Patent Application
20240401471
A resource field may be an accumulation, pool or group of pools of one or more resources (e.g., oil, gas, oil and gas) in a subsurface environment. A resource field may include at least one reservoir. A reservoir may be shaped in a manner that may trap hydrocarbons and may be covered by an impermeable or sealing rock. A bore may be drilled into an environment where the bore may be utilized to form a well that may be utilized in producing hydrocarbons from a reservoir.
A rig may be a system of components that may be operated to form a bore in an environment, to transport equipment into and out of a bore in an environment, etc. As an example, a rig may include a system that may be used to drill a bore and to acquire information about an environment, about drilling, etc. A resource field may be an onshore field, an offshore field or an onshore and offshore field. A rig may include components for performing operations onshore and/or offshore. A rig may be, for example, vessel-based, offshore platform-based, onshore, etc.
Field planning may occur over one or more phases, which may include an exploration phase that aims to identify and assess an environment (e.g., a prospect, a play, etc.), which may include drilling of one or more bores (e.g., one or more exploratory wells, etc.). Other phases may include appraisal, development and production phases.
As explained, a bore may be drilled into an environment where the bore may be utilized to form a well. Such a well may be utilized for one or more purposes (e.g., fluid production, fluid injection, etc.). As explained, a reservoir may be a hydrocarbon reservoir or, for example, an aqueous reservoir. In various instances, water injection (e.g., water flooding, etc.) may be utilized to help produce hydrocarbons where, for example, hydrocarbons are replaced at least in part by water. In various instances, a reservoir may be suitable for carbon sequestration where, for example, a well may be utilized to inject carbon dioxide (CO2) and/or one or more other fluids (e.g., liquids and/or gases). In such an example, the reservoir may be a produced reservoir where field operations for production of hydrocarbons have taken place.
A well in fluid communication with a reservoir may serve various functions over time. For example, consider hydrocarbon production, followed by water injection, followed by CO2 injection, etc. Quality of a well can depend on drilling practices. For example, where detrimental events occur during drilling, the occurrence of such events and/or resolution thereof (e.g., timing, techniques, etc.) can impact well quality. In such an example, a well with one or more well quality issues may be a suboptimal candidate for one or more treatments, uses, etc.
An individual well may have its own well history, which may be documented by various types of data (e.g., as may be stored in one or more databases). A framework that may provide for assessing well history (e.g., past field operations) and/or that may provide for control of drilling (e.g., current field operations) may improve decision-making as to potential field operations using already drilled wells and/or improve field operations as to wells being drilled and/or wells to be drilled. Such a framework may provide for improving well quality assessments and/or well quality.
A method can include acquiring data for rig operations that move a drillstring in a borehole in a subsurface geologic region, where the drillstring includes connected stands of drill pipe and a drill bit for drilling into the subsurface geologic region, and where the data include measured depth data, inclination data, mud density data, and measured hook load data; generating an estimated hook load value for a measured depth in the borehole using at least a trained model that receives a portion of the data as associated with the measured depth; performing a comparison between the estimated hook load value and a measured hook load value of the measured hook load data as associated with the measured depth; and, based at least in part on the comparison, determining a level of sticking of the drillstring in the borehole. A system can include a processor; memory accessible by the processor; processor-executable instructions stored in the memory and executable to instruct the system to: acquire data for rig operations that move a drillstring in a borehole in a subsurface geologic region, where the drillstring includes connected stands of drill pipe and a drill bit for drilling into the subsurface geologic region, and where the data include measured depth data, inclination data, mud density data, and measured hook load data; generate an estimated hook load value for a measured depth in the borehole using at least a trained model that receives a portion of the data as associated with the measured depth; perform a comparison between the estimated hook load value and a measured hook load value of the measured hook load data as associated with the measured depth; and, based at least in part on the comparison, determine a level of sticking of the drillstring in the borehole. One or more computer-readable storage media can include processor-executable instructions to instruct a computing system to: acquire data for rig operations that move a drillstring in a borehole in a subsurface geologic region, where the drillstring includes connected stands of drill pipe and a drill bit for drilling into the subsurface geologic region, and where the data include measured depth data, inclination data, mud density data, and measured hook load data; generate an estimated hook load value for a measured depth in the borehole using at least a trained model that receives a portion of the data as associated with the measured depth; perform a comparison between the estimated hook load value and a measured hook load value of the measured hook load data as associated with the measured depth; and, based at least in part on the comparison, determine a level of sticking of the drillstring in the borehole. Various other apparatuses, systems, methods, etc., are also disclosed.
This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter.
Features and advantages of the described implementations may be more readily understood by reference to the following description taken in conjunction with the accompanying drawings.
The following description includes the best mode presently contemplated for practicing the described implementations. This description is not to be taken in a limiting sense, but rather is made merely for the purpose of describing the general principles of the implementations. The scope of the described implementations should be ascertained with reference to the issued claims.
The equipment 170 includes a platform 171, a derrick 172, a crown block 173, a line 174, a traveling block assembly 175, drawworks 176 and a landing 177 (e.g., a monkeyboard). As an example, the line 174 may be controlled at least in part via the drawworks 176 such that the traveling block assembly 175 travels in a vertical direction with respect to the platform 171. For example, by drawing the line 174 in, the drawworks 176 may cause the line 174 to run through the crown block 173 and lift the traveling block assembly 175 skyward away from the platform 171; whereas, by allowing the line 174 out, the drawworks 176 may cause the line 174 to run through the crown block 173 and lower the traveling block assembly 175 toward the platform 171. Where the traveling block assembly 175 carries pipe (e.g., casing, etc.), tracking of movement of the traveling block 175 may provide an indication as to how much pipe has been deployed.
A derrick may be a structure used to support a crown block and a traveling block operatively coupled to the crown block at least in part via line. A derrick may be pyramidal in shape and offer a suitable strength-to-weight ratio. A derrick may be movable as a unit or in a piece-by-piece manner (e.g., to be assembled and disassembled).
As an example, drawworks may include a spool, brakes, a power source and assorted auxiliary devices. Drawworks may controllably reel out and reel in line. Line may be reeled over a crown block and coupled to a traveling block to gain mechanical advantage in a “block and tackle” or “pulley” fashion. Reeling out and in of line may cause a traveling block (e.g., and whatever may be hanging underneath it), to be lowered into or raised out of a bore. Reeling out of line may be powered by gravity and reeling in by a motor, an engine, etc. (e.g., an electric motor, a diesel engine, etc.). As an example, a drawworks may include a transmission (e.g., a gearbox, etc.) that can be utilized to adjust spool speed with respect to drive speed. As an example, a drawworks may include one or more types of brakes. For example, consider regenerative dynamic, fail-safe disk (e.g., emergency, parking, etc.), eddy current brake (ECB) to assist fast dynamic braking, etc.
As an example, a drawworks may include one or more drivers (e.g., one or more prime movers, etc.) that can deliver more than 500 kW (e.g., consider 3260 kW or more). As an example, a drawworks may provide for hoisting in a range that may exceed 100 metric tons (e.g., consider 1000 metric tons or more). As an example, consider an AC gear-driven drawworks for onshore and/or offshore applications. As an example, consider single-speed or multi-speed transmissions (e.g., two, three, etc., speed). In various instances, a drawworks system may be gear-driven with active-heave-compensation (AHC) with a relatively high rating (e.g., 6000 kW or more), which may be suitable for compensating for movements on large floaters and drillships.
As an example, a crown block may include a set of pulleys (e.g., sheaves) that may be located at or near a top of a derrick or a mast, over which line is threaded. A traveling block may include a set of sheaves that may be moved up and down in a derrick or a mast via line threaded in the set of sheaves of the traveling block and in the set of sheaves of a crown block. A crown block, a traveling block and a line may form a pulley system of a derrick or a mast, which may enable handling of heavy loads (e.g., drillstring, pipe, casing, liners, etc.) to be lifted out of or lowered into a bore. As an example, line may be about a centimeter to about five centimeters in diameter as, for example, steel cable. Through use of a set of sheaves, such line may carry loads heavier than the line could support as a single strand.
As an example, a derrickman may be a rig crew member that works on a platform attached to a derrick or a mast. A derrick may include a landing on which a derrickman may stand. As an example, such a landing may be about 10 meters or more above a rig floor. In an operation referred to as trip out of the hole (TOH), a derrickman may wear a safety harness that enables leaning out from the work landing (e.g., monkeyboard) to reach pipe in located at or near the center of a derrick or a mast and to throw a line around the pipe and pull it back into its storage location (e.g., fingerboards), for example, until a time at which it may be desirable to run the pipe back into the bore. As an example, a rig may include automated pipe-handling equipment such that the derrickman controls the machinery rather than physically handling the pipe.
As an example, a trip may refer to the act of pulling equipment from a bore and/or placing equipment in a bore. As an example, equipment may include a drillstring that may be pulled out of a hole and/or placed or replaced in a hole. As an example, a pipe trip may be performed where a drill bit has dulled or has otherwise ceased to drill efficiently and is to be replaced.
In the example system of
As shown in the example of
The wellsite system 200 may provide for operation of the drillstring 225 and other operations. As shown, the wellsite system 200 includes the platform 211 and the derrick 214 positioned over the borehole 232. As mentioned, the wellsite system 200 may include the rotary table 220 where the drillstring 225 pass through an opening in the rotary table 220.
As shown in the example of
As to a top drive example, the top drive 240 may provide functions performed by a kelly and a rotary table. The top drive 240 may turn the drillstring 225. As an example, the top drive 240 may include one or more motors (e.g., electric and/or hydraulic) connected with appropriate gearing to a short section of pipe called a quill, that in turn may be screwed into a saver sub or the drillstring 225 itself. The top drive 240 may be suspended from the traveling block 211, so the rotary mechanism is free to travel up and down the derrick 214. As an example, a top drive 240 may allow for drilling to be performed with more joint stands than a kelly/rotary table approach.
In the example of
In the example of
The mud pumped by the pump 204 into the drillstring 225 may, after exiting the drillstring 225, form a mudcake that lines the wellbore which, among other functions, may reduce friction between the drillstring 225 and surrounding wall(s) (e.g., borehole, casing, etc.). A reduction in friction may facilitate advancing or retracting the drillstring 225. During a drilling operation, the entire drillstring 225 may be pulled from a wellbore and optionally replaced, for example, with a new or sharpened drill bit, a smaller diameter drillstring, etc. As mentioned, the act of pulling a drillstring out of a hole or replacing it in a hole is referred to as tripping. A trip may be referred to as an upward trip or an outward trip or as a downward trip or an inward trip depending on trip direction.
