Patent Application
20250237133
The disclosed technology belongs to the technical field of oil and gas production, specifically focusing on the field of downhole well integrity prediction and management. The technical field encompasses the application of machine learning and data analysis techniques to ensure the reliability and safety of downhole well systems, with potential applications in various oil and gas production scenarios, including well intervention operations and proactive well integrity management. The disclosed technology may combine elements of data science, automation, and oilfield operations to enhance the performance and safety of oil and gas wells.
Various operations are performed at a well site during the lifetime of a producing well to maintain hydrocarbon recovery. Failures may occur, such as downhole pressure or flow communication issues, that increase risk of production problems. Moreover, failures may result in production losses as well as decreasing operational efficiencies and safety. However, detecting such failures typically requires manual inspections by well personnel as part of routine inspection operations or maintenance operations.
This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter.
In general, in one aspect, embodiments relate to a method that includes obtaining static well data for a well. The static well data describes one or more well design parameters of the well. The method further includes obtaining dynamic well data for the well. The dynamic well data describes one or more well properties that change over a predetermined time period. The method further includes obtaining inspection data regarding the well. The method further includes obtaining maintenance data regarding the well. The maintenance data corresponds to one or more maintenance operations that are performed at the well. The method further includes determining, by a computer processor, predicted well integrity data for the well using a machine-learning model, the static well data, the dynamic well data, the inspection data, and the maintenance data. The machine-learning model is trained using an ensemble learning algorithm. The method further includes determining, by the computer processor, a well operation for the well based on the predicted well integrity data. The method further includes transmitting, by the computer processor and to a control system coupled to the well, a command that causes the well operation to be performed at the well.
In general, in one aspect, embodiments relate to a system that includes a well control system coupled to a well at a well site. The well includes various pipe components that are installed in a wellbore. The system further includes a well integrity manager coupled to the well control system. The well integrity manager includes a computer processor. The well integrity manager obtains static well data for the well. The static well data describes one or more well design parameters of the well. The well integrity manager obtains dynamic well data for the well. The dynamic well data describes one or more well properties that change over a predetermined time period. The well integrity manager obtains inspection data regarding the well. The well integrity manager obtains maintenance data regarding the well. The maintenance data corresponds to one or more maintenance operations that are performed at the well. The well integrity manager determines predicted well integrity data for the well using a machine-learning model, the static well data, the dynamic well data, the inspection data, and the maintenance data. The machine-learning model is trained using an ensemble learning algorithm. The well integrity manager determines a well operation for the well based on the predicted well integrity data. The well integrity manager transmits a command to the well control system that causes the well operation to be performed at the well.
In some embodiments, a machine-learning model includes various models. A respective model among the models may generate a respective prediction using an input dataset to produce a set of respective predictions. The machine-learning models may use the respective predictions to determine a final prediction corresponding to predicted well integrity data. The ensemble learning algorithm is based on a max voting technique, an averaging technique, a stacking technique, and/or a weighted averaging technique. In some embodiments, an initial model is obtained. Training data may be obtained that includes static well data, dynamic well data, inspection data, and maintenance well data. A training operation may be performed on the initial model using a machine-learning algorithm, various machine-learning epochs, and the training data to produce a trained model. The trained model may be configured to determine predicted well integrity data for a well.
In some embodiments, a machine-learning model is a random forest model that includes various decision tree nodes. The machine-learning model is trained using a bootstrap and aggregation operation. In some embodiments, various well operations are presented on a graphical user interface that is provided by a user device and based on predicted well integrity data. A user selection of a well operation among the well operations is obtained in response to a user input within the graphical user interface. A command may be transmitted in response to the user selection.
In some embodiments, maintenance data is obtained from a service provider server and regarding various maintenance operations performed at various wells. In some embodiments, inspection data is obtained regarding various inspection operations from a remote server. At least one inspection operation among the inspection operations is a casing-casing annulus inspection. A second set of inspection data is used by a machine-learning model to determine predicted well integrity data. In some embodiments, inspection data includes mechanical inspection data and electrical inspection data from various inspection entities.
In some embodiments, maintenance data is automatically obtained from a service provider server after a control system on a well intervention network uploads information regarding a completed maintenance operation. In some embodiments, a well operation that is determined from predicted well integrity data is a slickline operation, a wireline operation, a well maintenance operation, a snubbing operation, a workover operation, a stimulation operation, or a coiled tubing operation. In some embodiments, a packer failure of a packer is detected based on predicted well integrity data and during a predetermined time period at a well. A packer replacement operation may be performed before the predetermined time period and based on the predicted well integrity data. The packer replacement operation includes replacing the packer with a different packer.
In some embodiments, predicted well integrity data are determined for a first well, a second well, and a third well, respectively. A priority ranking may be determined based on the predicted well integrity data. Various commands may be transmitted to various control systems coupled to the wells. The commands may implement various well intervention operations based on the priority ranking.
In light of the structure and functions described above, embodiments of the invention may include respective means adapted to carry out various steps and functions defined above in accordance with one or more aspects and any one of the embodiments of one or more aspect described herein.
Other aspects and advantages of the claimed subject matter will be apparent from the following description and the appended claims.
Specific embodiments of the disclosed technology will now be described in detail with reference to the accompanying figures. Like elements in the various figures are denoted by like reference numerals for consistency.
In the following detailed description of embodiments of the disclosure, numerous specific details are set forth in order to provide a more thorough understanding of the disclosure. However, it will be apparent to one of ordinary skill in the art that the disclosure may be practiced without these specific details. In other instances, well-known features have not been described in detail to avoid unnecessarily complicating the description.
Throughout the application, ordinal numbers (e.g., first, second, third, etc.) may be used as an adjective for an element (i.e., any noun in the application). The use of ordinal numbers is not to imply or create any particular ordering of the elements nor to limit any element to being only a single element unless expressly disclosed, such as using the terms “before,” “after,” “single,” and other such terminology. Rather, the use of ordinal numbers is to distinguish between the elements. By way of an example, a first element is distinct from a second element, and the first element may encompass more than one element and succeed (or precede) the second element in an ordering of elements.
In general, embodiments of the disclosure include systems and methods for determining predicted well integrity data using machine learning. In some embodiments, for example, downhole well integrity issues are detected in oil and gas wells using various data inputs, such as wellhead pressure data, tubular casing annulus (TCA) pressure data, geological data, well design features (e.g., well geometry, such as a vertical or inclined well, and a wellbore radius), inspection data, and/or maintenance data. Examples of such well integrity issues may include packer failures, wellhead communication issues, tubing leaks, and casing leaks that can result in operational inefficiencies, production losses, and safety risks at various well sites. Rather than identifying well integrity by deploying special logging devices within the wellbore, predictive modelling may analyze historical data for a particular well in order to identify such well integrity issues in real-time. For example, some embodiments may eliminate the need for generating acoustic logs, temperature logs, and/or electromagnetic logs through predictive analytics to diagnose well integrity issues within a wellbore. Using machine-learning techniques, a machine-learning model may uncover various non-linear patterns and physical interactions at a specific well that may prove undiscovered by traditional data analysis methods.
Furthermore, a machine-learning model may use collected data to classify a particular type of well integrity problem as well as predict a future problem during a specific time period when the well integrity issue could impact the well. In other words, some machine-learning models may provide a binary classification based on whether a well integrity issue exists, as well as more complex multiple classifications, such as a predicted date that the well integrity problem impacts production, an amount of probability or degree of uncertainty that the well integrity problem occurs, and/or the type of well integrity problem (e.g., a packer failure or a casing leak). Moreover, machine learning may be used in connection with various well intervention operations to both predict well integrity issues before they significantly impact a well in addition to prioritizing well operations to address such issues throughout an oil and gas field. For example, some machine-learning models may rank various well integrity issues throughout an oil field in order to optimize the order that well integrity issues are handled accordingly.
