Well trajectory builder and scheduler accounting for Anti-collision

Abstract

Certain aspects of the disclosure are directed to methods for obtaining a drilling sequence. The method may include accessing data for actual and planned well trajectories; identifying potential collisions between the actual and planned well trajectories; adjusting the planned well trajectories to mitigate the identified potential collisions; and generating a drilling schedule based on the adjusted planned well trajectories.

Description

WELL TRAJECTORY BUILDER AND SCHEDULER ACCOUNTING FOR ANTICOLLISIONCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This Application claims the benefit of and priority to U.S. Provisional Patent Application No. 63 / 595,123, filed on November 1, 2023, the entire contents of which are hereby incorporated by reference.BACKGROUND

[0002] Hydrocarbon drilling operations may involve extensive planning to determine optimal well locations and trajectories. A common practice is to plan well trajectories and generate drilling schedules for multiple wells, often 50-100 wells based on field size, within a field over a given period of time, such as a year. The initial planning aims to design trajectories that avoid collision with existing wells while reaching specified geological targets, taking into account various drilling constraints and objectives.

[0003] However, uncertainties during drilling operations often cause actual well trajectories to deviate from the planned well trajectories. After drilling begins, a fraction of the planned wells may be drilled according to the initial trajectories. Adjustments are then needed based on actual drilling data to modify remaining planned well trajectories and schedules to maintain collision avoidance. Conventionally, these adjustments require extensive manual review, analysis, and modification by domain experts, a tedious and time-consuming process. With congested and complex fields often containing hundreds of existing and planned wells in close proximity, the risk of well collisions rises significantly.

[0004] Well placement planning requires considering many factors, including collision risk management. Initially proposed well trajectories are designed to avoid nearby existing wells based on survey data. As wells are drilled, planned geological targets may shift based on new data from well logs. These target updates can render originally planned well trajectories unreachable. While new traj ectories are designed for the revised targets, collision risks may become unacceptably high, forcing well cancellations. Due to collision risks, operators may only be able to drill some of theplanned wells, failing to meet initial production expectations. Managing collision risks and altered trajectories is important and technically challenging when drilling, especially when drilling in areas containing multiple stacked reservoirs in a field.SUMMARY

[0005] Certain aspects are directed to a well planning system that integrates an anti-collision engine, trajectory design engine, and scheduler. The anti-collision engine may analyze uncertainties in actual and planned well positions to identify collision risks. The trajectory design engine may generate optimized well trajectories that avoid identified collisions while minimizing deviation from initial drilling targets. The scheduler may optimize the order and timing of drilling remaining planned wells based on updated trajectory information.

[0006] Certain aspects are directed to a method for planning well trajectories and scheduling drilling operations. The method may include accessing data for actual and planned well trajectories; identifying potential collisions between actual and planned well trajectories; adjusting the planned well trajectories to mitigate the identified potential collisions; and generating a drilling schedule based on the adjusted planned well trajectories.

[0007] Certain aspects are directed to a method for performing well anti-collision and obtaining a drilling sequence. The method may include accessing data for actual and planned well trajectories; calculating uncertainty ranges along the planned well trajectories; establishing exclusion zones around actual well traj ectories; identifying well collisions, wherein a well collision occurs when an uncertainty range for a specific well encroaches an exclusion zone established for an actual well or when an uncertainty range for a first planned well encroaches an uncertainty range for a second planned well; adjusting well trajectories to avoid an occurrence of a collision and generating one or more valid well trajectories based on the adjusted well trajectories; evaluating at least one risk factor for a plurality of drilling sequences including the one or more valid well trajectories; and selecting a drilling sequence for an optimized drilling schedule based on the evaluated at least one risk factor.

[0008] Other aspects provide processing systems configured to perform the aforementioned methods as well as those described herein; non-transitory, computer-readable media comprising instructions that, when executed by a processors of a processing system, cause the processingsystem to perform the aforementioned methods as well as those described herein; a computer program product embodied on a computer readable storage medium comprising code for performing the aforementioned methods as well as those further described herein; and a processing system comprising means for performing the aforementioned methods as well as those further described herein.

[0009] The following description and the related drawings set forth in detail certain illustrative features of one or more aspects.DESCRIPTION OF THE DRAWINGS

[0010] The appended figures depict certain aspects and are therefore not to be considered limiting of the scope of this disclosure.

[0011] FIG. 1 depicts an example system having various equipment in a geologic environment in accordance with examples of the present disclosure.

[0012] FIG. 2 depicts additional details of the equipment in a geologic environment in accordance with examples of the present disclosure.

[0013] FIG. 3 depicts details of a well planning system and associated workflow in accordance with examples of the present disclosure.

[0014] FIG. 4 depicts an example method for well planning and collision avoidance in accordance with examples of the present disclosure.

[0015] FIG. 5 depicts example data utilized for well planning and collision avoidance in accordance with examples of the present disclosure.

[0016] FIG. 6 depicts details of actual and planned trajectories in accordance with examples of the present disclosure.

[0017] FIG. 7 depicts another example method for well planning and collision avoidance in accordance with examples of the present disclosure.

[0018] FIG. 8 depicts an example processing system on which aspects of the present disclosure can be performed.

[0019] To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements that are common to the drawings. It is contemplated that elements and features of one embodiment may be beneficially incorporated in other embodiments without further recitation.DETAILED DESCRIPTION

[0020] In well drilling operations, collision between multiple planned wells can cause problems. Initially, proposed well trajectories are designed to avoid colliding with nearby existing wells based on survey data. However, uncertainties during drilling and based on conducted surveys mean that actual well paths or trajectories may diverge from planned well paths or trajectories. In addition, well targets often have to be shifted over time as new logging data is acquired and a reservoir model is updated. Such target adjustments can render originally planned trajectories unworkable. While revised trajectories are designed, collision risks may become unacceptably high, forcing cancellation of some planned wells. Therefore, there is need for an automated system that can continuously monitor actual drilling operations, modify planned well trajectories as needed to maintain collision avoidance, and re-optimize drilling schedules based on updated data. Such a system provides advantages in improving efficiency, reducing risks, and increasing hydrocarbon recovery from complex oil and gas assets such as stacked reservoirs with limited space at the surface due to well congestion, surface facilities, pipelines etc.

[0021] In certain aspects, a well planning system is configured to operate in a continuous, closed-loop manner. Initial well trajectories and a drilling schedule are defined for a field containing both existing and planned wells. As actual wells are drilled, data on the trajectories and landing locations are fed back into the well planning system. In certain aspects, the anti-collision engine identifies any potential collisions based on comparisons between planned and actual trajectories. In certain aspects, the trajectory design engine then modifies the remaining planned well trajectories as needed to maintain proper collision avoidance spacing. In certain aspects, the scheduler re-optimizes the order and timing of drilling the remaining wells to minimize risk based on the updated trajectories. This process, which can be an automated process, repeats whenever new actual drilling data is available, enabling continuous optimization of well plans over an entire drilling program.

[0022] In certain aspects, the well planning system utilizes a scheduling algorithm based on a weighted objective function. The scheduling algorithm evaluates thousands of different well sequence permutations. Each permutation is scored based on risk factors, such as well collision avoidance and target deviation. Constraints, such as rig availability, limits on well count per period, are also incorporated. The permutation with the lowest total risk score is selected as the optimal drilling sequence. This automated, data-driven approach replaces conventional manual scheduling techniques to improve efficiency.

[0023] In certain aspects, techniques for autonomous well planning and collision avoidance provide a technical solution to the problem of inefficient manual processes for managing complex well environments. The integration of real-time data with automated computational engines enables continuous optimization not feasible with manual approaches. This improves upon conventional static planning unable to adapt to evolving conditions. Further, the automated uncertainty modeling and risk analysis provides adaptive decision-making capabilities beyond human limitations. The techniques therefore advance well planning by enabling optimized, collision-free trajectories tailored to changing needs. In examples, “real-time” refers to a system’s ability to process, respond to, or act on external inputs or changes almost immediately after they occur, wherein the response time is fast enough to be perceived as instantaneous or nearly instantaneous by the system’ s users or relevant stakeholders. This implies that the system’ s outputs or actions are produced in a time frame that is relevant and appropriate for the application’s requirements, often resulting in immediate or near-immediate feedback.

[0024] In certain aspects, the automated well planning system described herein provides the ability to interface real-time drilling data with computational models and provides a flexible technical architecture. This allows well planners to dynamically optimize trajectories and schedules aligned with field requirements. The techniques enable a customized technical solution tailored to evolving drilling conditions versus being limited by static plans. Further, the automated risk analysis and schedule optimization provides technical improvements in evaluating drilling options.

