Hydraulic Fracturing in a Subsurface Formation

This disclosure relates to hydraulic fracturing in a subsurface formation.

Hydraulic fracturing is a process of injecting pressurized fluids (e.g., water, chemicals) with or without solids (e.g., sand, proppant) into a well in a subsurface formation to cause fractures in the rock of the formation. The fractures provide pathways for hydrocarbons contained within the subsurface formation to flow to the well for extraction. Hydraulic fracturing can be used in unconventional reservoirs (e.g., reservoirs with low porosity and/or permeability) to increase the ability to produce hydrocarbons from the reservoir.

Unconventional reservoirs, such as tight reservoirs, can be primary candidates for current and future hydrocarbon production as hydrocarbons in conventional reservoirs are depleted. To profitably develop tight reservoirs, horizontal wells can be used to increase the reservoir contact. Multi-stage hydraulic fracturing techniques can be applied to the wells to increase reservoir conductivity to the wellbores by creating flow channels in the tight reservoirs. Multi-stage hydraulic fracturing includes performing fracturing operations on individually isolated portions (stages) of a well.

This disclosure provides an approach for multi-stage hydraulic fracturing in a subsurface formation. Multi-stage hydraulic fracture models can be generated from a geo-mechanics simulation and hydraulic fracture simulation. The fracture models can be propagated to additional stages in the same well or different wells using artificial intelligence techniques. The fracture models can be integrated into a reservoir model using, for example, a local grid refinement (LGR) method, multiple-continuum models, or using embedded discrete fracture models (EDFM). Reservoir simulations can be performed to determine well spacing, cluster spacing, and/or hydraulic fracture designs.

Implementations of the systems and methods of this disclosure can provide various technical benefits. Production in unconventional reservoirs can be modelled using data-driven methods using artificial intelligence (AI). Various machine-learning techniques such as support vector regression (SVR), multiple linear regression (MLR), and backpropagation (BP) neural networks trained using numerical simulation data can be utilized to forecast the production performance of unconventional resources before drilling or fracturing wells in the reservoir. The reusability of machine learning techniques can reduce computational cost; however, the complex transport mechanisms of shale reservoirs may not be readily accounted for. Numerical simulations that account for various complex mechanisms such as phase transition, nonlinear flow behavior, and non-Darcy flow, can achieve the most accurate predictions without production data. Numerical simulations typically involve both geo-mechanics simulation and reservoir simulation. Numerical simulations can be more computationally expensive than data driven or analytical techniques. Training machine learning models using numerical simulation data to forecast future production performance enables these methods to be utilized in well fracturing design and production planning schemes like analytical methods (e.g., material balance equations (MBE), decline curve analysis (DCA), and rate transient analysis (RTA)) or machine learning models that rely on previous production data from the unconventional resource.

Using data-driven, artificial intelligence techniques to propagate hydraulic fracture models in a reservoir model can reduce computational cost and computation time by decreasing the amount of computation to generate representations of the hydraulic fractures in the reservoir model as compared with generating representations of the hydraulic fractures using geomechanical simulations. The reduced computations result in the ability to perform multiple reservoir simulations with varying parameters to optimize well spacing and/or cluster spacing in the reservoir. Training the artificial intelligence techniques using simulated data enables forecasting hydrocarbon production performance before a well is drilled or fractured to enable alterations to the well placement or hydraulic fracture design to improve hydrocarbon production after completion of the well. The prediction results generated by this approach can have high accuracy (e.g., similar to accuracy of full numerical simulations) while being generated with reduced computational cost compared with numerical simulations.

The details of one or more implementations of these systems and methods are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of these systems and methods will be apparent from the description and drawings, and from the claims.

Like reference symbols in the various drawings indicate like elements.

Unconventional reservoirs, such as tight reservoirs, can be primary candidates for current and future hydrocarbon production as hydrocarbons in conventional reservoirs are depleted. To profitably develop tight reservoirs, horizontal wells can be used to increase the reservoir contact. Multi-stage hydraulic fracturing techniques can be applied to the wells to increase reservoir conductivity to the wellbores by creating flow channels in the tight reservoirs. Multi-stage hydraulic fracturing includes performing fracturing operations on individually isolated portions (stages) of a well.

