Downhole Valve Optimization for Pump Intake Pressure Using Machine Learning

Various operations are performed at a well site during the lifetime of a producing well to maintain hydrocarbon recovery. Wells that produce from more than one zone may use a downhole valve or choke in hydraulic communication with one or more zones to control the flow from each of the zones. Electrical submersible pumps (ESPs) in hydraulic communication with the downhole chokes may provide hydraulic lift to the produced fluids. The downhole chokes may be used to control the flow from each zone to the inlet of the ESP. The settings of the downhole chokes and the operational conditions of the ESP may be adjusted to decrease risk of production problems such as production losses, and to increase operational efficiencies.

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.

This disclosure presents, in accordance with one or more embodiments, 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 includes obtaining dynamic well data for the well. The dynamic well data describes one or more well properties that change over time. The method includes obtaining first choke index setting data regarding a first choke disposed in a producing zone of interest of the well. The method includes generating, by a computer processor, first predicted electrical submersible pump (ESP) input data for a first ESP in hydraulic connection with the first choke based on the static well data, the dynamic well data, and the first choke index setting data using a recurrent neural network. The method includes determining, by the computer processor, well performance data for the well based on the first predicted ESP input data. The method includes determining, by the computer processor, well operations for the well based on the well performance data and transmitting, by the computer processor and to a control system coupled to the well, a command that causes the well operations to be performed at the well.

This disclosure presents, in accordance with one or more embodiments, a system that includes a well control system coupled to a well at a well site. The well includes a plurality of choke components that are installed in a wellbore. The system includes a well performance manager coupled to the well control system. The well performance manager includes a computer processor and the well performance manager is configured to perform a method. The method includes obtaining static well data for a well. The static well data describes one or more well design parameters of the well. The method includes obtaining dynamic well data for the well. The dynamic well data describes one or more well properties that change over time. The method includes obtaining first choke index setting data regarding a first choke included in the plurality of choke components. The first choke is disposed in a producing zone of interest of the well. The method includes generating first predicted electrical submersible pump (ESP) input data for a first ESP in hydraulic connection with the first choke based on the static well data, the dynamic well data, and the first choke index setting data using a recurrent neural network. The method includes determining well performance data for the well based on the first predicted ESP input data and determining well operations for the well based on the well performance data. The method includes transmitting to the well control system a command that causes the well operations to be performed at the well.

This disclosure presents, in accordance with one or more embodiments, a non-transitory computer-readable memory including computer-executable instructions stored thereon that, when executed on a processor, cause the processor to perform various steps. The steps include obtaining static well data for a well. The static well data describes one or more well design parameters of the well. The steps include obtaining dynamic well data for the well. The dynamic well data describes one or more well properties that change over time. The steps include obtaining first choke index setting data regarding a first choke disposed in a producing zone of interest of the well. The steps include generating first predicted electrical submersible pump (ESP) input data for a first ESP in hydraulic connection with the first choke based on the static well data, the dynamic well data, and the first choke index setting data using a recurrent neural network. The steps include determining well performance data for the well based on the first predicted ESP input data. The steps include determining well operations for the well based on the well performance data and transmitting to a control system coupled to the well a command that causes the well operations to be performed at the well.

Other aspects and advantages of the claimed subject matter will be apparent from the following description and the appended claims.

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.

Regarding the figures described herein, when using the term “down” the direction is toward or at the bottom of a respective figure and “up” is toward or at the top of the respective figure. “Up” and “down” are oriented relative to a local vertical direction. In the oil and gas industry, one or more activities take place in a vertical, substantially vertical, deviated, substantially horizontal, or horizontal well. Therefore, one or more figures may represent an activity in deviated or horizontal wellbore configuration. “Uphole” or “upper” may refer to objects, units, or processes that are positioned relatively closer to the surface entry in a wellbore than another. “Downhole” or “lower” may refer to objects, units, or processes that are positioned relatively farther from the surface entry in a wellbore than another. Measured depth (MD) is the length of the wellbore. True vertical depth (TVD) is the vertical distance from a point in the well at a location of interest to a reference point on the surface.

