Hybrid Machine Learning Modeling for Evaluating Hydraulic Fracture Conductivity Using Strain Data from Fiber Optics

This application is the U.S. National Stage Application of the International Application No. PCT/US2023/068185, entitled “HYBRID MACHINE LEARNING MODELING FOR EVALUATING HYDRAULIC FRACTURE CONDUCTIVITY USING STRAIN DATA FROM FIBER OPTICS,” filed on Jun. 9, 2023, the disclosure of which is hereby incorporated by reference in its entirety, which claims the benefit of U.S. Provisional Application No. 63/366,416, filed Jun. 15, 2022, the disclosure of which is herein incorporated by reference in its entirety.

The techniques described herein relate generally to the field of hydrocarbon well completions and hydraulic fracturing operations. More specifically, the techniques described herein relate to hybrid machine learning modeling for evaluating hydraulic fracture conductivity using strain data from fiber optics.

This section is intended to introduce various aspects of the art, which may be associated with embodiments of the present techniques. This discussion is believed to assist in providing a framework to facilitate a better understanding of particular aspects of the present techniques. Accordingly, it should be understood that this section should be read in this light, and not necessarily as admissions of prior art.

During the drilling and completion of a hydrocarbon well, a wellbore is drilled into a subterranean formation to promote the production of hydrocarbon fluids from a corresponding subterranean formation (or reservoir). In many cases, the subterranean formation needs to be stimulated in some manner to promote the production of the hydrocarbon fluids. Stimulation operations include any operation performed upon the matrix of a subterranean formation to improve hydraulic conductivity through such matrix. Hydraulic fracturing, in particular, is a common stimulation operation for unconventional reservoirs.

Hydraulic fracturing operations involve pumping large quantities of a pressurizing fluid stream (often referred to as “fracturing fluid”) into a subterranean formation under high hydraulic pressure to promote the formation of fractures within the matrix of the subterranean formation and to create high-conductivity flow paths. Moreover, as the pressurizing fluid stream is pumped into the formation, primary fractures extending from the wellbore and, in some instances, secondary fractures extending from the primary fractures, are formed. These fractures may be vertical, horizontal, or a combination of directions forming a tortuous path.

Once the pressurizing fluid stream has created the fractures within the subterranean formation, a proppant (e.g., typically consisting primarily of sand and/or ceramic beads) is pumped into the fractures to “prop” the fractures open after the hydraulic pressure has been released following the hydraulic fracturing operation. Specifically, upon reaching the fractures, the proppant settles within the fractures to form a proppant pack that prevents the fractures from closing once the hydraulic pressure has been released. In this manner, the proppant provides a long-term increase in fluid permeability within the near-wellbore region of the formation.

The success of the hydraulic fracturing process has a direct impact on the amount of hydrocarbon fluids that may be recovered from the reservoir. Specifically, the numbers, sizes, compliances, and locations of the fractures corresponding to the perforation clusters within each stage of the hydrocarbon well directly impact the amount of hydrocarbon fluids that are able to mobilize and flow into the wellbore. However, difficulties are often encountered during hydraulic fracturing operations, such as, in particular, difficulties associated with the deposition of proppant in fractures that have been created or extended under hydraulic pressure. In particular, effective transport of the proppant may be difficult due to settling, making it challenging to distribute the proppant into more remote reaches of a network of fractures. Therefore, it is desirable to obtain information regarding the manner in which the proppant transports through the fractures, particularly to far field. Such information can be used for well planning purposes, such as by enabling the estimation of optimal well spacing for the field. However, currently there is very limited understanding regarding how proppant transports through fractures. As a result, there is room for much improvement in this area.

An embodiment described herein provides a method for hydraulic fracturing conductivity evaluation with respect to a producer well. The method is executed via a processor of a computing system. The method includes measuring fracture-related data corresponding to a hydraulic fracturing operation performed with respect to one or more producer wells in a field, where the fracture-related data include strain data measured using one or more optical fibers deployed within one or more offset wells in the field. The method also includes extracting deep convolutional neural network (DCNN)-based features, physics-based features, and statistics-based features using the fracture-related data, as well as training a hybrid machine learning model for evaluating hydraulic fracture conductivity corresponding to the hydraulic fracturing operation, where the training is performed using the DCNN-based features, the physics-based features, and the statistics-based features. The method further includes applying the trained hybrid machine learning model to generate a well spacing plan for the field.

