Determining Boundaries for Subsurface Features Through Pixel-Wise Inferencing of Inversion Images

Many natural resources are located underground, including water reservoirs and hydrocarbon reservoirs, such as natural gas and oil. To access these resources, downhole drilling systems drill a wellbore along a trajectory away from a surface location to a target location, formation, or geological feature. Modern drilling systems use measurements underground to determine geological features along the trajectory. However, in many cases it may be difficult to ascertain from measurement data the location, shape, orientation and/or boundaries of subsurface features.

This disclosure describes a drilling system that uses a boundary detection system to identify boundaries of subsurface features. For example, the boundary detection system uses a subsurface boundary machine learning model that is generated to process individual pixels of input inversion images to identify subsurface features. The boundary information may facilitate generating a boundary mask for the input inversion images to facilitate identifying boundaries of subsurface features in the inversion images. In this way, the boundary detection system can efficiently and accurately locate subsurface features within inversion images that may not otherwise be identifiable in the inversion images.

In particular, this disclosure relates to devices, systems, and methods for determining boundary information using machine learning models, training data, and/or real-time inputs. In this disclosure, these devices, systems, and methods are described in the context of a boundary detection system, which may automatically identify boundary information and/or generate boundary masks for inversion images for identifying subsurface features within the inversion images.

To illustrate, in some embodiments, the boundary detection system identifies inversion training images indicating a portion of a subsurface feature, and obtains corresponding simulated inversion images from a subsurface model corresponding to a wellbore position of the inversion images. Based on generating simulated boundary masks for the simulated inversion images and based on the inversion training images, the boundary detection system generates a subsurface boundary machine learning model that processes pixels of the inversion training images through a decision-based architecture to generate predicted boundary masks indicating boundaries of the subsurface feature.

As will be discussed in further detail below, the present disclosure includes a number of practical applications having features described herein that provide benefits and/or solve problems associated with identifying subsurface features and/or boundaries of subsurface features. Some example benefits are discussed herein in connection with various features and functionalities provided by a boundary detection system implemented on one or more computing devices. It will be appreciated that benefits explicitly discussed in connection with one or more embodiments described herein are provided by way of example and are not intended to be an exhaustive list of all possible benefits of the boundary detection system.

For example, the boundary identification system described herein implements a subsurface boundary machine learning model that processes each pixel of an inversion image individually. This simplified manner of processing inversion images facilitates generating boundary information for the inversion images efficiency and quickly. For example, in contrast, some conventional image-processing techniques consider images as a whole and process many or all of the pixels simultaneously resulting in significant latency due to the high dimensionality and complexity of the image-processing operations. Additional efficiency benefits arise from the fact that the subsurface boundary machine learning model processes pixel number objects (e.g., pixel values as described herein) representative of each pixel of an inversion image as opposed to processing more complex image data directly from the pixels of target images. Further, the subsurface boundary machine learning model is generated based on a tree-or decision-based architecture which can process the target pixels through the trained decision tree(s) much more quickly than, for example, machine learning models having deep learning or neural network architectures, which due to their complexity, may be computationally expensive and inefficient, resulting in generating outputs with a significant delay.

These efficiency benefits not only facilitate the subsurface boundary machine learning model being implemented in real time to facilitate real-time identification of subsurface features, but may also advantageously facilitate generating or training the subsurface boundary machine learning model in real time or near real time. For example, tree-based models may typically have a simple and intuitive structure composed of a series of decision nodes and leaf nodes which, during training, are recursively split based on feature thresholds in the training data. These relatively straightforward computations may be trained and tuned considerably faster than the more complex operations often involved in the training of neural networks and other deep learning models, which involves the optimization of millions or even billions of parameters. Indeed, the simplicity of the tree-based model, which drives the efficiency of the subsurface boundary machine learning model, is facilitated by the simplicity of the input data being implemented on a pixel-by-pixel basis as well as the input data being simpler, number objects which are more computationally manageable. Thus, the subsurface boundary machine learning model may be trained in a manner of seconds as opposed to a manner of hours or days for deep learning and other machine learning models.

