MULTIWELL STRUCTURE DRILLING FRAMEWORK
A method may include receiving input for a multiwell structure and subsurface target locations, where the multiwell structure includes slots; generating, based at least in part on the input, a position for the multiwell structure, slot-well assignments for the slots, and well trajectories, where each of the well trajectories extends from an assigned one of the slots to one or more of the subsurface target locations; and outputting a set of specifications for the position for the multiwell structure, the well trajectories, and an order for drilling the well trajectories.
This application claims priority to and the benefit of a US Provisional Application having Ser. No. 63/578,059, filed 22 Aug. 2023, which is incorporated by reference herein in its entirety.
BACKGROUNDA reservoir may be a subsurface formation that may 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 may 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.).
In oil and gas exploration, interpretation is a process that involves analysis of data to identify and locate various subsurface structures (e.g., horizons, faults, geobodies, etc.) in a geologic environment. Various types of structures (e.g., stratigraphic formations) may be indicative of hydrocarbon traps or flow channels, as may be associated with one or more reservoirs (e.g., fluid reservoirs). In the field of resource extraction, enhancements to interpretation may allow for construction of a more accurate model of a subsurface region, which, in turn, may improve characterization of the subsurface region for purposes of resource extraction. Characterization of one or more subsurface regions in a geologic environment may guide, for example, performance of one or more operations (e.g., field operations, etc.). As an example, a plan may depend on a model of a subsurface region where the plan may specify how a drilling operation may accurately construct a borehole according to a trajectory that penetrates a reservoir, etc., where fluid may be produced via the borehole (e.g., as a completed well, etc.). As an example, one or more workflows may be performed using one or more computational frameworks, systems, etc., for one or more of analysis, acquisition, model building, control, etc., for exploration, interpretation, drilling, fracturing, production, etc.
SUMMARYA method may include receiving input for a multiwell structure and subsurface target locations, where the multiwell structure includes slots; generating, based at least in part on the input, a position for the multiwell structure, slot-well assignments for the slots, and well trajectories, where each of the well trajectories extends from an assigned one of the slots to one or more of the subsurface target locations; and outputting a set of specifications for the position for the multiwell structure, the slot-well assignments, the well trajectories, and an order for drilling the well trajectories. A system may 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 input for a multiwell structure and subsurface target locations, where the multiwell structure includes slots; generate, based at least in part on the input, a position for the multiwell structure, slot-well assignments for the slots, and well trajectories, where each of the well trajectories extends from an assigned one of the slots to one or more of the subsurface target locations; and output a set of specifications for the position for the multiwell structure, the slot-well assignments, the well trajectories, and an order for drilling the well trajectories. One or more computer-readable storage media may include processor-executable instructions to instruct a computing system to: receive input for a multiwell structure and subsurface target locations, where the multiwell structure includes slots; generate, based at least in part on the input, a position for the multiwell structure, slot-well assignments for the slots, and well trajectories, where each of the well trajectories extends from an assigned one of the slots to one or more of the subsurface target locations; and output a set of specifications for the position for the multiwell structure, the slot-well assignments, the well trajectories, and an order for drilling the well trajectories. 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.
The following detailed description refers to the accompanying drawings. Wherever convenient Features and advantages of the described implementations may be more readily understood by reference to the following description taken in conjunction with the accompanying drawings.
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.
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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 DRILLOPS framework may execute a digital drilling plan and ensures plan adherence, while delivering goal-based automation. The DRILLOPS framework may generate activity plans automatically individual operations, whether they are monitored and/or controlled on the rig or in town. Automation may utilize data analysis and learning systems to assist and optimize tasks, such as, for example, setting ROP to drilling a stand. A preset menu of automatable drilling tasks may be rendered, and, using data analysis and models, a plan may be executed in a manner to achieve a specified goal, where, for example, measurements may be utilized for calibration. The DRILLOPS framework provides flexibility to modify and replan activities dynamically, for example, based on a live appraisal of various factors (e.g., equipment, personnel, and supplies). Well construction activities (e.g., tripping, drilling, cementing, etc.) may be continually monitored and dynamically updated using feedback from operational activities. The DRILLOPS framework may provide for various levels of automation based on planning and/or re-planning (e.g., via the DRILLPLAN framework), feedback, etc.
The PETREL framework may be part of the DELFI environment for utilization in geosciences and geoengineering, for example, to analyze subsurface data from exploration to production of fluid from a reservoir. The DELFI cognitive exploration and production (E&P) environment (SLB, Houston, Texas), referred to herein as the DELFI environment or DELFI framework, is a secure, cognitive, cloud-based collaborative environment that integrates data and workflows with digital technologies, such as artificial intelligence and machine learning.
The PETREL framework provides components that allow for optimization of various exploration, development and production operations. The PETREL framework includes seismic to simulation software components that may output information for use in increasing reservoir performance, for example, by improving asset team productivity. Through use of such a framework, various professionals (e.g., geophysicists, geologists, and reservoir engineers) may develop collaborative workflows and integrate operations to streamline processes (e.g., with respect to one or more geologic environments, etc.). Such a framework may be considered an application (e.g., executable using one or more devices) and may be considered a data-driven application (e.g., where data is input for purposes of modeling, simulating, etc.).
The TECHLOG framework may handle and process field and laboratory data for a variety of geologic environments (e.g., deepwater exploration, shale, etc.). The TECHLOG framework may structure wellbore data for analyses, planning, etc.
The PETROMOD framework provides petroleum systems modeling capabilities that may combine one or more of seismic, well, and geological information to model the evolution of a sedimentary basin. The PETROMOD framework may predict if, and how, a reservoir has been charged with hydrocarbons, including the source and timing of hydrocarbon generation, migration routes, quantities, and hydrocarbon type in the subsurface or at surface conditions.
The ECLIPSE framework provides a reservoir simulator (e.g., as a computational framework) with numerical solutions for fast and accurate prediction of dynamic behavior for various types of reservoirs and development schemes.
The INTERSECT framework provides a high-resolution reservoir simulator for simulation of detailed geological features and quantification of uncertainties, for example, by creating accurate production scenarios and, with the integration of precise models of the surface facilities and field operations, the INTERSECT framework may produce reliable 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 may acquire data during one or more types of field operations, etc.). The INTERSECT framework may provide completion configurations for complex wells where such configurations may be built in the field, may provide detailed enhanced-oil-recovery (EOR) formulations where such formulations may be implemented in the field, may analyze application of steam injection and other thermal FOR 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. For example, a workflow may utilize one or more of the DELFI environment on demand reservoir simulation features.
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
As an example, a workflow may progress to a geology and geophysics (“G&G”) service provider, which may generate a well trajectory, which may involve execution of one or more G&G frameworks (e.g., consider the PETREL framework, etc.).
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As an example, a visualization process may implement one or more of various features that may be suitable for one or more web applications. For example, a template may involve use of the JAVASCRIPT object notation format (JSON) and/or one or more other languages/formats. As an example, a framework may include one or more converters. For example, consider a JSON to PYTHON converter and/or a PYTHON to JSON converter. Such an approach may provide for compatibility of devices, frameworks, etc., with respect to one or more sets of instructions.
As an example, visualization features may provide for visualization of various earth models, properties, etc., in one or more dimensions. As an example, visualization features may provide for rendering of information in multiple dimensions, which may optionally include multiple resolution rendering. In such an example, information being rendered may be associated with one or more frameworks and/or one or more data stores. As an example, visualization features may include one or more control features for control of equipment, which may include, for example, field equipment that may perform one or more field operations. As an example, a workflow may utilize one or more frameworks to generate information that may 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. As an example, reflection seismology may provide seismic data representing waves of elastic energy (e.g., as transmitted by P-waves and S-waves, in a frequency range of approximately 1 Hz to approximately 100 Hz). Seismic data may be processed and interpreted, for example, to understand better composition, fluid content, extent and geometry of subsurface rocks. Such interpretation results may 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 may include values organized with respect to time and/or depth (e.g., consider 1 D, 2D, 3D or 4D seismic data). For example, consider acquisition equipment that acquires digital samples at a rate of one sample per approximately 4 ms. Given a speed of sound in a medium or media, a sample rate may be converted to an approximate distance. For example, the speed of sound in rock may be on the order of around 5 km per second. Thus, a sample time spacing of approximately 4 ms would correspond to a sample “depth” spacing of about 10 meters (e.g., assuming a path length from source to boundary and boundary to sensor). As an example, a trace may be about 4 seconds in duration; thus, for a sampling rate of one sample at about 4 ms intervals, such a trace would include about 1000 samples where latter acquired samples correspond to deeper reflection boundaries. If the 4 second trace duration of the foregoing example is divided by two (e.g., to account for reflection), for a vertically aligned source and sensor, a deepest boundary depth may be estimated to be about 10 km (e.g., assuming a speed of sound of about 5 km per second).
As an example, a model may be a simulated version of a geologic environment. As an example, 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, may simulate fluid flow in a geologic environment based at least in part on a model that may be generated via a framework that receives seismic data. A simulator may be a computerized system (e.g., a computing system) that may execute instructions using one or more processors to solve a system of equations that describe physical phenomena subject to various constraints. In such an example, the system of equations may be spatially defined (e.g., numerically discretized) according to a spatial model that that includes layers of rock, geobodies, etc., that have corresponding positions that may be based on interpretation of seismic and/or other data. A spatial model may be a cell-based model where cells are defined by a grid (e.g., a mesh). A cell in a cell-based model may represent a physical area or volume in a geologic environment where the cell may be assigned physical properties (e.g., permeability, fluid properties, etc.) that may be germane to one or more physical phenomena (e.g., fluid volume, fluid flow, pressure, etc.). A reservoir simulation model may be a spatial model that may be cell-based.
A simulator may be utilized to simulate the exploitation of a real reservoir, for example, to examine different productions scenarios to find an optimal one before production or further production occurs. A reservoir simulator does not provide an exact replica of flow in and production from a reservoir at least in part because the description of the reservoir and the boundary conditions for the equations for flow in a porous rock are generally known with an amount of uncertainty. Certain types of physical phenomena occur at a spatial scale that may be relatively small compared to size of a field. A balance may be struck between model scale and computational resources that results in model cell sizes being of the order of meters; rather than a lesser size (e.g., a level of detail of pores). A modeling and simulation workflow for multiphase flow in porous media (e.g., reservoir rock, etc.) may include generalizing real micro-scale data from macro scale observations (e.g., seismic data and well data) and upscaling to a manageable scale and problem size. Uncertainties may exist in input data and solution procedure such that simulation results too are to some extent uncertain. A process known as history matching may involve comparing simulation results to actual field data acquired during production of fluid from a field. Information gleaned from history matching, may provide for adjustments to a model, data, etc., which may help to increase accuracy of simulation.
As an example, a simulator may utilize various types of constructs, which may be referred to as entities. Entities may include earth entities or geological objects such as wells, surfaces, reservoirs, etc. Entities may include virtual representations of actual physical entities that may be reconstructed for purposes of simulation. Entities may include entities based on data acquired via sensing, observation, etc. (e.g., consider entities based at least in part on seismic data and/or other information). As an example, an entity may be characterized by one or more properties (e.g., a geometrical pillar grid entity of an earth model may be characterized by a porosity property, etc.). Such properties may represent one or more measurements (e.g., acquired data), calculations, etc.
As an example, a simulator may utilize an object-based software framework, which may include entities based on pre-defined classes to facilitate modeling and simulation. As an example, an object class may encapsulate reusable code and associated data structures. Object classes may be used to instantiate object instances for use by a program, script, etc. For example, borehole classes may define objects for representing boreholes based on well data. A model of a basin, a reservoir, etc. may include one or more boreholes where a borehole may be, for example, for measurements, injection, production, etc. As an example, a borehole may be a wellbore of a well, which may be a completed well (e.g., for production of a resource from a reservoir, for injection of material, etc.).
While several simulators are illustrated in the example of
As an example, data may include geochemical data. For example, consider data acquired using X-ray fluorescence (XRF) technology, Fourier transform infrared spectroscopy (FTIR) technology and/or wireline geochemical technology.
As an example, one or more probes may be deployed in a bore via a wireline or wirelines. As an example, a probe may emit energy and receive energy where such energy may be analyzed to help determine mineral composition of rock surrounding a bore. As an example, nuclear magnetic resonance may be implemented (e.g., via a wireline, downhole NMR probe, etc.), for example, to acquire data as to nuclear magnetic properties of elements in a formation (e.g., hydrogen, carbon, phosphorous, etc.).
