DATA DRIVEN DESCALING

Abstract

A computer implemented method that enables data driven descaling is described. The method includes obtaining data associated with a descaling target and deriving engineered features from the data associated with the descaling target. A machine learning model is selected and trained to predict the use of chemical descaling operations or mechanical descaling operations using the engineered features.

Description

TECHNICAL FIELD

This disclosure relates to descaling operations.

BACKGROUND

Scale may be caused by precipitation due to chemical reactions, such as a chemical reaction with the surface, precipitation due to a change in pressure or temperature, or precipitation due to a change in the composition of a solution. In some cases, scale may occur on well tubing and components as the saturation of produced water is affected by changing temperature and pressure conditions during production.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 shows a workflow the describes data driven descaling.

FIG. 2 is a process flow diagram of a process that enables data driven descaling.

FIG. 3 illustrates hydrocarbon production operations that include both one or more field operations and one or more computational operations, which exchange information and control exploration for the production of hydrocarbons.

FIG. 4 is a schematic illustration of an example controller (or control system) for that enables data driven descaling.

DETAILED DESCRIPTION

Scale forms in pipelines, on wellbores, on surface instruments, and on other equipment and the like due to thermodynamic, kinetic, and chemical interchange among hydrocarbons. Scale can form in, for example, hydrocarbon and water producing wells. Scale can reduce hydrocarbon flow, overpower production, and cause the collapse of downhole equipment such as electrical submersible pumps, chokes, and valves.

Descaling operations are executed to remove scale from pipelines, wellbores, surface instruments, equipment and the like used in oil and gas exploration, production, refining, and transportation. In examples, descaling operations use milling, chemicals, or gelling agents with coiled tubing that is equipped with a motor and drill bit to apply the selected descaling modality. Coiled tubing is used to place fluid and mechanical tools accurately and precisely at specific depths where scale occurs. In examples, chemical descaling refers to the use of chemicals or gelling agents to remove scale, and mechanical descaling refers to the use of physical tools to remove scale, such as milling.

The present techniques are directed to data driven descaling. In some embodiments, data associated with a descaling target is obtained, and feature engineering is used to derive features from the data associated with a descaling target. A machine learning model is selected and trained to predict the use of chemical descaling operations or mechanical descaling operations using the engineered features for a relevant descaling area, such as a drilled interval.

Some advantages of the present techniques include using a combination of features to robustly predict a descaling operation. The features are developed based on the data associated with the descaling target, thereby harnessing the power of historical data from past descaling operations executed at the descaling target to make informed decisions on the most suitable solution, such as mechanical descaling operations or chemical operations, while also determining an optimal number of coiled tubing runs associated with the drilling interval. Through this comprehensive approach, the present techniques increase the overall success rate of descaling operations by engineering features for each respective descaling target, and ensure enhanced efficiency and performance of descaling operations.

FIG. 1 shows a workflow 100 that enables data driven descaling. The data driven descaling predicts an optimal solution for descaling based on data associated with a descaling target. At data collection 102, data associated with a descaling target is obtained. For ease of description, the descaling target is described as a well. However, the present techniques can be used to remove scale from pipelines, wells, surface instruments, equipment, pumps, chokes, valves and the like as used in oil and gas exploration, production, refining, and transportation. In examples, the data obtained includes a previous descaling operation (if any), oil rate, water cut, reservoir, geochemical analysis, well intervention history, temperature, pressure, scale composition, and tag depth. The data associated with the previous descaling operation describes if the candidate well was previously subjected to descaling. Wells that are previously descaled are more likely to be subjected to subsequent descaling operations. Reservoir data includes a name of the reservoir associated with each respective well. In fields, one or more reservoirs can be found. In some embodiments, wells associated with the same reservoir are analyzed for consistency. Geochemical analysis data shows the chemical concentrations for substances such as Cl, Na, K, Ca, and Mg. Well intervention history data includes a written report associated with respective wells. The report is completed after each major operation on a well. In examples, when a well is subjected to descaling, a well intervention history item is reported. The well intervention history provides information on the sequence and events that occur in wells similar to the candidate well. In examples, scale composition refers to the chemical composition of the scale that is collected, and then analyzed in the lab. By including the chemical composition in the model, the model can optimize the approach of removing this specific scale. Additionally, tag depth refers to a depth that usually cannot be passed with coiled tubing due to the obstruction of scale that is formed in the well. Tag depth is found out through the deployment of a gauge cutter on slickline.

