APPROACHES TO NEEDLE VALVE OPTIMIZATION

BACKGROUND

Liquid loading is a common issue in gas wells where the velocity of gas is often insufficient to carry liquids to the surface, causing those liquids to accumulate in a wellbore. This problem is particularly common in wells that are controlled by needle valves. Indeed, loading in these tight gas wells has troubled the industry for some time causing productivity loss and costly workovers. In conventional systems, site-specific experts are typically tasked with identifying and mitigating effects of liquid loading using time and energy consuming processes. Due to the unique nature of individual sites, and due to many parameters contributing to the trends associated gas pressure increasing and/or decreasing, conventional approaches for predicting gas production and scheduling opening and closing of needle valves are often inefficient and imprecise. The inefficient and imprecise nature of controlling production leads to many inefficiencies and less than optimal production.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example environment in which drilling may occur in accordance with one or more embodiments.

FIG. 2 illustrates an example of a wellsite system in accordance with one or more embodiments.

FIG. 3 illustrates a schematic view of such an example computing or processor system, in accordance with one or more embodiments.

FIG. 4 illustrates an example computing device having a SNV optimization system implemented thereon in accordance with one or more embodiments.

FIG. 5 illustrates an example workflow showing a procedure for training and implementing a pressure buildup and production prediction model in accordance with one or more embodiments.

FIG. 6 illustrates an example series of acts for determining a predicted production metric over a production cycle of a wellsite in accordance with one or more embodiments.

DETAILED DESCRIPTION

This disclosure relates to collecting and analyzing historical data related to pressure buildup and gas production for a particular well environment and training one or more models to predict gas production and pressure trends over one or more production cycles. In particular, this disclosure relates to training an implementing a pressure buildup and production model to be used in connection with determining when to open and/or close a needle valve that controls the flow of gas within a wellsite environment. As will be discussed in further detail below, the following disclosure describes features and characteristics related to specifically training a number of machine learning models and, based on a combination of predictions and real-time production and pressure data, further training the machine learning models to recommend a timing when the needle valve should be open or closed to maximize production of the wellsite over time.

As an illustrative example, systems, methods, and computer-readable media (or simply “systems”) described herein involve training a pressure buildup and prediction model to generate a given output indicating a predicted production metric for an upcoming production period upon opening a needle valve to initiate gas production of a well. In one or more embodiments, the pressure buildup and prediction model are trained based on historical production data and historical pressure data that is collected and maintained within a historical data database. In one or more embodiments described herein, systems obtain input pressure and production data for several previous closing periods (e.g., a previous production cycle). The trained pressure buildup and production prediction model may be applied to the input pressure and production data to generate an output indicating a predicted production metric for a next production period of the wellsite. The output of the pressure buildup and prediction model may be used to inform an operator of the needle valve to open (or close) the needle valve based on the predicted production metric for the next production period.

The present disclosure includes features and functionalities that implement practical applications that provide benefits and/or solve problems associated with forecasting pressure buildup and production over a production cycle and determining specific timings of when a needle valve should be open or closed to optimize production of the wellsite over a period of time. Examples of some of these applications and benefits are discussed in further detail below.

In one or more embodiments described herein, a Smart Needle Valve (SNV) optimization system trains machine learning models based on a combination of historical data and current data. By training machine learning models based on both historical data and current data, the SNV optimization system is able to update the respective models to more accurately recommend the needle valve opening times that will optimize production over time. This is an improvement over conventional systems that involve a single model and/or manual-driven systems that rely on expert domain knowledge for a particular wellsite.

As will be discussed in further detail below, the SNV optimization system facilitates a fine-tuning process in which the various models are refined over time based on additional data as it becomes available. Thus, while the model(s) described herein may refer to models that are generally trained based on a collection of historical data that may or may not be applicable to a particular wellsite, by retraining or otherwise updating the training over time based on production data and pressure data for the particular wellsite, the SNV optimization system can adjust the algorithms and parameters of the models to be more accurate over time for specific wellsites based on unique characteristics and gas production behavior for the particular wellsite(s).

As illustrated in the foregoing discussion, the present disclosure utilizes a variety of terms to described features and advantages of one or more embodiments of the SNV optimization system. Additional detail will now be provided regarding the meaning of some of these terms. Further terms will also be discussed in detail in connection with one or more embodiments and specific examples below.

As used herein, a “production cycle” may refer to a cycle inclusive of a period in which a needle valve is subsequently closed and open. For example, in one or more embodiments described herein, a production cycle includes both a first period of time in which a needle valve is closed and a second period of time in which the needle valve is open. Alternatively, in one or more embodiments, a production cycle refers to a first period of time in which the needle valve is open following by a second period of time in which the needle valve is closed.

