Drilling data analytics tool

Abstract

A method for determining insights about a drilling operation is disclosed. The method also includes generating a prediction for drilling a potential well in a formation in a subsurface based upon the first data and the second data. The method also includes generating a recommendation for drilling the potential well based upon the prediction. The recommendation includes surface coordinates for the potential well, a trajectory of the potential well, an azimuth of the potential well, a well design profile of the potential well, a drilling rig used to drill the potential well, a mud type pumped into the potential well, a bottom hole assembly (BHA) used to drill the potential well, a drill bit used to drill the potential well, or a weight on the drill bit.

Description

DRILLING DATA ANALYTICS TOOLCross-Reference to Related Applications

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 595,041, filed on November 1, 2023, which is incorporated by reference in its entirety.Background

[0002] Analyzing drilling data from one or more oil and gas wells in the scope of a single field is challenging due to a large amount of data, various formats, and missing details. In addition, understanding historical challenges while drilling, extracting drilling performance indicators, and identifying potential risks when planning future wells takes time (e.g., up to several weeks). Modem resources allow the aggregation of the data in a single place; however, processing the information to get insights from the data is still a challenge.Summary

[0003] A method for determining insights about a drilling operation is disclosed. The method includes receiving first data and second data. The method also includes combining the first data and the second data to produce combined data. The method also includes processing the combined data to produce processed data. The method also includes generating a prediction for drilling a potential well in a formation in a subsurface based upon the processed data. The method also includes generating a recommendation for drilling the potential well based upon the prediction. The recommendation includes surface coordinates for the potential well, a trajectory of the potential well, an azimuth of the potential well, a well design profile of the potential well, a drilling rig used to drill the potential well, a mud type pumped into the potential well, a bottom hole assembly (BHA) used to drill the potential well, a drill bit used to drill the potential well, or a weight on the drill bit.

[0004] A computing system is also disclosed. The computing system includes one or more processors and a memory system. The memory system includes one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations. The operations include receiving first data from a database. The first data includes daily drilling reports and time seriesdata from rig sensors. The operations also include receiving second data. The second data includes well trajectory data, mud reports, completion reports, or cementing reports. The operations also include combining the first data and the second data to produce combined data. The operations also include processing the combined data to produce processed data. Processing includes structuring the combined data, cleaning the combined data, aggregating the combined data, filtering the combined data, classifying the combined data, identifying first features in the combined data, identifying second features in the combined data based upon analytical goals, or labeling the combined data. The operations also include generating predictions for drilling different potential wells in a plurality of different formations in a subsurface based upon the processed data. The predictions involve a planned construction time for the different potential wells, a non-productive time (NPT) while drilling the different potential wells, drilling risks while drilling the different potential wells, and severity levels of the drilling risks. The operations also include generating recommendations for drilling the different potential wells based upon the processed data and the predictions. The recommendations include surface coordinates for the different potential wells, trajectories of the different potential wells, azimuths of the different potential wells, well design profdes of the different potential wells, a drilling rig used to drill the different potential wells, mud types pumped into the different potential wells, a bottom hole assembly (BHA) used to drill the different potential wells, drill bits used to drill the different potential wells, or weights on the drill bits. The operations also include generating a visualization based upon the predictions and the recommendations. The visualization includes a map, a chart, a table, or a graph. The visualization is a probabilistic 2D or 3D risk map.

