Patent Publication Number: US-2023141334-A1

Title: Systems and methods of modeling geological facies for well development

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application claims priority to U.S. patent application Ser. No. 63/276,884 filed on Nov. 8, 2021, which is incorporated by reference in its entirety herein. 
    
    
     FIELD 
     Aspects of the present disclosure relate generally to systems and methods for developing resources from subterranean reservoirs, and more particularly to modeling geological facies with decision tree-based models for well development. 
     BACKGROUND 
     Large scale oil and natural gas reservoirs are generally complex in terms of geology and development. Some reservoirs, such as shales, are highly heterogeneous due to nanoscale pore size and highly variable structures. Characterizing reservoir geology in terms of geological facies, permeability, and natural fractures requires huge amounts of resources and remains a pervasive challenge. Performance and cost of a well is strongly driven by well development techniques, such as approaches in drilling, well placement, and completion over the life cycle of reservoir development. However, technology to reliably characterize and model physical properties of a reservoir to optimize well development is computationally expensive and insufficient. 
     Exacerbating these challenges is the cost-prohibitive nature of core sample extraction. Core samples are only extracted for a limited number of well sites. The vast majority of well sites do not undergo core sampling, leaving core sample data for reservoirs incomplete and fragmented. Moreover, the process of aggregating core samples into a three-dimensional map of the reservoir&#39;s geological structure is time consuming for complex, heterogeneous reservoirs with dozens of intersecting multi-layered geographic facies. Interpretation bias can be introduced by a subject matter expert (SME) during this process. The challenges only increase when modeling multiple wells with interference or communication between fractures. 
     As such, different data analytics techniques are limited in their ability to generate predictions for geological facies. Well location selection can be risky because of uncertain well development costs and production results when portions of a reservoir do not have complete or accurate geological structure data. It is with these observations in mind, among others, that various aspects of the present disclosure were conceived and developed. 
     SUMMARY 
     Implementations described and claimed herein address the foregoing problems by providing systems and methods for generating a geological facies model using one or more decision tree-based models. In some instances, a method for modeling geological facies of a subsurface reservoir comprises: generating a predictive analytical model of the subsurface reservoir by: creating one or more decision tree-based models trained with an input data set including well log data associated with the subsurface reservoir; and assigning geological facies class as a target variable; receiving target well data corresponding to a target well associated with the subsurface reservoir; and generating, using the target well data and the predictive analytical model, a geological facies model for the target well. 
     In some examples, the well log data of the input data set includes core data associated with a plurality of wells at the subsurface reservoir. The method can further comprise labeling, using a subject matter expert (SME), the input data set with a plurality of geological facie class labels. The target well data can lack a core data set associated with the target well. Generating the predictive analytical model can include artificially balancing a plurality of geological facies class labels associated with the input data set to create a balanced input data set. 
     In some instances, the plurality of geological facies class labels includes between two and 20 geological facies class labels. Generating the predictive analytical model can further include providing vertical context data to the one or more decision tree-based models. The one or more decision tree-based models can include a gradient boosted decision tree. The well log data of the input data set can represent between five and 20 wells at the subsurface reservoir. The input data set can include at least one of resistivity data, gamma ray data, neutron porosity data, or bulk density data, sonic log data, dielectric log data, or nuclear magnetic resonance (NMR) logs. Generating the geological facies model for the target well can include numerically mapping the target well data to specific geographic facies represented by the input data set. The method can further comprise selecting, based at least partly on the geological facies model, a section of the subsurface reservoir for resource characterization. Moreover, in some instances, the target well is a candidate well for drilling, and the method further comprises: determining, based at least partly on the geological facies model, an optimal drilling location for the candidate well; and drilling the candidate well at the optimal drilling location. 
     In some examples, one or more tangible non-transitory computer-readable storage media storing computer-executable instructions for performing a computer process on a computing system, the computer process comprising the method(s) or any steps of the method(s) discussed herein. A system can be adapted to carry out the method(s) or any steps of the method(s) discussed herein. The system can comprise a wellbore modeling platform including the predictive analytics model trained with the decision tree-based models, the wellbore modeling platform receiving the target well data and generating the geological facies model for the target well. 
     Other implementations are also described and recited herein. Further, while multiple implementations are disclosed, still other implementations of the presently disclosed technology will become apparent to those skilled in the art from the following detailed description, which shows and describes illustrative implementations of the presently disclosed technology. As will be realized, the presently disclosed technology is capable of modifications in various aspects, all without departing from the spirit and scope of the presently disclosed technology. Accordingly, the drawings and detailed description are to be regarded as illustrative in nature and not limiting. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The foregoing summary, as well as the following detailed description, will be better understood when read in conjunction with the appended drawings. For the purpose of illustration, there is shown in the drawings certain embodiments of the disclosed subject matter. It should be understood, however, that the disclosed subject matter is not limited to the precise embodiments and features shown. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate implementations of systems, methods, and apparatuses consistent with the disclosed subject matter and, together with the description, serve to explain advantages and principles consistent with the disclosed subject matter, in which: 
         FIG.  1    illustrates an example network environment that may implement various systems and methods discussed herein; 
         FIG.  2    is a block diagram illustrating an example data flow for generating a geological facies model using decision tree-based models, which may form at least a portion of any of the systems or methods discussed herein; 
         FIG.  3    illustrates an example system for optimizing a well development action using a predictive analytical model, which may form at least a portion of any of the systems or methods discussed herein; 
         FIG.  4    illustrates an example geological structure modeling tool for geological facie modeling, which may form at least a portion of any of the systems or methods discussed herein; 
         FIG.  5    illustrates example operations for optimizing a well development action by generating the geological facies model, which may form at least a portion of any of the systems or methods discussed herein; 
         FIG.  6    illustrates example operations for generating a geological facies model with a decision tree-based model, which may form at least a portion of any of the systems or methods discussed herein; 
         FIG.  7    illustrates example operations for generating a predictive analytical model to generate a geological facies model, which may form at least a portion of any of the systems or methods discussed herein; and 
         FIG.  8    illustrates an example computing system that may implement any of the systems or methods discussed herein. 