At data pre-processing 104, the obtained data is preprocessed. In examples, preprocessing the data includes removing outliers and handling missing data. In the data preprocessing stage, missing data is filled using various techniques, such as mean imputation, and outliers are identified and subsequently removed to avoid their adverse impact on the model performance.

At feature engineering 106, the most important features are selected. In examples, the most important features include previous descaling operation, water cut, and scale composition. The most important features that have a significant impact on model accuracy are selected to train a machine learning model. In some embodiments, the most important features are selected based on their respective importance and correlating impact on the model accuracy. However, selection of other features can be performed on the fly so that other features deemed important can be used to build the model.

In some embodiments, the most important features are selected from features that are engineered from the input data. Underlying patterns are detected in the input data and used to create engineered features. In examples, the most important features are selected from the engineered features. Using engineered features further improves predictive capabilities of the trained machine learning model.

At modeling and evaluation 108, a best machine learning model is selected. In some embodiments, multiple machine learning models are trained using the most important features selected from the engineered features. In examples, the best machine learning model is a K-Nearest Neighbors (KNN) classifier. In examples, the machine learning model is iteratively updated by continuously evaluating the trained model's performance and validating its predictions to identify areas of improvement. One effective approach to enhance model accuracy is by incorporating new descaling operations data into the model. The inclusion of descaling operation data enables the model to learn from the latest information and adapt to evolving patterns and trends. As the model encounters new data, it can refine its understanding of the underlying patterns and improve its predictive capabilities. Through ongoing evaluation and validation, model performance is assessed on the new descaling operations data. This process helps identify any limitations or areas where the model may be underperforming. Consequently, corrective measures are implemented, such as fine-tuning the model's parameters or adjusting the feature engineering techniques, to further enhance its accuracy. By regularly updating the model with new data and leveraging the insights gained through evaluation and validation, the model remains effective and accurate in its predictions. This iterative approach enables continuous improvement and ensures that the model aligns with the evolving dynamics of the descaling operations domain.

At applications 110, the model will select an intervention type where an output of 1 corresponds to for mechanical descaling operations and an output of 2 corresponds to chemical descaling operations. To output an intervention type, the trained machine learning model is executed. In examples, a visualization platform renders the model output as shown at reference number 112 For ease of illustration, the output is shown in text form. However, the present techniques can render the output using images, video, text, or any combinations thereof. In examples, the rendered output includes the input variables and the predicted descaling operations. For example, a drilled interval corresponds to inputs of Oil rate=0.5 mbod; Scale composition=4; Pressure=922; and WC=15%; a trained machine learning model predicts 2 for chemical descaling at the subject interval. In examples, the rendered output is an image of a well with descaling operations predicted for one or more intervals.

FIG. 2 is a process flow diagram of a process 200 that enables data driven descaling. At block 202, data associated with the descaling target is obtained. At block 204, features are selected from the data associated with the descaling target. In some examples, feature engineering is performed on the data associated with the descaling target to derive features based on patterns found in the historical data. In examples, the most significant features are extracted from the data associated with the descaling target to create a training dataset.

At block 206, a selected machine learning model is trained using the training dataset. In examples, the machine learning model is selected based on trials of different machine learning models. In some embodiments, a visualization is rendered that shows predicted descaling operations associated with at least one drilled interval and the predicted number of coiled tubes used in the interval. For example, the visualization can show the predicted descaling operations for multiple intervals of a drilled well.

FIG. 3 illustrates hydrocarbon production operations 300 that include both one or more field operations 310 and one or more computational operations 312, which exchange information and control exploration for the production of hydrocarbons. In some implementations, outputs of techniques of the present disclosure can be performed before, during, or in combination with the hydrocarbon production operations 300, specifically, for example, either as field operations 310 or computational operations 312, or both.