This disclosure provides an approach for multi-stage hydraulic fracturing in a subsurface formation. Multi-stage hydraulic fracture models can be generated from a geo-mechanics simulation and hydraulic fracture simulation. The fracture models can be propagated to additional stages in the same well or different wells using artificial intelligence techniques. The fracture models can be integrated into a reservoir model using, for example, a local grid refinement (LGR) method, multiple-continuum models, or using embedded discrete fracture models (EDFM). Reservoir simulations can be performed to determine well spacing, cluster spacing, and/or hydraulic fracture designs.

is a schematic illustration of an example implementation of a hydraulic fracturing system. Generally, systemmay be operated to apply a fracture treatment to a subsurface formation(e.g., rock formation, geologic formation) from a wellborethat extends from a surfaceto the subsurface formation. Fracture treatments can be used, for example, to form or propagate fractures in a rock layer of the subsurface formationby injecting pressurized fluid. The fracture treatment can enhance or otherwise influence production of petroleum, natural gas, coal seam gas, or other types of reservoir resources.

The wellboreshown inincludes vertical and horizontal sections, as well as a curved section that connects the vertical and horizontal portions. Generally, and in alternative implementations, the wellborecan include horizontal, vertical (e.g., only vertical), slant, curved, and other types of wellbore geometries and orientations, and the fracture treatment can generally be applied to any portion of a subsurface formation. The wellbore, in this example, includes a casingthat is cemented or otherwise secured to the wellbore wall to define a boreholein the inner volume of the casing. In alternative implementations, the wellborecan be uncased or include uncased sections. Perforations (not specifically labeled) can be formed in the casingto allow fracturing fluids and/or other materials to flow into the boreholeand to the terranean surface. Perforations can be formed using shape charges, a perforating gun, and/or other tools. Although illustrated as generally vertical portions and generally horizontal portions, such parts of the wellboremay deviate from exactly vertical and exactly horizontal (e.g., relative to the surface) depending on the formation techniques of the wellbore, type of rock formation in the subsurface formation, and other factors.

The surfacecan be any appropriate surface from which drilling and completion equipment may be staged to recover hydrocarbons from a subsurface zone. For example, in some aspects, the surfacemay represent a body of water, such as a sea, gulf, ocean, lake, or otherwise. In some aspects, all or part of a drilling and completion system, including hydraulic fracturing system, may be staged on the body of water or on a floor of the body of water (e.g., ocean or gulf floor). Thus, references to surfaceincludes reference to bodies of water, terranean surfaces under bodies of water, as well as land locations.

Subsurface formationincludes one or more rock or geologic formations that bear hydrocarbons (e.g., oil, gas) or other fluids (e.g., water) to be produced to the surface. For example, the rock or geologic formations can be shale, sandstone, or other type of rock, typically, that may be hydraulically fractured to initiate, increase, or enhance the production of such hydrocarbons.

The example hydraulic fracturing systemincludes a hydraulic fracturing liquid circulation systemthat is fluidly coupled to the boreholethrough conduitand also fluidly coupled to a first hydraulic fracturing liquidand a second hydraulic fracturing liquid. In some aspects, there may be multiple first hydraulic fracturing liquidsand/or multiple second hydraulic fracturing liquids(e.g., each liquid stored separately). In some aspects, each of the multiple liquids (whetheror) may be the same composition (e.g., each first hydraulic fracturing liquidis the same or each second hydraulic fracturing liquidis the same).

In some aspects, as shown, the hydraulic fracturing liquid circulation systemis fluidly coupled to the subsurface formation(which could include a single formation, multiple formations or portions of a formation) through a working string(e.g., a tubular string that may be lowered and raised through the borehole). Generally, the hydraulic fracturing liquid circulation systemcan be deployed, for example, via skid equipment, a marine vessel, sub-sea deployed equipment, or other types of equipment and include hoses, tubes, fluid tanks or reservoirs, pumps, valves, and/or other suitable structures and equipment arranged to circulate a hydraulic fracturing liquidthrough the working stringand into the subsurface formation. The working stringis positioned to communicate the hydraulic fracturing liquidinto the wellboreand can include coiled tubing, sectioned pipe, and/or other structures that communicate fluid through the wellbore. The working stringcan also include flow control devices, bypass valves, ports, and or other tools or well devices that control the flow of fracturing fluid from the interior of the working stringinto the subsurface formation.