[0005] A non-transitory computer-readable medium is also disclosed. The medium stores instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations. The operations include receiving first data from a database. The first data includes daily drilling reports and time series data from rig sensors. The first data includes a plurality of different data formats and different data types. The first data is structured or unstructured. The operations also include receiving second data. The second data includes well trajectory data, mud reports, completion reports, and cementing reports. The second data includes the plurality of different data formats and different data types. The second data is structured or unstructured. The operations also include converting the first data into converted first data. Converting the first data includes converting the different data formats and differentdata types of the first data into one or more first tables. The operations also include converting the second data into converted second data. Converting the second data includes converting the different data formats and different data types of the second data into one or more second tables. The operations also include combining the converted first data and the converted second data to produce combined data. The operations also include processing the combined data to produce processed data. Processing includes structuring the combined data, cleaning the combined data, aggregating the combined data, filtering the combined data, classifying the combined data, identifying first features in the combined data, identifying second features in the combined data based upon analytical goals, and labeling the combined data based on new features or a combination of attributes. The first features include drilling parameters, mud parameters, and mechanical specific energy (MSE) parameters. The second features include a downhole loss rate, key performance indicators (KPIs), and non-productive time (NPT). The processed data includes operational historical drilling data. The operational historical drilling data includes durations of a plurality of drilling risks per formation depth interval or section size in a plurality of different formations in a subsurface. The drilling risks include downhole losses, twist-off of a bottom hole assembly (BHA), extended backreaming, a health, safety, and environment (HSE) incident, fishing, influx, lost circulation, failure of a measurement-while-drilling (MWD) tool, failure of a mud motor, failure of a rotary steerable system (RSS), the non-productive time of a drilling rig, a stuck pipe, a surface waiting time at the drilling rig, a tight hole portion, and an instability. The operations also include generating predictions for drilling different potential wells in the plurality of different formations based upon the processed data. The predictions are also generated based upon user input. The user input includes surface coordinates for the different potential wells, trajectories of the different potential wells, total depths of the different potential wells, well design profiles of the different potential wells, planned drilling times for the different potential wells, and the drilling rig used to drill the different potential wells. The predictions involve a planned construction time for the different potential wells, the non-productive time while drilling the different potential wells, the drilling risks while drilling the different potential wells, and severity levels of the drilling risks. The operations also include generating recommendations for drilling the different potential wells based upon the processed data, the predictions, and the user input. The recommendations include the surface coordinates for the different potential wells, the trajectories of the different potential wells, azimuths of the different potential wells, the well design profilesof the different potential wells, the drilling rig used to drill the different potential wells, mud types pumped into the different potential wells, the BHA used to drill the different potential wells, drill bits used to drill the different potential wells, and weights on the drill bits. The operations also include generating a visualization based upon the predictions and the recommendations. The visualization includes a map, a chart, a table, or a graph. The visualization is a probabilistic 2D or 3D risk map.Brief Description of the Drawings

[0006] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present teachings and together with the description, serve to explain the principles of the present teachings. In the figures:

[0007] Figure 1 illustrates an example of a system that includes various management components to manage various aspects of a geologic environment, according to an embodiment.

[0008] Figure 2 illustrates a flowchart of a method for determining insights about a drilling operation, according to an embodiment.

[0009] Figure 3 illustrates a schematic view of the method in Figure 2, according to an embodiment.

[0010] Figure 4 illustrates a chart showing historical data distribution, according to an embodiment.

[0011] Figure 5 illustrates a schematic view of a portion of the method (e.g., generating predictions) in Figure 2, according to an embodiment.

[0012] Figure 6 illustrates a schematic view of a portion of the method (e.g., generating recommendations) in Figure 2, according to an embodiment.

[0013] Figure 7 illustrates a schematic view of a computing system for performing at least a portion of the method(s) herein, according to an embodiment.Detailed Description

[0014] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings and figures. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to one of ordinary skill in the art that the invention may be practicedwithout these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0015] It will also be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first object or step could be termed a second object or step, and, similarly, a second object or step could be termed a first object or step, without departing from the scope of the present disclosure. The first object or step, and the second object or step, are both, objects or steps, respectively, but they are not to be considered the same object or step.

[0016] The terminology used in the description herein is for the purpose of describing particular embodiments and is not intended to be limiting. As used in this description and the appended claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and / or” as used herein refers to and encompasses any possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. Further, as used herein, the term “if’ may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context.

[0017] Attention is now directed to processing procedures, methods, techniques, and workflows that are in accordance with some embodiments. Some operations in the processing procedures, methods, techniques, and workflows disclosed herein may be combined and / or the order of some operations may be changed. The present disclosure includes a drilling data analytics tool that may serve drilling team members by processing data and providing insights for efficient decisionmaking. The drilling data analytics tool (and corresponding method) may provide interactivity for a user to display insights from the data. Thus, the user may not have to go over multiple files (e.g., Excel, PDFs, Word documents) to read them and extract certain information.

[0018] Drilling operations generate vast amounts of data from multiple sources, including sensors, logs, geological surveys, and reports. The drilling data analytics tool (and correspondingmethod) may help to analyze and make sense of this diverse and high-volume data. Drilling engineers and operators make complex decisions regarding well placement, drilling parameters, and / or equipment usage. The drilling data analytics tool (and corresponding method) may provide decision support by offering insights and recommendations based on historical data. In addition, the drilling data analytics tool (and corresponding method) may provide insight of drilling events without the user having to open the reports (e.g., daily drilling reports or completion reports).