Examples of field operations 310 include forming/drilling a wellbore, hydraulic fracturing, producing through the wellbore, injecting fluids (such as water) through the wellbore, to name a few. In some implementations, methods of the present disclosure can trigger or control the field operations 310. For example, the methods of the present disclosure can generate data from hardware/software including sensors and physical data gathering equipment (e.g., seismic sensors, well logging tools, flow meters, and temperature and pressure sensors). The methods of the present disclosure can include transmitting the data from the hardware/software to the field operations 310 and responsively triggering the field operations 310 including, for example, generating plans and signals that provide feedback to and control physical components of the field operations 310. Alternatively or in addition, the field operations 310 can trigger the methods of the present disclosure. For example, implementing physical components (including, for example, hardware, such as sensors) deployed in the field operations 310 can generate plans and signals that can be provided as input or feedback (or both) to the methods of the present disclosure.

Examples of computational operations 312 include one or more computer systems 320 that include one or more processors and computer-readable media (e.g., non-transitory computer-readable media) operatively coupled to the one or more processors to execute computer operations to perform the methods of the present disclosure. The computational operations 312 can be implemented using one or more databases 318, which store data received from the field operations 310 and/or generated internally within the computational operations 312 (e.g., by implementing the methods of the present disclosure) or both. For example, the one or more computer systems 320 process inputs from the field operations 310 to assess conditions in the physical world, the outputs of which are stored in the databases 318. For example, seismic sensors of the field operations 310 can be used to perform a seismic survey to map subterranean features, such as facies and faults. In performing a seismic survey, seismic sources (e.g., seismic vibrators or explosions) generate seismic waves that propagate in the earth and seismic receivers (e.g., geophones) measure reflections generated as the seismic waves interact with boundaries between layers of a subsurface formation. The source and received signals are provided to the computational operations 312 where they are stored in the databases 318 and analyzed by the one or more computer systems 320.

In some implementations, one or more outputs 322 generated by the one or more computer systems 320 can be provided as feedback/input to the field operations 310 (either as direct input or stored in the databases 318). The field operations 310 can use the feedback/input to control physical components used to perform the field operations 310 in the real world.

For example, the computational operations 312 can process the seismic data to generate three-dimensional (3D) maps of the subsurface formation. The computational operations 312 can use these 3D maps to provide plans for locating and drilling exploratory wells. In some operations, the exploratory wells are drilled using logging-while-drilling (LWD) techniques which incorporate logging tools into the drill string. LWD techniques can enable the computational operations 312 to process new information about the formation and control the drilling to adjust to the observed conditions in real-time.

The one or more computer systems 320 can update the 3D maps of the subsurface formation as information from one exploration well is received and the computational operations 312 can adjust the location of the next exploration well based on the updated 3D maps. Similarly, the data received from production operations can be used by the computational operations 312 to control components of the production operations. For example, production well and pipeline data can be analyzed to predict slugging in pipelines leading to a refinery and the computational operations 312 can control machine operated valves upstream of the refinery to reduce the likelihood of plant disruptions that run the risk of taking the plant offline.

In some implementations of the computational operations 312, customized user interfaces can present intermediate or final results of the above-described processes to a user. Information can be presented in one or more textual, tabular, or graphical formats, such as through a dashboard. The information can be presented at one or more on-site locations (such as at an oil well or other facility), on the Internet (such as on a webpage), on a mobile application (or app), or at a central processing facility.

The presented information can include feedback, such as changes in parameters or processing inputs, that the user can select to improve a production environment, such as in the exploration, production, and/or testing of petrochemical processes or facilities. For example, the feedback can include parameters that, when selected by the user, can cause a change to, or an improvement in, drilling parameters (including drill bit speed and direction) or overall production of a gas or oil well. The feedback, when implemented by the user, can improve the speed and accuracy of calculations, streamline processes, improve models, and solve problems related to efficiency, performance, safety, reliability, costs, downtime, and the need for human interaction.

In some implementations, the feedback can be implemented in real-time, such as to provide an immediate or near-immediate change in operations or in a model. The term real-time (or similar terms as understood by one of ordinary skill in the art) means that an action and a response are temporally proximate such that an individual perceives the action and the response occurring substantially simultaneously. For example, the time difference for a response to display (or for an initiation of a display) of data following the individual's action to access the data can be less than 1 millisecond (ms), less than 1 second (s), or less than 5 s. While the requested data need not be displayed (or initiated for display) instantaneously, it is displayed (or initiated for display) without any intentional delay, taking into account processing limitations of a described computing system and time required to, for example, gather, accurately measure, analyze, process, store, or transmit the data.