Another embodiment described herein provides a method for developing a field of producer wells. The method includes executing a hydraulic fracturing operation for a producer well in a field and generating fracture-related data corresponding to the hydraulic fracturing operation for the producer well using an offset well, where the fracture-related data include strain data measured using an optical fiber deployed within the offset well. The method also includes extracting DCNN-based features, physics-based features, and statistics-based features using the fracture-related data and training a hybrid machine learning model for evaluating hydraulic fracture conductivity corresponding to the hydraulic fracturing operation, where the training is performed using the DCNN-based features, the physics-based features, and the statistics-based features. The method further includes applying the trained hybrid machine learning model to generate a well spacing plan for the field and developing the field according to the generated well spacing plan.

Another embodiment described herein provides a computing system including a processor and a non-transitory, computer-readable storage medium. The non-transitory, computer-readable storage medium includes code configured to direct the processor to access fracture-related data corresponding to a hydraulic fracturing operation performed with respect to one or more producers well in a field, where the fracture-related data include strain data measured using one or more optical fibers deployed within one or more offset wells in the field. The non-transitory, computer-readable storage medium also includes code configured to direct the processor to extract DCNN-based features using the fracture-related data by performing deep-learning-based feature extraction, where performing the deep-learning-based feature extraction includes generating two-dimensional (2D) strain maps from the strain data, converting each 2D strain map to a strain rate map, averaging each strain rate map to a time series of averaged strain rate, calculating a Gramian Angular Field (GAF) map for each time series, loading each GAF map into a pretrained DCNN, removing top layers of the DCNN to generate an array of features, mapping the array of features to two or more principal components, and outputting the principal components as at least a portion of the DCNN-based features. The non-transitory, computer-readable storage medium also includes code configured to direct the processor to extract physics-based features using the fracture-related data by calculating at least a portion of the physics-based features from the 2D strain maps, as well as to extract statistics-based features using the fracture-related data. The non-transitory, computer-readable storage medium further includes code configured to direct the processor to train a hybrid machine learning model for evaluating hydraulic fracture conductivity corresponding to the hydraulic fracturing operation, where the training is performed using the pretrained DCNN, in combination with the DCNN-based features, the physics-based features, and the statistics-based features, as well as to apply the trained hybrid machine learning model to generate a well spacing plan for the field.

These and other features and attributes of the disclosed embodiments of the present techniques and their advantageous applications and/or uses will be apparent from the detailed description that follows.

It should be noted that the figures are merely examples of the present techniques and are not intended to impose limitations on the scope of the present techniques. Further, the figures are generally not drawn to scale, but are drafted for purposes of convenience and clarity in illustrating various aspects of the techniques.

In the following detailed description section, the specific examples of the present techniques are described in connection with preferred embodiments. However, to the extent that the following description is specific to a particular embodiment or a particular use of the present techniques, this is intended to be for exemplary purposes only and simply provides a description of the embodiments. Accordingly, the techniques are not limited to the specific embodiments described below, but rather, include all alternatives, modifications, and equivalents falling within the true spirit and scope of the appended claims.

At the outset, and for case of reference, certain terms used in this application and their meanings as used in this context are set forth. To the extent a term used herein is not defined below, it should be given the broadest definition those skilled in the art have given that term as reflected in at least one printed publication or issued patent. Further, the present techniques are not limited by the usage of the terms shown below, as all equivalents, synonyms, new developments, and terms or techniques that serve the same or a similar purpose are considered to be within the scope of the present claims.

As used herein, the singular forms “a,” “an,” and “the” mean one or more when applied to any embodiment described herein. The use of “a,” “an,” and/or “the” does not limit the meaning to a single feature unless such a limit is specifically stated.