[0019] Additionally, the method may provide recommendations and predictions to the user based on input from user. More particularly, the method may provide recommendations based on analyses performed. For example, based on a proposed location by a user of the subject (e.g., planned) well, a recommendation such as well design, trajectory, azimuth, expected risks, bit, bottom hole assembly, mud type and weight, drilling parameters may be provided. As another example, based on user input about a subject well such as well design, trajectory and drilling rig to be used, a prediction of following the features may be provided: well construction time, nonproductive time, drilling risks with severity level, etc. In another example, based on user input such as well design, well profile, well target depth, planned well construction time, the method may propose surface coordinates.System Overview

[0020] Figure 1 illustrates an example of a system 100 that includes various management components 110 to manage various aspects of a drilling data what includes data from rig sensors, downhole sensors, data from drilling reports, mud reports, and mud logging reports. Also, it manages data produced by engineering team such as summaries, reports, trajectories, etc.

[0021] In the example of Figure 1, the management components 110 include a data from rig sensors and / or bottom hole assembly sensors (e.g., WTSML format) component 112. The management components 110 also include a historical data (reports, summaries, etc.) in various formats (excel, pdf, word, etc.) component 114 (e.g., well / logging data). The management components 110 may a processing component 116, a database component 122, an analysis / visualization component 142, recommendations component 145, prediction component 146, and a workflow component 144. The database component 122 may aggregate, store, and / or process the data (e.g., data from the sensors and / or historical data). The analysis / visualization component 142 may generate one or more visualizations (e.g., a map, a chart, a graph) based uponthe processed data, as described in greater detail below. The recommendations component 145 may generate one or more recommendations (e.g., to perform a particular action) based at least partially upon the processed data and / or user input, as described in greater detail below. The prediction component 146 may generate one or more predictions (e.g., of future events that may occur) based at least partially upon the processed data and / or user input, as described in greater detail below.

[0022] In the example of Figure 1, the database component 122 may contain various features such as well name, surface coordinates, holesize, report date, trajectories, depths, operations summaries, key performance indicators, formation names, casing sizes, casing points, well design, well shape, time series data and other features based on analytic targets. As an example, output from the database component 122 may be input to one or more other workflows, as indicated by the workflow component 144.

[0023] As an example, the domain objects 182 can include entity objects, property objects, and optionally other objects. Entity objects may be used to geometrically represent wells, etc., while property objects may be used to provide property values as well as data versions and display parameters. For example, an entity object may represent a well where a property object provides log information as well as version information and display information (e.g., to display the well as part of a model).

[0024] In the example of Figure 1, data may be stored in one or more data sources (or data stores, generally physical data storage devices), which may be at the same or different physical sites and accessible via one or more networks. The data analytics engine 180 may be configured to model projects. As such, a particular project may be stored where stored project information may include inputs, models, results and cases. Thus, upon completion of a modeling session, a user may store a project.

[0025] In the example of Figure 1, the drilling environment 150 may include well that includes a wellbore, different sections, and data about drilling by seconds, minutes, hours, and / or days. As an example, the drilling environment 150 may be outfitted with any of a variety of sensors, detectors, actuators, etc. Such information may include information associated with downhole equipment 154, which may be equipment to acquire information, to assist with resource recovery, etc. Other equipment 156 may be located remote from a well site and include sensing, detecting, emitting or other circuitry. Such equipment 156A may include storage and communicationcircuitry to store and to communicate data, instructions, etc. As an example, one or more satellites 156B may be provided for purposes of communications, data acquisition, etc. For example, Figure 1 shows a satellite 156B in communication with the network 155 that may be configured for communications, noting that the satellite may additionally or instead include circuitry for imagery (e.g., spatial, spectral, temporal, radiometric, etc.).

[0026] Figure 1 also shows the drilling environment 150 as including equipment 157 and 158 associated with a drilling a well. As mentioned, the system 100 may be used to perform one or more workflows. A workflow may be a process that includes a number of worksteps. A workstep may operate on data, for example, to create new data, to update existing data, etc. As an example, a may operate on one or more inputs and create one or more results, for example, based on one or more algorithms. As an example, a system may include a workflow editor for creation, editing, executing, etc. of a workflow.Exemplary Method

[0027] Figure 2 illustrates a flowchart of a method 200 for determining insights about a drilling operation, according to an embodiment. More particularly, the method 200 may be used to control a drilling operation. Figure 3 illustrates a schematic view of the flowchart in Figure 2, according to an embodiment. An illustrative order of the method 200 is provided below; however, one or more portions of the method 200 may be performed in a different order, simultaneously, repeated, or omitted.

