Patent Application
20230145859
The present disclosure relates to a system and methods for projecting the trajectory of a drilling assembly in a subsurface formation, and more specifically to systems and methods for generating stochastic trajectory projections in real-time to predict the movement of a bottom hole assembly coupled to a drill string across a depth horizon and thereby improve control of the bottom hole assembly across the depth horizon.
Boreholes drilled into subsurface formations may enable recovery of desirable fluids, including, without limitation, hydrocarbons, using any number of different techniques. In drilling operations, typical drilling processes may be relatively complex and involve considerable expense. Many of these drilling operations may be done manually with experienced operators running the drilling platform. There are continual efforts to improve safety, improve fluid recovery, and lower costs associated with subsurface drilling and advancements in computerized and automated systems in drilling processes may support these efforts.
Model-based control methods are now widely utilized to control the trajectory of borehole placement during exploration of and extraction operations in subsurface formations. Due to the complexity and uncertainty in drilling operations, it is challenging to find effective models for control. High-fidelity models have been established in the past, but often cannot be used for real-time dynamic control of subsurface drilling operations as these high-fidelity models are generally high dimension and computationally expensive, thus cannot be used in real-time. Reduced physics-based models have also been developed. These reduced physics-based models are simpler and may provide more confidence for a short range that may be suitable for real-time control if they are updated frequently using the measurements from subsurface equipment. However, due to uncertainties in the bit-rock interactions, drilling parameter changes, sensor noise or malfunctions, downhole vibrations, and model/system discrepancies, reduced physics-based models with deterministic parameters may not be sufficient for real-time control of drilling operations.
A more complete understanding of the present embodiments and advantages thereof may be acquired by referring to the following description taken in conjunction with the accompanying drawings, in which like reference numbers indicate like features.
While embodiments of this disclosure have been depicted and described and are defined by reference to exemplary embodiments of the disclosure, such references do not imply a limitation on the disclosure and no such limitation is to be inferred. The subject matter disclosed is capable of considerable modification, alteration, and equivalents in form and function, as will occur to those skilled in the pertinent art and having the benefit of this disclosure. The depicted and described embodiments of this disclosure are examples only and are not exhaustive of the scope of the disclosure.
The present disclosure relates to a system and methods for projecting the trajectory of a drilling assembly in a subsurface formation, and more specifically to systems and methods for generating stochastic trajectory projections in real-time to predict the movement of a bottom hole assembly coupled to a drill string across a depth horizon and thereby improve control of the bottom hole assembly across the depth horizon.
The system and method disclosed herein uses a stochastic trajectory projection module. The stochastic trajectory projection module may use a plurality of inputs to generate a projected trajectory and confidence regions for a bottom hole assembly across a depth horizon in a subsurface formation. The stochastic trajectory projection module may use one or more stochastic models, including, without limitation, Monte Carlo simulation methods, to simulate and project the future trajectories. The stochastic trajectory projection module disclosed herein supports two modes, where selection of the mode and any corresponding settings may be dependent on any prior data analyses, including without limitation higher fidelity models, or knowledge of one or more of the subsurface formation and the equipment of the drilling system.
The stochastic trajectory projection module further enables real-time probabilistic projections for trajectories and corresponding confidence regions based on system model parameters, steering inputs, and bottom hole assembly initial conditions.
The system and method disclosed herein provide a unique way to project the borehole trajectories. This enables drilling personnel and steering control systems to plan ahead and improve steering decisions, resulting in improved well placement and, thereby, improving fluid recovery and lowering costs associated with subsurface drilling operations. As discussed herein, real-time trajectory projections enable feedback during drilling operations that enables an operator to refine the steering inputs to the drilling operation equipment during measurement-while drilling (MWD) or logging-while-drilling (LWD) operations.
In one or more aspects of the present disclosure, a borehole environment may utilize an information handling system to control one or more operations associated with the borehole environment. For purposes of this disclosure, an information handling system may include any instrumentality or aggregate of instrumentalities operable to compute, classify, process, transmit, receive, retrieve, originate, switch, store, display, manifest, detect, record, reproduce, handle, or utilize any form of information, intelligence, or data for business, scientific, control, or other purposes. For example, an information handling system may be a personal computer, a network storage device, or any other suitable device and may vary in size, shape, performance, functionality, and price. The information handling system may include random access memory (RAM), one or more processing resources such as a central processing unit (CPU) or hardware or software control logic, ROM, and/or other types of nonvolatile memory. Additional components of the information handling system may include one or more disk drives, one or more network ports for communication with external devices as well as various input and output (I/O) devices, such as a keyboard, a mouse, and a video display. The information handling system may also include one or more buses operable to transmit communications between the various hardware components. The information handling system may also include one or more interface units capable of transmitting one or more signals to a controller, actuator, or like device.