As noted above, the production cycle may include a closing period and a production period. As used herein, a “closing period” refers to a period of time in which the needle valve is closed and is not producing gas. As will be discussed in further detail below, the closing period may also be referred to as a pressure buildup period as pressure within the well is typically building while the needle valve is closed. As used herein, a “production period” refers to a period of time in which the needle valve is open and producing gas. As will be discussed in further detailed below, the production period may be characterized by metrics of gas production and, in some instances, an accompanying pressure of the well as the pressure typically decreases over the time period in which the needle valve is open.

Additional detail will now be provided regarding systems and methods described herein in relation to illustrative figures portraying example implementations. Wherever convenient, the same reference numbers are used in the drawings and the following description to refer to the same or similar parts. While several embodiments and features of the present disclosure are described herein, modifications, adaptations, and other implementations are possible, without departing from the spirit and scope of the present disclosure.

Although the terms “first”, “second”, etc. may be used herein to describe various elements, these terms are used to distinguish one element from another. For example, a first object or step could be termed a second object or step, and, similarly, a second object or step could be termed a first object or step, without departing from the scope of the present disclosure. The first object or step, and the second object or step, are both, objects or steps, respectively, but they are not to be considered the same object or step.

The terminology used in the description herein is for the purpose of describing particular embodiments and is not intended to be limiting. As used in this description and the appended claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Further, as used herein, the term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context.

Although the terms “first”, “second”, etc. may be used herein to describe various elements, these terms are used to distinguish one element from another. For example, a first object or step could be termed a second object or step, and, similarly, a second object or step could be termed a first object or step, without departing from the scope of the present disclosure. The first object or step, and the second object or step, are both objects or steps respectively, but they are not to be considered the same object or step.

FIG. 1 illustrates one example of an environment 100 in which drilling may occur. The environment may include a reservoir 102 and various geological features, such as stratified layers. The geological aspects of the environment 100 may contain other features such as faults, basins, and others. The reservoir 102 may be located on land or offshore.

The environment 100 may be outfitted with sensors, detectors, actuators, etc. to be used in connection with the drilling process. FIG. 1 illustrates equipment 104 associated with a well 106 being constructed using downhole equipment 108. The downhole equipment 108 may be, for example, part of a bottom hole assembly (BHA). The BHA may be used to drill the well 106. The downhole equipment 108 may communicate information to the equipment 104 at the surface, and may receive instructions and information from the surface equipment 104 as well. The surface equipment 104 and the downhole equipment 108 may communicate using various communications techniques, such as mud-pulse telemetry, electromagnetic (EM) telemetry, or others depending on the equipment and technology in use for the drilling operation.

The surface equipment 104 may also include communication means to communicate over a network 110 to remote computing devices 112. For example, the surface equipment 104 may communicate data using a satellite network to computing devices 112 supporting a remote team monitoring and assisting in the creation of the well 106 and other wells in other locations. Depending on the communications infrastructure available at the wellsite, various communication equipment and techniques (cellular, satellite, wired Internet connection, etc.) may be used to communicate data from the surface equipment 104 to the remote computing devices 112. In some embodiments, the surface equipment 104 sends data from measurements taken at the surface and measurements taken downhole by the downhole equipment 108 to the remote computing devices 112.

During the well construction process, a variety of operations (such as cementing, wireline evaluation, testing, etc.) may also be conducted. In such embodiments, the data collected by tools and sensors and used for reasons such as reservoir characterization may also be collected and transmitted by the surface equipment 104.

In FIG. 1, the well 106 includes a substantially horizontal portion (e.g., lateral portion) that may intersect with one or more fractures. For example, a well in a shale formation may pass through natural fractures, artificial fractures (e.g., hydraulic fractures), or a combination thereof. Such a well may be constructed using directional drilling techniques as described herein. However, these same techniques may be used in connection with other types of directional wells (such as slant wells, S-shaped wells, deep inclined wells, and others) and are not limited to horizontal wells.

FIG. 2 shows an example of a wellsite system 200 (e.g., at a wellsite that may be onshore or offshore). As shown, the wellsite system 200 can include a mud tank 201 for holding mud and other material (e.g., where mud can be a drilling fluid), a suction line 203 that serves as an inlet to a mud pump 204 for pumping mud from the mud tank 201 such that mud flows to a vibrating hose 206, a drawworks 207 for winching drill line or drill lines 212, a standpipe 208 that receives mud from the vibrating hose 206, a kelly hose 209 that receives mud from the standpipe 208, a gooseneck or goosenecks 210, a traveling block 211, a crown block 213 for carrying the traveling block 211 via the drill line or drill lines 212, a derrick 214 a kelly 218 or a top drive 240, a kelly drive bushing 219, a rotary table 220, a drill floor 221, a bell nipple 222, one or more blowout preventors (BOPs) 223, a drillstring 225, a drill bit 226, a casing head 227 and a flow pipe 228 that carries mud and other material to, for example, the mud tank 201.

In the example system of FIG. 2, a borehole 232 is formed in subsurface formations 230 by rotary drilling; noting that various example embodiments may also use one or more directional drilling techniques, equipment, etc.