The term “and/or” placed between a first entity and a second entity means one of (1) the first entity, (2) the second entity, and (3) the first entity and the second entity. Multiple entities listed with “and/or” should be construed in the same manner, i.e., “one or more” of the entities so conjoined. Other entities may optionally be present other than the entities specifically identified by the “and/or” clause, whether related or unrelated to those entities specifically identified. Thus, as a non-limiting example, a reference to “A and/or B,” when used in conjunction with open-ended language such as “including,” may refer, in one embodiment, to A only (optionally including entities other than B); in another embodiment, to B only (optionally including entities other than A); in yet another embodiment, to both A and B (optionally including other entities). These entities may refer to elements, actions, structures, steps, operations, values, and the like.

As used herein, the term “any” means one, some, or all of a specified entity or group of entities, indiscriminately of the quantity.

The phrase “at least one,” when used in reference to a list of one or more entities (or elements), should be understood to mean at least one entity selected from any one or more of the entities in the list of entities, but not necessarily including at least one of each and every entity specifically listed within the list of entities, and not excluding any combinations of entities in the list of entities. This definition also allows that entities may optionally be present other than the entities specifically identified within the list of entities to which the phrase “at least one” refers, whether related or unrelated to those entities specifically identified. Thus, as a non-limiting example, “at least one of A and B” (or, equivalently, “at least one of A or B,” or, equivalently, “at least one of A and/or B”) may refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including entities other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including entities other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other entities). In other words, the phrases “at least one,” “one or more,” and “and/or” are open-ended expressions that are both conjunctive and disjunctive in operation. For example, each of the expressions “at least one of A, B, and C,” “at least one of A, B, or C,” “one or more of A, B, and C,” “one or more of A, B, or C,” and “A, B, and/or C” may mean A alone, B alone, C alone, A and B together, A and C together, B and C together, A, B, and C together, and optionally any of the above in combination with at least one other entity.

As used herein, the phrase “based on” does not mean “based only on,” unless expressly specified otherwise. In other words, the phrase “based on” means “based only on,” “based at least on,” and/or “based at least in part on.”

As used herein, the terms “example,” exemplary,” and “embodiment,” when used with reference to one or more components, features, structures, or methods according to the present techniques, are intended to convey that the described component, feature, structure, or method is an illustrative, non-exclusive example of components, features, structures, or methods according to the present techniques. Thus, the described component, feature, structure, or method is not intended to be limiting, required, or exclusive/exhaustive; and other components, features, structures, or methods, including structurally and/or functionally similar and/or equivalent components, features, structures, or methods, are also within the scope of the present techniques.

As used herein, the term “field” (sometimes referred to as an “oil and gas field” or a “hydrocarbon field”) refers to an area for which hydrocarbon production operations are to be performed to provide for the extraction of hydrocarbon fluids from one or more corresponding subterranean formation.

The term “fracture” refers to a crack or surface of breakage induced by an applied pressure or stress within a subterranean formation.

As used herein, the term “fracture conductivity” or “hydraulic fracture conductivity” refers to the ability of a fluid to flow through a fracture at various stress (or pressure) levels, which is based, at least in part, on the permeability and thickness of the fracture.

The term “hydraulic fracturing” refers to a process for creating fractures that extend from a wellbore into a reservoir, so as to stimulate the flow of hydrocarbon fluids from the reservoir into the wellbore. A fracturing fluid is generally injected into the reservoir with sufficient pressure to create and extend multiple fractures within the reservoir, and a proppant material is used to “prop” or hold open the fractures after the hydraulic pressure used to generate the fractures has been released.

As used herein, the term “proppant” refers to particles that are mixed with fracturing fluid to hold open fractures that are formed within a near-wellbore region of a reservoir using a hydraulic fracturing process. The size, shape, strength, and density of the proppant material have a significant impact on the hydraulic fracturing process. Currently, commercial proppant materials include natural proppants, such as natural sands, resin-coated natural sands, shell fragments, and the like, and artificial proppants, such as sintered bauxite and ceramics, resin-coated or metal-coated ceramics, carbon-based proppants, lightweight proppants, ultra-lightweight proppants, and the like.