For the purposes of this disclosure, computer-readable media may include any instrumentality or aggregation of instrumentalities that may retain data and/or instructions for a period of time. Computer-readable media may include, for example, without limitation, storage media such as a sequential access storage device (for example, a tape drive), direct access storage device (for example, a hard disk drive or floppy disk drive), compact disk (CD), CD read-only memory (ROM) or CD-ROM, DVD, RAM, ROM, electrically erasable programmable read-only memory (EEPROM), and/or flash memory, biological memory, molecular or deoxyribonucleic acid (DNA) memory as well as communications media such wires, optical fibers, microwaves, radio waves, and other electromagnetic and/or optical carriers; and/or any combination of the foregoing.
Illustrative embodiments of the present disclosure are described in detail herein. In the interest of clarity, not all features of an actual implementation may be described in this specification. It will of course be appreciated that in the development of any such actual embodiment, numerous implementation-specific decisions may be made to achieve the specific implementation goals, which may vary from one implementation to another. Moreover, it will be appreciated that such a development effort might be complex and time-consuming, but would nevertheless be a routine undertaking for those of ordinary skill in the art having the benefit of the present disclosure.
Throughout this disclosure, a reference numeral followed by an alphabetical character refers to a specific instance of an element and the reference numeral alone refers to the element generically or collectively. Thus, as an example (not shown in the drawings), widget “l a” refers to an instance of a widget class, which may be referred to collectively as widgets “1” and any one of which may be referred to generically as a widget “1”. In the figures and the description, like numerals are intended to represent like elements.
To facilitate a better understanding of the present disclosure, the following examples of certain embodiments are given. In no way should the following examples be read to limit, or define, the scope of the disclosure. Embodiments of the present disclosure may be applicable to drilling operations that include but are not limited to target (such as an adjacent well) following, target intersecting, target locating, well twinning such as in SAGD (steam assist gravity drainage) well structures, drilling relief wells for blowout wells, river crossings, construction tunneling, as well as horizontal, vertical, deviated, multilateral, u-tube connection, intersection, bypass (drill around a mid-depth stuck fish and back into the well below), or otherwise nonlinear boreholes in any type of subsurface formation. Embodiments may be applicable to injection wells, and production wells, including natural resource production wells such as hydrogen sulfide, hydrocarbons or geothermal wells; as well as wellbore or borehole construction for river crossing tunneling and other such tunneling boreholes for near surface construction purposes or borehole u-tube pipelines used for the transportation of fluids such as hydrocarbons. Embodiments described below with respect to one implementation are not intended to be limiting.
As depicted in
With continued reference to
Bottom hole assembly 130 may comprise any one or more of tools, transmitters, and receivers to perform downhole measurement operations. For example and without limitation, bottom hole assembly 130 may comprise one or more of any number of assemblies for one or more of measurement, communication, energy storage, and the like. For example and without limitation, bottom hole assembly 130 may comprise measurement assembly 134. In one or more embodiments, measurement assembly 134 may comprise at least one transducer 136a, which may be disposed at the surface of measurement assembly 134. While
In one or more embodiments, bottom hole assembly 130 may be one or more of coupled to and controlled by information handling system 138, which may be disposed on surface 108. In one or more embodiments, information handling system 138 may be disposed down hole in bottom hole assembly 130. Processing of information recorded may occur at one or more of down hole and on surface 108. Processing occurring downhole may be transmitted to surface 108 to be one or more of recorded, observed, and further analyzed. In one or more embodiments, information recorded on information handling system 138 that may be disposed down hole may be stored until bottom hole assembly 130 may be brought to surface 108. In one or more embodiments, information handling system 138 may communicate with bottom hole assembly 130 through a communication line (not shown) disposed in or on drill string 116. In one or more embodiments, wireless communication may be used to transmit information back and forth between information handling system 138 and bottom hole assembly 130. Information handling system 138 may transmit information to bottom hole assembly 130 and may receive as well as process information recorded by bottom hole assembly 130. In one or more embodiments, a downhole information handling system (not shown) may include suitable circuitry, for example and without limitation, a microprocessor, for estimating, receiving, and processing signals from bottom hole assembly 130. Downhole information handling system (not shown) may further comprise one or more of additional components, including, without limitation, memory, input devices, output devices, interfaces, and the like. In one or more embodiments, while not shown, bottom hole assembly 130 may include one or more additional components, including, without limitation, analog-to-digital converters, filters, and amplifiers, among others, that may be used to process the measurements of bottom hole assembly 130 before they may be transmitted to surface 108. In one or more embodiments, raw measurements from bottom hole assembly 130 may be transmitted to surface 108.