Events can include readings or measurements captured by downhole equipment such as sensors, pumps, bottom hole assemblies, or other equipment. The readings or measurements can be analyzed at the surface, such as by using applications that can include modeling applications and machine learning. The analysis can be used to generate changes to settings of downhole equipment, such as drilling equipment. In some implementations, values of parameters or other variables that are determined can be used automatically (such as through using rules) to implement changes in oil or gas well exploration, production/drilling, or testing. For example, outputs of the present disclosure can be used as inputs to other equipment and/or systems at a facility. This can be especially useful for systems or various pieces of equipment that are located several meters or several miles apart, or are located in different countries or other jurisdictions.

FIG. 4 is a schematic illustration of an example controller 400 (or control system) for that enables data driven descaling. For example, the controller 400 may be operable according to the workflow 100 of FIG. 1 or the process 200 of FIG. 2. In some embodiments, the controller 400 is the same as or similar to the computer systems 320 of FIG. 3. The controller 400 is intended to include various forms of digital computers, such as printed circuit boards (PCB), processors, digital circuitry, or otherwise parts of a system for supply chain alert management. Additionally the system can include portable storage media, such as, Universal Serial Bus (USB) flash drives. For example, the USB flash drives may store operating systems and other applications. The USB flash drives can include input/output components, such as a wireless transmitter or USB connector that may be inserted into a USB port of another computing device.

The controller 400 includes a processor 410, a memory 420, a storage device 430, and an input/output interface 440 communicatively coupled with input/output devices 460 (for example, displays, keyboards, measurement devices, sensors, valves, pumps). Each of the components 410, 420, 430, and 440 are interconnected using a system bus 450. The processor 410 is capable of processing instructions for execution within the controller 400. The processor may be designed using any of a number of architectures. For example, the processor 410 may be a CISC (Complex Instruction Set Computers) processor, a RISC (Reduced Instruction Set Computer) processor, or a MISC (Minimal Instruction Set Computer) processor.

In one implementation, the processor 410 is a single-threaded processor. In another implementation, the processor 410 is a multi-threaded processor. The processor 410 is capable of processing instructions stored in the memory 420 or on the storage device 430 to display graphical information for a user interface on the input/output interface 440.

The memory 420 stores information within the controller 400. In one implementation, the memory 420 is a computer-readable medium. In one implementation, the memory 420 is a volatile memory unit. In another implementation, the memory 420 is a nonvolatile memory unit.

The storage device 430 is capable of providing mass storage for the controller 400. In one implementation, the storage device 430 is a computer-readable medium. In various different implementations, the storage device 430 may be a floppy disk device, a hard disk device, an optical disk device, or a tape device.

The input/output interface 440 provides input/output operations for the controller 400. In one implementation, the input/output devices 460 includes a keyboard and/or pointing device. In another implementation, the input/output devices 460 includes a display unit for displaying graphical user interfaces.

There can be any number of controllers 400 associated with, or external to, a computer system containing controller 400, with each controller 400 communicating over a network. Further, the terms “client,” “user,” and other appropriate terminology can be used interchangeably, as appropriate, without departing from the scope of the present disclosure. Moreover, the present disclosure contemplates that many users can use one controller 400 and one user can use multiple controllers 400.

EMBODIMENTS

According to some non-limiting embodiments or examples, provided is a computer-implemented method that enables data driven descaling, including: obtaining, using at least one hardware processor, data associated with a descaling target; deriving, using the at least one hardware processor, engineered features from the data associated with the descaling target, wherein the engineered features are created based on patterns found in the data associated with the descaling target; and training, using the at least one hardware processor, a selected machine learning model to predict use of chemical descaling operations or mechanical descaling operations based on the engineered features.

According to some non-limiting embodiments or examples, provided is an apparatus including a non-transitory, computer readable, storage medium that stores instructions that, when executed by at least one processor, cause the at least one processor to perform operations including: obtaining data associated with a descaling target; deriving engineered features from the data associated with the descaling target, wherein the engineered features are created based on patterns found in the data associated with the descaling target; and training a selected machine learning model to predict use of chemical descaling operations or mechanical descaling operations based on the engineered features.