As shown in the example of FIG. 2, the drillstring 225 is suspended within the borehole 232 and has a drillstring assembly 250 that includes the drill bit 226 at its lower end. As an example, the drillstring assembly 250 may be a bottom hole assembly (BHA).

The wellsite system 200 can provide for operation of the drillstring 225 and other operations. As shown, the wellsite system 200 includes the traveling block 211 and the derrick 214 positioned over the borehole 232. As mentioned, the wellsite system 200 can include the rotary table 220 where the drillstring 225 passes through an opening in the rotary table 220.

As shown in the example of FIG. 2, the wellsite system 200 can include the kelly 218 and associated components, etc., or a top drive 240 and associated components. As to a kelly example, the kelly 218 may be a square or hexagonal metal/alloy bar with a hole drilled therein that serves as a mud flow path. The kelly 218 can be used to transmit rotary motion from the rotary table 220 via the kelly drive bushing 219 to the drillstring 225, while allowing the drillstring 225 to be lowered or raised during rotation. The kelly 218 can pass through the kelly drive bushing 219, which can be driven by the rotary table 220. As an example, the rotary table 220 can include a master bushing that operatively couples to the kelly drive bushing 219 such that rotation of the rotary table 220 can turn the kelly drive bushing 219 and hence the kelly 218. The kelly drive bushing 219 can include an inside profile matching an outside profile (e.g., square, hexagonal, etc.) of the kelly 218; however, with slightly larger dimensions so that the kelly 218 can freely move up and down inside the kelly drive bushing 219.

As to a top drive example, the top drive 240 can provide functions performed by a kelly and a rotary table. The top drive 240 can turn the drillstring 225. As an example, the top drive 240 can 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 can 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 FIG. 2, the mud tank 201 can hold mud, which can be one or more types of drilling fluids. As an example, a wellbore may be drilled to produce fluid, inject fluid or both (e.g., hydrocarbons, minerals, water, etc.).

In the example of FIG. 2, the drillstring 225 (e.g., including one or more downhole tools) may be composed of a series of pipes threadably connected together to form a long tube with the drill bit 226 at the lower end thereof. As the drillstring 225 is advanced into a wellbore for drilling, at some point in time prior to or coincident with drilling, the mud may be pumped by the pump 204 from the mud tank 201 (e.g., or other source) via the lines 206, 208 and 209 to a port of the kelly 218 or, for example, to a port of the top drive 240. The mud can then flow via a passage (e.g., or passages) in the drillstring 225 and out of ports located on the drill bit 226 (see, e.g., a directional arrow). As the mud exits the drillstring 225 via ports in the drill bit 226, it can then circulate upwardly through an annular region between an outer surface(s) of the drillstring 225 and surrounding wall(s) (e.g., open borehole, casing, etc.), as indicated by directional arrows. In such a manner, the mud lubricates the drill bit 226 and carries heat energy (e.g., frictional or other energy) and formation cuttings to the surface where the mud (e.g., and cuttings) may be returned to the mud tank 201, for example, for recirculation (e.g., with processing to remove cuttings, etc.).

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 can 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 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 can 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 FIG. 2, an uphole control and/or data acquisition system 262 may include circuitry to sense pressure pulses generated by telemetry equipment 252 and, for example, communicate sensed pressure pulses or information derived therefrom for process, control, etc.

The assembly 250 of the illustrated example includes a logging-while-drilling (LWD) module 254, a measurement-while-drilling (MWD) module 256, an optional module 258, a rotary-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 can include a plurality of tools.

As to a 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 can 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 can 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 can 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 can 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 can operate in a so-called sliding mode, when the drillstring is not rotated from the surface. In such an example, a bit RPM can be determined or estimated based on the RPM of the mud motor.

A RSS can drill directionally where there is continuous rotation from surface equipment, which can alleviate the sliding of a steerable motor (e.g., a PDM). A RSS may be deployed when drilling directionally (e.g., deviated, horizontal, or extended-reach wells). A RSS can aim to minimize interaction with a borehole wall, which can help to preserve borehole quality. A RSS can 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 can 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 can be employed, for example, as represented by the module 256 of the drillstring assembly 250. Where the position of an LWD module is mentioned, as an example, it may refer to a module at the position of the LWD module 254, the module 256, etc. An LWD module can 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 can 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 can 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.

FIG. 2 also shows some examples of types of holes that may be drilled. For example, consider a slant hole 272, an S-shaped hole 274, a deep inclined hole 276 and a horizontal hole 278.

As an example, a drilling operation can 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 can 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 a downhole motor and/or a turbine. As to a motor, for example, a drillstring can include a positive displacement motor (PDM).

As an example, a system may be a steerable system and include equipment to perform method such as geosteering. As mentioned, a steerable system can be or include an RSS. As an example, a steerable system can include a PDM or a turbine on a lower part of a drillstring which, just above a drill bit, a bent sub can 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 can 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, can allow for implementing a geosteering method. Such a method can include navigating a subsurface environment, for example, to follow a desired route to reach a desired target or targets.