Any suitable technique may be used for transmitting signals from bottom hole assembly 130 to surface 108, including, without limitation, wired pipe telemetry, mud-pulse telemetry, acoustic telemetry, and electromagnetic telemetry. While not shown, bottom hole assembly 130 may include a telemetry subassembly that may transmit telemetry data to surface 108. At surface 108, pressure transducers (not shown) may convert the pressure signal into electrical signals for a digitizer (not shown). The digitizer may supply a digital form of the telemetry signals to information handling system 138 via a communication link 140, which may be a wired or wireless link. The telemetry data may be analyzed and processed by information handling system 138.
As depicted in
{dot over (x)}=f(x,u,p) (1)
where x represents the initial conditions of the bottom hole assembly 224, which may include one or more of inclination, azimuth, build rate, walk rate, true vertical depth and similar values; u represents steering inputs 222, which may include one or more of steering ratios and tool face angles and which may be provided as a sequence of inputs; and p denotes the system model parameter probability distributions 220. In one or more embodiments, the steering inputs 222 may be derived using a dynamic control scheme such as model predictive control. In one or more embodiments, the steering inputs 222 may be quantitative values specified by the drilling personnel or the control system. In one or more embodiments, the probability distributions of p can be developed in real-time using one or more parameter data sets. In one or more embodiments, a parameter data set may comprise any one or more of a real-time (or online) calibration method or data analytics, non-real-time (or offline) calibration method or data analytics, any one or more models of varying degrees of fidelity, and the experience of one or more persons skilled in the art of drilling or control systems. In one or more embodiments, system model parameter set p may depicted by Equation (3). In one or more embodiments, system model parameter set p may be directly obtained from an online identification method. In one or more embodiments, an identification method or system identification may refer to one or more methods of using one or more of measurements and known external influence to determine one or more system model parameters. In one or more embodiments, a known external influence may comprise one or more system inputs. In one or more embodiments, the term “online” may be used to denote a real-time method or system in which a model controller for the bottom hole assembly is operating simultaneously with and controlling the bottom hole assembly. An online method or system enables identification of one or more new values for system model parameter set p as one or more of new measurements and inputs are obtained. In one or more embodiments, the new measurements and inputs may enable improved controller performance by refining and updating prior measurements and inputs during one or more drilling operations. In one or more embodiments, the term “offline” may be synonymous with a method or system that is not operating in real-time. In one or more embodiments, the system model parameter probability distributions 220 may be assumed to follow a normal distribution, as is assumed in Equations (3)-(5). In one or more embodiments, the system model parameters probability distributions 220 may be any alternative type of distribution.
In one or more embodiments, the system model parameters probability distributions 220 may be one or more of the elements of a steering model. For example and without limitation, the steering model may be used to estimate the position of a drill bit 122 (depicted in
τ{umlaut over (θ)}=−{dot over (θ)}+Kactu+Kbias, initial conditions: θ0, {dot over (θ)}0 (2)
identified as T is a depth constant, Kact is the magnitude of the bottom hole assembly 130 turning capability, Kbias represents both the inherent steering tendency of bottom hole assembly 130 as well as any external forces on bottom hole assembly 130, θ0 is the initial angle (inclination or azimuth), and θ0 is the initial curvature (build rate or walk rate). The dot notation in this equation represents a derivative with respect to distance, not time. Without limitation, θ is also used to represent a vector of the system model parameter probability distributions 220. In one or more embodiments, the system model parameter probability distributions 220 may be generated using prior experience, knowledge of the subsurface formation and the equipment of the drilling system, prior analyses, and the like.
In one or more embodiments, model parameter set p may be described by a multivariate normal probability distribution using one or more of the model parameter set's mean and variances or covariances. The multivariate normal distribution of an n-dimensional parameter vector P=(P, P2, . . . Pn) may be written as:
P˜N(μ, Kpp) (3)
where μ is an n-dimensional mean vector:
μ=E[P]=(E[P1], E[P2], . . . , E[Pn]) (4)
and where Kpp is an n×n covariance matrix:
K
pp
=E[(Pi−μi) (Pj−μj)] (5)
such that 1≤i and j≤n. That is, in one or more embodiments, the probability distribution for Kact may have one or more interactions with Kbias. In one or more embodiments, the multivariate distribution may be any alternative type of distribution.