[0028] The method 200 may include receiving first data, as at 205. This is also shown at 305 in Figure 3. The first data may be captured while drilling a well (e.g., using a bottom hole assembly). The first data may be received from a database. The first data may be structured, unstructured, or both. The first data may include daily drilling reports and / or time series data (e.g., from rig sensors). In one embodiment, the first data may be presented in one or more first tables. In another embodiment, the first data may not be presented in the one or more first tables and may instead include a plurality of different data formats and / or types (e.g., Word, Excel, CSV, PDF).

[0029] The method 200 may also include converting the first data into converted first data, as at 210. This is also shown at 310 in Figure 3. This may include converting the different data formats and / or types of the first data into one or more first tables.

[0030] The method 200 may also include receiving second data, as at 215. This is also shown at 315 in Figure 3. The second data may be captured while drilling the well and / or during an evaluation phase (e.g., 2-3 weeks after the well is completed). The second data may be added and / or received manually (e.g., by uploading and / or downloading various fdes). The second data may be structured, unstructured, or both. The second data may include well trajectory data, mud reports, completion reports, cementing reports, or a combination thereof. The second data may include a plurality of different data formats and / or types (e.g., Word, Excel, CSV, PDF). In one embodiment, the second data may not be in the data format and / or type of one or more second tables when received. The first and second data can be stored in different databases. The method 200 integrates, structures, and stores the data in a local space (e.g., in the first database, the second database, or both).

[0031] The method 200 may also include converting the second data into converted second data, as at 220. This is also shown at 320 in Figure 3. This may also or instead include converting the different data formats and / or types of the second data into one or more second tables.

[0032] The method 200 may also include combining the converted first data and the converted second data to produce combined data, as at 225. This is also shown at 325 in Figure 3. The combined data may include the one or more first tables, the one or more second tables, or both. The table(s) may be or include structured tables.

[0033] The method 200 may also include processing the combined data to produce processed data, as at 230. This is also shown at 330 in Figure 3. The processed data may be presented in an analytic-based table. Processing may include structuring the combined data, cleaning the combined data, aggregating the combined data, filtering the combined data, classifying the combined data, identifying or generating first features in the combined data (e.g., drilling parameters, mud parameters, mechanical specific energy (MSE) parameters, drilling performance indicators, total well construction duration, etc.), identifying second features in the combined data (e.g., downhole loss rate, key performance indicators, non-productive time) based upon analytical goals, labeling the combined data based upon new features or a combination of attributes, or a combination thereof. The combined data may be processed using machine learning (ML) algorithms, natural language processing algorithms, or both (e.g., which may help to recognize the text in the summaries). The combined data may be processed based upon the data format and / ortype of the second data before being converted. In one embodiment, the processed data may then be further processed (e.g., using Python).Operational Historical Drilling Data

[0034] Figure 4 illustrates a chart (e.g., a histogram) 400 showing historical data distribution, according to an embodiment. The processed data may include operational historical drilling data. The operational historical drilling data may include durations of a plurality of risks per formation depth interval or section size in a plurality of different formations 410, 420, 430 in a subsurface. The risks may include downhole losses, twist-off of a bottom hole assembly (BHA), extended backreaming, a health, safety, and environment (HSE) incident, fishing, influx, lost circulation, failure of a measurement-while-drilling (MWD) tool, failure of a mud motor, failure of a rotary steerable system (RSS), the non-productive time of a drilling rig, a stuck pipe, a surface waiting time at the drilling rig, a tight hole portion, instability, or a combination thereof.

[0035] The method 200 may also include generating a prediction, as at 235. This is also shown at 335 in Figure 3. The prediction may include one or more predictions related to drilling different potential wells in the plurality of different formations. The prediction may be based upon the processed data and user input. The user input may be or include surface coordinates for the different potential wells, trajectories of the different potential wells, total depths of the different potential wells, well design profiles of the different potential wells, planned drilling times for the different potential wells, the drilling rig used to drill the different potential wells, or a combination thereof. The predictions may involve a planned construction time for the different potential wells, the non-productive time while drilling the different potential wells, and drilling risks and severity levels while drilling the different potential wells.