As an example, lithology scanning technology may be employed to acquire and analyze data. For example, consider the LITHO SCANNER technology (SLB, Houston, Texas). As an example, a LITHO SCANNER tool may be or include a gamma ray spectroscopy tool.
As an example, a tool may be positioned to acquire information in a portion of a borehole. Analysis of such information may reveal vugs, dissolution planes (e.g., dissolution along bedding planes), stress-related features, dip events, etc. As an example, a tool may acquire information that may help to characterize a fractured reservoir, optionally where fractures may be natural and/or artificial (e.g., hydraulic fractures). Such information may assist with completions, stimulation treatment, etc. As an example, information acquired by a tool may be analyzed using a framework such as the aforementioned TECHLOG framework.
As an example, a workflow may utilize one or more types of data for one or more processes (e.g., stratigraphic modeling, basin modeling, completion designs, drilling, production, injection, etc.). As an example, one or more tools may provide data that may be used in a workflow or workflows that may implement one or more frameworks (e.g., PETREL, TECHLOG, PETROMOD, ECLIPSE, etc.).
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A well may include a substantially horizontal portion (e.g., lateral portion) that may intersect with one or more fractures. For example, a well in a shale formation may pass through natural fractures, artificial fractures (e.g., hydraulic fractures), or a combination thereof. Such a well may be constructed using directional drilling techniques as described herein. However, these same techniques may be used in connection with other types of directional wells (such as slant wells, S-shaped wells, deep inclined wells, and others) and are not limited to horizontal wells.
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The wellsite system 200 may provide for operation of the drillstring 225 and other operations. As shown, the wellsite system 200 includes the traveling block 211 and the derrick 214 positioned over the borehole 232. As mentioned, the wellsite system 200 may include the rotary table 220 where the drillstring 225 pass through an opening in the rotary table 220.
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As to a top drive example, the top drive 240 may provide functions performed by a kelly and a rotary table. The top drive 240 may turn the drillstring 225. As an example, the top drive 240 may include one or more motors (e.g., electric and/or hydraulic) connected with appropriate gearing to a short section of pipe called a quill, that in turn may be screwed into a saver sub or the drillstring 225 itself. The top drive 240 may be suspended from the traveling block 211, so the rotary mechanism is free to travel up and down the derrick 214. As an example, a top drive 240 may allow for drilling to be performed with more joint stands than a kelly/rotary table approach.
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The mud pumped by the pump 204 into the drillstring 225 may, after exiting the drillstring 225, form a mudcake that lines the wellbore which, among other functions, may reduce friction between the drillstring 225 and surrounding wall(s) (e.g., borehole, casing, etc.). A reduction in friction may facilitate advancing or retracting the drillstring 225. During a drilling operation, the entire drillstring 225 may be pulled from a wellbore and optionally replaced, for example, with a new or sharpened drill bit, a smaller diameter drillstring, etc. As mentioned, the act of pulling a drillstring out of a hole or replacing it in a hole is referred to as tripping. A trip may be referred to as an upward trip or an outward trip or as a downward trip or an inward trip depending on trip direction.
As an example, consider a downward trip where upon arrival of the drill bit 226 of the drillstring 225 at a bottom of a wellbore, pumping of the mud commences to lubricate the drill bit 226 for purposes of drilling to enlarge the wellbore. As mentioned, the mud may be pumped by the pump 204 into a passage of the drillstring 225 and, upon filling of the passage, the mud may be used as a transmission medium to transmit energy, for example, energy that may encode information as in mud-pulse telemetry.
As an example, mud-pulse telemetry equipment may include a downhole device configured to effect changes in pressure in the mud to create an acoustic wave or waves upon which information may modulated. In such an example, information from downhole equipment (e.g., one or more modules of the drillstring 225) may be transmitted uphole to an uphole device, which may relay such information to other equipment for processing, control, etc.
As an example, telemetry equipment may operate via transmission of energy via the drillstring 225 itself. For example, consider a signal generator that imparts coded energy signals to the drillstring 225 and repeaters that may receive such energy and repeat it to further transmit the coded energy signals (e.g., information, etc.).
As an example, the drillstring 225 may be fitted with telemetry equipment 252 that includes a rotatable drive shaft, a turbine impeller mechanically coupled to the drive shaft such that the mud may cause the turbine impeller to rotate, a modulator rotor mechanically coupled to the drive shaft such that rotation of the turbine impeller causes said modulator rotor to rotate, a modulator stator mounted adjacent to or proximate to the modulator rotor such that rotation of the modulator rotor relative to the modulator stator creates pressure pulses in the mud, and a controllable brake for selectively braking rotation of the modulator rotor to modulate pressure pulses. In such an 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.
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The assembly 250 of the illustrated example includes a logging-while-drilling (LWD) module 254, a measurement-while-drilling (MWD) module 256, an optional module 258, a rotary-steerable system (RSS) and/or motor 260, and the drill bit 226. Such components or modules may be referred to as tools where a drillstring may include a plurality of tools.
As to an RSS, it involves technology utilized for directional drilling. Directional drilling involves drilling into the Earth to form a deviated bore such that the trajectory of the bore is not vertical; rather, the trajectory deviates from vertical along one or more portions of the bore. As an example, consider a target that is located at a lateral distance from a surface location where a rig may be stationed. In such an example, drilling may commence with a vertical portion and then deviate from vertical such that the bore is aimed at the target and, eventually, reaches the target. Directional drilling may be implemented where a target may be inaccessible from a vertical location at the surface of the Earth, where material exists in the Earth that may impede drilling or otherwise be detrimental (e.g., consider a salt dome, etc.), where a formation is laterally extensive (e.g., consider a relatively thin yet laterally extensive reservoir), where multiple bores are to be drilled from a single surface bore, where a relief well is desired, etc.
One approach to directional drilling involves a mud motor; however, a mud motor may present some challenges depending on factors such as rate of penetration (ROP), transferring weight to a bit (e.g., weight on bit, WOB) due to friction, etc. A mud motor may be a positive displacement motor (PDM) that operates to drive a bit (e.g., during directional drilling, etc.). A PDM operates as drilling fluid is pumped through it where the PDM converts hydraulic power of the drilling fluid into mechanical power to cause the bit to rotate.
As an example, a mud motor (e.g., PDM) may be operated in different modes, which may include a rotating mode and a sliding mode. A sliding mode involves drilling with a mud motor rotating the bit downhole without rotating the drillstring from the surface. Such an operation may be conducted when a BHA has been fitted with a bent sub or a bent housing mud motor, or both, for directional drilling. Sliding may be used in building and controlling or adjusting hole angle. In directional drilling, pointing of a bit may be accomplished through a bent sub, which may have a relatively small angle offset from the axis of a drillstring, and a measurement device to determine the direction of offset. Without turning the drillstring, the bit may be rotated with mud flow through the mud motor to drill in the direction it is pointed. With steerable motors, when a desired wellbore direction is attained, the entire drillstring may be rotated to drill straight rather than at an angle. By controlling the amount of hole drilled in the sliding mode versus the rotating mode, a wellbore trajectory may be controlled rather precisely.
As an example, a PDM may operate in a combined rotating mode where surface equipment is utilized to rotate a bit of a drillstring (e.g., a rotary table, a top drive, etc.) by rotating the entire drillstring and where drilling fluid is utilized to rotate the bit of the drillstring. In such an example, a surface RPM (SRPM) may be determined by use of the surface equipment and a downhole RPM of the mud motor may be determined using various factors related to flow of drilling fluid, mud motor type, etc. As an example, in the combined rotating mode, bit RPM may be determined or estimated as a sum of the SRPM and the mud motor RPM, assuming the SRPM and the mud motor RPM are in the same direction.
As an example, a PDM mud motor may operate in a so-called sliding mode, when the drillstring is not rotated from the surface. In such an example, a bit RPM may be determined or estimated based on the RPM of the mud motor.
An RSS may drill directionally where there is continuous rotation from surface equipment, which may alleviate the sliding of a steerable motor (e.g., a PDM). An RSS may be deployed when drilling directionally (e.g., deviated, horizontal, or extended-reach wells). An RSS may aim to minimize interaction with a borehole wall, which may help to preserve borehole quality. An RSS may aim to exert a relatively consistent side force akin to stabilizers that rotate with the drillstring or orient the bit in the desired direction while continuously rotating at the same number of rotations per minute as the drillstring.
The LWD module 254 may be housed in a suitable type of drill collar and may contain one or a plurality of selected types of logging tools. It will also be understood that more than one LWD and/or MWD module may be employed. Where the position of an LWD module is mentioned, as an example, it may refer to a module at the position of the LWD module 254, the MWD module 256, etc. An LWD module may include capabilities for measuring, processing, and storing information, as well as for communicating with the surface equipment. In the illustrated example, the LWD module 254 may include a seismic measuring device.
The MWD module 256 may be housed in a suitable type of drill collar and may contain one or more devices for measuring characteristics of the drillstring 225 and the drill bit 226. As an example, the MWD module 256 may include equipment for generating electrical power, for example, to power various components of the drillstring 225. As an example, the MWD module 256 may include the telemetry equipment 252, for example, where the turbine impeller may generate power by flow of the mud; it being understood that other power and/or battery systems may be employed for purposes of powering various components. As an example, the MWD module 256 may include one or more of the following types of measuring devices: a weight-on-bit measuring device, a torque measuring device, a vibration measuring device, a shock measuring device, a stick slip measuring device, a direction measuring device, and an inclination measuring device.
As an example, a drilling operation may include directional drilling where, for example, at least a portion of a well includes a curved axis. For example, consider a radius that defines curvature where an inclination with regard to the vertical may vary until reaching an angle between about 30 degrees and about 60 degrees or, for example, an angle to about 90 degrees or possibly greater than about 90 degrees.
As an example, a directional well may include several shapes where each of the shapes may aim to meet particular operational demands. As an example, a drilling process may be performed on the basis of information as and when it is relayed to a drilling engineer. As an example, inclination and/or direction may be modified based on information received during a drilling process.
As an example, deviation of a bore may be accomplished in part by use of a downhole motor and/or a turbine. As to a motor, for example, a drillstring may include a positive displacement motor (PDM).
As an example, a system may be a steerable system and include equipment to perform method such as geosteering. As mentioned, a steerable system may be or include an RSS. As an example, a steerable system may include a PDM or of a turbine on a lower part of a drillstring which, just above a drill bit, a bent sub may be mounted. As an example, above a PDM, MWD equipment that provides real time or near real time data of interest (e.g., inclination, direction, pressure, temperature, real weight on the drill bit, torque stress, etc.) and/or LWD equipment may be installed. As to the latter, LWD equipment may make it possible to send to the surface various types of data of interest, including for example, geological data (e.g., gamma ray log, resistivity, density and sonic logs, etc.).
The coupling of sensors providing information on the course of a well trajectory, in real time or near real time, with, for example, one or more logs characterizing the formations from a geological viewpoint, may allow for implementing a geosteering method. Such a method may include navigating a subsurface environment, for example, to follow a desired route to reach a desired target or targets.
As an example, a drillstring may include an azimuthal density neutron (ADN) tool for measuring density and porosity; a MWD tool for measuring inclination, azimuth and shocks; a compensated dual resistivity (CDR) tool for measuring resistivity and gamma ray related phenomena; one or more variable gauge stabilizers; one or more bend joints; and a geosteering tool, which may include a motor and optionally equipment for measuring and/or responding to one or more of inclination, resistivity and gamma ray related phenomena.
As an example, geosteering may include intentional directional control of a wellbore based on results of downhole geological logging measurements in a manner that aims to keep a directional wellbore within a desired region, zone (e.g., a pay zone), etc. As an example, geosteering may include directing a wellbore to keep the wellbore in a particular section of a reservoir, for example, to minimize gas and/or water breakthrough and, for example, to maximize economic production from a well that includes the wellbore.
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As an example, one or more of the sensors 264 may be provided for tracking pipe, tracking movement of at least a portion of a drillstring, etc.