In general, embodiments of the disclosure include systems and methods for determining a well performance optimization using machine learning (ML). In some embodiments, for example, a well performance optimization may be selected based on predicted well performance inputs that include various well performance considerations. The considerations may be based on real-time data, recent data, and historical data from well performance such as well production flowrate, well production temperature, electrical submersible pump (ESP) data, etc., and from inflow control valve, interval control valve (ICV, hereafter choke) index setting data such as those from zone control chokes, etc. As such, well performance optimization may be a complex process based on the variables from static well data, dynamic well data, ESP data, and choke data. Accordingly, some embodiments include a well performance manager that may be a smart system or expert system that automatically predicts well performance, generates well performance scenarios, selects well performance scenarios, then selects priorities for implementing the scenarios. A well performance 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 include a well performance manager with self-decision functionality that operates independently and with flexibility. For example, the well performance manager may perform a learning process that detects well performance issues in oil and gas wells. For example, a well performance manager may detect well performance problems, such as a flowrate discrepancy between a predicted flowrate and a measured flowrate, that can lead to well performance issues. Manual inspections or rule-based systems may be used for identifying these issues. Manual approaches may be time-consuming, subjective, and less accurate. The disclosed system and method use a machine-learned model and predictive modelling of a well performance manager along with static and dynamic well data, inflow control valve index data, and ESP data to predict ESP input data, well performance data, and to recommend one or more well operations.

The well performance manager may determine statistical trends based on well data (such as static and dynamic data), inflow control valve (choke) index data, and/or ESP operational data. Likewise, a well performance manager may determine one or more additional values or weights for arranging the well performance operations based on the priority ranking. This flexibility may accommodate changes in real time, e.g., on-the-fly observed matters, like health, environment, safety, and production concerns. Historical well data builds trends and improves robustness of various well performance plans by advising about possible issues. These different factors may provide the data inputs that are adjusted over time to optimize a particular performance operation criterion.

Furthermore, some embodiments use one or more machine-learning algorithms to determine which data inputs to use for a well performance criterion. For example, different sets of data inputs may maximize operational efficiency and hydrocarbon production at one or more wells, while also minimizing operational costs for those same wells. For example, an optimized set of data inputs may be a subset of a larger aggregated set of data inputs identified over a well management network. These aggregated data inputs may be recognized by a well performance manager, where the well performance manager may prescribe different weights or significances to various data inputs. Thus, a well performance manager may provide a flexible method to accommodate multiple performance criteria (e.g., swapping importance/relevance of different data inputs that correspond to various incidents, inspection results, maintenance activities, production performances, the static well data, and the dynamic well data, etc.) in real time (as applicable) to re-arrange different well performance plans. Thus, a well performance manager may automatically readjust variables for a well performance plan based on predicting their future importance to a user. In some embodiments, a well performance plan may be updated based on real-time observed matters during actual performance of the well performance plan (e.g., in response to changing safety concerns, dynamic well data, etc.)

Some embodiments describe, as a suitable solution, a predictive model that utilizes machine-learned methods to detect performance problems such as well performance issues in oil and gas wells. Well performance refers to wellbore pressure and/or fluid communication between a first location and a second location. Pressure or fluid communication in this context is, for example, pressure variations, i.e., pressure increases or decreases, and fluid flows between the first location and the second location. Downhole isolation refers to lack of communication between the locations. Pressure and fluid communication are a performance issue when the well design calls for downhole isolation but well zone-to-zone communication happens. A suitable solution is leveraging machine-learned methods and predictive modeling with machine learning methods to improve well performance management, optimize operations, and mitigate risks in oil and gas wells applications.