According to some non-limiting embodiments or examples, provided is a system, including: one or more memory modules; one or more hardware processors communicably coupled to the one or more memory modules, the one or more hardware processors configured to execute instructions stored on the one or more memory modules to perform operations including: obtaining data associated with a descaling target; deriving engineered features from the data associated with the descaling target, wherein the engineered features are created based on patterns found in the data associated with the descaling target; and training a selected machine learning model to predict use of chemical descaling operations or mechanical descaling operations based on the engineered features.

Further non-limiting aspects or embodiments are set forth in the following numbered embodiments:

Embodiment 1: A computer-implemented method that enables data driven descaling, including: obtaining, using at least one hardware processor, data associated with a descaling target; deriving, using the at least one hardware processor, engineered features from the data associated with the descaling target, wherein the engineered features are created based on patterns found in the data associated with the descaling target; and training, using the at least one hardware processor, a selected machine learning model to predict use of chemical descaling operations or mechanical descaling operations based on the engineered features.

Embodiment 2: The computer implemented method of any preceding embodiment, wherein the selected machine learning model is trained to predict a number of coiled tubes used in the chemical descaling operations or the mechanical descaling operations.

Embodiment 3: The computer implemented method of any preceding embodiment, wherein the descaling target is a well and the selected machine learning model is trained to predict the use of chemical descaling operations or mechanical descaling operations for a drilled interval of the well using the engineered features and a number of coiled tubes used in the chemical descaling operations or the mechanical descaling operations.

Embodiment 4: The computer implemented method of any preceding embodiment, pre-processing the data associated with the descaling target to removing outliers and insert missing data.

Embodiment 5: The computer implemented method of any preceding embodiment, iteratively training the trained, selected machine learning model to enhance model accuracy.

Embodiment 6: The computer implemented method of any preceding embodiment, rendering a visualization that shows output of the trained, selected machine learning model that shows predicted descaling operations for at least one interval of a drilled well.

Embodiment 7: The computer implemented method of any preceding embodiment, wherein the selected machine learning model is selected from machine learning models trained to predict the use of chemical descaling operations or mechanical descaling operations.

Embodiment 8: An apparatus including a non-transitory, computer readable, storage medium that stores instructions that, when executed by at least one processor, cause the at least one processor to perform operations including: obtaining data associated with a descaling target; deriving engineered features from the data associated with the descaling target, wherein the engineered features are created based on patterns found in the data associated with the descaling target; and training a selected machine learning model to predict use of chemical descaling operations or mechanical descaling operations based on the engineered features.

Embodiment 9: The apparatus of any preceding embodiment, wherein the selected machine learning model is trained to predict a number of coiled tubes used in the chemical descaling operations or the mechanical descaling operations.

Embodiment 10: The apparatus of any preceding embodiment, wherein the descaling target is a well and the selected machine learning model is trained to predict the use of chemical descaling operations or mechanical descaling operations for a drilled interval of the well using the engineered features and a number of coiled tubes used in the chemical descaling operations or the mechanical descaling operations.

Embodiment 11: The apparatus of any preceding embodiment, pre-processing the data associated with the descaling target to removing outliers and insert missing data.

Embodiment 12: The apparatus of any preceding embodiment, iteratively training the trained, selected machine learning model to enhance model accuracy.

Embodiment 13: The apparatus of any preceding embodiment, rendering a visualization that shows output of the trained, selected machine learning model that shows predicted descaling operations for at least one interval of a drilled well.

Embodiment 14: The apparatus of any preceding embodiment, wherein the selected machine learning model is selected from machine learning models trained to predict the use of chemical descaling operations or mechanical descaling operations.

Embodiment 15: A system, including: one or more memory modules; one or more hardware processors communicably coupled to the one or more memory modules, the one or more hardware processors configured to execute instructions stored on the one or more memory modules to perform operations including: obtaining data associated with a descaling target; deriving engineered features from the data associated with the descaling target, wherein the engineered features are created based on patterns found in the data associated with the descaling target; and training a selected machine learning model to predict use of chemical descaling operations or mechanical descaling operations based on the engineered features.

Embodiment 16: The system of any preceding embodiment, wherein the selected machine learning model is trained to predict a number of coiled tubes used in the chemical descaling operations or the mechanical descaling operations.

