FIELD OPERATIONS FRAMEWORK

BACKGROUND

A reservoir can be a subsurface formation that can be characterized at least in part by its porosity and fluid permeability. As an example, a reservoir may be part of a basin such as a sedimentary basin. A basin can be a depression (e.g., caused by plate tectonic activity, subsidence, etc.) in which sediments accumulate. As an example, where hydrocarbon source rocks occur in combination with appropriate depth and duration of burial, a petroleum system may develop within a basin, which may form a reservoir that includes hydrocarbon fluids (e.g., oil, gas, etc.). Various operations may be performed in the field to access such hydrocarbon fluids and/or produce such hydrocarbon fluids. For example, consider equipment operations where equipment may be controlled to perform one or more operations. In such an example, control may be based at least in part on characteristics of rock where drilling into such rock forms a borehole that can be completed to form a well to produce from a reservoir and/or to inject fluid into a reservoir. While hydrocarbon fluid reservoirs are mentioned as an example, a reservoir that includes water and brine may be assessed, for example, for one or more purposes such as, for example, carbon storage (e.g., sequestration), water production or storage, geothermal production or storage, metallic extraction from brine, etc.

SUMMARY

A method can include receiving downhole formation testing time series data acquired at a location along a borehole in a subsurface region during a formation testing operation performed by a downhole tool; processing the downhole formation testing time series data using a machine learning model to generate smoothed time series data; resampling the smoothed time series data; generating fluid and formation characteristics with respect to time based on the smoothed time series data; and outputting the fluid and formation characteristics with respect to time. A system can include one or more processors; memory accessible to at least one of the one or more processors; processor-executable instructions stored in the memory and executable to instruct the system to: receive downhole formation testing time series data acquired at a location along a borehole in a subsurface region during a formation testing operation performed by a downhole tool; process the downhole formation testing time series data using a machine learning model to generate smoothed time series data; resample the smoothed time series data; generate fluid and formation characteristics with respect to time based on the smoothed time series data; and output the fluid and formation characteristics with respect to time. One or more computer-readable storage media can include processor-executable instructions to instruct a computing system to: receive downhole formation testing time series data acquired at a location along a borehole in a subsurface region during a formation testing operation performed by a downhole tool; process the downhole formation testing time series data using a machine learning model to generate smoothed time series data; resample the smoothed time series data; generate fluid and formation characteristics with respect to time based on the smoothed time series data; and output the fluid and formation characteristics with respect to time. 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.

BRIEF DESCRIPTION OF THE DRAWINGS

Features and advantages of the described implementations can be more readily understood by reference to the following description taken in conjunction with the accompanying drawings.

FIG. 1 illustrates an example system that includes various framework components associated with one or more geologic environments;

FIG. 2 illustrates an example of a system;

FIG. 3 illustrates an example of a drilling equipment and examples of borehole shapes;

FIG. 4 illustrates an example of a system;

FIG. 5 illustrates an example of a downhole tool;

FIG. 6 illustrates an example of a downhole tool;

FIG. 7 illustrates an example of a workflow;

FIG. 8 illustrates an example of a graphical user interface (GUI);

FIG. 9 illustrates an example of a graphical user interface (GUI);

FIG. 10 illustrates an example of a workflow;

FIG. 11 illustrates an example of a workflow;

FIG. 12 illustrates an example of a method and an example of a system; and

FIG. 13 illustrates examples of computer and network equipment.

DETAILED DESCRIPTION

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.

FIG. 1 shows an example of a system 100 that includes a workspace framework 110 that can provide for instantiation of, rendering of, interactions with, etc., a graphical user interface (GUI) 120. In the example of FIG. 1, the GUI 120 can include graphical controls for computational frameworks (e.g., applications) 121, projects 122, visualization 123, one or more other features 124, data access 125, and data storage 126.

In the example of FIG. 1, the workspace framework 110 may be tailored to a particular geologic environment such as an example geologic environment 150. For example, the geologic environment 150 may include layers (e.g., stratification) that include a reservoir 151 and that may be intersected by a fault 153. A geologic environment 150 may be outfitted with a variety of sensors, detectors, actuators, etc. In such an environment, various types of equipment such as, for example, equipment 152 may include communication circuitry to receive and to transmit information, optionally with respect to one or more networks 155. Such information may include information associated with downhole equipment 154, which may be equipment to acquire information, to assist with resource recovery, etc. Other equipment 156 may be located remote from a wellsite and include sensing, detecting, emitting, or other circuitry. Such equipment may include storage and communication circuitry to store and to communicate data, instructions, etc. One or more satellites may be provided for purposes of communications, data acquisition, etc. For example, FIG. 1 shows a satellite 170 in communication with the network 155 that may be configured for communications, noting that the satellite may additionally or alternatively include circuitry for imagery (e.g., spatial, spectral, temporal, radiometric, etc.).

FIG. 1 also shows the geologic environment 150 as optionally including equipment 157 and 158 associated with a well that includes a substantially horizontal portion that may intersect with one or more fractures 159. For example, consider a well in a formation that may include natural fractures, artificial fractures (e.g., hydraulic fractures) or a combination of natural and artificial fractures. As an example, a well may be drilled for a reservoir that is laterally extensive. In such an example, lateral variations in properties, stresses, etc., may exist where an assessment of such variations may assist with planning, operations, etc., to develop a laterally extensive reservoir (e.g., via fracturing, injecting, extracting, etc.). As an example, the equipment 157 and/or 158 may include components, a system, systems, etc. for fracturing, seismic sensing, analysis of seismic data, assessment of one or more fractures, etc.

In the example of FIG. 1, the GUI 120 shows some examples of computational frameworks, including the DRILLPLAN, PETREL, TECHLOG, PETROMOD, ECLIPSE, and INTERSECT frameworks (SLB, Houston, Texas).

The DRILLPLAN framework provides for digital well construction planning and includes features for automation of repetitive tasks and validation workflows, enabling improved quality drilling programs (e.g., digital drilling plans, etc.) to be produced quickly with assured coherency.

The PETREL framework can be part of the DELFI cognitive exploration and production (E&P) environment (SLB, Houston, Texas, referred to as the DELFI environment) for utilization in geosciences and geoengineering, for example, to analyze subsurface data from exploration to production of fluid from a reservoir.

One or more types of frameworks may be implemented within or in a manner operatively coupled to the DELFI environment, which is a secure, cognitive, cloud-based collaborative environment that integrates data and workflows with digital technologies, such as artificial intelligence (Al) and machine learning (ML). Such an environment can provide for operations that involve one or more frameworks. The DELFI environment may be referred to as the DELFI framework, which may be a framework of frameworks. The DELFI environment can include various other frameworks, which may operate using one or more types of models (e.g., simulation models, etc.).

The TECHLOG framework can handle and process field and laboratory data for a variety of geologic environments (e.g., deepwater exploration, shale, etc.). The TECHLOG framework can structure wellbore data for analyses, planning, etc.

The PIPESIM simulator includes solvers that may provide simulation results such as, for example, multiphase flow results (e.g., from a reservoir to a wellhead and beyond, etc.), flowline and surface facility performance, etc. The PIPESIM simulator may be integrated, for example, with the AVOCET production operations framework (SLB, Houston Texas). The PIPESIM simulator may be an optimizer that can optimize one or more operational scenarios at least in part via simulation of physical phenomena.

The ECLIPSE framework provides a reservoir simulator with numerical solvers for prediction of dynamic behavior for various types of reservoirs and development schemes.

The INTERSECT framework provides a high-resolution reservoir simulator for simulation of geological features and quantification of uncertainties, for example, by creating production scenarios and, with the integration of precise models of the surface facilities and field operations, the INTERSECT framework can produce results, which may be continuously updated by real-time data exchanges (e.g., from one or more types of data acquisition equipment in the field that can acquire data during one or more types of field operations, etc.). The INTERSECT framework can provide completion configurations for complex wells where such configurations can be built in the field, can provide detailed chemical-enhanced-oil-recovery (EOR) formulations where such formulations can be implemented in the field, can analyze application of steam injection and other thermal EOR techniques for implementation in the field, advanced production controls in terms of reservoir coupling and flexible field management, and flexibility to script customized solutions for improved modeling and field management control. The INTERSECT framework, as with the other example frameworks, may be utilized as part of the DELFI environment, for example, for rapid simulation of multiple concurrent cases.