Some embodiments use a data-driven approach by analyzing input variables (e.g., relevant input variables, primary input variables, and combinations of input variables) such as wellhead pressures, well flowrates, ESP input frequencies, choke index settings, and other relevant parameters, to accurately predict these outputs, namely, well performance and ESP wear. The data-driven decision making may overcome limitations of subjective or experience-based decision making. Well performance issues such as reduced hydrocarbon production may be discovered through evaluation and processing, e.g., with a machine-learned model and with quantities such as flowrate, water cut, and pressure. Some embodiments may enable proactive well management and maintenance, leading to improved operational efficiency and reduced downtime. Embodiments may detect or capture complex relationships between input variables and well performance issues and provide an output, including a ranking of outputs. For example, an output may include zone index settings and ESP maintenance intervals.

Some embodiments may be robust to noise and variability in the data for handling real-world well data that may contain uncertainties or measurement errors, thereby providing reliable predictions in diverse operational conditions. Some embodiments may include ranking of the input variables and feature engineering to extract meaningful information from a dataset. Selection of relevant features improves model performance and interpretability. The model may include practical implementation considerations, such as computational efficiency, scalability, and integration into existing well performance management systems, thereby contributing to the practicality and usability of the developed predictive model. Some embodiments include data gathered and curated to form a comprehensive dataset of problematic and non-problematic zones, wells, or equipment. The data may include the input parameters collected and preprocessed for training the model to form an accurate and reliable predictive model.

By accurately predicting well performance issues, some embodiments facilitate early detection, identification, and proactive management, enabling timely well operations (such as well interventions), choke operations (such as index changes or index setting changes), and/or ESP operations (such as frequency changes) to prevent or mitigate potential problems before they escalate. Utilizing machine-learning techniques, the machine-learning model may uncover non-linear patterns, relationships, and interactions. The proactive assessment of well performance issues may help optimize well operations, minimize production losses, reduce downtime, and improve overall operational efficiency. Some embodiments assist in optimizing well performance management practices by prioritizing maintenance activities, allocating resources effectively, and identifying potential risks before they lead to costly failures. The predictive model may provide decision support for well operators and engineers, aiding in risk mitigation strategies, resource allocation, and maintenance planning based on predicted well performance issues.

In accordance with one or more embodiments the disclosed system and method enhance the maintenance of optimal pump intake pressure for ESPs for efficient and safe extraction. One or more embodiments use a type of machine learning model known as a long short-term memory (LSTM) network to predict the outcomes of choke indexer settings on well performance, specifically focusing on intake pressure, temperature, and flow rate. LSTMs are capable of learning from sequences of data, making them particularly suited for time-series data that characterizes well operations. The LSTM-driven system can continuously learn from new data to improve its predictions over time. This adaptability makes it highly effective in dynamic well environments where conditions change frequently. The LSTM-driven system optimizes choke indexer settings using predictions from the LSTM, potentially incorporating a broader range of operational data into the decision-making process.

In accordance with one or more embodiments the disclosed system and method adapt in real time. The real-time adaptability using the LSTM-driven system offers real-time advice on choke indexer settings, leveraging the ability of the LSTM model to quickly process new information and update predictions. The LSTM-driven system allows customization to specific well characteristics and scalability across different operational scenarios without significant reconfiguration.

The LSTM-Driven downhole valve optimization system represents a significant advancement in the application of deep learning to downhole valve optimization. Its use of LSTM networks for real-time, adaptive prediction and optimization of ICV settings offers potential improvements over traditional simulation-based approaches in terms of adaptability, prediction accuracy, and operational efficiency.

shows a schematic diagram in accordance with one or more embodiments. As shown in,illustrates a well sitethat includes a hydrocarbon reservoir (e.g., reservoir) located in a subsurface hydrocarbon-bearing (e.g., formation) and a well system. The formationmay include a porous or fractured rock formation that resides underground, below the surface of the earth or below a seabed (hereafter surface e.g., surface). In the case of the well systembeing a hydrocarbon well, the reservoirmay include a portion of the formation. The formationand the reservoirmay include different layers of rock having varying characteristics, such as varying degrees of permeability, porosity, and resistivity. In the case of the well systembeing operated as a production well, the well systemmay facilitate the extraction of hydrocarbons from the reservoir.