As an example, consider a downward trip where upon arrival of the drill bit 226 of the drillstring 225 at a bottom of a wellbore, pumping of the mud commences to lubricate the drill bit 226 for purposes of drilling to enlarge the wellbore. As mentioned, the mud may be pumped by the pump 204 into a passage of the drillstring 225 and, upon filling of the passage, the mud may be used as a transmission medium to transmit energy, for example, energy that may encode information as in mud-pulse telemetry.
As an example, mud-pulse telemetry equipment may include a downhole device configured to effect changes in pressure in the mud to create an acoustic wave or waves upon which information may be modulated. In such an example, information from downhole equipment (e.g., one or more modules of the drillstring 225) may be transmitted uphole to an uphole device, which may relay such information to other equipment for processing, control, etc.
As an example, telemetry equipment may operate via transmission of energy via the drillstring 225 itself. For example, consider a signal generator that imparts coded energy signals to the drillstring 225 and repeaters that may receive such energy and repeat it to further transmit the coded energy signals (e.g., information, etc.).
As an example, the drillstring 225 may be fitted with telemetry equipment 252 that includes a rotatable drive shaft, a turbine impeller mechanically coupled to the drive shaft such that the mud may cause the turbine impeller to rotate, a modulator rotor mechanically coupled to the drive shaft such that rotation of the turbine impeller causes said modulator rotor to rotate, a modulator stator mounted adjacent to or proximate to the modulator rotor such that rotation of the modulator rotor relative to the modulator stator creates pressure pulses in the mud, and a controllable brake for selectively braking rotation of the modulator rotor to modulate pressure pulses. In such example, an alternator may be coupled to the aforementioned drive shaft where the alternator includes at least one stator winding electrically coupled to a control circuit to selectively short the at least one stator winding to electromagnetically brake the alternator and thereby selectively brake rotation of the modulator rotor to modulate the pressure pulses in the mud.
In the example of
The assembly 250 of the illustrated example includes a logging-while-drilling (LWD) module 254 (e.g., a LWD tool), a measuring-while-drilling (MWD) module 256 (e.g., a MWD tool), an optional module 258, a roto-steerable system (RSS) and/or motor 260, and the drill bit 226. Such components or modules may be referred to as tools where a drillstring may include a plurality of tools.
As to an RSS, it involves technology utilized for directional drilling. Directional drilling involves drilling into the Earth to form a deviated bore such that the trajectory of the bore is not vertical; rather, the trajectory deviates from vertical along one or more portions of the bore. As an example, consider a target that is located at a lateral distance from a surface location where a rig may be stationed. In such an example, drilling may commence with a vertical portion and then deviate from vertical such that the bore is aimed at the target and, eventually, reaches the target. Directional drilling may be implemented where a target may be inaccessible from a vertical location at the surface of the Earth, where material exists in the Earth that may impede drilling or otherwise be detrimental (e.g., consider a salt dome, etc.), where a formation is laterally extensive (e.g., consider a relatively thin yet laterally extensive reservoir), where multiple bores are to be drilled from a single surface bore, where a relief well is desired, etc.
One approach to directional drilling involves a mud motor; however, a mud motor may present some challenges depending on factors such as rate of penetration (ROP), transferring weight to a bit (e.g., weight on bit, WOB) due to friction, etc. A mud motor may be a positive displacement motor (PDM) that operates to drive a bit (e.g., during directional drilling, etc.). A PDM operates as drilling fluid is pumped through it where the PDM converts hydraulic power of the drilling fluid into mechanical power to cause the bit to rotate.
As an example, a PDM may operate in a combined rotating mode where surface equipment is utilized to rotate a bit of a drillstring (e.g., a rotary table, a top drive, etc.) by rotating the entire drillstring and where drilling fluid is utilized to rotate the bit of the drillstring. In such an example, a surface RPM (SRPM) may be determined by use of the surface equipment and a downhole RPM of the mud motor may be determined using various factors related to flow of drilling fluid, mud motor type, etc. As an example, in the combined rotating mode, bit RPM may be determined or estimated as a sum of the SRPM and the mud motor RPM, assuming the SRPM and the mud motor RPM are in the same direction.
As an example, a PDM mud motor may operate in a so-called sliding mode, when the drillstring is not rotated from the surface to drive a drill bit in a particular cutting direction. In such an example, a bit RPM may be determined or estimated based on the RPM of the mud motor. As an example, during a sliding mode, oscillation of a drillstring may be provided by surface equipment, for example, to oscillate the drillstring in a clockwise and a counterclockwise direction, which may, for example, help to reduce risk of sticking, etc.
An RSS may drill directionally where there is continuous rotation from surface equipment, which may alleviate the sliding of a steerable motor (e.g., a PDM). An RSS may be deployed when drilling directionally (e.g., deviated, horizontal, or extended-reach wells). An RSS may aim to minimize interaction with a borehole wall, which may help to preserve borehole quality (e.g., a factor in well quality). An RSS may aim to exert a relatively consistent side force akin to stabilizers that rotate with the drillstring or orient the bit in the desired direction while continuously rotating at the same number of rotations per minute as the drillstring.
The LWD module 254 may be housed in a suitable type of drill collar and may contain one or a plurality of selected types of logging tools. It will also be understood that more than one LWD and/or MWD module may be employed. Where the position of a module is mentioned, as an example, it may refer to a module at the position of the LWD module 254, the MWD module 256, etc. An LWD module may include capabilities for measuring, processing, and storing information, as well as for communicating with the surface equipment. In the illustrated example, the LWD module 254 may include a seismic measuring device.
The MWD module 256 may be housed in a suitable type of drill collar and may contain one or more devices for measuring characteristics of the drillstring 225 and the drill bit 226. As an example, the MWD module 256 may include equipment for generating electrical power, for example, to power various components of the drillstring 225. As an example, the MWD module 256 may include the telemetry equipment 252, for example, where the turbine impeller may generate power by flow of the mud; it being understood that other power and/or battery systems may be employed for purposes of powering various components. As an example, the MWD module 256 may include one or more of the following types of measuring devices: a weight-on-bit measuring device, a torque measuring device, a vibration measuring device, a shock measuring device, a stick slip measuring device, a direction measuring device, and an inclination measuring device.
As an example, a drilling operation may include directional drilling where, for example, at least a portion of a well includes a curved axis. For example, consider a radius that defines curvature where an inclination with regard to the vertical may vary until reaching an angle between about 30 degrees and about 60 degrees or, for example, an angle to about 90 degrees or possibly greater than about 90 degrees.
As an example, a directional well may include several shapes where each of the shapes may aim to meet particular operational demands. As an example, a drilling process may be performed on the basis of information as and when it is relayed to a drilling engineer. As an example, inclination and/or direction may be modified based on information received during a drilling process.
As an example, deviation of a bore may be accomplished in part by use of one or more of an RSS, a downhole motor and/or a turbine. As to a motor, for example, a drillstring may include a positive displacement motor (PDM).
As an example, a system may be a steerable system and include equipment to perform a method such as geosteering. As an example, a steerable system may include a PDM or a turbine on a lower part of a drillstring which, just above a drill bit, a bent sub may be mounted. As an example, above a PDM, MWD equipment that provides real time or near real time data of interest (e.g., inclination, direction, pressure, temperature, real weight on the drill bit, torque stress, etc.) and/or LWD equipment may be installed. As to the latter, LWD equipment may make it possible to send to the surface various types of data of interest, including for example, geological data (e.g., gamma ray log, resistivity, density and sonic logs, etc.).
The coupling of sensors providing information on the course of a well trajectory, in real time or near real time, with, for example, one or more logs characterizing the formations from a geological viewpoint, may allow for implementing a geosteering method. Such a method may include navigating a subsurface environment, for example, to follow a desired route to reach a desired target or targets.
As an example, a drillstring may include an azimuthal density neutron (ADN) tool for measuring density and porosity; a MWD tool for measuring inclination, azimuth and shocks; a compensated dual resistivity (CDR) tool for measuring resistivity and gamma ray related phenomena; one or more variable gauge stabilizers; one or more bend joints; and a geosteering tool, which may include a motor and optionally equipment for measuring and/or responding to one or more of inclination, resistivity and gamma ray related phenomena.
As an example, geosteering may include intentional directional control of a wellbore based on results of downhole geological logging measurements in a manner that aims to keep a directional wellbore within a desired region, zone (e.g., a pay zone), etc. As an example, geosteering may include directing a wellbore to keep the wellbore in a particular section of a reservoir, for example, to minimize gas and/or water breakthrough and, for example, to maximize economic production from a well that includes the wellbore.
Referring again to
As an example, one or more of the sensors 264 may be provided for tracking pipe, tracking movement of at least a portion of a drillstring, etc.
As an example, the system 200 may include one or more sensors 266 that may sense and/or transmit signals to a fluid conduit such as a drilling fluid conduit (e.g., a drilling mud conduit). For example, in the system 200, the one or more sensors 266 may be operatively coupled to portions of the standpipe 208 through which mud flows. As an example, a downhole tool may generate pulses that may travel through the mud and be sensed by one or more of the sensors 266. In such an example, the downhole tool may include associated circuitry such as, for example, encoding circuitry that may encode signals, for example, to reduce demands as to transmission. As an example, circuitry at the surface may include decoding circuitry to decode encoded information transmitted at least in part via mud-pulse telemetry. As an example, circuitry at the surface may include encoder circuitry and/or decoder circuitry and circuitry downhole may include encoder circuitry and/or decoder circuitry. As an example, the system 200 may include a transmitter that may generate signals that may be transmitted downhole via mud (e.g., drilling fluid) as a transmission medium.
As an example, one or more portions of a drillstring may become stuck. The term stuck may refer to one or more of varying degrees of inability to move or remove a drillstring from a bore. As an example, in a stuck condition, it might be possible to rotate pipe or lower it back into a bore or, for example, in a stuck condition, there may be an inability to move the drillstring axially in the bore, though some amount of rotation may be possible. As an example, in a stuck condition, there may be an inability to move at least a portion of the drillstring axially and rotationally.