As depicted in
In the second working mode, multiple different models are used at different depths within the projected. For a first predetermined depth interval (or length of the borehole), a first set of model parameters may be selected from the system model parameter probability distributions 220 input to the stochastic trajectory projection module 210. The first set of model parameters may then used in combination with the steering inputs 222 to generate the stochastic trajectory projections for the bottom hole assembly for the first predetermined depth interval. After projecting the stochastic trajectory projections for the bottom hole assembly for the first predetermined depth interval, a second set of model parameters is selected from the system model parameter probability distributions 220 input to the stochastic trajectory projection module 210 and used to generate the stochastic trajectory projections for the bottom hole assembly for the second predetermined depth interval. This process is repeated until the final trajectory for the bottom hole assembly, which is the combined stochastic trajectory projections, traverses the entire depth horizon. The second working mode may be referred to as a multi-model mode. In one or more embodiments, the multi-model mode may reduce the size of the confidence regions of the stochastic trajectory projections. In one or more embodiments, the multi-model mode may provide more confident predictions, across the depth horizon than a one-model model because, for example, the multi-model mode supports updates to the model parameter set across the depth horizon.
The selection of the mode and other settings, including, without limitation, the depth interval for stochastic trajectory projections may be dependent on one or more factors, including, without limitation, any prior data analyses or knowledge of one or more of the subsurface formation and the equipment of the drilling system. In one or more embodiments, the number of stochastic trajectory projections for the entire depth horizon may be provided as an input to the stochastic trajectory projection module 210.
In one or more embodiments, a number of stochastic trajectory projections over the entire depth horizon may be generated, where the number of stochastic trajectory projections 228 may be represented by N and where N is a positive integer. In one or more embodiments, N may be 100 or more. In one or more embodiments, a median or mean value for the N stochastic trajectory projections may be calculated. In one or more embodiments, any outliers in the stochastic trajectory projections may be identified and eliminated before the median or mean value is calculated for the N stochastic trajectory projections.
In one or more embodiments, the N stochastic trajectory projections 228 may be used to generate a vector including N data points, where each data point corresponds to particular position within the subsurface formation. In one or more embodiments, the position within the subsurface formation may comprise a depth point, horizontal point, or any other identifier for the location of the position within the subsurface formation. Quantiles between the cumulative probabilities of q1 and q2 of the elements in the vector can be calculated where q1, q2 are in the interval of [0, 1] and q1<q2. With quantiles derived at each depth point, a confidence region is established in which the abovementioned resulting trajectory is at the center. For example, a 95% confidence region means a pair of 2.5% and 97.5% quantiles need to be calculated and the remaining data points in the vector are used to determine the confidence region. Multiple confidence regions or quantiles can be obtained and plotted and presented in the same or separate displays.
In one or more embodiments, one or more of data, analysis, experience, and knowledge may be used to select identify desired confidence regions, where smaller confidence regions generally indicate greater confidence that the actual trajectory of the bottom hole assembly will align with the stochastic trajectory projections. In one or more embodiments, a multi-model mode may generate narrower confidence regions, indicating increased confidence in the multi-model trajectory projections than in the stochastic trajectory projections generated by a one-model mode. For example and without limitation, a multi-model trajectory projection may generate narrower confidence regions by better representing the variations in subsurface operating conditions based on generation of multiple trajectory projections using multiple models from the system model parameter probability distributions 220. In one or more embodiments, the number of models utilized may affect the area of confidence regions given the same distributions. For example and without limitation, over a given depth horizon, a one-model mode may use only a single model parameter set drawn from data set p while a multi-model mode may use, for example, ten parameter sets drawn from model parameter set p for the same depth horizon. Accordingly, it may be desirable to sample multiple models from data set p to attempt to generate a better representation of the probability distribution of model parameters.