The aforementioned DELFI environment provides various features for workflows as to subsurface analysis, planning, construction and production, for example, as illustrated in the workspace framework 110. As shown in FIG. 1, outputs from the workspace framework 110 can be utilized for directing, controlling, etc., one or more processes in the geologic environment 150, and feedback 160 can be received via one or more interfaces in one or more forms (e.g., acquired data as to operational conditions, equipment conditions, environment conditions, etc.).

In the example of FIG. 1, the visualization features 123 may be implemented via the workspace framework 110, for example, to perform tasks as associated with one or more of subsurface regions, planning operations, constructing wells and/or surface fluid networks, and producing from a reservoir.

Visualization features may provide for visualization of various earth models, properties, etc., in one or more dimensions. As an example, visualization features may include one or more control features for control of equipment, which can include, for example, field equipment that can perform one or more field operations. A workflow may utilize one or more frameworks to generate information that can be utilized to control one or more types of field equipment (e.g., drilling equipment, wireline equipment, fracturing equipment, etc.).

As to a reservoir model that may be suitable for utilization by a simulator, consider acquisition of seismic data as acquired via reflection seismology, which finds use in geophysics, for example, to estimate properties of subsurface formations. Seismic data may be processed and interpreted, for example, to understand better composition, fluid content, extent and geometry of subsurface rocks. Such interpretation results can be utilized to plan, simulate, perform, etc., one or more operations for production of fluid from a reservoir (e.g., reservoir rock, etc.). Field acquisition equipment may be utilized to acquire seismic data, which may be in the form of traces where a trace can include values organized with respect to time and/or depth (e.g., consider 1D, 2D, 3D or 4D seismic data).

A model may be a simulated version of a geologic environment where a simulator may include features for simulating physical phenomena in a geologic environment based at least in part on a model or models. A simulator, such as a reservoir simulator, can simulate fluid flow in a geologic environment based at least in part on a model that can be generated via a framework that receives seismic data. A simulator can be a computerized system (e.g., a computing system) that can execute instructions using one or more processors to solve a system of equations that describe physical phenomena subject to various constraints. While several simulators are illustrated in the example of FIG. 1, one or more other simulators may be utilized, additionally or alternatively.

FIG. 2 shows an example of a system 200 that can be operatively coupled to one or more databases, data streams, etc. For example, one or more pieces of field equipment, laboratory equipment, computing equipment (e.g., local and/or remote), etc., can provide and/or generate data that may be utilized in the system 200.

As shown, the system 200 can include a geological/geophysical data block 210, surface models block 220 (e.g., for one or more structural models), a volume modules block 230, an applications block 240, a numerical processing block 250 and an operational decision block 260. As shown in the example of FIG. 2, the geological/geophysical data block 210 can include data from well tops or drill holes 212, data from seismic interpretation 214, data from outcrop interpretation and optionally data from geological knowledge. As an example, the geological/geophysical data block 210 can include data from digital images, which can include digital images of cores, cuttings, cavings, outcrops, etc. As to the surface models block 220, it may provide for creation, editing, etc. of one or more surface models based on, for example, one or more of fault surfaces 222, horizon surfaces 224 and optionally topological relationships 226. As to the volume models block 230, it may provide for creation, editing, etc. of one or more volume models based on, for example, one or more of boundary representations 232 (e.g., to form a watertight model), structured grids 234 and unstructured meshes 236.

As shown in the example of FIG. 2, the system 200 may allow for implementing one or more workflows, for example, where data of the data block 210 are used to create, edit, etc. one or more surface models of the surface models block 220, which may be used to create, edit, etc. one or more volume models of the volume models block 230. As indicated in the example of FIG. 2, the surface models block 220 may provide one or more structural models, which may be input to the applications block 240. For example, such a structural model may be provided to one or more applications, optionally without performing one or more processes of the volume models block 230 (e.g., for purposes of numerical processing by the numerical processing block 250). Accordingly, the system 200 may be suitable for one or more workflows for structural modeling (e.g., optionally without performing numerical processing per the numerical processing block 250).

As to the applications block 240, it may include applications such as a well prognosis application 242, a reserve calculation application 244 and a well stability assessment application 246. As to the numerical processing block 250, it may include a process for seismic velocity modeling 251 followed by seismic processing 252, a process for facies and petrophysical property interpolation 253 followed by flow simulation 254, and a process for geomechanical simulation 255 followed by geochemical simulation 256. As indicated, as an example, a workflow may proceed from the volume models block 230 to the numerical processing block 250 and then to the applications block 240 and/or to the operational decision block 260. As another example, a workflow may proceed from the surface models block 220 to the applications block 240 and then to the operational decisions block 260 (e.g., consider an application that operates using a structural model).

In the example of FIG. 2, the operational decisions block 260 may include a seismic survey design process 261, a well rate adjustment process 252, a well trajectory planning process 263, a well completion planning process 264 and a process for one or more prospects, for example, to decide whether to explore, develop, abandon, etc. a prospect.

Referring again to the data block 210, the well tops or drill hole data 212 may include spatial localization, and optionally surface dip, of an interface between two geological formations or of a subsurface discontinuity such as a geological fault; the seismic interpretation data 214 may include a set of points, lines or surface patches interpreted from seismic reflection data, and representing interfaces between media (e.g., geological formations in which seismic wave velocity differs) or subsurface discontinuities; the outcrop interpretation data 216 may include a set of lines or points, optionally associated with measured dip, representing boundaries between geological formations or geological faults, as interpreted on the earth surface; and the geological knowledge data 218 may include, for example knowledge of the paleo-tectonic and sedimentary evolution of a region.

As to a structural model, it may be, for example, a set of gridded or meshed surfaces representing one or more interfaces between geological formations (e.g., horizon surfaces) or mechanical discontinuities (fault surfaces) in the subsurface. As an example, a structural model may include some information about one or more topological relationships between surfaces (e.g., fault A truncates fault B, fault B intersects fault C, etc.).

As to the facies and petrophysical property interpolation 253, it may include an assessment of type of rocks and of their petrophysical properties (e.g., porosity, permeability), for example, optionally in areas not sampled by well logs or coring. As an example, such an interpolation may be constrained by interpretations from log and core data, and by prior geological knowledge.

As to the various applications of the applications block 240, the well prognosis application 242 may include predicting type and characteristics of geological formations that may be encountered by a drill bit, and location where such rocks may be encountered (e.g., before a well is drilled); the reserve calculations application 244 may include assessing total amount of hydrocarbons or ore material present in a subsurface environment (e.g., and estimates of which proportion can be recovered, given a set of economic and technical constraints); and the well stability assessment application 246 may include estimating risk that a well, already drilled or to-be-drilled, will collapse or be damaged due to underground stress.

As to the operational decision block 260, the seismic survey design process 261 may include deciding where to place seismic sources and receivers to optimize the coverage and quality of the collected seismic information while minimizing cost of acquisition; the well rate adjustment process 262 may include controlling injection and production well schedules and rates (e.g., to maximize recovery and production); the well trajectory planning process 263 may include designing a well trajectory to maximize potential recovery and production while minimizing drilling risks and costs; the well trajectory planning process 264 may include selecting proper well tubing, casing and completion (e.g., to meet expected production or injection targets in specified reservoir formations); and the prospect process 265 may include decision making, in an exploration context, to continue exploring, start producing or abandon prospects (e.g., based on an integrated assessment of technical and financial risks against expected benefits).

The system 200 can include and/or can be operatively coupled to a system such as the system 100 of FIG. 1. For example, the workspace framework 110 may provide for instantiation of, rendering of, interactions with, etc., the graphical user interface (GUI) 120 to perform one or more actions as to the system 200. In such an example, access may be provided to one or more frameworks (e.g., DRILLPLAN, PETREL, TECHLOG, PIPESIM, ECLIPSE, INTERSECT, etc.). One or more frameworks may provide for geo data acquisition as in block 210, for structural modeling as in block 220, for volume modeling as in block 230, for running an application as in block 240, for numerical processing as in block 250, for operational decision making as in block 260, etc.

As an example, the system 200 may provide for monitoring data, which can include geo data per the geo data block 210. In various examples, geo data may be acquired during one or more operations. For example, consider acquiring geo data during drilling operations via downhole equipment and/or surface equipment. As an example, the operational decision block 260 can include capabilities for monitoring, analyzing, etc., such data for purposes of making one or more operational decisions, which may include controlling equipment, revising operations, revising a plan, etc. In such an example, data may be fed into the system 200 at one or more points where the quality of the data may be of particular interest. For example, data quality may be characterized by one or more metrics where data quality may provide indications as to trust, probabilities, etc., which may be germane to operational decision making and/or other decision making.