As to the term “stuck pipe”, this term may refer to a portion of a drillstring that cannot be rotated or moved axially. As an example, a condition referred to as “differential sticking” may be a condition whereby the drillstring cannot be moved (e.g., rotated or reciprocated) along the axis of the bore. Differential sticking may occur when high-contact forces caused by low reservoir pressures, high wellbore pressures, or both, are exerted over a sufficiently large area of the drillstring. Differential sticking may have time and financial cost.
As an example, a sticking force may be a product of the differential pressure between the wellbore and the reservoir and the area that the differential pressure is acting upon. This means that a relatively low differential pressure (delta p) applied over a large working area may be just as effective in sticking pipe as may a high differential pressure applied over a small area.
As an example, a condition referred to as “mechanical sticking” may be a condition where limiting or prevention of motion of the drillstring by a mechanism other than differential pressure sticking occurs. Mechanical sticking may be caused, for example, by one or more of junk in the hole, wellbore geometry anomalies, cement, keyseats or a buildup of cuttings in the annulus.
Various types of data associated with field operations may be 1-D series data. For example, consider data as to one or more of a drilling system, downhole states, formation attributes, and surface mechanics being measured as single or multi-channel time series data.
The hoisting system 300 may be part of a wellsite system (see, e.g.,
As to the distance range of a traveling block, it may be sufficient for adding and removing drill pipe and/or other components. As an example, a stand may be two or three single joints of drill pipe or drill collars that remain screwed together during tripping operations. As an example, a stand may be a three-joint stand. As an example, a drill pipe length may be approximately 10 m (e.g., consider from about 27 ft to about 30 ft); noting that shorter or longer drill pipe may be utilized. Where a stand is composed of three lengths of approximately 10 m drill pipe, the stand may have an over length of approximately 30 m (e.g., approximately 100 ft). As such, a traveling block that has a total excursion of approximately 50 m may be raised and lowered to accommodate a stand of approximately 30 m (e.g., for addition to a drillstring or removal from a drillstring).
BPOS is a type of real-time channel that reflects surface mechanical properties of a rig. Another example of a channel is hook load, which may be referred to as HKLD. HKLD may be a 1-D series measurement of the load of a hook. As to a derivative, a first derivative may be a load velocity and a second derivative may be a load acceleration. Such data channels may be utilized to infer and monitor various operations and/or conditions. In some examples, a rig may be represented as being in one or more states, which may be referred to as rig states.
As to the HKLD channel, it may help to detect if a rig is “in slips”, while the BPOS channel may be a primary channel for depth tracking during drilling. For example, BPOS may be utilized to determine a measured depth in a geologic environment (e.g., a borehole being drilled, etc.). As to the condition or state “in slips”, HKLD is at a much lower value than in the condition or state “out of slips”.
The term slips refers to a device or assembly that may be used to grip a drillstring (e.g., drill collar, drill pipe, etc.) in a relatively non-damaging manner and suspend it in a rotary table (e.g., or other structure). Once suspended, the slips, as seated structurally in an opening, can provide for carrying the weight of a drillstring, for example, such that a stand or drill pipe may be removed from or added to the drillstring. Slips may include three or more steel wedges that are hinged together, forming a near circle around a drill pipe. On the drill pipe side (inside surface), the slips are fitted with replaceable, hardened tool steel teeth that embed slightly into the side of the pipe. The outsides of the slips are tapered to match the taper of an opening in the rotary table (e.g., or other structure). After the rig crew places the slips around the drill pipe and in the rotary table (e.g., or other structure), a driller may control a rig to slowly lower the drillstring. As the teeth on the inside of the slips grip the pipe, the slips are pulled down. This downward force pulls the outer wedges down, providing a compressive force inward on the drill pipe and effectively locking components together. Then the rig crew may unscrew the upper portion of the drillstring (e.g., a kelly, saver sub, a joint or stand of pipe) while the lower part is suspended. After some other component is screwed onto the lower part of the drillstring, the driller raises the drillstring to unlock the gripping action of the slips, and a rig crew may remove the slips from the rotary table (e.g., or other structure).
A hook load sensor may be used to measure a weight of load on a drillstring and may be used to detect whether a drillstring is in-slips or out-of-slips. When the drillstring is in-slips, motion from the blocks or motion compensator do not have an effect on the depth of a drill bit at the end of the drillstring (e.g., it will tend to remain stationary). Where movement of a traveling block is via a drawworks encoder (DWE), which may be mounted on a shaft of the drawworks, acquired DWE information (e.g., BPOS) will not augment the recorded drill bit depth. When a drillstring is out-of-slips (e.g., drilling ahead), DWE information (e.g., BPOS) may augment the recorded bit depth. The difference in hook load weight (HKLD) between in-slips and out-of-slips tends to be distinguishable. As to marine operations, heave of a vessel may affect bit depth whether a drillstring is in-slips or out-of-slips. As an example, a vessel may include one or more heave sensors, which may sense data that may be recorded as 1-D series data.
As to marine operations, a vessel may experience various types of motion, such as, for example, one or more of heave, sway and surge. Heave is a linear vertical (up/down) motion, sway is linear lateral (side-to-side or port-starboard) motion, and surge is linear longitudinal (front/back or bow/stem) motion imparted by maritime conditions. As an example, a vessel may include one or more heave sensors, one or more sway sensors and/or one or more surge sensors, each of which may sense data that may be recorded as 1-D series data.
As to hook load (HKLD), it may be appropriately defined, for example, for consistent utilization. As an example, HKLD may be defined as a sum of vertical components of forces acting on a drillstring attached to a hook; a total force that includes weight of a drillstring in air, drill collars and ancillary equipment, reduced by force that tends to change that weight; etc. The weight of pipes and equipment that make up a drillstring can be known at surface conditions before going down into a borehole. As explained, during drilling, mud circulation can be established such that a drillstring feels a buoyant uplifting force. As a borehole increases in depth, the borehole might deviate from vertical, which may result in friction forces between a drillstring (e.g., or other tool string) and borehole walls; noting that drilling fluid and/or other fluid (e.g., reservoir fluid) may be present. As explained, mud (e.g., drilling fluid) characteristics and/or flow rate may provide for formation stability (e.g., reducing risks of influx, etc.), appropriate lubrication, sufficient cleaning capacity as to cuttings, etc.
As explained, hook load can depend on various factors, which may include history of a borehole (e.g., a wellbore, etc.) along with operational activity such as, for example, drilling, tripping, circulation, temperature changes, stop in circulation, high circulation, etc., noting that operational activity may relate to borehole history.
As an example, BPOS alone, or combined with one or more other channels, may be used to detect whether a rig is “on bottom” drilling or “tripping”, etc. An inferred state may be further consumed by one or more systems such as, for example, an automatic drilling control system, which may be a dynamic field operations system or a part thereof. In such an example, the conditions, operations, states, etc., as discerned from BPOS and/or other channel data may be predicates to making one or more drilling decisions, which may include one or more control decisions (e.g., of a controller that is operatively coupled to one or more pieces of field equipment, etc.).
A block may be a set of pulleys used to gain mechanical advantage in lifting or dragging heavy objects. There may be two blocks on a drilling rig, the crown block and the traveling block. Each may include several sheaves that are rigged with steel drilling cable or line such that the traveling block may be raised (or lowered) by reeling in (or out) a spool of drilling line on the drawworks. As such, block position may refer to the position of the traveling block, which may vary with respect to time.
A hook may be high-capacity J-shaped equipment used to hang various equipment such as a swivel and kelly, elevator bails, or a topdrive.
Hook load may be the total force pulling down on a hook as carried by a traveling block. The total force includes the weight of the drillstring in air, the drill collars and ancillary equipment, reduced by forces that tend to reduce that weight. Some forces that might reduce the weight include friction along a bore wall (especially in deviated wells) and buoyant forces on a drillstring caused by its immersion in drilling fluid (e.g., and/or other fluid). If a blowout preventer (BOP) (e.g., or BOPs) is closed, pressure in a bore acting on cross-sectional area of a drillstring in the BOP may also exert an upward force.
A standpipe may be a rigid metal conduit that provides a high-pressure pathway for drilling fluid to travel approximately one-third of the way up the derrick, where it connects to a flexible high-pressure hose (e.g., kelly hose). A large rig may be fitted with more than one standpipe so that downtime is kept to a minimum if one standpipe demands repair.
As to surface torque, such a measurement may be provided by equipment at a rig site. As an example, one or more sensors may be utilized to measure surface torque, which may provide for direct and/or indirect measurement of surface torque associated with a drillstring. As an example, equipment may include a drill pipe torque measurement and controller system with one or more of analog frequency output and digital output. As an example, a torque sensor may be associated with a coupling that includes a resilient element operatively joining an input element and an output element where the resilient element allows the input and output elements to twist with respect to one another in response to torque being transmitted through the torque sensor where the twisting may be measured and used to determine the torque being transmitted. As an example, such a coupling may be located between a drive and drill pipe. As an example, torque may be determined via an inertia sensor or sensors. As an example, equipment at a rig site may include one or more sensors for measurement and/or determination of torque (e.g., in units of Nm, etc.).
As an example, equipment may include a real-time drilling service system that may provide data such as weight transfer information, torque transfer information, equivalent circulation density (ECD) information, downhole mechanical specific energy (DMSE) information, motion information (e.g., as to stall, stick-slip, etc.), bending information, vibrational amplitude information (e.g., axial, lateral and/or torsional), rate of penetration (ROP) information, pressure information, differential pressure information, flow information, etc. As an example, sensor information may include inclination, azimuth, total vertical depth, etc. As an example, a system may provide information as to whirl (e.g., backward whirl, etc.) and may optionally provide information such as one or more alerts (e.g., “severe backward whirl: stop and restart with lower surface RPM”, etc.).
As to DMSE, it may be a MSE as associated with downhole energy. MSE may be utilized as a measure of drilling efficiency. MSE may be defined as the energy required to remove a unit volume of rock. For optimal drilling efficiency, field operations may aim to minimize the MSE and to maximize ROP. As an example, to control MSE, field equipment may be controlled as to factors such as, for example, one or more of WOB, torque, ROP and RPM.