     
    
    
     DETAILED DESCRIPTION 
     The present disclosure involves systems and methods for optimizing well development by generating a geological facies model for a candidate location. The system creates a predictive analytical model using decision tree-based models to classify input data with geological facies class being the target variable for the decision tree-based models. The input data can include well log data and core data. Due to the cost of collecting core samples, the core data may be a smaller (e.g., “minority”) data set relative to the well log data set. 
     Additionally, core data may be lacking for target well data associated with the candidate location. However, the techniques disclosed herein, in some instances, overcome these issues and provide accurate predictions for underlying geological structures at the candidate location. For instance, the system can artificially balance the input data set to generate a balanced data set, boost the decision tree to generate a boosted decision tree, and use vertical context data to further refine the models. Subject matter experts (SME)s can generate geological facies class labels to be associated with the input data, which can improve the accuracy and efficiency of the systems discussed herein. Moreover, geological facie models can be generated to predict geological structures at particular depths regardless of whether core data is available for the candidate location. The techniques can result in geological modeling with higher accuracy than other models (e.g., as measured with a randomly selected test population) and reduced processing requirements and memory requirements. 
     Moreover, because of the lower computational cost for predicting geological facies for candidate locations, a larger candidate area or zone of the subsurface reservoir (or reservoir field) can be assessed by generating multiple, iterative geological facies models for multiple locations in the candidate zone. A candidate location can be selected for well drilling, or a candidate zone for performing other well development actions (e.g., further resource characterization). These selections can be optimized based on the geological facies models. For instance, the geological facies models can indicate whether a predicted geological structure at the candidate location (e.g., based on the geology, petrophysics, rock properties, fluid properties, and/or the like) will output a resource amount above a threshold value. Furthermore, the predictions generated by the system can indicate equipment requirements, and corresponding costs, for well development at the candidate location. Accordingly, the efficiency of well development resource allocation is significantly improved. 
     Overall, the systems and methods improve reservoir development by providing predictions extrapolated beyond data limits for critical development decisions throughout the life of a reservoir field and its wells. Other advantages will become apparent from the present disclosure. 
     To begin a detailed discussion of an example system for modeling geological structures for a target well, reference is made to  FIG.  1   .  FIG.  1    illustrates an example network environment  100  for implementing the various systems and methods, as described herein including a wellbore modeling platform  102 . A network  104  is used by one or more computing or data storage devices for implementing the wellbore modeling platform  102 . The wellbore modeling platform  102  may be a remote service, software as a service (SaaS) and/or cloud service for collecting and aggregating well-related and geological-related data from multiple sources. The wellbore modeling platform  102  can include software modules for analyzing the well-related and geological-related data and presenting the results. For instance, any of the software components (e.g., the decision tree-based models  206 , the predictive analytics model  204 , the geological facies model  202 , etc.) discussed herein can be incorporated into the wellbore modeling platform  102  (e.g., as executable python script) to scale-up the software components and make them accessible to a variety of users in a multiple locations using many different types of computing devices. 
     In some implementations, various components of the wellbore modeling platform  102 , one or more user devices  106 , one or more databases  110 , and/or other network components or computing devices described herein are communicatively connected to the network  104 . Examples of the user devices  106  include a terminal, personal computer, a smart-phone, a tablet, a mobile computer, a workstation, and/or the like. 
     A server  108  may, in some instances, host the system. In one implementation, the server  108  also hosts a website or an application that users may visit to access the network environment  100 , including the wellbore modeling platform  102 . The server  108  may be one single server, a plurality of servers with each such server being a physical server or a virtual machine, or a collection of both physical servers and virtual machines. In another implementation, a cloud hosts one or more components of the system. The wellbore modeling platform  102 , the user devices  106 , the server  108 , and other resources connected to the network  104  may access one or more additional servers for access to one or more websites, applications, web services interfaces, etc. that are used for production decline modeling and/or generating a well production profile. 
       FIG.  2    is a block diagram illustrating an example data flow  200  for generating a geological facies model  202  with a predictive analytical model  204  by utilizing one or more decision tree-based models  206  and/or other machine learning techniques. The data flow  200  can be performed by any of the computing systems discussed herein and can include operations of an application or embedded plugin of the wellbore modeling platform  102 . Through the data flow  200  of  FIG.  2   , the geological facies model  202  may be generated for a target well (and/or a candidate well location) based on target well data  208 , which may lack corresponding core sample data (e.g., or a portion of core sample data). Rather, supervised machine learning, artificial intelligence, decision tree based model(s)  206 , and/or other algorithms or techniques may be trained through an iterative and validation process to map the target well data  208  to specific geologic facies, represented by an input data set  210 , for a faster and more accurate geological facies model  202  that does not necessarily rely on core data collected at the target well location. In some implementations, the steps outlined in the data flow  200  of  FIG.  2    may be executed by the wellbore modeling platform  102  automatically or in response to inputs provided through a user interface to generate the geological facies model  202 . In other instances, however, any component of the network environment  100  may execute one or more applications as described in relation to the data flow  200  of  FIG.  2   . 