In this example, a control systemis communicably coupled to the hydraulic fracturing liquid circulation system(and may also be communicably coupled to one or more other components in the hydraulic fracturing system, such as flow control devices in the conduit, the working string, or other components). Generally, the control system, which may be electronic, electric, electro-mechanical, mechanical, pneumatic, or a combination thereof, may control (e.g., automatically without real-time human intervention, by a human operator, or a combination thereof) the hydraulic fracturing liquid circulation systemto deliver the hydraulic fracturing liquidat specified flowrates, pressures, and time durations to the working stringand to the subsurface formationto hydraulically fracture a geologic or rock formation. Control of the hydraulic fracturing liquid circulation systemmay include, for example, opening, closing, and modulating one or more valves that fluidly couple the circulation systemto the first and second hydraulic fracturing liquid sourcesand, as well as the conduitand the working string. Control of the hydraulic fracturing liquid circulation systemmay also include, for example, controlling one or more pump motor controllers (e.g., variable frequency drives) to circulate one or both of the first and second hydraulic fracturing liquid sourcesandinto the working stringand to the subsurface formation.

Generally, the first hydraulic fracturing liquidincludes a pad or pre-pad liquid that does not include proppant. For example, in some examples, the first hydraulic fracturing liquid(which may be mixed, generated, and stored at the wellsite or delivered to the wellsite) may include a slickwater liquid. For instance, the slickwater hydraulic fracturing liquid may consist of water mixed with a low concentration of a friction reducer to reduce a friction pressure in the working stringas the first hydraulic fracturing liquidis circulated to the subsurface formationby the hydraulic fracturing liquid circulation system. The friction reducer may be based on acrylamide polymers or copolymers. In some specific examples, the first hydraulic fracturing liquidmay include a water or brine-based Acrylamido Methyl Propane Sulfonate (AMPS)-polyacrylamide slickwater.

The second hydraulic fracturing liquidincludes a liquid that does include proppant (e.g., plastic-based or coated with resin or polymer or other softer materials to mitigate embedment issues). For example, in some examples, the second hydraulic fracturing liquid(which may be mixed, generated, and stored at the wellsite or delivered to the wellsite) may also include a slickwater liquid, such as a slickwater that is seawater- or brine-based and includes proppant. Moreover, in some aspects, the second hydraulic fracturing liquidmay include two separate hydraulic fracturing liquids. For example, one of the second hydraulic fracturing liquidmay be a slickwater hydraulic fracturing liquid, while another of the second hydraulic fracturing liquidmay be a linear or crosslinked hydraulic fracturing liquid that also includes proppant. Thus, for the present disclosure, the difference between the one or more first hydraulic fracturing liquids(e.g., pad and pre-pad liquids) and the one or more second hydraulic fracturing liquidsis that the one or more first hydraulic fracturing liquidsdoes not include proppant and the one or more second hydraulic fracturing liquidsdoes include proppant. Both, however, may be circulated by the hydraulic fracturing liquid circulation systeminto the working stringas the hydraulic fracturing liquid(e.g., based on a particular step being implemented in the hydraulic fracture job or operation).

As shown in, there may be multiple fracture zones (or stages),, andwithin the subsurface formation. In some aspects, the first and second hydraulic fracturing liquidsandmay be circulated in a specified order, and at specified times within a hydraulic fracturing job (e.g., multi-stage) to fractures in the zones,, and. Although three fracture zones or stages are shown, more or fewer fracture zones or stages can be used. In some aspects, each zone,, andmay be fluidly isolated, e.g., with packersor other zonal isolation devices or techniques. Such isolation may be implemented within the hydraulic fracturing process, e.g., after or prior to certain circulations of the first or second hydraulic fracturing liquidsor.

The design of a multi-stage fracturing operation can depend on the geomechanics of the subsurface formation. The design of the fracturing operation can change from stage to stage when geomechanical, geological, and/or petrophysical properties of the subsurface formationchange along the length of the well bore. For example, the fracturing pressure, the amount of proppant, size of proppant, duration of the fracturing operation, etc. can be modified depending on the properties of the subsurface formationat a particular fracture zone. Modelling of the subsurface formation can be used to design multi-stage hydraulic fractures that maximize production from the subsurface formation, reduce costs, and reduce overlap between adjacent wells.

is a block diagram of an example systemfor modelling hydraulic fractures in a subsurface formation. The systemincludes several modules that generate, store, and/or transform data and make the data available to one or more other modules. The systemcan be implemented on one or more data processing systems (e.g., computer systems or control systems).