Embodiment 17: The system of any preceding embodiment, wherein the descaling target is a well and the selected machine learning model is trained to predict the use of chemical descaling operations or mechanical descaling operations for a drilled interval of the well using the engineered features and a number of coiled tubes used in the chemical descaling operations or the mechanical descaling operations.

Embodiment 18: The system of any preceding embodiment, pre-processing the data associated with the descaling target to removing outliers and insert missing data.

Embodiment 19: The system of any preceding embodiment, iteratively training the trained, selected machine learning model to enhance model accuracy.

Embodiment 20: The system of any preceding embodiment, rendering a visualization that shows output of the trained, selected machine learning model that shows predicted descaling operations for at least one interval of a drilled well.

Implementations of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, in tangibly embodied computer software or firmware, in computer hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Software implementations of the described subject matter can be implemented as one or more computer programs. Each computer program can include one or more modules of computer program instructions encoded on a tangible, non-transitory, computer-readable computer-storage medium for execution by, or to control the operation of, data processing apparatus. Alternatively, or additionally, the program instructions can be encoded in/on an artificially generated propagated signal. The example, the signal can be a machine-generated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus. The computer-storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of computer-storage mediums.

The terms “data processing apparatus,” “computer,” and “electronic computer device” (or equivalent as understood by one of ordinary skill in the art) refer to data processing hardware. For example, a data processing apparatus can encompass all kinds of apparatus, devices, and machines for processing data, including by way of example, a programmable processor, a computer, or multiple processors or computers. The apparatus can also include special purpose logic circuitry including, for example, a central processing unit (CPU), a field programmable gate array (FPGA), or an application specific integrated circuit (ASIC). In some implementations, the data processing apparatus or special purpose logic circuitry (or a combination of the data processing apparatus or special purpose logic circuitry) can be hardware- or software-based (or a combination of both hardware- and software-based). The apparatus can optionally include code that creates an execution environment for computer programs, for example, code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of execution environments. The present disclosure contemplates the use of data processing apparatuses with or without conventional operating systems, for example, LINUX, UNIX, WINDOWS, MAC OS, ANDROID, or IOS.

A computer program, which can also be referred to or described as a program, software, a software application, a module, a software module, a script, or code, can be written in any form of programming language. Programming languages can include, for example, compiled languages, interpreted languages, declarative languages, or procedural languages. Programs can be deployed in any form, including as stand-alone programs, modules, components, subroutines, or units for use in a computing environment. A computer program can, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data, for example, one or more scripts stored in a markup language document, in a single file dedicated to the program in question, or in multiple coordinated files storing one or more modules, sub programs, or portions of code. A computer program can be deployed for execution on one computer or on multiple computers that are located, for example, at one site or distributed across multiple sites that are interconnected by a communication network. While portions of the programs illustrated in the various figures may be shown as individual modules that implement the various features and functionality through various objects, methods, or processes, the programs can instead include a number of sub-modules, third-party services, components, and libraries. Conversely, the features and functionality of various components can be combined into single components as appropriate. Thresholds used to make computational determinations can be statically, dynamically, or both statically and dynamically determined.

The methods, processes, or logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output. The methods, processes, or logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, for example, a CPU, an FPGA, or an ASIC.

Computers suitable for the execution of a computer program can be based on one or more of general and special purpose microprocessors and other kinds of CPUs. The elements of a computer are a CPU for performing or executing instructions and one or more memory devices for storing instructions and data. Generally, a CPU can receive instructions and data from (and write data to) a memory. A computer can also include, or be operatively coupled to, one or more mass storage devices for storing data. In some implementations, a computer can receive data from, and transfer data to, the mass storage devices including, for example, magnetic, magneto optical disks, or optical disks. Moreover, a computer can be embedded in another device, for example, a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive.