     The data flow  200  may include generating the input data set  210  for input to the decision tree-based model(s)  206 . The input data set  210  may include any well-related data, reservoir-related data, or geological-related data, such as but not limited to, well log data  212 , core data  214 , historical geological survey data, and petrophysical or other rock property data, rock property models, and/or flow simulation information data associated with a particular subsurface reservoir (e.g., a subterranean reservoir). The geological-related data can further include seismic data obtained through any known or hereafter developed seismic-based measurement techniques for determining subsurface characteristics. Well log data  212  may include well development data, well completion data, well production data, and/or any other records related to wells at the reservoir. For instance, well log data  212  can include data associated with a particular stage of the well life cycle, such as exploration and appraisal data or well abandonment data. Moreover, well log data  212  can indicate subsurface parameters, development and completion parameters, operation and facilities parameters, performance parameters, and/or the like. The subsurface parameters may include, without limitation, BVH, thickness, fractures, faults, FEV features, frac hits, landing targets, and/or the like. The development and completion parameters may include, without limitation, wellbore geometry, orientation, completion size, zipper, completion design, such as ppg, number per cluster, and/or the like. The operation and facilities parameters may include, without limitation, operating strategy, facility network, artificial lift, workover, remedials, water management, and/or the like. The performance parameters may include, without limitation, impacted, non-impacted, confined, unconfined, degradation, well life, event size, and/or the like. 
     In some examples, the input data set  210  can include the core data  214  associated with the reservoir. The core data  214  can be obtained, among other techniques, through analysis of one or more well-drilled core samples to determine the geological make-up at the well. The core data  214  can indicate geological data from the core sample, such as sedimentary compositions of geological facies sampled by the core sample and other geological-related information of the facies structures (e.g. a facies starting depth, a facies terminating depth, a facies interval size, a facies sedimentary composition, a facies porosity, a facies permeability, a facies wettability, one or more facies rock properties of the facies, a facies organic matter content, a presence of hydrocarbons or other fluids in the facies, a geomechanical property of the facies a fluid sensitivity of the facies, and the like). Additional geological-related data can be obtained from any known or hereafter developed physical model of rock characteristics, measurements, simulations, and the like. The number and types of data included in the input data set  210  may vary from model to model such that no particular types or amount of data is required to generate the geological facies model  202 . Rather, any data sets can be supplied as input to the decision tree-based model(s)  206 , although additional data may result in a more detailed geological facies model  202  being provided by the wellbore modeling platform  102 . 
     In some examples, the well log data  212  and/or the core data  214  represent information related to a plurality of wells at a particular subsurface reservoir (e.g., an oil and/or natural gas reservoir region, reservoir field, reservoir zone, etc.) The core data  214  may represent core samples of between five wells and 20 wells (e.g., 8, 9, 10, 11, 12, etc.) which may be a small fraction of total number of wells at the reservoir (e.g., the total number of wells may be greater than 200 wells). The core data  214  may represent between 2% and 25% of total wells at the reservoir. The input data  210  (e.g., the well log data  212  and/or the core data  214 ) can include one or more of electrical resistivity data, gamma ray detection data, neutron porosity data, bulk density data, sonic log data, dielectric log data, nuclear magnetic resonance (NMR) logs, and/or the like. At least a portion of the core data  214  may be a subset or otherwise included in the well log data  212  and/or at least a portion of the core data  214  may be separate data (e.g., a different data file, data structure, and/or data format) from the well log data  212 . 
     In some examples, geological facies class labels  216  are included in the input data set  210  or otherwise provided to the decision-tree based model(s)  206 . The geological facies class labels are identifiers for distinct geological facies (e.g., including one or more alphanumeric symbols and/or identifying words). The geological facies class labels  216  can be provided by one or more subject matter experts (SME), for instance to reduce computational resources and/or improve accuracy of the predictive analytical model  204 . For instance, the wellbore modeling platform  102  can present a user interface to receive input from the SME defining one or more geological facies class labels  216 . Additionally, or alternatively, at least some of the geological facies class labels  216  can be generated by a machine learning technique. In some instances, the input data set  210  set can be artificially balanced to generate a balanced input data set so that under sampled geological facies class labels  216  (e.g., forming a minority class) are given a greater weight. The input data set  210  can include any number of geological facies class labels, such as between two and 20 geological facies class labels  216 . 
     The collection of reservoir-based data may be combined into the input data set  210  for use by a supervised machine learning system, such as the decision tree-based model(s)  206 , to generate the predictive analytical model  204 . A target variable  218  is assigned for the decision tree-based model(s)  206 , such as geological facies class, such that relationships between the different geological facies classes and their factors can be identified from the input data set  210 . In some implementation, the decision tree-based model(s)  206  uses pattern recognition techniques to generate classification trees indicating how particular well log data characteristics correlate to particular geological facies class labels. For instance, the decision tree-based model(s)  206  may determine conjunctions of features from the input data set  210  (e.g. well-related features, reservoir-related features, and/or geological-related features) and interrelations of the conjunctions that have a higher statistical probability of corresponding to a particular geological facie class label. Moreover, vertical context data  220  can be provided to the decision tree-based model(s)  206  to further refine the decision tree-based model(s)  206 . The vertical context data  220  can include supplementary data indicating depth values (e.g., measured values and/or predicted values) associated with the well log data  212  and/or the core data  214  of the input data set  210 . The decision tree-based model(s)  206  can categorize various decision branches based on the vertical context data  220  so that multiple models can represent multiple different depths, resulting in predicted geological facies classes for particular depths. 