As an example, a drillstring can 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 optional equipment for measuring and/or responding to one or more of inclination, resistivity and gamma ray related phenomena.

As an example, geosteering can 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 FIG. 2, the wellsite system 200 can include one or more sensors 264 that are operatively coupled to the control and/or data acquisition system 262. As an example, a sensor or sensors may be at surface locations. As an example, a sensor or sensors may be at downhole locations. As an example, a sensor or sensors may be at one or more remote locations that are not within a distance of the order of about one hundred meters from the wellsite system 200. As an example, a sensor or sensors may be at an offset wellsite where the wellsite system 200 and the offset wellsite are in a common field (e.g., oil and/or gas field).

As an example, one or more of the sensors 264 can be provided for tracking pipe, tracking movement of at least a portion of a drillstring, etc.

As an example, the system 200 can include one or more sensors 266 that can 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 can be operatively coupled to portions of the standpipe 208 through which mud flows. As an example, a downhole tool can generate pulses that can travel through the mud and be sensed by one or more of the one or more sensors 266. In such an example, the downhole tool can include associated circuitry such as, for example, encoding circuitry that can 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 can include a transmitter that can generate signals that can 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 can 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 can refer to a portion of a drillstring that cannot be rotated or moved axially. As an example, a condition referred to as “differential sticking” can 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 can have time and financial cost.

As an example, a sticking force can 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 can be just as effective in sticking pipe as can a high differential pressure applied over a small area.

As an example, a condition referred to as “mechanical sticking” can be a condition where limiting or preventing motion of the drillstring by a mechanism other than differential pressure sticking occurs. Mechanical sticking can 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.

FIG. 3 illustrates a schematic view of such a computing or processor system 300, according to an embodiment. The processor system 300 may include one or more processors 302 of varying core configurations (including multiple cores) and clock frequencies. The one or more processors 302 may be operable to execute instructions, apply logic, etc. It will be appreciated that these functions may be provided by multiple processors or multiple cores on a single chip operating in parallel and/or communicably linked together. In at least one embodiment, the one or more processors 302 may be or include one or more GPUs.

The processor system 300 may also include a memory system, which may be or include one or more memory devices 304 and/or computer-readable media of varying physical dimensions, accessibility, storage capacities, etc. such as flash drives, hard drives, disks, random access memory, etc., for storing data, such as images, files, and program instructions for execution by the processor 302. In an embodiment, the computer-readable media may store instructions that, when executed by the processor 302, are configured to cause the processor system 300 to perform operations. For example, execution of such instructions may cause the processor system 300 to implement one or more portions and/or embodiments of the method(s) described above.

The processor system 300 may also include one or more network interfaces 306. The network interfaces 306 may include any hardware, applications, and/or other software. Accordingly, the network interfaces 306 may include Ethernet adapters, wireless transceivers, PCI interfaces, and/or serial network components, for communicating over wired or wireless media using protocols, such as Ethernet, wireless Ethernet, etc.

As an example, the processor system 300 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 one or more IEEE 802.11 protocols, 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.

The processor system 300 may further include one or more peripheral interfaces 308 for communication with a display, projector, keyboards, mice, touchpads, sensors, other types of input and/or output peripherals, and/or the like. In some implementations, the components of processor system 300 need not be enclosed within a single enclosure or even located in close proximity to one another, but in other implementations, the components and/or others may be provided in a single enclosure. 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 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., 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 can 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.).

The memory device 304 may be physically or logically arranged or configured to store data on one or more storage devices 310. The storage device 310 may include one or more file systems or databases in any suitable format. The storage device 310 may also include one or more software programs 312, which may contain interpretable or executable instructions for performing one or more of the disclosed processes. When requested by the processor 302, one or more of the software programs 312, or a portion thereof, may be loaded from the storage devices 310 to the memory devices 304 for execution by the processor 302.

Those skilled in the art will appreciate that the above-described componentry is merely one example of a hardware configuration, as the processor system 300 may include any type of hardware components, including any accompanying firmware or software, for performing the disclosed implementations. The processor system 300 may also be implemented in part or in whole by electronic circuit components or processors, such as application-specific integrated circuits (ASICs) or field-programmable gate arrays (FPGAs).

The processor system 300 may be configured to receive a variety of data associated with a particular wellsite. Examples include drilling data, well data, production data, pressure data, etc. The received or otherwise obtained data may be collected using one or more sensors associated with surface equipment or with downhole equipment. The various data may include information such as data relating to the position of the BHA (such as survey data or continuous position data), drilling parameters (such as weight on bit (WOB), rate of penetration (ROP), torque, or others), production metrics, text information entered by individuals working at the wellsite, or other data collected during the construction of or operation of the well.

In one embodiment, the processor system 300 is part of a rig control system (RCS) for the rig. In another embodiment, the processor system 300 is a separately installed computing unit including a display that is installed at the rig site and receives data from the RCS. In such an embodiment, the software on the processor system 300 may be installed on the computing unit, brought to the wellsite, and installed and communicatively connected to the rig control system in preparation for constructing the well or a portion thereof.