As an example, the system 200 may include one or more sensors 266 that may sense and/or transmit signals to a fluid conduit such as a drilling fluid conduit (e.g., a drilling mud conduit). For example, in the system 200, the one or more sensors 266 may be operatively coupled to portions of the standpipe 208 through which mud flows. As an example, a downhole tool may generate pulses that may travel through the mud and be sensed by one or more of the one or more sensors 266. In such an example, the downhole tool may include associated circuitry such as, for example, encoding circuitry that may encode signals, for example, to reduce demands as to transmission. As an example, circuitry at the surface may include decoding circuitry to decode encoded information transmitted at least in part via mud-pulse telemetry. As an example, circuitry at the surface may include encoder circuitry and/or decoder circuitry and circuitry downhole may include encoder circuitry and/or decoder circuitry. As an example, the system 200 may include a transmitter that may generate signals that may be transmitted downhole via mud (e.g., drilling fluid) as a transmission medium.
As an example, one or more portions of a drillstring may become stuck. The term stuck may refer to one or more of varying degrees of inability to move or remove a drillstring from a bore. As an example, in a stuck condition, it might be possible to rotate pipe or lower it back into a bore or, for example, in a stuck condition, there may be an inability to move the drillstring axially in the bore, though some amount of rotation may be possible. As an example, in a stuck condition, there may be an inability to move at least a portion of the drillstring axially and rotationally.
As to the term “stuck pipe”, this may refer to a portion of a drillstring that cannot be rotated or moved axially. As an example, a condition referred to as “differential sticking” may be a condition whereby the drillstring cannot be moved (e.g., rotated or reciprocated) along the axis of the bore. Differential sticking may occur when high-contact forces caused by low reservoir pressures, high wellbore pressures, or both, are exerted over a sufficiently large area of the drillstring. Differential sticking may have time and financial cost.
As an example, a sticking force may be a product of the differential pressure between the wellbore and the reservoir and the area that the differential pressure is acting upon. This means that a relatively low differential pressure (delta p) applied over a large working area may be just as effective in sticking pipe as may a high differential pressure applied over a small area.
As an example, a condition referred to as “mechanical sticking” may be a condition where limiting or prevention of motion of the drillstring by a mechanism other than differential pressure sticking occurs. Mechanical sticking may be caused, for example, by one or more of junk in the hole, wellbore geometry anomalies, cement, keyseats or a buildup of cuttings in the annulus.
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As an example, the system 370 may be utilized to generate one or more rate of penetration drilling parameter values, which may, for example, be utilized to control one or more drilling operations.
As an example, a method may include automating operations of one or more types of downhole tools. For example, consider automating operations of one or more of mud motors, rotary steerable systems (RSSs) and at-bit steerable systems (ABSSs). As an example, one or more of such types of equipment, systems, etc., may be implemented using one or more features of the system 200 of
As an example, an ABSS may include an actuator with a pressure drop range, hold inclination and azimuth (HIA), and dual downlinking capabilities. As an example, an ABSS may include onboard near-bit sensors that may acquire continuous six-axis inclination and azimuth measurements with a 6-ft range, and optional natural gamma ray and azimuthal images with a 9-ft range. As an example, an ABSS may include one of more features of one or more of the NEOSTEER family of ABSSs (SLB, Houston, Texas).
As an example, a framework may provide for considering information from planning with offset analysis and adapting a hybrid model for real time execution; considering various downhole automation possibilities at a given time to optimize a recommended working trajectory execution, taking advantage of tool capabilities and minimizing unnecessary surface actions; considering real-time data information and derived tool health as well as tool state estimation at a given point in time to optimize current ongoing recommendation and recommend real time correction to handle deviations when it occurs. Such an approach may involve different computations and actions that may output an optimal recommendation, for example, for a fastest path with minimized risk based on the drilling constraints and the drilling context.
As to an optimal path recommendation, derivation may be via a framework, which may provide for a single-target approach and/or a multi-target approach and, for example, which may account for various factors, which may include energy, emissions, etc.
A system such as the system 400 may utilize various functions and constraints for generation of plans, which may provide for single or multiple target aiming. As explained, a plan may be generated that aims to provide for drilling operations for a multiwell structure.
Various challenges may arise in pad/rig drilling in onshore and/or offshore sites in a planning stage and/or in an execution stage as crowded well placements may pose substantial risks. Such challenges may stem from multiple well heads gathered within a relatively small area, which makes an escape strategy for anti-collision challenging. As an example, a system may automate and/or semi-automate a trajectory design workflow. Such a system may use various types of inputs, which may include, for example, surface locations, targets, and optional trajectory pattern inputs to generate trajectories on a pad.
A pad may be a temporary drilling site, for example, constructed of local materials such as gravel, shell, or even wood, noting that an offshore pad may be a steel plate with slots. For some long-drilling-duration, deep wells, such as the ultradeep wells of western Oklahoma, or some regulatory jurisdictions such as the Netherlands, pads may be paved with asphalt or concrete. After a drilling operation is over, most of the pad is usually removed or plowed back into the ground. Where a pad includes multiple wells, it may be referred to as a multiwell structure. As to number of wells, a multiwell structure may include from two to over one-hundred wells.
As an example, a system may utilize existing pad data such as from offset wells, which may be at one or more other pads. For example, consider one or more machine learning techniques that may be applied to pad data to generate a trained machine learning model that may generate output based on input. In such an example, an output may be an initial template for a pad where the initial template may include initial well trajectories. In such an example, the initial well trajectories may be shaped and dimensioned to provide some number of anti-collision assurances. As an example, an initial template may be generated using constraints and trajectory shapes that are associated with one or more anti-collision strategies. Data analytics of pads on land and/or offshore may be implemented to help identify principal features to classify pads into enumerable pad templates. Such features may include, for example, surface slot layout (e.g., line, matrix, or circle, etc.), nudge strategy (e.g., S nudge, J nudge, or mixed on a single pad), and landing preferences (e.g., single curve landing or multiple curves), etc. As an example, a template may provide a pattern as to how a trajectory connects surface locations and targets, indications of an S nudge, a J nudge, alignment, nudge kickoff, build kickoff, etc. As an example, a template may specify that a landing is to be achieved using a single curve or optionally one or more curves. For example, consider a build kickoff point (build KOP) that may be a starting point for a curve (e.g., a curve start point) that has an ending point (e.g., a curve end point) that may be a landing point from which a straight line may extend to a target, which may be a target line.
As an example, a system may provide for generation of trajectories for a multiwell structure using one or more templates where control points (e.g., key points) may be utilized for curve forming, defining straight line portions, etc., where one or more control points may be one or more geometric points that may or may not lie on a trajectory. Such a system may provide for computation of point positions (e.g., control points and/or trajectory points) and adjustments to point positions, which may be performed using adjustments to one or more control points. In such an approach, geometric techniques may be implemented rapidly to construct trajectories for a pad where the trajectories may have various anti-collision assurances. As an example, a pad design with multiple trajectories may be output with various associated specifications that may pertain to drilling. For example, consider torque and drag (T&D), BHA specifications, hookload (HKLD), weight on bit (WOB), etc., which may be implemented by a drilling framework to expedite drilling of the trajectories. As an example, during drilling, a re-planning process may be called for responsive to data acquired using one or more sensors. For example, if T&D differs from specified T&D, if an obstacle is encountered (e.g., as evidenced by HKLD and/or WOB), then a drilling framework may instruct a trajectory generation framework to re-plan (e.g., re-design) one or more trajectories from a multiwell structure. As explained, where a system implements a geometric control point based approach, planning and/or re-planning may be performed expeditiously in a manner that accounts for various constraints.
As to the term nudge, it may refer to a process whereby a trajectory is altered in one or more manners. For example, a nudge may alter a trajectory in a multidimensional coordinate space (e.g., consider a north nudge, an east nudge, etc.). As an example, a nudge may alter a trajectory with respect to an angular direction (e.g., an inclination, an azimuth, etc.). As an example, a nudge may be a noun that refers to a particular shape of a portion of a trajectory of a well such as an S nudge or a J nudge, where “S” and “J” may correspond to shapes. As to an S nudge it may have a first, upper vertical end and a second, lower vertical end where a curve exists between the first and second ends or, for example, an upper end may be non-vertical (e.g., a portion with a non-zero inclination). Such a nudge may be implemented as part of an anti-collision strategy and/or to avoid one or more subsurface objects and/or regions. In practice, introduction of an S nudge may complicate drilling in that a drillstring has to drill the trajectory in a manner that deviates from straight (e.g., deviates from vertical, etc.). An S nudge may have at least two arc curves between two vertical portions (e.g., or inclined if an S nudge is introduced from an inclined well), where one bends up (build) and one bends down (drop), while a J nudge may have one or multiple arc curves which bend up (build). During drilling, one or more techniques may be implemented to create a curve in a borehole that aims to conform to a curved trajectory (e.g., a planned trajectory).
Another aspect of a trajectory is referred to as a dogleg, which may be characterized in part by a dogleg severity (DLS or dls). A dogleg may be defined as a particularly crooked place in a wellbore where the trajectory of the wellbore in three-dimensional space changes relatively rapidly. While a dogleg is sometimes created intentionally by directional drillers, the term may also refer to a section of a hole that changes direction faster than expected or desired, possibly with detrimental side effects. In surveying wellbore trajectories, a standard calculation of dogleg severity may be made, usually expressed in two-dimensional degrees per 100 feet (e.g., or degrees per 30 m) of wellbore length.
Doglegs may account for various aspects of drilling, well completions, well treatments, etc. For example, a planned casing string may be taken into account such that it may fit through a curved section. As another example, the impact of repeated abrasion by a drillstring in a particular location of a dogleg may be taken into account to help reduce risks of one or more worn spots (e.g., one or more keyseats) in which a BHA may potentially stick. As yet another example, consider accounting for casing that may be cemented through a dogleg where a dogleg is designed and/or drilled in a manner that aims to reduce risk of casing wear due to higher contact forces between a drillstring and an inner diameter surface of the casing through the dogleg. Other aspects that may be taken into account may include, for example, BHA stiffness and/or resiliency such that a BHA or BHAs may fit through the dogleg section and/or friction to a drillstring, which may increase likelihood of getting stuck or not reaching a planned target. In various instances, if a dogleg impairs a well, one or more remedial actions may be taken, such as, for example, reaming or underreaming through the dogleg, or even sidetracking in extreme situations.
As an example, a system may generate a plan for a multiwell structure with trajectories that are amenable to one or more remedial actions. For example, consider a system that may generate a plan where the plan may be rendered to a display with information as to various regions as to where one or more remedial action may be available if one or more types of issues arise during drilling, completing, injection, production, etc.
As an example, a system may generate and optimize a layout and geometry of trajectories on a common pad. In one embodiment, each pad template may be generated accordingly. In one embodiment, given surface locations and subsurface targets information for pad wells, assuming the expected wells are horizontal wells with similar target line direction, a system may determine a nudge kickoff depth (nudge KOP specified using depth), a nudge inclination (e.g., specified using an angle) and an azimuth (e.g., specified using an angle) to maximize well trajectory separation and landing to target lines with relatively simple geometry such as, for example, single curve. As explained, drilling may be facilitated where one or more curves are designed in a manner that takes into account one or more types of constraints. For example, a curve may be a curve designed through use of a minimum curvature technique such that issues are less likely to arise during drilling, completions, etc. As an example, a system may utilize specified targets, which may be connected by one or more curve-hold combination sequences. As to output from a system, pad trajectories may be expected to minimize collision risks and optimize drilling performance under given conditions.
As to inclination, it may be defined as the deviation from vertical, irrespective of compass direction, expressed in degrees. Inclination may be measured initially with a pendulum mechanism, and confirmed with MWD accelerometers or gyroscopes. For most vertical wellbores, inclination may be the sole measurement of a path of a wellbore. For intentionally deviated wellbores, or wells close to legal boundaries, directional information may be measured.
As to azimuth, it may be defined as the compass direction of a directional survey or of a wellbore as planned and/or measured by a directional survey. The azimuth is usually specified in degrees with respect to the geographic or magnetic north pole.
In one embodiment, to evaluate the results and assist users for decision making, a system may implement one or more performance indicators (e.g., key performance indicator (KPI)), which may be output via an estimation table. As an example, an estimation table of KPIs may be generated for output sets of pad trajectories, with information of geometry as well as anti-collision (AC or A/C), torque and drag (T&D), and other factors. As an example, a system may provide for comparison of trajectories and, for example, adjustments on a pad level. Such an approach may lessen demand to iterate one by one. Once pad trajectories are finalized, a system may include features that provide for fine-tuning single borehole trajectories.