FIG. 3 shows an example of a wellsite system 300 (e.g., at a wellsite that may be onshore or offshore). As shown, the wellsite system 300 can include a mud tank 301 for holding mud and other material (e.g., where mud can be a drilling fluid), a suction line 303 that serves as an inlet to a mud pump 304 for pumping mud from the mud tank 301 such that mud flows to a vibrating hose 306, a drawworks 307 for winching drill line or drill lines 312, a standpipe 308 that receives mud from the vibrating hose 306, a kelly hose 309 that receives mud from the standpipe 308, a gooseneck or goosenecks 310, a traveling block 311, a crown block 313 for carrying the traveling block 311 via the drill line or drill lines 312, a derrick 314, a kelly 318 or a top drive 340, a kelly drive bushing 319, a rotary table 320, a drill floor 321, a bell nipple 322, one or more blowout preventors (BOPs) 323, a drillstring 325, a drill bit 326, a casing head 327 and a flow pipe 328 that carries mud and other material to, for example, the mud tank 301.

In the example system of FIG. 3, a borehole 332 is formed in subsurface formations 330 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. 3, the drillstring 325 is suspended within the borehole 332 and has a drillstring assembly 350 that includes the drill bit 326 at its lower end. As an example, the drillstring assembly 350 may be a bottom hole assembly (BHA).

The wellsite system 300 can provide for operation of the drillstring 325 and other operations. As shown, the wellsite system 300 includes the traveling block 311 and the derrick 314 positioned over the borehole 332. As mentioned, the wellsite system 300 can include the rotary table 320 where the drillstring 325 pass through an opening in the rotary table 320.

As shown in the example of FIG. 3, the wellsite system 300 can include the kelly 318 and associated components, etc., or the top drive 340 and associated components. As to a kelly example, the kelly 318 may be a square or hexagonal metal/alloy bar with a hole drilled therein that serves as a mud flow path. The kelly 318 can be used to transmit rotary motion from the rotary table 320 via the kelly drive bushing 319 to the drillstring 325, while allowing the drillstring 325 to be lowered or raised during rotation. The kelly 318 can pass through the kelly drive bushing 319, which can be driven by the rotary table 320. As an example, the rotary table 320 can include a master bushing that operatively couples to the kelly drive bushing 319 such that rotation of the rotary table 320 can turn the kelly drive bushing 319 and hence the kelly 318. The kelly drive bushing 319 can include an inside profile matching an outside profile (e.g., square, hexagonal, etc.) of the kelly 318; however, with slightly larger dimensions so that the kelly 318 can freely move up and down inside the kelly drive bushing 319.

As to a top drive example, the top drive 340 can provide functions performed by a kelly and a rotary table. The top drive 340 can turn the drillstring 325. As an example, the top drive 340 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 325 itself. The top drive 340 can be suspended from the traveling block 311, so the rotary mechanism is free to travel up and down the derrick 314. As an example, a top drive 340 may allow for drilling to be performed with more joint stands than a kelly/rotary table approach.

In the example of FIG. 3, the mud tank 301 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. 3, the drillstring 325 (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 326 at the lower end thereof. As the drillstring 325 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 304 from the mud tank 301 (e.g., or other source) via the lines 306, 308 and 309 to a port of the kelly 318 or, for example, to a port of the top drive 340. The mud can then flow via a passage (e.g., or passages) in the drillstring 325 and out of ports located on the drill bit 326 (see, e.g., a directional arrow). As the mud exits the drillstring 325 via ports in the drill bit 326, it can then circulate upwardly through an annular region between an outer surface(s) of the drillstring 325 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 326 and carries heat energy (e.g., frictional or other energy) and formation cuttings to the surface where the mud may be returned to the mud tank 301, for example, for recirculation with processing to remove cuttings and other material.

In the example of FIG. 3, processed mud pumped by the pump 304 into the drillstring 325 may, after exiting the drillstring 325, form a mudcake that lines the wellbore which, among other functions, may reduce friction between the drillstring 325 and surrounding wall(s) (e.g., borehole, casing, etc.). A reduction in friction may facilitate advancing or retracting the drillstring 325. During a drilling operation, the entire drillstring 325 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 326 of the drillstring 325 at a bottom of a wellbore, pumping of the mud commences to lubricate the drill bit 326 for purposes of drilling to enlarge the wellbore. As mentioned, the mud can be pumped by the pump 304 into a passage of the drillstring 325 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. Characteristics of the mud can be utilized to determine how pulses are transmitted (e.g., pulse shape, energy loss, transmission time, etc.).

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 325) 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 325 itself. For example, consider a signal generator that imparts coded energy signals to the drillstring 325 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 325 may be fitted with telemetry equipment 352 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. 3, an uphole control and/or data acquisition system 362 may include circuitry to sense pressure pulses generated by telemetry equipment 352 and, for example, communicate sensed pressure pulses or information derived therefrom for process, control, etc.

The assembly 350 of the illustrated example includes a logging-while-drilling (LWD) module 354, a measurement-while-drilling (MWD) module 356, an optional module 358, a rotary-steerable system (RSS) and/or motor 360, and the drill bit 326. Such components or modules may be referred to as tools where a drillstring can 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 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.

The LWD module 354 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 356 of the drillstring assembly 350. 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 354, the module 356, 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 354 may include a seismic measuring device.

The MWD module 356 may be housed in a suitable type of drill collar and can contain one or more devices for measuring characteristics of the drillstring 325 and the drill bit 326. As an example, the MWD tool 354 may include equipment for generating electrical power, for example, to power various components of the drillstring 325. As an example, the MWD tool 354 may include the telemetry equipment 352, 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 356 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. 3 also shows some examples of types of holes that may be drilled. For example, consider a slant hole 372, an S-shaped hole 374, a deep inclined hole 376 and a horizontal hole 378.

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 approximately 30 degrees and approximately 60 degrees or, for example, an angle to approximately 90 degrees or possibly greater than approximately 90 degrees.

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 explained, a system may be a steerable system and may include equipment to perform a method such as geosteering. A steerable system can include equipment on a lower part of a drillstring which, just above a drill bit, a bent sub may be mounted. Above directional drilling equipment, a drillstring can include 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. 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 to follow a desired route to reach a desired target or targets.

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.

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. 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. 3, the wellsite system 300 can include one or more sensors 364 that are operatively coupled to the control and/or data acquisition system 362. 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 approximately one hundred meters from the wellsite system 300.

The system 300 can include one or more sensors 366 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 300, the one or more sensors 366 can be operatively coupled to portions of the standpipe 308 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 366. 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. Circuitry at the surface may include decoding circuitry to decode encoded information transmitted at least in part via mud-pulse telemetry. 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 300 can include a transmitter that can generate signals that can be transmitted downhole via mud (e.g., drilling fluid) as a transmission medium.

FIG. 4 shows an example of an environment 401 that includes a subterranean portion 403 where a rig 410 is positioned at a surface location above a bore 420. In the example of FIG. 4, various wirelines services equipment can be operated to perform one or more wirelines services including, for example, acquisition of data from one or more positions within the bore 420.

As an example, a wireline tool and/or a wireline service may provide for acquisition of data, analysis of data, data-based determinations, data-based decision making, etc. Some examples of wireline data can include gamma ray (GR), spontaneous potential (SP), caliper (CALI), shallow resistivity (LLS and ILD), deep resistivity (LLD and ILD), density (RHOB), neutron porosity (BPHI or TNPH or NPHI), sonic (DT), photoelectric (PEF), permittivity and conductivity.

In the example of FIG. 4, the bore 420 includes drillpipe 422, a casing shoe 424, a cable side entry sub (CSES) 423, a wet-connector adaptor 426 and an openhole section 428. As an example, the bore 420 can be a vertical bore or a deviated bore where one or more portions of the bore may be vertical and one or more portions of the bore may be deviated, including substantially horizontal.

In the example of FIG. 4, the CSES 423 includes a cable clamp 425, a packoff seal assembly 427 and a check valve 429. These components can provide for insertion of a logging cable 430 that includes a portion 432 that runs outside the drillpipe 422 to be inserted into the drillpipe 422 such that at least a portion 434 of the logging cable runs inside the drillpipe 422. In the example of FIG. 4, the logging cable 430 runs past the casing shoe 424 and the wet-connect adaptor 426 and into the openhole section 428 to a logging string 440.

