Patent Publication Number: US-2020291758-A1

Title: Machine Learning Systems and Methods for Isolating Contribution of Geospatial Factors to a Response Variable

Description:
RELATED APPLICATIONS 
     The present application claims the priority of U.S. Provisional Application Ser. No. 62/816,481 filed on Mar. 11, 2019, the entire disclosure of which is expressly incorporated by reference. 
    
    
     BACKGROUND 
     Technical Field 
     The present disclosure relates generally to the field of computer-based machine learning systems. More specifically, the present disclosure relates to machine learning systems and methods for isolating contribution of geospatial factors to a response variable. 
     Related Art 
     The field of machine learning has rapidly advanced in recent years. As a branch of artificial intelligence, machine learning involves systems that can learn from data, identify patterns, and make decisions with minimal human intervention. Machine learning has been applied to various fields of endeavor, including in the petroleum field. 
     Drilling is a process whereby a hole is bored to create a well for oil and natural gas production. Drilling wells is an expensive and time-consuming process. The process can be further complicated by geological factors and variables, such as formation depth and rock brittleness. Additionally, the performance (e.g., oil production) of each well can differ based on production/productivity factors, such as stock tank original oil-initially-in-place (“STOIIP”) and intensity (a measure of productivity related to proppant and fracking fluid). As such, it is desirable to predict certain features of a potential oil well, such as productivity, costs and safety characteristics, when grading a land parcel (acreage) for potential drilling sites. At present, machine learning systems cannot adequately learn (isolate) the contribution of geospatial factors to a response variable (such as oil well productivity, etc.), thereby reducing the effectiveness, speed, and accuracy of such computer-based tools in the petroleum industry. These and other needs are addressed by the machine learning systems and methods of the present disclosure. 
     SUMMARY 
     The present disclosure relates to machine learning systems and methods for isolating contribution of geospatial factors to a response variable. Specifically, the system assembles a dataset comprising a response variable and predictors for each observation and joins the dataset to geospatial data based on a location of an observation. The system then develops or trains a first predictive model on all or a subset of non-geospatial data and divides the response variable actual value by a first predictive value from the first predictive model to generate a second response variable (e.g., a ratio of the actual value over the predicted value). Next, the system develops or trains a second predictive model for the second response variable based on all or a subset of the geospatial data. Lastly, the system calculates a second predicted value for the second response variable using the second predictive model. The value of the second predicted value (referred to herein as a “GeoFactor”) represents a continuous variable (e.g., a score) that acts as a multiplier for oil/gas well production. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The foregoing features of the invention will be apparent from the following Detailed Description of the Invention, taken in connection with the accompanying drawings, in which: 
         FIG. 1  is a flowchart illustrating overall process steps carried out by the machine learning system of the present disclosure; 
         FIG. 2  is illustration showing oil production of Bakken Horizontal wells; 
         FIG. 3  is a graph showing a Bakken Well performance histogram; 
         FIG. 4A  shows graphs illustrating averages of Bakken Well performance trends from a time period of 2012-2016; 
         FIG. 4B  is a graph showing selected features/factors which drive well performance; 
         FIGS. 5A and 5B  are graphs showing the Intensity of the Bakken Wells; 
         FIG. 6  is an illustration showing the system of the present disclosure generating a GeoFactor from subsurface characteristics and applying the GeoFactor to oil production data; 
         FIG. 7A  is a graph showing the Intensity of the Bakken Wells as seen and discussed in  FIG. 5A ; 
         FIG. 7B  is a graph showing a GeoFactor normalized Intensity of the Bakken Wells Intensity; 
         FIG. 8A  is a graph showing well performance data graphed in relation to pounds of proppant per foot vs gallons of fracking fluid per foot; 
         FIG. 8B  is a graph showing the well performance in  FIG. 8A  normalized by the GeoFactor, according the present disclosure; 
         FIG. 9  is a graph showing sensitivity on Intensity based on the data shown in  FIG. 8B ; 
         FIG. 10A  is a graph showing the GeoFactor of a plurality of wells graphed in relation to rock brittleness vs formation depth, according to the present disclosure; 
         FIG. 10B  is a graph showing the sensitivity of the GeoFactor of  FIG. 10A , according to the present disclosure; 
         FIG. 11A  is a graph showing the GeoFactor of a plurality of wells graphed in relation to Net-STOIIP vs formation depth, according to the present disclosure; 
         FIG. 11B  is a graph showing the sensitivity of the GeoFactor of  FIG. 11A , according to the present disclosure; 
         FIG. 12A  is a graph of drivers for an F-Score, according to the present disclosure. 