[0036] An example of generating a prediction is also shown in Figure 5. In this example, the data may be collected and pre-processed, as at 525. This corresponds to steps 205-225 in Figure 2. This may yield processed historical data, as at 530. This corresponds to step 230 in Figure 2. Then, one or more properties may be predicted and / or a risk distribution may be predicted, as at 535. This corresponds to steps 235 in Figure 2. Finally, a visualization may be generated, as at 545. This corresponds to steps 245 in Figure 2.

[0037] The method 200 may also include generating a recommendation, as at 240. This is also shown at 340 in Figure 3. The recommendation(s) may be generated based upon the combineddata, the processed data, the prediction, user input, or a combination thereof. In one example, the user input may include a proposed location of a well, and the recommendation may include a design of the well, a trajectory of the well, an azimuth of the well, an expected risk while drilling the well, a drill bit used to drill the well, a bottom hole assembly that includes the drill bit, a mud type pumped into the well, a weight on the drill bit, or a combination thereof. In another example, the user input may include the design of the well, a profde of the well, a target depth of the well, a planned construction time for the well, or a combination thereof, and the recommendation may include surface coordinates for the well. In yet another example, the recommendations may include the surface coordinates for the different potential wells, the trajectories of the different potential wells, azimuths of the different potential wells, the well design profdes of the different potential wells, the drilling rig used to drill the different potential wells, the drilling risks and severity levels while drilling the different potential wells, mud types pumped into the different potential wells, the bottom hole assembly (BHA) used to drill the different potential wells, drill bits used to drill the different potential wells, and weights on the drill bits.

[0038] An example of generating a recommendation is also shown in Figure 6. In this example, the drilling risks may be determined as at 635. This corresponds to step 235 in Figure 2. Then, one or more recommendations may be generated, as at 640. This corresponds to step 240 in Figure 2. The recommendations may also be based upon subject well details such as surface coordinates, well shape, and / or the target formation. Finally, a visualization may be generated, as at 645. This corresponds to steps 245 in Figure 2.

[0039] The method 200 may also include generating a visualization of the processed data, as at 245. This is also shown at 345 in Figure 3. The visualization may be based upon the prediction and / or the recommendation. The visualization may include a map, a chart, a graph, or a combination thereof. For example, the visualization may include a probabilistic 2D or 3D risk map. The visualization may display drilling events that occurred in a selected depth interval or section size. The drilling events may include downhole losses, bottom hole assembly (BHA) twist- off, well control events, extended backreaming, stuck pipe events, health, safety, and environmental (HSE) incidents, or a combination thereof. The visualization (e.g., maps) may be interactive. For example, when hovering over a well on the map, one or more details about the drilling events may be displayed, such as non-productive time related to the event, mud weightused, summary of the event, depth, data and time of the event, etc. The visualization may also or instead include various charts (e.g., bar, scatter plot, histogram, etc.).

[0040] The method 200 may also include selecting one of the different potential wells, as at 250. The selection may be based upon the predictions, the recommendations, the visualization, or a combination thereof.

[0041] The method 200 may also include performing an action, as at 255. The action may be or include a wellsite action that is based upon the combined data, the processed data, the prediction, the recommendation, the visualization, the selected different potential well, or a combination thereof. The wellsite action may be or include generating and / or transmitting a signal (e.g., using a computing system) that instructs or causes a physical action to occur at a wellsite. The wellsite action may also or instead include performing the physical action at the wellsite. The physical action may include implementing the recommendation in / or the selected different potential well. The physical action may also or instead include changing drilling parameters (e.g., weight on bit, rotation, flow rate), changing in drilling practice (e.g., pre-connection or / and post connection), changing wiper trips, changing drilling mud parameters (e.g., weight, lubricant concentration, etc.) The wellsite action may also or instead include any other action such as well planning (e.g., well design selection, trajectory planning, and / or a method or technology implementation to improve drilling performance or to reduce non-productive time).

[0042] In one embodiment, the drilling data analytics tool (and corresponding method 200) may provide a web-based solution (e.g., Dataiku) that can be connected to a database with the input. Additional input can be added, as mentioned above. Dataiku has an engine for the data processing part and artificial intelligence. The visualization part can be performed in Dataiku or transferred through an established connection to the Power BI or a like software.