     In some examples, the data flow  200  generates the predictive analytical model  204  by iteratively training multiple decision tree model(s)  206  with a regression algorithm  224  and training/validation diagnostics algorithm  226 . For example, the decision tree-based model(s)  206  can train and validate the various generated models with the input data set  210 . In one implementation, the training/validation diagnostics algorithm  226  may be applied to each generated decision tree-based model  206  to determine an accuracy of the model with respect to the input data set  210 . Through a determined error obtained from application of the various decision tree-based models  206  to the training/validation diagnostics algorithm  226 , the data flow  200  can determine how accurate or how closely the generated model corresponds to the input data set  210 . The data flow  200  can then alter the generated decision tree-based model  206  based on the determined error to address and attempt to eliminate the error. This process of model generation, regression, validation, and alteration may be repeated until the determined error of the decision tree-based model(s)  206  (according to the training/validation diagnostics algorithm  226 ), falls below a threshold value. In this manner, the data flow  200  utilizes regression techniques to generate or alter the decision tree-based model(s)  206  that are trained, through the above-described iterative process, to accurately represent the input data set  210 . 
     In some instances, the predictive analytical model  204  uses a gradient boosting model, which provides predictions in the form of an ensemble of weak prediction decision tree-based model(s)  206 , and builds the prediction in a stage-wise fashion. More particularly, gradient boosting involves using an additive model to add weak learners to minimize a loss function. Any of the features discussed herein can be used to generate the predictive analytical model  204 . 
     In some examples, the predictive analytical model  204  receives the target well data  208  and, using the decision tree-based model(s)  206 , generates the geological facies model  202 . The target well data  208  can include location data (e.g., representing a single location, multiple locations, and/or a zone), well-related data, reservoir-related data, and/or geological-related data (e.g., like the input data set  210 ) for a particular geographic location or geographic area. For instance, the target well data  208  may be related to a candidate well or a candidate location for drilling the candidate well. Moreover, the target well data  208  can represent a geographic area being considered for drilling or surveying, such as a candidate section, area, or zone of a reservoir field or a candidate section of the reservoir below the reservoir field. The target well data  208  can include geographic information (e.g., Global Positioning System (GPS) data), and/or the vertical context data  220  related to the target well data  208  (e.g., for predicting geological facies class labels  216  at particular depths). In some instances, the target well data  208  includes well log data for the target location, but lacks core data for the target location. The target well data  208  may be an incomplete data set with respect to core sampling information. 
     In some examples, the predictive analytical model  204  provides the target well data  208  to the one or more decision tree-based model(s)  206  and outputs the geological facies model  202  based on one or more classifications generated by the decision tree-based model(s)  206 . The one or more classifications can include a numerical (e.g., weighted) mapping of depths to particular geological facies class labels  216  predicted by the decision tree based model(s)  206  for the particular depths, based on the target well data  208  (e.g., and as categorized or defined by the vertical context data  220 ). For instance, multiple geological facies class labels  216  may be predicted sequentially for a sequence of depth values, starting at a surface level or sub-surface level depth, at a location (e.g., or multiple locations) associated with the target well data  208 . The numerical mapping of the target well data  208  at various depths to the geological facies class labels  216  forms the geological facies model  202 . 
     In some instances, the data flow  200  (e.g., as executed by the wellbore modeling platform  102 ) can provide the geological facies model  202  to a well location selection optimizer  228  for optimizing a well development selection. The well development selection can include selecting a section of the subsurface reservoir for further resource characterization and/or determining an optimal drilling location for a candidate well. Operations of the well location selection optimizer  228  are discussed in greater detail below regarding  FIG.  3   . 
       FIG.  3    illustrates an example system  300  for generating the geological facies model  202  and optimizing a well development selection, which can be performed by any of the systems discussed herein. The system  300  can include a reservoir field  302  (e.g., an oil and/or natural gas field) with a plurality of wells  304 . The well log data  212  corresponds to the plurality of wells  304  and the core data  214  corresponds to the plurality of wells  304  or a subset of the plurality of wells  304 . Generally, due to the expenses associated with extracting and analyzing core samples, the core data  214  represents less wells than the well log data  212 . 
     In some examples, the target well data  208  represents a target or candidate location  306  for potentially drilling a target or candidate well, or performing another well development action. Additionally, or alternatively, the target well data  208  represents a target area or section  308  of the reservoir field  302  (e.g., and/or a section of the reservoir below the section  308  of the reservoir field  302 ) being assessed for determining an optimal location within the section  308  of the reservoir field  302  to perform the well development action and/or whether to perform additional resource characterization for the section  308  of the reservoir. The target well data  208  can represent multiple candidate locations  306  and/or multiple sections  308  of the reservoir field  302 , for instance, to generate multiple geological facies models  202  to be compared against each other via an optimization process. 