In some embodiments, the well systemincludes a wellboreand a well control system. The well control systemmay control various operations of the well system, such as well production operations, well completion operations, well maintenance operations, and reservoir monitoring, assessment, and development operations. In some embodiments, the well control systemincludes a computer system that is the same as or similar to that of computer system (e.g., a computer) described below inand the accompanying description.

The wellboremay include a bored hole that extends from the surfaceinto a target zone of the formation, such as the reservoir. An upper end of the wellbore, terminating at or near the surface, may be referred to as the “up-hole” end of the wellbore, and a lower end of the wellbore, terminating in the formation, may be referred to as the “downhole” end of the wellbore. The wellboremay facilitate the circulation of drilling fluids during drilling operations, conveyance of produced fluids including the flow of hydrocarbon (e.g., oil and gas) production (e.g., production) from the reservoirto the surfaceduring production operations, the injection of substances (e.g., water) into the formationor the reservoirduring injection operations, or the communication of monitoring devices (e.g., logging tools) into the formationor the reservoirduring monitoring operations (e.g., during in situ logging operations).

In some embodiments, during operation of the well system, the well control systemcollects and records wellhead datafor the well systemand other data regarding downhole equipment and downhole sensors (e.g., using an automatic computer-controlled management system described herein.) The wellhead datamay include, for example, a record of measurements of wellhead pressure (P) (e.g., wellhead pressures (measured pressures at the wellhead) including flowing wellhead pressure (FWHP), shut-in wellhead pressure (SIWHP)), wellhead temperature (T) (e.g., including flowing wellhead temperature), wellhead production rate (Q) over some or all of the life of the well system, and water cut data (data regarding water cut.) 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 datamay be referred to as “real-time” wellhead data. Real-time wellhead data may enable an operator of the well systemto assess a relatively current state of the well system, and to make real-time decisions regarding development of the well systemand the reservoir, such as on-demand adjustments in regulation of production flow from the well.

With respect to water cut data, the well systemmay include one or more water cut sensors. 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 wellborecompared to the total volume of liquids produced from the wellbore. In some embodiments, a water-to-gas ratio (WGR) is determined using a multiphase flow meter. For example, a multiphase flow meter may use magnetic resonance information to determine the number of hydrogen atoms in a particular fluid flow. Since oil, gas and water all contain hydrogen atoms, a multiphase flow may be measured using magnetic resonance. In particular, a fluid may be magnetized and subsequently excited by radio frequency pulses. The hydrogen atoms may respond to the pulses and emit echoes that are subsequently recorded and analyzed by the multiphase flow meter.

In some embodiments, the well systemincludes a wellhead. The wellheadmay include a rigid structure installed at the “up-hole” end of the wellbore, at or near where the wellboreterminates at the surface. The wellheadmay include structures for supporting (or “hanging”) casing and production tubing extending into the wellbore. Productionmay flow through the wellhead, after exiting the wellbore, including, for example, the casing and the production tubing. In some embodiments, the well systemincludes flow regulating devices that are operable to control the flow of substances into and out of the wellbore. For example, the well systemmay include one or more of a production valvethat are operable to control the flow of production. For example, a production valvemay be fully opened to enable unrestricted flow of productionfrom the wellbore, the production valvemay be partially opened to partially restrict (or “throttle”) the flow of productionfrom the wellbore, and production valvemay be fully closed to fully restrict (or “block”) the flow of productionfrom the wellbore.

Keeping with, in some embodiments, the well systemmay include sensor devices for sensing characteristics of substances passing through the well system, including production. The characteristics may include, for example, pressure, temperature, and flowrate of productionflowing through the wellhead, or other conduits of the well system, after exiting the wellbore.