The term “substantially,” when used in reference to a quantity or amount of a material, or a specific characteristic thereof, refers to an amount that is sufficient to provide an effect that the material or characteristic was intended to provide. The exact degree of deviation allowable may depend, in some cases, on the specific context.

As used herein, the term “surface” refers to the uppermost land surface of a land well, or the mud line of an offshore well, while the term “subsurface” (or “subterranean”) generally refers to a geologic strata occurring below the earth's surface. Moreover, as used herein, “surface” and “subsurface” are relative terms. The fact that a particular piece of equipment is described as being on the surface does not necessarily mean it must be physically above the surface of the earth but, rather, describes only the relative placement of the surface and subsurface pieces of equipment. In that sense, the term “surface” may generally refer to any equipment that is located above the casing strings and other equipment that is located inside the wellbore. Moreover, according to embodiments described herein, the terms “downhole” and “subsurface” are sometimes used interchangeably, although the term “downhole” is generally used to refer specifically to the inside of the wellbore.

The term “wellbore” refers to a borehole drilled into a subterranean formation. The borehole may include vertical, deviated, highly deviated, and/or horizontal sections. The term “wellbore” also includes the downhole equipment associated with the borehole, such as the casing strings, production tubing, gas lift valves, and other subsurface equipment. Relatedly, the term “hydrocarbon well” (or simply “well”) includes the wellbore in addition to the wellhead and other associated surface equipment.

Turning now to details of the present techniques, as described above, information regarding the manner in which proppant transports through fractures, particularly to far field, can be used for well planning purposes, such as by enabling the estimation of optimal well spacing and, potentially, reducing the total number of wells drilled for the field. This, in turn, increases the efficiency of the overall hydrocarbon production operation. However, according to current techniques, there is very limited understanding regarding how proppant transports through fractures. Accordingly, the present techniques alleviate this difficulty and provide related advantages as well. In particular, the present techniques provide hybrid machine learning modeling methods and systems for evaluating and comparing hydraulic fracture conductivity with respect to one or more producer wells using fiber optic strain data collected from one or more offset wells. More specifically, the present techniques combine features from a deep convolutional neural network (DCNN), physics-based features (including features obtained from the strain data, as described further herein), and statistics-based features. The combination of such features is then used to produce a hybrid machine learning model that can be used to evaluate and compare hydraulic fracture conductivity, as well as to predict the arrival of proppant at the offset well(s) in real-time.

According to the present techniques, one or more optical fibers are deployed within one or more offset wells (e.g., one or more offset producer wells and/or one or more dedicated monitor wells) to collect physical measurements of optical phase shift caused by hydraulic fracturing operations in one or more producer wells. The optical phase shift measurements are then converted to dynamic strain measurements, and a two-dimensional (2D) strain map is generated. A deep-learning-based feature extraction technique is then used to identify key features from the 2D strain map, including features relating to strain evolution with time at every point along the fiber optic cable(s). In addition, several additional physics-based features (including, for example, strain rate frequency and average strain magnitude) are calculated from the 2D strain map. These features are then used, in combination with DCNN-based features and statistics-based features, to generate a hybrid machine learning model that can be used to evaluate and compares fracture conductivities, as well as to predict the arrival of proppant at the offset well(s) in real-time. A metric that combines the number of observed conductive fractures may then be used to determine an optimal well spacing plan for the field, and producer wells may then be drilled according to the well spacing plan.

Turning now to a detailed description of the figures,provide examples of wells that may be utilized to perform the techniques described herein. Within such figures, elements that serve a similar (or at least substantially similar) purpose may be labeled with like numbers. Moreover, in general, elements that are likely to be included in a particular embodiment are illustrated in solid lines, while elements that may be optional (depending, in part, on the particular type of well and/or the details of the particular embodiment) are illustrated in dashed lines. However, elements that are shown in solid lines may not be essential to all embodiments and, in some embodiments, may be omitted without departing from the scope of the present techniques. Generally speaking, those skilled in the art will appreciate that the schematic views ofare not intended to indicate that the well(s) described herein are to include all of the components shown in the figures in every embodiment, or that the well(s) are limited to only such components. Rather, any number of components may be added to, or omitted from, the well(s) without departing from the scope of the present techniques.