In addition to providing quick and timely boundary indications, the efficient manner in which the subsurface boundary machine learning model is implemented facilitates a more efficient use of computing resources. For example, some conventional machine learning models, such as neural networks and other deep learning models require implementation on GPU hardware, for example, to facilitate parallel processing operations. In contrast, the subsurface boundary machine learning model may be implemented entirely on CPU hardware, for example, based on the simplicity of the tree-based architecture and input data allowing for implementation through serial computations. Thus, the boundary detection system, and more specifically the subsurface boundary machine learning model, may realistically be implemented on any computing device without requiring robust, specialized, or excess computing and hardware components, allowing for ease and flexibility of deployment of the boundary detection techniques described herein.

Further, the pixel-wise manner in which the boundary detection system processes inversion images facilitates identifying any number of subsurface features in inversion images, including any number of boundaries for each subsurface feature. For example, because the boundary detection system is trained to determine on a pixel-by-pixel basis whether each pixel corresponds to a subsurface feature (e.g., reservoir) or whether it does not, the boundary detection system may generate a boundary mask that may indicate any and every subsurface feature, including multiple features, that may be depicted in an underlying inversion image. In contrast, some conventional methods may be limited to only detecting a singular reservoir, and/or only detecting a singular (e.g., top or bottom) boundary of a reservoir at a time.

The pixel-wise implementation of the boundary detection system may additionally facilitate training the boundary detection system accurately and efficiently based on a very limited set of inversion training images. For example, image-processing techniques that evaluate images as a whole (or large parts of an image) often require hundreds, thousands, or more images in order to generate an accurately trained model. Because the boundary detection system processes individual pixels of inversion images as independent inputs or samples, each inversion image contains thousands of unique training samples for tuning the parameters of the decision-tree architecture. Thus, the subsurface boundary machine learning model may be trained to be highly accurate based on as few as 20 inversion images, whereas some conventional models may require at least 300 or more inversion images for less accurate results.

As illustrated in the following discussion, this disclosure uses a variety of terms to describe the features and advantages of one or more implementations described in this disclosure. Additional details are provided to clarify the meaning of some of these terms, while details regarding other terms may be provided later in the document.

As used herein, “wellbore measurement data,” “wellbore data,” and “measurement data” refer to data which each describe an aspect, value, rate, property, state, etc. of some feature detected, observed, or otherwise measured with respect to a downhole operation. For example, the wellbore data may include formation evaluation data such as resistivity data, porosity data, gamma-ray data, density data, acoustic data, seismic data, electromagnetic data, etc. The wellbore data may include drilling parameter data such as flow rate, temperature, pressure, speed, torque (TOR), rate of penetration (ROP), and weight on bit (WOB). The wellbore measurement data may include measurements of formation evaluation, wellbore stability, mud properties, survey data, and equipment health and status. Indeed, the measurement data may include any measurement, metric, or value relevant to a downhole operation, and combinations thereof. The wellbore measurement data may include measurements taken from various downhole and/or surface sensors and/or measurements received from one or more computing devices.

As used herein, a “log” such as a data log, wellbore data log, or downhole data log may refer to data contained or documented within an operation report or log for a downhole operation. For example, the wellbore data logs may document the various measurements taken during or in pursuit of one or more downhole operations. The logs may document relevant times and/or depths of the measurements. In some cases, the downhole logs may be generated or aggregated manually, such as by a drilling engineer compiling various measurement data for a downhole operation. The logs may be generated while downhole (e.g., drilling) activities are being conducted, or may be generated after the completion of one or more activities such as part of an upload or transmission of various data for one or more downhole activities.

As used herein, a “feature” such as a geological feature, downhole feature, or a subsurface feature may be any element of a geological formation. A geological feature may include a reservoir, pay zone, subterranean target, or any other underground feature for which it may be desirable to know its location, orientation, position, etc. For instance, a geological feature may include a geological structure, such as a formation. A feature may include the entire geological structure. A downhole feature may include a volume of space, including one or more structures, rock types, material types, and so forth. In some embodiments, a feature may include a boundary between two geological structures, such as a boundary between strata. In some embodiments, a feature may include a boundary between rock types. In some embodiments, a downhole feature may include a specific structure of a set of structures, such as a fluid reservoir. A feature may be three-dimensional. For example, a feature may include a three-dimensional surface having variations in latitude, longitude, and depth. In some embodiments, a feature may be a reservoir, pay zone, or underground resource, such as an oil, gas, or water reservoir, a source of geothermal energy, or any other subterranean target.