         FIG. 12B  is a list of other data elements that can enable the system of the present disclosure to differentiate well performance; 
         FIGS. 13A-14C  are graphs showing the GeoFactor and the F-Score increasing the discriminatory power of the system of the present disclosure; and 
         FIG. 15  is a diagram illustrating sample hardware and software components capable of being used to implement the system of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     The present disclosure relates to machine learning systems and methods for isolating contribution of geospatial factors to a response variable, as described in detail below in connection with  FIGS. 1-14 . 
       FIG. 1  is a flowchart illustrating the overall process steps carried out by the system of the present disclosure, indicated generally at method  10 . The objective of the system is to create models which isolate the contributions to oil/gas well performance that completion (e.g., code completion) engineering factors and geology. Method  10  of the system calculates a single coefficient from multiple factors. Advantageously, by isolating the aforementioned contributions, the models developed by the systems and methods of the present disclosure function with increased speed and accuracy, thereby improving the functionality of computer-based modeling systems. 
     In step  12 , the system assembles a dataset comprising a response variable and predictors (also known as features) for each observation. A response variable (e.g., target variable) is a variable that is or should be the output. In a classifying process, the response variable can be a binary 0 or 1. In a regression process, the response variable can be a continuous variable. A predictor is input data or a variable that is mapped to the response variable through an empirical relationship. An observation is a set of predictors. 
     In step  14 , the system joins the dataset to geospatial data based on a location of an observation. Geospatial data is derived from location specific data points through a variety of methods of interpolation and distance calculations from any given geospatial location to the locations of the original geospatial data. Typical geospatial predictors for this application include geological information, vertical depths below sea level of formations of interest, and characteristics of the lithology of said formations. In step  16 , the system develops or trains a first predictive model on all or a subset of non-geospatial data. Non-geospatial data relates specifically to information about the observed data point (the well) that is not specific to its location. Typical non-geospatial data includes the length of the well, the engineering parameters of the well&#39;s drilling and completion, and non-physical characteristics such as what entity owns the well or when it was drilled. In step  18 , the system divides the response variable actual value by a first predictive value from the first predictive model to generate a second response variable (e.g., a ratio of the actual value over the predicted value). 
     In step  20 , the system develops or trains a second predictive model for the second response variable based on all or a subset of the geospatial data. In step  22 , the system calculates a second predicted value for the second response variable using the second predictive model (referred to herein as a “GeoFactor”). The GeoFactor represents a continuous variable (e.g., a score) which acts as a multiplier for oil/gas well production. For example, a GeoFactor of 1.5 means that a well drilled in that acreage will produce 50% more than a similar well drilled in an average acreage (which would have a score of 1.0). 
     The system can use the GeoFactor as a coefficient to multiply the predicted values (e.g., the first predicted value) from models (e.g., the first predictive model) developed using features that are not part of the calculation for the GeoFactor. The system can also use the GeoFactor to normalize response variable data and develop new models or to retrain existing models on a response variable that has been divided by the GeoFactor. Additionally, the system can use the GeoFactor as develop or train models and update or retrain the GeoFactor(s) in an iterative manner. Such processing steps greatly improve the accuracy of predictions relating to oil well production (and other predictions) by the machine learning system. This system enables methods of visualizing and mapping how machine learning is detecting patterns between variables that are easier for human experts to communicate, audit, and understand. It enables faster processing time of subsequent processes and uses by converting multiple complex variable interactions into a single coefficient. 
     The properties and characteristics of wells will now be discussed, followed by the application of the GeoFactor onto data determined of the wells.  FIG. 2  is an illustration showing oil production of Bakken Horizontal wells. Specifically,  FIG. 2  shows approximately a dataset 4,100 Bakken Horizontal wells with sufficient data points on key completion parameters. The dataset expressed as IP365, which measures how many barrels (“bbls”) of oil a new well produces over an initial production rate of 365 days. 
       FIG. 3  is a graph showing a Bakken Well performance histogram. Specifically,  FIG. 3  shows a distribution and variability of performance of the Bakken Wells. The x-axis represents a frequency of wells and the y-axis represents the IP365 (e.g., an initial production of barrels for a 365 day period). 