Geo-mechanics modelling modulegenerates a model of a hydraulic fracture for one or more stages in a well in a subsurface formation. The subsurface formation can include an unconventional reservoir (e.g., a tight reservoir). The geo-mechanics modelling moduleperforms a numerical simulation (e.g., a finite-element simulation) based on geomechanical properties of the subsurface formation (e.g., Young's modulus, Poisson's ratio, permeability, etc.) to generate a model of the hydraulic fracture. The numerical simulation can also include parameters for the hydraulic fracturing including, for example, fracture spacing, injection rate, a time series of pumping schedules, proppant concentration, well static pressure, well bottom hole pressure, fracture pressure, etc. The geophysical, geo-mechanical and hydraulic fracturing properties can enable the geo-mechanics modelling moduleto model stress and strain for considering stress shadowing effects on complex fracture geometry, which can cause differences in fracture geometry for each stage of the well. The output of the geomechanics modelling modulecan be, for example, a rectangle shape of a hydraulic fracture centralized in a well perforation location or the width and half-length of a fracture generated from the inputs. The output can also include discrete values of fracture conductivities (millidarcy feet (md*ft), millidarcy meter (md*m), m) mapped to fracture geometry. The output (e.g., the hydraulic fracture) can be stored in the hydraulic fracture database.

Hydraulic fracture databasestores hydraulic fractures generated by the geo-mechanics module. The hydraulic fractures stored in hydraulic fracture databasecan be used to correlate input parameters (e.g., geomechanical properties, hydraulic fracture parameters) with resulting hydraulic fractures. For example, a regression method can be used to correlate the input parameters with the hydraulic fractures. The hydraulic fracture databasecan also be used to form training and validation datasets for machine learning models.

The AI modulecan generate AI models that link the geo-mechanical, geological, and hydraulic fracture inputs to the resulting hydraulic fracture outputs based on the data generated by the geo-mechanics moduleand/or the data stored in the hydraulic fractures database. The AI modulecan incorporate data-driven methods such as machine learning models and deep learning models. The AI modulecan also use statistical techniques (e.g., linear and non-linear regression) or geometric techniques (e.g., affine transformations) to establish correlations between inputs and outputs. The AI moduletakes inputs(e.g., reservoir properties, geo-mechanics properties, hydraulic fracturing properties, microseismic data) and produces outputs(e.g., hydraulic fracture geometry). The AI models generated by AI modulecan reduce computation time for simulating hydrocarbon production for a hydraulically fractured well by reducing the computational time and complexity to generate hydraulic fractures in a reservoir model for each stage in a multi-stage well.

For example, for a simple case (e.g., homogeneous reservoir and neglecting mechanisms affecting fracture stress geometry), the AI modulecan replicate the geometry of a hydraulic fracture (e.g., produced by the geo-mechanics modelling moduleor from the hydraulic fractures database), by performing a three-dimensional (3D) mapping using an affine transformation including scaling, reflection, translation, and rotation. An affine transformation can be an input matrix multiplied by a weight matrix in terms. The weight matrix can be determined using a machine learning model based on the input parameters. In a more complex case, the AI modulecan use, for example, a deep learning framework that is trained based on the hydraulic fractures stored in the hydraulic fracture database. After training, the deep learning framework (e.g., a neural network) can be used to generate hydraulic fractures for one or more additional well stages. The deep learning framework can also be continually updated based on newly acquired or newly simulated data.

The outputsof the AI moduleare stored in hydraulic fracture module. The hydraulic fracture modulecan store the hydraulic fractures for each stage in a multi-stage well and/or hydraulic fractures in multiple multi-stage wells with a subsurface formation.

Geological model modulegenerates a reservoir modelincorporating geological, geomechanical, and/or petrophysical properties. One or more wells can be represented in the reservoir model, and execution of the reservoir modelcan simulate hydrocarbon production of the one or more wells.

The systemcan integratethe hydraulic fractureswith the reservoir modelto perform a numerical simulationof the hydrocarbon production from one or more fractured wells in the reservoir model. Hydraulic fractures can be integratedinto the reservoir modelusing, for example, local grid refinement (LGR), an unstructured grid, a multiple continuum model, discrete fracture models (DFM), and/or embedded discrete fracture models (EDFM).

The numerical simulationusing hydraulic fracturesand reservoir modelscan predict the production performance of multi-stage fractured horizontal wells in unconventional reservoir. The numerical simulationcan incorporate complex mechanism such as compaction and adsorption, nonlinear flow behavior, non-Darcy flow, etc. in unconventional reservoirs. The numerical simulationcan be used in simulation studies to optimize construction design and maximize production benefits, by predicting the production performance before drilling and hydraulic fracturing begin.