As used herein, “machine-learning model” refers to a computer model or computer representation that may be trained (e.g., optimized) based on inputs to approximate unknown functions. For instance, a machine-learning model may include, but is not limited to, a neural network (e.g., a convolutional neural network (CNN), LSTM, graph neural network, or deep learning model), a decision tree (e.g., a gradient-boosted decision tree), a linear regression model, a logistic regression model, Dirichlet allocation (LDA) model, multi-arm bandit model, random forest model, support vector machine (SVM) model, or a combination of these models.

As used herein, “resistivity image” refers to an image that includes resistivity measurements of subsurface geological features. A resistivity image may include a geosphere inversion image and/or electromagnetic (EM) field measurements and/or mappings of a surrounding area of a wellbore measured using a downhole resistivity sensor. In some instances, the measurements are in a specific direction, typically along the length of the borehole or drill hole, which is done to gather information about subsurface structures and geological formations.

As used herein, “inversion image,” “inversion result,” and the like refers to an image representation of subsurface formations encountered by or from a wellbore. Inversion images are generated by processing raw measurement data collected from downhole tools such as logging-while-drilling tools or wireline tools. Generating inversion images involves the mathematical transformation of the measured data to an image of the subsurface formation indicating one or more details, aspects, or other qualities of the formation as sensed or observed by the underlying measurements. For example, inversion includes forward modeling to simulate the response of underlying measurement data and then solving an inversion problem to adjust model parameters by inferring the subsurface properties from the measurement data. Once the model converges, the model parameters are used to generate an inversion image of the subsurface formations. For example, inversion images may depict information about lithology, fluid content, structural features, reservoir characteristics, or other subsurface features.

In some cases, inversion images may be described as being generated from resistivity data. In some instance, inversion images may be generated from any downhole measurement data such as, for example, porosity data, density data, acoustic data, seismic data, and the like. Inversion images are made up of an array of pixels, with each pixel having a value (e.g., color) corresponding to a specific value or magnitude of an aspect of the underlying measurement data. For example, an inversion image may include an array of 128×128 pixels. In some embodiments inversion images have a square resolution, or may have any other resolution.

As used herein, a “mask,” “boundary mask” and the like refers to a binary image corresponding to an identified feature of an associated inversion image. For example, a mask may be a binary labelling of a reservoir and/or boundary depicted in an inversion image. For instance, a given feature of interest may be demarked within an inversion image by setting pixels corresponding to the feature to a certain value (e.g., white or 1) while other pixels are set to another value (e.g., black or 0). The mask may be applied to (e.g., overlaid on) the inversion image, or referenced separately, to provide visual cues about a feature, for example by isolating the feature within the inversion image. In this way, a mask highlights the spatial locations of a given feature within an inversion image.

As used herein, “resistivity data,” “resistivity measurements,” and the like refer to electromagnetic (EM) field measurements and/or mappings of a surrounding area of a wellbore measured using a downhole resistivity sensor. In some instances, the measurements are in a specific direction, such as transverse or perpendicular to the length of the wellbore, which is done to gather information about the surrounding subsurface structures and geological formations.

Additional details will now be provided regarding systems described herein in relation to illustrative figures portraying example implementations. For example,shows one example of a downhole systemfor drilling an earth formationto form a wellbore. The downhole systemincludes a drill rigused to turn a drilling tool assemblywhich extends downward into the wellbore. The drilling tool assemblymay include a drill string, a bottomhole assembly (“BHA”), and a bit, attached to the downhole end of the drill string.

The drill stringmay include several joints of drill pipeconnected end-to-end through tool joints. The drill stringtransmits drilling fluid through a central bore and transmits rotational power from the drill rigto the BHA. In some embodiments, the drill stringfurther includes additional downhole drilling tools and/or components. The drill pipeprovides a hydraulic passage through which drilling fluid is pumped from the surface to the bit.