In another embodiment, the processor system 300 may be at a location remote from the wellsite and receives the various data over a communications medium using a protocol such as well-site information transfer specification or standard (WITS) and markup language (WITSML). In such an embodiment, the software on the processor system 300 may be a web-native application that is accessed by users using a web browser. In such an embodiment, the processor system 300 may be remote from the wellsite where the well is being constructed, and the user may be at the wellsite or at a location remote from the wellsite.

While the discussion above references drilling, the approaches discussed herein may be applied to other wellsite operations. These include, but are not limited to, wireline operations, production operations (such as artificial lift operations), and others. Similarly, the various data may be data from other wellsite operations.

Disclosed herein is an approach that combines machine learning and deep learning techniques to improve the gas production of liquid loading wells controlled by needle valve. In one embodiment, the approach is used in tight gas reservoirs using high frequency production data including Tubing Holding Pressure (THP), Casing Holding Pressure (CHP) and Gas production rate.

Additional detail in connection with one or more embodiments will now be discussed in connection with FIG. 4. FIG. 4 illustrates a block diagram illustrating an example environment 400 including a computing device(s) 402 and a historical data database 404. As shown in FIG. 4, the historical data database 404 includes historical pressure data 406 and historical production data 408. The historical pressure data 406 refers to pressure readings or measurements that have been collected or otherwise sensed by one or more sensors (e.g., on-site sensors). In one or more embodiments described herein, the historical pressure data 406 refers to tubing holding pressure (THP) and casing holding pressure (CHP) readings that have been obtained over a period of time. In one or more embodiments, the historical pressure data 406 refers to sensor data over a predetermined period of time leading up to a current period being evaluated by the SNV optimization system.

As noted above, the historical data database 404 includes the historical production data 408. In one or more embodiments, the historical production data 408 includes historical readings of gas production over a previous period of time. In one or more embodiments, the historical production data 408 refers specifically to a metric of production v. pressure that is observed, detected, or otherwise captured over a period of time. Similar to the historical pressure data 406, the historical production data 408 may refer to historical readings of gas production over a predetermined period of time leading up to a current period being evaluated by the SNV optimization system.

The data 406, 408 on the historical data database 404 may be accessible by the computing device(s) 402. In one or more embodiments, the historical data database 404 includes data that is stored on one or more remote devices from the computing device(s) 402 with the respective types of data 406, 408 being accessible via components of the SNV optimization system 410 over a network, such as a wireless or wired network or any communication medium over which data may be communicated to the computing device(s) 402.

As mentioned above, the environment 400 includes the computing device(s) 402 including the SNV optimization system 410 implemented thereon. The computing device(s) 402 may refer to one or multiple computing devices. The computing device(s) 402 may refer to a client device, server device, or any other type of computing device on which the SNV optimization system 410 may be implemented. For example, the computing device(s) 402 may refer to a mobile phone, tablet, desktop, or non-mobile server device. In one or more embodiments, the computing device(s) 402 is a singular device in which each of the components of the SNV optimization system 410 are implemented thereon. In other examples, one or more components of the SNV optimization system 410 may be implemented across different computing devices. For example, in one or more embodiments, a model training manager may be implemented on a different device (or system of devices) as a prediction engine.

As shown in FIG. 4, the SNV optimization system 410 includes a plurality of components, which will be discussed in further detail below in connection with various examples. As shown in FIG. 4, the SNV optimization system 410 includes a database manager 412. The database manager 412 manages any function related to accessing data on the historical data database 404 and/or placing additional data on the historical data database 404. In one or more embodiments, the database manager 412 collects historical pressure data 406 and historical production data 408 over a previous period of time. The database manager 412 may obtain the historical data from a variety of sources. In one or more embodiments, the database manager 412 downloads historical data from a storage and stores or otherwise maintains the historical data within the historical data database 404. In one or more embodiments, the database manager 412 collects sensor data (e.g., pressure sensor data, production sensor data) as the data is captured and stored incrementally on the historical data database 404. In one or more embodiments, the database manager 412 maintains APIs between sensors and the historical data database 404 that facilitates collection of data streams including the sensor data from which the historical pressure data 406 and historical production data 408 originates.

In addition to initiating and carrying out an initial collection of historical data to be stored on the historical data database 404, the database manager 412 may additionally add new data to the historical data database 404 as the new data is received, captured, or otherwise obtained. For example, in one or more embodiments, the database manager 412 facilitates measuring gas production and/or pressure buildup during a production and closing period representing real-time data of the wellsite. The database manager 412 may add the measured gas production and the measured pressure buildup to the historical data database 404. As will be discussed in further detail below, this additional data (in combination with the previously obtained and stored historical data) may be used to retrain or further refine the training of one or more machine learning models implemented by the SNV optimization system 410.