In summary, as shown in
In one or more embodiments, the projected trajectory 230 and projected confidence region 232 may be used for a variety of purposes. In one or more embodiments, the projected trajectory 230 and projected confidence region 232 may be used to update future steering decisions, resulting in new steering inputs 222 to the stochastic trajectory projection module 210. In one or more embodiments, an actual trajectory that poorly aligns with the stochastic projected trajectory 230 or lies outside the projected confidence region 232 may indicate changes in the drilling conditions and signal an operator to recalibrate and update the model parameter set drawn from data set p, for example and without limitation, by running the system identification again using the new measurements. In one or more embodiments, the projected trajectory 230 and projected confidence region 232 may be used to mitigate any possible risks of the borehole 102 interfering with other boreholes in a subsurface formation 106 or falling behind the target. For example and without limitation, an operator may provide new inputs to trajectory controller 242 if there is a significant overlap between the projected trajectory 230 and an area of possible collision.
In an additional example without limitation, an operator may provide new inputs to trajectory controller 242 such as applying additional steering power if the projected trajectory 230 shows a risk of deviating from a well plan for borehole 102 and, in one or more embodiments, the additional steering power can be repeatedly provided to the trajectory controller 242 if the projected trajectory 230 continues to show the borehole 102 deviating from the well plan. For example, if the actual borehole has begun to deviate from the well plan, the projected trajectory 230 and projected confidence region 232 for a given set of steering inputs 222 enables an operator or controller to determine that the projected trajectory 230 will return to or overlap with the well plan.
In one or more embodiments, a projected confidence region generated by the stochastic trajectory projection module 210 using the one-model mode may be larger, and in some cases substantially larger, than the projected confidence region generated by the stochastic trajectory projection module 210 using the multi-model mode.
In one or more embodiments, the stochastic trajectory projection module 210 may use one or more stochastic simulation methods including, without limitation, Monte Carlo simulation methods, to simulate and project the stochastic trajectory projections. In one or more embodiments, one or more additional inputs may be provided to the stochastic trajectory projection module 210, including, without limitation, weight on bit, RPM, flow rate. These additional inputs may enable the stochastic trajectory projection module 210 to account for changes in one or more drilling parameters and may thereby improve the quality of the stochastic trajectory projections.
The data 854 may include treatment data, geological data, fracture data, microseismic data, mud candidate data, borehole imager measured data, inversion-estimated imaging properties, or any other appropriate data. The one or more applications 858 may include one or more machine learning models, applications for one or more of down-sampling measured data, calculating misfits or to minimize cost functions, to perform petrochemical inversions, to solve for formation permittivity, to align measured data based on depth, azimuth, resolution, or any other measurement, extrapolating permittivity, scaling coefficients to match borehole imager measurements with dielectric tool measurements, calculate dispersion curves of permittivity, calibrating coefficients, or any other appropriate applications. In one or more embodiments, a memory of a computing device includes additional or different data, application, models, or other information. In one or more embodiments, the data 854 may include treatment data relating to fracture treatment plans. For example, the treatment data may indicate a pumping schedule, parameters of a previous injection treatment, parameters of a future injection treatment, or one or more parameters of a proposed injection treatment. Such one or more parameters may include information on flow rates, flow volumes, slurry concentrations, fluid compositions, injection locations, injection times, or other parameters. The treatment data may include one or more treatment parameters that have been optimized or selected based on numerical simulations of fracture propagation. In one or more embodiments, the data 854 may include one or more signals received by one or more transducers 136a-c of
The one or more applications 858 may comprise one or more software programs or applications, one or more scripts, one or more functions, one or more executables, or one or more other modules that are interpreted or executed by the processor 801. For example, the one or more applications 858 may include a fracture design module, a reservoir simulation tool, a hydraulic fracture simulation model, or any other appropriate function block. The one or more applications 858 may include machine-readable instructions for performing one or more of the operations related to any one or more embodiments of the present disclosure. The one or more applications 858 may include machine-readable instructions for generating a user interface or a plot, for example, depicting fracture geometry (for example, length, width, spacing, orientation, etc.), pressure plot, hydrocarbon production performance. The one or more applications 858 may obtain input data, such as treatment data, geological data, fracture data, measurement data, or other types of input data, from the memory 803, from another local source, or from one or more remote sources (for example, via the one or more communication links 814). The one or more applications 858 may generate output data and store the output data in the memory 803, hard drive 807, in another local medium, or in one or more remote devices (for example, by sending the output data via the communication link 814).