In some embodiments, a machine-learning model is based on an ensemble learning architecture to determine predicted well integrity data. In particular, examples of ensemble learning methods may include bagging, stacking, and boosting techniques. For example, a bagging technique may fit multiple decision trees on different data samples of the same dataset in order to average various predictions. On the other hand, a stacking technique may fit different model types to the same data, while using another model to learn how to best combine different well integrity predictions. Likewise, a boosting technique may include various ensemble members that sequentially correct different predictions made by prior models in order to output a weighted average of the predictions. As such, various ensemble learning techniques may enhance generalization of well integrity problems and help mitigate overfitting by aggregating predictions from multiple models into a particular ensemble learning architecture.
In some embodiments, a well integrity manager collects relevant data over a well management network that couples multiple wells and well systems using various computer systems. The well integrity manager may determine predicted well integrity data for various wells, while also scheduling, prioritizing, and/or implementing various well intervention operations to address the predicted well integrity issues. To predict well integrity issues, the well integrity manager may obtain real-time data and historical data from various well maintenance operations and well inspection operations for use with well data (e.g., wellhead pressure data, well operation conditions such as temperature and production rates, etc.) and related geological and reservoir data. As such, the well integrity manager may provide automated maintenance planning to streamline a complex maintenance process based on multiple variables from static well data, dynamic well data, inspection data, and maintenance data. Additionally, a well integrity manager that may be a smart system or expert system that automatically predicts well integrity for multiple wells, generates well intervention plans, and prioritizes different well operations for implementing various well remediation scenarios. A well integrity manager may be an artificial intelligence entity operation on a well management network (e.g., as a network controller) that performs such functionality.
Moreover, some embodiments address one or more technical problems associated with detecting and/or remediating well integrity issues. For example, various conventional approaches for determining well integrity can rely on reactive maintenance that leads to production well downtime and potential environmental risks. As such, there is a lack of robust predictive capabilities to anticipate and prevent downhole well integrity issues before the issues escalate. Additionally, some well systems may experience data overload such that these systems may generate vast amounts of data from various sources, including sensors, well logs, and maintenance records. As such, these well systems may be unable to analyze and extract actionable insights from collected data from being overwhelmed, thereby leading to missed opportunities for early well issue detection. Likewise, because downhole environments are harsh and complex with numerous factors affecting well integrity, such as fluid dynamics, temperature, pressure, and material wear, some embodiments can manage these variables in real-time in order to predict their impact on well integrity, thus overcoming a formidable data processing challenge in well technology.
Moreover, some embodiments both ensure the integrity of downhole well systems for operational efficiency as well as safety and environmental compliance. Failing to address well integrity issues promptly can lead to accidents and environmental damage around well sites. Some embodiments further overcome these issues using automated resource allocation, where oil and gas companies often face resource allocation dilemmas when deciding which wells require immediate attention. The lack of prioritization based on predictive well integrity data can lead to inefficiencies in maintenance operations for different wells. Accordingly, some embodiments overcome these challenges by leveraging machine learning and data analysis techniques to predict downhole well integrity issues proactively. For example, historical and real-time data may be combined using machine learning to generate predictive models that help operators make informed decisions, allocate resources effectively, and ensure the safety and reliability of well systems at a significant time in advance of actual well integrity issues occurring.
Turning to
In some embodiments, the well system (106) includes a wellbore (120), a well sub-surface system (122), a well surface system (124), and a well control system (126). The control system (126) may control various operations of the well system (106), such as well production operations, well completion operations, well maintenance operations, and reservoir monitoring, assessment and development operations. Control systems may include a programmable logic controller (PLC), a distributed control system (DCS), a supervisory control and data acquisition (SCADA), and/or a remote terminal unit (RTU). For example, a programmable logic controller may control valve states, fluid levels, pipe pressures, warning alarms, and/or pressure releases throughout a well facility or power-generation facility. In particular, a programmable logic controller may be a ruggedized computer system with functionality to withstand vibrations, extreme temperatures, wet conditions, and/or dusty conditions, for example, around a production well. A distributed control system may be a computer system for managing various processes at various facilities using multiple control loops. As such, a distributed control system may include various autonomous controllers (such as remote terminal units) positioned at different locations throughout the facility to manage operations and monitor processes. Likewise, a distributed control system may include no single centralized computer for managing control loops and other operations. On the other hand, a SCADA system may include a control system that includes functionality for enabling monitoring and issuing of process commands through local control at a facility as well as remote control outside the facility. With respect to an RTU, an RTU may include hardware and/or software, such as a microprocessor, that connects sensors and/or actuators using network connections to perform various processes in the automation system. Likewise, a control system may be coupled to one or more well devices.
In some embodiments, a well control system includes functionality for transmitting commands to another control system to implement a particular production operation or stimulation operation. For example, a well control system coupled to a reservoir simulator may transmit a network message over a machine-to-machine protocol to a control system based on predicted flow rate data. A command (e.g., command Y (295) in
The wellbore (120) may include a bored hole that extends from the surface (108) into a target zone of the hydrocarbon-bearing formation (104), such as the reservoir (102). An upper end of the wellbore (120), terminating at or near the surface (108), may be referred to as the “uphole” end of the wellbore (120), and a lower end of the wellbore, terminating in the hydrocarbon-bearing formation (104), may be referred to as the “downhole” end of the wellbore (120). The wellbore (120) may facilitate the circulation of drilling fluids during drilling operations, the flow of hydrocarbon production (“production”) (121) (e.g., oil and gas) from the reservoir (102) to the surface (108) during production operations, the injection of substances (e.g., water) into the hydrocarbon-bearing formation (104) or the reservoir (102) during injection operations, or the communication of monitoring devices (e.g., logging tools) into the hydrocarbon-bearing formation (104) or the reservoir (102) during monitoring operations (e.g., during in situ logging operations).
In some embodiments, during operation of the well system (106), the control system (126) collects and records wellhead data (140) for the well system (106). The wellhead data (140) may include, for example, a record of measurements of wellhead pressure (P) (e.g., including flowing wellhead pressure), wellhead temperature (T) (e.g., including flowing wellhead temperature), wellhead production rate (Q) over some or all of the life of the well system (106), and water cut data. In some embodiments, the measurements are recorded in real-time, and are available for review or use within seconds, minutes or hours of the condition being sensed (e.g., the measurements are available within 1 hour of the condition being sensed). In such an embodiment, the wellhead data (140) may be referred to as “real-time” wellhead data (140). Real-time wellhead data (140) may enable an operator of the well system (106) to assess a relatively current state of the well system (106) and make real-time decisions regarding development of the well system (106) and the reservoir (102), such as on-demand adjustments in regulation of production flow from the well.
In some embodiments, the well surface system (124) includes a wellhead (130). The wellhead (130) may include a rigid structure installed at the “uphole” end of the wellbore (120), at or near where the wellbore (120) terminates at the Earth's surface (108). The wellhead (130) may include structures for supporting (or “hanging”) casing and production tubing extending into the wellbore (120). Production (121) may flow through the wellhead (130), after exiting the wellbore (120) and the well sub-surface system (122), including, for example, the casing and the production tubing. In some embodiments, the well surface system (124) includes flow regulating devices that are operable to control the flow of substances into and out of the wellbore (120). For example, the well surface system (124) may include one or more production valves (132) that are operable to control the flow of production (121). For example, a production valve (132) may be fully opened to enable unrestricted flow of production (121) from the wellbore (120), the production valve (132) may be partially opened to partially restrict (or “throttle”) the flow of production (121) from the wellbore (120), and production valve (132) may be fully closed to fully restrict (or “block”) the flow of production (121) from the wellbore (120), and through the well surface system (124).