The BHAmay include other downhole drilling tools and components. Examples of additional BHA components include measurement-while-drilling (“MWD”) tools, logging-while-drilling (“LWD”) tools, and measurement sensors.

To elaborate, while performing downhole (e.g., drilling) activities, wellbore measurement data may be taken, measured, or observed through a variety of (e.g., downhole and/or surface) sensors. In this way, various information may be collected related to the wellbore and/or downhole activity in order to facilitate the techniques described herein. Additionally, in some cases, reports or logs may be generated for documenting various downhole activities or operations.

The BHAmay further include a rotary steerable system (RSS). The RSS may include directional drilling tools that change a direction of the bit, and thereby the trajectory of the wellbore. At least a portion of the RSS may maintain a geostationary position relative to an absolute reference frame, such as one or more of gravity, magnetic north, or true north. Using measurements obtained with the geostationary position, the RSS may locate the bit, change the course of the bit, and direct the directional drilling tools on a projected trajectory. The RSS may steer the bitin accordance with or based on a trajectory for the bit. For example, a trajectory may be determined for directing the bittoward one or more subterranean targets such as an oil or gas reservoir.

The downhole systemmay include or may be associated with a client devicewith a boundary detection systemimplemented thereon (e.g., or with a client application implemented thereon for accessing the boundary detection systemas described herein). The boundary detection systemmay facilitate identifying subsurface features and/or boundaries of subsurface features, for example, to facilitate directing or steering the bitto access and/or remain within a subsurface feature.

illustrates an example environmentin which a boundary detection systemis implemented in accordance with one or more embodiments describe herein. As shown in, the environmentincludes a server device. The server devicemay include one or more computing devices (e.g., including processing units, data storage, etc.) organized in an architecture with various network interfaces for connecting to and providing data management and distribution across one or more client systems. As shown in, the server devicemay be connected to and may communicate with (either directly or indirectly) a client devicethrough a network. The networkmay include one or multiple networks and may use one or more communication platforms and/or technologies suitable for transmitting data. The networkmay refer to any data link that enables transport of electronic data between devices of the environment. The networkmay refer to a hardwired network, a wireless network, or a combination of a hardwired network and a wireless network. In one or more embodiments, the networkincludes the internet. The networkmay be configured to facilitate communication between the various computing devices via well-site information transfer standard markup language (WITSML) or similar protocol, or any other protocol or form of communication.

The client devicemay be representative of one or multiple client devices, and may refer to various types of computing devices. For example, the client devicemay include a mobile device such as a mobile telephone, a smartphone, a personal digital assistant (PDA), a tablet, a laptop, or any other portable device. Additionally, or alternatively, the client devicemay include one or more non-mobile devices such as a desktop computer, server device, surface or downhole processor or computer (e.g., associated with a sensor, system, or function of the downhole system), or other non-portable device. In one or more implementations, the client deviceincludes graphical user interfaces (GUI) thereon (e.g., a screen of a mobile device). In addition, or as an alternative, one or more of the client devicemay be communicatively coupled (e.g., wired or wirelessly) to a display device having a graphical user interface thereon for providing a display of system content. The server devicemay similarly refer to various types of computing devices. Each of the devices of the environmentmay include features and/or functionalities described below in connection with.

As shown in, the environmentmay include a boundary detection systemimplemented on the server device. While shown on the server device, the boundary detection systemmay be implemented wholly or in part on the client device, across the server deviceand the client device, or on or across one or more additional devices, such that different portions or components of the boundary detection systemare implemented on different computing devices in the environment.

The client devicemay include a client application. The client applicationmay include an application or interface for interacting with and/or receiving the features of the boundary detection systemas described herein. In some embodiments, one or more of the functionalities or features of the boundary detection systemmay be carried out or performed on or by the client application. In this way, the environmentmay be a cloud computing environment, and the boundary detection systemmay be implemented across one or more devices of the cloud computing environment in order to leverage the processing capabilities, memory capabilities, connectivity, speed, etc., of such cloud computing environments in order to facilitate the features and functionalities described herein.

illustrates an example implementation of the boundary detection systemas described herein, according to at least one embodiment of the present disclosure. The boundary detection systemmay include a downhole data manager, a machine learning manager, and a boundary mask manager. The boundary detection systemmay also include a data storagehaving inversion images, a subsurface boundary machine learning model, and boundary masksstored thereon. While one or more embodiments described herein describe features and functionalities performed by specific components-of the boundary detection system, it will be appreciated that specific features described in connection with one component of the boundary detection systemmay, in some examples, be performed by one or more of the other components of the boundary detection system.