     Providing the target well data  208  to the predictive analytical model  204  can improve well development by optimizing selection of the section  308  or the candidate location  306  (e.g., from among multiple candidate locations  306  and/or multiple candidate sections  308 ) for a well development action, even if the target well data  208  is incomplete (e.g., lacks corresponding core data at the candidate location  306  or section  308 ). For instance, the predictive analytical model  204  can generate the geological facies model  202  or multiple geological facies models  202  representing the candidate location  306  and/or the section  308 . The geological facies model  202  can map (e.g., with one or more numerical weights) the geological facies class labels  216  to the target well data  208 . Accordingly, the geological facies model  202  indicates a prediction or likelihood of which geological facies represented by the geological facies class labels  216  are present at different depths at the candidate location  306  and/or section  308  For instance, the geological facies model  202  can indicate that a first geological facie associated with a first geological facie class label is likely to be present at a first depth, a second geological facie associated with second geological facie class label is likely to be present at a second depth, and so forth. The geological facies model  202  can indicate starting depths and terminating depths of the different geological facies, and changes in the geological facies at different depths (e.g., changes in sedimentary composition at various depths). The system  300  can use the vertical context data  220  (e.g., which may include depth values associated with the input data set  210  and/or the target well data  208 ) to correlate or aggregate data based on relations to similar or identical depth values (e.g., relative to sea level or a surface depth value). 
     The well location selection optimizer  228  can assess the geological facies model  202  or multiple geological facies models  202  and determine, based at least partly on the geological facies model(s)  202 , that the candidate location  306  and/or the section  308  satisfy one or more criteria for the development action. For instance, the geological facies model(s)  202  can indicate that likelihood of a petroleum trap being present at the candidate location  306  and/or section  308  is greater than a predetermined threshold value, that a predicted 12-month cumulative in barrels of oil equivalent (BOE) per foot (boe/ft) is greater than the predetermined threshold value, an estimated ultimate recovery (EUR) in millions of BOE is greater than the predetermined threshold value, a predicted bulk volume hydrocarbon (BVH) is greater than the predetermined threshold value, and the like. The well development action can be drilling a well at the candidate location  306  or at an optimal location in the section  308  of the reservoir field  302 , conducting additional surveying at the candidate location  306  and/or the section  308  (e.g., performing resource characterization), using a particular well spacing, well orientation and placement, well length, completion, central infrastructure, making a depth-based equipment decision, and/or combinations thereof. In making the selection, the selection optimizer can consider the geological facies model  202  and/or a combinations of other variables that would maximize the resource output or increase efficiency. 
       FIG.  4    illustrates an example block diagram of a geological structure modeling tool  400  for generating the geological facies model  202 . In general, the geological structure modeling tool  400  may include a predictive analytical model generator  402  for generating the predictive analytical model  204  according to the data flow  200 . The geological structure modeling tool  400  may form at least a portion of the wellbore modeling platform  102  of  FIG.  1   . As shown in  FIG.  4   , the geological structure modeling tool  400  may include or be in communication with a computing device  404  providing a user interface  406 . As explained in more detail below, the geological structure modeling tool  400  may be accessible to various users to generate the predictive analytical model  204  and the geological facies model  202  based on the target well data  208  and/or the input data set  210  (e.g., which may be provided to the geological structure modeling tool  400  by the user). Access to the geological structure modeling tool  400  may occur through the user interface  406  executed on the computing device  404 . 
     As explained above, the geological structure modeling tool  400  may generate the geological facies model  202  based on the input data set  210 . Thus, the geological structure modeling tool  400  may include the predictive analytical model generator  402  executed to perform one or more of the systems and operations described herein. The predictive analytical model generator  402  may be an application stored in a computer-readable media  408  (e.g., memory) and executed on a processing system  410  of the geological structure modeling tool  400  or other type of computing system, such as that described below regarding  FIG.  8   . For example, predictive analytical model generator  402  may include instructions that may be executed in an operating system environment, such as a Microsoft Windows™ operating system, a Linux operating system, or a UNIX operating system environment. The computer-readable media  408  includes volatile media, nonvolatile media, removable media, non-removable media, and/or another available medium. By way of example and not limitation, non-transitory computer-readable media  408  comprises computer storage media, such as non-transient storage memory, volatile media, nonvolatile media, removable media, and/or non-removable media implemented in a method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. 
     The predictive analytical model generator  402  may also utilize a data source  412  of the computer-readable media  408  for storage of data and information associated with the geological structure modeling tool  400 . For example, the predictive analytical model generator  402  may store information associated with iterations of the decision tree-based model(s)  206 , outputs of the predictive analytical model  204 , training/validation diagnostic information or data, model accuracy scoring, geological facies model(s)  202 , and the like. As described in more detail below, various generated models and profiles may be stored and used via the user interface  406  to simulate or otherwise determine geological facies models  202  such that trained or optimized models and profiles for various target wells may be stored in the data source  412 . 