Keeping with
In some embodiments, the surface sensing system (134) includes a surface pressure sensor (136) operable to sense the pressure of production (121) flowing through the well surface system (124), after it exits the wellbore (120). The surface pressure sensor (136) may include, for example, a wellhead pressure sensor that senses a pressure of production (121) flowing through or otherwise located in the wellhead (130). In some embodiments, the surface sensing system (134) includes a surface temperature sensor (138) operable to sense the temperature of production (121) flowing through the well surface system (124), after it exits the wellbore (120). The surface temperature sensor (138) may include, for example, a wellhead temperature sensor that senses a temperature of production (121) flowing through or otherwise located in the wellhead (130), referred to as “wellhead temperature” (T). In some embodiments, the surface sensing system (134) includes a flow rate sensor (139) operable to sense the flow rate of production (121) flowing through the well surface system (124), after it exits the wellbore (120). The flow rate sensor (139) may include hardware that senses a flow rate of production (121) (Q) passing through the wellhead (130).
In some embodiments, the well system (106) includes a reservoir simulator (160). For example, the reservoir simulator (160) may include hardware and/or software with functionality for generating one or more reservoir models regarding the hydrocarbon-bearing formation (104) and/or performing one or more reservoir simulations. For example, the reservoir simulator (160) may store well logs and data regarding core samples for performing simulations. A reservoir simulator may further analyze the well log data, the core sample data, seismic data, and/or other types of data to generate and/or update the one or more reservoir models. While the reservoir simulator (160) is shown at a well site, embodiments are contemplated where reservoir simulators are located away from well sites. In some embodiments, the reservoir simulator (160) may include a computer system that is similar to the computer system (602) described below with regard to
In some embodiments, downhole pressure sensors include absolute pressure transmitters, differential-pressure transmitters, and/or multivariable transmitters. Absolute pressure transmitters may include sensors that measure pressure with respect to a full vacuum, while differential-pressure transmitters may include sensors that are used in flow applications. Multivariable transmitters may measure pressure in addition to other variables, such as temperature. For example, a multivariable transmitter may be a gauge sensor that measures both pressure and temperature at a single point, such as a single quartz crystal. Multivariable transmitters may be transmit-only devices in a well providing pressure and temperature (PT) measurements at fixed time intervals, e.g., using one or more electric lines and one or more hydraulic lines. Likewise, multivariable transmitters may transmit pressure and temperature data to a well surface using a high-speed digital telemetry link. Similar to downhole pressure sensors, downhole temperature sensors may include downhole temperature gauges, temperature transmitters, and/or multivariable transmitters. In some embodiments, permanent downhole measurement systems (PDHMSs), such as monitoring systems that include permanent downhole gauges (PDGs), are used that are permanently installed in a well For example, PDHMSs may be used to detect pressure data and/or temperature data downhole a wellbore.
In some embodiments, a flow rate sensor is a multiphase flow meter. For example, a multiphase flow meter may include hardware and/or software for determining individual flow rates of different components within a three-phase flow. More specifically, a multiphase flow meter may determine a mass flow rate of a gas component and a mass flow rate of a liquid component (e.g., a component of the three-phase flow that includes oil and water) of the three-phase flow. As such, a multiphase flow meter may be used to determine an amount of oil or a portion of oil within a multiphase flow that travels through a wellhead during a given period of time. A multiphase flow meter may also include hardware that uses various types of sensors based on different sensing technologies (e.g., nuclear magnetic resonance, electromagnetic sensors, acoustic sensors, etc.) and interpretation models. For example, a multiphase flow meter may use a sensor response of magnetic resonance information to determine the number of hydrogen atoms in a particular fluid flow. Since oil, gas, and water each contain hydrogen atoms, properties of a multiphase flow may be measured using magnetic resonance. The hydrogen atoms in a magnetized fluid may respond to radio frequency pulses and emit echoes that are subsequently recorded and analyzed by the multiphase flow meter. Thus, multiphase flow rate measurements may be used for production monitoring, well control, and/or reservoir optimization.
Moreover, a multiphase flow metering system may include a multiphase flow meter and a host device. In response to determining flow rate data regarding a multiphase flow, a multiphase flow meter may transmit flow rate data to a host device, such as a well control system or another type of computer system, over a network. The multiphase flow meter may be coupled to one or more flow tubes in order to determine the flow rate data, such as individual flow rates and/or oil, gas, and/or water fractions of a corresponding multiphase flow. A flow tube may be a fluid conduit, such as pipe, that may provide a fluid sampling for analysis by the multiphase flow meter. Examples of flow tubes may include a bent flow tube, a straight flow tube, or another type of flow tube. Furthermore, a flow model may be stored within a multiphase flow meter as a portion of a database and/or as one or more flow regime maps that are associated with various sensor values. By analyzing sensor data in connection with one or more flow models, a flow meter may determine flow rate data that corresponds to acquired sensor data. Flow rate data may include corresponding fractional data (e.g., gas fraction of a multiphase flow) and/or velocity data (e.g., an individual flow rate of oil or water in the multiphase flow).
Furthermore, the multiphase flow meter may include a flow meter controller that controls sensing operations and/or the flow analysis operations. In some embodiments, a flow controller uses one or more flow models to determine flow rate data regarding a particular flow. Phase distribution information may describe the respective fractions of one or more phases (e.g., gas phase, oil phase, water phase), in a particular flow. Flow regime information may refer to a specific manner that two or three phases flow through a flow tube. For example, a flow regime may be expressed using various superficial velocities. One example of a flow regime may be a “bubble regime,” in which gas is entrained as bubbles within a liquid. Another example of a flow regime is a “slug regime” that may correspond to a series of liquid “slugs” or “plugs” separated by relatively large gas pockets. Accordingly, a flow model may describe changes in a multiphase flow between transitions from high-liquid compositions to high-gas compositions and vice versa. Other flow regimes may include an annular flow regime, a dispersed flow regime, and a froth flow regime.
In some embodiments, one or more production logging tools (PLT) are used to determine production data at one or more depth intervals in a production well. For example, PLT data at a particular depth may include wellbore temperature measurements, pressure measurements, fluid density measurements, flow velocity measurements, and holdup data (e.g., volume fraction of a pipe occupied by fluid). While measurements of pressure, temperature and flow rate can be obtained at the surface, surface measurements may not necessarily reflect what is happening in the reservoir. As such, PLT data may be acquired downhole using various logging tools. More specifically, fluid velocity data may be acquired using a spinner flowmeter. In particular, a spinner flowmeter may include a rotating blade that turns when fluid moves past the device (e.g., the rotational speed of the blade in revolutions per second (RPS) may be proportional to the fluid velocity). Moreover, PLT data may be acquired using a production logging toolstring. This toolstring may include a fullbore spinner, various fluid holdup and bubble count probes, a pipe diameter caliper tool, a bearing sensor, a pressure sensor, a temperature sensor, a gamma ray tool, a casing collar locator, one or more batteries, and/or a data recorder. Other production logging tools include markers/tracers such as oxygen activation logs or radioactive iodine tracer logs as well as anemometers. An anemometer may be an instrument that measures the speed or velocity of gases in a contained flow. As such, PLT operations may include temperature logging, radioactive tracer logging, noise logging, focused gamma ray density logging, unfocused gamma ray density logging, fluid capacitance logging, fluid identification logging in high angle wells, and flowmeter logging at different depth intervals.