Computer readable media (transitory or non-transitory, as appropriate) suitable for storing computer program instructions and data can include all forms of permanent/non-permanent and volatile/non-volatile memory, media, and memory devices. Computer readable media can include, for example, semiconductor memory devices such as random access memory (RAM), read only memory (ROM), phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory devices. Computer readable media can also include, for example, magnetic devices such as tape, cartridges, cassettes, and internal/removable disks. Computer readable media can also include magneto optical disks and optical memory devices and technologies including, for example, digital video disc (DVD), CD ROM, DVD+/−R, DVD-RAM, DVD-ROM, HD-DVD, and BLURAY. The memory can store various objects or data, including caches, classes, frameworks, applications, modules, backup data, jobs, web pages, web page templates, data structures, database tables, repositories, and dynamic information. Types of objects and data stored in memory can include parameters, variables, algorithms, instructions, rules, constraints, and references. Additionally, the memory can include logs, policies, security or access data, and reporting files. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.

Implementations of the subject matter described in the present disclosure can be implemented on a computer having a display device for providing interaction with a user, including displaying information to (and receiving input from) the user. Types of display devices can include, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), a light-emitting diode (LED), and a plasma monitor. Display devices can include a keyboard and pointing devices including, for example, a mouse, a trackball, or a trackpad. User input can also be provided to the computer through the use of a touchscreen, such as a tablet computer surface with pressure sensitivity or a multi-touch screen using capacitive or electric sensing. Other kinds of devices can be used to provide for interaction with a user, including to receive user feedback including, for example, sensory feedback including visual feedback, auditory feedback, or tactile feedback. Input from the user can be received in the form of acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to, and receiving documents from, a device that is used by the user. For example, the computer can send web pages to a web browser on a user's client device in response to requests received from the web browser.

The term “graphical user interface,” or “GUI,” can be used in the singular or the plural to describe one or more graphical user interfaces and each of the displays of a particular graphical user interface. Therefore, a GUI can represent any graphical user interface, including, but not limited to, a web browser, a touch screen, or a command line interface (CLI) that processes information and efficiently presents the information results to the user. In general, a GUI can include a plurality of user interface (UI) elements, some or all associated with a web browser, such as interactive fields, pull-down lists, and buttons. These and other UI elements can be related to or represent the functions of the web browser.

Implementations of the subject matter described in this specification can be implemented in a computing system that includes a back end component, for example, as a data server, or that includes a middleware component, for example, an application server. Moreover, the computing system can include a front-end component, for example, a client computer having one or both of a graphical user interface or a Web browser through which a user can interact with the computer. The components of the system can be interconnected by any form or medium of wireline or wireless digital data communication (or a combination of data communication) in a communication network. Examples of communication networks include a local area network (LAN), a radio access network (RAN), a metropolitan area network (MAN), a wide area network (WAN), Worldwide Interoperability for Microwave Access (WIMAX), a wireless local area network (WLAN) (for example, using 802.11 a/b/g/n or 802.20 or a combination of protocols), all or a portion of the Internet, or any other communication system or systems at one or more locations (or a combination of communication networks). The network can communicate with, for example, Internet Protocol (IP) packets, frame relay frames, asynchronous transfer mode (ATM) cells, voice, video, data, or a combination of communication types between network addresses.

The computing system can include clients and servers. A client and server can generally be remote from each other and can typically interact through a communication network. The relationship of client and server can arise by virtue of computer programs running on the respective computers and having a client-server relationship. Cluster file systems can be any file system type accessible from multiple servers for read and update. Locking or consistency tracking may not be necessary since the locking of exchange file system can be done at application layer. Furthermore, Unicode data files can be different from non-Unicode data files.

While this specification contains many specific implementation details, these should not be construed as limitations on the scope of what may be claimed, but rather as descriptions of features that may be specific to particular implementations. Certain features that are described in this specification in the context of separate implementations can also be implemented, in combination, in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations, separately, or in any suitable sub-combination. Moreover, although previously described features may be described as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can, in some cases, be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.

Particular implementations of the subject matter have been described. Other implementations, alterations, and permutations of the described implementations are within the scope of the following claims as will be apparent to those skilled in the art. While operations are depicted in the drawings or claims in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed (some operations may be considered optional), to achieve desirable results. In certain circumstances, multitasking or parallel processing (or a combination of multitasking and parallel processing) may be advantageous and performed as deemed appropriate.

Moreover, the separation or integration of various system modules and components in the previously described implementations should not be understood as requiring such separation or integration in all implementations, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

Accordingly, the previously described example implementations do not define or constrain the present disclosure. Other changes, substitutions, and alterations are also possible without departing from the spirit and scope of the present disclosure.