The systemcan be used to efficiently conduct well spacing and cluster spacing sensitivity studies. Well spacing can include, for example, the spacing between vertical or horizontal wells in the subsurface formation. Optimizing the well spacing can be important since large well spacing may lead to unrecovered oil/gas between wells, and small well spacing can make hydraulic fracture from one well overlaps in the other, wells may interfere with each other in production. Small well spacing results in drilling more wells, and thus increases the cost. An optimal well spacing can maximize the hydrocarbon recovery and minimize the cost. Determining an optimal well spacing can require many simulation runs of the reservoir model with the integrated hydraulic fractures. Cluster spacing can include the number of fractures in a stage and/or the number of stages within a well in the subsurface formation. Capturing the architecture of hydraulic fractures in a simulator offers an opportunity to understand the impacts of the hydraulic fractures on production before drilling wells in the subsurface formation and/or before performing a hydraulic fracturing operation. In some implementations, the systemcan be used for history matching and production forecasts.

is a flow chart of an example methodfor hydraulic fracturing a subsurface formation. The methodcan be implemented on a data processing system (e.g., the computer system of).

The data processing system obtains a reservoir model representing a subsurface formation (step). For example, the data processing system may access the reservoir model from a data store, or the data processing system may generate the reservoir model.

The data processing system obtains a baseline hydraulic fracture model for one or more stages of a well in the subsurface formation based on the reservoir model (step). For example, the data processing system obtains the hydraulic fracture model by generating a hydraulic fracture model by performing a geomechanics simulation of the subsurface formation based on geomechanical and geophysical properties of the subsurface formation. In some implementations, the data processing system obtains the baseline hydraulic fracture model by accessing a data store storing one or more hydraulic fracture models.

The data processing system generates additional hydraulic fracture models for one or more additional stages in one or more wells in the subsurface formation using a machine learning model trained based on the baseline hydraulic fracture model (step). The machine learning model can include an affine transformation or an artificial neural network. Inputs to the machine learning model can include one or more of geomechanical properties, geophysical properties, and time-series data for a hydraulic fracturing operation. Geomechanical properties can include Young's modulus, Poisson's ratio, permeability, etc. The machine learning model can include hydraulic fracturing parameters including, for example, fracture spacing, injection rate, a time series of pumping schedules, proppant concentration, well static pressure, well bottom hole pressure, fracture pressure, etc.

The data processing system can train the machine learning model. The training data to train the machine learning model can include input features such as geomechanical and geophysical properties of the subsurface formation and labeled output including the obtained hydraulic fracture model. The data processing system can obtain the input and output data for the training data from a data store (e.g., a hydraulic fracture database), or from a geomechanics simulation, or both.

The data processing system integrates the additional hydraulic fracture models into the reservoir model (step). For example, the data processing system can integrate the additional hydraulic fracture models into the reservoir model by using local grid refinement, an unstructured grid, or embedded discrete fracture models to represent the fractures in the reservoir model.

The data processing system simulates hydrocarbon production from the subsurface formation using the reservoir model with the integrated additional hydraulic fracture models (step). For example, the data processing system executes a massively parallel simulation (e.g., hundreds to thousands of processes) of the reservoir model to simulate hydrocarbon production from the subsurface formation by solving coupled flow equations in parallel.

The data processing system determines a well spacing for the one or more wells or a cluster spacing for hydraulic fractures in the one or more wells based on the simulated hydrocarbon production (step). For example, the data processing system can perform a sensitivity analysis by iteratively simulating hydrocarbon production from the subsurface formation while altering spacing parameters of wells or clusters in the reservoir model.

In some implementations, the data processing system causes the drilling of one or more wells in the subsurface formation based on the determined well spacing (step). For example, the data processing system generates control commands to control drilling equipment to drill wells in the subsurface formation based on the well spacing. In some implementations, the data processing system causes performance of a hydraulic fracturing operation on the one or more wells based on the determined cluster spacing (step). For example, the data processing system can generate control commands to control hydraulic fracturing equipment in a hydraulic fracturing operation (e.g., hydraulic fracturing operation) based on the determined cluster spacing.

is a schematic of an example artificial neural networkthat can be used to generate hydraulic fracture models. Artificial neural networkincludes four layers, an input layer, two hidden layers,, and an output layer. Artificial neural networkis a fully connected neural network (e.g., each node in a first layer is connected to each node in the next consecutive layer). The input layer, as shown, includes three input nodes-. The noderepresents Poisson's ratio, the noderepresents Young's modulus, and the noderepresents permeability, fracture spacing, or injection rate. In some implementations, more input nodes can be used representing additional geomechanical and hydraulic fracture parameters. The output layerincludes a node representing a hydraulic fracture. The hydraulic fracturecan represent, for example, the shape, fracture half length, or fracture height of the hydraulic fracture.