Keeping with production logging tools, various types of logging tools may be used to provide information during production operations and afterwards. For example, production logging may determine an axial flow rate based on axial velocity data and an internal diameter of a pipe component. Likewise, production logging may be used to track movement of fluid either inside or immediately outside the casing of a wellbore. Examples of production logs include temperature surveys, mechanical flowmeter surveys, and borehole fluid-density surveys, and fluid-capacitance surveys. Moreover, PLT data may be used to determine whether a production problem exists, such as excessive water or gas production. In particular, PLT data may be used to determine whether a production problem is the result of a completion problem or a reservoir problem. In some embodiments, production logging is used to determine the location of casing damage or collars. Likewise, PLT data may also determine water holdup in a wellbore or a well's gas volume fraction. Thus, PLT data may provide detailed, multiphase evaluation of fluid velocity and phase identification in vertical, deviated, and horizontal wells. More specifically, PLT data may identify fluid entry, gas leaks, injection zones, and cement tops through various well or reservoir analyses.
In some embodiments, the well system (106) includes a water cut sensor. For example, a water cut sensor may be hardware and/or software with functionality for determining the water content in oil, also referred to as “water cut.” Measurements from a water cut sensor may be referred to as water cut data and may describe the ratio of water produced from the wellbore (120) compared to the total volume of liquids produced from the wellbore (120). Water cut sensors may implement various water cut measuring techniques, such as those based on capacitance measurements, Coriolis effect, infrared (IR) spectroscopy, gamma ray spectroscopy, and microwave technology. Water cut data may be obtained during production operations to determine various fluid rates found in production from the well system (106).
With regard to microwave-based water cut sensors, certain microwave-based water cut sensors may rely on measuring a phase difference between transmitted and received microwave signals. As such, the phase difference may have a direct link with the effective permittivity of the oil and water mixture from the wellbore (120). In some embodiments, microwave-based water cut sensors employ transmit (Tx) antennas and receive (Rx) antennas disposed inside of well system pipe, such that the antennas are at least partially immersed in the fluid mixture as the fluid flows through the pipe.
In some embodiments, the well system (106) includes a water cut sensing system that includes a water cut (WC) sensor, a cylindrical pipe, and/or a measurement processing system. The WC sensor may be disposed on (or otherwise integrated within) the cylindrical pipe. As such, the WC sensor may include a signal conductor (SC) (e.g., a first conductive plane), such as a T-resonator, disposed at a first/upper/top surface of the cylindrical pipe, and a ground conductor (GC) (e.g., a second conductive plane) disposed at a second/lower/bottom surface of the cylindrical pipe that is opposite the first/upper/top surface of the pipe. In such a configuration, the WC sensing system may be employed to sense a water cut of fluid obtained from the wellbore (120) (e.g., a water and oil mixture, or other substrate). In some embodiments, a WC sensor includes multiple waveguides that are attached to a production pipe, where a network analyzer may be connected to the waveguides. The network analyzer may be communicatively coupled with the well control system (126) to determine water cut data.
In some embodiments, production wells and/or injection wells are involved in one or more stimulation operations. For example, one type of stimulation operation is a water-alternating-gas (WAG) operation. A WAG operation may be a cyclic process of injecting water followed by gas. Using a WAG injection, macroscopic or microscopic sweep efficiency may be improved for a reservoir, e.g., by maintaining nearly initial high pressure, slow down any gas breakthroughs, and reduce oil viscosity. Likewise, WAG injections may also decrease residual oil saturation resulting from three phase flows and effects associated with relative permeability hysteresis. Thus, some stimulation operations may produce gas flooding, which is a type of enhanced oil recovery (EOR) method for increasing recovery of light to moderate oil reservoirs. In some stimulation operations, water may be injected during the initial phase of the operation and followed by a gas (e.g., carbon dioxide) because water may have a higher mobility ratio than the injected gas, thereby preventing breakthroughs in the reservoir. Injected gas may be a mixture of hydrocarbon gas or nonhydrocarbon gases. With hydrocarbon gases, the gas mixture may include methane, ethane, and propane for achieving a miscible or immiscible gas-oil system in the reservoir. With nonhydrocarbon gases, the gas mixture may include carbon dioxide (CO2), nitrogen (N2), and some exotic gases that displace fluid in the reservoir. Likewise, gas may also be injected directly into a reservoir, e.g., into the gas cap, to compensate for the reservoir's pressure decline.
Furthermore, a stimulation injection during a stimulation operation may correspond to various injection parameters, such as bank size, cycle time, and a predetermined water-gas ratio (also called a “WAG ratio”). Bank size may refer to a size of sequential banks of fluids (e.g., oil, CO2 and water) formed in the reservoir rock in response to a stimulation operation that migrate from the injection to the production wells. For illustration, a WAG ratio of 1:1 may result in a high oil production for one or more production wells, such as production wells coupled to a miscible reservoir. Based on some reservoir parameters such as oil composition, gas flooding can be carried out in miscible or immiscible conditions. Moreover, different types of stimulation operations may use different stimulation parameters. Examples of different stimulation operations may include: (1) continuous gas injections; (2) WAG injections; (3) simultaneous water-alternating-gas (SWAG) injections; and (4) tapered WAG injections. Different strategies have been developed by the petroleum industry to cope with these conditions.
Keeping with
In one well delivery example, the sides of the wellbore (120) may require support, and thus casing may be inserted into the wellbore (120) to provide such support. After a well has been drilled, casing may ensure that the wellbore (120) does not close in upon itself, while also protecting the wellstream from outside incumbents, like water or sand. Likewise, if the formation is firm, casing may include a solid string of steel pipe that is run on the well and will remain that way during the life of the well. In some embodiments, the casing includes a wire screen liner that blocks loose sand from entering the wellbore (120).
In another well delivery example, a space between the casing and the untreated sides of the wellbore (120) may be cemented to hold a casing in place. This well operation may include pumping cement slurry into the wellbore (120) to displace existing drilling fluid and fill in this space between the casing and the untreated sides of the wellbore (120). Cement slurry may include a mixture of various additives and cement. After the cement slurry is left to harden, cement may seal the wellbore (120) from non-hydrocarbons that attempt to enter the wellstream. In some embodiments, the cement slurry is forced through a lower end of the casing and into an annulus between the casing and a wall of the wellbore (120). More specifically, a cementing plug may be used for pushing the cement slurry from the casing. For example, the cementing plug may be a rubber plug used to separate cement slurry from other fluids, reducing contamination and maintaining predictable slurry performance. A displacement fluid, such as water, or an appropriately weighted drilling fluid, may be pumped into the casing above the cementing plug. This displacement fluid may be pressurized fluid that serves to urge the cementing plug downward through the casing to extrude the cement from the casing outlet and back up into the annulus.
Keeping with well operations, some embodiments include perforation operations. More specifically, a perforation operation may include perforating casing and cement at different locations in the wellbore (120) to enable hydrocarbons to enter a wellstream from the resulting holes. For example, some perforation operations include using a perforation gun at different reservoir levels to produce holed sections through the casing, cement, and sides of the wellbore (120). Hydrocarbons may then enter the wellstream through these holed sections. In some embodiments, perforation operations are performed using discharging jets or shaped explosive charges to penetrate the casing around the wellbore (120).
In another well delivery, a filtration system may be installed in the wellbore (120) in order to prevent sand and other debris from entering the wellstream. For example, a gravel packing operation may be performed using a gravel-packing slurry of appropriately sized pieces of coarse sand or gravel. As such, the gravel-packing slurry may be pumped into the wellbore (120) between a casing's slotted liner and the sides of the wellbore (120). The slotted liner and the gravel pack may filter sand and other debris that might have otherwise entered the wellstream with hydrocarbons. In another well delivery, a wellhead assembly may be installed on the wellhead of the wellbore (120). A wellhead assembly may be a production tree (also called a Christmas tree) that includes valves, gauges, and other components to provide surface control of subsurface conditions of a well.