As shown in the example of FIG. 4, a logging truck 450 (e.g., a wirelines services vehicle) can deploy the wireline 430 under control of a system 460. As shown in the example of FIG. 4, the system 460 can include one or more processors 462, memory 464 operatively coupled to at least one of the one or more processors 462, instructions 466 that can be, for example, stored in the memory 464, and one or more interfaces 468. As an example, the system 460 can include one or more processor-readable media that include processor-executable instructions executable by at least one of the one or more processors 462 to cause the system 460 to control one or more aspects of equipment of the logging string 440 and/or the logging truck 450. In such an example, the memory 464 can be or include the one or more processor-readable media where the processor-executable instructions can be or include instructions. As an example, a processor-readable medium can be a computer-readable storage medium that is not a signal and that is not a carrier wave.

FIG. 4 also shows a battery 470 that may be operatively coupled to the system 460, for example, to power the system 460. As an example, the battery 470 may be a back-up battery that operates when another power supply is unavailable for powering the system 460 (e.g., via a generator of the wirelines truck 450, a separate generator, a power line, etc.). As an example, the battery 470 may be operatively coupled to a network, which may be a cloud network. As an example, the battery 470 can include smart battery circuitry and may be operatively coupled to one or more pieces of equipment via a SMBus or other type of bus.

As an example, the system 460 can be operatively coupled to a client layer 480. In the example of FIG. 4, the client layer 480 can include features that allow for access and interactions via one or more private networks 482, one or more mobile platforms and/or mobile networks 484 and via the “cloud” 486, which may be considered to include distributed equipment that forms a network such as a network of networks. As an example, the system 460 can include circuitry to establish a plurality of connections (e.g., sessions). As an example, connections may be via one or more types of networks. As an example, connections may be client-server types of connections where the system 460 operates as a server in a client-server architecture. For example, clients may log-in to the system 460 where multiple clients may be handled, optionally simultaneously.

While the example of FIG. 4 shows the system 460 as being associated with the logging truck 450, one or more features of the system 460 may be included in a downhole assembly, which may be a wireline assembly and/or a LWD assembly. In such an approach, various computations may be performed downhole where results thereof may be optionally transmitted to surface (e.g., to the logging truck 450, etc.) using one or more telemetric technologies and/or techniques (e.g., mud-pulse telemetry, wireline, etc.).

As an example, a tool can include one or more features of the ORA platform (SLB, Houston, Texas). The ORA platform includes various tool options, which include metrology options (e.g., various types of sensors that may be disposed in a sensor array, etc.). For example, consider a tool that includes a fluid in situ scanner that can measure one or more of density and viscosity, resistivity, and full-spectrum viscosity. As an example, a tool can include one or more pressure sensors (e.g., quartz, etc.) and/or one or more temperature sensors. As an example, a tool can include one or more sensors for measurement of oil, water and gas volume fraction, composition, color, etc. As to composition sensing, consider sensing of C₁to C₆or C₆₊ (e.g., with uncertainty less than approximately 6 weight percent) and, for example, sensing of CO₂. As to fluid density, consider a range from approximately 0.01 to 2.0 g/cm³. As to fluid viscosity, consider a range from 0.1 to 300 cP. As to color, consider optical density as a measurement. As to optical measurements, for example, a tool can include a spectrophotometer, a fluorescence meter, etc.

FIG. 5 shows an example of a tool 500 that can include various features of the ORA platform. For example, the tool 500 is shown as including a sensor array 506. In the example of FIG. 5, the tool 500 is shown as including a pumping assembly 505, power and/or data lines 507 and a fluid controller 508. Such a fluid controller can include passages and one or more valves 509. As an example, the fluid controller 508 can receive fluid in one or more manners. For example, consider receipt of fluid via an upper inlet, a lower inlet and/or a side inlet where an inlet may be in fluid communication with a passage, where flow may be controlled. As an example, the fluid controller 508 can include one or more outlets where, for example, an inlet may be operable as an outlet or vice versa. In the example of FIG. 5, fluid may be directed from a lower portion of the tool 500 upwardly to an upper portion of the tool 500 where the fluid may flow via one or more outlets to an annular space that may be defined at least in part by an outer surface of a portion of the tool 500.

In the example of FIG. 5, the tool 500 may include one or more passages that extend at least in part axially along the tool 500. As an example, fluid may be directed, for example, in a controllable manner, to one or more sensors of the sensor array 506, which may include a port or ports. For example, consider flowing fluid from the inlet 504 to a passage and to a sensor of the sensor array 506 for performing one or more measurements where the fluid may pass out of the tool 500 via a port. As an example, the pump assembly 505 may include one or more pumps that can be in fluid communication with one or more passages, which may be via control of one or more valves, etc. As an example, a pump may be utilized for one or more purposes, which can include, for example, one or more of sampling, moving fluid along the tool, moving fluid out of the tool, moving fluid to a sensor, moving fluid to sensors, moving fluid into the tool, inflating one or more packers, etc. As an example, one or more packer assemblies may be positioned along a length of the tool 500 and/or operatively coupled to the tool 500 and/or inflatable to form a seal against a surface of the tool 500.

As explained, a tool can include one or more packer assemblies where, for example, an inflatable seal or seals may be transitioned from an uninflated state to an inflated state. For example, consider introduction of fluid at hydrostatic pressure into the interior of an inflatable seal that can cause the inflatable seal to inflate until one or more layers of the inflatable seal have contacted a formation or a wellbore wall (e.g., casing or open wellbore wall). In such an example, the inflatable seal can inflate due to the hydrostatic pressure within the interior of the inflatable seal being greater than a pressure in a drawdown zone (e.g., a sampling zone for inflow of formation fluid, etc.). A process referred to as drawdown may include use of one or more pumps (e.g., rotating, reciprocating, piston, etc.) in the tool 500, which may be utilized to decrease the pressure in a drawdown zone to cause fluid from the formation to enter one or more inlets (e.g., on a packer assembly, between packers, etc.). When the pressure in a drawdown zone is less than a formation pressure, the differential pressure may cause fluid to flow out from the formation and into the drawdown zone. An inflatable seal or seals can help to hinder fluid in a sampling zone from mixing with other fluid.

As an example, the tool 500 can include various types of circuitry, including digital circuitry, which may provide for control of one or more features. The tool 500 can include a multi-flowline architecture and downhole automation capabilities. As an example, the tool 500 can include one or more wideband downhole pumps that may be automated via flow control, for example, with flow rates that may be, for example, in a range from approximately 0.001 bbl/d to over 100 bbl/d. As an example, the sensor array 506 may be operable from an inside out approach and/or an outside in approach, for example, depending on direction of fluid flow. As an example, a multi-flowline fluid analyzer can be included in the sensor array 506 that can help to quantify fluid properties with laboratory-accuracy metrology (e.g., consider one or more of multi-flowline spectrometry, calibrated resistivity, high-accuracy density, wide-spectrum viscosity, high-accuracy and -resolution pressure gauges, etc.).

As to some examples of formation fluid samples, consider a well that may produce 100 bbl/day or more where a sample may be in a range from less than a barrel to more than 20 barrels (e.g., consider 25 bbl of formation fluid). As an example, a sample may be of a volume from a few liters to a thousand liters or more. As explained, sampling may be controlled optionally using one or more types of sensor data. For example, the flow controller 508 may be controlled locally downhole and/or from surface (e.g., a surface station, etc.) using one or more types of sensor data. As an example, consider a method where sensor data values acquired using the sensor array 506 can automatically control (e.g., trigger) one or more valves of the flow controller 508. In such an example, where formation fluid sampled via the inlet 504 (e.g., where the packer 502 is inflated, etc.) reaches a relatively “clean” (e.g., relatively constant readings) state, the flow controller 508 may direct flow to an annular region (e.g., via an outlet of the tool 500, an outlet of the flow controller 508, etc.). As an example, the flow controller 508 may be part of the tool 500 or may be operatively coupled to the tool 500 to perform one or more tasks as to sampled fluid, etc.

As to sizes of a riser, casing, a drillstring, etc., diameters may be generally less than approximately 1 meter. For example, consider a tool that is capable of being deployed in a borehole in a diameter with a range from approximately 20 cm to approximately 35 cm. In such an example, the tool can have a diameter of about 12 cm and include one or more packers that can be extendable to a diameter sufficient to contact a borewall of a borehole (e.g., for isolation of a formation region for sampling, etc.). As an example, a tool may have a length that is in excess of 1 meter, which may be as long as 30 meters or more. As to mass, consider a tool that has a mass of approximately 100 kg to approximately 1000 kg or more.