By way of example, one or more of the data receiving, gathering, or storing features of the downhole data managermay be delegated to other components of the boundary detection system. As another example, while boundary masks may be generated by the boundary mask manager, in some instances, some or all of these features may be performed by the machine learning manager(or other component of the boundary detection system). Indeed, it will be appreciated that some or all of the specific components may be combined into other components and specific functions may be performed by one or across multiple components-of the boundary detection system.

Additionally, while, for example, depicts the boundary detection systemimplemented on a client deviceof the downhole system, it should be understood that some or all of the features and functionalities of the boundary detection systemmay be implemented on or across multiple client devicesand/or server devices. For example, data may be input and/or received by the downhole data manageron a (e.g., local) client device, and the subsurface features and/or boundaries may be identified as described herein on one or more of a remote, server, or cloud device. Indeed, it will be appreciated that some or all of the specific components-may be implemented on or across multiple client devicesand/or server devices, including individual functions of a specific component being performed across multiple devices.

The boundary detection systemmay be implemented to identify subsurface features in inversion images, which may include identifying one or more boundaries of the subsurface feature. Identifying subsurface features in this way may inform the directing or steering of the wellbore in order to access the subsurface feature. For example, one or more downhole drilling parameters or steering parameters may be adjusted based on subsurface features identified by the boundary detection system. In some embodiments, the boundary detection systemmay adjust one or more downhole drilling parameters automatically and without user input. For example, based on the subsurface features and/or boundaries of the subsurface features identified in the inversion images and/or based on the boundary masks, the boundary detection systemmay identify one or more steering parameters for maintaining a trajectory of a downhole tool within the boundaries of the subsurface feature, and may automatically send, transmit, or apply the steering parameters to the downhole tool in order to steer the downhole tool, without use input or intervention.

In some embodiments, the downhole data managermay receive, generate, and access a variety of types of data. For example, the downhole data managermay receive formation evaluation data, such as resistivity data, and may generate inversion images from the formation evaluation data. The downhole data managermay also receive a three-dimensional model of the formation which may depict subsurface features with increased accuracy, precision, resolution, etc., over that of the inversion images. The downhole data managermay generate simulated, two-dimensional inversion images from the three-dimensional model that correspond to the inversion images, such as by slicing a cross-section of the three-dimensional model. Based on the simulated inversion images, the downhole data managermay generate simulated boundary masks that indicate subsurface features in the simulated inversion images and the corresponding inversion images. In some embodiments, the downhole data managermay identify various information associated with the inversion images and, along with the underlying measurement data, may generate a table or pixel data set for the inversion images.

In some embodiments, the machine learning managermay utilize the simulated boundary masks and the associated pixel data sets as training data for training a subsurface boundary machine learning modelto predict boundary information based on the inversion images. Once trained, the subsurface boundary machine learning modelmay be implemented to generate accurate boundary information for identifying subsurface features in target inversion images. For example, the boundary mask managermay generate boundary masks from the boundary information for identifying the position and locations of subsurface features. In some embodiments, the boundary mask managermay generate a three-dimensional model from several consecutive inversion images and associated boundary masks. In this way, the boundary detection systemmay facilitate characterizing subsurface formations and identifying features of interest in order that the wellbore may be directed to access these features. For example, the boundary detection systemmay be implemented in real time or near real time to receive inversion images, train and implement the subsurface boundary machine learning model, and produce boundary masks for the inversion images.

Turning now to, this figure illustrates an example workflowof generating inversion images from downhole measurement data, according to at least one embodiment of the present disclosure. The workflowmay be performed by the boundary detection system. For example, the boundary detection systemmay implement the downhole data manager(and/or other components) to perform some or all of the workflow.