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Furthermore, the well management network may also collect data regarding various well maintenance and inspection operations (e.g., a portion of user data (233), maintenance and inspection data C (292), maintenance data N (271), inspection data N (272)) from one or more user device and/or data servers (e.g., user device M (230), service provider server C (250), service provider server N (270)). For example, historical maintenance and inspection data may include records of well interventions, repairs, and maintenance activities that are manually created by human users as well as automatically generated based on well operations performed at a well site. For example, a user device associated with an inspection or maintenance operation may automatically generate an inspection or maintenance report that is uploaded at a predetermined time after completion of the inspection or maintenance operation.
In some embodiments, inspection data corresponds to one or more inspection operations of various types of well equipment at a well site, such as photographs of the well equipment, location data, timestamp, well identification information, and/or captions or analysis of the well equipment provided by one or more inspectors. In some embodiments, inspection operations include one or more ultrasonic operations that detect anomalies and sizing capabilities. For example, various ultrasonic sensors may provide direct wall thickness data of casing or other pipe components in a wellbore (e.g., determining an amount of metal loss over a predetermined amount of time or identification of one or more holes in a corresponding pipe component).
In some embodiments, a service provider server is a remote server that includes hardware and/or software for managing and tracking inspection data and/or maintenance data. For example, the service provider server may obtain raw inspection data and/or raw maintenance data from various well sites over an oil field. Accordingly, a service provider server may transmit inspection data and/or maintenance data to a well integrity manager.
In some embodiments, the well integrity manager (e.g., well integrity manager X (260)) may include hardware and/or software that obtains and aggregates well data (e.g., well data X (261)), maintenance and inspection data (e.g., maintenance and inspection data C (292)), geological data (e.g., geological data X (262)), reservoir data (e.g., reservoir data X (263)), and/or historical well integrity data (e.g., historical well integrity data X (267)) from data inputs (e.g., user data (233), well data A (291)). The user device (e.g., user device M (230)) may include hardware and/or software to receive real-time user selections (e.g., user selections N (231)) regarding predicted well integrity provided by a well integrity manager. For example, the user selection may be obtained by interacting with a user via a graphical display (e.g., user interface O (232)). Specifically, a user may interact with a well integrity manager using the user device (e.g., user device M (230)) to implement one or more well intervention plans based on predicted and/or historical well integrity data. Depending on the priority of various well intervention operations, a well integrity manager may automatically determine an order that the well intervention operations are implemented based on future well problems (e.g., predicted time of a packer failure).
Keeping with
In some embodiments, well intervention operations may include various operations carried out by one or more service entities for an oil or gas well during its productive life (e.g., fracking operations, CT, flow back, separator, pumping, wellhead and Christmas tree maintenance, slickline, wireline, well maintenance, stimulation, braded line, coiled tubing, snubbing, workover, subsea well intervention, packer replacement, etc.). For example, well intervention activities may be similar to well completion operations, well delivery operations, and/or drilling operations in order to modify the state of a well or well geometry. In some embodiments, well intervention operations may provide well diagnostics, and/or manage the production of the well. With respect to service entities, a service entity may be a company or other actor that performs one or more types of oil field services, such as well intervention operations, at a well site.
Turning to geological data and reservoir data, geological data and reservoir data may be acquired using a logging system that includes one or more logging tools for use in generating well logs of the formation. For example, a logging tool may be lowered into the wellbore to acquire measurements as the tool traverses a depth interval (e.g., a targeted reservoir section) of the wellbore. The plot of the logging measurements versus depth may be referred to as a “log” or “well log”. Well log data may provide depth measurements of the wellbore that describe such reservoir characteristics as formation porosity, formation permeability, resistivity, water saturation, and the like. The resulting logging measurements may be stored and/or processed, for example, by a control system, to generate corresponding well logs for the well. A well log may include, for example, a plot of a logging response time versus true vertical depth (TVD) across the depth interval of the wellbore.
Turning to examples of logging techniques, multiple types of logging techniques are available for determining various reservoir characteristics. In some embodiments, gamma ray logging is used to measure naturally occurring gamma radiation to characterize rock or sediment regions within a wellbore. In particular, different types of rock may emit different amounts and different spectra of natural gamma radiation. For example, gamma ray logs may distinguish between shales and sandstones/carbonate rocks because radioactive potassium may be common to shales. Another logging technique is nuclear magnetic resonance (NMR) logging that may measure the induced magnetic moment of hydrogen nuclei (i.e., protons) contained within the fluid-filled pore space of porous media (e.g., reservoir rocks). Thus, NMR logs may measure the magnetic response of fluids present in the pore spaces of the reservoir rocks. In so doing, NMR logs may measure both porosity and permeability, as well as the types of fluids present in the pore spaces. Thus, NMR logging may be a subcategory of electromagnetic logging that responds to the presence of hydrogen protons rather than a rock matrix. Because hydrogen protons may occur primarily in pore fluids, NMR logging may directly or indirectly measure the volume, composition, viscosity, and distribution of pore fluids. Another type of logging is spontaneous potential (SP) logging that determines the permeabilities of rocks in the formation by measuring the amount of electrical current generated between drilling fluid produced by the drilling system and formation water that is held in pore spaces of the reservoir rock. Porous sandstones with high permeabilities may generate more electricity than impermeable shales. Thus, SP logs may be used to identify sandstones from shales. Another type of electrical logging technique is resistivity logging. Resistivity logging may measure the electrical resistivity of rock or sediment in and around the wellbore. In particular, resistivity measurements may determine what types of fluids are present in the formation by measuring how effective these rocks are at conducting electricity. Because fresh water and oil are poor conductors of electricity, they have high resistivities. As such, resistivity measurements obtained via such logging can be used to determine corresponding reservoir water saturation (Sw).
Keeping with well logging, another electrical logging technique is dielectric logging. For example, dielectric permittivity may be defined as a physical quantity that describes the propagation of an electromagnetic field through a dielectric medium. As such, dielectric permittivity may describe a physical medium's ability to polarize in response to an electromagnetic field, and thus reduce the total electric field inside the physical medium. In a portion of reservoir rock, water may have a large dielectric permittivity that is higher than any associated rock or hydrocarbon fluids within the portion. In particular, water permittivity may depend on a frequency of an electromagnetic wave, water pressure, water temperature, and salinity of the reservoir rock mixture. Likewise, a multi-frequency dielectric logging tool may determine a value of the water-filled porosity in the reservoir rock. Other types of logging include density logging and neutron logging. Density logging may determine porosity measurements by directly measuring the density of the rocks in the formation. Furthermore, neutron logging may determine porosity measurements by assuming that the reservoir pore spaces within the formation are filled with either water or oil and then measuring the amount of hydrogen atoms (i.e., neutrons) in the pores. While Pulsed-Neutron-Lifetime (PNL) devices may measure hydrogen atoms, these devices may only transmit neutrons by shooting hydrogen atoms into a particular formation and analyzing reflected content. As such, PNL devices may not directly measure oil, water, or gas, but instead may measure how much of a rock formation can absorb the generated nuclear particles.
Turning to coring, reservoir characteristics may be determined using core sample data acquired from a well site. For example, certain reservoir characteristics can be determined via coring (e.g., physical extraction of rock specimens) to produce core specimens and/or logging operations (e.g., wireline logging, logging-while-drilling (LWD) and measurement-while-drilling (MWD)). Coring operations may include physically extracting a rock specimen from a region of interest within the wellbore for detailed laboratory analysis. For example, when drilling an oil or gas well, a coring bit may cut core plugs (or “cores” or “core specimens”) from the formation and bring the core plugs to the surface, and these core specimens may be analyzed at the surface (e.g., in a lab) to determine various characteristics of the formation at the location where the specimen was obtained.