[0025] FIG. 1 shows an example of a geologic environment 120. In FIG. 1, the geologic environment 120 may be a sedimentary basin that includes layers (e.g., stratification) that include a reservoir 121 that may be, for example, intersected by a fault 123 (e.g., or faults). The geologicenvironment 120 may be outfitted with any of a variety of sensors, detectors, actuators, etc. For example, equipment 122 may include communication circuitry to receive and to transmit information with respect to one or more networks 125. Such information may include information associated with downhole equipment 124, where downhole equipment 124 may be equipment to acquire information, to assist with resource recovery, etc. Other equipment 126 may be located remote from a well site and include sensing, detecting, emitting or other equipment. Such equipment may include storage and communication circuitry to store and to communicate data, instructions, etc. As an example, one or more pieces of equipment may provide for measurement, collection, communication, storage, analysis, etc. of data (e.g., for one or more produced resources, etc ). As another example, one or more satellites may be provided for purposes of communications, data acquisition, etc. For example, FIG. 1 shows a satellite in communication with the network 125 that may be configured for communications, noting that the satellite may additionally or alternatively include circuitry for imagery (e.g., spatial, spectral, temporal, radiometric, etc.).

[0026] FIG. 1 also shows the geologic environment 120 as optionally including equipment 127 and 128 associated with a well that includes a substantially horizontal portion (e.g., a lateral portion) that may intersect with one or more fractures 129. For example, consider a well in a shale formation that may include natural fractures, artificial fractures (e.g., hydraulic fractures) or a combination of natural and artificial fractures. As an example, a well may be drilled for a reservoir that is laterally extensive. In such an example, lateral variations in properties, stresses, etc. may exist where an assessment of such variations may assist with planning, operations, etc. to develop the reservoir (e.g., via fracturing, injecting, extracting, etc.). As an example, the equipment 127 and / or 128 may include components, a system, systems, etc. for fracturing, seismic sensing, analysis of seismic data, assessment of one or more fractures, injection, production, etc. As another example, the equipment 127 and / or 128 may provide for measurement, collection, communication, storage, analysis, etc. of data such as, for example, production data (e.g., for one or more produced resources). As another example, one or more satellites may be provided for purposes of communications, data acquisition, etc.

[0027] FIG. 1 also shows an example of example equipment 170 and example equipment 180. Such equipment, which may include systems of components, may be suitable for use in the geologic environment 120. While the example equipment 170 and 180 are illustrated as land-based, various components may be suitable for use in an offshore system (e.g., an offshore rig, etc ). An example wellsite system 160 may include the example equipment 170 and 180.

[0028] The example equipment 170 includes a platform 171, a derrick 172, a crown block 173, a line 174, a traveling block assembly 175, drawworks 176, and a landing 177 (e.g., a monkeyboard). As an example, the line 174 may be controlled at least in part via the drawworks 176 such that the traveling block assembly 175 travels in a vertical direction with respect to the platform 171. For example, by drawing the line 174 in, the drawworks 176 may cause the line 174 to run through the crown block 173 and lift the traveling block assembly 175 skyward away from the platform 171; whereas, by allowing the line 174 out, the drawworks 176 may cause the line 174 to run through the crown block 173 and lower the traveling block assembly 175 toward the platform 171. Where the traveling block assembly 175 carries pipe (e.g., casing, etc.), tracking of movement of the traveling block assembly 175 may provide an indication as to how much pipe has been deployed.

[0029] As depicted in the example wellsite system 160 of FIG. 1, a drill string 178 is suspended within the borehole 181 and has a drill string assembly 182 that includes the drill bit 184 at its lower end. As an example, the drill string assembly 182 may be a bottom hole assembly (BHA). The drill string 178 ( e.g., including one or more downhole tools) may be composed of a series of pipes threadably connected together to form a long tube with the drill bit 184 at the lower end thereof.

[0030] As an example, telemetry equipment may operate via transmission of energy via the drill string 178 itself. For example, consider a signal generator that imparts coded energy signals to the drill string 178 and repeaters that may receive such energy and repeat it to further transmit the coded energy signals (e.g., information, etc.).

[0031] As an example, the drill string 178 may be fitted with telemetry equipment 186 that includes a rotatable drive shaft, a turbine impeller mechanically coupled to the drive shaft such that the mud can cause the turbine impeller to rotate, a modulator rotor mechanically coupled to the drive shaft such that rotation of the turbine impeller causes said modulator rotor to rotate, a modulator stator mounted adjacent to or proximate to the modulator rotor such that rotation of the modulator rotor relative to the modulator stator creates pressure pulses in the mud, and a controllable brake for selectively braking rotation of the modulator rotor to modulate pressurepulses. 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.

[0032] In the example of FIG. 1, an uphole control and / or data acquisition system 196 may include circuitry to sense pressure pulses generated by telemetry equipment 186 and, for example, communicate sensed pressure pulses or information derived therefrom for process, control, etc.

[0033] The drill string assembly 182 of the illustrated example includes a logging-whiledrilling (LWD) module 188, a measurement-while-drilling (MWD) module 190, an optional module 192, a rotary-steerable system (RSS) and / or motor 194, and the drill bit 184. Such components or modules may be referred to as tools where a drill string can include a plurality of tools.

[0034] As to a RSS, it involves technology utilized for directional drilling. Directional drilling involves drilling into the Earth to form a deviated bore such that the trajectory of the bore is not vertical; rather, the trajectory deviates from vertical along one or more portions of the bore. As an example, consider a target that is located at a lateral distance from a surface location where a rig may be stationed. In such an example, drilling can commence with a vertical portion and then deviate from vertical such that the bore is aimed at the target and, eventually, reaches the target. Directional drilling may be implemented where a target may be inaccessible from a vertical location at the surface of the Earth, where material exists in the Earth that may impede drilling or otherwise be detrimental (e.g., consider a salt dome, etc.), where a formation is laterally extensive (e.g., consider a relatively thin yet laterally extensive reservoir), where multiple bores are to be drilled from a single surface bore, where a relief well is desired, etc.

[0035] The LWD module 188 may be housed in a suitable type of drill collar and can contain one or a plurality of selected types of logging tools. It will also be understood that more than one LWD and / or MWD module can be employed, for example, as represented at by the MWD module 190 of the drill string assembly 182. 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 188, the MWD module 190, etc. An LWD module can include capabilities for measuring, processing, and storing information,as well as for communicating with the surface equipment. In the illustrated example, the LWD module 188 may include a seismic measuring device.

[0036] The MWD module 190 may be housed in a suitable type of drill collar and can contain one or more devices for measuring characteristics of the drill string 178 and the drill bit 184. As an example, the MWD module 190 may include equipment for generating electrical power, for example, to power various components of the drill string 178. As an example, the MWD module 190 may include the telemetry equipment, for example, where the turbine impeller can generate power by flow of the mud; it being understood that other power and / or battery systems may be employed for purposes of powering various components. As an example, the MWD module 190 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.

[0037] The coupling of sensors 198 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, enables implementing a geosteering method. Such a method can include navigating a subsurface environment, for example, to follow a desired route to reach a desired target or targets.

[0038] As an example, a drill string can include an azimuthal density neutron (ADN) tool for measuring density and porosity; a MWD tool for measuring inclination, azimuth and shocks; a compensated dual resistivity (CDR) tool for measuring resistivity and gamma ray related phenomena; one or more variable gauge stabilizers; one or more bend joints; and a geosteering tool, which may include a motor and optionally equipment for measuring and / or responding to one or more of inclination, resistivity and gamma ray related phenomena.

[0039] As an example, geosteering can include intentional directional control of a wellbore based on results of downhole geological logging measurements in a manner that aims to keep a directional wellbore within a desired region, zone (e.g., a pay zone), etc. As an example, geosteering may include directing a wellbore to keep the wellbore in a particular section of a reservoir, for example, to minimize gas and / or water breakthrough and, for example, to maximize economic production from a well that includes the wellbore.

[0040] Referring again to FIG. 1, the example wellsite system 160 can include one or more sensors 198 that are operatively coupled to the control and / or data acquisition system 196. As an example, a sensor or sensors may be at surface locations. As an example, a sensor or sensors may be at downhole locations. As an example, a sensor or sensors may be at one or more remote locations that are not within a distance of the order of about one hundred meters from the example wellsite system 160. As an example, a sensor or sensor may be at an offset wellsite where the example wellsite system 160 and the offset wellsite are in a common field (e.g., oil and / or gas field). As an example, one or more of the sensors 198 can be provided for tracking pipe, tracking movement of at least a portion of a drill string, etc.

[0041] FIG. 2 shows an example of a system 200 that includes various stages includes, but not limited to, an evaluation stage 210, a planning stage 220, an engineering stage 230 and an operations stage 240. For example, a drilling workflow framework 201, a seismic-to-simulation framework 202, and a technical data framework 203 may be implemented to perform one or more processes such as evaluating a formation 214, evaluating a process 218, generating a trajectory 224, validating a trajectory 228, formulating constraints 234, designing equipment and / or processes based at least in part on design equipment / processes 238 constraints, performing drilling 244, and evaluating drilling I formation 248.

[0042] In the example of FIG. 2, the seismic-to-simulation framework 202 can be, for example, the PETREL® framework (SLB, Houston, Texas) and the technical data framework 203 can be, for example, the TECHLOG® framework (SLB, Houston, Texas).