Modifications, additions, or omissions may be made to
Memory controller hub 802 may include a memory controller for directing information to or from various system memory components within the information handling system 800, such as memory 803, storage element 806, and hard drive 807. The memory controller hub 802 may be coupled to memory 803 and a graphics processing unit (GPU) 804. Memory controller hub 802 may also be coupled to an I/O controller hub (ICH) or south bridge 805. I/O controller hub 805 is coupled to storage elements of the information handling system 800, including a storage element 806, which may comprise a flash ROM that includes a basic input/output system (BIOS) of the computer system. I/O controller hub 805 is also coupled to the hard drive 807 of the information handling system 800. I/O controller hub 805 may also be coupled to an I/O chip or interface, for example, a Super I/O chip 808, which is itself coupled to several of the I/O ports of the computer system, including a keyboard 809, a mouse 810, a monitor 812 and one or more communications link 814. Any one or more input/output devices receive and transmit data in analog or digital form over one or more communication links 814 such as a serial link, a wireless link (for example, infrared, radio frequency, or others), a parallel link, or another type of link. The one or more communication links 814 may comprise any type of communication channel, connector, data communication network, or other link. For example, the one or more communication links 814 may comprise a wireless or a wired network, a Local Area Network (LAN), a Wide Area Network (WAN), a private network, a public network (such as the Internet), a WiFi network, a network that includes a satellite link, or another type of data communication network.
A memory or storage device primarily stores one or more software applications or programs, which may also be described as program modules containing computer-executable instructions, which may be executed by the computing unit for implementing one or more embodiments of the present disclosure. The memory, therefore, may include one or more applications including, for example, a transmitter control application, a receiver control application, and one or more applications enabling one or more of the processes or sub-processes illustrated in
Although the computing device 800 is shown as having one or more generalized memories, the computing device 800 typically includes a variety of non-transitory computer readable media. By way of example, and not limitation, non-transitory computer readable media may comprise computer storage media and communication media. The memory may include computer storage media, such as a ROM and RAM in the form of volatile memory, nonvolatile memory, or both. A BIOS containing the basic routines that help to transfer information between elements within the computing unit, such as during start-up, is typically stored in the ROM. RAM typically contains data, program modules, other executable instructions, or any combination thereof that are immediately accessible to, presently being operated on, or both by the processing unit. By way of example, and not limitation, the computing device 800 may include an operating system, application programs, other program modules, and program data.
The components shown in the memory may also be included in other removable/non-removable, volatile/nonvolatile non-transitory computer storage media or the components may be implemented in the computing device 800 through an application program interface (“API”) or cloud computing, which may reside on a separate computing device coupled through a computer system or network (not shown). For example and without limitation, a hard disk drive may read from or write to non-removable, nonvolatile magnetic media, a magnetic disk drive may read from or write to a removable, nonvolatile magnetic disk, and an optical disk drive may read from or write to a removable, nonvolatile optical disk such as a CD-ROM or other optical media. Other removable/non-removable, volatile/nonvolatile computer storage media that may be used in the exemplary operating environment may include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, or the like. The drives and their associated computer storage media discussed above provide storage of computer readable instructions, data structures, program modules, and other data for the computing unit.
The computing device 800 may receive commands or information from a user through one or more input devices such as the keyboard 809 and the mouse 810. Additional input devices may comprise a microphone, joystick, touchscreen, scanner, voice or gesture recognition, one or more sensors including one or more seismic sensors, and the like (not shown). These and other input devices may be coupled to the processing unit through the Super I/O chip 808 that is coupled to the ICH 805, but may be coupled by other interface and bus structures, such as a parallel port or a universal serial bus (USB) (not shown).
A monitor or other type of display device (not shown) may be coupled to the MCH 802 via an interface, such as the GPU 804 or via Super I/O chip 808. A graphical user interface (“GUI”) may also be used with the video interface 804 to receive instructions from a user and transmit instructions to the central processing unit 801. A GUI may be used to display the outputs of the processes described in in
Any one or more input/output devices may receive and transmit data in analog or digital form over one or more communication links 814 such as a serial link, a wireless link (for example, infrared, radio frequency, or others), a parallel link, or another type of link. The one or more communication links 814 may comprise any type of communication channel, connector, data communication network, or other link. For example, the one or more communication links 814 may comprise a wireless or a wired network, a Local Area Network (LAN), a Wide Area Network (WAN), a private network, a public network (such as the Internet), a wireless fidelity or WiFi network, a network that includes a satellite link, or another type of data communication network.
Although many other internal components of the computing device 800 are not shown, those of ordinary skill in the art will appreciate that such components and their interconnection are well known.