As described herein, a drilling tool assembly may be implemented in a wellbore to form the wellbore along a trajectoryin order to access an underground target. In some embodiments, the drilling tool assembly includes a measurement tool, such as an LWD tool for taking formation evaluation measurements. For instance, the measurement toolmay measure resistivity data, neutron density data, gamma-ray data, acoustic data, and the like. The formation evaluation measurements may include measurements of resistivity, density, porosity, or any other property or aspect of the formation. The resistivity measurements may be recorded in a downhole data log. The downhole data logmay record any number and any type of downhole measurement data, such as any formation evaluation measurements, drilling parameters, or any other measurements.

In some embodiments, the formation evaluation measurements and/or the downhole data log may include measurements from sensors and/or sources other than those included in the measurement tool, such as measurements from surface sensors, other downhole tools, or other devices. For example, some or all of the downhole data logmay be accessed from libraries, databases, user input, other devices, etc.

In some embodiments, inversion imagesare generated from the downhole data log. For example, resistivity data of the downhole data logmay be inverted to generate the inversion images. The inversion imagesmay be generated to correspond with periodic positions of the trajectory. For example, the inversion imagesmay be generated at 10-30 ft intervals (or any other interval) along the trajectory. In some embodiments, the inversion imagesare generated at intervals of 1 ft, 2 ft, 3 ft, 4 ft, 5 ft, 10 ft, 15 ft, 20 ft, 30 ft, 50 ft, or any other interval and combinations thereof. In some embodiments, the inversion imagesmay be generated at various different intervals. For example, a longer interval (e.g., 10-30 ft) may be used for performing the boundary detection techniques described herein in real time, while a shorter interval (e.g., 1-5 ft) may be utilized for post-operation boundary detection from the inversion images.

In some embodiments, the inversion imagesmay be two-dimensional images that are transvers to the wellbore and the trajectory. For example, the inversion images may capture and/or characterize the formation surrounding the wellbore at a given position of the trajectory. In some embodiments, the inversion images may be one-dimensional, such as a ribbon indicating formation properties in only 1-dimension. In some embodiments, the inversion images may be three-dimensional, representing formation properties in all dimensions. The dimensionality of the inversion imagesmay be determined based on one or more properties of a subsurface feature of interest. For example, one-dimensional inversion images may be implemented based on a subsurface features that are known to change or fluctuate in only one dimension, two-dimensional inversion images may be implemented based on subsurface features that are known to change in two dimensions, etc. In this way, inversion images of different dimensionalities may be implemented to facilitate flexibility with different situations. Indeed, while the boundary detection techniques may be described herein with respect to two-dimensional, transvers inversion images, it should be understood that the boundary detection system may be implemented with respect to inversion images of any dimensions, such as one-dimensional and/or three-dimensional inversion images.

In some embodiments, the boundary detection systemgenerates the inversion images. For example, the downhole data managermay receive the downhole data logand may invert some or all of the information included therein to generate the inversion images. In some embodiments, the boundary detection systemreceives or accesses the inversion images. For example, another system may generate the inversion imagesand may provide the inversion imagesto the boundary detection system.

In some embodiments, the inversion images may depict or indicate at least a portion of a subsurface feature and/or one or more boundaries of a subsurface feature. In some embodiments, it may be difficult to identify the subsurface feature from the inversion images and/or it may be desirable to automatically identify boundaries of the subsurface feature in the inversion images through computer implemented techniques and without user input.

illustrates an example workflowfor generating simulated inversion images and simulated boundary masks corresponding to inversion images, according to at least one embodiment of the present disclosure. The workflowmay be performed by the boundary detection system. For example, the boundary detection systemmay implement the downhole data manager(and/or other components) to perform some or all of the workflow.