       FIG. 4A  shows four graphs  32 ,  34 ,  36 ,  38  illustrating averages of Bakken Well performance trends from a time period of 2012-2016. Specifically, graph  32  shows the amount of barrels produced during the time period, graph  34  shows the amount (in pounds) of proppant used per foot during the time period, graph  36  shows the amount (in gallons) of fracking fluid used per foot during the time period, and graph  38  show the formation depth (in feet) during the time period. 
       FIG. 4B  is a graph showing selected features/factors which drive well performance. As seen, the selected features/factors include a formation depth, the amount of proppant used (in pounds per foot), the amount of fracking fluid used (in gallons per foot), the total organic carbon (“TOC”) or amount of concentration of organic material in source rocks as represented by the weight percent of organic carbon, the brittleness of the rocks, the amount of water saturation in the ground, the volume of clay (“VClay”), spacing between wells (“stage spacing), a maximum treatment rate, a thickness, a NetNutechPerm, and a maximum treatment PSI (pressure per square inch). It is noted some features/factors have a large impact on well performance (e.g., formation depth, amount of proppant used) while other have a smaller impact on well performance (NetNutechPerm, maximum treatment PSI). 
       FIGS. 5A and 5B  are graphs showing the Intensity of the Bakken Wells. Intensity is a measure of productivity related to proppant and fracking fluid. Specifically, Intensity is an estimate of the average amount of production (per lateral foot) expected from any given volume of proppant and fracking fluid.  FIG. 5A  shows measurements of proppant loading in pounds per foot on the x-axis, and the IP 365 (normalized by lateral length) per foot on the y-axis. As seen, there is a square root trend line due to diminishing returns to proppant loading.  FIG. 5B  shows measurements of fracking fluid volume in pounds per foot on the x-axis, and IP 365 (normalized by lateral length) per foot on the y-axis. 
       FIG. 6  is an illustration showing the system of the present disclosure generating a GeoFactor from subsurface characteristics, and applying the GeoFactor to oil production data. The GeoFactor acts as a multiplier to Intensity, and estimates how much geology will drive oil production above or below average productivity. Specifically,  FIG. 6  shows STOIIP data  52 , brittleness data  54 , and formation depth data  56 ) fed into the system  58 , then applied to oil production of Bakken Horizontal wells data discussed in  FIG. 2 , to generate a model  62  that estimates the GeoFactor (e.g., productivity multiplier) based on the subsurface characteristics (e.g., the STOIIP data  52 , the brittleness data  54 , and the formation data  56 ). 
       FIG. 7A  is a graph showing the Intensity of the Bakken Wells as seen and discussed in  FIG. 5A .  FIG. 7B  is a graph showing a GeoFactor normalized Intensity of the Bakken Wells Intensity. As seen, the graphs illustrate the GeoFactor normalized Intensity producing a better correlation between completion Intensity with production performance. 
       FIG. 8A  is a graph  72  showing well performance data, in barrels per foot over a 365 days average, graphed in relation to pounds of proppant per foot (represented by the x-axis) vs gallons of fracking fluid per foot (represented by the y-axis). Bar graph  74  shows a pounds of proppant per foot row count and bar graph  76  shows a gallons of fracking fluid per foot row count of graph  72 .  FIG. 8B  is a graph  78  showing the well performance in  FIG. 8A  normalized by the GeoFactor. Bar graph  80  shows a pounds of proppant per foot row count and bar graph  82  shows a gallons of fracking fluid per foot row count of graph  78 . As seen, the GeoFactor reveals clearer trends between completion and well results. 
       FIG. 9  shows a sensitivity on Intensity graph  84  based on the data shown in  FIG. 8B . Intensity is calculated as 0.49 multiplied by the square root of proppant loading (lb/ft) plus 0.0025 multiplied by fluid intensity (gal/ft). Bar graph  86  shows a pounds of proppant per foot row count and bar graph  88  shows a gallons of fracking fluid per foot row count of graph  84 . 