[0043] As an example, a framework can include entities that may include earth entities, geological objects or other objects such as wells, surfaces, reservoirs, etc. Entities can include virtual representations of actual physical entities that are reconstructed for purposes of one or more of evaluation, planning, engineering, operations, etc. In some aspects, entities may be based on data acquired via sensing, observation, etc. (e.g., seismic data and / or other information). 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). Such properties may represent one or more measurements (e.g., acquired data), calculations, etc.

[0044] A framework may be an object-based framework. In such a framework, entities may be based on pre-defined classes, for example, to facilitate modeling, analysis, simulation, etc. Anexample of an object-based framework is the MICROSOFT .NET® framework (Redmond, Wash.), which provides a set of extensible object classes. In the .NET framework, an object class encapsulates a module of reusable code and associated data structures. Object classes can be used to instantiate object instances for use in by a program, script, etc. For example, borehole classes may define objects for representing boreholes based on well data.

[0045] As another example, a framework may be implemented within or in a manner operatively coupled to the DELFI® cognitive exploration and production (E&P) environment (SLB, Houston, Texas), which is a secure, cognitive, cloud-based collaborative environment that integrates data and workflows with digital technologies, such as artificial intelligence and machine learning. As an example, such an environment can provide for operations that involve one or more frameworks.

[0046] As an example, a framework can include an analysis component that may allow for interaction with a model or model-based results (e.g., simulation results, etc.). As to simulation, a framework may operatively link to or include a simulator such as the ECLIPSE® reservoir simulator (SLB, Houston, Texas), the INTERSECT® reservoir simulator (SLB, Houston Texas), etc.

[0047] The aforementioned PETREL® framework provides components that allow for optimization of exploration and development operations. The PETREL® framework includes seismic to simulation software components that can 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, well engineers, reservoir engineers, etc.) can develop collaborative workflows and integrate operations to streamline processes. Such a framework may be considered an application and may be considered a data- driven application (e.g., where data is input for purposes of modeling, simulating, etc.).

[0048] As mentioned with respect to the DELFI® environment, one or more frameworks may interoperate and / or run upon one or another. As an example, a framework environment marketed as the OCEAN® framework environment (SLB, Houston, Texas) may be utilized, which allows for integration of add-ons (or plug-ins) into a PETREL framework workflow. In an example embodiment, various components may be implemented as add-ons (or plug-ins) that conform toand operate according to specifications of a framework environment (e.g., according to application programming interface (API) specifications, etc.).

[0049] As an example, a framework can include a model simulation layer along with a framework services layer, a framework core layer and a modules layer. In a framework environment (e.g., OCEAN®, DELFI®, etc.), a model simulation layer can include or operatively link to a model-centric framework. In an example embodiment, a framework may be considered to be a data-driven application. For example, the PETREL® framework can include features for model building and visualization. As an example, a model may include one or more grids where a grid can be a spatial grid that conforms to spatial locations per acquired data (e.g., satellite data, logging data, seismic data, etc.).

[0050] As an example, a model simulation layer may provide domain objects, act as a data source, provide for rendering and provide for various user interfaces. Rendering capabilities may provide a graphical environment in which applications can display their data while user interfaces may provide a common look and feel for application user interface components.

[0051] As an example, domain objects can include entity objects, property objects and optionally other objects. Entity objects may be used to geometrically represent wells, surfaces, reservoirs, etc., while property objects may be used to provide property values as well as data versions and display parameters. For example, an entity object may represent a well where a property object provides log information as well as version information and display information (e.g., to display the well as part of a model).

[0052] As an example, data may be stored in one or more data sources (or data stores, generally physical data storage devices), which may be at the same or different physical sites and accessible via one or more networks. As an example, a model simulation layer may be configured to model projects. As such, a particular project may be stored where stored project information may include inputs, models, results and cases. Thus, upon completion of a modeling session, a user may store a project. At a later time, the project can be accessed and restored using the model simulation layer, which can recreate instances of the relevant domain objects.

[0053] As an example, the system 200 may be used to perform one or more workflows. A workflow may be a process that includes a number of worksteps. A workstep may operate on data,for example, to create new data, to update existing data, etc. As an example, a workflow may operate on one or more inputs and create one or more results, for example, based on one or more algorithms. As an example, a system may include a workflow editor for creation, editing, executing, etc. of a workflow. In such an example, the workflow editor may provide for selection of one or more pre-defined worksteps, one or more customized worksteps, etc. As an example, a workflow may be a workflow implementable at least in part in the PETREL® framework, for example, that operates on seismic data, seismic attribute(s), etc.

[0054] As an example, seismic data can be data acquired via a seismic survey where sources and receivers are positioned in a geologic environment to emit and receive seismic energy where at least a portion of such energy can reflect off subsurface structures. As an example, a seismic data analysis framework or frameworks (e.g., consider the OMEGA™ framework, marketed by SLB, Houston, Texas) may be utilized to determine depth, extent, properties, etc. of subsurface structures. As an example, seismic data analysis can include forward modeling and / or inversion, for example, to iteratively build a model of a subsurface region of a geologic environment. As an example, a seismic data analysis framework may be part of or operatively coupled to a seismic-to- simulation framework (e g., the PETREL® framework, etc.).

[0055] As an example, a workflow may be a process implementable at least in part in a framework environment and by one or more frameworks. As an example, a workflow may include one or more worksteps that access a set of instructions such as a plug-in (e.g., external executable code, etc.). As an example, a framework environment may be cloud-based where cloud resources are utilized that may be operatively coupled to one or more pieces of field equipment such that data can be acquired, transmitted, stored, processed, analyzed, etc., using features of a framework environment. As an example, a framework environment may employ various types of services, which may be backend, frontend or backend and frontend services. For example, consider a clientserver type of architecture where communications may occur via one or more application programming interfaces, one or more microservices, etc.

[0056] As an example, a framework may provide for modeling petroleum systems. For example, the modeling framework marketed as the PETROMOD® framework (SLB, Houston, Texas), which includes features for input of various types of information (e.g., seismic, well, geological, etc.) to model evolution of a sedimentary basin. The PETROMOD® frameworkprovides for petroleum systems modeling via input of various data such as seismic data, well data and other geological data, for example, to model evolution of a sedimentary basin. The PETROMOD® framework may predict if, and how, a reservoir has been charged with hydrocarbons, including, for example, the source and timing of hydrocarbon generation, migration routes, quantities, pore pressure and hydrocarbon type in the subsurface or at surface conditions. In combination with a framework such as the PETREL framework, workflows may be constructed to provide basin-to-prospect scale exploration solutions. Data exchange between frameworks can facilitate construction of models, analysis of data (e.g., PETROMOD® framework data analyzed using PETREL framework capabilities), and coupling of workflows.

[0057] As mentioned, a drill string can include various tools that may make measurements. As an example, a wireline tool or another type of tool may be utilized to make measurements. As an example, a tool may be configured to acquire electrical borehole images. As an example, the fullbore Formation MicroImager (FMI)™ tool (SLB, Houston, Texas) can acquire borehole image data. A data acquisition sequence for such a tool can include running the tool into a borehole with acquisition pads closed, opening and pressing the pads against a wall of the borehole, delivering electrical current into the material defining the borehole while translating the tool in the borehole, and sensing current remotely, which is altered by interactions with the material.

[0058] Analysis of formation information may reveal features such as, for example, 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 reservoir, optionally a fractured reservoir where fractures may be natural and / or artificial (e.g., hydraulic fractures). As an example, information acquired by a tool or tools may be analyzed using a framework such as the TECHLOG® framework. As an example, the TECHLOG® framework can be interoperable with one or more other frameworks such as, for example, the PETREL® framework.

[0059] As an example, various aspects of a workflow may be completed automatically, may be partially automated, or may be completed manually, as by a human user interfacing with a software application that executes using hardware (e.g., local and / or remote). As an example, a workflow may be cyclic, and may include, as an example, four stages such as, for example, an evaluation stage 210, a planning stage 220, an engineering stage 230, and an operations stage 240.As an example, a workflow may commence at one or more stages, which may progress to one or more other stages (e.g., in a serial manner, in a parallel manner, in a cyclical manner, etc.).

[0060] As an example, a workflow can commence with an evaluation stage, which may include a geological service provider evaluating a formation (see, e.g., the evaluate formation 214 block). As an example, a geological service provider may undertake the formation evaluation using a computing system executing a software package tailored to such activity; or, for example, one or more other suitable geology platforms may be employed (e.g., alternatively or additionally). As an example, the geological service provider may evaluate the formation, for example, using earth models, geophysical models, basin models, petrotechnical models, combinations thereof, and / or the like. Such models may take into consideration a variety of different inputs, including offset well data, seismic data, pilot well data, other geologic data, etc. The models and / or the input may be stored in the database maintained by the server and accessed by the geological service provider.