As mentioned, a tool can include one or more pumps. For example, the ORA platform provides a tool that can include multiple pumps. In such an example, a pump may be dedicated for sample and guard flowlines where pump control circuitry can provide for control of rate and pressure in each line. As an example, flow rates may be in a range from approximately 0.05 cm³/s to approximately 200 cm³/s or more (e.g., 0.025 to 108 bbl/day) and with differential pressure up to approximately 55 MPa or more (e.g., 8,000 psi or more).

As an example, a downhole tool (e.g., ORA tool, etc.) may be utilized to assess one or more characteristics of a formation (e.g., a reservoir, etc.). Operation of such a downhole tool can depend on permeability (e.g., mobility). For example, consider submillidarcy permeability of a heterogeneous, naturally fractured carbonate formation where formation testing is desired to collect one or more samples for analysis (e.g., PVT, etc.) and/or for conducting interval pressure transient testing (IPTT). In such an example, consider a well drilled in the formation to a measured depth of approximately 8000 meters, which may pose challenging conditions for collecting representative gas condensate samples due to miscible contamination, bottomhole temperature of in excess of 175 deg C. [338 deg F.], 20,000-psi pressure, and <0.1-mD permeability. In such a well, two sampling stations, one at the top and one at the bottom of the hydrocarbon-bearing interval in the well, may be identified where formation testing operations can be performed. As explained, permeability may be quite low (e.g., lower than 0.03 mD, as may be confirmed by core analysis and/or a drillstem test (DST)) where temperature may be relatively high (e.g., approximately 182-deg C. [360-deg F.]). As explained, a downhole tool can perform formation testing to collect one or more samples. In the foregoing example, consider collection of gas condensate samples of sufficient representative quality for PVT analysis and for conducting IPTT analysis. Such samples and analysis can be utilized in a workflow that can generate reserves estimates in an expeditious manner, which, in turn, can expedite designing a completion strategy (e.g., for well completions equipment to complete the well for production, etc.).

As mentioned, equipment may be provided for handling of gas. For example, consider a separator that can separate one or more components of formation fluid as sampled using a downhole tool. As an example, handling of gas can include handling of pressurized samples and/or unpressurized samples of fluids, which may be collected in a sample chamber, a tank, etc.

As an example, a tool can include one or more features of the MDT platform (SLB, Houston, Texas). The MDT platform includes features for formation testing such as, for example, formation pressure measurement and fluid contact identification, formation fluid sampling, permeability measurement, permeability anisotropy measurement, mini-drillstem test (DST) and productivity assessment, and in-situ stress and minifrac testing. The MDT platform can include features that provide for acquiring multiple samples in a single trip, multiprobe and inflatable dual packer options, efficient integration with one or more other tools, pressure measurement using crystal quartz gauge(s), programmable pretest pressure with rate and volume, filtrate pumpout prior to sampling, fluid resistivity and temperature measurements at a probe, quantitative sample contamination measurement with optical spectroscopy, low-shock and single-phase sampling, database-driven pumpout time guidance, etc.

FIG. 6 shows an example of a tool 600 that can include various features of the MDT platform. For example, the tool 600 can include a power cartridge 610, multisample modules 620, sample chambers 630, a pumpout module 640, a live fluid analyzers 650, a hydraulic power module 660 and a probe module 670 (e.g., single-probe, multi-probe, etc.).

A single-probe module (MRPS) can include a probe assembly, (with packer and telescoping backup pistons), pressure gauges, fluid resistivity and temperature sensors, and a pretest chamber (e.g., 20 cc). A MRPS can also include a strain gauge and an accurate, high-resolution, quick response quartz gauge. A volume, rate and drawdown of a chamber can be controlled from the surface to adjust to a test situation. A dual-probe module (MRPD) can include two probes mounted back-to-back, 180 degrees apart on a common block. When combined with an MRPS module, it can form a multiprobe system capable of determining horizontal and vertical permeability. During a test with an MRPD module, formation fluid can be diverted through a sink probe to a pretest chamber in the flow control module (e.g., 1 liter). The MRPD module, in conjunction with the pressure measured at the vertical probe from the MRPS module, measures the pressure at both probes. These measurements can be used to determine near-wellbore permeability anisotropy. Flexible probe configurations are a unique feature of the MDT tool. By running multiple probe modules, pressure communication between adjacent formations can be monitored during an interference test. The MDT multiprobe configuration also allows in-situ verification of gauge quality and utilization of two different probe assemblies for redundancy under various conditions.

As mentioned, a test may be a pressure test as a downhole type of pressure test. Various types of pressure tests may be downhole and/or surface types. Whether downhole or surface, a pressure test may aim to measure or isolate a particular portion of a borehole (e.g., a borehole or wellbore interval). A transient pressure test is a type of pressure test that can provide for assessing reservoir performance by measuring flow rates and pressures under a range of flowing conditions and applying acquired data to one or more types of models. As an example, data relating to an interval under test, such as reservoir height and details of the reservoir fluids, may be model inputs. In such an example, resulting outputs can include, for example, an assessment of reservoir permeability, the flow capacity of the reservoir and indications of damage that may be restricting productivity. Results from various types of pressure tests may be combined, for example, to develop a more accurate model of a subsurface region with respect to fluid, pressure, rock, etc., properties, locations, etc.

As an example, a test may be in a particular type of subsurface region that is characterized at least in part based on temperature and/or pressure. For example, consider a high pressure and high temperature (HPHT) borehole, well or wellbore, which may, for example, have an undisturbed bottomhole temperature of greater than 300 deg F. (e.g., 149 deg C.) and a pore pressure of at least 0.8 psi/ft (e.g., ˜15.3 lbm/galUS) or requiring a blowout protector with a rating in excess of 10,000 psi (e.g., 68.95 MPa). As an example, depending on downhole conditions and depth, testing may be performed in a particular manner, where expedited testing may aim to reduce wear and tear on a downhole tool.

As an example, a tool can be a drillstring tool, a wireline tool, a coiled tubing tool, etc. As an example, a tool may be run on an electric logging cable that pushes a probe into the formation, which then allows production into a small closed chamber. Such a tool may be used to obtain formation pressures at chosen locations in an interval, and, with an accurate quartz gauge, permeability estimates may be obtained. As an example, such a tool may include features to acquire formation-fluid samples.

Various types of tools can acquire formation-related field data (e.g., formation test data). For example, consider the ORA tool, the MDT tool and the XPT tool (SLB, Houston, Texas) as wireline examples along with various “while-drilling” examples such as STETHOSCOPE tool (SLB, Houston, Texas). The STETHOSCOPE tool provides formation pressure-while-drilling (FPWD) measurements that can be used to predict pore pressure trends in a wellbore where profiles generated via real-time calibration points can be combined with one or more other LWD logs to model dynamic reservoir pressure (e.g., for optimizing recovery). As an example, one or more pore pressure models generating using one or more tools can foster a better understanding of production systems.

As an example, a framework can provide for digital fluid analysis. Such a framework can be a computational framework that is a digital fluid analyzer and computer (DiFAC) that can generate analytics for formation fluid properties. In such an example, the framework can utilize one or more machine learning techniques and/or data analytic techniques.

A downhole fluid sampling operation can acquire one or more representative formation fluid samples using one or more types of formation testing tools (e.g., downhole tools). Such sample or samples can be utilized in one or more decision-making processes, which can depend on an accurate knowledge of reservoir fluid properties.

As an example, a framework can receive field data, process the field data and output improved data, which can include interpreted data. Such a framework may include and/or be operatively coupled to one or more machine learning models that can be trained using the improved data. In various instances, machine learning can depend on data quality. For example, where immense amounts of data are available, suitable training of a machine learning model may be possible through filtering of the immense amounts of data to thereby remove undesirable data and hence its influence on training. However, where immense amounts of data are not available, a simple filtering approach can substantially reduce the amount of data for training, which can lead to issues in training and/or performance of a trained machine learning model (ML model). To facilitate training, a framework can provide for processing field data where such processing can help to assure that features in the field data are suitably retained for training one or more ML models.