[0043] The drilling data analytics tool (and corresponding method 200) may provide insights such as: bottom hole assembly details, bit type, bit dull grading, summary of the drilling or HSE event, mud weight, mud type, depth and time of incident, and more based on user requests and / or analysis purpose. The drilling data analytics tool (and corresponding method 200) may be applied to plan new wells (e.g., offset well analysis), evaluate drilling performance, modify (e.g., optimize) drilling operations, reduce non-productive time, reduce invisible lost time, reduce drilling risks, or a combination thereof.Exemplary Computing System

[0044] In some embodiments, the methods of the present disclosure may be executed by a computing system. Figure 7 illustrates an example of such a computing system 700, in accordance with some embodiments. The computing system 700 may include a computer or computer system 701A, which may be an individual computer system 701A or an arrangement of distributed computer systems. The computer system 701A includes one or more analysis modules 702 that are configured to perform various tasks according to some embodiments, such as one or more methods disclosed herein. To perform these various tasks, the analysis module 702 executes independently, or in coordination with, one or more processors 704, which is (or are) connected to one or more storage media 706. The processor(s) 704 is (or are) also connected to a network interface 707 to allow the computer system 701A to communicate over a data network 709 with one or more additional computer systems and / or computing systems, such as 70 IB, 701C, and / or 70 ID (note that computer systems 70 IB, 701C and / or 70 ID may or may not share the same architecture as computer system 701A, and may be located in different physical locations, e.g., computer systems 701 A and 70 IB may be located in a processing facility, while in communication with one or more computer systems such as 701 C and / or 70 ID that are located in one or more data centers, and / or located in varying countries on different continents).

[0045] A processor may include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.

[0046] The storage media 706 may be implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment of Figure 7 storage media 706 is depicted as within computer system 701 A, in some embodiments, storage media 706 may be distributed within and / or across multiple internal and / or external enclosures of computing system 701 A and / or additional computing systems. Storage media 706 may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories, magnetic disks such as fixed, floppy and removable disks, other magnetic media including tape, optical media such as compact disks (CDs) or digital video disks (DVDs), BLURAY® disks, or other types of optical storage, or other types of storage devices. Note that theinstructions discussed above may be provided on one computer-readable or machine-readable storage medium, or may be provided on multiple computer-readable or machine-readable storage media distributed in a large system having possibly plural nodes. Such computer-readable or machine-readable storage medium or media is (are) considered to be part of an article (or article of manufacture). An article or article of manufacture may refer to any manufactured single component or multiple components. The storage medium or media may be located either in the machine running the machine-readable instructions, or located at a remote site from which machine-readable instructions may be downloaded over a network for execution.

[0047] In some embodiments, computing system 700 contains one or more data analytics module(s) 708. It should be appreciated that computing system 700 is merely one example of a computing system, and that computing system 700 may have more or fewer components than shown, may combine additional components not depicted in the example embodiment of Figure 7, and / or computing system 700 may have a different configuration or arrangement of the components depicted in Figure 7. The various components shown in Figure 7 may be implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and / or application specific integrated circuits.

[0048] Further, the steps in the processing methods described herein may be implemented by running one or more functional modules in information processing apparatus such as general purpose processors or application specific chips, such as ASICs, FPGAs, PLDs, or other appropriate devices. These modules, combinations of these modules, and / or their combination with general hardware are included within the scope of the present disclosure.

[0049] Computational interpretations, models, and / or other interpretation aids may be refined in an iterative fashion; this concept is applicable to the methods discussed herein. This may include use of feedback loops executed on an algorithmic basis, such as at a computing device (e.g., computing system 700, Figure 7), and / or through manual control by a user who may make determinations regarding whether a given step, action, template, model, or set of curves has become sufficiently accurate for the evaluation of the subsurface three-dimensional geologic formation under consideration.

[0050] The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or limiting to the precise forms disclosed. Many modifications and variations arepossible in view of the above teachings. Moreover, the order in which the elements of the methods described herein are illustrate and described may be re-arranged, and / or two or more elements may occur simultaneously. The embodiments were chosen and described in order to best explain the principles of the disclosure and its practical applications, to thereby enable others skilled in the art to best utilize the disclosed embodiments and various embodiments with various modifications as are suited to the particular use contemplated.