As an example, a system may define multiple trajectories which are drillable and satisfy constraints such as, for example, one or more anti-collision constraints. Such a system may generate trajectories that may be visualized and evaluated. The approach may use a pre-trained model from historic data to recommend initial design patterns and parameters. The layout and geometry of the well trajectories for the pad may be optimized to balance the need of maximizing the trajectory separation with other drilling concerns.
As an example, a system may handle complex trajectory design where many trajectories are to be designed. Such a system may improve efficiency and lessen demand for human labor when a workflow calls for design of many trajectories gathering within a comparably small area of a field.
As an example, a pad sketcher component may be provided to generate trajectories based on given inputs and context data. In such an example, the pad sketcher component may use inputs that include surface locations, targets and positions. As an example, additional data may be provided that describes a detailed setup about trajectories to design and one or more offset wells to be considered, including, for example: what trajectory profile is desired, such as a J nudge single curve landing or other detailed setups; and whether there are existing offset wells or definitive designs to consider for anti-collision. A pad sketcher component may generate trajectories on a pad, which may be assessed by one or more other components.
As an example, a pad template component may define and/or generate a pad trajectory template and one or more techniques to achieve trajectories and recommend proper templates from offset well analysis for the new pad. As an example, a pad template component may be driven by one or more machine learning models (one or more ML models). As explained, a trained ML model may receive input and generate output, which may be a template or, for example, a selected template. For example, consider a trained ML model that may determine what template is an optimal template for a particular design problem. In such an example, the trained ML model may utilize data from offset wells that have been designed and drilled, and optionally completed and put into operation. Offset wells may provide optional data (e.g., directly provided by a user, accessed from one or more databases, etc.) and may also provide suggestions to recommend a pad template configuration, as appropriate. As an example, a template may be selected via interaction with a graphical user interface (GUI), which may provide a number of templates, optionally ranked based on an algorithmic technique that receives at least some input information regarding a multiwell structure.
As an example, an evaluator component may evaluate trajectories output by a pad sketcher component, for example, against one or more specified KPIs and/or one or more other criteria. As an example, KPIs may include, but are not limited to, whether targets are hit, geometric index (e.g., a Drilling Difficulty Index (DDI), an Along Hole Departure (AHD), an Extended Reach Ratio (ERR), etc.), anti-collision indicator (e.g., separation factor, minimum allowable separation against new designs in this pad, definitive designs and existing wells defined from other sources, etc.), torque and drag performance (T&D, final value and/or along one or more portions of a trajectory, etc.), hydraulics validation, well stability, whether hazard zones may be avoided, etc.
As an example, an optimizer component may adjust one or more trajectory parameters to optimize one or more KPIs under given constraints through either single target optimization which may reflect the combination effect of the required KPIs or multi-target optimization, for example, to generate a Pareto surface or Pareto frontier and provide several solution candidates.
As explained, a system may operate automatically or semi-automatically such that human intervention and/or interactions may be performed. As an example, a system may provide for human interaction to set up trajectory preference in more detail through optional input data. One example implementation for manual intervention for trajectories may be that one or more of nudge inclination and nudge azimuth may be defined through batch and/or individual manual adjustment, which may be used for an initial draft pad trajectory design and/or for adjustment after draft pad trajectories have been generated and one or more users want to have some refinement for the trajectories.
As an example, a pad sketcher component may be implemented to perform part of a workflow that generates pad trajectories based on one or more existing pad templates. A pad sketcher component may use basic input for pad information, including the surface locations for each well, the target points, dogleg severity (DLS or dls) for nudge build, nudge drop and build sections. As an example, target assignment for wells may be predefined with user input and/or computed by a system, for example, based on the geometrical characteristics of surface locations and targets. As an example, optional input may include detailed trajectory geometry parameters setup and/or parameter range setup. As an example, trajectory optional geometry parameters may include, without limitation, nudge inclination, nudge azimuth, nudge kickoff point, etc. Such parameters may be used to generate points that are used to link up trajectories.
As mentioned, a system may include a variety of techniques for handling trajectory generation. For example, consider techniques that may be associated with scenarios, which may be referred to as cases (e.g., Case 1, Case 2, etc.), which may be appropriately selected and executed to design one or more portions of one or more trajectories.
As an example, candidate techniques may operate in part based on assignment of a surface location and one or more targets to each well. For example, consider one or more techniques that may be setup to: use a straight line segment to connect a surface location and one or more targets and to minimize total length of a connecting line length (e.g., iteratively, line-by-line, over line portions, etc.); and use parallel straight lines to fit the target points, and where the line number is equals to the surface location number, to minimize a total displacement of targets from the parallel straight lines and assign each line to one surface location to minimize the overlapping or collision of the trajectories.
In the example of
As an example, a framework may provide for optimized structure trajectory planning. For example, consider the DRILLPLAN framework as including features for such planning and/or as being operatively coupled to one or more frameworks for such planning. As an example, one or more frameworks may be implemented for performance of an integrated workflow that may optimize surface location and surface-targets assignment for drilling structures, platforms, locations, etc., with an aim to ensure drilling concerns, such as, for example, anti-collision and drilling difficulty, are adequately managed.
In a planning stage of a drilling structure/platform construction, to drill to targets via wells, planning of placement of surface locations (e.g., slots) and assignment of an appropriate slot to each well may have an impact on drilling, production, etc. Further, planning of surface locations and assignments of slots to wells may have an impact on successive single trajectory (e.g., wellbore) planning. For example, where a framework may effectively place a structure and assign slots to wells, refined planning of trajectories, which may be on a trajectory-by-trajectory basis, may be expedited. Such an approach may be in contrast to a trajectory-by-trajectory approach that does not involve multiwell planning at the structure/platform planning stage. As an example, a framework that may employ multiwell planning at a stage that is before a trajectory refinement stage may reduce computational burden by generating base trajectories for multiple wells that may have some assurances as to concerns, such as, for example, anti-collision and drilling difficulty. For example, a framework may generate a structure plan with slot assignments that provide for reduced collision concerns, reduced drilling difficulty, etc., which may thereby expedite refinements, if appropriate, to one or more trajectories.
As an example, a framework may include features for planning an optimized slot template placement and slot-well assignments. As explained, without such results, drilling engineers may have to resort to detailed single well trajectory planning to adjust each individual wellbore one-by-one while viewing other wellbores on the same structure as offset wells, which is not instinctive nor efficient.
As explained, a framework may provide for implementation of a slot planning workflow that may optimize design of slot structure placement and slot assignments with wells. As an example, a framework may include instructions for generation of graphical user interfaces (GUIs) that may provide for guidance, visualization, optimization, etc. As an example, a GUI or series of GUIs may allow a user to interact with a framework, for example, to design wellbores on a structure level. Such a framework may improve planning, generation of a plan and execution of a plan, for example, in a manner that demands less effort spent on successive single wellbore design involving manual iterations.
As an example, a framework may provide for implementation of a workflow that involves wellbore trajectory design technology applied to various level. For example, consider a field level where a field may be defined in a manner consistent with reservoir engineering and drillings (e.g., when referring to an area with one or more platforms, structures, groups of wells, etc.). Field planning may involve engineers with domain expertise in reservoirs, geology, and production to design locations of production plants and logistics in a field.
At a structure level, a structure may be defined as including a group of wells that have relatively close surface locations. For example, consider surface locations of a common pad, a common structure, a common platform, etc. As an example, a pad may be a type of structure and a platform may be a type of structure. In reservoir and production, one structure may have one surface location as a drilling center. For example, consider scenarios where the distance between surfaces is too small compared with reservoir scale or where the sensitivity on how much well construction may affect the field plan may not be comparable with field production economic performance indicators (e.g., KPIs).
In the field of drilling, without a multiwell structure approach, planning may demand substantial manual work of professional engineering design to plan the placement of each surface location for each well on a common structure. Such work demands separation of a drilling center into dedicated slot locations on a slot template or common operation area and assignment or connection of slot location to the appropriate well for drilling KPIs (e.g., anti-collision, torque and drag (T&D), drilling difficulty, etc.). As an example, a framework may compute various KPIs that may expedite planning. For example, an automated approach may utilize anti-collision computations, T&D computations, drilling difficulty computations, etc. As to drilling difficulty, as an example, consider computation of a drilling difficulty metric such as a drilling difficulty index (DDI) for each well (e.g., each trajectory). In such an example, DDIs may be compared, constrained, etc. As an example, a framework may aim to optimize structure position and slot assignments in a manner that maintains DDIs below a threshold value. As an example, a framework may order drilling of wells (e.g., trajectories) in an order that may be based at least in part on DDI (e.g., consider less difficult wells first, to provide for learning as to more difficult wells, or more difficult wells first to assure success in drilling of such wells).
As an example, a DDI may be or include one or more factors of a directional difficulty index. For example, consider one or more factors set forth in an article by Oag and Williams. (2000) (“The Directional Difficulty Index—A New Approach to Performance Benchmarking.” Paper presented at the IADC/SPE Drilling Conference, New Orleans, Louisiana, February 2000. doi: https://doi.org/10.2118/59196-MS), which is incorporated by reference herein in its entirety. As an example, a directional difficulty index may be defined as follows:
Directional Difficulty Index=log10((TD*AHD*TORT)/TVD)
-
- where TD is total depth, AHD is along-hole displacement, TORT is tortuosity, and TVD is total vertical depth.
As an example, variables or factors may include one or more of azimuth, inclination, and step-out. Factors may include one or more of rig capabilities, temperature, pressure, geological data, emissions, etc. As an example, a directional difficulty index may be utilized for classifying wells. For example, consider an index value less than six being for relatively short wells that may include relatively simple profiles with relatively low tortuosity, an index value from 6.0 to 6.4 being for shorter wells with relatively high tortuosity or longer wells with lower tortuosity, an index value of 6.4 to 6.8 being for longer wells with relatively tortuous well paths, and an index value of greater than 6.8 being for long tortuous well profiles.
As mentioned, one or more ML models may be utilized. For example, consider a workflow where offset well data may be utilized and assessed as to difficulty, which may include one or more factors indicated in the offset well data as may be available via drilling reports, time spent, issues encountered, cost, energy expenditure, emissions generated, etc. In such an example, a pattern may be assessed for a site that is offset from a planned site where an order may be generated for the pattern using difficulty where that order may differ from an actual order for wells drilled at that offset site. As an example, a pattern-based approach may provide for generating patterns with an aim to provide incremental learning in drilling wells in a sequence. As an example, such an approach may provide for improving slot template positioning and slot assignment in a manner that accounts for difficulty and incremental learning.
As an example, a framework may utilize offset well data as to actual drilling time versus planned drilling time. In various instances where such data are not available, a correlation between percentage of actual to planned drilling time and difficulty may be utilized where such a percentage may be increased based on an increase in difficulty.
As an example, a drilling difficulty index (DDI) may be or include a directional difficulty index. For example, consider using an index as described in the aforementioned article of Oag and Williams.
U.S. Pat. No. 7,460,957 B2, entitled “Geometrical optimization of multi-well trajectories”, issued 2 Dec. 2008, to Schlumberger Technology Corporation, is incorporated by reference herein in its entirety.
As an example, a framework may consider or operate on a single wellbore level. For example, given a surface location, a well, with or without drafted wellbore trajectory, via GUI interactions, one or more drilling engineers may design or refine a trajectory. As an example, a framework may generate a base design or plan that may be considered a draft for refinement where the base design or plan may be generated in a manner that accounts for anti-collision concerns that may be adequately handled on a structure level. In such an example, demand for manual iterations may be substantially reduced.
As an example, a framework may provide for implementation of a workflow to automatically recommend surface locations of multiple wells and assign them to a proper target group first where a user may update one or more specific aspects of a surface location and/or may update one or more slot-well assignments. In such an example, user interactions may be driven by renderings of one or more graphics, plots, etc. For example, consider multidimensional views of slots, trajectories, offset wells, etc., which may be viewed along with renderings of graphics for one or more KPIs (e.g., consider viewing of KPIs and trajectories with a structure level).