is a schematic view of an exemplary wellthat may be utilized in accordance with the present techniques, whileis a simplified schematic view of multiple wellsthat may be utilized in accordance with the present techniques, including a producer wellA and an offset wellB. More specifically,is a more detailed illustration of examples of components/structures that may be included in wells according to the present techniques. In some embodiments, the wellofis a producer well for which the present techniques are performed to make determinations regarding fracture conductivity within the hydraulic fractures corresponding to the well. In other embodiments, the wellofis an offset well that is used to collect data relating to physics-based features for the present techniques. In such embodiments, the offset well may be either a separate producer well, a dedicated monitor well, or any other suitable type of well that is offset from the producer well, depending on the details of the particular implementation. Moreover,is an illustration of an exemplary orientation and/or configuration of two wells, including the producer well(s) and the offset well(s) that are utilized according to embodiments described herein. With this in mind, any of the structures, components, functions, and/or features that are illustrated herein with reference to the wellofmay be included within either the producer wellA or the offset wellB (or, in some cases, both wellsA andB) of, depending on the details of the particular implementation.

As shown in, the wellincludes a wellborethat extends between a surface regionand a subsurface region. In various embodiments, the subsurface regionincludes a subterranean formation (or reservoir) from which hydrocarbon fluids are to be extracted and produced using the well(and/or one or more other wells within the field).

In various embodiments, the wellincludes a number of structures/components that enable the collection of data relating to physics-based features of the well(and/or one or more other wells within the field). In various embodiments, such structures/components include one or more optical fibers, which extend and/or are positioned within the wellbore. Moreover, in some embodiments, such structures/components also include a spectral gamma logging tooland/or a series of distributed pressure gauges, both of which extend and/or are positioned within the wellbore.

In various embodiments, the wellincludes a computing systemthat is configured to direct and control the execution of at least a portion of the present techniques. More specifically, the computing systemis configured to execute (either alone or with the aid of one or more remote computing systems, including, for example, one or more computing systems corresponding to one or more other wells within the field) a hybrid machine learning modeling process for hydraulic fracture conductivity evaluation. To that end, the computing systemmay include any suitable component(s), structure(s), and/or device(s) that are adapted, configured, designed, constructed, and/or programmed to perform the techniques described herein. As examples, the computing systemmay include an electronic controller, dedicated controller, special-purpose controller, personal computer, special-purpose computer, or the like. In various embodiments, the computing systemincludes one or more processors and one or more non-transitory, computer-readable storage mediathat include, define, and/or store computer-executable instructions, programs, and/or code, where such computer-executable instructions direct the processor(s) to perform any suitable portion, or subset, of the present techniques, as described further herein. Moreover, the computing systemmay also include any number of additional components, including (but not limited to) one or more display devices, one or more memory devices, one or more communication connection devices, and the like.

Additionally or alternatively, the computing systemmay include one or more separate computing systems, optionally corresponding to multiple wells in the field. In such embodiments, the computing systemmay be communicably coupled to such remote computing system(s), with at least a portion of the computer-executable instructions corresponding to the present techniques being stored and/or executed by the remote computing system(s). In some embodiments, a single computing system (e.g., either the computing systemor another remote computing system) acts as the main computing system for executing the present techniques, with other computing system(s) cooperating to provide data and/or execute various functions throughout the process.

In various embodiments, the computing systemincludes (and/or is communicably coupled to) an optical fiber controllerthat permits and/or facilitates the initiation, regulation, and/or control of the measurement of strain data with the optical fiber(s). In such embodiments, the optical fiber controllermay include an optical signal generator, optical signal receiver, and/or optical signal analyzer. In some embodiments, the optical fiber controllerand the optical fiber(s)together may be referred to as a “distributed acoustic sensing (DAS) system.”