[0061] As an example, a workflow may progress to a geology and geophysics (“G&G”) service provider, which may generate a well trajectory (e.g., the generate trajectory 224 block), which may involve execution of one or more G&G software packages. Examples of such software packages include the PETREL framework. As an example, a G&G service provider may determine a well trajectory or a section thereof, based on, for example, one or more model(s) provided by a formation evaluation (e.g., per the evaluate formation 214 block), and / or other data, e.g., as accessed from one or more databases (e.g., maintained by one or more servers, etc.). As an example, a well trajectory may take into consideration various “basis of design” (BOD) constraints, such as general surface location, target (e.g., reservoir) location, and the like. As an example, a trajectory may incorporate information about tools, bottom-hole assemblies, casing sizes, etc., that may be used in drilling the well. A well trajectory determination may take into consideration a variety of other parameters, including risk tolerances, fluid weights and / or plans, bottom-hole pressures, drilling time, etc.

[0062] As an example, a workflow may progress to a first engineering service provider (e.g., one or more processing machines associated therewith), which may validate a well trajectory and, for example, relief well design (see, e.g., the validate trajectory228 block). Such a validation process may include evaluating physical properties, calculations, risk tolerances, integration with other aspects of a workflow, etc. As an example, one or more parameters for such determinationsmay be maintained by a server and / or by the first engineering service provider; noting that one or more model(s), well trajectory(ies), etc. may be maintained by a server and accessed by the first engineering service provider. For example, the first engineering service provider may include one or more computing systems executing one or more software packages. As an example, where the first engineering service provider rejects or otherwise suggests an adjustment to a well trajectory, the well trajectory may be adjusted or a message or other notification sent to the G&G service provider requesting such modification.

[0063] As an example, one or more engineering service providers (e.g., first, second, etc.) may provide a casing design, bottom-hole assembly design, fluid design, and / or the like, to implement a well trajectory (see, e.g., the design equipment / processes 238 block). In some embodiments, a second engineering service provider may perform such design using one of more software applications. Such designs may be stored in one or more databases maintained by one or more servers, which may, for example, employ STUDIO® framework tools (SLB, Houston, Texas), and may be accessed by one or more of the other service providers in a workflow.

[0064] As an example, a second engineering service provider may seek approval from a third engineering service provider for one or more designs established along with a well trajectory. In such an example, the third engineering service provider may consider various factors as to whether the well engineering plan is acceptable, such as economic variables (e.g., oil production forecasts, costs per barrel, risk, drill time, etc.), and may request authorization for expenditure, such as from the operating company’s representative, well-owner’s representative, or the like (e.g., the formulate constraint 234 block). As an example, at least some of the data upon which such determinations are based may be stored in one or more database maintained by one or more servers. As an example, a first, a second, and / or a third engineering service provider may be provided by a single team of engineers or even a single engineer, and thus may or may not be separate entities.

[0065] As an example, where economics may be unacceptable or subject to authorization being withheld, an engineering service provider may suggest changes to casing, a bottom-hole assembly, and / or fluid design, or otherwise notify and / or return control to a different engineering service provider, so that adjustments may be made to casing, a bottom-hole assembly, and / or fluid design. Where modifying one or more of such designs is impracticable within well constraints, trajectory, etc., the engineering service provider may suggest an adjustment to the well trajectory and / or aworkflow may return to or otherwise notify an initial engineering service provider and / or a G&G service provider such that either or both may modify the well trajectory.

[0066] As an example, a workflow can include considering a well trajectory, including an accepted well engineering plan, and a formation evaluation. Such a workflow may then pass control to a drilling service provider, which may implement the well engineering plan, establishing safe and efficient drilling, maintaining well integrity, and reporting progress as well as operating parameters (e.g., the perform drilling 244 and evaluate drilling / formation 248 blocks). As an example, operating parameters, formation encountered, data collected while drilling (e.g., using logging-while-drilling or measuring-while-drilling technology), may be returned to a geological service provider for evaluation. As an example, the geological service provider may then reevaluate the well trajectory, or one or more other aspects of the well engineering plan, and may, in some cases, and potentially within predetermined constraints, adjust the well engineering plan according to the real-life drilling parameters (e.g., based on acquired data in the field, etc.).

[0067] Whether the well is entirely drilled, or a section thereof is completed, depending on the specific embodiment, a workflow may proceed to a post review (e.g., the evaluate process 218 block). As an example, a post review may include reviewing drilling performance. As an example, a post review may further include reporting the drilling performance (e.g., to one or more relevant engineering, geological, or G&G service providers).

[0068] Various activities of a workflow may be performed consecutively and / or may be performed out of order (e.g., based partially on information from templates, nearby wells, etc. to fill in any gaps in information that is to be provided by another service provider). As an example, undertaking one activity may affect the results or basis for another activity, and thus may, either manually or automatically, call for a variation in one or more workflow activities, work products, etc. As an example, a server may allow for storing information on a central database accessible to various service providers where variations may be sought by communication with an appropriate service provider, may be made automatically, or may otherwise appear as suggestions to the relevant service provider. Such an approach may be considered a holistic approach to a well workflow, in comparison to a sequential, piecemeal approach.

[0069] As an example, various actions of a workflow may be repeated multiple times during drilling of a wellbore. For example, in one or more automated systems, feedback from a drillingservice provider may be provided at or near real-time, and the data acquired during drilling may be fed to one or more other service providers, which may adjust its piece of the workflow accordingly. As there may be dependencies in other areas of the workflow, such adjustments may permeate through the workflow, e.g., in an automated fashion. In some embodiments, a cyclic process may additionally or instead proceed after a certain drilling goal is reached, such as the completion of a section of the wellbore, and / or after the drilling of the entire wellbore, or on a per- day, week, month, etc., basis.

[0070] Well planning can include determining a path of a well (e.g., a trajectory) that can extend to a reservoir, for example, to economically produce fluids such as hydrocarbons therefrom. Well planning can include selecting a drilling and / or completion assembly which may be used to implement a well plan. As an example, various constraints can be imposed as part of well planning that can impact design of a well. As an example, such constraints may be imposed based at least in part on information as to known geology of a subterranean domain, presence of one or more other wells (e.g., actual and / or planned, etc.) in an area (e.g., consider collision avoidance), etc. As an example, one or more constraints may be imposed based at least in part on characteristics of one or more tools, components, etc. As an example, one or more constraints may be based at least in part on factors associated with drilling time and / or risk tolerance.

[0071] As an example, a system can allow for a reduction in waste, for example, as may be defined according to LEAN, where LEAN refers to a systematic approach or methodology aimed at reducing waste and inefficiencies in a process. In the context of LEAN, consider one or more of the following types of waste: transport (e g., moving items unnecessarily, whether physical or data); inventory (e.g., components, whether physical or informational, as work in process, and finished product not being processed); motion (e.g., people or equipment moving or walking unnecessarily to perform desired processing); waiting (e.g., waiting for information, interruptions of production during shift change, etc.); overproduction (e.g., production of material, information, equipment, etc. ahead of demand); over processing (e.g., resulting from poor tool or product design creating activity); and defects (e.g., effort involved in inspecting for and fixing defects whether in a plan, data, equipment, etc.). As an example, a system that allows for actions (e.g., methods, workflows, etc.) to be performed in a collaborative manner can help to reduce one or more types of waste.

[0072] As an example, a system can be utilized to implement a method for facilitating distributed well engineering, planning, and / or drilling system design across multiple computation devices where collaboration can occur among various different users (e.g., some being local, some being remote, some being mobile, etc.). In such a system, the various users via appropriate devices may be operatively coupled via one or more networks (e.g., local and / or wide area networks, public and / or private networks, land-based, marine-based and / or areal networks, etc.).

[0073] As an example, a system may allow well engineering, planning, and / or drilling system design to take place via a subsystems approach where a wellsite system is composed of various subsystem, which can include equipment subsystems and / or operational subsystems (e.g., control subsystems, etc.). As an example, computations may be performed using various computational platforms / devices that are operatively coupled via communication links (e g., network links, etc.). As an example, one or more links may be operatively coupled to a common database (e.g., a server site, etc.). As an example, a particular server or servers may manage receipt of notifications from one or more devices and / or issuance of notifications to one or more devices. As an example, a system may be implemented for a project where the system can output a well plan, for example, as a digital well plan, a paper well plan, a digital and paper well plan, etc. Such a well plan can be a complete well engineering plan or design for the particular project.

[0074] FIG. 3 illustrates a well planning system 300 and associated workflow according to examples of the present disclosure. The well planning system 300 may perform a portion of a workflow for planning, engineering, operations, and evaluation stages as described with respect to FIG. 2. The well planning system 300 may include an anti-collision engine 310, a trajectory design engine 320, a scheduler 330, and one or more data acquisition engines 340.

[0075] In some examples, the anti-collision engine 310 may analyze data for actual and planned well trajectories and positions. Based on the analysis, the anti-collision engine 310 may identify potential collision risks based on uncertainty ranges around wells. In some examples, an uncertainty range may refer to spatial regions around a planned well trajectory that represents the possible deviation of the actual well path from the planned path due to various uncertainties, such as measurement errors, drilling equipment limitations, and / or geological variability. These uncertainty ranges may be modeled as three-dimensional ellipsoids.