Claims

CLAIMSWhat is claimed is:

1. A method for determining insights about a drilling operation, the method comprising: receiving first data and second data; combining the first data and the second data to produce combined data; processing the combined data to produce processed data; generating a prediction for drilling a potential well in a formation in a subsurface based upon the processed data; and generating a recommendation for drilling the potential well based upon the prediction, wherein the recommendation comprises surface coordinates for the potential well, a trajectory of the potential well, an azimuth of the potential well, a well design profile of the potential well, a drilling rig used to drill the potential well, a mud type pumped into the potential well, a bottom hole assembly (BHA) used to drill the potential well, a drill bit used to drill the potential well, or a weight on the drill bit.

2. The method of claim 1, wherein the first data comprises daily drilling reports and time series data from rig sensors.

3. The method of claim 1, wherein the second data comprises well trajectory data, mud reports, completion reports, or cementing reports.

4. The method of claim 1, wherein processing comprises structuring the combined data, cleaning the combined data, aggregating the combined data, filtering the combined data, classifying the combined data, identifying first features in the combined data, identifying second features in the combined data based upon analytical goals, or labeling the combined data.

5. The method of claim 4, wherein the first features comprise drilling parameters, mud parameters, or mechanical specific energy (MSE) parameters, and wherein the second features comprise a downhole loss rate, key performance indicators (KPIs), or a non-productive time (NPT).

6. The method of claim 1, wherein the processed data comprises operational historical drilling data, and wherein the operational historical drilling data comprises a duration of a drilling risk per formation depth interval or section size in the formation, and wherein the drilling risk comprises downhole losses, twist-off of the BHA, extended backreaming, a health, safety, and environment (HSE) incident, fishing, influx, lost circulation, failure of a measurement-while-drilling (MWD) tool, failure of a mud motor, failure of a rotary steerable system (RSS), the non-productive time of the drilling rig, a stuck pipe, a surface waiting time at the drilling rig, a tight hole portion, or an instability.

7. The method of claim 6, wherein the prediction involves a planned construction time for the potential well, a non-productive time (NPT) while drilling the potential well, the drilling risk while drilling the potential well, or a severity level of the drilling risk.

8. The method of claim 1, wherein the prediction is also generated based upon user input, and wherein the user input comprises the surface coordinates for the potential well, the trajectory of the potential well, a total depth of the potential well, the well design profile of the potential well, a planned drilling time for the potential well, or the drilling rig used to drill the potential well.

9. The method of claim 1, further comprising generating a visualization based upon the prediction and the recommendation, wherein the visualization comprises a map, a chart, a table, or a graph.

10. The method of claim 1, further comprising performing a wellsite action in response to the prediction and the recommendation.

11. A computing system, comprising: one or more processors; and a memory system comprising one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations, the operations comprising:receiving first data from a database, wherein the first data comprises daily drilling reports and time series data from rig sensors; receiving second data, wherein the second data comprises well trajectory data, mud reports, completion reports, or cementing reports; combining the first data and the second data to produce combined data; processing the combined data to produce processed data, wherein processing comprises structuring the combined data, cleaning the combined data, aggregating the combined data, filtering the combined data, classifying the combined data, identifying first features in the combined data, identifying second features in the combined data based upon analytical goals, or labeling the combined data; generating predictions for drilling different potential wells in a plurality of different formations in a subsurface based upon the processed data, wherein the predictions involve a planned construction time for the different potential wells, a non-productive time (NPT) while drilling the different potential wells, drilling risks while drilling the different potential wells, and severity levels of the drilling risks; generating recommendations for drilling the different potential wells based upon the processed data and the predictions, wherein the recommendations comprise surface coordinates for the different potential wells, trajectories of the different potential wells, azimuths of the different potential wells, well design profiles of the different potential wells, a drilling rig used to drill the different potential wells, mud types pumped into the different potential wells, a bottom hole assembly (BHA) used to drill the different potential wells, drill bits used to drill the different potential wells, or weights on the drill bits; and generating a visualization based upon the predictions and the recommendations, wherein the visualization comprises a map, a chart, a table, or a graph, and wherein the visualization comprises a probabilistic 2D or 3D risk map.

12. The computing system of claim 11, wherein the first features comprise drilling parameters, mud parameters, or mechanical specific energy (MSE) parameters, and wherein the second features comprise a downhole loss rate, key performance indicators (KPIs), or the non-productive time.

13. The computing system of claim 11, wherein the processed data comprises operational historical drilling data, and wherein the operational historical drilling data comprises durations of the drilling risks per formation depth interval or section size in the plurality of different formations.