The optical signal generatormay be configured to generate optical signals and/or to provide the optical signals to initiation location(s), such as uphole end(s), of the optical fiber(s). The optical signals may then be conveyed away from the initiation location(s), in a downhole direction, and/or along the length(s) of the optical fiber(s)and may be scattered at a number of distributed sensing locationsthat are spaced apart along the length(s) of the optical fiber(s). Respective scattered fractions of the optical signals, which are scattered at each distributed sensing location, may then be conveyed along the length(s) of the optical fiber(s), in an uphole direction, and/or toward the initiation location(s)and may be detected, with the optical signal receiver, at detection location(s)of the optical fiber(s). The optical signal receivermay then convey data regarding the respective scattered fractions of the optical signals to the optical signal analyzer, which may analyze and/or quantify the respective scattered fractions of the optical signals.

The above-described process may be repeated a number of times, or even continuously, as hydraulic fractures, such as exemplary hydraulic fracture, are propped with proppantduring the hydraulic fracturing operation. For example, in various embodiments, pressure signals induced in a producer well, such as via the pumping down of plugs at a slow rate, introduces pressure signals that cause deformation of the optical fiber(s), which may cause strain within the optical fiber(s). This strain within the optical fiber(s)may be measured, detected, and/or quantified via changes in the respective scattered fractions of the optical signals that are scattered at each distributed sensing location, thereby permitting and/or facilitating the generation of data regarding strain in the optical fiber(s), both as a function of position along the length(s) of the optical fiber(s)and as a function of time during the progression of the hydraulic fracturing operation. In this manner, the optical fiber(s), in combination with the optical fiber controller, provide for the collection of strain data that are used to determine physics-based features to be used as input for the hybrid machine learning model training process described herein.

In some embodiments, the computing systemalso includes (and/or is communicably coupled to) a spectral gamma ray logging tool controllerthat is configured to direct, facilitate, and/or control the operation of the spectral gamma ray logging toolthat is deployed within the wellbore. In such embodiments, hydraulic fractures, such as the exemplary hydraulic fracture, include radioactive proppant tracersthat are deployed along with the proppantand thereby become scattered throughout the respective fractures. Moreover, the spectral gamma ray logging tool, in combination with (or under the direction of) the spectral gamma ray logging tool controller, may be configured to detect the presence of such radioactive proppant tracersby detecting gamma radiation emitted by such radioactive proppant tracersand then converting the corresponding gamma rays to electronic pulses that can be measured, counted, and/or analyzed to determine the locations of the radioactive proppant tracerswithin the fractures, including the hydraulic fracture. In this manner, the spectral gamma ray logging tool, in combination with the spectral gamma ray logging tool controller, may provide for the collection of radioactive proppant tracer data that may be used to determine physics-based features to be used as additional input for the hybrid machine learning model training process described herein.

In some embodiments, the computing systemalso includes (and/or is communicably coupled to) a distributed pressure gauge controllerthat is configured to direct, facilitate, and/or control the operation of the distributed pressure gaugesthat are deployed within the wellbore. In particular, the distributed pressure gauges, in combination with (or under the direction of) the distributed pressure gauge controller, may be configured to measures depletion as a function of distance from a producer well, where the amount of depletion in a particular region may indicate the extent to which hydraulic fractures in such region are propped with proppant. In this manner, distributed pressure gauges, in combination with the distributed pressure gauge controller, may provide for the collection of pressure depletion data that may be used to determine physics-based features to be used as additional input for the hybrid machine learning model training process described herein.

The correlation between the data obtained using the optical fiber(s), the spectral gamma ray logging tool, and the distributed pressure gauges, as well as the manner in which such data correspond to the fracture conductivity within the corresponding hydraulic fractures, are shown below in Table 1.

Therefore, as shown in Table 1, a large amount of information regarding hydraulic fracture conductivity for a producer well may be obtained using optical fibers, spectral gamma ray logging tools, and/or distributed pressure gauges deployed within one or more offset wells.

As shown in, the wellmay also include a downhole tubular, such as a casing string. The downhole tubular, when present, may extend within the wellboreand may define, or at least partially bound, a tubular conduit. In such a configuration, the wellboreand the downhole tubulartogether may define, or at least partially bound, an annular space. Also in such a configuration, the optical fiber(s), the spectra gamma ray logging tool, and/or the distributed pressure gaugesmay extend within the tubular conduitand/or within the annular space, as illustrated.