Any one or more embodiments of the present disclosure may be implemented through a computer-executable program of instructions, such as program modules, generally referred to as software applications or application programs executed by a computer. A software application may include, for example, routines, programs, objects, components, data structures, any other executable instructions, or any combination thereof, that perform particular tasks or implement particular abstract data types. The software application forms an interface to allow a computer to react according to a source of input. For example, an interface application may be used to implement any one or more embodiments of the present disclosure. The software application may also cooperate with other applications or code segments to initiate a variety of tasks based, at least in part, on data received, a source of data, or any combination thereof. Other applications or code segments may provide optimization components including, but not limited to, neural networks, earth modeling, history-matching, optimization, visualization, data management, and economics. The software application may be stored, carried, or both on any variety of memory such as CD-ROM, magnetic disk, optical disk, bubble memory, and semiconductor memory (for example, various types of RAM or ROM). Furthermore, the software application and one or more inputs or outputs may be transmitted over a variety of carrier media including, but not limited to wireless, wired, optical fiber, metallic wire, telemetry, any one or more networks (such as the Internet), or any combination thereof.
Moreover, those skilled in the art will appreciate that one or more of the embodiments may comprise a variety of computer-system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and any combination thereof. Any number of computer-systems and computer networks are acceptable for use with the present disclosure. The disclosure may be practiced in distributed-computing environments where tasks are performed by remote-processing devices that are linked through a communications network. In a distributed-computing environment, program modules may be located in both local and remote computer-storage media including memory storage devices. The present disclosure may, therefore, be implemented in connection with various hardware, software, or any combination thereof, in a computer system, information handling system, or other processing system.
In one or more embodiments, a method for stochastically projecting a well trajectory of a bottom hole assembly in a subsurface formation comprises receiving a first one or more system model parameters from a system model parameter probability distribution, receiving a first one or more steering inputs, receiving a first one or more values corresponding to the bottom hole assembly initial conditions at a first position within the subsurface formation, and stochastically projecting a first one or more trajectories of the bottom hole assembly from the first position within the subsurface formation to a second position within the subsurface formation based at least in part on one or more of the first one or more system model parameters, the first one or more steering inputs, and the first one or more values corresponding to the bottom hole assembly initial conditions.
In one or more embodiments, the method of further comprises stochastically projecting the first confidence region between the first position and the second position based at least in part one or more of the first one or more stochastically projected trajectories, the first one or more system model parameters, the received one or more steering inputs, and the received one or more values corresponding to the bottom hole assembly initial conditions. In one or more embodiments, the method further comprises providing one or more of the first one or more stochastically projected trajectories and the first confidence region to one or more of a display and a trajectory controller. In one or more embodiments, the method further comprises discarding one or more outliers in the first one or more stochastically projected trajectories of the bottom hole assembly before stochastically projecting the first confidence region. In one or more embodiments, the method further comprises advancing the bottom hole assembly from the first position to the second position. In one or more embodiments, the method further comprises stochastically projecting a second one or more trajectories of the bottom hole assembly from the second position to a third position based at least in part on one or more of the first one or more system model parameters, the received one or more steering inputs, and the received one or more values corresponding to the bottom hole assembly initial conditions. In one or more embodiments, the method further comprises receiving a second one or more system model parameters from the system model parameter probability distribution, stochastically projecting a second one or more trajectories of the bottom hole assembly from the second position to a third position based at least in part on one or more of the second one or more system model parameters, the received one or more steering inputs, and the received one or more values corresponding to the bottom hole assembly initial conditions.
In one or more embodiments, the first one or more system model parameters may be randomly selected from the system model parameter probability distribution. In one or more embodiments, the method further comprises generating a second one or more one or more steering inputs and stochastically projecting a second one or more trajectories of the bottom hole assembly from the second position to a third position based at least in part on one or more of the selected one or more system model parameters, the second one or more steering inputs, and the received one or more values corresponding to the bottom hole assembly initial conditions. In one or more embodiments, stochastically projecting the first one or more trajectories of the bottom hole assembly occurs in real-time. In one or more embodiments, selecting a second one or more steering inputs may be based at least in part on one or more of the first one or more stochastically projected trajectories and the first confidence region to one or more of a display and a trajectory controller. In one or more embodiments, the method further comprises receiving a second one or more system model parameters from the system model parameter probability distribution, receiving a second one or more steering inputs, receiving a second one or more values corresponding to the bottom hole assembly initial conditions at a second position within the subsurface formation, and stochastically projecting a second one or more trajectories of the bottom hole assembly from the second position in the subsurface formation to a third position in the subsurface formation based at least in part on one or more of the second one or more system model parameters, the second one or more steering inputs, and the second one or more values corresponding to the bottom hole assembly initial conditions.