14. The computing system of claim 13, wherein the drilling risks comprise downhole losses, twist-off of the BHA, extended backreaming, a health, safety, and environment (HSE) incident, fishing, influx, lost circulation, failure of a measurement-while-drilling (MWD) tool, failure of a mud motor, failure of a rotary steerable system (RSS), the non-productive time of the drilling rig, a stuck pipe, a surface waiting time at the drilling rig, a tight hole portion, or an instability.

15. The computing system of claim 11, wherein the predictions are also generated based upon user input, and wherein the user input comprises the surface coordinates for the different potential wells, the trajectories of the different potential wells, total depths of the different potential wells, the well design profdes of the different potential wells, planned drilling times for the different potential wells, or the drilling rig used to drill the different potential wells.

16. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations, the operations comprising: receiving first data from a database, wherein the first data comprises daily drilling reports and time series data from rig sensors, wherein the first data comprises a plurality of different data formats and different data types, and wherein the first data is structured or unstructured; receiving second data, wherein the second data comprises well trajectory data, mud reports, completion reports, and cementing reports, wherein the second data comprises the plurality of different data formats and different data types, and wherein the second data is structured or unstructured; converting the first data into converted first data, wherein converting the first data comprises converting the different data formats and different data types of the first data into one or more first tables;converting the second data into converted second data, wherein converting the second data comprises converting the different data formats and different data types of the second data into one or more second tables; combining the converted first data and the converted second data to produce combined data; processing the combined data to produce processed data, wherein processing comprises structuring the combined data, cleaning the combined data, aggregating the combined data, filtering the combined data, classifying the combined data, identifying first features in the combined data, identifying second features in the combined data based upon analytical goals, and labeling the combined data based on new features or a combination of attributes, wherein the first features comprise drilling parameters, mud parameters, and mechanical specific energy (MSE) parameters, wherein the second features comprise a downhole loss rate, key performance indicators (KPIs), and non-productive time (NPT), wherein the processed data comprises operational historical drilling data, wherein the operational historical drilling data comprises durations of a plurality of drilling risks per formation depth interval or section size in a plurality of different formations in a subsurface, wherein the drilling risks comprise downhole losses, twist- off of a bottom hole assembly (BHA), extended backreaming, a health, safety, and environment (HSE) incident, fishing, influx, lost circulation, failure of a measurement-while-drilling (MWD) tool, failure of a mud motor, failure of a rotary steerable system (RSS), the non-productive time of a drilling rig, a stuck pipe, a surface waiting time at the drilling rig, a tight hole portion, and an instability; generating predictions for drilling different potential wells in the plurality of different formations based upon the processed data, wherein the predictions are also generated based upon user input, wherein the user input comprises surface coordinates for the different potential wells, trajectories of the different potential wells, total depths of the different potential wells, well design profdes of the different potential wells, planned drilling times for the different potential wells, and the drilling rig used to drill the different potential wells, wherein the predictions involve a planned construction time for the different potential wells, the non-productive time while drilling the different potential wells, the drilling risks while drilling the different potential wells, and severity levels of the drilling risks;generating recommendations for drilling the different potential wells based upon the processed data, the predictions, and the user input, wherein the recommendations comprise the surface coordinates for the different potential wells, the trajectories of the different potential wells, azimuths of the different potential wells, the well design profiles of the different potential wells, the drilling rig used to drill the different potential wells, mud types pumped into the different potential wells, the BHA used to drill the different potential wells, drill bits used to drill the different potential wells, and weights on the drill bits; and generating a visualization based upon the predictions and the recommendations, wherein the visualization comprises a map, a chart, a table, or a graph, and wherein the visualization comprises a probabilistic 2D or 3D risk map.

17. The non-transitory computer-readable medium of claim 16, wherein the operations further comprise selecting one of the different potential wells based upon the predictions, the recommendations, and the visualization.

18. The non-transitory computer-readable medium of claim 17, wherein the operations further comprise performing a wellsite action in response to the predictions, the recommendations, and the selected different potential well.

19. The non-transitory computer-readable medium of claim 18, wherein performing the wellsite action comprises generating and / or transmitting a signal that instructs or causes a physical action to occur at a wellsite of the selected different potential well.

20. The non-transitory computer-readable medium of claim 19, wherein the physical action comprises selecting where to drill the selected different potential well, drilling the selected different potential well, varying a weight and / or torque on the drill bit that is drilling the selected different potential well, varying the trajectory of the selected different potential well, or varying a concentration and / or flow rate of a fluid pumped into the selected different potential well.

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