A drill bit may be defined as a tool used to crush and/or cut rock. As explained, various rig equipment may directly and/or indirectly assist a drill bit in crushing and/or cutting the rock. Various drill bits may work by scraping or crushing the rock, or both, usually as part of a rotational motion; noting that some bits, known as hammer bits, pound rock. During drilling, various equipment may be controlled to deliver energy to a drill bit to crush and/or cut rock to thereby lengthen a borehole. As explained, drilling may aim to minimize MSE and maximize ROP while maintaining borehole quality (e.g., wellbore integrity, etc.). As an example, various equipment may be controlled as to energy delivered to a drillstring and/or a drill bit, for example, to address one or more conditions, which may include, for example, one or more conditions that may cause sticking of a drillstring and/or increase risk of sticking of a drillstring and/or one or more conditions involving actual sticking of a drillstring (e.g., getting a drillstring unstuck, etc.). As various physical interactions may occur between a drillstring and a formation (e.g., a borehole wall), controlled delivery of energy, material(s) (e.g., drilling fluid additives, etc.), etc., may provide for reduced risk of damage to the drillstring and/or the formation.
As explained, a drillstring may include a tool or tools that include various sensors that may make various measurements. For example, consider the OPTIDRILL® tool (SLB, Houston, Texas), which includes strain gauges, accelerometers, magnetometer(s), gyroscope(s), etc. For example, such a tool may acquire weight on bit measurements (WOB) using a strain gauge (e.g., 10 second moving window with bandwidth of 200 Hz), torque measurements using a strain gauge (e.g., 10 second moving window with bandwidth of 200 Hz), bending moment using a strain gauge (e.g., 10 second moving window with bandwidth of 200 Hz), vibration using one or more accelerometers (e.g., 30 second RMS with bandwidth of 0.2 to 150 Hz), rotational speed using a magnetometer and a gyroscope (e.g., 30 second moving window with bandwidth of 4 Hz), annular and internal pressures using one or more strain gauges (e.g., 1 second average with bandwidth of 200 Hz), annular and internal temperatures using one or more temperature sensors (1 second average with bandwidth of 10 Hz), and continuous inclination using an accelerometer (30 second average with bandwidth of 10 Hz).
Inclination (e.g., Incl.) may represent deviation from vertical, which may be irrespective of compass direction. As an example, inclination may be expressed in degrees. Inclination may be measured via one or more types of sensors. For example, consider a tiered approach where inclination may be initially measured using a pendulum type of sensor and then confirmed using one or more drillstring accelerometers and/or gyroscopes (e.g., consider one or more MWD tool sensors, etc.).
As mentioned, mud density may be provided, which may be provided via measurement acquired using one or more sensors. Mud density may be defined as mass per unit volume of a drilling fluid. Mud density may be referred to as drilling fluid density, mud weight, or simply density (e.g., in the context of use of drilling fluid). Mud density can impact hydrostatic pressure in a borehole, which may be utilized to reduce risk of unwanted flow into a borehole (e.g., flow of fluid from a formation to a borehole, etc.). The weight of mud can help to reduce risk of collapse of casing and/or a borewall in an openhole section. Excessive mud weight may increase risk of lost circulation by propagating, and then filling, fractures in rock. As an example, mud density test procedures may involve using a mud balance. As an example, a test or measurement process may be performed or taken according to a standard or standards such as, for example, a standard or standards of the American Petroleum Institute (API).
As to measured depth (MD), one or more techniques, technologies, etc., may be utilized to provide MD values. For a deviated borehole, MD can differ from true vertical depth (TVD); noting, for vertical wells, MD and TVD may be substantially the same (e.g., where corkscrewing, etc., does not occur). As an example, MD may be estimated using lengths of individual joints of drill pipe, drill collars and/or one or more other drillstring elements. As an example, pipe may be measured while in a derrick or laying on a pipe rack, in an untensioned, unstressed state. When pipe lengths are coupled together (e.g., via screwing, etc.) and put into a borehole, the pipe may stretch under its own weight (e.g., and that of a BHA). In various instances, an actual borehole may be slightly longer than a reported MD value. As an example, MD may be measured using optical techniques, block position techniques, etc., and, for example, may be adjusted using one or more techniques that may account for stretching of one or more drillstring components.
As mentioned, channels of real time drilling operation data may be received and characterized using generated synthetic data, which may be generated based at least in part on one or more operational parameters associated with the real time drilling operation. Such real time drilling operation data may include surface data and/or downhole data. As mentioned, data availability may differ temporally (e.g., frequency, gaps, etc.) and/or otherwise (e.g., resolution, etc.). Such data may differ as to noise level and/or noise characteristics. While various types of sensors are mentioned, equipment may be utilized that may not include one or more types of downhole sensors. In such instances, a method may be utilized that may determine one or more downhole values.
In
Pre-connection may be where a downhole tool (e.g., a drill bit) has completed drilling operations for a current section of pipe, but the slips assembly has not begun to move (e.g., radially-inward) into engagement with the drillstring. During pre-connection, the flow rate of fluid being pumped into the drillstring may increase and/or decrease, the rate of rotation of the drillstring may increase and/or decrease, the downhole tool (e.g., the drill bit) may move upwards and/or downwards, or a combination thereof.
Connection may be where a slips assembly is engaged with, and supports, a drillstring (e.g., the drillstring is “in-slips”). When a connection is occurring, a segment (e.g., a pipe, a stand, etc.) may be added to the drillstring to increase the length of the drillstring, or a segment may be removed from the drillstring to reduce the length of the drillstring.
Post-connection may be where the drillstring is released by a slips assembly, and a downhole tool (e.g., the drill bit) are lowered to be on-bottom (e.g., bottom of hole or BOH). During post-connection, the flow rate of fluid being pumped into a drillstring may increase and/or decrease, the rate of rotation of a drillstring may increase and/or decrease, a downhole tool (e.g., the drill bit) may move upwards and/or downwards, or a combination thereof.
As to an absent state, it may indicate a scenario where data are not being received (e.g., at least one of a plurality of inputs is missing).
As an example, a method may be utilized to determine a slips status. For example, slips status may include one or more of the following: In-slips where a slips assembly is engaged with, and supports, a drillstring (“in-slips”); out-of-slips where the slips assembly is not engaged with, and does not support, the drillstring; and absent where data are not received (e.g., at least one of the inputs is missing).
The method 400 of
As an example, in the method 400, measurements (e.g., data) may include a depth of a wellbore (e.g., a measured depth as hole depth (HD)), a depth of a drill bit (e.g., a measured depth as bit depth (BD)), a position of a travelling block (e.g., BPOS), or a combination thereof. A set of measurements may or may not include weight on hook (e.g., HKLD), or weight on a drill bit (e.g., WOB). Each set of measurements may be captured/received a predetermined amount of time after a previous set of measurements is captured/received. A predetermined amount of time may be, for example, about three seconds; however, the predetermined amount of time may be shorter or longer.
As an example, a method may be for determining a drilling activity that includes receiving a set of measurements at different times. The set of measurements may include a depth of a wellbore, a depth of a drill bit, and a position of a travelling block. Such a method may also include identifying a connection by determining when the position of the travelling block changes but the depth of the drill bit does not change. Such a method may also include determining when the depth of the wellbore does not increase between two different connections. Such a method may also include determining a direction that the drill bit moves between the two connections.
As an example, a GUI may provide for rendering one or more broomstick model plots with respect to time (e.g., horizontally, vertically, etc.). In such an example, a broomstick model plot may be utilized to ascertain one or more friction factors with respect to time. As an example, a broomstick plot or broomstick model plot (e.g., a plot of model results, etc.), may be a full broomstick plot, a half broomstick plot or another portion of a broomstick plot. For example, where PU and SO are concerned, they may correspond to different directions such that a full broomstick plot may be generated; noting that a half broomstick plot for PU and/or a half broomstick plot for SO may be generated. As to TQLS, where the torque is in a particular rotational direction (e.g., a rotational direction of a bit for drilling), a broomstick plot may be a half broomstick plot; noting that torque may be acquired in two rotational directions (e.g., clockwise and counterclockwise), which may provide for rendering a plot in a full broomstick manner.
As to a friction factor, it may be a factor that can be defined to be positive or zero and that aims to account for various types of physical phenomena that may occur when a drillstring is in a borehole (e.g., cased, uncased, cased and uncased, etc.). For example, a friction factor may aim to account for one or more of pipe stiffness effects, viscous drag (e.g., fluid resistance caused by pipe movement), cutting beds, occurrence of lost circulation (e.g., leading to loss of lubricity), stabilizers and/or centralizers on a drillstring (e.g., which may affect string stiffness), tortuosity of a borehole, one or more hole cleaning issues, geometry of a borehole, etc.
As an example, a friction factor may depend on type of fluid (e.g., drilling fluid or mud) and type of borehole (e.g., cased, uncased (open), or cased and uncased (open). Below, Table 1 provides some examples of friction factors.
In the examples of Table 1, a friction factor may be lower in a cased hole when compared to an open hole (e.g., uncased hole) where use of polymers and/or synthetic materials may provide for reductions in friction factor values.
As an example, a system may provide for real-time (RT) torque and drag (T&D) monitoring. Abnormal torque and drag, which commonly refers to overpull, underpull, and high-torque load, are indications of excess frictional effects between the drillstring and the wellbore. Various conditions may cause these effects, including tight hole, differential sticking, poor hole cleaning, key seats, etc. Failing to detect these anomalies may cause excessive wear on a drillstring and may eventually lead to severe stuck pipe conditions.
As to torque, it may be defined as the moment required to rotate a drillstring, which may be with a bit on-bottom and engaged with rock or with a bit off-bottom (e.g., during a phase of drilling, during tripping, etc.). This moment can act to overcome rotational friction of a drillstring against a borewall (e.g., cased and/or open), which may be referred to as frictional torque, as may be related to viscous force between drillstring and drilling fluid (e.g., dynamic torque) and, where appropriate, bit torque. As to magnitude of torque, factors may include tension and/or compression in a drillstring, DLS, size(s) of a drillstring and hole, weight of a drillstring, directional changes of a borehole (e.g., inclination and azimuth), lubricity or friction factor, etc.
As to drag, it may be considered to be a force required to pull or lower a drillstring through a borehole. As an example, drag may be considered to be an axial force, for example, a force along an axis of a borehole, an axis of a drillstring, etc. As an example, magnitude of drag, as may be associated with sliding friction forces or borehole friction, may depending on factors such as, for example, the normal contact force and the coefficient of friction (COF or CoF) between contact surfaces (e.g., based on Coulomb's friction model, etc.). As explained, a friction factor may be utilized with respect to drag. Drag may be defined as force required to overcome axial friction between a drillstring and a borehole wall, as may be related to hydrodynamic viscous force between a drillstring and drilling fluid.