Turning to various coring technique examples, conventional coring may include collecting a cylindrical specimen of rock from the wellbore using a core bit, a core barrel, and a core catcher. The core bit may have a hole in its center that allows the core bit to drill around a central cylinder of rock. Subsequently, the resulting core specimen may be acquired by the core bit and disposed inside the core barrel. More specifically, the core barrel may include a special storage chamber within a coring tool for holding the core specimen. Furthermore, the core catcher may provide a grip to the bottom of a core and, as tension is applied to the drill string, the rock under the core breaks away from the undrilled formation below coring tool. Thus, the core catcher may retain the core specimen to avoid the core specimen falling through the bottom of the drill string. In some embodiments, a micro computed tomography (micro-CT) scan is performed on a core sample. Several types of micro-CT scanning may be used, such as a desktop micro-CT scanner that uses an X-ray generation tube, and a synchrotron X-ray micro-tomography. In particular, a micro-CT scanner may use various X-rays to penetrate from different viewpoints in a core sample to produce an attenuated projection profile that is used for later reconstruction using a filtered back projection algorithm.
In some embodiments, cutting samples are acquired and analyzed from one or more drilling operations to determine various geological properties of one or more formations. In particular, cuttings may be initially cleaned in liquid detergent to remove drilling additives and before being dried on a ‘hotplate’. Dried cutting samples may be passed through one or more sieves to remove fragments of various sizes. Likewise, a magnet may be placed over a sieved cutting sample to remove any metallic fragments acquired during a drilling operation. After selecting various desired samples from the sieving and other preparation processes, selected samples may be ground into a fine powder for analysis using X-ray fluorescence (XRF) spectrometry processing and/or and inductively coupled plasma (ICP) spectrometry processing.
Turning to downhole sampling, downhole samples are used to determine pressure-volume-temperature (PVT) properties of one or more regions in an unconventional reservoir. In particular, a PVT laboratory test on a downhole fluid sample may use multiple stages. For example, separator test experiments may be carried out for both oil and gas condensate mixtures. A sample of reservoir fluid may be placed in a laboratory cell and brought to reservoir temperature and bubble-point pressure. Afterwards, fluid may be expelled from the laboratory cell through a number of stages of separation. Usually, two or three stages of separation are used, with the last stage at atmospheric pressure and near-ambient temperature.
Keeping with PVT data, PVT properties may be used for hydrocarbon reserve estimations, reservoir modeling, production and pressure analysis, and for predicting well production performance. Thus, PVT properties may be identified by relating specific properties of unconventional reservoir fluids with various reservoir measurements, such as saturation pressure and oil formation volume factor may be correlated with reservoir temperature, stock tank oil gravity, specific gas gravity, and/or solution gas-oil-ratios. More specifically, PVT may be determined using various PVT correlation methods, such as non-parametric correlation methods that provide a multivariate optimization without using a specific model. Examples of PVT correlation methods may include exponential-polynomial functions and rational polynomial functions. In addition to PVT correlation methods, PVT properties may be further determined using equation-of-state (EOS). Equations-of-state may be computationally complex, thereby requiring detailed compositions of reservoir fluids. An example of EOS is a mathematical function that relates pressure, molar volume, temperature, and composition for modelling a fluid system (e.g., a reservoir region).
Turning to machine learning, a well integrity manager may include hardware and/or software with functionality for generating and/or updating one or more machine-learning models (e.g., machine-learning models X (265)) to determine predicted well integrity data (e.g., predicted well integrity data X (266)) using various data inputs. Examples of machine-learning models may include random forest models and artificial neural networks, such as convolutional neural networks, deep neural networks, and recurrent neural networks. Machine-learning (ML) models may also include support vector machines (SVMs), Naïve Bayes models, ridge classifier models, gradient boosting models, decision trees, inductive learning models, deductive learning models, supervised learning models, unsupervised learning models, reinforcement learning models, and the like. In a deep neural network, for example, a layer of neurons may be trained on a predetermined list of features based on the previous network layer's output. Thus, as data progresses through the deep neural network, more complex features may be identified within the data by neurons in later layers. In some embodiments, two or more different types of machine-learning models are integrated into a single machine-learning architecture, e.g., a machine-learning model may include a random forest model and various neural networks. In some embodiments, a well integrity manager may generate augmented data or synthetic data to produce a large amount of interpreted data for training a particular model.
In some embodiments, a well integrity manager uses various machine-learning techniques, such as ensemble modeling (e.g., Random Forest, Gradient Boosting) and data preprocessing (e.g., feature selection, engineering) to generate a trained model. A model's performance may be assessed through rigorous validation using cross-validation, while hyperparameter tuning may be performed using techniques such as a grid search, a random search, and/or Bayesian optimization. In particular, a well integrity manager may load a selected dataset, such as to preview an initial set of rows in the dataset. Next, the well integrity manager may obtain a statistical summary of the data. Likewise, the well integrity manager may check for missing values and perform data imputation if necessary. Moreover, the well integrity manager may perform feature engineering by creating new features relevant to various downhole fluid communication issues that may affect well integrity. The well integrity manager may check for outliers in the dataset using boxplots and handle any outliers if needed. Thus, a well integrity manager may plot histograms of the data variables for data exploration. The selected dataset may be prepared selecting relevant input variables (e.g., wellhead pressures, tubing casing annulus pressures) and the target variable (presence of downhole communication issues). After splitting the selected data into training and testing datasets, various input features may be scaled using techniques like standardization or normalization. The well integrity manager may further determine a machine-learning model based on an ensemble model architecture suitable for various classification tasks. A machine-learning model may be subsequently trained using one or more training operations with a training dataset and incorporating various ensemble methods (e.g., Random Forest, Gradient Boosting, Bagging, etc.).
After determining the best hyperparameters and their corresponding evaluation metrics (e.g., accuracy, precision, recall) for a machine-learning model, the machine-learning model may be evaluated by a testing dataset using the best hyperparameters. The testing dataset's evaluation metrics may be analyzed to determine the model's performance in detecting downhole fluid communication issues. Afterwards, a confusion matrix or other relevant visualization may be plotted to analyze the model's performance on the validation set. Finally, well integrity predictions may be made based on the testing dataset to determine the model's accuracy in predicting well integrity issues, such as packer failure, tubing leaks, casing leaks, and other well integrity problems prior to deployment. After the model's deployment, real-time well integrity predictions may be made in various production wells (e.g., using the machine-learning model by a control system at a well site or using a well integrity manager for multiple well sites) to detect downhole fluid communication issues and enable proactive maintenance and intervention.
In some embodiments, a well intervention manager uses one or more ensemble learning methods to produce a hybrid-model architecture. For example, an ensemble learning method may use multiple types of machine-learning models to obtain better predictive performance than available with a single machine-learning model. In some embodiments, for example, an ensemble architecture may combine multiple base models to produce a single machine-learning model. One example of an ensemble learning method is a BAGGing model (i.e., BAGGing refers to a model that performs Bootstrapping and Aggregation operations) that combines predictions from multiple neural networks to add a bias that reduces variance of a single trained neural network model. Another ensemble learning method includes a stacking method, which may involve fitting many different model types on the same data and using another machine-learning model to combine various predictions.