As explained, a framework can provide for interpretation of field data. In such an example, interpretation can be machine-based and hence more objective than manual interpretation. Such an approach can provide for output of interpretations that are more objective and hence exhibiting less human-based interpretation bias. Where human-based interpretation bias is reduced, the resulting output can be improved for purposes of training one or more ML models. For example, interpretations absent human-based interpretation bias can be more consistent and thereby enhance training of a ML model and performance of a trained ML model.

As an example, a framework can provide for real-time processing of field data. For example, consider a framework that can be implemented at a field site at surface and/or downhole in a tool or at a remote location linked to the field site via one or more networks. As an example, a downhole tool with a built-in framework may output interpretations that may be transmissible with a lesser bandwidth demand than raw field data. In such an example, raw field data may be stored in memory of the downhole tool while interpretations may be transmitted uphole to surface equipment (e.g., using a wire, a fiber, mud-pulse telemetry, etc.). As to surface equipment that can include a framework, consider a logging truck, a drilling workstation, an edge computing device, etc., that can provide for real-time interpretations responsive to receipt of field data (e.g., raw field data from a downhole tool or downhole tools). In such examples, decisions may be made more rapidly and with lesser concerns as to the influence of human interpretation bias.

As an example, a framework can enhance real-time monitoring of a clean-up operation using downhole formation tester field data, for example, to ensure one or more clean, representative downhole fluid samples are captured with minimum level of drilling fluid contamination. Such an approach can aid in a better understanding of fluid composition and characteristics. In various examples, sampling may take a number of hours. For example, sampling can involve considerable time for clean-up to assure that a representative sample of formation fluid can be acquired.

As an example, a framework can generate output as to contamination level of a sample, which may provide for feedback as to one or more clean-up operations. For example, if an output contamination level is above a threshold, more time, more pumping, etc., during clean-up may be indicated; whereas, if an output contamination level is below a threshold, it may be possible to expedite clean-up to conserve time (e.g., and energy and wear on a downhole tool). As an example, a downhole tool can include a built-in framework that can generate output that can be utilized for control of the downhole tool (e.g., in performing one or more testing operations).

As an example, a framework can include components for implementing one or more computational methods, language-processing, back-end interpretation, etc., to deliver formation fluid outputs, which, as explained can be suited for ML model training, ML model inputs, data analytics, etc.

As explained, a manual process for interpretation of field data can introduce human bias. Such a manual process involves reading of raw acquisition data and various user dependent and time-consuming actions. In addition, results tend to be subjective, at times including errors and approximations that are not representative to true sensor readings. As mentioned, for purposes of machine learning, issues as to data quality and data quantity can exist. As an example, a framework can provide output that is suitable for machine learning where, depending on tasks, such machine learning may be supplemented with one or more other types of data (e.g., consider formation fluid interpretations based on lab analysis reports, which tend to be limited in quantity).

FIG. 7 shows an example of a workflow 700 that can be implemented at least in part by a framework 710. As shown, the framework 710 can receive field data 704 and generate output 708 based on the field data 704. As an example, the framework 710 can include a scanner component 720 that scans the field data 704, a wrangler component 730 that organizes scanned field data, a quality control component 740 that performs one or more quality control processes on the organized field data and a fluid properties component 750 that can provide for computations via a computation library 760 and interpretations via an automatic interpretation component 770. As shown, the fluid properties component 750 can generate the output 708, which, as explained, can be suitable for one or more purposes (e.g., additional workflow actions, etc.). For example, the output 708 can be formatted appropriately for use in one or more processes. As an example, the output 708 may be formatted in a spreadsheet format.

As explained, a framework can provide for automatic scanning and extraction of raw digital acquisition files from one or more downhole tools and can provide for one or more automatic quality control checks (e.g., for outliers, etc.), where a back-end automatic interpreter can be used to validate, analyze and process data. As to a computation library, it can provide various models to compute various fluid properties which may not necessarily be recorded by a formation tester. As explained, output from a framework can be or can include data that are suitable for machine learning and/or data analytics tasks. Such output can include formatted values of formation fluid properties.

As an example, output may be utilized in one or more PVT workflows. Physical property and phase behavior PVT workflows can be utilized to manage reservoir production. Such workflows can involve initial measurements of fluid compressibility and shrinkage factors to determine oil in place and gas in place, provide input for recovery estimates, evaluate reservoir material balance calculations, etc. As an example, a workflow can include using compositional analysis results and physically measured fluid properties as input and a basis for tuning one or more equations-of-state (EOS) modeling frameworks.

FIG. 8 shows an example of a graphical user interface (GUI) 800 that includes various tracks with respect to time, such as, for example, tracks for processed composition, processed fluids original composition, original fluids, viscosity, density, gas-oil ratio (GOR), resistivity, rate, and flowing pressure with respect to time. As shown, the processed composition track and the original composition track can indicate amounts of hexanes, persistent room-temperature phosphorescence (PRTP) constituents, ethane, methane, and CO2; and the processed fluids and the original fluids track can indicate amounts of various hydrocarbons and water. These tracks can be generated from downhole tool sensor data for various fluid constituents. In the example GUI 800, the viscosity, density, and resistivity tracks can be generated from corresponding downhole tool sensor data that pertain to physical properties of fluids while the GOR track can relate to amounts of gas and oil and the rate and flowing pressure tracks can pertain to transport phenomena (e.g., fluid dynamics).

FIG. 9 shows an example GUI 900 that includes various tracks with respect to time for framework output, such as, for example, cumulative variables of hydrocarbons and CO2 (e.g., C6_plus_R, C3_C5_R, C2_R, C1_R and CO2_R); resistivity, viscosity and GOR; cumulative variables of water, gas, oil and contamination; mobility (e.g., relative permeability divided by viscosity); pump rate, flowing pressure and formation pressure; and total volume pumped. The framework output in the GUI 900 may be generated using a framework, such as, for example, the framework 710 of FIG. 7.

Mobility can be defined as the ratio of effective permeability to phase viscosity. The overall mobility can be determined as a sum of individual phase viscosities. Well productivity tends to be directly proportional to the product of the mobility and the layer thickness product. Effective permeability can be defined as the ability to preferentially flow or transmit a particular fluid when other immiscible fluids are present in the reservoir (e.g., effective permeability of gas in a gas-water reservoir). Relative saturations of fluids as well as the nature of a reservoir affect effective permeability. Absolute permeability can be defined as a measurement of permeability conducted when a single fluid or phase is present in rock. Pressure tests and/or various other tests can depend on mobility. For example, one type of test equipment may be suitable for a low mobility while another type of test equipment may be suitable for a high mobility. As a particular example, consider test equipment that involves pumping using one or more pumps. In such an example, a stronger pump (e.g., higher pressure differential or pressure head rating) may be appropriate for low mobility (e.g., due to low effective permeability and/or high viscosity). As to mobility, it can be utilized in planning one or more formation testing operations and/or controlling one or more formation testing operations. For example, a cleanup process and/or a sampling process can depend at least in part on mobility. In such an example, a pump rating, a pumping rate, a pumping time, etc., may be selected and/or controlled based at least in part on mobility and, for example, one or more properties of drilling fluid (e.g., mud). As an example, a framework can generate output that can facilitate analyzing, planning and/or controlling one or more formation testing operations.

FIG. 10 shows an example workflow 1000 that includes input 1004 and output 1008 as generated using a framework 1010. In the example of FIG. 10, the workflow 1000 can be for one or more types of downhole tool data. For example, consider MDT tool data and ORA tool data. These data can be utilized as the input 1004 to generate the output 1008 where the output 1008 can include, for example, sample bottles fluids fractions and unknown contamination models (e.g., drilling fluid, etc.), which may be output in one or more formats (e.g., spreadsheet format, chart format, etc.). As shown in FIG. 10, the framework 1010 can provide for parameter preparation, data preparation, data processing, data resampling, data computations and data finalization for output. In the example of FIG. 10, one or more processes of the framework 1010 can be automated. For example, data preparation, data resampling and data finalization may be fully automated while parameter preparation, data processing and data computation may optionally provide for human intervention, human validation, etc. As an example, the framework 1010 can provide for generating one or more graphical user interfaces (GUIs) that can indicate when and/or how to use a human-in-the-loop (HITL).