In one or more embodiments, a system for stochastically projecting a well trajectory of a bottom hole assembly comprises a bottom hole assembly comprising one or more transducers, a trajectory controller coupled to the bottom hole assembly, and an information handling system coupled to the transducers, where the information system comprises a processor, and a non-transitory computer readable medium for storing one or more instructions that, when executed, causes the processor to receive a first one or more system model parameters from a system model parameter probability distribution, receive a first one or more steering inputs, receive a first one or more values corresponding to the bottom hole assembly initial conditions from the one or more transducers at a first position within a subsurface formation, and stochastically project a first one or more trajectories of the bottom hole assembly from the first position within the subsurface formation to a second position within the subsurface formation based at least in part on one or more of the first one or more system model parameters, the first one or more steering inputs, and the first one or more values corresponding to the bottom hole assembly initial conditions.
In one or more embodiments, the one or more instructions, when executed, further causes the processor to stochastically project a confidence region for the projected trajectory of the bottom hole assembly between the first position within the subsurface formation to the second position within the subsurface formation. In one or more embodiments, the system further comprises a display and the one or more instructions, when executed, further causes the processor to provide one or more of the first one or more stochastically projected trajectories and the first confidence region to one or more of the display and the trajectory controller. In one or more embodiments, the one or more instructions, when executed, further causes the processor to randomly select the first one or more system model parameters from the system model parameter probability distribution. In one or more embodiments, the one or more instructions, when executed, further causes the processor to one or more of stochastically project the trajectory of the bottom hole assembly or stochastically project the confidence region for the projected trajectory of the bottom hole assembly in real time. In one or more embodiments, the one or more instructions, when executed, further causes the processor to receive a second one or more system model parameters from the system model parameter probability distribution, receive a second one or more steering inputs; receive a second one or more values corresponding to the bottom hole assembly initial conditions at the second position within the subsurface formation, and stochastically project a second one or more trajectories of the bottom hole assembly from the second position in the subsurface formation to a third position in the subsurface formation based at least in part on one or more of the second one or more system model parameters, the second one or more steering inputs, and the second one or more values corresponding to the bottom hole assembly initial conditions.
In one or more embodiments, a method for stochastically projecting a well trajectory of a bottom hole assembly in a subsurface formation in real time comprises receiving a first one or more system model parameters from a system model parameter probability distribution, receiving a first one or more steering inputs, receiving a first one or more values corresponding to the bottom hole assembly initial conditions at a first position within the subsurface formation, stochastically projecting a first one or more trajectories of the bottom hole assembly from the first position within the subsurface formation to a second position within the subsurface formation, advancing the bottom hole assembly from the first position to the second position, receiving a second one or more system model parameters from the system model parameter probability distribution, receiving a second one or more steering inputs, receiving a second one or more values corresponding to the bottom hole assembly initial conditions at a second position within the subsurface formation, and stochastically projecting a second one or more trajectories of the bottom hole assembly from the second position within the subsurface formation to a third position within the subsurface formation. In one or more embodiments, the method further comprises stochastically projecting the first one or more confidence regions based on the stochastically projected first one or more trajectories of the bottom hole assembly between the first position within the subsurface formation and the second position within the subsurface formation and further comprising stochastically projecting the second one or more confidence regions based on the stochastically projected second one or more trajectories of the bottom hole assembly between the second position within the subsurface formation and the third position within the subsurface formation.
While the present disclosure has been described in connection with presently preferred embodiments, it will be understood by those skilled in the art that it is not intended to limit the disclosure to those embodiments. It is therefore, contemplated that various alternative embodiments and modifications may be made to the disclosed embodiments without departing from the spirit and scope of the disclosure defined by the appended claims and equivalents thereof. In particular, with regards to the methods disclosed, one or more steps may not be required in all embodiments of the methods and the steps disclosed in the methods may be performed in a different order than was described. Furthermore, no limitations are intended to the details of construction or design herein shown, other than as described in the claims below. It is therefore evident that the particular illustrative embodiments disclosed above may be altered or modified and all such variations are considered within the scope and spirit of the present disclosure. In particular, every range of values (for example, “from about a to about b,” or, equivalently, “from approximately a to b,” or, equivalently, “from approximately a-b”) disclosed herein is to be understood as referring to the power set (the set of all subsets) of the respective range of values. The terms in the claims have their plain, ordinary meaning unless otherwise explicitly and clearly defined by the patentee.