In one or more embodiments, the database manager 412 is tasked with collecting various types of pressure data and/or production data over variable periods of time. In one or more embodiments, the historical pressure data 406 refers specifically to measured values of THP and CHP over two or more previous production cycles including valve closing periods and associated production periods. In one or more embodiments, the historical production data 408 refers to measurements of gas production over two or more previous production cycles. Other implementations may include additional pressure and/or production data observed over significantly greater numbers of production cycles.

As further shown in FIG. 4, the SNV optimization system 410 includes a model training manager 414. The model training manager 414 trains a pressure buildup and production model to generate a given output indicating a predicted production metric for an upcoming production period upon opening a needle valve to initiate gas production of a wellsite. In one or more embodiments, the model training manager 414 trains the pressure buildup and production model to generate the output based on the historical pressure data 406 and historical production data 408.

As will be discussed below, the pressure buildup and production model may include multiple models that are each trained individually to generate respective outputs. For example, in one or more embodiments, the pressure buildup and production model include a first model related to predicting pressure buildup metrics. For instance, the pressure buildup and production model may include a first deep neural network referred to herein as a pressure buildup model that is trained to generate a first output indicating a predicted pressure buildup metric based on a first input including a first pressure metric associated with several previous valve closing periods. In one or more embodiments, the pressure buildup model is trained based on historical pressure data 406 to predict an output trend of pressure metrics based on an initial pressure reading at a particular point or across several previous closing periods associated with the needle valve. As noted above, this pressure metric may refer to a single pressure metric at a particular point in time or based on an input including multiple pressure metrics that are observed over several previous valve closing periods. In one or more embodiments, the pressure metric refers to a measurement of pressure at a time corresponding to when the needle valve is closed at the beginning of the previous valve closing period. In one or more embodiments, the pressure metric refers to multiple measurements of pressure over a range of time corresponding to the valve closing period.

In addition to the pressure buildup model, one or more embodiments of the pressure buildup and production model includes a second model related to predicting a production metric for a next production period. In one or more embodiments, the pressure buildup and production model include a tree-based machine learning model referred to herein as a production model that is trained to generate a second output indicating a predicted production metric for a next production period based at least in part on a pressure metric input to the production model. In one or more embodiments, the pressure metric input refers to a predicted pressure input generated by the pressure buildup model. It will be appreciated that each of the neural networks may refer to deep neural networks including any number of layers and having a particular structure as may serve a particular embodiment. The layers of the deep neural networks enables the models described herein to consider complex patterns and relationships in the data to more effectively and accurately predict pressure buildup and gas production based on a variety of input signals and parameters.

In addition to the database manager 412 and model training manager 414, the SNV optimization system 410 additionally includes a prediction engine 416. The prediction engine 416 may include or otherwise implement the trained models (pressure buildup model and/or production model) to predict pressure and/or production metrics over an upcoming period of time. In one or more embodiments, the prediction engine 416 initiates these predictions by first obtaining input pressure and production data for several previous production and closing periods. In one or more embodiments, these previous periods refer specifically to a production period and a closing period immediately proceeding a current or upcoming production period and/or closing period. Indeed, in this example, previous production periods and previous closing periods refer to most recent previous production periods and most recent previous closing periods.

Similar to the other metrics mentioned above, the input pressure and production data may refer to a variety of different portions of data. In one or more embodiments, the input pressure and production data includes a first pressure metric associated with several previous valve closing periods. In this example, the first pressure metric includes measured values of THP and CHP at a time when the needle valve is closed (e.g., at the beginning or at another point of the previous valve closing period). In one or more embodiments, the input pressure and production data further includes observed (e.g., detected or captured) production data for a production period immediately a valve closing period (e.g., a current valve closing period or a valve closing period immediately preceding the previous valve closing period). In one or more embodiments, the input pressure and production data includes to a production metric refers to a measurement of production v. pressure.

In one or more embodiments, the prediction engine 416 applies the pressure buildup and production model (e.g., including the pressure buildup model and the production model) to the input pressure and production data to generate an output. In one or more embodiments, the output includes a predicted production metric for a next production period of the wellsite. More specifically the output may include a predicted production metric based on a first pressure metric of the previous closing periods. In one or more embodiments, the output refers specifically to a predicted trend or value of production over a production period. In one or more embodiments, the output refers to a recommendation for a specific time or timing when the needle valve should be opened at an end of the closing period. The recommendation may specifically refer to a time or timing when opening the needle valve will maximize gas production over a next production period or over multiple production periods including the next production period.

By selectively identifying a specific time when the needle valve should be opened, the prediction engine 416 provides a mechanism whereby an operator of a needle valve may optimize gas production over multiple production cycles. Indeed, where gas production often decreases gradually over time based on pressure buildup failing to reach a maximum pressure for one or more previous periods of time, opening and/or closing the needle valve at fixed intervals may result in reduced gas production over time. By training and implementing the pressure buildup and gas production models as discussed herein, the prediction engine 416 enables an operator of a wellsite to more selectively time when the needle valve is open and/or closed so as to maximum gas production over multiple periods of time and (in some instances) reduce many of the inefficiencies and obstacles to maximum gas production that exist in conventional systems in which needle valve intervals are fixed and/or determined manually (e.g., based on a limited perspective of the factors and parameters that affect gas production).