As an example, output may include organized information as to one or more aspects of one or more formation testing operations. For example, consider output regarding pumpout duration when first signs of formation fluid are observed, volume pumped when first signs of formation fluid are observed, cumulative pumping time, increment in pumping time, PTA buildup duration, total station duration, total volume pumped, incremental volume pumped, pump rate increment average, etc. Such operational data can be utilized for purposes of planning, controlling, etc., one or more formation testing operations, which may be in view of one or more other metrics (e.g., contamination level, mobility, etc.). As an example, output may be provided on a station-by-station basis, where station-to-station comparisons may be made. Such comparisons may provide information germane to performance of formation testing at one or more other stations and/or provide an indication for revisiting a station (e.g., using an improved or revised operational protocol, etc.). As explained, operations at stations may be performed with respect to time where equipment is relatively stationary for some period of time and then moved to a subsequent station.

As an example, output may provide for an assessment of multiple stations with respect to time where stations may be marked within such output (e.g., to indicate station location at a particular time and/or for a duration of time). As an example, output as to mobility and/or sample contamination level for a series of stations can provide for planning, control, etc., as to operations at one or more subsequent stations.

In the example of FIG. 10, the framework 1010 can include one or more components for performing data processing tasks. For example, consider one or more types of machine learning models for data processing. As an example, a ML model can be a support vector machine (SVM) for classification. In such an example, an SVM can utilize a noise level as input for reduction of spikes in data such that processed data output by the SVM is smoother. In such an example, the SVM may be employed in an unsupervised manner. As an example, a one-class SVM can be utilized in an unsupervised manner to learn a decision function for noise detection, for example, classifying new data as similar or different to a training set where noise, which may occur as spikes, can be different from expected data (e.g., acceptable training data). As an example, a noise level may be approximately 5 percent to approximately 20 percent and tailored for acceptable spike and/or outlier classification where such classified data may be removed as part of data processing. As an example, one or more types of SVMs may be implemented, which may utilize one or more types of functions (e.g., radial basis functions, etc.).

As an example, GOR data may be noisy, including spikes. In such an example, an SVM approach may be implemented along with use of one or more time intervals (e.g., windows) for smoothing. As an example, one time interval may be approximately 0.1 minute while another, subsequent, time interval is approximately 30 minutes (e.g., for resampling). In such an approach, the GOR data may be subjected to an SVM at the first time interval and then resampled at the second time interval. Such an approach can smooth and reduce data (e.g., with respect to time resolution). In such an approach, the processed data can be more indicative of actual response of formation fluid (e.g., without spiking, outliers, etc.). As explained, a framework can output various types of data, which can include data indicative of cleaning time, response time, etc. Such times can be more accurately generated through use of data processing.

In various instances, downhole formation testing can aim to collect one or more samples with minimal contamination from drilling fluid (e.g., mud). As an example, a framework may process field data from a downhole tool in real-time to help reduce contamination. As an example, a ML model may be trained using output from a framework where output from a trained ML model may help to plan, control, etc., a downhole formation testing operation. As an example, a downhole tool probe for downhole sampling may include a single inlet to take formation fluid where the inlet is surrounded by a sealing material to isolate formation fluids from the wellbore mud column (e.g., drilling fluid column in an annulus between tubing and a wellbore wall). As an example, a probe can use a hydraulic ring inlet (guard) surrounding the probe inlet (sample) to create a barrier between the probe and the borehole fluid (e.g., drilling fluid). The guard and sample can be connected to separate hydraulic systems that allow controlling pump rates to maintain pressure in the guard at or slightly below the pressure in the sample. By doing this, most of the fluid drawn into the sample tend to be less contaminated (e.g., uncontaminated formation fluid). After cleanout pumping, fluid from the sample inlet can be directed to one or more sample chambers (e.g., sample bottles, etc.).

FIG. 11 shows an example of a workflow 1100 that can be performed using a framework, such as, for example, the framework 710 of FIG. 7, the framework 1010 of FIG. 10, etc. As shown, the workflow 1100 includes an extraction process for extracting parameters where such parameters can be organized with respect to various well-related categories, such as, for example, bottles, PPT sampling summary, final fluid analysis, and final pretest analysis. As shown, files from downhole formation testing can be received (e.g., MDT[3]_Sampling_600T), which may be in a DLIS (Dlis) format, which is a binary file format for well logs (e.g., as acquired from downhole tool operations). In the example of FIG. 11, the workflow 1100 can include a data preparation process that follows parameter preparation where, for example, data are cleaned at intervals of approximately 0.1 minute (e.g., approximately 6 seconds), which may be in a format suitable for plotting (e.g., generating graphics of data with respect to time). After data preparation, the workflow 1100 can include data processing, which may provide for determinations as to first fluid arrival and generation of a most representative reading versus time. After data processing, the workflow 1100 can include data resampling, which may resample the cleaned data using, for example, an interval of approximately 30 minutes. As shown in the example of FIG. 11, after resampling, the workflow 1100 can include data computation, which may generate results for mobility, fluids, contamination, etc. (see, e.g., the GUI 900 of FIG. 9) using field data as processed by prior actions of the workflow 1100. As an example, one or more data interpretation processes may be implemented, for example, using output from the data computation process. In the example of FIG. 11, the workflow 1100 can include outputting data in one or more formats such as, for example, a spreadsheet format (e.g., an EXCEL format, etc.). As explained, such data may be suitable for use in one or more additional workflows, which can include, for example, machine learning, data analytics, etc.

As an example, a framework may be implemented within another framework and/or be operatively coupled to another framework. For example, consider the framework 710 or the framework 1010 being integrated with the TECHLOG framework. In such an example, integration may involve one or more application programming interfaces, add-ons, etc. As explained, a framework may be implemented within a downhole tool such that output can be generated downhole. In such an example, output generated downhole may be received by a framework, such as, for example, the TECHLOG framework.

As an example, output from a framework may provide information akin to that provided from a PVT laboratory analysis; noting that a PVT laboratory analysis may take considerable time (e.g., a day, a week, a month, etc.); whereas, a framework may provide output in real-time. As an example, output from a framework may be compared to a PVT laboratory analysis, which may provide for one or more types of feedback for improving framework operation. For example, an interpretation component may be tuned using feedback from a PVT laboratory analysis.

As explained, a framework can generate output using field data acquired from downhole formation testing operations where the output can be quality controlled and interpreted. Such output can be suitable for use in machine learning, data analytics, etc. Such a framework can provide output in an agnostic manner independent of formation fluid tester where, for example, output is of reduced human bias and approximations independent.

Planning of testing processes tend to involve manual, subjective determinations that rely on personal expertise and knowledge, which can vary from individual to individual. Such an approach can make planning less consistent and can make improving planning more challenging. As an example, a framework can generate output that can be utilized for improved planning. For example, where such output, utilized to plan, exhibits reduced human bias, planning and actual testing may become more consistent.

As an example, a data science platform such as the DATAIKU platform may be utilized for one or more purposes. The DATAIKU platform provides various libraries for ML models and associated techniques. As an example, an ML model-based approach may utilize one or more of supervised and/or unsupervised learning. As an example, an F1 score (e.g., harmonic mean of precision and recall) can be used as a metric and/or a confusion matrix or matching matrix. In such an approach, various types of ML models may be excluded from further consideration. Once one or more ML models are selected as suitable for a task or tasks, the one or more ML models may be subjected to tuning, which can include hyperparameter tuning.

As an example, cross-validation can be utilized as a statistical technique to estimate skill of one or more ML models. Cross-validation can be used in applied machine learning, for example, to compare and select a model for a given predictive modeling problem because it is easy to understand, easy to implement, and results in skill estimates that generally have a lower bias than other methods. As an example, a k-fold cross-validation procedure can be implemented for estimating skill of one or more ML models.

As an example, a downhole tool may include one or more processors, memory accessible by at least one of the processors and instructions stored in the memory and executable by at least one of the processors such that a ML model can be executed by the downhole tool such that the downhole tool can generate ML model-based output. For example, consider a downhole tool that can acquire logs, process the logs using a downhole framework to generate output where the output or a portion thereof is input to a trained ML model executing in the downhole tool. Such an approach can provide for additional types of output, control, etc.

FIG. 12 shows an example of a method 1200 and an example of a system 1290. As shown, the method 1200 can include a reception block 1210 for receiving downhole formation testing time series data acquired at a location along a borehole in a subsurface region during a formation testing operation performed by a downhole tool; a process block 1220 for processing the downhole formation testing time series data using a machine learning model to generate smoothed time series data; a resample block 1230 for resampling the smoothed time series data; a generation block 1240 for generating fluid and formation characteristics with respect to time based on the smoothed time series data; and an output block 1250 for outputting the fluid and formation characteristics with respect to time.