     The predictive analytical model generator  402  may include several components to perform one or more of the operations described herein. For example, the predictive analytical model generator  402  may include a training data manager  414  to manage the input data set  210  for the decision tree-based model(s)  206  to generate the geological facies model  202  based on the input data set  210 . The training data manager  414  may, in some instances, receive various types of data, such as well logs (e.g., the well log data  212 ), core logs (e.g., the core log data  214 ), well construction data, production data, seismic data, attribute data, and/or other types of well-related data and combine the data into the input data set  210  for use in generating the geological facies model  202 . Further, the training data manager  414  may also manage training/validation diagnostic information and data used in determining an accuracy of the decision tree-based model(s)  206  and/or the geological facies model  202  as compared to the input data set  210 . For example, the training data manager  414  may compare simulated results of the geological facies model  202  and determine a difference between the simulated results and the input data set  210  to determine an accuracy of the generated model. Past results of the training of the model may also be stored and/or maintained by the training data manager  414  for comparison to current results to determine if the generated model is becoming more accurate or less accurate in response to operations performed by the geological structure modeling tool  400 . In general, any information or data provided as inputs to the decision tree-based model(s)  206  and/or utilized to train or validate the predictive analytical model  204  may be managed by the training data manager  414 . 
     The predictive analytical model generator  402  may also include a decision tree-based model trainer  416  and regression trainer  418  to generate and/or train one or more decision tree-based model(s)  206  based on the input data set  210  received from the training data manager  414 . As explained above, the decision tree-based model trainer  416  may include any machine learning, deep learning, or artificial intelligence techniques (e.g., a supervised machine learning system) to generate the decision tree-based models  206 , such as multiple decision tree-based models  206  via an iterative process. The regression trainer  418  may reduce the complexity of the generated models and profiles and apply the models to the training/validation diagnostics algorithm  226  for iterative training. Together, the decision tree-based model trainer  416  and regression trainer  418  may develop a plurality of trained decision tree-based models  206 . 
     A parallelization implementer  420  may also be included and executed by the predictive analytical model generator  402 . In one non-limiting example, the parallelization implementer  420  may manage the parallelization of the training of the generated decision tree-based models  206  and the predictive analytical model  204  and/or model scoring with a high-performance cluster (HPC). For instance, the overall data flow process described above with relation to  FIG.  2    may be distributed across an HPC of computing devices. The various trained models (e.g., trained decision tree-based models  206  and/or trained predictive analytical models  204 ) generated by the iterative process may be scored in parallel through a distribution of the trained models onto various computing machines of the HPC. In this manner, the simulations executed on the trained models and the accuracy scores of the various models may be obtained simultaneously to reduce the time needed to complete the model evaluations. In a similar manner, multiple computing devices may execute the deep learning techniques in a parallel manner to generate the multiple trained models for the target well simultaneously such that the trained models may be generated at a faster rate than previous implementations that may generate the trained models serially. For example, the parallelization implementer  420  may provide the generated models to one or more computing devices of the HPC for training, simulation, and comparing to the diagnostic data. Similarly, the parallelization implementer  420  may communicate with one or more computing devices of the HPC to apply measured data to the trained models to determine an accuracy of the trained models. In general, any communication between the predictive analytical model generator  402  and the HPC may be managed by the parallelization implementer  420  to reduce the time to generate the decision tree-based model(s)  206 , the predictive analytical model  204 , and/or the geological facies model  202 . It will be appreciated that HPC clusters are just one example of learning techniques that may be utilized. 
     Further, it will be appreciated that the components described herein are provided only as examples, and that the geological structure modeling tool  400  may have different components, additional components, or fewer components than those described herein. For example, one or more components as described in  FIG.  4    may be combined into a single component. As another example, certain components described herein may be encoded on, and executed on other computing systems. Any components of the geological structure modeling tool  400  may be combined or included with the components of the computing system  800  discussed in greater detail below regarding  FIG.  8   , the wellbore modeling platform  102 , and/or the user device  106 . 
     Several advantages over previous techniques for predicting geological facies may be gained through the methods and systems described herein. For example, the wellbore modeling platform  102  may facilitate data loading, pre-processing, transformation and alignment to the well log data, a dynamic and flexible model construction process, and data handling, generation, augmentation during model training. Other advantages include automated techniques for model validation, automated capture of model training results, and automated implementation of model hyper-parameter optimization to repeatedly train new models in a search for the optimal model configuration. In some non-limiting examples, the described modeling framework may also be used to streamline user access to Graphical Processing Unit (GPU) resources in the HPC to improve model training speed and a visualization and data framework allows users to track model optimization. The model prediction framework may also distribute the prediction tasks out to as many computational resources as desired in order to speed up the process while automatically taking care of the hardware resourcing, setup, and take-down tasks. Still other advantages include an efficient process that makes it easy for users to connect their data to the modeling tools while receiving the results a short time later, even if core data related to the target well  208  is lacking. Moreover, automating portions of the modeling process with the machine learning and artificial intelligence techniques described herein can reducing interpretation bias common in previous reservoir model generation systems. 
       FIG.  5    illustrates example operations of a method  500  for optimizing a well development action by generating the geological facies model  202 , which can be performed by any of the systems discussed herein. At operation  502 , the method  500  can include generating the predictive analytical model  204  with the one or more decision tree-based model(s)  206  using the input data set  210  of the well log data  212 , the core data  214 , and/or the geological facies class labels  216 . At operation  504 , the method  500  can include receiving the target well data  208  associated with a target well. At operation  506 , the method  500  can include generating, using the predictive analytical model  204  and the target well data  208 , the geological facies model  202  for the target well. At operation  508 , the method  500  can include selecting, based at least partly on the geological facies model  202 , the section  308  of a reservoir for resource characterization. At operation  510 , the method  500  can include characterizing resources at the section  308  of the reservoir. At operation  512 , the method  500  can include selecting, based at least partly on the geological facies model  202 , the candidate location  306  for drilling or developing a well. At operation  514 , the method  500  can include drilling or developing the well at the candidate location  306 . 