In another ensemble method, a machine-learning model may be generated using gradient boosting. Similar to other ensemble techniques, gradient boosting may be used in regression, binary classification, and multi-class classification. For example, gradient boosting may be a machine-learning technique based on boosting in a functional space. For classification, a gradient boosting classifier may apply a logistic regression, where the machine-learning model uses log-odds to make a prediction, convert log-odds to various probabilities through a logistic function, and then make a classification based on a self-defined threshold. Moreover, gradient boosting may determine a prediction model in the form of an ensemble of weak prediction models, i.e., models that make very few assumptions about the data (e.g., simple decision trees).
Turning to random forests, a random forest model may an algorithmic model that combines the output of multiple decision trees to reach a single predicted result. For example, a random forest model may be composed of a collection of decision trees, where training the random forest model may be based on three main hyperparameters that include node size, a number of decision trees, and a number of input features being sampled. During training, a random forest model may allow different decision trees to randomly sample from a dataset with replacement (e.g., from a bootstrap sample) to produce multiple final decision trees in the trained model. For example, when multiple decision trees form an ensemble in the random forest model, this ensemble may determine more accurate predicted data, particularly when the individual trees are uncorrelated with each other. In some embodiments, a random forest model implements a software algorithm that is an extension of a bagging method. As, a random forest model may use both bagging and feature randomness to create an uncorrelated forest of decision trees. Feature randomness (also referred to as “feature bagging”) may generate a random subset of input features. This random subject may thereby result in low correlation among decision trees in the random forest model. In a training operation for a random forest model, a training operation may search for decision trees that provide the best split to subset particular data, such as through a Classification and Regression Tree (CART) algorithm. Different metrics, such as information gain or mean square error (MSE), may be used to determine the quality of a data split for various decision trees.
Keeping with random forests, a random forest model may be a classifier that uses data having discrete labels or classes. Likewise, a random forest model may also be used as a random forest regressor to solve regression problems. Depending on the type of problem being addressed by the random forest model, how predicted data is determined may vary accordingly. For a regression task, the individual decision trees may be averaged in a predicted result. For a classification task, a majority vote (e.g., predicting an output based on the most frequent categorical variable) may determine a predicted class. In a random forest regressor, the model may work with data having a numeric or continuous output, which cannot be defined by distinct classes.
In some embodiments, various types of machine-learning algorithms (e.g., machine-learning algorithm X (264)) may be used to train the model, such as a backpropagation algorithm. In a backpropagation algorithm, gradients are computed for each hidden layer of a neural network in reverse from the layer closest to the output layer proceeding to the layer closest to the input layer. As such, a gradient may be calculated using the transpose of the weights of a respective hidden layer based on an error function (also called a “loss function”). The error function may be based on various criteria, such as mean squared error function, a similarity function, etc., where the error function may be used as a feedback mechanism for tuning weights in the machine-learning model.
In some embodiments, a machine-learning model is trained using multiple epochs. For example, an epoch may be an iteration of a model through a portion or all of a training dataset. As such, a single machine-learning epoch may correspond to a specific batch of training data, where the training data is divided into multiple batches for multiple epochs. Thus, a machine-learning model may be trained iteratively using epochs until the model achieves a predetermined criterion, such as predetermined level of prediction accuracy or training over a specific number of machine-learning epochs or iterations. Thus, better training of a model may lead to better predictions by a trained model.
With respect to artificial neural networks, for example, an artificial neural network may include one or more hidden layers, where a hidden layer includes one or more neurons. A neuron may be a modelling node or object that is loosely patterned on a neuron of the human brain. In particular, a neuron may combine data inputs with a set of coefficients, i.e., a set of network weights for adjusting the data inputs. These network weights may amplify or reduce the value of a particular data input, thereby assigning an amount of significance to various data inputs for a task being modeled. Through machine learning, a neural network may determine which data inputs should receive greater priority in determining one or more specified outputs of the artificial neural network. Likewise, these weighted data inputs may be summed such that this sum is communicated through a neuron's activation function to other hidden layers within the artificial neural network. As such, the activation function may determine whether and to what extent an output of a neuron progresses to other neurons where the output may be weighted again for use as an input to the next hidden layer.
Turning to recurrent neural networks, a recurrent neural network (RNN) may perform a particular task repeatedly for multiple data elements in an input sequence (e.g., a sequence of temperature values or flow rate values), with the output of the recurrent neural network being dependent on past computations. As such, a recurrent neural network may operate with a memory or hidden cell state, which provides information for use by the current cell computation with respect to the current data input. For example, a recurrent neural network may resemble a chain-like structure of RNN cells, where different types of recurrent neural networks may have different types of repeating RNN cells. Likewise, the input sequence may be time-series data, where hidden cell states may have different values at different time steps during a prediction or training operation. For example, where a deep neural network may use different parameters at each hidden layer, a recurrent neural network may have common parameters in an RNN cell, which may be performed across multiple time steps. To train a recurrent neural network, a supervised learning algorithm such as a backpropagation algorithm may also be used. In some embodiments, the backpropagation algorithm is a backpropagation through time (BPTT) algorithm. Likewise, a BPTT algorithm may determine gradients to update various hidden layers and neurons within a recurrent neural network in a similar manner as used to train various deep neural networks.
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In Block 400, static well data are obtained regarding one or more wells in a geological region of interest in accordance with one or more embodiments. For example, static well data may correspond to various types of well data that are typically constant at a well, such as well design features such as well geometry, and other well site data such as geological and reservoir data. Examples of reservoir data may include porosity, permeability, and reservoir pressure.
In Block 402, dynamic well data are obtained regarding one or more wells in a geological region of interest in accordance with one or more embodiments. Dynamic well data may correspond to well data that changes as a function of time, one or more well parameters, and/or well operating conditions. Some examples of dynamic well data include wellhead pressure data, tubing casing annulus (TCA) pressure data, fluid property data (e.g., flow rate measurements), well production parameter data, well operation condition data, and various time-series data.
In Block 404, inspection data are obtained regarding one or more inspection operations in accordance with one or more embodiments. Inspection data may be similar to inspection data described above in
In Block 406, maintenance data are obtained regarding one or more maintenance operations in accordance with one or more embodiments. Maintenance data may be similar to maintenance data described above in
In Block 408, geological data and/or reservoir data are obtained regarding a geological region of interest in accordance with one or more embodiments. In some embodiments, for example, geological data and/or reservoir data are acquired using well logging, coring techniques, and/or downhole sampling.
In Block 410, an initial machine-learning model is obtained in accordance with one or more embodiments. For example, the initial machine-learning model may include multiple base models for use in a training operation with a prepared dataset. Each base model may be subsequently trained on a subset of the data or with different hyperparameters. As such, an ensemble model may combine the predictions of these base models to make a final prediction.
In some embodiments, for example, the initial machine-learning model may be a default model with default weights and biases. In some embodiments, the initial machine-learning model is a pre-trained model. For example, some training operations may benefit from “transfer learning” between models trained using similar problems with different training datasets. Thus, a pre-trained model may refer to a model that was trained on a large benchmark dataset to solve a similar problem, such as predicting well integrity for a similar type of well or a well in a similar formation. Accordingly, different types of pre-training processes may be performed to prepare for an actual training operation.
In Block 412, a trained model is generated using an initial machine-learning model, one or more machine-learning algorithms, static well data, dynamic well data, inspection data, maintenance data, geological data, and/or reservoir data in accordance with one or more embodiments. The trained model may be trained using various ensemble methods, such as Random Forest or Gradient Boosting, to build a predictive model that establishes the relationship between the input variables and downhole fluid communication issues.
For a training operation, data may be collected and preprocessed that includes both problematic and non-problematic well integrity examples. Data preprocessing techniques may include handling missing values, outliers, and normalization. For a training operation, various input variables are selected in a feature engineering process based on their relevance to downhole fluid communication issues.