As an example, a location may be referred to as a station, for example, a location where a downhole tool may be positionally stationary for a period of time to perform one or more formation testing operations (e.g., cleanup, sampling, etc.). For example, consider the example scenario where gas condensate samples were collected using a downhole tool. As explained, conditions may be challenging given measured depth of a location (e.g., a station). As an example, one or more of cost, resources, time, tool exposure concerns, quality of sample(s), etc., can be addressed using a method such as, for example, the method 1200, which may be implemented at least in part using one or more frameworks.

The method 1200 is shown in FIG. 12 in association with various computer-readable media (CRM) blocks 1211, 1221, 1231, 1241 and 1251. Such blocks generally include 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. While various blocks are shown, a single medium may be configured with instructions to allow for, at least in part, performance of various actions of the method 1200. As an example, a computer-readable medium (CRM) may be a computer-readable storage medium that is non-transitory and that is not a carrier wave. As an example, one or more of the blocks 1211, 1221, 1231, 1241 and 1251 may be in the form of processor-executable instructions.

In the example of FIG. 12, the system 1290 includes one or more information storage devices 1291, one or more computers 1292, one or more networks 1295 and instructions 1296. As to the one or more computers 1292, each computer may include one or more processors (e.g., or processing cores) 1293 and memory 1294 for storing the instructions 1296, for example, executable by at least one of the one or more processors 1293 (see, e.g., the blocks 1211, 1221, 1231, 1241 and 1251). As an example, a computer may include one or more network interfaces (e.g., wired or wireless), one or more graphics cards, a display interface (e.g., wired or wireless), etc.

As to types of machine learning models that may be implemented for one or more purposes, 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 can 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 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 of 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 can be implemented for machine learning applications that can include neural networks. As an example, the CAFFE framework may be implemented, which is a DL framework developed by Berkeley Al 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 can include various actions that can operate on a dataset to train a ML model. As an example, a dataset can be split into training data and test data where test data can provide for evaluation. A method can include cross-validation of parameters and best parameters, which can be provided for model training.

The TENSORFLOW framework can 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 can 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 can be referred to as “tensors”.

As an example, a device may utilize TENSORFLOW LITE (TFL) or another type of lightweight framework. TFL is a set of tools that enables on-device machine learning where models may run on mobile, embedded, and IoT devices. TFL is optimized for on-device machine learning, by addressing latency (no round-trip to a server), privacy (no personal data leaves the device), connectivity (Internet connectivity is demanded), size (reduced model and binary size) and power consumption (e.g., efficient inference and a lack of network connections). TFL offers multiple platform support, covering ANDROID and iOS devices, embedded LINUX, and microcontrollers. TFL offers diverse language support, which includes JAVA, SWIFT, Objective-C, C++, and PYTHON. TFL offers high performance, with hardware acceleration and model optimization.

As an example, a TFL or other lightweight framework approach may be implemented in the field, optionally within a downhole tool string that can execute framework processes downhole, which may provide for real-time decision making, control, etc.

As an example, a method can include receiving downhole formation testing time series data acquired at a location along a borehole in a subsurface region during a formation testing operation performed by a downhole tool; processing the downhole formation testing time series data using a machine learning model to generate smoothed time series data; resampling the smoothed time series data; generating fluid and formation characteristics with respect to time based on the smoothed time series data; and outputting the fluid and formation characteristics with respect to time. In such an example, receiving can include receiving the downhole formation testing time series data by a computational framework implemented in the downhole tool where, for example, outputting includes transmitting the fluid and formation characteristics to surface equipment. As explained, for a downhole tool, transmitting may be via wireline transmission or acoustic transmission (e.g., or one or more other transmission technologies).

As an example, a machine learning model can be or include a support vector machine. For example, consider a one-class SVM implemented to handle noise and/or outliers.

As an example, a machine learning model can include a tunable parameter for identification of one or more of noise and outliers. For example, consider spikes as a type of noise where a spike may be defined using an amplitude and a time. As an example, a time interval analysis approach may be implemented before and/or after spike identification and removal.

As an example, downhole formation testing time series data can include data indicative of a cleanup process and data indicative of a sampling process that acquires a fluid sample. In such an example, fluid and formation characteristics can include a fluid sample contamination characteristic. In such an example, a method can include controlling a formation testing operation based at least in part on the fluid sample contamination characteristic. For example, consider controlling that extends or shortens a cleanup time based at least in part on the fluid sample contamination characteristic. As an example, a fluid sample contamination characteristic may indicate a level of drilling fluid contamination in a fluid sample. As explained, drilling can utilize drilling fluid (e.g., mud) for lubrication, carrying away cuttings, etc. Where a formation testing operation aims to collect a sample of formation fluid (e.g., reservoir fluid, etc.), cleanup can help to clean away drilling fluid such that the sample has less drilling fluid contamination. As explained, such an operation may be performed under challenging circumstances and at a considerable depth where an ability to assess sample quality (e.g., contamination level) prior to bringing a sample to surface and/or collecting additional samples can be beneficial.

As an example, a method can include processing that processes downhole formation testing time series data according to a first time interval where resampling resamples at a second time interval that is at least an order of magnitude greater than the first time interval. For example, consider a first time interval that is less than approximately 1 minute and a second time interval that is greater than approximately 10 minutes. As explained, noise may be reduced in data prior to resampling (e.g., consider spike elimination prior to resampling).

As an example, downhole formation testing time series data can include one or more of viscosity data and density data. As an example, downhole formation testing time series data can include gas-oil ratio data. As an example, downhole formation testing time series data can include one or more of flow rate data and flowing pressure data. As an example, data can include one or more other types of data such as, for example, temperature data, operational time data, etc.

As an example, fluid and formation characteristics can include one or more of mobility, fluid composition and level of contamination.

As an example, a method can include performing machine learning model training using fluid and formation characteristics. In such an example, a trained machine learning model may be generated that can be utilized for one or more purposes, which may help to facilitate formation testing operations, PVT analysis, completions planning and/or execution, etc.

As an example, a system can include one or more processors; memory accessible to at least one of the one or more processors; processor-executable instructions stored in the memory and executable to instruct the system to: receive downhole formation testing time series data acquired at a location along a borehole in a subsurface region during a formation testing operation performed by a downhole tool; process the downhole formation testing time series data using a machine learning model to generate smoothed time series data; resample the smoothed time series data; generate fluid and formation characteristics with respect to time based on the smoothed time series data; and output the fluid and formation characteristics with respect to time.

As an example, one or more computer-readable storage media can include processor-executable instructions to instruct a computing system to: receive downhole formation testing time series data acquired at a location along a borehole in a subsurface region during a formation testing operation performed by a downhole tool; process the downhole formation testing time series data using a machine learning model to generate smoothed time series data; resample the smoothed time series data; generate fluid and formation characteristics with respect to time based on the smoothed time series data; and output the fluid and formation characteristics with respect to time.

As an example, a computer program product can include one or more computer-readable storage media that can include processor-executable instructions to instruct a computing system to perform one or more methods and/or one or more portions of a method.

In some embodiments, a method or methods may be executed by a computing system. FIG. 13 shows an example of a system 1300 that can include one or more computing systems 1301-1, 1301-2, 1301-3 and 1301-4, which may be operatively coupled via one or more networks 1309, which may include wired and/or wireless networks. As shown, the system 1300 may include one or more other components 1308.

As an example, a system can include an individual computer system or an arrangement of distributed computer systems. In the example of FIG. 13, the computer system 1301-1 can include one or more modules 1302, which may be or include processor-executable instructions, for example, executable to perform various tasks (e.g., receiving information, requesting information, processing information, simulation, outputting information, etc.).

As an example, a module may be executed independently, or in coordination with, one or more processors 1304, which is (or are) operatively coupled to one or more storage media 1306 (e.g., via wire, wirelessly, etc.). As an example, one or more of the one or more processors 1304 can be operatively coupled to at least one of one or more network interfaces 1307; noting that one or more other components 1308 may also be included. In such an example, the computer system 1301-1 can transmit and/or receive information, for example, via the one or more networks 1309 (e.g., consider one or more of the Internet, a private network, a cellular network, a satellite network, etc.).

As an example, the computer system 1301-1 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 1301-2, etc. A device may be located in a physical location that differs from that of the computer system 1301-1. 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 1306 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 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 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.).

Although only a few example embodiments have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the example embodiments. 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.

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