       FIG.  6    illustrates example operations of a method  600  for generating the geological facies model  202  with one or more decision tree-based model(s)  206 , which can be performed by any of the systems discussed herein. At operation  602 , the method  600  can include generating the predictive analytical model  204  with the one or more decision tree-based model(s)  206  using the input data set  210  of the well log data  212 , the core data  214 , and/or the geological facies class labels  216 . At operation  604 , the method  600  can include receiving the target well data  208  associated with a target well, the target well data  208  indicating the candidate well location  306  and/or a candidate section of a subsurface reservoir (e.g., the section  308  of the reservoir field  302  above the candidate section of the subsurface reservoir). At operation  606 , the method  600  can include predicting, using the one or more decision tree-based model(s)  206 , one or more geological facie classes numerically mapped to one or more depth values at the candidate well location  306  and/or the candidate section of the subsurface reservoir. At operation  608 , the method  600  can include generating the geological facies model  202  for the target well that numerically maps the geological facies class labels  216  to the target well data  208 . At operation  610 , the method  600  can include performing a well development action at least partly based on the geological facies model  202 . For instance, the method  600  can include drilling a well at the candidate location  306  or at the section  308  of the reservoir field  302  and/or performing resource characterization at the candidate location  306  or the section  308  of the reservoir field  302 . 
       FIG.  7    illustrates example operations of a method  700  for generating the predictive analytical model  204  to generate the geological facies model  202 , which can be performed by any of the systems discussed herein. At operation  702 , the method  700  can include receiving the input data set  210  corresponding to a plurality of wells at a subsurface reservoir, the input data set  210  including the well log data  212 , the core data  214 , and/or the geological facies class labels  216  generated by an SME. At operation  704 , the method  700  can include artificially balancing the input data set  210  based on geological facies class label  216  occurrences to create a balanced input data set. At operation  706 , the method  700  can include generating, using the input data set  210 , the one or more decision tree-based model(s)  206  with geological facie class as the target variable  218 . At operation  708 , the method  700  can include providing the vertical context data  220  to the one or more decision tree-based model(s)  206 . At operation  710 , the method  700  can include boosting the one or more decision tree-based model(s) to generate one or more boosted decision tree-based model(s). At operation  712 , the method  700  can include generating, using the one or more boosted decision tree-based models, the geological facies model  202  for a target well based on the target well data  208  associated with the target well. 
     It is to be understood that the specific order or hierarchy of operations in the methods depicted in  FIGS.  5 - 7    are instances of example approaches and can be rearranged while remaining within the disclosed subject matter. For instance, any of the operations depicted in  FIGS.  5 - 7    may be omitted, repeated, performed in parallel, performed in a different order, and/or combined with any other of the operations depicted in 5-7 or throughout this disclosure. 
     Referring to  FIG.  8   , a detailed description of an example computing system  800  having one or more computing units that may implement various systems and methods discussed herein is provided. The computing system  800  may be applicable to the wellbore modeling platform  102 , the network environment  100 , and other computing or network devices. In some instances, the computing system  800  may be similar or identical to the user device  106 , the geological structure modeling tool  400 , the computing device  400 , the server  108 , and the like. It will be appreciated that specific implementations of these devices may be of differing possible specific computing architectures not all of which are specifically discussed herein but will be understood by those of ordinary skill in the art. 
     The computing system  800  may be capable of executing a computer program product to execute a computer process. Data and program files may be input to the computing system  800 , which reads the files and executes the programs therein. Some of the elements of the computing system  800  are shown in  FIG.  8   , including one or more hardware processors  802  (e.g., which may be similar or identical to the processing system  410  in  FIG.  4   ), one or more data storage devices  804 , such as memory devices (e.g., which may be similar or identical to the computer-readable media  408  in  FIG.  4   ), and/or one or more ports  806  or  808 . Additionally, other elements that will be recognized by those skilled in the art may be included in the computing system  800  but are not explicitly depicted in  FIG.  8    or discussed further herein. Various elements of the computing system  800  may communicate with one another by way of one or more communication buses, point-to-point communication paths, or other communication means not explicitly depicted in  FIG.  8   . 
     The processor  802  may include, for example, a central processing unit (CPU), a microprocessor, a microcontroller, a digital signal processor (DSP), and/or one or more internal levels of cache. There may be one or more processors  802 , such that the processor  802  comprises a single central-processing unit, or a plurality of processing units capable of executing instructions and performing operations in parallel with each other, commonly referred to as a parallel processing environment. 
     The computing system  800  may be a conventional computer, a distributed computer, or any other type of computer, such as one or more external computers made available via a cloud computing architecture. The presently described technology is optionally implemented in software stored on the data stored device(s)  804 , (e.g., memory device(s)), and/or communicated via one or more of the ports  806  or  808 , thereby transforming the computing system  800  in  FIG.  8    to a special purpose machine for implementing the operations described herein. Examples of the computing system  800  include personal computers, terminals, workstations, mobile phones, tablets, laptops, personal computers, multimedia consoles, gaming consoles, set top boxes, and the like. 