In some embodiments the boundary detection systemreceives a three-dimensional subsurface model of downhole formations. In some embodiments, the three-dimensional subsurface modelmay include higher quality, higher fidelity, higher resolution, and/or more detailed, complete, or robust information about the formation than, for example, the inversion images. For instance, the three-dimensional subsurface modelmay be generated based on more refined measurement data and/or more sources of measurement data. For instance, the three-dimensional subsurface modelmay be generated from an assembly of several formation evaluation measurements, including seismic data measurements. The three-dimensional subsurface modelmay be generated from measurements taken at the surface, measurements taken by a wireline tool, measurements taken from a nearby or offset wellbore, and/or measurements taken at an earlier stage (e.g., uphole) of the same wellbore.

Based on the three-dimensional subsurface model, one or more subsurface features (and boundaries of the features) may be identifiable, for example, with a higher confidence than based on the inversion images. For instance, one or more automatic and/or computer-implemented techniques may be suited for identifying subsurface features from the three-dimensional model which may otherwise not be possible or practical based on the inversion images. In many cases, however, a detailed, three-dimensional subsurface model may not be generated or otherwise available for a given location of the wellbore and/or trajectory. For example, it may be computationally expensive and/or slow to generate a three-dimensional subsurface model such that these models may only be generated periodically and/or for select portions of the trajectory. In another example, generating a three-dimensional subsurface model may be performed after drilling operations are completed or paused. Thus, it may not be possible to rely on models like the three-dimensional subsurface modelto identify subsurface feature and inform the steering of the wellbore.

In some embodiments, the boundary detection systemmay generate simulated inversion imagesfrom the three-dimensional subsurface model. For instance, as shown in box, based on the inversion images, the boundary detection systemmay identify the locations or portions of the trajectorycorresponding to the inversion images. The boundary detection systemmay accordingly identify the corresponding portions of the three-dimensional subsurface modelthat align with or correspond to the inversion imagesand may generate the simulated inversion images. For example, the boundary detection systemmay segment, slice, section, or otherwise isolate transverse portions (e.g., cross-sections) of the three-dimensional subsurface modelthat correspond to the inversion images. In some embodiments, the simulated inversion imagesmay be an entire cross-section of the three-dimensional subsurface model, or may be a cropped portion of a cross-section of the three-dimensional subsurface model. The simulated inversion imagesmay have the same dimensionality as the inversion images(e.g., one-dimensional, two-dimensional, or three-dimensional). In some embodiments, the simulated inversion imagesmay have the same vertical and/or horizontal dimensions and/or resolution as the inversion images. In some embodiments, the simulated inversion imagesmay have a higher resolution than the inversion images. In this way, the simulated inversion imagesmay capture substantially the same view or window of the formation as the inversion images, but may include more detail, more information, and/or a higher resolution of the formation.

Based on the simulated inversion images, the boundary detection systemmay generate simulated boundary masks. For example, the boundary detection systemmay identify changes or contrasts in the simulated inversion imagesto identify boundaries of a subsurface feature identified in the simulated inversion images. For example, pixels (e.g., colors) of the simulated inversion imagesmay correspond to measured values of one or more underlying measurements of the three-dimensional subsurface modeland/or simulated inversion images. In some embodiments, the simulated boundary masksmay be generated based on a threshold as applied to the pixels of the simulated inversion images. For instance, pixels having a color or value above a given threshold may correspond with a 1 or white value of the simulated boundary masks, and pixels having a color or value below a (same or different) threshold may correspond with a 0 or black value of the simulated boundary masks. In this way, the simulated boundary masksmay be generated based on identifying, pixel by pixel, which pixels correspond to an identified feature and which pixels do not (or correspond with some other feature).

Accordingly, the simulated boundary masksmay be generated to indicate one or more subsurface features depicted in the simulated inversion images, including boundaries of the features. Moreover, the simulated boundary masks, and the subsurface features and/or boundaries identified in the simulated boundary masks, may similarly correspond to the inversion imagesbased on the simulated inversion imagesbeing generated to align and correspond with the inversion images. As described herein, the simulated boundary masksalong with the inversion imagesmay facilitate training data to facilitate training the subsurface boundary machine learning modelas described herein.

illustrates an example workflowfor generating pixel data setsfor the inversion images, according to at least one embodiment of the present disclosure. The workflowmay be performed by the boundary detection system. For example, the boundary detection systemmay implement the downhole data manager(and/or other components) to perform some or all of the workflow.