Using one or more training operations, a machine-learning model may be evaluated and optimized accordingly. For example, a trained ensemble model may be evaluated using appropriate performance metrics such as accuracy, precision, recall, and F1-score. Model hyperparameters may be tuned using techniques like cross-validation to optimize the model's performance. For prediction and decision support, a trained model may be used to predict downhole fluid communication issues for new or unseen wells after optimization.
In Block 414, a well is selected for predicting well integrity in accordance with one or more embodiments. For example, the well may be selected automatically by a well integrity manager for analysis. On the other hand, a user may select a particular well within a graphical user interface.
In Block 416, new data are obtained for a selected well in accordance with one or more embodiments. The new data may be similar to the static well data, the dynamic well data, the inspection data, the maintenance data, geological data, and/or reservoir data described above in Blocks 400-408.
In Block 418, predicted well integrity data are determined using a trained model and new data in accordance with one or more embodiments. For example, a trained model may use the new data for a selected well as one or more data inputs and output a binary classification label or a multi-classification label set that predicts well integrity of the selected well. In some embodiments, the predicted well integrity data may indicate whether a particular well is likely to have a downhole communication problem. This predicted well integrity data may support various automated decision-making processes related to well integrity management, such as maintenance operation prioritization and well operational planning.
Furthermore, one relationship between various data inputs (e.g., wellhead pressures and tubing casing annulus pressures) and predicted well integrity data (i.e., the presence or absence of specific downhole fluid communication issues) is illustrated in Tables 1 and 2 below. More specifically, predicted well data may be determined for different wells associated with different well identifiers (i.e., “Well ID”).
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In some embodiments, for example, a user may set one of more well integrity thresholds that automatically implement one or more well operations based on a monitored well failing a well integrity threshold. For example, a well integrity threshold may correspond to a specific reduction in metal thickness in tubing, casing, or another pipe component that indicates a current or future problem. As such, the graphical user interface may provide decision support and risk mitigation for multiple well operators and engineers.
In Block 430, a determination is made whether predicted well integrity data satisfies a well integrity criterion in accordance with one or more embodiments. For example, well integrity criteria may be used to determine whether a predicted integrity problem warrants any remediation action as well as the optimum time period for performing the remediation. As such, the predicted well integrity data may enable timely intervention to prevent or mitigate potential problems before the problems escalate in the respective wells. If no well integrity problems are detected, the process may return to Block 416 for continued monitoring of the selected well. On the other hand, if a well integrity problem is detected based on failing to satisfy the well integrity criterion, the process may proceed to Block 432 for remediation or determining a well remediation plan accordingly.
In Block 432, one or more well operations are determined based on predicted well integrity data in accordance with one or more embodiments. For example, early detection of downhole fluid communication issues may facilitate early detection of necessary well intervention operation and proactive management of well operations among multiple wells and multiple oil fields. The well operations may be well intervention operations as well as other well operations, such as adjusting well operating conditions to alleviate future well integrity issues.
In some embodiments, predicted well integrity data is used to improve operation proficiency at one or more well sites. The proactive assessment of downhole fluid communication issues may be used optimize well operations (e.g., adjust well operation conditions to reduce or eliminate future well integrity issues), minimize production losses, and reduce well downtime.
In Block 434, one or more commands are transmitted to perform one or more well operations in accordance with one or more embodiments. After predicting well integrity issues, such as one or more packer failures, a tubing leak, a casing leak, or an obstruction to wellhead fluid communication, one or more commands may be transmitted over a well management network to implement well intervention operations to remediate the well integrity issues.
Embodiments may be implemented on a computer system.
The computer (602) can serve in a role as a client, network component, a server, a database or other persistency, or any other component (or a combination of roles) of a computer system for performing the subject matter described in the instant disclosure. The illustrated computer (602) is communicably coupled with a network (630). In some implementations, one or more components of the computer (602) may be configured to operate within environments, including cloud-computing-based, local, global, or other environment (or a combination of environments).
At a high level, the computer (602) is an electronic computing device operable to receive, transmit, process, store, or manage data and information associated with the described subject matter. According to some implementations, the computer (602) may also include or be communicably coupled with an application server, e-mail server, web server, caching server, streaming data server, business intelligence (BI) server, or other server (or a combination of servers).
The computer (602) can receive requests over network (630) from a client application (for example, executing on another computer (602)) and responding to the received requests by processing the said requests in an appropriate software application. In addition, requests may also be sent to the computer (602) from internal users (for example, from a command console or by other appropriate access method), external or third-parties, other automated applications, as well as any other appropriate entities, individuals, systems, or computers.
Each of the components of the computer (602) can communicate using a system bus (603). In some implementations, any or all of the components of the computer (602), both hardware or software (or a combination of hardware and software), may interface with each other or the interface (604) (or a combination of both) over the system bus (603) using an application programming interface (API) (612) or a service layer (613) (or a combination of the API (612) and service layer (613). The API (612) may include specifications for routines, data structures, and object classes. The API (612) may be either computer-language independent or dependent and refer to a complete interface, a single function, or even a set of APIs. The service layer (613) provides software services to the computer (602) or other components (whether or not illustrated) that are communicably coupled to the computer (602). The functionality of the computer (602) may be accessible for all service consumers using this service layer. Software services, such as those provided by the service layer (613), provide reusable, defined business functionalities through a defined interface. For example, the interface may be software written in JAVA, C++, or other suitable language providing data in extensible markup language (XML) format or other suitable format. While illustrated as an integrated component of the computer (602), alternative implementations may illustrate the API (612) or the service layer (613) as stand-alone components in relation to other components of the computer (602) or other components (whether or not illustrated) that are communicably coupled to the computer (602). Moreover, any or all parts of the API (612) or the service layer (613) may be implemented as child or sub-modules of another software module, enterprise application, or hardware module without departing from the scope of this disclosure.
The computer (602) includes an interface (604). Although illustrated as a single interface (604) in
The computer (602) includes at least one computer processor (605). Although illustrated as a single processor (605) in
The computer (602) also includes a memory (606) that holds data for the computer (602) or other components (or a combination of both) that can be connected to the network (630). For example, memory (606) can be a database storing data consistent with this disclosure. Although illustrated as a single memory (606) in
The application (607) is an algorithmic software engine providing functionality according to particular needs, desires, or particular implementations of the computer (602), particularly with respect to functionality described in this disclosure. For example, application (607) can serve as one or more components, modules, applications, etc. Further, although illustrated as a single application (607), the application (607) may be implemented as multiple applications (607) on the computer (602). In addition, although illustrated as integral to the computer (602), in alternative implementations, the application (607) can be external to the computer (602).
There may be any number of computers (602) associated with, or external to, a computer system containing computer (602), each computer (602) communicating over network (630). Further, the term “client,” “user,” and other appropriate terminology may be used interchangeably as appropriate without departing from the scope of this disclosure. Moreover, this disclosure contemplates that many users may use one computer (602), or that one user may use multiple computers (602).
In some embodiments, the computer (602) is implemented as part of a cloud computing system. For example, a cloud computing system may include one or more remote servers along with various other cloud components, such as cloud storage units and edge servers. In particular, a cloud computing system may perform one or more computing operations without direct active management by a user device or local computer system. As such, a cloud computing system may have different functions distributed over multiple locations from a central server, which may be performed using one or more Internet connections. More specifically, cloud computing system may operate according to one or more service models, such as infrastructure as a service (IaaS), platform as a service (PaaS), software as a service (SaaS), mobile “backend” as a service (MBaaS), serverless computing, and/or function as a service (FaaS).
Although only a few example embodiments have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the example embodiments without materially departing from this invention. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the following claims.