As to the workflow 805, there may be some common configurations for wellbores on a common structure, which may not necessary be differentiated at the structure level. For example, consider commonalities that may include one or more of surface nudge related: nudge kickoff depth, nudge DLS, minimum allowable inclination for nudge sector, maximum allowable inclination for nudge; one or more build and target segments related: build DLS, target DLS; and/or one or more preferred shape list ordered in priority: no nudge 2D landing, S nudge 2D landing, and J nudge curve landing.
As to scenarios, consider the scenario 810 as, given structure reference and a piece of slot template, together with a group of wells, recommendation of the placement and rotation of the slot template and assignment of each slot to the appropriate well with output trajectories. The scenario 810 may be handled by a framework using a particular process, referred herein as A2, which may aim to achieve the template placement, template rotation, and slot-target assignment.
As to the scenario 820, given well-defined surface locations and a group of wells, a framework may implement a workflow to assign each slot to the appropriate well with output trajectories. The scenario 820 may be handled by a framework using a particular process, referred herein as A1 (e.g., a slot-target assignment process).
As to the scenario 830, given two or more drafted structure designs from a scenario such as the scenario 810 or the scenario 820, or, for example, user manual input, a framework may provide for analysis of the different drafted structure designs, for example, via comparison of one or more KPIs. In such an example, one of the different drafted structure designs may be select as an optimal design or a design to be further optimized. As an example, KPIs may include KPIs defined scientifically, according to factors associated with drilling, offset wells, one or more wells of a group of wells, equipment capabilities, geologic characteristics, interactions between equipment and material (e.g., fluid, mud, rock, etc.).
As to the scenario 840, given a draft structure design, a framework may provide one or more GUIs for user adjustment of one or more specific slot-well assignments, for example, by dropping and dragging graphics, altering entries in a table, etc., where, for example, a plan may be updated automatically. As an example, upon swapping one slot-well assignment for another, a framework may provide for automatically re-computing the two corresponding trajectories from their updated surface locations.
As to the scenario 850, given a drafted structure design, a framework may provide one or more GUIs for user adjustment of one or more specific parameters and/or shapes of a wellbore, for example, within a structure view. The scenario 850 may be handled by a framework using a particular process, referred herein as A3 (e.g., an update of a trajectory from particular parameters and/or shapes).
As to the scenario 860, given well-defined surface locations, a group of wells, and assignment of surface-well relationships, a framework may provide design trajectories. In such an example, the framework may expedite generation of a draft with trajectories. Such an approach may expedite a workflow compared to a manual workflow where a client may have a draft plan of the structure and want drilling experts start engineering design. The scenario 860 may be handled by a framework using a particular process, referred herein as A4 (e.g., for output of trajectories with best drilling performance, which may consider at least anti-collision (AC or A/C)).
As to the scenario 870, with designed trajectories for one structure, a framework may provide one or more GUIs for user creation of a group of single well engineering plan project (e.g., consider a DRILLPLAN framework project, etc.). In such an example, consider a framework that may operate responsive to actuation of a graphical control (e.g., optionally by a single click) such that the corresponding surface location, target sets, and trajectories will be automatically copied to a desired project.
As to the scenarios 880 and 890, they may utilize one or more features of a framework, for example, denoted FX and FY.
As an example, a slot-target assignment process may be flexible and extensible and may provide various options. As an example, a definition of a best assignment process may depend on various factors, which may include local practices such as basin and client preferences.
As an example, akin to a manual design example, slots on a common side may be assigned to the wells that have landing points on that side. The graphics 910 illustrate such logic, which may be part of a manual design process (e.g., A1.1).
As an example, the A1 process (e.g., A1.2) may order wells by landing points in a clockwise direction, and order slots in clockwise direction too. Such a process may try to assign slots and wells in the same order with different starting slot. In this way, there will tend to be fewer intersection and crosses between the trajectories (e.g., consider lesser risk of collision).
As an example of an A1.2 process, consider the following example of pseudo-code:
As an example, the A2 process may include optimizing the placement of a slot template, including positioning (x,y) and rotation angle theta, such that the interested KPI performance is best. That is, the objective function may be one or multiple KPIs, such as oriented separation factor (OSF) for AC (or A/C), the variables (x,y) and angle theta may be continuous within the possible ranges. It may be solved by one or more optimization techniques, such as, for example, minimum trust region, or random search. As an example, if performance allowed, grid search may be an option.
As an example, as to the process A3, it may involve updating a new trajectory from specific parameters and/or shapes. The process A3 may include features for automated trajectory design (ATD), for example, using an engine of a planning framework, which may provide for labeling.
As an example, the process A4 may provide for computation of trajectories with the best drilling performance, which may include anti-collision assurance.
As an example, a framework may configure a structure as a subset of configurations of one single wellbore trajectory on the structure. In such an example, specific details such as, for example, one or more of nudge azimuth, build inclination, etc., may be included in generated output.
As an example, various parameters may be set by default and/or set by a user. For example, in the workflow 1200 consider pad level parameters:
-
- NudgeKop=100 m;
- NudgeDls=1.5 deg/100 ft;
- StartType=StartSegmentTypeEnum.Nudge_BackToVertical_CHCH (automatic, to define a logic)
- NudgeInclination_Min=5 deg;
- NudgeInclination_Max=20 deg;
- BuildType=BuildSegmentTypeEnum.C_2D;
- BuildDls=10 deg/100 ft;
As an example, a framework may provide for parameter values for a structure level as part of a configuration. For example, consider one or more of:
-
- NudgeKop=100 m;
- NudgeDls=1.5 deg/100 ft;
- NudgeInclination_Min=5 deg;
- NudgeInclination_Max=20 deg;
- BuildDls=10 deg/100 ft;
- Preferred shape list:
- No nudge 2D landing
- S nudge 2D landing
- J nudge curve landing
As an example, a GUI may include graphical controls, etc., for representing wells, slots, a trajectory table and KPIs. For example, in wells list, wells may be assigned to slots, along with target sets. Various parameters may be set, which may be per an initialization file or process. For example, parameters may include KOP, DLS, inclination, azimuth, measured depth (MD) and total vertical depth (TVD). In a 2D plan view, the trajectories are shown along with the target sets where the trajectories emanate from the slot template as positioned.
As an example, rig capacity may be taken into account in a plan workflow. For example, each trajectory to be drilled may demand certain capabilities of a rig. As shown in
As shown in the example of
As explained, difficulty may be computed for already drilled wells, which may be wells associated with a structure such as a slot structure with multiple slots. As to difficulty, factors may include geometric factors for individual wells and/or one or more well branches (e.g., laterals, offshoots, etc.), number of wells associated with a slot structure, number of slot structures, geometric factors for a single slot structure, geometric factors for a number of slot structures, collision risks with other wells of a slot structure, collision risks with other wells of a different slot structure, etc.
As an example, difficulty may be associated with one or more types of risks. For example, a well may be difficult to drill but not necessarily risky to drill. As an example, difficulty may be associated with time and expense and not necessarily risk. However, in various instances, difficulty and risk may be intertwined, for example, where difficulty and collision risks are intertwined such that a borehole is difficult to drill due to collision risks (e.g., anti-collision factors, etc.).
As to borehole quality, consider scenarios where one or more BHA runs, passes, etc., may cause damage to a portion of a borehole. In various instances, damage to a borehole may lead to risk of sticking (e.g., stuck pipe), increased cost and/or time of completions, etc. In the example of
As to producer and/or injector status of a well, consider a trained machine learning model that may determine an arrangement of one or more producers and/or one or more injectors. As an example, such a trained ML model may provide for determining an order to drill, for example, drilling a producer before an injector. In various instances, a producer may suffice for production of fluid over a particular period of time where an injector may be utilized thereafter, for example, to improve production from the producer (e.g., consider steam injection, water injection, chemical injection, etc.). In various instances, a producer may be changed from a producer to an injector, which may be indicated via output of a trained ML model. For example, consider an arrangement of wells from one or more structures where an order and/or timing for changing a producer to an injector is noted. In such an example, this may be presented as an option as an alternative to drilling one or more injectors (e.g., boreholes to be used as injector wells). For example, consider a design that includes drilling a number of injectors after drilling a number of producers versus a design that includes switching one or more producers to be one or more injectors. In such an example, initial production from a reservoir may be high such that a number of producers is practical. However, when the production drops, it may be that a fewer number of producers suffices such that one or more of the producers may be switched (e.g., changed) to be one or more injectors. In such an approach, resources (e.g., time, equipment, etc.) may be conserved by having a design where a producer may become an injector without having to drill a separate injector.
As an example, a framework may provide for generation of output as to one or more infill wells where, for example, an infill well may be drilled in an effort to increase production from a field, increase thermal energy extraction, increase storage of carbon (e.g., carbon sequestration), increase ability to inject water to increase production, etc. As an example, an infill well may provide for meeting one or more production goals (e.g., production rate, production stability, total production, production efficiency, etc.).
As to relief wells, in various scenarios, a relief well may provide for mitigating one or more types of issues that may arise during drilling and/or deliberate production of fluid. As an example, a relief well may be drilled to intersect an oil and/or gas well for one or more reasons. For example, consider a producer that has experienced a blowout. In such an example, specialized liquid, such as heavy (dense) drilling mud followed by cement, may be pumped down the relief well in order to stop the flow from the reservoir in the damaged well. As an example, data for offset wells may include relief well data. As relief wells may be considered as contingency types of wells, they may not normally be planned at a time when other wells are being planned (e.g., producer, injector, etc.). As an example, a framework may provide for designing one or more relief wells such that one or more relief well trajectories are available if one or more issues arise that may be mitigated by relief operations. In such an example, action may be taken more quickly, for example, responsive to a blowout. As an example, where multiple structures with multiple wells are planned, spacing between structures and/or well trajectories may be planned to accommodate drilling of one or more relief wells. For example, consider a scenario where a relief well may be drilled from a common structure and/or a neighboring structure. In various instances, a relief well may be constructed from an existing well, for example, by drilling an offshoot or branch from the existing well to deliberately intersect another well. As an example, anti-collision metrics may be utilized in relief well design. For example, consider an anti-collision metric as to closeness to another well being utilized as a possible metric for designing a branch off point to deliberate connect two wells for purposes of relief and/or one or more other operations. As an example, one or more anti-collision metrics may be utilized to design a relief well trajectory where, for example, the relief well may be suitable for relieving one or more of a number of wells. In such an example, the design may be for a portion of a relief well that may then be tailored for further drilling to reach a well that may be in need of relief. Where relief well information is known a priori, a location may be prepared at a time when equipment is on site for drilling of other wells. For example, consider preparing one or more pads for one or more relief wells when earth moving equipment is onsite to construct one or more other pads.
As an example, a framework may provide for automatically evaluating risk for producers, for example, to identify one or more producers that may be at a high risk of blowout. In such an example, the framework may provide for planning other wells around one or more high blowout risk producers where, for example, one or more of those wells may be amenable to being changed and/or drilled to function as relief well, if warranted. In such an example, a framework may operate in a blowout risk centric manner that aims to provide built-in precautions (e.g., contingencies, etc.) to handle and/or mitigate a blowout or blowouts. As an example, a framework may provide for reducing risk of a blowout when conditions may become favorable for a blowout. For example, consider a framework that may provide for defining one or more conditions that may indicate a risk of blowout for a well where such one or more conditions may be utilized in practice to trigger drilling of a relief well and/or drilling a branch on an existing well to provide relief. As explained, a framework may provide for designing a relief strategy where such a strategy may be accompanied by one or more metrics to be utilized in monitoring, control, etc., to reduce risk of a blowout, prevent a blowout, mitigate a blowout, etc.
As an example, a framework may provide for design with respect to one or more mechanical barriers, which may include one or more blowout preventers (BOPs). Such barriers may be controllably closed to isolate a well while hydrostatic balance may be regained through circulation of fluids in the well. However, if a well is not shut in by actuation of a BOP, a kick may quickly escalate into a blowout when the formation fluids reach surface (e.g., land surface, seafloor, etc.), especially when an influx includes gas that may expand rapidly with reduced pressure as it flows up a wellbore, which may further decrease effective weight of drilling fluid.
Some examples of warning signs of an impending kick while drilling may include a sudden change in drilling rate, a reduction in drillpipe weight, a change in pump pressure, a change in drilling fluid return rate, etc. One or more other warning signs during drilling operations may include returning mud cut by (e.g., contaminated by) gas, oil and/or water, and/or connection gases, high background gas units, and high bottoms-up gas units detected in a mudlogging unit.