[0076] In some examples, an exclusionary zone may refer to spatial regions established around actual well trajectories to prevent planned wells from encroaching upon them. In some aspects, exclusionary zones may account for the positional uncertainties of actual wells and provide safety margins to avoid collisions. One or more defined exclusionary zones around existing wells should not be penetrated by planned trajectories. If the uncertainty range of a planned well falls within the exclusion zone of an existing well, a potential collision may be flagged. Similarly, if the uncertainty range of a planned well falls within the exclusion zone of another planned well, a potential collision may be flagged. As another example, for a given well pair, intersecting position uncertainty ranges may indicate a collision probability. Both lateral and vertical separation can be considered. The anti-collision engine 310 can model uncertainty ranges as 3D ellipsoids around the well paths, allowing efficient computation of intersection. A risk value can then be assigned based on the probability and consequences of collision. If the risk value exceeds a threshold, the potential collision between the well pair can be flagged.

[0077] When determining the trajectory of a well, a certain degree of deviation based on the known reliability of the equipment or the conditions under which the data is collected and the well is drilled can be accounted for. The inclusion of these uncertainty ranges may take into account not just the most likely trajectory, but also potential deviations from a most likely trajectory when planning collision avoidance measures. Alternative collision risk metrics, such as calculating closest approach distances between wells, can also be implemented. In examples, well information including a well trajectory may be provided to the anti -collision engine 310 via the data acquisition engine 340. In examples, the data acquisition engine 340 may access data from one or more sensors 198 (FIG. 1) that are operatively coupled to the control and / or data acquisition system 196, such as described in FIG. 1. In examples, such data is accessed in real-time.

[0078] In examples, the trajectory design engine 320 may generate initial well path plans that avoid known wells and meet user-specified trajectory criteria. In an example, the trajectory design engine parameterizes trajectory dimensions like curvature, dogleg severity, and turn radius based on drilling equipment limitations. In some examples, a dogleg severity may refer to a measure of the change in direction of a wellbore, and may be expressed in degrees per 100 feet or degrees per 30 meters. A dogleg severity may represent the curvature of the well-path and may be constrained by the mechanical limitations of drilling equipment and a need to reduce stress on the drill string.The trajectory design engine 320 may then computes realistic trajectories using optimization techniques to satisfy the parameters while focusing on hitting geological targets. In some examples, and where the trajectory design engine 320 operates on existing and planned well trajectory information, the trajectory design engine 320 may adjust planned well trajectories to avoid collisions identified by the anti-collision engine 310. The adjustments may be performed to meet collision avoidance criteria specified by the anti-collision engine 310 while minimizing deviations from the original drilling targets. Common trajectory parameters that can be optimized include surface coordinates, depth, inclination, azimuth, and dogleg severity. The trajectory design engine 320 may then compute modification parameters to adjust planned trajectories using optimization techniques to satisfy the parameters while hitting geological targets. In some instances, the geological target (e.g., location, or target generally) may be modified to account for a potential collision.

[0079] In examples, the trajectory design engine 320 predicts and quantifies likely uncertainty ranges for the planned trajectories. Sources of error include survey and positioning inaccuracy, limitations of trajectory control, and inherent rock variability. Uncertainty typically increases with depth and lateral distance along the well-path. Mathematical techniques including, but not limited to, covariance propagation methods may be used to model an expanding uncertainty envelope occurring during a trajectory. In some examples, covariance propagation methods may refer to mathematical techniques used to determine the cumulative effect of individual uncertainties on the overall system. In the context of well trajectories, covariance propagation methods may calculate how measurement errors and other uncertainties accumulate along a trajectory. The resulting probabilistic well position ranges may be used to assess collision risks between wells.

[0080] In examples, well information including a well trajectory is provided to the trajectory design engine 320 via the data acquisition engine 340. In examples, the data acquisition engine 340 may access data from one or more sensors 198 (FIG. 1) that are operatively coupled to the control and / or data acquisition system 196 (FIG. 1). In examples, such data is accessed in realtime.

[0081] In examples, the scheduler 330 determines an optimal sequence and timing for drilling the remaining planned wells based on the updated trajectories from the trajectory design engine 320. In examples, the scheduler 330 utilizes one or more models operating on an objectivefunction, where such objective function maximizes resource usage to ensure a number of wells are drilled within a defined timeline while minimizing the risk of collision. In one implementation, an algorithm evaluates a large number (e.g., thousands) of permutations of drilling sequences and schedules using a weighted objective function that accounts for parameters such as well collision risk, deviation from targets, rig availability, drilling costs, and desired well count per period, to name a few examples. Optimization techniques are employed to select the lowest risk permutation. The output is an optimized drilling schedule for the remaining planned wells. In examples, one or more processes are implemented that automatically modify trajectory parameters subject to relevant drilling constraints to generate revised well paths avoiding collisions.

[0082] In some examples, the scheduler 330 determines an optimal drilling order and schedule that minimizes collision risks while meeting other constraints. In one implementation, a cost function incorporating collision probability and risk factors is mathematically optimized using techniques like combinatorial optimization and genetic algorithms. The scheduler 330 may also consider secondary goals, such as drilling rate, rig usage, and operational dependencies between wells. As new well data is incorporated, the scheduler 330 can re-optimize the schedule for the remaining planned wells to integrate the updated risk assessments. In examples, well information data is provided to the scheduler 330 via the data acquisition engine 340. In examples, the data acquisition engine 340 may access data from one or more sensors 198 that are operatively coupled to the control and / or data acquisition system 196. In examples, such data is accessed in real-time. In some examples, the scheduler 330 may provide data to the data acquisition engine 340; such data may include but is not limited to drill sequence information, updated trajectory information, etc.

[0083] The anti-collision engine 310, trajectory design engine 320, and scheduler 330 work together in a closed loop fashion. Actual drilling data, such as logs of well landing locations, can be provided back to the anti-collision engine 310 in real-time or near real-time as new wells are drilled. Any resulting changes to planned trajectories from the trajectory design engine 320 prompt re-optimization of the drilling schedule from the scheduler 330. For example, modified trajectories undergo re-assessment to confirm risk reduction before being finalized. In some examples, users are provided with interactive visualizations of suggested adjustments for analysis and approval. The integrated workflow allows quick iteration to converge on optimal solutions that balance targetobjectives, collision avoidance, and plan requirements. By automating adjustments, the system resolves issues faster than manual planning. By operating in a closed loop fashion, the process can repeat over the life of a drilling program as new data is acquired, enabling adaptive optimization of well plans.

[0084] FIG. 4 depicts an example method 400 for automated well planning and collision avoidance according to examples of the present disclosure. Method 400 may be performed by one or more processor(s) of a computing device, such as processor(s) 802 of processing system 800 described below with respect FIG. 8.

[0085] In examples, the method may begin at 402, where the well planning system (e.g., planning system 300 of FIG. 3) receives input data, including well trajectories / surface coordinates, measured depth, azimuth, inclination, and true vertical depth. In examples, the method 400 can be performed for all wells in a project or associated with a drilling plan, in which event the input data may be for one or more wells. In some examples, the method 400 can be performed for a single well, in which event, the input data may be for a single well. That is, the input data provides information related to the drilling process for one or more wells. The input data may be provided from one or more sensors 198 that are operatively coupled to the control and / or data acquisition system 196.

[0086] Method 400 then proceeds to 404, where based on the input data, the system constructs and in some cases visualizes the trajectory of the well, allowing operators to view the path and potential points of intersection with other wells. In some examples, well trajectories may be displayed in a visual manner.

[0087] Method 400 then proceeds to 406, where an anti-collision scan can be initiated. The anti-collision scan evaluates potential collisions by considering various parameters, such as the number of wells per year. In examples, the anti-collision engine (e.g., 310 of FIG. 3) performs the anti-collision scan as previously described and identifies one or more wells that are likely to collide.

[0088] The method 400 then proceeds to 408, where the well planning system can determine if a current trajectory for a well will result in a collision.

[0089] If the current trajectory will not result in a collision, then the method 400 proceeds to 410 and selects a best drilling scenario and outputs a well sequence. A well sequence can be identified in accordance with the scheduler (e.g., 330 of FIG. 3) as previously described.

[0090] If the current trajectory will result in a collision, then method 400 proceeds to 412, where the well planning system provides suggestions to modify the trajectories of the wells in question, typically by shifting the targets to avoid potential collisions. In some examples, the trajectory design engine (e.g., 320 of FIG. 3) generates new or modified trajectories for one or more wells as previously described.

[0091] The method 400 then proceeds to 414, where well planning system 300 can perform additional anti-collision scanning to verify that the new or modified trajectories do not have a high likelihood of colliding. Method 400 can repeat at 416. In some examples, method 400 is performed in accordance with a timed event or at the request of a parameter or variable (e.g., initiated by a user).

[0092] FIG. 5 depicts example information associated with one or more wells of a drilling project. For example, at a first time instance, a scheduler (e.g., 330 of FIG. 3) may schedule a drilling sequence as depicted in 502. After performing an anti-collision scan by an anti-collision engine (e.g., 310 of FIG. 3) and then modifying trajectories by a trajectory engine (e.g., 320 of FIG. 3), the scheduler (e.g., 330 of FIG. 3) may generate a drilling sequence as depicted in 504, where the drilling sequence for the wells has changed.