     The one or more data storage devices  804  may include any non-volatile data storage device capable of storing data generated or employed within the computing system  800 , such as computer executable instructions for performing a computer process, which may include instructions of both application programs and an operating system (OS) that manages the various components of the computing system  800 . The data storage devices  804  may include, without limitation, magnetic disk drives, optical disk drives, solid state drives (SSDs), flash drives, and the like. The data storage devices  804  may include one or more memory devices such as removable data storage media, non-removable data storage media, and/or external storage devices made available via a wired or wireless network architecture with such computer program products, including one or more database management products, web server products, application server products, and/or other additional software components. Examples of removable data storage media include Compact Disc Read-Only Memory (CD-ROM), Digital Versatile Disc Read-Only Memory (DVD-ROM), magneto-optical disks, flash drives, and the like. Examples of non-removable data storage media include internal magnetic hard disks, SSDs, and the like. The one or more memory devices can include volatile memory (e.g., dynamic random access memory (DRAM), static random access memory (SRAM), etc.) and/or non-volatile memory (e.g., read-only memory (ROM), flash memory, etc.). 
     Computer program products containing mechanisms to effectuate the systems and methods in accordance with the presently described technology may reside in the data storage devices  804 , which may be referred to as machine-readable media. It will be appreciated that machine-readable media may include any tangible non-transitory medium that is capable of storing or encoding instructions to perform any one or more of the operations of the present disclosure for execution by a machine or that is capable of storing or encoding data structures and/or modules utilized by or associated with such instructions. Machine-readable media may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more executable instructions or data structures. The machine-readable media may store instructions that, when executed by the processor, cause the systems to perform the operations disclosed herein. 
     In some implementations, the computing system  800  includes one or more ports, such as an input/output (I/O) port  806  and a communication port  808 , for communicating with other computing, network, or reservoir development devices. It will be appreciated that the ports  806  and  808  may be combined or separate and that more or fewer ports may be included in the computing system  800 . 
     The I/O port  806  may be connected to an I/O device, or other device, by which information is input to or output from the computing system  800 . Such I/O devices may include, without limitation, one or more input devices, output devices, and/or environment transducer devices. 
     In some implementations, the input devices convert a human-generated signal, such as, human voice, physical movement, physical touch or pressure, and/or the like, into electrical signals as input data into the computing system  800  via the I/O port  806 . Similarly, the output devices may convert electrical signals received from computing system  800  via the I/O port  806  into signals that may be sensed as output by a human, such as sound, light, and/or touch. The input device may be an alphanumeric input device, including alphanumeric and other keys for communicating information and/or command selections to the processor  802  via the I/O port  806 . The input device may be another type of user input device including, but not limited to: direction and selection control devices, such as a mouse, a trackball, cursor direction keys, a joystick, and/or a wheel; one or more sensors, such as a camera, a microphone, a positional sensor, an orientation sensor, a gravitational sensor, an inertial sensor, and/or an accelerometer; and/or a touch-sensitive display screen (“touchscreen”). The output devices may include, without limitation, a display, a touchscreen, a speaker, a tactile and/or haptic output device, and/or the like. In some implementations, the input device and the output device may be the same device, for example, in the case of a touchscreen. 
     In some implementations, a communication port  808  is connected to a network by way of which the computing system  800  may receive network data useful in executing the methods and systems set out herein as well as transmitting information and network configuration changes determined thereby. Stated differently, the communication port  808  connects the computing system  800  to one or more communication interface devices configured to transmit and/or receive information between the computing system  800  and other devices by way of one or more wired or wireless communication networks or connections. Examples of such networks or connections include, without limitation, Universal Serial Bus (USB), Ethernet, Wi-Fi, Bluetooth®, Near Field Communication (NFC), Long-Term Evolution (LTE), and so on. One or more such communication interface devices may be utilized via the communication port  808  to communicate one or more other machines, either directly over a point-to-point communication path, over a wide area network (WAN) (e.g., the Internet), over a local area network (LAN), over a cellular (e.g., third generation (3G) or fourth generation (4G) or fifth generation (5G) network), or over another communication means. Further, the communication port  808  may communicate with an antenna or other link for electromagnetic signal transmission and/or reception. 
     The computing system  800  set forth in  FIG.  8    is but one possible example of a computer system that may employ or be configured in accordance with aspects of the present disclosure. It will be appreciated that other non-transitory tangible computer-readable storage media storing computer-executable instructions for implementing the presently disclosed technology on a computing system may be used. In the present disclosure, the methods and operations disclosed herein may be implemented as sets of instructions or software readable by a device. 
     For instance, the described disclosure may be provided as a computer program product, or software, that may include a non-transitory machine-readable medium having stored thereon instructions, which may be used to program a computer system (or other electronic devices) to perform a process according to the present disclosure. A machine-readable medium includes any mechanism for storing information in a form (e.g., software, processing application) readable by a machine (e.g., a computer). The machine-readable medium may include, but is not limited to, magnetic storage medium, optical storage medium; magneto-optical storage medium, read only memory (ROM); random access memory (RAM); erasable programmable memory (e.g., EPROM and EEPROM); flash memory; or other types of medium suitable for storing electronic instructions. 
     While the present disclosure has been described with reference to various implementations, it will be understood that these implementations are illustrative and that the scope of the present disclosure is not limited to them. Many variations, modifications, additions, and improvements are possible. More generally, implementations in accordance with the present disclosure have been described in the context of particular implementations. Functionality may be separated or combined in blocks differently in various implementations of the disclosure or described with different terminology. These and other variations, modifications, additions, and improvements may fall within the scope of the disclosure as defined in the claims that follow.