As an example, a framework may account for formation pressure and distribution thereof, which may include pre-production and during production pressures. As explained, a framework may consider declines in pressure where, for example, an injector may be beneficial to maintain or increase production. As such, a framework may be pressure-aware for one or more purposes. As explained, a framework may provide for assessing pressure related aspects of various wells and determining blowout risks for such wells. As explained, where blowout risk is concentrated within a particular region, a framework may provide for design of one or more paths for relief, which may be via one or more of a relief specific well and/or a non-relief specific well that may be adapted (e.g., further drilled, etc.) for purposes of relief.
As explained, a method such as the method 3500 may be performed in portions. For example, consider a training portion to generate one or more ML models and an implementation portion to implement one or more trained ML models. In various instances, implementation may be utilized to generate an initial design, which may be according to one or more inputs received from a human, humans, a machine, machines, etc. Such an approach may provide for expedited design of wells and associated structures.
As an example, a framework may operate in a responsive manner, for example, where the framework may be accessed via one or more interfaces, which may include one or more application programming interfaces (APIs).
As shown in
As an example, the framework 3610 may be operatively coupled to one or more frameworks for one or more purposes. As an example, the framework 3610 may be utilized for one or more carbon related projects, which may be part of a carbon capture, utilization and/or storage (CCUS) project. For example, carbon such as CO2 may be injected to one or more subsurface locations for purposes of sequestration.
As explained, a framework may provide for learning such that easier, less difficult wells may be drilled first, which may expedite an ability to capitalize on production or other goals (e.g., geothermal, carbon sequestration, etc.). For example, in the scenario 3800, consider drilling the wells associated with the multiwell structure 1 and then brining these wells online for production such that production may commence before drilling one or more other wells associated with the multiwell structures 2 and/or 3; or, for example, production network equipment assembly may be commenced to expedite project goals. In the example scenario 3800, fluid produced from the wells may be routed via a production network to a common point, which may facilitate collection, transportation, processing, etc. As explained, a framework may take into account one or more aspects of routing, networking, collection, transportation, processing, etc., when generating output. In such an approach, order of drilling may be organized to facilitate one or more of routing, networking, collection, transportation, processing, etc. As to carbon sequestration, a framework may account for infrastructure associated with delivery of carbon (e.g., CO2, etc.) for subsurface injection. As explained, a framework may facilitate various types of operations that involve wells.
As explained, a framework may provide various options that are not necessarily considered during conventional producer well planning. As explained, a framework may provide for collaborative (e.g., team, etc.) interactions. For example, where multiple types of wells may be considered, individuals and/or machines from different domains may interact via framework utilities (e.g., tools, APIs, GUIs, etc.). As explained, a framework may account for borehole quality, completions, etc., which may involve interactions with individuals and/or machines from associated domains. As explained, a framework may provide for expedited and comprehensive design of wells and their associated structures.
As explained, a framework may provide for generation of multiple well trajectories from offshore platforms and/or onshore pads, to the subsurface targets in view of contextually progressive constraints. As explained, a framework may provide for generation of comprehensive KPIs from concept design to engineering design, that may enable integrated workflow across multiple frameworks.
During a design process for platform wells, an asset team and a well construction team may conventionally tend to work in silos, which may be driven by frameworks that are tailored to each of their distinct domains. In such an approach, domain specific constrains and solution KPIs may result in a complete interruption between concept design, engineering planning, and revised design, for example, during performance of one or more operations. As drilling trajectory computations may be subject to strict industry standards, computations corresponding to one set of constraints may result in a computation fail, which may present even more challenges to teams without specific domain knowledge. As explained, a framework may provide for flexibility to allow for scenario generation such that a computation fail is unlikely to occur and impede development.
As explained, a framework may provide for generation of multiple solutions, for example, from platform(s) to targets in situation such as one or more of automatic targets assignment to platform, trajectory design from platform or platforms (e.g., one or more points) to targets, optimization of slot-target assignment, slot template placement optimization, re-plan remaining portions of one or more trajectories when one or more parts of one or more wells are fixed (e.g., already drilled, etc.), provide trajectory solutions evaluation via KPIs (e.g., total length, drilling difficulty index, azimuth change, anti-collision index, hook load, surface torque, borehole quality, BHA compatibility, dogleg severity, relief capability, injector/producer status capabilities, etc.), etc. As explained, a framework may provide for training and/or implementation of one or more machine learning models (e.g., artificial intelligence, etc.), whether for initial design, updated design, re-design, recommendations, etc.
As an example, a framework may generate one or more data models, which may be created including configuration at structure level such as formation limit and drilling tool ability, slot template information with slot numbers and spacing, target locations, and well trajectories. As an example, a framework may implement a series of algorithms and optimizers to provide drilling solutions. As an example, a framework may provide for output of well trajectory profiles that are labelled according to target direction and geometric shapes. As an example, a success of trajectory computation connecting surface and targets within constraints may be generated via optimizing one or more geometric parameters, for example, for preferred profiles (e.g., consider S-shape, J-shape, etc.). As to particular profiles, these may be for escape, to assure for appropriate near surface (e.g., or ocean bottom) separation of wells. As explained, slots and targets may be ordered to achieve an optimized assignment. As explained, escape directions may be optimized to help ensure that collision risk is properly managed.
As explained, a framework may help to breakdown silos between various domains. For example, consider a framework that may promote collaboration and workflows of an asset team and a well construction team in platform well design. Such a framework may bridge knowledge, constraints and results from various domains and enable a progressive optimization from concept to engineering. As explained, a framework may be implemented as an integrated and/or a stand-alone framework. As explained, a framework may help to execute continuous workflows where, for example, cross-team iterations and updates may be reduced.
In a cloud-based workspace within a framework environment, a framework may efficiently decrease instances of duplicated development of multiple trajectory design in various platforms. As an example, a framework may provide for expediting and improving facilities planning (e.g., piping, separators, compressors, processing facilities, transport facilities, etc.). In such an example, the framework may be linked to a facilities framework such that wells and facilities are planned in an integrated manner. As mentioned, a framework may be utilized for geothermal fields. For example, consider a framework that may provide for bulk trajectory replanning for geothermal wells where a specialized framework performs heat energy related computations for purposes of optimization. In such an example, the framework may operate responsive to a specialized framework calling for re-planning to get one or more updated designs, for example, when a planned well that has been drilled has been validated to be efficient for purposes of geothermal goals.
As explained, one or more ML models may be utilized for learning for offset pads, slot templates, and/or wells. Such an approach may include, for example, assessing offset data with respect to difficulty and/or order in which wells were drilled. Such an approach may include generating a better starting point (e.g., as a design, etc.) that may account for difficulty and propose an order for drilling the wells. Such an approach may include helping to avoid drilling of wells that have proven to be difficult according to a difficulty index and/or one or more other factors. As explained, difficulty and risk may be independent or associated. As explained, a risk may exist of drilling a well with one or more borehole quality issues, where such a well may be deemed to be difficult or not. As an example, a framework may provide for generating output for wells where borehole quality may be above a desired threshold according to one or more metrics. In such an example, the framework may provide for assessing risks such as, for example, risk of sticking, which may be associated with borehole quality and, for example, one or more other factors. As an example, a framework may provide for estimating time to drill based on a correlation with difficulty (e.g., per a difficulty index). In such an example, a framework may include accessing data, assessing difficulty and correlating difficulty with a time to drill, which may be compared to an actual time to drill if such data are available. As an example, a framework may provide for determining uncertainty as to one or more outputs. For example, consider an uncertainty in difficulty and/or time to drill that may be based at least in part on assessments performed for offset wells.
As an example, a framework may provide for determining an order for drilling wells from one or more structures. In such an example, one or more criteria, inputs, etc., may be utilized for determining an order. As an example, one or more ML models may provide for determining an order or orders for wells of one or more structures. As an example, an inner slot may be prioritized over an outer slot as to order. As an example, difficulty, density, number of neighboring wells (e.g., offset wells), number of wells associated with a structure, types of wells, etc., may be taken into account. As explained, producers may be prioritized over injectors. As an example, a framework may generate one or more order related KPIs. For example, consider an ease of order KPI that may demonstrate how one order may be more beneficial than another order. As an example, a time KPI, a cost KPI, a learning KPI, etc., may be provided to assess one or more orders of drilling.
As explained, time may be a factor to be optimized and/or reported as a KPI. As an example, a framework may implement one or more techniques as to time estimations, time comparisons, time correlations, etc. As explained, a framework may provide for generating time estimates for offset wells if not already available, which may include utilizing a trained ML model to estimate time to drill, which may account for difficulty. Such an approach may include accounting for time to drill and drilling faster wells before slower wells (e.g., as to order of wells for a structure or structures). In such an example, a framework may include assessing estimated time to drill and re-optimization if an estimated time is too high (e.g., relatively, per a threshold, etc.).
As explained, a framework may provide for nudging with respect to aspects of shape. In such an example, nudging may be part of an initial scenario generation, multiple scenarios generation, re-planning, etc. As to an S-shape, it may refer to a borehole trajectory that includes two substantially vertical portions with a curve between these two portions; whereas, a J-shape may refer to a borehole trajectory that includes a substantially vertical portion and a deviated portion (e.g., horizontal, etc.) that does not return to a substantially vertical portion within a particular distance. Such shapes may be utilized for escape from a structure (e.g., a slot structure). As an example, a framework may provide for selection of one or more shapes for escape where, for example, such selection may occur automatically and/or be pre-determined (e.g., via input, constraints, etc.). As an example, a framework may generate one or more shape KPIs that may associate shape with one or more aspects of drilling, production, injection, borehole quality, dogleg severity, etc. In such an example, a human and/or machine may assess shape related aspects to refine, optimize, re-run, etc., a workflow for structures and wells (e.g., actual, planned, contingent, etc.) extending therefrom.
As an example, nudging and anti-collision may be related. For example, a framework may include nudging for purposes of anti-collision. In such an example, nudging may be part of a process to adequately separate borehole trajectories as early as possible within a distance from a structure (e.g., surface slots, platform slots, subsea slots, etc.).
As an example, a framework may aim to provide nudging as part of an anti-collision mechanism and/or for constructing a simpler shape, which may be, for example, a 2D curve to landing point with a relatively large dogleg that does not present challenges of a smaller, tighter dogleg. As explained, nudges may refer to shape such as an S nudge and a J nudge. Such shapes may be particularly use for wells in North American land fields.
As to dogleg size, dogleg severity (DLS) may be utilized as a metric, which may be defined according to one or more standards such as, for example, one or more of degrees per 100 feet, degrees per 30 meters, degrees per 30 feet, and/or degrees per 10 meters. A larger DLS tends to increase difficulty and/or risk. Often both of S nudges and J nudges tend to be drilled with relatively small DLS. Geometrically, such shapes may be combined with a series of curve and hold profiles. As explained, an end of an S nudge may be defined to be substantially vertically down, while a J nudge may be defined to end at a non-vertical direction, as desired. As limited by formation condition, inclinations may be relatively small (e.g., less than 20 degrees, 30 degrees, as may depend on one or more other factors).
As an example, with respect to difficulty and/or risk, a framework may provide for input of and/or selection of one or more difficulty formulations and/or risk formulations. In such an example, a formulation may be associated with a particular type of formation, a particular type of drilling, a particular service provider, etc. In such an approach, a framework may be flexible and extensible to be able to handle various different concepts of difficulty and/or risk, whether quantitative and/or qualitative.
As an example, a framework may provide for generation of statistics and/or probabilities for output. For example, consider 100 wells where a histogram for 100 wells may be challenging to assess. In such an example, a framework may provide for filtering, sorting, classifying, etc., the 100 wells. For example, consider generation of probabilities, averages, standard deviations, etc. Such an approach may provide for sorting wells and, for example, ordering wells. As to ordering of wells, where more than one rig may be available, an order for wells may include drilling in a manner where multiple wells may be drilled at the same time (e.g., at least in part overlapping in time) using multiple rigs.
U.S. Pat. No. 11,015,433 B2, issued 25 May 2021, is incorporated by reference herein in its entirety, which describes in part a method that may include acquiring trajectory information; based at least in part on a portion of the trajectory information, generating a set of candidate trajectories with associated performance indicator values; rendering a graphical user interface to a display; via the graphical user interface, receiving input that adjusts one of the performance indicator values; and, responsive to the adjustment of the one of the performance indicator values, selecting one of the set of candidate trajectories. As an example, one or more of the technologies and/or techniques therein may be utilized by a framework, for example, directly and/or indirectly.