[0093] As further depicted in FIG. 5, a trajectory design engine (e.g., 320 of FIG. 3) may receive information 506 that includes well identifiers, associated targets, and drilling criteria information. The trajectory design engine (e.g., 320 of FIG. 3) may then modify or change the trajectories associated with the well identifiers as a result of performing trajectory modification and provide a revised drilling sequence as depicted in 508. In some examples, the trajectory design engine (e.g., 320 of FIG. 3) may generate one or more trajectories for a well; accordingly, the scheduler (e.g., 330 of FIG. 3) may consider multiple trajectories for a given well when creating a drilling sequence.

[0094] FIG. 6 depicts additional details of actual and planned well trajectories in accordance with examples of the present disclosure. More specifically, a first trajectory 602 may correspondto an actual trajectory, as in, the well has been drilled the first trajectory 602 represents the actual well path. One or more exclusion zones 604 may be placed around the first trajectory 602 as discussed with respect to FIG. 3. A second trajectory 606 may correspond to a planned trajectory, as in, a planned well path that has yet to be drilled or has yet to be completed. One or more areas of uncertainty 608 may be placed around the second trajectory 606 as discussed with respect to FIG. 3

[0095] In certain aspects, 610 represents a data store, which may encompass one or more physical or virtual data storage units, each configured to hold data essential for well trajectory planning and collision avoidance. The data store 610 may incorporate one or more tables, exemplified by table 612, that may organize and may store data relevant to planned and / or actual well operations. A table within data store 610 may include, but is not limited to, fields or records for a well identifier, target identifier, trajectory identifier, exclusion zone information (e.g., 604), and area of uncertainty information (e.g., 608). Of course, a table may include fields or records for a well identifier, another table may include fields or records for a target identifier, another table may include fields or records for a trajectory identifier, another table may include fields or records for a exclusion zone information (e.g., 604), and another table may include fields or records for an area of uncertainty information (e.g., 608).

[0096] The well identifier may uniquely label each well within the dataset, allowing for efficient retrieval and organization of data specific to individual wells. The target identifier may correspond to designated geological target locations, which planned well trajectories aim to reach. The trajectory identifier may distinguish between various actual or planned paths associated with a given well, permitting multiple trajectory records for wells with adjusted paths or alternative planning scenarios.

[0097] Additionally, exclusion zone information, such as represented by exclusion zones 604, may be stored in the data store to define spatial boundaries around drilled well trajectories, creating a safe operating margin to mitigate collision risks. Similarly, area of uncertainty information, represented by 608, provides spatial uncertainty buffers around planned well paths. These buffers account for potential deviations in drilling trajectories due to environmental factors, equipment constraints, or operational variability. By incorporating fields for exclusion zones and areas of uncertainty, the data store 610 facilitates dynamic assessment of well paths against proximityconstraints and risk factors, thereby supporting real-time adjustments and enhanced collision management capabilities.

[0098] FIG. 7 illustrates a flowchart depicting an example method 700 for planning well trajectories and scheduling drilling operations to avoid collisions, in accordance with examples of the present disclosure. Method 700 may be performed by one or more processor(s) of a computing device, such as processor(s) 802 of processing system 800 described below with respect FIG. 8.

[0099] Method 700 may begin at block 702 with accessing data for actual and planned well trajectories. In some aspects, this data may include, but is not limited to, positional coordinates, depth, inclination, azimuth, trajectory paths, and other associated uncertainty ranges. In some aspects, the data for actual well trajectories may be obtained from downhole logs, surveys, and real-time drilling data. Data for planned well trajectories may include proposed paths, target locations, and design parameters.

[0100] Method 700 may proceed to block 704 with identifying potential collisions between the actual and planned well trajectories. In some examples, identifying potential collisions between the actual and planned well trajectories involves analyzing spatial relationships and calculating separation distances. In some examples, uncertainty ranges and exclusion zones may be used to determine if planned wells encroach upon actual wells or other planned wells. In some examples, one or more of three-dimensional modeling or collision detection algorithms may be used to assess collision risks.

[0101] Method 700 may proceed to block 706 with adjusting the planned well trajectories to mitigate the identified potential collisions. In some examples, adjustments may involve modifying trajectory parameters such as surface coordinates, depth, inclination, azimuth, and dogleg severity. The adjustments may be made to avoid collisions while maintaining alignment with geological targets and adhering to drilling constraints and equipment limitations. In some examples, optimization techniques may be employed to find the most feasible trajectory adjustments.

[0102] Method 700 may then end at block 708, with generating an optimized drilling schedule based on the adjusted planned well trajectories. In some examples, this schedule takes into account the adjusted trajectories, operational constraints, rig availability, drilling priorities, and desiredwell counts per period. In some examples, a sequence of drilling operations may be obtained that minimizes collision risks and maximizes operational efficiency.

[0103] In some examples, method 700 may include a decision point where, upon the availability of new data (e.g., updated actual trajectories, new planned trajectories, or changes in target coordinates), the method 700 loops back to block 702. Accordingly, the method 700 can continuously update and optimize the well planning and drilling schedule in response to real-time data.

[0104] In some embodiments, the methods of the present disclosure may be executed by a computing system. Note that FIG. 7 is just one example of a method, and other methods including fewer, additional, or alternative operations are possible consistent with this disclosure.

[0105] Method 700 provides a technical solution to the problem of planning well trajectories and scheduling drilling operations to avoid collisions in complex and congested fields. By systematically accessing data for actual and planned well trajectories, identifying potential collisions, adjusting planned trajectories, and generating optimized drilling schedules, method 700 enables proactive collision avoidance without requiring extensive manual intervention. The implementation of method 700 improves drilling safety and operational efficiency by automating trajectory adjustments and schedule optimization, thereby reducing the risk of well collisions, minimizing downtime, and optimizing resource utilization.

[0106] FIG. 8 depicts an example processing system 800 configured to perform various aspects described herein, including, for example, method 700 as described above with respect to FIG. 7

[0107] Processing system 800 is generally an example of an electronic device configured to execute computer-executable instructions, such as those derived from compiled computer code, including without limitation personal computers, tablet computers, servers, smart phones, smart devices, wearable devices, augmented and / or virtual reality devices, and others.

[0108] In the depicted example, method 400 includes one or more processor(s) 802, one or more input / output device(s) 804, one or more display device(s) 806, one or more network interface(s) 808 through which processing system 800 is connected to one or more networks (e.g., a local network, an intranet, the Internet, or any other group of processing systemscommunicatively connected to each other), and computer-readable medium 812. In the depicted example, the aforementioned components are coupled by a bus 810, which may generally be configured for data exchange amongst the components. Bus 810 may be representative of multiple buses, while only one is depicted for simplicity.

[0109] Processor(s) 802 are generally configured to retrieve and execute instructions stored in one or more memories, including local memories like computer-readable medium 812, as well as remote memories and data stores. Similarly, processor(s) 802 are configured to store application data residing in local memories like the computer-readable medium 812, as well as remote memories and data stores. More generally, bus 810 is configured to transmit programming instructions and application data among the processor(s) 802, display device(s) 806, network interface(s) 808, and / or computer-readable medium 812. In certain embodiments, processor(s) 802 are representative of a one or more central processing units (CPUs), graphics processing unit (GPUs), tensor processing unit (TPUs), accelerators, and other processing devices.

[0110] Input / output device(s) 804 may include any device, mechanism, system, interactive display, and / or various other hardware and software components for communicating information between processing system 800 and a user of processing system 800. For example, input / output device(s) 804 may include input hardware, such as a keyboard, touch screen, button, microphone, speaker, and / or other device for receiving inputs from the user and sending outputs to the user.

[0111] Display device(s) 806 may generally include any sort of device configured to display data, information, graphics, user interface elements, and the like to a user. For example, display device(s) 806 may include internal and external displays such as an internal display of a tablet computer or an external display for a server computer or a projector. Display device(s) 806 may further include displays for devices, such as augmented, virtual, and / or extended reality devices. In various embodiments, display device(s) 806 may be configured to display a graphical user interface.

[0112] Network interface(s) 808 provide processing system 800 with access to external networks and thereby to external processing systems. Network interface(s) 808 can generally be any hardware and / or software capable of transmitting and / or receiving data via a wired or wireless network connection. Accordingly, network interface(s) 808 can include a communication transceiver for sending and / or receiving any wired and / or wireless communication.

[0113] Computer-readable medium 812 may be a volatile memory, such as a random access memory (RAM), or a nonvolatile memory, such as nonvolatile random access memory (NVRAM), or the like. In this example, computer-readable medium 812 may include a data accessing component 814, a collision identifying component 816, a trajectory adjusting component 818, a schedule generating component 820, a user interface component 822, and a data updating component 824.