In addition to the prediction engine 416, the SNV optimization system 410 includes a valve manager 418. The valve manager 418 may actuate one or more mechanisms that open and/or close the valve based on the outputs generated by the models described herein. For example, in one or more embodiments, the valve manager 418 opens or closes the needle valve based on an output recommendation indicating a timing at an end of a valve closing period when the valve should be open based on a predicted production metric over a next production period. More specifically, in one or more embodiments, the valve manager 418 causes the needle valve to open based on the predicted production metric for a next production period that is indicated by the output of the pressure buildup and production model (e.g., an output of a gas production model).

In addition to the components 412-418, the SNV optimization system 410 additionally includes a data storage 420. The data storage 420 may include any information or data relied on by any of the components of the SNV optimization system 410. For example, the data storage 420 may include model data, sensor data, valve configuration data, or any other information that enables the respective components 412-418 of the SNV optimization system 410 to perform any of the acts described herein. As shown in FIG. 4, the data storage 420 may include information stored on the computing device(s) 402. Alternatively, the data storage 420 may refer to data stored on another device that is accessible to one or more of the components 412-418. In one or more embodiments, rather than implementing a separate historical data database 404, the data storage 420 includes some or all of the historical pressure data 406 and/or historical production data 408 discussed above in connection with the historical data database 404.

As will be discussed in further detail below, the SNV optimization system 410 may further train or refine the pressure buildup and production model over time. For example, in one or more embodiments, the model training manager 414 may retrain the pressure buildup and production model by first retraining a deep neural network (e.g., the pressure buildup model) based on measured pressure buildup of a previous (or current) valve closing cycle. Further, in one or more embodiments, the model training manager 414 may retrain or further refine the pressure buildup and production model by retraining the tree-based machine learning model (e.g., the gas production model) based on a combination of the measured pressure buildup and the measured gas production. Additional detail in connection with collecting and retraining the models will be discussed below in connection with FIG. 5.

Additional detail will discuss an implementation of the SNV optimization system 410 in connection with an example workflow showing a particular implementation of one or more embodiments described herein. In particular, FIG. 5 illustrates an example workflow 500 illustrating some of the features of the SNV optimization system 410 in accordance with one or more embodiments described herein.

As shown in FIG. 5, the SNV optimization system 410 may obtain production and pressure data 502. In one or more embodiments, the production and pressure data 502 refers to real production data (e.g., production data captured by sensors at the wellsite) for one or more previous periods. In an information gathering phase, this may involve the SNV optimization system 410 collecting historical pressure data and historical production data over some previous period of time (e.g., two or more production cycles). As shown in FIG. 5, the historical data database 404 is shown including historical pressure data and historical production data.

As shown in FIG. 5, the data from the historical data database 404 is provided to a model training manager 414 to train a pressure buildup model 504a and a gas production model 506a. In this example, the pressure buildup model 504a and the gas production model 506a represent versions of the trained models based on the pressure and production data that is available at a time prior to a current production cycle. For instance, in one or more embodiments, the pressure buildup model 504a and the gas production model 506a refer to initially trained neural networks having been trained based on available historical data prior to observing pressure and/or production data for a current or upcoming production cycle.

Once the models have been trained, the prediction engine 416 applies a copy or trained version of the pressure buildup model 504b and gas production model 506b to input pressure and production data to generate an output. More specifically, the prediction engine 416 receives pressure data for several previous periods from the historical data database 404 and applies the model to the pressure and production data to generate an output prediction 510. In one or more embodiments, the pressure data for the previous period refers specifically to pressure data (e.g., actual pressure data) in a valve closing phase of the current (or immediately previous) period.

More specifically, as shown in FIG. 5, the pressure buildup model 504b is applied to an input including the pressure data for previous periods 508 to generate a first intermediate input to provide as an input to the gas production model. This intermediate input refers specifically to an output of the pressure buildup model 504b which includes a predicted pressure buildup metric associated with a valve closing period. This predicted pressure buildup metric is provided as an input to the gas production model 506b. Based on the predicted pressure buildup metric (e.g., as output by the pressure buildup model 504b), the gas production model 506b generates the output prediction 510. As noted above, the output prediction 510 may refer to a predicted metric of production and/or pressure for a next production cycle. In addition, or as an alternative, in one or more embodiments, the output prediction 510 may refer to a timing or other recommendation associated with when to open the needle valve.

As an illustrated example consistent with the example illustrated in FIG. 5, the gas production model 506 may receive a predicted buildup trend generated by the pressure buildup model 504b to predict a production metric when the well is reopened at various timestamps and within some time ranges (e.g., a few hours). For instance, the output prediction 510 may include a production prediction based on opening the well after two hours, after four hours, etc. As discussed in examples described herein, this prediction may be used in determining when to open the needle valve.