An international patent application publication WO 2023/178066 A1, published 21 Sep. 2023, is incorporated by reference herein in its entirety, which describes in part a method that may include receiving input for a multiwell pad; selecting, based at least in part on a portion of the input, a template for the multiwell pad; generating, based at least in part on the template, well trajectories for the multiwell pad, where each of the well trajectories extends from a surface location of the multiwell pad to one or more reservoir target locations and where each of the well trajectories includes at least one curve generated using one or more dogleg severity values and one or more geometric control points that are not required to lie on one or more of the well trajectories; and outputting specifications for the generated trajectories of the multiwell pad. As an example, one or more of the technologies and/or techniques therein may be utilized by a framework, for example, directly and/or indirectly.
As explained, anti-collision concerns may be handled in one or more manners. An article by Lesso and Paton, entitled “High Fidelity Directional Survey Calculations Can Improve Hydraulic Fracture Positioning in Unconventional Laterals”, paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, Virtual, July 2020 (doi: https://doi.org/10.15530/urtec-2020-2636) is incorporated by reference herein in its entirety.
As an example, a framework may provide for selection and/or customization of computational approaches to anti-collision, for example, via a rule set, measuring tool codes, etc., which may provide for computation of OSF and/or MAS.
As an example, a framework may provide for handling and/or accounting for various risks, which may be qualitatively and/or quantitatively assessed. As an example, risk factors may include geological, operational, etc., which may be mitigated by adding one or more geometric constraints. For example, consider one or more of importation of 3D models, with formations classification (e.g., via the PETREL framework). In such an example, a framework may associate risks are marked on different formations accordingly (e.g., consider salt formation leading to a design constraint as to vertical or hold interval, low ROP formation leading to a design constraint as to vertical through, completion concern leading to a design constraint as to hold interval for pump.
As explained, a difficulty index may be defined in one or more manners. For example, consider utilization of a combination of KPIs, risk factors, etc., which may provide a total score.
As to techniques to characterize structures, trajectories, etc., as explained, feature engineering may be employed. For example, consider an approach where offset well data may be tagged or otherwise assessed (e.g., by human and/or machine) as to features such that these data may be utilized for machine learning. Features may include hybrid features that account for slots in relationship to wells. Such features may be descriptive as to geometric aspects, which may include dimensions, angles, shapes, orientations, etc. Such features may be associated with one or more other factors such as, for example, cost, time, borehole quality, risk, etc. As explained, a framework may provide for data access, processing, and ML model training and may provide for ML model implementation. As an example, a framework may provide for feature selection on a job-by-job basis such that a human and/or a machine may leverage certain features. For example, features associated with time and borehole quality may be leveraged over features associated with overall space (e.g., volume, etc.). In such an example, a framework may generate output tailored to particular concerns as may be guided by one or more domain experts, whether human and/or machine. In such an example, the framework may provide for outputting an order to drill wells from one or more structures where, for example, an order may be determined at least in part on time and/or borehole quality. To move a project forward in an expeditious manner, wells that take less time to drill may be drilled before other wells. For example, a framework may rank wells based on time to drill (e.g., estimated time to drill, etc.) where an order may be balanced against one or more other factors, which may be taken into account manually, semi-automatically, automatically, etc. As explained, a KPI-based approach to assessment of structure(s) and associated wells may provide for determining an order for drilling of the wells. In such an example, well order may be determined in a context dependent manner, as may be tailored to a particular site or sites. As an example, a framework may provide for formulating an equation and/or rules for order determinations, which may include, for example, one or more KPIs as variables. As explained, an order may be determined by a framework using one or more ML models. In such an approach, the framework may provide for tailoring and/or optimizing the order, for example, using one or more KPIs. As an example, a formula for determining an order and/or refining an order may include ranking based at least in part on one or more KPIs, noting that weighting may be applied to consider the effect of a KPI or KPIs on order determination. In various instances, order may be beneficial to know prior to drilling such that particulars of each well may be taken into account with respect to provisioning, risks, reporting, etc. Further, as explained, a framework may provide for output of one or more metrics concerning when to drill, whether to drill, purpose of a well, etc. As explained, a purpose may be a producer, injector, relief, etc., where timings as to drilling, further drilling, etc., may be relevant with respect to actual conditions in a field.
As an example, a system, which may be a framework, may interact with one or more other applications, frameworks, systems, etc., to adjust trajectory geometry. For example, an output trajectory may be transformed into a specific format or data structure which may be modified with other applications. As an example, output from a system may be introduced to a simulator that may be a drilling simulator, a wellbore geomechanics simulator, a reservoir simulator, a stimulation simulator, etc. In such an example, one or more of multiple trajectories from a pad sketcher system may be subject to rigorous analysis, which may provide for generation of feedback that may be implemented in another round of trajectory generation, for example, to refine one or more trajectories to meet certain goals, to optimize production, to minimize NPT, etc. As an example, consider a reservoir simulation where drainage from a reservoir may be less than expected such that spacing of targets may be adjusted in an effort to increase production from a gang of wells stemming from a common pad. As another example, consider a drilling simulator that indicates one BHA may perform drilling more efficiently than another BHA, however, that a DLS criterion may differ for the two BHAs. In such an example, a pad sketcher system may be executed to generate trajectories for the more efficient BHA, where results from the two may be evaluated (e.g., using KPIs, additional simulations, etc.), to arrive at a final plan.
As an example, apart from manual input and various types of information computed within a trajectory design workflow, optional input data may also come from one or more other sources, for example, from an input recommendation service to define a quite detailed setup for a whole pad and/or for each well.
As an example, trajectory design may be integrated with one or more other applications such as, for example, single trajectory design, re-plan while drilling and even formation interpretation.
As an example, trajectory design may integrate with one or more single trajectory design workflows. As an example, consider an approach that converts output trajectories from pad design to trajectory design input and uses trajectory design tools to generate a group of trajectory candidates within a range, where one or more users may explore solutions and select the best one according to their engineering constraint and experience.
As an example, trajectory design may be integrated into one or more while-drilling processes to monitor one or more as-drilled trajectories and to assist with one or more re-planning processes, for example, when a deviation from plan is too large (e.g., according to one or more criteria). As an example, trajectory design may be integrated into a formation interpretation framework, for example, to combine geological target definition process and pad trajectory design process. In such an example, lithology information may be utilized, which may be from one or more pilot wells, offset wells, seismic survey analyses, etc. In such an approach, drilling mechanics may be taken into account, for example, to reduce drilling distances in one or more type of materials, etc., which may expedite drilling, provide for improved borehole integrity, etc.
In the example of
As an example, a computational framework may include a solver, which may be implemented via executable instructions. For example, consider a computational framework that includes a processor and memory accessible to the processor where executable instructions may be stored in the memory and accessed for execution by the processor to cause the computational framework to perform one or more actions. Such a computational framework may include one or more interfaces for receipt of information and/or for output of information, which may include values of parameters, an instruction, etc. As an example, a computational framework may be part of a controller. As an example, a computational framework may be part of a system.
As an example, various systems, methods, etc., may implement one or more ML models. As to types of ML models, consider one or more of a support vector machine (SVM) model, a k-nearest neighbors (KNN) model, an ensemble classifier model, a neural network (NN) model, etc. As an example, a machine learning model may be a deep learning model (e.g., deep Boltzmann machine, deep belief network, convolutional neural network, stacked auto-encoder, etc.), an ensemble model (e.g., random forest, gradient boosting machine, bootstrapped aggregation, AdaBoost, stacked generalization, gradient boosted regression tree, etc.), a neural network model (e.g., radial basis function network, perceptron, back-propagation, Hopfield network, etc.), a regularization model (e.g., ridge regression, least absolute shrinkage and selection operator, elastic net, least angle regression), a rule system model (e.g., cubist, one rule, zero rule, repeated incremental pruning to produce error reduction), a regression model (e.g., linear regression, ordinary least squares regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing, logistic regression, etc.), a Bayesian model (e.g., naïve Bayes, average on-dependence estimators, Bayesian belief network, Gaussian naïve Bayes, multinomial naïve Bayes, Bayesian network), a decision tree model (e.g., classification and regression tree, iterative dichotomiser 3, C4.5, C5.0, chi-squared automatic interaction detection, decision stump, conditional decision tree, M5), a dimensionality reduction model (e.g., principal component analysis, partial least squares regression, Sammon mapping, multidimensional scaling, projection pursuit, principal component regression, partial least squares discriminant analysis, mixture discriminant analysis, quadratic discriminant analysis, regularized discriminant analysis, flexible discriminant analysis, linear discriminant analysis, etc.), an instance model (e.g., k-nearest neighbor, learning vector quantization, self-organizing map, locally weighted learning, etc.), a clustering model (e.g., k-means, k-medians, expectation maximization, hierarchical clustering, etc.), etc.
As an example, a system may utilize one or more generative artificial intelligence techniques (e.g., GenAI). For example, consider a self-attention technique as a mechanism to achieve GenAI which may allow for a model to weigh importance of different tokens in a sequence when encoding each of a number of segments. In such an approach, this technique may help a model understand context by considering relationships between tokens, which may be regardless of their distance from each other in a sequence. As an example, a GenAI approach may use a transformer deep learning model architecture. As an example, a system may utilize one or more recurrent neural networks (RNNs). One type of RNN is referred to as long short-term memory (LSTM), which may be a unit or component (e.g., of one or more units) that may be in a layer or layers. A LSTM component may be a type of artificial neural network (ANN) designed to recognize patterns in sequences of data, such as time series data. When provided with time series data, LSTMs take time and sequence into account such that an LSTM may include a temporal dimension. For example, consider utilization of one or more RNNs for processing temporal data from one or more sources, optionally in combination with spatial data. Such an approach may recognize temporal patterns, which may be utilized for making predictions (e.g., as to a pattern or patterns for future times, etc.).
As an example, the TENSORFLOW framework (Google LLC, Mountain View, California) may be implemented, which is an open-source software library for dataflow programming that includes a symbolic math library, which may be implemented for machine learning applications that may include neural networks. As an example, the CAFFE framework may be implemented, which is a DL framework developed by Berkeley A1 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 A1 framework may be utilized (APOLLO.AI GmbH, Germany). As mentioned, a framework such as the PYTORCH framework may be utilized.
As an example, a training method may include various actions that may operate on a dataset to train a ML model. As an example, a dataset may be split into training data and test data where test data may provide for evaluation. A method may include cross-validation of parameters and best parameters, which may be provided for model training.
The TENSORFLOW framework may run on multiple CPUs and GPUs (with optional CUDA (NVIDIA Corp., Santa Clara, California) and SYCL (The Khronos Group Inc., Beaverton, Oregon) extensions for general-purpose computing on graphics processing units (GPUs)). TENSORFLOW is available on 64-bit LINUX, MACOS (Apple Inc., Cupertino, California), WINDOWS (Microsoft Corp., Redmond, Washington), and mobile computing platforms including ANDROID (Google LLC, Mountain View, California) and IOS (Apple Inc.) operating system-based platforms.
TENSORFLOW computations may be expressed as stateful dataflow graphs; noting that the name TENSORFLOW derives from the operations that such neural networks perform on multidimensional data arrays. Such arrays may be referred to as “tensors”.