[0114] The data accessing component 814 may be configured to access data for actual and planned well trajectories, as described in block 702 of FIG. 7. In some examples, the data accessing component 814 may retrieve data from various data sources, including databases and real-time data feeds, and stores them in the relevant data stores. The collision identifying component 816 may be configured to identify potential collisions between actual and planned well trajectories, as described in block 704 of FIG. 7. The collision identifying component 816 may analyze spatial relationships, calculate separation distances, and consider uncertainty ranges and exclusion zones to determine collision risks. The trajectory adjusting component 818 may be configured to adjust the planned well trajectories to mitigate identified potential collisions, as described in block 706 of FIG. 7. The trajectory adjusting component 818 may modify trajectory parameters and recalculate paths using one or more optimization algorithms while adhering to one or more drilling constraints.

[0115] The schedule generating component 820 may be configured to generate an optimized drilling schedule based on the adjusted planned well trajectories, as described in block 708 of FIG. 7. The schedule generating component 820 may sequence drilling operations to minimize collision risks and maximize operational efficiency. In some examples, an optimization algorithm for trajectory adjustment and drilling schedule generation may be used to minimize collision risks and maximize operational efficiency. In some examples, the data updating component 824 may be configured to monitor for new data availability and update relevant data stores, enabling the system to operate with the most current information. In some examples, the user interface component 822 may be configured to generate interactive visualizations and interfaces, allowing users to analyze and approve adjusted trajectories and drilling schedules.

[0116] In some examples, the computer-readable medium 812 may include one or more data stores storing or otherwise providing access to data. For example, an actual well trajectories datastore 826 may be configured to store data related to actual wells, including positional coordinates, depth, inclination, azimuth, and associated uncertainty ranges for example. In some examples, a planned well trajectories data store 828 may include data for proposed well paths, target locations, design parameters, and initial trajectory plans for example. In some examples, a potential collisions data store 830 may include information for identified potential collisions, including but not limited to collision points, risk assessments, and separation distances. In some examples, an adjusted trajectories data store 832 may include adjusted planned well trajectories after modifications to avoid collisions, including updated parameters and paths. In some examples, a drilling schedule data store 834 may include, but is not limited to, a generated drilling schedule, detailing the sequence, timing, and operational considerations for drilling the adjusted planned wells. In some examples, an uncertainty ranges data store 836 may include data for both actual and planned well trajectories, accounting for survey inaccuracies, equipment limitations, and / or geological variability. In some examples, an operational constraints data store 838 may include information on drilling constraints, equipment specifications, rig availability, and other factors influencing trajectory adjustments and scheduling for example.

[0117] In some examples, the data accessing component 814, collision identifying component 816, trajectory adjusting component 818, schedule generating component 820, user interface component 822, and data updating component 824 may be configured to perform the corresponding blocks of method 700 as described and depicted with respect to the method flow diagram in FIG. 7. For example, the data accessing component 814 may implement aspects of block 702 by retrieving data for actual and planned well trajectories from the actual well trajectories data store 826 and the planned well trajectories data store 828. The collision identifying component 816 may perform aspects of block 704 by analyzing the data and storing potential collision information in the potential collisions data store 830. The trajectory adjusting component 818 may perform aspects of block 706 by adjusting planned trajectories and updating the adjusted trajectories data store 832. The schedule generating component 820 may perform aspects of block 708 by generating an optimized drilling schedule, which may be stored in the drilling schedule data store 834. The user interface component 822 may interacts with display device(s) 806 and input / output device(s) 804 to present visualizations and receive user input throughout the process.

[0118] Note that FIG. 8 is just one example of a processing system consistent with aspects described herein, and other processing systems having additional, alternative, or fewer components are possible consistent with this disclosure.Example Clauses

[0119] Implementation examples are described in the following numbered clauses:

[0120] Clause 1 : A method for planning well trajectories and scheduling drilling operations to avoid collisions, comprising: accessing data for actual and planned well trajectories; identifying potential collisions between the actual and planned well trajectories; adjusting the planned well trajectories to mitigate the identified potential collisions; and generating an optimized drilling schedule based on the adjusted planned well trajectories.

[0121] Clause 2: The method of Clause 1, wherein identifying potential collisions comprises: calculating uncertainty ranges along the planned well trajectories; establishing exclusion zones around actual well trajectories; and determining that a potential collision occurs when at least one of an uncertainty range for a planned well encroaches upon an exclusion zone established for an actual well or when an uncertainty range for a first planned well encroaches upon an uncertainty range for a second planned well.

[0122] Clause 3 : The method of Clause 2, wherein calculating uncertainty ranges along the planned well trajectories comprises: modeling an expansion of uncertainty along each planned trajectory segment utilizing covariance propagation; incorporating systematic and random error components into the uncertainty calculations; and generating composite uncertainty ranges that reflect a cumulative effect of multiple sources of error.

[0123] Clause 4: The method of Clause 3, wherein the uncertainty ranges are modeled as three- dimensional ellipsoids around well paths.

[0124] Clause 5: The method of any one of clauses 2-4, wherein establishing exclusion zones around actual well trajectories comprises: determining safety margins based on positional uncertainties of the actual well trajectories; defining spatial regions surrounding the actual well trajectories using the determined safety margins; and designating the spatial regions as exclusion zones to prevent planned wells from encroaching upon them.

[0125] Clause 6: The method of Clause 1, wherein adjusting the planned well trajectories comprises: modifying at least one trajectory parameter selected from surface coordinates, depth, inclination, azimuth, and dogleg severity; recalculating the planned well trajectories to satisfy predefined trajectory parameters based on specified geological targets; and verifying that the adjusted trajectories no longer encroach upon exclusion zones or uncertainty ranges of other wells.

[0126] Clause 7: The method of Clause 6, wherein modifying trajectory parameters includes shifting planned geological target locations based on new data from well logs.

[0127] Clause 8: The method of any one of Clause 6 or Clause 7, further comprising defining a maximum allowable dogleg severity for the planned well trajectories based on drilling equipment specifications, wherein modifying trajectory parameters includes incorporating a maximum allowable dogleg severity.

[0128] Clause 9: The method of Clause 1, further comprising: evaluating at least one risk factor for a plurality of drilling sequences including the adjusted planned well trajectories; assigning weights to risk factors that include at least one of collision probability, deviation from target objectives, rig availability, drilling costs, or desired well counts per period; calculating a total risk score for each drilling sequence permutation by combining the weighted risk factors; comparing the total risk scores of the drilling sequence permutations; and selecting the drilling sequence with the lowest total risk score.

[0129] Clause 10: The method of Clause 9, wherein generating the optimized drilling schedule comprises: generating a plurality of drilling sequence permutations including the adjusted planned well trajectories; evaluating each permutation using a weighted objective function that accounts for collision risks and operational constraints; and selecting the drilling sequence based on resource utilization and risk minimization.

[0130] Clause 11 : The method of Clause 1, further comprising: monitoring actual drilling data as wells are drilled; incorporating the actual drilling data into the well planning system in near real-time; repeating the steps of identifying potential collisions and adjusting the planned well trajectories based on the real-time drilling data; and re-generating the drilling schedule for the remaining planned wells.

[0131] Clause 12: The method of Clause 1, further comprising: generating interactive visualizations of the adjusted planned well trajectories and the optimized drilling schedule; presenting the visualizations to users for analysis and approval; receiving user input regarding the suggested adjustments; and updating the planned well trajectories and drilling schedule based on the received user input.

[0132] Clause 13: The method of Clause 12, wherein generating interactive visualizations includes providing at least one of alerts or notifications when a potential collision risk exceeds a predefined threshold.

[0133] Clause 14: The method of Clause 1, further comprising: storing historical drilling and collision data; using the stored data to refine uncertainty models and collision risk assessments; and updating future well planning processes based on insights gained from historical data analysis.

[0134] Clause 15: The method of Clause 14, wherein updating future well planning processes includes refining trajectory adjustment algorithms and collision avoidance criteria.

[0135] Clause 16: The method of Clause 1, wherein the constraints for adjusting planned well trajectories include limitations based on drilling equipment capabilities and geological formation characteristics.

[0136] Clause 17: The method of Clause 1, wherein the uncertainty ranges consider at least one of survey and positioning inaccuracies, limitations of trajectory control, or material variability.

[0137] Clause 18: A method for performing well anti-collision and obtaining a drilling sequence, comprising: accessing data for actual and planned well trajectories; calculating uncertainty ranges along the planned well trajectories; establishing exclusion zones around actual well trajectories; identifying one or more predicted well collisions, wherein a predicted well collision occurs when an uncertainty range for a specific well encroaches an exclusion zone established for an actual well or when an uncertainty range for a first planned well encroaches an uncertainty range for a second planned well; adjusting well trajectories to avoid an occurrence of a collision and generating one or more valid well trajectories based on the adjusted well trajectories; evaluating at least one risk factor for a plurality of drilling sequences including the one or more valid well trajectories; and selecting a drilling sequence for an optimized drilling schedule based on the evaluated at least one risk factor.

[0138] Clause 19: A method according to clause 18, further comprising generating visualizations of the optimized drilling schedule.

[0139] Clause 20: A method according to any one of clauses 18-19, further comprising generating visualizations of the optimized drilling schedule.