As an example, a T&D workflow may be executed using surface sensor measurements and contextual data for extraction of information from relevant operations. Such information may then be used for modeling calibration and predictions, for example, based on a finite element method-based stiff-string T&D model. In such an approach, a T&D workflow may generate alarms, instructions, etc., based on one or more detected anomalies.
As an example, a workflow may use one or more physical models together with knowledge acquired from drilling data. A workflow may be fully automatic without demand for manual calibration and fixed thresholds. In such an example, the workflow may be adaptive to changing conditions of a well being drilled.
As explained, a rig system can include various sensors that can receive signals and covert the signals to digital data, which can be transmitted, for example, as a data stream. In such an example, a data stream can be a stream of real-time data. For example, as WOB changes during a drilling operation, the data stream can be a time series of data that includes values that vary over time correspondingly as the WOB varies. While WOB is mentioned, a data stream as time series data may be provided for HKLD too.
As an example, a sensor may provide for WOB and HKLD data (e.g., measurements). For example, consider a transducer that can measure tension of a wire-rope deadline that may span between a crown block sheave and a deadline anchor. In such an example, changes in tension may be converted to WOB and/or HKLD measurements. As an example, a transducer may utilize one or more types of circuitry, whether electronic and/or fluidic, to measure tension.
As an example, during various operations, tension on a drilling line may increase where, for example, hydraulic fluid may be forced through a gauge, turning the hands of an indicator of the gauge, generating a digital response, etc. In such an example, the weight that is measured tends to includes substantially everything exerting tension on the drilling line, including the traveling block(s) and the drilling line itself. Hence, to have an accurate weight measurement of a drillstring, the driller can make a zero offset adjustment to account for the traveling block(s) and items other than the drillstring. With adjustments, the indicated weight will represent the drillstring (e.g., drill pipe and bottom hole assembly (BHA)).
During drilling operations, a driller may be interested in the measured weight for one or more operations. As mentioned, the weight of interest can be the weight applied to the bit on the bottom of the hole. As an example, a driller can take the rotating and hanging off bottom weight, for example 136,200 kg, and subtract from that the amount of rotating on bottom weight, for example 113,500 kg, to get a bit weight of 22,700 kg. Various rigs can include a weight indicator that has a second indicator dial that can be set to read zero (“zeroed”) with the drillstring hanging free, where the second indicator dial works backwards from the main indicator dial. After proper zeroing, a weight set on bottom (that takes weight away from the main dial), has the effect of adding weight to this secondary dial, so that the driller can read weight on bit directly from the dial.
As may be appreciated, WOB can be approximate. Factors such as friction, fluid, debris, buoyancy, etc. can have effects on WOB measurements (e.g., as scalar values), stability of WOB measurements, etc. Hysteresis can exist such that WOB measurements differ depending on a direction of a drillstring moving in a hole. For example, moving in a direction of gravity may result in different time series data than moving in a direction contrary to gravity. Further, friction may differ depending on direction of movement.
As an example, a surface HKLD measurement can drop as soon as the bottom of the hole is engaged with the bit and the surface torque measurement can show an increased torque demand as the bit interacts with a formation (e.g., rock) and, if there is a downhole motor, surface pressure can increase, signaling an increase in differential pressure as the motor drills away. Such physical indicators can be present on the rig floor with relatively adequate fidelity and provide a sense of awareness for the driller that the equipment being operated is operating to crush through rock and make steady progress drilling ahead. The way a driller infers an operational state as being one of on or off bottom may be through experience and with some amount of uncertainty as one or more transition states can exist between the two states of on and off bottom; noting that one or more state detection systems may be implemented to determine or estimate a state such as being on bottom or being off bottom.
As explained, WOB measurements may be based on a difference in HKLD between off bottom and on bottom states. When a portion of a hanging drillstring weight is supported by a bit resting on the bottom of a borehole, HKLD is reduced by that portion. As an example, a difference between a current HKLD and a pre-set tare value (e.g., or TARE value) may be utilized as a reference for the amount of weight put on the bit. A TARE value may be obtained by measuring HKLD while suspending the drillstring in in a borehole, and without the drillstring being supported on the bottom of the borehole. As drillstring weight changes as drill pipe segments are added to or removed from a drillstring, applying a designated WOB demands that the TARE weight be monitored. For example, consider the aforementioned zeroing approach where a gauge may include a secondary indicator that works backwards from a primary indicator (e.g., a main indicator) such that a driller may read WOB directly from the gauge.
As an example, a framework may provide for implementation of one or more automated methods that may be utilized for rig-based operations (e.g., drilling, etc.). As an example, a method can provide for detecting tare values for weight correction from a well construction process using one or more data-driven techniques. As an example, a method may provide for detecting a possible post-connection procedure for computing off bottom rotating weight from sensor data, which may include noise and/or artefacts, during a well construction process and combining that with a Gaussian Process (GP) model to propose an optimal TARE value. In such an example, the method may also utilize information available in one or more pre-connection procedures to enhance reliability of a TARE value prediction.
As an example, a framework may provide for implementation of one or more automated methods that may be utilized for rig-based operations, such as, for example, drilling operations where a risk of sticking may exist. For example, consider a framework that may provide for automated sticking of drill pipe (e.g., drill pipe of a drillstring) in a borehole.
As an example, a data-driven model may be generated using various types of data, which may include series data as time series data and/or depth series data. For example, consider a workflow that may utilize data for various operations at multiple rig sites that include measured depth (MD) data, inclination (Incl.) data, and mud density data (e.g., mud weight data, given as mass per unit volume of a drilling fluid). As an example, a data-driven model may provide for detection of sticking of drill pipe, which may be at one or more levels of sticking. For example, consider a scale that extends from micro sticking (e.g., minor sticking) to macro sticking (e.g., major sticking) where various levels of sticking may contribute to non-productive time (NPT), invisible lost time (ILT), risks to a drillstring, risks to rig equipment, risks to borehole damage, etc.
As an example, micro sticking may be defined as a type of relatively short-term sticking or stuck pipe events that may tend to go unreported as, for example, one or more metrics (e.g., duration, magnitude, etc.) do not meet one or more corresponding reporting criteria (e.g., duration threshold, magnitude threshold, etc.). However, such micro events may accumulate as ILT. In various instances, such micro events may still potentially cause NPT and/or increase risk of major and catastrophic stuck pipe events. As an example, a framework may provide for detection of events such as micro stuck pipe events.
Stuck pipe events can be a major cause of NPT in well construction and drilling operations. As an example, a stuck pipe event may be reported as NPT when it has an impact on planned activities (e.g., consequences of time delays and corresponding resource impacts, etc.). As explained, various relatively short-term stuck pipe events may go unreported due to not meeting one or more reporting thresholds; however, while still potentially causing NPT and increasing risk of major and/or catastrophic stuck pipe events. As explained, such events may be referred to as micro stuck pipe, micro stuck pipe events, micro sticking, etc. As an example, a framework may provide for identifying the presence of micro stuck pipe during operations such as, for example, tripping in hole (e.g., running in hole (RIH)) or tripping out of hole (e.g., pulling out of hole (POOH)). As an example, a framework may provide for micro stuck pipe detection and quantifying risk of a catastrophic stuck pipe in the near future based at least in part thereon. As an example, a framework may provide for detecting micro stuck pipe and predicting impact if the level of sticking may increase to above the micro level (e.g., to major level and/or a catastrophic level). As an example, a data-driven model may provide for micro stuck pipe detection, for example, by comparing a predicted drag force value from an ideal data-driven drag model to acquired hook load (HKLD) data in the field (e.g., from one or more sensors, etc.).
As an example, a method may provide for detecting micro sticking of a drillstring in a borehole. In such an example, a reduced order model may be utilized to detect micro sticking. For example, consider a reduced order model that is a torque and drag model, which may be, for example, a multi-well data-driven model. As an example, a multi-well data-driven model may be developed using a Gaussian Process (GP)-based approach such as, for example, a GP Regression approach, which may be referred to as a GP Regressor approach, either of which may be abbreviated as GPR.
As an example, a micro stuck pipe detector (see, e.g., the micro stuck pipe detection block 630) may utilize a drag prediction model that may be or include a reduced order model of torque and drag (T&D), which may be a physics-based model. As an example, a drag prediction model may be utilized that is a data-driven model. For example, consider a data-driven model that is or includes a Gaussian Process Regressor (e.g., utilizes Gaussian Process Regression (GPR)). As an example, a data-driven model may be relatively lightweight for rapid execution using available computing resources. As an example, a data-driven model may be relatively rapid and suitable for implementation and, for example, for calibration to each operation via comparison to output of a physics-based model. As an example, a data-driven model may utilize inputs such as, for example, MD, Incl., and density (e.g., mud density) to generate output such as, for example, HKLD during free rotation, HKLD during tripping, etc.; noting that tripping may be considered a free rotation type of state where a drill bit is not on-bottom. In such an example, the HKLD output may be considered as corresponding to an ideal condition with zero friction. As an example, HKLD with different friction factors may be computed as a function of HKLD during free rotation, friction factor and depth.
As an example, a framework may utilize a data-driven model that may be driven at least in part with real-time measurements. As an example, a framework may provide for combining real-time measurements with data-driven model output in a Bayesian manner (e.g., consider utilization of a Bayesian estimator, etc.).
As an example, a GPR may be utilized, for example, via the scikit-learn framework, the GPy framework, etc. In the scikit-learn framework the GPR is an implementation based on Algorithm 2.1 of Gaussian Processes for Machine Learning (GPML) (C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006, ISBN 026218253X, at p. 19), which is incorporated by reference herein. In addition to standard scikit-learn estimator API, GPR: allows prediction without prior fitting (based on the GP prior); provides an additional method sample_y(X), which evaluates samples drawn from the GPR (prior or posterior) at given inputs; exposes a method log_marginal_likelihood(theta), which can be used externally for other ways of selecting hyperparameters, e.g., via Markov chain Monte Carlo, etc.
The example Algorithm 2.1 provides for predictions and log marginal likelihood for GPR. The implementation addresses the matrix inversion required in lines 3 and 4 using Cholesky factorization in lines 5 and 6. For multiple test cases lines 4-6 can be repeated. The log determinant required in line 7 can be computed from the Cholesky factor (noting that for large n it may not be possible to represent the determinant itself). The computational complexity is n3/6 for the Cholesky decomposition in line 2, and n2/2 for solving triangular systems in line 3 and (for each test case) in line 5. The example Algorithm 2.1 uses Cholesky decomposition, instead of directly inverting the matrix, as it can be faster and numerically more stable. The algorithm returns the predictive mean and variance for noise free test data where, for example, to compute the predictive distribution for noisy test data, the algorithm can include adding the noise variance to the predictive variance.