As an example, a method may include receiving input for a multiwell structure and subsurface target locations, where the multiwell structure includes slots; generating, based at least in part on the input, a position for the multiwell structure, slot-well assignments for the slots, and well trajectories, where each of the well trajectories extends from an assigned one of the slots to one or more of the subsurface target locations; and outputting a set of specifications for the position for the multiwell structure, the slot-well assignments, the well trajectories, and an order for drilling the well trajectories. In such an example, the generating may include computing performance metrics. For example, performance metrics may include one or more of an anti-collision metric and a drilling difficulty metric. As an example, an anti-collision metric may be an oriented separation factor (OSF) metric or a minimum allowable separation (MAS) metric. As an example, multiple anti-collision metrics may be utilized (e.g., OSF, MAS, etc.). Anti-collision assurances may provide for improved drilling, completions, production, injection, etc. As explained, collisions are to be avoided as a collision between one trajectory with another trajectory may damage both trajectories and result in substantial non-productive time (NPT). As explained, various metrics, thresholds, etc., may be utilized to help assure separation of trajectories such that one trajectory does not impact integrity of another trajectory. As a drilling difficulty metric may be a drilling difficulty index, which may be a scientifically-based metric that accounts for various difficulties that may be encountered in drilling a trajectory, which may include equipment related factors and/or formation related factors. As an example, performance metrics may include one or more of a total length metric, a curve length metric, and a build length metric. As an example, performance metrics may include rig capacity metrics. For example, consider one or more of a pump pressure metric, a hookload metric, and a torque metric. As an example, a method may include generating rig capacity metrics for each well of a group of wells of a multiwell structure. As an example, one or more rig capacity metrics may be utilized to determine drilling difficulty, drilling order, slot-well assignments, etc.
As an example, a method may include receiving input for more than one multiwell structure, generating more than one position for the more than one multiwell structure, and outputting one or more sets of specifications for the more than one position for the more than one multiwell structure. For example, consider the examples of
As an example, a method may include outputting instructions for rendering of a graphical user interface that includes a graphic of at least well trajectories that extend from slots to subsurface target locations.
As an example, a method may include, responsive to receipt of an additional input, modifying one or more of slot-well assignments and re-generating at least two of well trajectories. As explained, a method may include swapping slot-well assignments for two slot-well assignments and/or otherwise modifying slot-well assignments.
As an example, a method may include, responsive to receipt of an additional input, modifying the position for the multiwell structure and re-generating the slot-well assignments.
As an example, a set of specifications output by a method may be a first set of specifications with a first set of performance metrics and such a method may include generating a second set of specifications with a second set of performance metrics. In such an example, the method may include outputting instructions for rendering of a graphical user interface that includes a graphic for comparison of the first set of performance metrics and the second set of performance metrics. In such an example, the method may include receiving a signal responsive to selection of one of the first set of specifications and the second set of specifications. In such an example, the method may include outputting the selected set of specifications for receipt by a drill planning framework. In such an example, the method may include executing the drill planning framework to generate a digitally executable drill plan for drilling at least one of the wells.
As explained, such a drill plan may be executable by a framework for performing drilling operations. For example, consider generation of a drill plan using a framework such as the DRILLPLAN framework based at least in part on a draft design for trajectories from a multiwell structure to subsurface target locations where the trajectories provide for anti-collision assurances and utilizing the drill plan by a framework such as the DRILLOPS framework, which may issue instructions to control drilling operations to drill one or more trajectories using rig and a drill string with a drill bit that breaks rock to length a borehole in a subsurface geologic region. As explained, a framework that generates a draft plan may generate various performance metrics, which may include metrics for one or more of anti-collision, drilling difficulty, rig capacity, etc. Such metrics may provide for tailoring instructions issued by a framework such as, for example, the DRILLOPS framework.
As an example, a method may include generating, based at least in part on input, a position for a multiwell structure, slot-well assignments for slots, and well trajectories, where each of the well trajectories extends from an assigned one of the slots to one or more of the subsurface target locations, where the generating may include determining a scenario and selecting a process based on the scenario. For example, input may include particular information that may be analyzed to determine a scenario. As explained with respect to various examples of
As an example, a system may 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 input for a multiwell structure and subsurface target locations, where the multiwell structure includes slots; generate, based at least in part on the input, a position for the multiwell structure, slot-well assignments for the slots, and well trajectories, where each of the well trajectories extends from an assigned one of the slots to one or more of the subsurface target locations; and output a set of specifications for the position for the multiwell structure, the slot-well assignments, the well trajectories, and an order for drilling the well trajectories.
As an example, one or more computer-readable storage media may include processor-executable instructions to instruct a computing system to: receive input for a multiwell structure and subsurface target locations, where the multiwell structure includes slots; generate, based at least in part on the input, a position for the multiwell structure, slot-well assignments for the slots, and well trajectories, where each of the well trajectories extends from an assigned one of the slots to one or more of the subsurface target locations; and output a set of specifications for the position for the multiwell structure, the slot-well assignments, the well trajectories, and an order for drilling the well trajectories.
As an example, a computer program product that may include computer-executable instructions to instruct a computing system to perform one or more methods such as one or more of the methods described herein (e.g., in part, in whole and/or in various combinations).
In some embodiments, a method or methods may be executed by a computing system.
As an example, a system may include an individual computer system or an arrangement of distributed computer systems. In the example of
As an example, a module may be executed independently, or in coordination with, one or more processors 4004, which is (or are) operatively coupled to one or more storage media 4006 (e.g., via wire, wirelessly, etc.). As an example, one or more of the one or more processors 4004 may be operatively coupled to at least one of one or more network interface 4007. In such an example, the computer system 4001-1 may transmit and/or receive information, for example, via the one or more networks 4009 (e.g., consider one or more of the Internet, a private network, a cellular network, a satellite network, etc.). As shown, one or more other components 4008 may be included in the computer system 4001-1.
As an example, the computer system 4001-1 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 4001-2, etc. A device may be located in a physical location that differs from that of the computer system 4001-1. 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 4006 may be implemented as one or more computer-readable or machine-readable storage media. As an example, storage may be distributed within and/or across multiple internal and/or external enclosures of a computing system and/or additional computing systems.
As an example, a storage medium or storage media may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories, magnetic disks such as fixed, floppy and removable disks, other magnetic media including tape, optical media such as compact disks (CDs) or digital video disks (DVDs), BLUERAY disks, or other types of optical storage, or other types of storage devices.
As an example, a storage medium or media may be located in a machine running machine-readable instructions, or located at a remote site from which machine-readable instructions may be downloaded over a network for execution.
As an example, various components of a system such as, for example, a computer system, may be implemented in hardware, software, or a combination of both hardware and software (e.g., including firmware), including one or more signal processing and/or application specific integrated circuits.
As an example, a system may include a processing apparatus that may be or include a general-purpose processors or application specific chips (e.g., or chipsets), such as ASICs, FPGAs, PLDs, or other appropriate devices.
As an example, a device may be a mobile device that includes one or more network interfaces for communication of information. For example, a mobile device may include a wireless network interface (e.g., operable via IEEE 802.11, ETSI GSM, BLUETOOTH, satellite, etc.). As an example, a mobile device may include components such as a main processor, memory, a display, display graphics circuitry (e.g., optionally including touch and gesture circuitry), a SIM slot, audio/video circuitry, motion processing circuitry (e.g., accelerometer, gyroscope), wireless LAN circuitry, smart card circuitry, transmitter circuitry, GPS circuitry, and a battery. As an example, a mobile device may be configured as a cell phone, a tablet, etc. As an example, a method may be implemented (e.g., wholly or in part) using a mobile device. As an example, a system may include one or more mobile devices.
As an example, a system may be a distributed environment, for example, a so-called “cloud” environment where various devices, components, etc. interact for purposes of data storage, communications, computing, etc. As an example, a device or a system may include one or more components for communication of information via one or more of the Internet (e.g., where communication occurs via one or more Internet protocols), a cellular network, a satellite network, etc. As an example, a method may be implemented in a distributed environment (e.g., wholly or in part as a cloud-based service).
As an example, information may be input from a display (e.g., consider a touchscreen), output to a display or both. As an example, information may be output to a projector, a laser device, a printer, etc. such that the information may be viewed. As an example, information may be output stereographically or holographically. As to a printer, consider a 2D or a 3D printer. As an example, a 3D printer may include one or more substances that may be output to construct a 3D object. For example, data may be provided to a 3D printer to construct a 3D representation of a subterranean formation. As an example, layers may be constructed in 3D (e.g., horizons, etc.), geobodies constructed in 3D, etc. As an example, holes, fractures, etc., may be constructed in 3D (e.g., as positive structures, as negative structures, etc.).
Although only a few examples have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the examples. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the following claims. In the claims, means-plus-function clauses are intended to cover the structures described herein as performing the recited function and not only structural equivalents, but also equivalent structures. Thus, although a nail and a screw may not be structural equivalents in that a nail employs a cylindrical surface to secure wooden parts together, whereas a screw employs a helical surface, in the environment of fastening wooden parts, a nail and a screw may be equivalent structures.
Claims
1. A method comprising:
- receiving input for a multiwell structure and subsurface target locations, wherein the multiwell structure comprises slots;
- generating, based at least in part on the input, a position for the multiwell structure, slot-well assignments for the slots, and well trajectories, wherein each of the well trajectories extends from an assigned one of the slots to one or more of the subsurface target locations; and
- outputting a set of specifications for the position for the multiwell structure, the slot-well assignments, the well trajectories, and an order for drilling the well trajectories.
2. The method of claim 1, wherein the receiving comprises receiving input for more than one multiwell structure, wherein the generating comprises generating more than one position for the more than one multiwell structure, and wherein the outputting comprises outputting the set of specifications for the more than one position for the more than one multiwell structure.
3. The method of claim 1, wherein the generating comprises computing performance metrics.
4. The method of claim 3, wherein the performance metrics comprise one or more of an anti-collision metric and a drilling difficulty metric.
5. The method of claim 4, wherein the anti-collision metric comprises an oriented separation factor (OSF) metric or a minimum allowable separation (MAS) metric.
6. The method of claim 3, wherein the performance metrics comprise one or more of a total length metric, a curve length metric, and a build length metric.
7. The method of claim 3, wherein the performance metrics comprise rig capacity metrics.
8. The method of claim 7, wherein the rig capacity metrics comprise one or more of a pump pressure metric, a hookload metric, and a torque metric.
9. The method of claim 7, wherein the rig capacity metrics are generated for each of the wells.
10. The method of claim 1, comprising outputting instructions for rendering of a graphical user interface that comprises a graphic of at least the well trajectories that extend from the slots to the subsurface target locations.
11. The method of claim 1, comprising, responsive to receipt of an additional input, modifying one or more of the slot-well assignments and re-generating at least two of the well trajectories.
12. The method of claim 1, comprising, responsive to receipt of an additional input, modifying the position for the multiwell structure and re-generating the slot-well assignments.
13. The method of claim 1, wherein the set of specifications comprises a first set of specifications with a first set of performance metrics and generating a second set of specifications with a second set of performance metrics.
14. The method of claim 13, comprising outputting instructions for rendering of a graphical user interface that comprises a graphic for comparison of the first set of performance metrics and the second set of performance metrics.
15. The method of claim 14, comprising receiving a signal responsive to selection of one of the first set of specifications and the second set of specifications.
16. The method of claim 15, comprising outputting the selected set of specifications for receipt by a drill planning framework.
17. The method of claim 16, comprising executing the drill planning framework to generate a digitally executable drill plan for drilling at least one of the wells.
18. The method of claim 1, wherein the generating comprises determining a scenario and selecting a process based on the scenario, wherein determining the scenario comprises analyzing the input.
19. A system comprising:
- 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 input for a multiwell structure and subsurface target locations, wherein the multiwell structure comprises slots; generate, based at least in part on the input, a position for the multiwell structure, slot-well assignments for the slots, and well trajectories, wherein each of the well trajectories extends from an assigned one of the slots to one or more of the subsurface target locations; and output a set of specifications for the position for the multiwell structure, the slot-well assignments, the well trajectories, and an order for drilling the well trajectories.
20. One or more computer-readable storage media comprising processor-executable instructions to instruct a computing system to:
- receive input for a multiwell structure and subsurface target locations, wherein the multiwell structure comprises slots;
- generate, based at least in part on the input, a position for the multiwell structure, slot-well assignments for the slots, and well trajectories, wherein each of the well trajectories extends from an assigned one of the slots to one or more of the subsurface target locations; and
- output a set of specifications for the position for the multiwell structure, the slot-well assignments, the well trajectories, and an order for drilling the well trajectories.
Type: Application
Filed: Aug 22, 2024
Publication Date: Feb 27, 2025
Inventors: Qing Liu (Beijing), Xin Qiu (Beijing), Lu Jiang (Beijing), Peng Jin (Beijing), Zhenyu Chen (Beijing), Hai Feng Li (Beijing), Zhen Fan (Beijing), Lei Zhu (Beijing), Xue Lin (Beijing), Wanqiang Li (Beijing), Can Jin (Beijing), Wei Li (Beijing)
Application Number: 18/812,042