       FIG. 10A  is a graph  92  showing the GeoFactor of a plurality of wells graphed in relation to rock brittleness (represented by the x-axis) vs formation depth (represented by the y-axis). Bar graph  94  shows a brittleness row count and bar graph  96  shows a formation depth row count.  FIG. 10B  is a graph  98  showing the sensitivity of the GeoFactor of  FIG. 10A . Bar graph  100  shows a pounds of proppant per foot row count and bar graph  102  shows a gallons of fracking fluid per foot row count of graph  98 . It is noted that the system learns nonlinear relationships among geology variable and rock quality. 
       FIG. 11A  is a graph  112  showing the GeoFactor of a plurality of wells graphed in relation to Net-STOIIP (represented by the x-axis) vs formation depth (represented by the y-axis). Bar graph  114  shows a Net-STOIIP row count and bar graph  116  shows a formation depth row count.  FIG. 11B  is a graph  118  showing the sensitivity of the GeoFactor of  FIG. 11A . Bar graph  120  shows a Net-STOIIP row count and bar graph  122  shows a formation depth row count. 
     The system can use an F-score metric to capture remaining efficiency and effectiveness factors. Specifically, other information regarding the well and associated geology are processed into the F-Score to analyze remaining variability in well performance.  FIG. 12A  is a graph of drivers (variables) for the F-Score. The variables are split into four categories; 1) completion data, 2) geology data, 3) time, and 4) operator. The completion data can include stage spacing, maximum treatment rate, stage count, and maximum treatment PSI. The geology data can include an average Nutech Perm, a water saturation level, formation depth, TOC, thickness, VClay, and brittleness.  FIG. 12B  shows a list of other data elements that can enable the system to differentiate well performance. In the production category, the data examples include daily well, artificial lift, and choke size. In the completion category, the data examples include cluster spacing, entry points pumping schedule, fracking fluid composition, and proppant size and type. In the subsurface category, the data examples include horizontal and vertical spacing, fluid, gas to oil ratio (“GOR”), thermal material, pressure data, presence of natural fractures, and lateral placement in zone. 
     The  FIGS. 13-14C  graphs show the GeoFactor and the F-Score increasing the discriminatory power of the system. Specifically,  FIG. 13A  shows the Intensity,  FIG. 13B  shows the Intensity multiplied by the GeoFactor, and  FIG. 13C  shows the Intensity multiplied by the GeoFactor and the F-Score. As seen, Intensity explains 36% of over/under performance of wells, GeoFactor explains a further 33% of over/under performance of wells, and the F-Score explains an additional 7% of over/under performance of wells. 
       FIG. 15  is a diagram showing a hardware and software components of a computer system  202  on which the system of the present disclosure can be implemented. The computer system  202  can include a storage device  204 , machine learning software code  206 , a network interface  208 , a communications bus  210 , a central processing unit (CPU) (microprocessor)  212 , a random access memory (RAM)  214 , and one or more input devices  216 , such as a keyboard, mouse, etc. The server  202  could also include a display (e.g., liquid crystal display (LCD), cathode ray tube (CRT), etc.). The storage device  204  could comprise any suitable, computer-readable storage medium such as disk, non-volatile memory (e.g., read-only memory (ROM), eraseable programmable ROM (EPROM), electrically-eraseable programmable ROM (EEPROM), flash memory, field-programmable gate array (FPGA), etc.). The computer system  102  could be a networked computer system, a personal computer, a server, a smart phone, tablet computer etc. It is noted that the server  202  need not be a networked server, and indeed, could be a stand-alone computer system. 
     The functionality provided by the present disclosure could be provided by machine learning software code  206 , which could be embodied as computer-readable program code stored on the storage device  204  and executed by the CPU  212  using any suitable, high or low level computing language, such as Python, Java, C, C++, C#, .NET, MATLAB, etc. The network interface  208  could include an Ethernet network interface device, a wireless network interface device, or any other suitable device which permits the server  202  to communicate via the network. The CPU  212  could include any suitable single-core or multiple-core microprocessor of any suitable architecture that is capable of implementing and running the machine learning software code  206  (e.g., Intel processor). The random access memory  214  could include any suitable, high-speed, random access memory typical of most modern computers, such as dynamic RAM (DRAM), etc. 
     Having thus described the system and method in detail, it is to be understood that the foregoing description is not intended to limit the spirit or scope thereof. It will be understood that the embodiments of the present disclosure described herein are merely exemplary and that a person skilled in the art can make any variations and modification without departing from the spirit and scope of the disclosure. All such variations and modifications, including those discussed above, are intended to be included within the scope of the disclosure.