As to the GPy library, consider the model 750 of
In the GPy library, a kernel (GPy.kern), data and, optionally, a representation of noise may be assigned to a GPRM. Tailored models may demand, or may make use of, one or more types of additional information. As indicated, a kernel and/or noise may be controlled via hyperparameters, for example, by calling one or more optimization techniques (e.g., GPy.core.gp.GP.optimize) to be applied against the model to invoke an iterative process that may seek optimal hyperparameter values. As shown in the example of
Gaussian Processes (GP) may be defined as being a generic supervised learning method designed to solve regression and probabilistic classification problems. Some example advantages of Gaussian Processes include: the prediction interpolates the observations (at least for regular kernels); the prediction is probabilistic (Gaussian) so that one can compute empirical confidence intervals and decide based on those if one should refit (online fitting, adaptive fitting) the prediction in some region of interest; versatility in that different kernels can be specified (e.g., common kernels can be provided where it is also possible to specify custom kernels).
A GPR can implement one or more GPs for regression purposes. For this, the prior of the GP can be specified. The prior mean can be assumed to be constant and zero (for normalize_y=False) or the training data's mean (for normalize_y=True). The prior's covariance can be specified by passing a kernel object. The hyperparameters of the kernel can be optimized during fitting of GPR by maximizing the log-marginal-likelihood (LML) based on the passed optimizer. As the LML may have multiple local optima, the optimizer can be started repeatedly by specifying n_restarts_optimizer. As an example, a first run may be conducted starting from an initial set of hyperparameter values of a kernel; where subsequent runs can be conducted from hyperparameter values that have been chosen randomly from the range of allowed values. If the initial hyperparameters are to be kept fixed, none can be passed as optimizer.
As an example, noise level in the targets can be specified by passing it via the parameter alpha, either globally as a scalar or per datapoint. Note that a moderate noise level can also be helpful for dealing with numeric issues during fitting as it is effectively implemented as Tikhonov regularization, e.g., by adding it to the diagonal of the kernel matrix. As an example, an alternative to specifying the noise level explicitly is to include a WhiteKernel component into the kernel, which can estimate the global noise level from the data.
The form of the mean function and covariance kernel function in a GP prior may be chosen and tuned during model selection. The mean function may be constant, either zero or the mean of a training dataset. Various options exist for the covariance kernel function, which may be semi-positive definite and symmetric. Some kernel functions may include constant, linear, square exponential and Matern kernel, as well as a composition of multiple kernels. One example of a kernel is the composition of the constant kernel with the radial basis function (RBF) kernel, which encodes for smoothness of functions. Such a kernel has two hyperparameters: signal variance, σ2, and lengthscale, l. In the scikit-learn framework, a variety of kernels are available and it is possible to specify the initial value and bounds on the hyperparameters.
As an example, an approach to tune the hyperparameters of the covariance kernel function can involve maximizing the log marginal likelihood of the training data. For example, a gradient-based optimizer may be used for efficiency; if unspecified above, a default optimizer in the scikit-learn framework is fmin_l_bfgs_b. As the log marginal likelihood is not necessarily convex, multiple restarts of the optimizer with different initializations may be used (n_restarts_optimizer).
As mentioned, a model may be utilized that includes various inputs such as, for example, MD, Incl., and density. In such an example, output may be HLKD during free rotation with mud pumps running (e.g., flow or FLWI being greater than zero). For example, the method 700 of
As explained, a framework may utilize a data-driven model to provide for a predicted HLKD value, which may be utilized for appropriate assessment of sticking (e.g., micro sticking, etc.). As an example, a framework may provide for use of one or more sensor-based measurements and/or one or more model-based predictions. For example, consider an approach that may act to output a value based on both a measurement of HLKD and a prediction of HLKD. In such an example, the framework may implement a type of probabilistic filter that can determine how to combine a measurement and a prediction.
In the example of
In the example of
In statistics and control theory, Kalman filtering, also known as linear quadratic estimation (LQE), is a technique that uses a series of measurements observed over time, which may include statistical noise and/or other inaccuracies, producing estimates of unknown variables that tend to be more accurate than those based on a single measurement alone. This may be achieved by estimating a joint probability distribution over the variables for each timeframe.
A Kalman filter may be applied to understand the behavior of a system that changes or evolves over time. A Kalman filter may be utilized in scenarios where information may include uncertainties (e.g., statistical noise and/or other inaccuracies) about a current state of a system under consideration, to estimate information about what the system is going to look like in the future (e.g., in a next state). By capturing certain correlations in the processes pertaining to that system based on its current state, t=k−1, it may be possible to generate a reasonably accurate estimation regarding the next state, k.
As explained, a filter may be a Bayesian type of filter that may be implemented to combine output of a prediction model with a measurement for estimation of a value. As explained, a prediction model may include conditioning a data-driven GP modelled for a given depth (e.g., MD). As explained, measured depth, mud density and inclination may be predictor variables of a model and a weight on a drillstring may be the response.
As explained, during tripping, operations may include adding equipment to a drillstring (e.g., or other tool string) or removing equipment from a drillstring (e.g., or other tool string). As explained, a drillstring may be composed of stands of drill pipe together with a BHA where pipe handling operations may be performed at surface to add or remove one or more pipes at a time (e.g., a pipe at a time, a stand at a time, etc.).
Abnormal torque and/or drag may occur during various operations, where torque can be associated with rotary friction and where drag can be associated with axial friction. As an example, an abnormal torque value and/or drag value may be due at least in part due to frictional effects between a drillstring and a borewall. As an example, an abnormal value may be considered to be an anomaly. As an example, an anomalous value may be a value that differs from an expected value associated with normal operation, as may be defined using one or more criteria (e.g., upper limit, lower limit, standard deviation, etc.). As an example, overpull, underpull, and/or high-torque load may be indications of excess frictional effects between a drillstring and a borewall.
As to overpull, it may be defined as a load applied when pulling a drillstring that is in excess of actual drillstring weight; whereas, underpull may be defined as a load applied when pulling a drillstring that is not in excess of actual drillstring weight. For example, overpull and underpull may be defined with respect to a free rotating weight (FRW), which may be a hook load value (e.g., HKLD value); noting that one or more limits may be set as with respect to FRW (e.g., plus an amount and minus an amount). As an example, FRW may depend on flow rate of drilling fluid (e.g., mud flow rate).
As explained, friction may depend on one or more drilling fluid characteristics and/or flow of drilling fluid. As an example, one or more types of mud additives may be utilized for lowering torque (rotary friction) and/or drag (axial friction) of a drillstring in a borehole. As an example, a mud additive may be a lubricant, which may include solids (e.g., such as plastic beads, glass beads, nut hulls, graphite, etc.), liquids (e.g., oils, synthetic fluids, glycols, modified vegetable oils, fatty-acid soaps, surfactants, etc.), etc. As an example, a framework may provide for detection of sticking (e.g., micro sticking) and determining a control strategy for addressing such sticking and/or escalation thereof. As an example, a control strategy may involve adjusting one or more drilling fluid properties and/or flow (e.g., introduction of one or more additives, adjusting a mud pump as to flow rate, etc.).
As an example, one or more conditions may lead to an anomaly (e.g., an anomalous event). For example, consider one or more of tight hole, differential sticking, poor hole cleaning, key seats, etc. A failure to address one or more conditions may lead to an increased risk of excessive wear on a drillstring, an increased risk of a severe sticking event (e.g., a major or catastrophic event), an increased risk of borehole damage (e.g., damage to a borewall, etc.), etc.
As an example, a framework may provide for overlaying HKLD estimates (e.g., data-driven model estimates, hybrid model and real-value estimates, Bayesian estimates, etc.) for different friction factors with measured HKLD data. In such an example, the framework may provide for determining an actual friction factor, which may be via inference. For example, where different friction factors are utilized for HKLD estimates, values may be compared with measured HKLD data to determine a best match or best fit between the different friction factors and the measured HKLD data. For example, if 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, etc., are utilized for friction factors, if a best match exists for 0.25 with measured HKLD data, then a framework may infer that the actual friction factor is approximately 0.25; noting that an interpolation may be utilized (e.g., taking an average of a closest two friction factor values, etc.). As an example, a framework may infer friction factor during drilling and thereby identify the drag during tripping out of the hole (POOH, as an overpull condition) and tripping in hole (RIH, as a slack-off condition). As to detection of sticking such as, for example, micro sticking (e.g., micro stuck pipe), a framework may identify one or more instances where one or more measured HKLD values are greater than one or more corresponding predicted drag values.
In the example of
In the example GUI 1100, the hook load values may be provided on a stand-by-stand or one or more other bases. For example, each bar may represent a stand or other length of equipment (e.g., drill pipe, drill collar, etc.) that is added or removed from a drillstring (e.g., or other tool string).
As mentioned, a framework may implement a Bayesian type of filter that may utilize a measurement and a prediction, which may be implemented for one or more depths of a drillstring in a borehole. As an example, for a particular depth, a framework may include predicting and acquiring measurements where uncertainties may exist for predictions and measurements. As explained, a Bayesian approach may provide for generating estimates using predictions and/or measurements.
As an example, a framework may provide for combining a prediction (e.g., a model-based value) and a measurement (e.g., an observed value) with a Gaussian update through a Bayesian type of filter. In various instances, the challenge of combining a measurement with a prediction may be impacted by the prediction having a high uncertainty at times because a prediction model (e.g., GPRM, etc.) may be dense and also incomplete. In such instances, a Bayesian update can help in mitigating the uncertainty of the prediction model with observation data as may be generated via one or more sensors.
In various instances, measurements may be sparse as they may be contingent on attempts to infer a state (or states) from a sequence of operations, which may not be guaranteed to happen (e.g., may not be available). In these cases, a Bayesian update may rely on a data-driven model to fill a gap and still provide a reliable estimate of a weight value.
As an example, a framework may be operable in real-time to receive real-time data from a rig site (e.g., rig equipment) where the framework may generate an estimated hook load value based on one or more of a predicted hook load value and a measured hook load value. In such an example, the estimated hook load value may be of lesser uncertainty than the predicted hook load value and/or the measured hook load value. In such an example, the estimated hook load value may be compared to the measured hook load value for one or more purposes, which may include one or more of sticking detection, friction factor determination, sensor quality, data acquisition quality, rig equipment control, etc. As explained, a predicted hook load value may be generated using a data-driven model and parameters such as measured depth, inclination, and mud density (e.g., density) as inputs.