In some embodiments, the optical fiber(s)and/or the distributed pressure gaugesmay be rigidly and/or operatively attached to the wellboreand/or to the downhole tubular. As an example, cementmay be positioned within the annular space, and the optical fiber(s)and/or the distributed pressure gaugesmay extend within the cement. As another example, the optical fiber(s)and/or the distributed pressure gaugesmay be attached or otherwise secured, tethered, or coupled to an internal and/or external surface of the downhole tubularat a number of locations. Moreover, in some embodiments, the optical fiber(s)and/or the distributed pressure gaugesare permanently installed and/or positioned within the wellbore. In other embodiments, the optical fiber(s)and/or the distributed pressure gauges, along with the spectral gamma ray logging tool, may form a portion of one or more downhole assemblies, which may be temporarily and/or selectively positioned within the tubular conduit.

It should be noted that, according to embodiments described herein, the specific structures/components included within the wellmay vary depending on whether the well is a producer well for which the hydraulic fracturing operation is being monitored or an offset well (e.g., another producer well or a dedicated monitor well) that is being used to monitor such hydraulic fracturing operation. This is explained in more detail with respect to. Specifically,shows an exemplary producer wellA and an exemplary offset wellB. According to the embodiment shown in, each wellA andB includes both horizontal and vertical well regions that are substantially parallel to one another. However, those skilled in the art will appreciate that the specific orientations, locations, and/or configurations of the wellsA andB may vary widely, depending on the details of the specific implementation.

As shown in, hydraulic fractures, such as the exemplary hydraulic fracture, extend from the producer wellA, which may be undergoing a hydraulic fracturing operation that is being monitored in real-time according to the present techniques. In such embodiments, the hydraulic fractures, including the exemplary hydraulic fracture, are propped with the proppantand, optionally, may include the radioactive proppant tracersthat are deposited within the fractures along with the proppant.

As indicated in, the offset wellB may include the primary structure/components for generating and collecting data relating to the hydraulic fracturing operation that is being implemented with respect to the producer wellA. In particular, the offset wellB may include the structure/components for generating data relating to the physics-based features that are used for the hybrid machine learning model training process described herein. Such structure/components include the optical fiber(s)for collecting the stain data. In some embodiments, such structure/components also include the spectral gamma ray logging toolfor generating the radioactive proppant tracer data and/or the distributed pressure gaugesfor generating the pressure depletion data, as described with respect to.

In some embodiments, and as illustrated in, at least a portion of the offset wellB may extend within and/or through one or more hydraulic fractures, e.g., the hydraulic fracture, corresponding to the producer wellA. In other embodiments, the offset wellB may be spaced apart and/or distinct from the hydraulic fractures corresponding to the producer wellA. Moreover, those skilled in the art will appreciate that the fracture-related data obtained according to the present techniques may be interpreted differently depending upon the relative orientation and/or distance between the producer wellA and the offset wellB.

is a simplified schematic view of an of an exemplary methodfor developing a field of producer wells to provide for the extraction of hydrocarbon fluids from one or more corresponding subsurface reservoirs in accordance with the present techniques. In various embodiments, the methodis executed using at least one producer well and at least one corresponding offset well within a particular field, in combination with one or more computing systems that are communicably coupled to the wells, as described with respect to. As shown in, the methodmay begin by executing hydraulic fracturing operation(s) for one or more producer wells within the field at block. At block, fracture-related data corresponding to the hydraulic fracturing operation(s) are generated using one or more corresponding offset wells within the field. As described with respect to, such fracture-related data include strain data generated using one or more optical fibers (and corresponding optical fiber controller(s)) deployed within one or more offset wells. In various embodiments, such strain data are generated and collected simultaneously with the performance of particular stages of the hydraulic fracturing operation, in particular, during the pumping down of frac plugs and/or the positioning of perforations guns within the producer well(s). In some embodiments, the fracture-related data generated at blockalso include radioactive proppant tracer data generated using one or more spectral gamma ray logging tools and/or pressure depletion data generated using distributed pressure gauges. Moreover, those skilled in the art will appreciate that other wellbore monitoring tools or components may also be used to generate additional fracture-related data according to embodiments described herein.