illustrate example hydraulic fractures,integrated into a reservoir simulation. The hydraulic fractures,can be generated using, for example, a geomechanical simulation.illustrates a wellwith hydraulic fracturesreplicated in many stages of along the length of the well. For example, the hydraulic fracturesare replicated in each stage of the wellusing a machine learning model.

illustrate hydraulic fractures replicated in multiple wells in a reservoir model of a subsurface formation.shows a side view of three wells,,each with hydraulic fractures replicated along the length of the wells.shows a perspective view of many wells in a subsurface formationwith hydraulic fractures generated based on the methods and systems herein.

illustrates hydrocarbon production operationsthat include both one or more field operationsand one or more computational operations, which exchange information and control exploration for the production of hydrocarbons. In some implementations, outputs of techniques of the present disclosure (e.g., the method) can be performed before, during, or in combination with the hydrocarbon production operations, specifically, for example, either as field operationsor computational operations, or both.

Examples of field operationsinclude forming/drilling a wellbore, hydraulic fracturing, producing through the wellbore, injecting fluids (such as water) through the wellbore, to name a few. In some implementations, methods of the present disclosure can trigger or control the field operations. For example, the methods of the present disclosure can generate data from hardware/software including sensors and physical data gathering equipment (e.g., seismic sensors, well logging tools, flow meters, and temperature and pressure sensors). The methods of the present disclosure can include transmitting the data from the hardware/software to the field operationsand responsively triggering the field operationsincluding, for example, generating plans and signals that provide feedback to and control physical components of the field operations. Alternatively, or in addition, the field operationscan trigger the methods of the present disclosure. For example, implementing physical components (including, for example, hardware, such as sensors) deployed in the field operationscan generate plans and signals that can be provided as input or feedback (or both) to the methods of the present disclosure.

Examples of computational operationsinclude one or more computer systemsthat include one or more processors and computer-readable media (e.g., non-transitory computer-readable media) operatively coupled to the one or more processors to execute computer operations to perform the methods of the present disclosure. The computational operationscan be implemented using one or more databases, which store data received from the field operationsand/or generated internally within the computational operations(e.g., by implementing the methods of the present disclosure) or both. For example, the one or more computer systemsprocess inputs from the field operationsto assess conditions in the physical world, the outputs of which are stored in the databases. For example, seismic sensors of the field operationscan be used to perform a seismic survey to map subsurface features, such as facies and faults. In performing a seismic survey, seismic sources (e.g., seismic vibrators or explosions) generate seismic waves that propagate in the earth and seismic receivers (e.g., geophones) measure reflections generated as the seismic waves interact with boundaries between layers of a subsurface formation. The source and received signals are provided to the computational operationswhere they are stored in the databasesand analyzed by the one or more computer systems.

In some implementations, one or more outputsgenerated by the one or more computer systemscan be provided as feedback/input to the field operations(either as direct input or stored in the databases). The field operationscan use the feedback/input to control physical components used to perform the field operationsin the real world.

For example, the computational operationscan process the seismic data to generate three-dimensional (3D) maps of the subsurface formation. The computational operationscan use these 3D maps to provide plans for locating and drilling exploratory wells. In some operations, the exploratory wells are drilled using logging-while-drilling (LWD) techniques which incorporate logging tools into the drill string. LWD techniques can enable the computational operationsto process new information about the formation and control the drilling to adjust to the observed conditions in real-time.

The one or more computer systemscan update the 3D maps of the subsurface formation as information from one exploration well is received and the computational operationscan adjust the location of the next exploration well based on the updated 3D maps. Similarly, the data received from production operations can be used by the computational operationsto control components of the production operations. For example, production well and pipeline data can be analyzed to predict slugging in pipelines leading to a refinery and the computational operationscan control machine operated valves upstream of the refinery to reduce the likelihood of plant disruptions that run the risk of taking the plant offline.

In some implementations of the computational operations, customized user interfaces can present intermediate or final results of the above-described processes to a user. Information can be presented in one or more textual, tabular, or graphical formats, such as through a dashboard. The information can be presented at one or more on-site locations (such as at an oil well or other facility), on the Internet (such as on a webpage), on a mobile application (or app), or at a central processing facility.