As an example, the framework 1440 may output an estimated hook load value that is based on a filter where the filter includes an input for a predicted hook load value and an input for a measured hook load value. As an example, the filter may be a Bayesian filter such as, for example, a Bayesian Kalman filter. In such an example, the framework 1440 may output an estimated hook load value that may be based on one or more of a predicted hook load value and a measured hook load value. In such an example, uncertainty may be output for the estimated hook load value (see, e.g., the GUI 1300 of
As an example, one or more outputs may be utilized by a machine learning block 1450, for example, to perform machine learning and/or to generate one or more machine learning outputs (e.g., using one or more trained machine learning models). As an example, the framework 1440 may include one or more machine learning models that may be at least in part data-driven. As explained, a framework may output one or more values that may be based on output of a predictor, a measurement and/or a combination of both. As explained, a framework may implement a Bayesian approach to output generation.
As to a real-time scenario, the framework 1440 may generate output that may be utilized to control the rig equipment 1405. For example, if a friction factor estimate is trending high, the framework 1440 may call for one or more additives to be added to mud to help reduce the friction factor. As another example, consider detection of micro sticking where the framework 1440 may call for adjusting one or more operational parameters of the rig equipment 1405. As an example, the control instruction block 1446 may output a control instruction to one or more of the local RCS 1410 and the remote RCS 1420. In such an example, the control instruction may be based at least in part on a friction factor estimate and/or a sticking detection (e.g., micro sticking detection).
As an example, the framework 1440 may provide for increasing automation of control of the rig equipment 1410. As explained, uncertainty may be provided as part of a prediction, a measurement, an estimate, etc. In such an approach, where uncertainty is below a threshold, a value may be utilized for increased automation of one or more field operations performed at least in part by the rig equipment 1405 (e.g., via the local RCS 1410 and/or the remote RCS 1420). In contrast, if uncertainty is above a threshold, one or more field operations may involve having a human in the loop (HITL). In such an approach, automation may be implemented in a manner that depends on output uncertainty, for example, where level of automation may be automatically adjusted to be towards more automation or less automation.
As an example, where drilling is at least in part automated using one or more controllers, performance thereof may be improved via improved drilling and/or more autonomous drilling. As to automation, the ability to provide output as to uncertainty may be implemented in determining what level of automation to implement. For example, if uncertainty is above a threshold, a level of automation may be reduced, for example, to include more human involvement (e.g., a human-in-the-loop (HITL)); noting that upon a reduction in uncertainty, a level of automation may optionally be increased.
As explained, as an example, a framework may provide for computing more accurate values using a measurement as well as a prediction from a data-driven model by combining the measurement and the prediction through a Bayesian update mechanism to thereby provide a way to determine values with reduced uncertainty.
As an example, a framework may provide for assessing one or more measurements, which, as explained, may include uncertainty due to state (e.g., temporal uncertainty) and/or uncertainty due to measurement noise (e.g., sensor-based noise). As an example, where a measurement and a prediction are not in agreement, a framework may provide feedback as to the measurement, which may include calling for slowing operations down to reduce temporal uncertainty as to state and/or calling for investigation of a sensor and/or data acquisition equipment. As an example, where agreement is deemed acceptable, a framework may call for speeding up operations as a state may be adequately detected with relatively little uncertainty where such detection may be taking more time that is necessary. In such an example, drilling operations may be improved by reducing non-productive time (NPT), lost invisible time (ILT), etc.
As explained, a well in fluid communication with a reservoir may serve various functions over time (e.g., consider hydrocarbon production, followed by water injection, followed by CO2 injection, etc.). As explained, quality of a well can depend on drilling practices. For example, where detrimental events occur during drilling, the occurrence of such events and/or resolution thereof (e.g., timing, techniques, etc.) can impact well quality. In such an example, a well with one or more well quality issues may be a suboptimal candidate for one or more treatments, further drilling, uses, etc. In such an example, the well quality may be assumed or unknown where, for example, the framework 1440 may be utilized to perform an historical assessment per the block 1448 to determine one or more well quality metrics. In such an example, a well quality metric may be related to sticking at one or more depths, which may indicate one or more borewall issues, one or more completions issues, etc.
As an example, the framework 1440 may provide for assessing quality of a borehole with respect to one or more completions operations. For example, consider considering assessing acquired data for instances of sticking at one or more depths to plan and/or perform one or more completions operations. Well completions or completions operations may refer to one or more assemblies of downhole tubulars and equipment that aim to enable safe and efficient production and/or injection of fluid with respect to a well. The point at which a completion process begins may depend on type and design of a well. Various options may exist for performing actions during a construction phase of a well, which can have a substantial impact on how suitable the well is for one or more purposes.
In completing a well, operations can include cementing where cement may be used to hold casing in place and to hinder fluid migration between subsurface formations. Cementing operations may include primary cementing and remedial cementing. Along with supporting casing in a bore, cement may be utilized to isolate zones, for example, keeping each of a number of penetrated zones and their fluids from communicating with one or more other zones. To help keep zones isolated, cementing operations may consider borehole quality, borehole properties, etc. As an example, the framework 1440 may provide for assessing borehole quality and/or borehole properties to improve cementing. In such an example, the framework 1440 may identify instances of sticking at one or more depths, which may be associated with one or more borehole quality and/or borehole property issues germane to cementing or completions generally. In such an example, cementing for a well may be performed using a tailored cement (e.g., a cement slurry, etc.), one or more plugs, etc., to address one or more issues to thereby improve cementing for the well. As to some types of issues that may be identified through an assessment, as may be associated with sticking, consider one or more of drilling-induced fractures, chemically induced formation instability, natural fractures, vugs, etc.
As explained, an individual well may have its own well history, which may be documented by various types of data (e.g., as may be stored in one or more databases). As an example, the framework 1440 may provide for assessing well history (e.g., past field operations) and/or may provide for control of drilling (e.g., current field operations). As an example, the framework 1440 may improve decision-making as to potential field operations using already drilled wells and/or may improve field operations as to wells being drilled and/or wells to be drilled. Such a framework may provide for improving well quality assessments and/or well quality.
As explained, the framework 1440 may provide for reducing risks of stuck pipe. In various instances, complications related to stuck pipe may account for nearly half of a total well cost, making stuck pipe one of the most expensive problems that can occur during a drilling operation. In various instances, stuck pipe may be associated with well-control and/or lost-circulation events, which cause detrimental disruptions to drilling operations. Risks may increase, for example, in high-angle wells and/or horizontal wells. As an example, the framework 1440 may provide for indicating one or more depths where well-control and/or lost-circulation risks may be elevated (e.g., as may be associated with sticking, etc.).
In the example of
As to types of machine learning models, consider one or more of a support vector machine (SVM) model, a k-nearest neighbors (KNN) model, an ensemble classifier model, a neural network (NN) model, etc. As an example, a machine learning model may be a deep learning model (e.g., deep Boltzmann machine, deep belief network, convolutional neural network, stacked auto-encoder, etc.), an ensemble model (e.g., random forest, gradient boosting machine, bootstrapped aggregation, AdaBoost, stacked generalization, gradient boosted regression tree, etc.), a neural network model (e.g., radial basis function network, perceptron, back-propagation, Hopfield network, etc.), a regularization model (e.g., ridge regression, least absolute shrinkage and selection operator, elastic net, least angle regression), a rule system model (e.g., cubist, one rule, zero rule, repeated incremental pruning to produce error reduction), a regression model (e.g., linear regression, ordinary least squares regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing, logistic regression, etc.), a Bayesian model (e.g., naïve Bayes, average on-dependence estimators, Bayesian belief network, Gaussian naïve Bayes, multinomial naïve Bayes, Bayesian network), a decision tree model (e.g., classification and regression tree, iterative dichotomiser 3, C4.5, C5.0, chi-squared automatic interaction detection, decision stump, conditional decision tree, M5), a dimensionality reduction model (e.g., principal component analysis, partial least squares regression, Sammon mapping, multidimensional scaling, projection pursuit, principal component regression, partial least squares discriminant analysis, mixture discriminant analysis, quadratic discriminant analysis, regularized discriminant analysis, flexible discriminant analysis, linear discriminant analysis, etc.), an instance model (e.g., k-nearest neighbor, learning vector quantization, self-organizing map, locally weighted learning, etc.), a clustering model (e.g., k-means, k-medians, expectation maximization, hierarchical clustering, etc.), etc.
As an example, a machine model, which may be a machine learning model (ML model), may be built using a computational framework with a library, a toolbox, etc., such as, for example, those of the MATLAB framework (MathWorks, Inc., Natick, Massachusetts). The MATLAB framework includes a toolbox that provides supervised and unsupervised machine learning algorithms, including support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbor (KNN), k-means, k-medoids, hierarchical clustering, Gaussian mixture models, and hidden Markov models. Another MATLAB framework toolbox is the Deep Learning Toolbox (DLT), which provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. The DLT provides convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. The DLT includes features to build network architectures such as generative adversarial networks (GANs) and Siamese networks using custom training loops, shared weights, and automatic differentiation. The DLT provides for model exchange various other frameworks.
As an example, the TENSORFLOW framework (Google LLC, Mountain View, CA) may be implemented, which is an open-source software library for dataflow programming that includes a symbolic math library, which may be implemented for machine learning applications that may include neural networks. As an example, the CAFFE framework may be implemented, which is a DL framework developed by Berkeley AI Research (BAIR) (University of California, Berkeley, California). As another example, consider the SCIKIT platform (e.g., scikit-learn), which utilizes the PYTHON programming language. As an example, a framework such as the APOLLO AI framework may be utilized (APOLLO.AI GmbH, Germany). As an example, a framework such as the PYTORCH framework may be utilized (Facebook AI Research Lab (FAIR), Facebook, Inc., Menlo Park, California).
As an example, a training method may include various actions that may operate on a dataset to train a ML model. As an example, a dataset may be split into training data and test data where test data may provide for evaluation. A method may include cross-validation of parameters and best parameters, which may be provided for model training.