[0140] Clause 21 : A method according to any one of clauses 18-20, further comprising defining one or more constraints for generating valid well trajectories, wherein the constraints comprise at least one of dogleg severity limits and collision avoidance criteria.

[0141] Clause 22: A method according to any one of clauses 18-21, further comprising obtaining the data for actual and planned well trajectories in real-time.

[0142] Clause 23: A method according to any one of clauses 18-22, wherein actual well trajectories comprise drilling data from downhole logs of well locations.

[0143] Clause 24: One or more processing systems, comprising: one or more memories comprising computer-executable instructions; and one or more processors configured to execute the computer-executable instructions and cause the one or more processing systems to perform a method in accordance with any one of Clauses 1-23.

[0144] Clause 25 One or more processing systems, comprising means for performing a method in accordance with any one of Clauses 1-23.

[0145] Clause 26: One or more non-transitory computer-readable media comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to perform the operations of any one of Clauses 1-23.

[0146] Clause 27: One or more computer program products embodied on one or more computer-readable storage media comprising code for performing a method in accordance with any one of Clauses 1-23.Additional Considerations

[0147] The preceding description is provided to enable any person skilled in the art to practice the various embodiments described herein. The examples discussed herein are not limiting of the scope, applicability, or embodiments set forth in the claims. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles definedherein may be applied to other embodiments. For example, changes may be made in the function and arrangement of elements discussed without departing from the scope of the disclosure. Various examples may omit, substitute, or add various procedures or components as appropriate. For instance, the methods described may be performed in an order different from that described, and various steps may be added, omitted, or combined. Also, features described with respect to some examples may be combined in some other examples. For example, an apparatus may be implemented or a method may be practiced using any number of the aspects set forth herein. In addition, the scope of the disclosure is intended to cover such an apparatus or method that is practiced using other structure, functionality, or structure and functionality in addition to, or other than, the various aspects of the disclosure set forth herein. It should be understood that any aspect of the disclosure disclosed herein may be embodied by one or more elements of a claim.

[0148] As used herein, a phrase referring to “at least one of’ a list of items refers to any combination of those items, including single members. As an example, “at least one of: a, b, or c” is intended to cover a, b, c, a-b, a-c, b-c, and a-b-c, as well as any combination with multiples of the same element (e.g., a-a, a-a-a, a-a-b, a-a-c, a-b-b, a c c, b-b, b-b-b, b-b-c, c-c, and c-c-c or any other ordering of a, b, and c).

[0149] As used herein, the term “determining” encompasses a wide variety of actions. For example, “determining” may include calculating, computing, processing, deriving, investigating, looking up (e.g., looking up in a table, a database or another data structure), ascertaining and the like. Also, “determining” may include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory) and the like. Also, “determining” may include resolving, selecting, choosing, establishing and the like.

[0150] The methods disclosed herein comprise one or more steps or actions for achieving the methods. The method steps and / or actions may be interchanged with one another without departing from the scope of the claims. In other words, unless a specific order of steps or actions is specified, the order and / or use of specific steps and / or actions may be modified without departing from the scope of the claims. Further, the various operations of methods described above may be performed by any suitable means capable of performing the corresponding functions. The means may include various hardware and / or software component(s) and / or module(s), including, but not limited to a circuit, an application specific integrated circuit (ASIC), or processor. Generally, where there areoperations illustrated in figures, those operations may have corresponding counterpart means-plus- function components with similar numbering.

[0151] The following claims are not intended to be limited to the embodiments shown herein, but are to be accorded the full scope consistent with the language of the claims. Within a claim, reference to an element in the singular is not intended to mean “one and only one” unless specifically so stated, but rather “one or more.” Unless specifically stated otherwise, the term “some” refers to one or more. No claim element is to be construed under the provisions of 35 U.S.C. §112(f) unless the element is expressly recited using the phrase “means for” or, in the case of a method claim, the element is recited using the phrase “step for.” All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims.

Claims

CLAIMSWhat is claimed is:

1. A method for planning well trajectories and scheduling drilling operations, comprising: accessing data for actual and planned well trajectories; identifying potential collisions between actual and planned well trajectories; adjusting the planned well trajectories to mitigate the identified potential collisions; and generating a drilling schedule based on the adjusted planned well trajectories.

2. The method of Claim 1, wherein identifying potential collisions comprises: calculating uncertainty ranges along the planned well trajectories; establishing exclusion zones around actual well trajectories; and determining that a potential collision occurs when at least one of an uncertainty range for a planned well encroaches upon an exclusion zone established for an actual well or when an uncertainty range for a first planned well encroaches upon an uncertainty range for a second planned well.

3. The method of Claim 2, wherein calculating uncertainty ranges along the planned well trajectories comprises: modeling an expansion of uncertainty along each planned trajectory segment utilizing covariance propagation; incorporating systematic and random error components into the uncertainty calculations; and generating composite uncertainty ranges that reflect a cumulative effect of multiple sources of error.

4. The method of Claim 3, wherein the uncertainty ranges are modeled as three- dimensional ellipsoids around well paths.

5. The method of Claim 2, wherein establishing exclusion zones around actual well trajectories comprises: determining safety margins based on positional uncertainties of the actual well trajectories; defining spatial regions surrounding the actual well trajectories using the determined safety margins; and designating the spatial regions as exclusion zones to prevent planned wells from encroaching upon them.

6. The method of Claim 2, wherein the uncertainty ranges include data from at least one of survey and positioning inaccuracies, limitations of trajectory control, or material variability.

7. The method of Claim 1, wherein adjusting the planned well trajectories comprises: modifying at least one trajectory parameter comprising at least one of surface coordinates, depth, inclination, azimuth, or dogleg severity; recalculating the planned well trajectories to satisfy predefined trajectory parameters based on specified geological targets; and verifying that adjusted trajectories no longer encroach upon exclusion zones or uncertainty ranges of other wells.

8. The method of Claim 7, wherein modifying trajectory parameters includes shifting planned geological target locations based on new data from well logs.

9. The method of Claim 7, further comprising defining a maximum allowable dogleg severity for the planned well trajectories based on drilling equipment specifications, wherein modifying trajectory parameters includes incorporating a maximum allowable dogleg severity.

10. The method of Claim 1, further comprising: evaluating at least one risk factor for a plurality of drilling sequences including the adjusted planned well trajectories; assigning weights to one or more risk factors that include at least one of collision probability, deviation from target objectives, rig availability, drilling costs, or desired well counts per period;calculating a total risk score for each drilling sequence permutation by combining the one or more risk factors having an assigned weight; comparing the total risk scores of the drilling sequence permutations; and selecting the drilling sequence with a lowest total risk score.

11. The method of Claim 10, wherein generating the drilling schedule comprises: generating a plurality of drilling sequence permutations including the adjusted planned well trajectories; evaluating each permutation using a weighted objective function that accounts for collision risks and operational constraints; and selecting a drilling sequence based on resource utilization and risk minimization.

12. The method of Claim 11, wherein the operational constraints for adjusting planned well trajectories include limitations based on drilling equipment capabilities and geological formation characteristics.

13. The method of Claim 1, further comprising: obtaining real-time drilling data as wells are drilled; repeating the steps of identifying potential collisions and adjusting the planned well trajectories based on the obtained real-time drilling data; and re-generating the drilling schedule for remaining planned wells.

14. The method of Claim 1, further comprising: generating interactive visualizations of at least one of the adjusted planned well trajectories or the drilling schedule; receiving user input regarding at least one of the adjusted planned well trajectories and the drilling schedule; and updating the planned well trajectories and drilling schedule based on the received user input.

15. The method of Claim 14, wherein generating interactive visualizations includes providing at least one of alerts or notifications when a potential collision risk exceeds a predefined threshold.

16. A processing system, comprising: one or more memories comprising computer-executable instructions; and one or more processors configured to execute the computer-executable instructions and cause the processing system to: access data for actual and planned well trajectories; identify potential collisions between actual and planned well trajectories; adjust planned well trajectories to mitigate the identified potential collisions; and generate a drilling schedule based on the adjusted planned well trajectories.

17. The processing system of Claim 16, wherein the one or more processors are further configured to cause the processing system to: calculate uncertainty ranges along planned well trajectories; and establish exclusion zones around actual well trajectories.

18. The processing system of Claim 17, wherein the one or more processors are further configured to cause the processing system to determine that a potential collision occurs when an uncertainty range for a planned well encroaches upon an exclusion zone established for an actual well, or when an uncertainty range for a first planned well encroaches upon an uncertainty range for a second planned well.

19. The processing system of Claim 18, wherein the one or more processors are further configured to cause the processing system to: modify at least one trajectory parameter selected from surface coordinates, depth, inclination, azimuth, and dogleg severity; and recalculate planned well trajectories using optimization techniques to satisfy predefined trajectory parameters while aiming to reach specified geological targets.

20. The processing system of Claim 19, wherein the one or more processors are further configured to cause the processing system to: generate a minimum separation distances between uncertainty ranges of planned wells and exclusion zones of actual wells; compare the minimum separation distances to safety thresholds;identify well pairs having a separation distance that is less than the safety threshold; and quantify a collision risk for each identified well pair.

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