Furthermore, any claimed implementation is considered to be applicable to at least a computer-implemented method; a non-transitory, computer-readable medium storing computer-readable instructions to perform the computer-implemented method; and a computer system comprising a computer memory interoperably coupled with a hardware processor configured to perform the computer-implemented method or the instructions stored on the non-transitory, computer-readable medium.

Claims

1. A computer-implemented method that enables data driven descaling, comprising:

obtaining, using at least one hardware processor, data associated with a descaling target;

deriving, using the at least one hardware processor, engineered features from the data associated with the descaling target, wherein the engineered features are created based on patterns found in the data associated with the descaling target; and

training, using the at least one hardware processor, a selected machine learning model to predict use of chemical descaling operations or mechanical descaling operations based on the engineered features.

2. The computer implemented method of claim 1, wherein the selected machine learning model is trained to predict a number of coiled tubes used in the chemical descaling operations or the mechanical descaling operations.

3. The computer implemented method of claim 1, wherein the descaling target is a well and the selected machine learning model is trained to predict the use of chemical descaling operations or mechanical descaling operations for a drilled interval of the well using the engineered features and a number of coiled tubes used in the chemical descaling operations or the mechanical descaling operations.

4. The computer implemented method of claim 1, pre-processing the data associated with the descaling target to removing outliers and insert missing data.

5. The computer implemented method of claim 1, iteratively training the trained, selected machine learning model to enhance model accuracy.

6. The computer implemented method of claim 1, rendering a visualization that shows output of the trained, selected machine learning model that shows predicted descaling operations for at least one interval of a drilled well.

7. The computer implemented method of claim 1, wherein the selected machine learning model is selected from machine learning models trained to predict the use of chemical descaling operations or mechanical descaling operations.

8. An apparatus comprising a non-transitory, computer readable, storage medium that stores instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:

obtaining data associated with a descaling target;

deriving engineered features from the data associated with the descaling target, wherein the engineered features are created based on patterns found in the data associated with the descaling target; and

training a selected machine learning model to predict use of chemical descaling operations or mechanical descaling operations based on the engineered features.

9. The apparatus of claim 8, wherein the selected machine learning model is trained to predict a number of coiled tubes used in the chemical descaling operations or the mechanical descaling operations.

10. The apparatus of claim 8, wherein the descaling target is a well and the selected machine learning model is trained to predict the use of chemical descaling operations or mechanical descaling operations for a drilled interval of the well using the engineered features and a number of coiled tubes used in the chemical descaling operations or the mechanical descaling operations.

11. The apparatus of claim 8, pre-processing the data associated with the descaling target to removing outliers and insert missing data.

12. The apparatus of claim 8, iteratively training the trained, selected machine learning model to enhance model accuracy.

13. The apparatus of claim 8, rendering a visualization that shows output of the trained, selected machine learning model that shows predicted descaling operations for at least one interval of a drilled well.

14. The apparatus of claim 8, wherein the selected machine learning model is selected from machine learning models trained to predict the use of chemical descaling operations or mechanical descaling operations.

15. A system, comprising:

one or more memory modules;

one or more hardware processors communicably coupled to the one or more memory modules, the one or more hardware processors configured to execute instructions stored on the one or more memory modules to perform operations comprising:

obtaining data associated with a descaling target;

deriving engineered features from the data associated with the descaling target, wherein the engineered features are created based on patterns found in the data associated with the descaling target; and

training a selected machine learning model to predict use of chemical descaling operations or mechanical descaling operations based on the engineered features.

16. The system of claim 15, wherein the selected machine learning model is trained to predict a number of coiled tubes used in the chemical descaling operations or the mechanical descaling operations.

17. The system of claim 15, wherein the descaling target is a well and the selected machine learning model is trained to predict the use of chemical descaling operations or mechanical descaling operations for a drilled interval of the well using the engineered features and a number of coiled tubes used in the chemical descaling operations or the mechanical descaling operations.

18. The system of claim 15, pre-processing the data associated with the descaling target to removing outliers and insert missing data.

19. The system of claim 15, iteratively training the trained, selected machine learning model to enhance model accuracy.

20. The system of claim 15, rendering a visualization that shows output of the trained, selected machine learning model that shows predicted descaling operations for at least one interval of a drilled well.