The TENSORFLOW framework may run on multiple CPUs and GPUs (with optional CUDA (NVIDIA Corp., Santa Clara, California) and SYCL (The Khronos Group Inc., Beaverton, Oregon) extensions for general-purpose computing on graphics processing units (GPUs)). TENSORFLOW is available on 64-bit LINUX, MACOS (Apple Inc., Cupertino, California), WINDOWS (Microsoft Corp., Redmond, Washington), and mobile computing platforms including ANDROID (Google LLC, Mountain View, California) and IOS (Apple Inc.) operating system-based platforms.
TENSORFLOW computations may be expressed as stateful dataflow graphs; noting that the name TENSORFLOW derives from the operations that such neural networks perform on multidimensional data arrays. Such arrays may be referred to as “tensors”.
As an example, a method can include acquiring data for rig operations that move a drillstring in a borehole in a subsurface geologic region, where the drillstring includes connected stands of drill pipe and a drill bit for drilling into the subsurface geologic region, and where the data include measured depth data, inclination data, mud density data, and measured hook load data; generating an estimated hook load value for a measured depth in the borehole using at least a trained model that receives a portion of the data as associated with the measured depth; performing a comparison between the estimated hook load value and a measured hook load value of the measured hook load data as associated with the measured depth; and based at least in part on the comparison, determining a level of sticking of the drillstring in the borehole. In such an example, generating can generates the estimated hook load value using a friction factor value. In such an example, the method may include determining the friction factor value by comparing a number of estimated hook load values for different friction factors for a span of measured depths to a number of measured hook load values of the measured hook load data for the span of measured depths.
As an example, a trained model can receive an inclination value of inclination data and a mud density value of mud density data.
As an example, an estimated hook load value can depend on a measured depth value, an inclination value, and a mud density value.
As an example, acquiring can acquire real-time data during one or more types of rig operations. For example, consider one or more types of rig operations that can include a pulling out type of rig operation and a running in type of rig operation.
As an example, a method may include controlling one or more of rig operations based at least on a level of sticking. For example, consider a method that includes acquiring real-time data during one or more types of rig operations that involve a drillstring in a borehole, generating an estimated hook load value for a measured depth in a borehole using at least a trained model that receives a portion of the data as associated with the measured depth; performing a comparison between the estimated hook load value and a measured hook load value of the measured hook load data as associated with the measured depth; based at least in part on the comparison, determining a level of sticking of the drillstring in the borehole; and controlling one or more of the rig operations based at least on the level of sticking.
As an example, a level of sticking may be a less than micro sticking level or a micro sticking level. As an example, a micro sticking level can be associated with an increased risk of a higher level of sticking. For example, where a micro sticking level occurs, it may indicate that a more pronounced sticking level of sticking may occur that can result in one or more types of issues; whereas, a less than micro sticking level may not be an indicator of increased risk of a higher level of sticking. As an example, as to a micro sticking level, one or more techniques, technologies, criteria, etc., may be utilized to ascertain whether such a level is indicative of an increased risk of a higher level of sticking.
As an example, one or more types of data may be utilized such as, for example, historical data, synthetic data (e.g., from simulation runs, etc.). As shown in the example of
As an example, a method can include generating at least one control instruction associated with a level of sticking. For example, consider at least one control instruction that includes a control instruction to add an additive to drilling fluid to reduce risk of sticking, a control instruction to adjust speed of moving a drillstring in the borehole, a control instruction to adjust rotation (e.g., rpm) of a drillstring (e.g., unidirectional, bidirectional as in oscillating, etc.), etc.
As an example, a method can include generating an estimated hook load value at least in part by using a filter that includes an input for a measured hook load value and an input for a predicted hook load value. In such an example, the filter may be or include a Bayesian type of filter. For example, consider a Bayesian type of filter that is or includes a Bayesian Kalman filter. As explained with respect to the example GUI 1200 of
As an example, a method can include generating an estimated hook load value in a manner that includes estimating uncertainty of the estimated hook load value (see, e.g., the example GUI 1300 of
As an example, a method may include implementing a trained model that may be or include a Gaussian Process Regression (GPR) model.
As an example, a system can include a processor; memory accessible by the processor; processor-executable instructions stored in the memory and executable to instruct the system to: acquire data for rig operations that move a drillstring in a borehole in a subsurface geologic region, where the drillstring includes connected stands of drill pipe and a drill bit for drilling into the subsurface geologic region, and where the data include measured depth data, inclination data, mud density data, and measured hook load data; generate an estimated hook load value for a measured depth in the borehole using at least a trained model that receives a portion of the data as associated with the measured depth; perform a comparison between the estimated hook load value and a measured hook load value of the measured hook load data as associated with the measured depth; and, based at least in part on the comparison, determine a level of sticking of the drillstring in the borehole.
As an example, one or more computer-readable storage media can include processor-executable instructions to instruct a computing system to: acquire data for rig operations that move a drillstring in a borehole in a subsurface geologic region, where the drillstring includes connected stands of drill pipe and a drill bit for drilling into the subsurface geologic region, and where the data include measured depth data, inclination data, mud density data, and measured hook load data; generate an estimated hook load value for a measured depth in the borehole using at least a trained model that receives a portion of the data as associated with the measured depth; perform a comparison between the estimated hook load value and a measured hook load value of the measured hook load data as associated with the measured depth; and, based at least in part on the comparison, determine a level of sticking of the drillstring in the borehole.
As an example, a method may be implemented in part using computer-readable media (CRM), for example, as a module, a block, etc. that include information such as instructions suitable for execution by one or more processors (or processor cores) to instruct a computing device or system to perform one or more actions. As an example, a single medium may be configured with instructions to allow for, at least in part, performance of various actions of a method. As an example, a computer-readable medium (CRM) may be a computer-readable storage medium (e.g., a non-transitory medium) that is not a carrier wave. As an example, a computer-program product may include instructions suitable for execution by one or more processors (or processor cores) where the instructions may be executed to implement at least a portion of a method or methods.
According to an embodiment, one or more computer-readable media may include computer-executable instructions to instruct a computing system to output information for controlling a process. For example, such instructions may provide for output to sensing process, an injection process, drilling process, an extraction process, an extrusion process, a pumping process, a heating process, etc.
In some embodiments, a method or methods may be executed by a computing system.
As an example, a system may include an individual computer system or an arrangement of distributed computer systems. In the example of
As an example, a module may be executed independently, or in coordination with, one or more processors 1604, which is (or are) operatively coupled to one or more storage media 1606 (e.g., via wire, wirelessly, etc.). As an example, one or more of the one or more processors 1604 may be operatively coupled to at least one of the one or more network interface 1607. In such an example, the computer system 1601-1 may transmit and/or receive information, for example, via the one or more networks 1609 (e.g., consider one or more of the Internet, a private network, a cellular network, a satellite network, etc.). As shown, one or more other components 1608 may be included in the computer system 1601-1.
As an example, the computer system 1601-1 may receive from and/or transmit information to one or more other devices, which may be or include, for example, one or more of the computer systems 1601-2, etc. A device may be located in a physical location that differs from that of the computer system 1601-1. As an example, a location may be, for example, a processing facility location, a data center location (e.g., server farm, etc.), a rig location, a wellsite location, a downhole location, etc.
As an example, a processor may be or include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.
As an example, the storage media 1606 may be implemented as one or more computer-readable or machine-readable storage media. As an example, storage may be distributed within and/or across multiple internal and/or external enclosures of a computing system and/or additional computing systems.
As an example, a storage medium or storage media may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories, magnetic disks such as fixed, floppy and removable disks, other magnetic media including tape, optical media such as compact disks (CDs) or digital video disks (DVDs), BLUERAY disks, or other types of optical storage, or other types of storage devices.
As an example, a storage medium or media may be located in a machine running machine-readable instructions, or located at a remote site from which machine-readable instructions may be downloaded over a network for execution.
As an example, various components of a system such as, for example, a computer system, may be implemented in hardware, software, or a combination of both hardware and software (e.g., including firmware), including one or more signal processing and/or application specific integrated circuits.
As an example, a system may include a processing apparatus that may be or include a general-purpose processors or application specific chips (e.g., or chipsets), such as ASICs, FPGAs, PLDs, or other appropriate devices.
As an example, a device may be a mobile device that includes one or more network interfaces for communication of information. For example, a mobile device may include a wireless network interface (e.g., operable via IEEE 802.11, ETSI GSM, BLUETOOTH, satellite, etc.). As an example, a mobile device may include components such as a main processor, memory, a display, display graphics circuitry (e.g., optionally including touch and gesture circuitry), a SIM slot, audio/video circuitry, motion processing circuitry (e.g., accelerometer, gyroscope), wireless LAN circuitry, smart card circuitry, transmitter circuitry, GPS circuitry, and a battery. As an example, a mobile device may be configured as a cell phone, a tablet, etc. As an example, a method may be implemented (e.g., wholly or in part) using a mobile device. As an example, a system may include one or more mobile devices.
As an example, a system may be a distributed environment, for example, a so-called “cloud” environment where various devices, components, etc. interact for purposes of data storage, communications, computing, etc. As an example, a device or a system may include one or more components for communication of information via one or more of the Internet (e.g., where communication occurs via one or more Internet protocols), a cellular network, a satellite network, etc. As an example, a method may be implemented in a distributed environment (e.g., wholly or in part as a cloud-based service).
As an example, information may be input from a display (e.g., consider a touchscreen), output to a display or both. As an example, information may be output to a projector, a laser device, a printer, etc. such that the information may be viewed. As an example, information may be output stereographically or holographically. As to a printer, consider a 2D or a 3D printer. As an example, a 3D printer may include one or more substances that may be output to construct a 3D object. For example, data may be provided to a 3D printer to construct a 3D representation of a subterranean formation. As an example, layers may be constructed in 3D (e.g., horizons, etc.), geobodies constructed in 3D, etc. As an example, holes, fractures, etc., may be constructed in 3D (e.g., as positive structures, as negative structures, etc.).
Although only a few examples have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the examples. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the following claims. In the claims, means-plus-function clauses are intended to cover the structures described herein as performing the recited function and not only structural equivalents, but also equivalent structures. Thus, although a nail and a screw may not be structural equivalents in that a nail employs a cylindrical surface to secure wooden parts together, whereas a screw employs a helical surface, in the environment of fastening wooden parts, a nail and a screw may be equivalent structures.
This application claims priority to and the benefit of a US Provisional Application having Ser. No. 63/504,954, filed 30 May 2023, which is incorporated by reference herein in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 63504954 | May 2023 | US |