As noted above, and as shown in FIG. 5, the SNV optimization system 410 may receive additional pressure and production data as the data becomes available. In one or more embodiments, the SNV optimization system 410 collects this additional data and further refines the pressure buildup model 504 and gas production model 506. Indeed, the SNV optimization system 410 may compare accuracies or inaccuracies between the predicted metrics (e.g., pressure buildup and production metrics) and the actual metrics (e.g., the measured pressure and production metrics) and further train the model based on the differences and/or similarities. Indeed, where the predictions are accurate, the respective models or algorithms may be reinforced. Alternatively, where the predictions are inaccurate, the respective models or algorithms may be fine-tuned based on a combination of the historical data previously collected and the new, current data that is being collected with each subsequent production cycle.

Turning now to FIG. 6, this figure illustrates example flowcharts including series of acts for predicting production over a production period when a needle valve is opened in accordance with one or more embodiments described herein. While FIG. 6 illustrates acts according to one or more embodiments, alternative embodiments may omit, add to, reorder, and/or modify any of the acts shown in FIG. 6. The acts of FIG. 6 can be performed as part of a method. Alternatively, a non-transitory computer-readable medium can include instructions that, when executed by one or more processors, cause a computing device to perform the acts of FIG. 6. In still further embodiments, a system can perform the acts of FIG. 6.

As shown in FIG. 6, the series of acts 600 includes an act 610 of training a pressure buildup and production model to generate an output indicating a predicted production metric for an upcoming production period upon opening a needle valve to initiate gas production of a well. As further shown, the series of acts 600 includes an act 620 of obtaining input pressure and production data for previous production periods and previous closing periods.

As further shown, the series of acts 600 includes an act 630 of applying the pressure buildup and production model to the input pressure and production data to generate a predicted production metric for a next production period. As further shown, the series of act 600 includes an act 640 of causing the needle vale to open based on the prediction production metric. In one or more embodiments, the act 640 includes causing the needle valve to open based on the predicted production metric for the next production period indicated by the output of the pressure buildup and production model.

In one or more embodiments, the pressure buildup and production model includes a pressure buildup model comprising a deep neural network, the pressure buildup model being trained to generate a first output indicating a predicted pressure buildup metric based on a first input comprising a first pressure metric associated with previous valve closing periods. In one or more embodiments, the pressure buildup and production model includes a production model comprising a tree-base machine learning model, the production model being trained to generate a second output comprising a predicted production metric for a next production period based at least in part on the first pressure metric output by the pressure buildup model.

In one or more embodiments, the production model generates the output of the pressure buildup and production model based at least in part on a predicted pressure metric generated by the pressure buildup model. In one or more embodiments, the pressure buildup metric is a measurement of pressure at a time corresponding to when the needle valve is closed at the beginning of the previous valve closing period. In one or more embodiments, the pressure buildup metric is measurements of pressure over a range of time corresponding to the valve closing period.

In one or more embodiments, the series of acts 600 includes acts of measuring gas production of the next production period, measuring pressure buildup during the valve closing period, and adding the measured gas production and the measured pressure buildup to a database including the historical production data and historical pressure data. The series of acts 600 may additionally further include an act of retraining the pressure buildup and production model based on the measured gas production added to the database. In one or more embodiments, retraining the pressure buildup and production model includes retraining the deep neural network based on the measured pressure buildup. Retraining the pressure buildup and production model may additionally include retraining the tree-based machine learning model based on a combination of the measured pressure buildup and the measured gas production.

In one or more embodiments, the historical production data and historical pressure that includes measured values of tubing holding pressure (THP) and casing holding pressure (CHP) over two or more previous production cycles including valve closing periods and associated production periods and measure gas production over the two or more previous production cycles. In one or more embodiments, the output of the pressure buildup and production model indicates a recommended time for opening the needle valve at an end of the closing period to maximize gas production over multiple production periods including the next production period.

The embodiments disclosed in this disclosure are to help explain the concepts described herein. This description is not exhaustive and does not limit the claims to the precise embodiments disclosed. Modifications and variations from the exact embodiments in this disclosure may still be within the scope of the claims.

Likewise, the steps described need not be performed in the same sequence discussed or with the same degree of separation. Various steps may be omitted, repeated, combined, or divided, as appropriate. Accordingly, the present disclosure is not limited to the above-described embodiments, but instead is defined by the appended claims in light of their full scope of equivalents. In the above description and in the below claims, unless specified otherwise, the term “execute” and its variants are to be interpreted as pertaining to any operation of program code or instructions on a device, whether compiled, interpreted, or run using other techniques.

The claims that follow do not invoke section 112 (f) unless the phrase “means for” is expressly used together with an associated function.

APPROACHES TO NEEDLE VALVE OPTIMIZATION

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