In some embodiments, the well systemincludes a surface pressure sensoroperable to sense the pressure of productionafter it exits the wellbore. The surface pressure sensormay include, for example, a wellhead pressure sensor that senses a pressure of productionflowing through or otherwise located in the wellhead, referred to as “wellhead pressure” (P). In some embodiments, the well systemincludes a surface temperature sensoroperable to sense the temperature of productionafter it exits the wellbore. The surface temperature sensormay include, for example, a wellhead temperature sensor that senses a temperature of productionflowing through or otherwise located in the wellhead, referred to as “wellhead temperature” (T). In some embodiments, the well systemincludes a flowrate sensoroperable to sense the flowrate of productionafter it exits the wellbore. The flowrate sensormay include hardware that senses a flowrate of production(Q) passing through the wellhead.

Keeping with, when completing a well, one or more well completion operations may be performed prior to delivering the well to the party responsible for production or injection. Well completion operations may include casing operations, cementing operations, perforating the well, gravel packing, directional drilling, hydraulic stimulation of a reservoir region, and/or installing a production tree or wellhead assembly at the wellbore. Likewise, well operations may include open-hole completions or cased-hole completions. For example, an open-hole completion may refer to a well that is drilled to the top of the hydrocarbon reservoir. Thus, the well may be cased at the top of the reservoir and left open at the bottom of a wellbore. In contrast, cased-hole completions may include running casing into a reservoir region.

In one well completion example, the sides of the wellboremay require support, and thus casing may be inserted into the wellboreto provide such support. After a well has been drilled, casing may ensure that the wellboredoes not close in upon itself, while also protecting the wellstream from outside contaminants, like water or sand. Likewise, if the formation is firm, casing may include a solid string of steel pipe that is run in 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.

In another well operation example, a space between the casing and the untreated sides of the wellboremay be cemented to hold a casing in place. This well operation may include pumping cement slurry into the wellboreto displace existing drilling fluid and fill in this space between the casing and the untreated sides of the wellbore. Cement slurry may include a mixture of various additives and cement. After the cement slurry is left to harden, cement may seal the wellborefrom 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 bored hole of the wellbore. 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 wellboreto enable hydrocarbons to enter a wellstream from the resulting holes. For example, some perforation operations include using a perforation gun at one or more reservoir levels to produce holed sections through the casing, cement, and sides of the wellbore. 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.

In another well completion, a filtration system may be installed in the wellborein 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 wellborebetween a the slotted liner of a casing and the sides of the wellbore. 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 completion, a wellhead assembly may be installed on the wellhead of the wellbore. A wellhead assembly may include 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.

In some embodiments, a wellboreincludes one or more casing centralizers. For example, a casing centralizer may be a mechanical device that secures casing at various locations in a wellbore to prevent casing from contacting the walls of the wellbore. Thus, casing centralization may produce a continuous annular clearance around the casing such that cement may be used to completely seal the casing to walls of the wellbore. Without casing centralization, a cementing operation may experience mud channeling and poor zonal isolation. Examples of casing centralizers may include bow-spring centralizers, rigid centralizers, semi-rigid centralizers, and mold-on centralizers. In particular, bow springs may be slightly larger than a particular wellbore in order to provide complete centralization in vertical or slightly deviated wells. On the other hand, rigid centralizers may be manufactured from solid steel bar or cast iron with a fixed blade height in order to fit a specific casing or hole size. Rigid centralizers may perform well even in deviated wellbores regardless of any particular side forces. Semi-rigid centralizers may be made of double crested bows and operate as a hybrid centralizer that includes features of both bow-spring and rigid centralizers. The spring characteristic of the bow-spring centralizers may allow the semi-rigid centralizers to compress in order to be disposed in tight spots in a wellbore. Mold-on centralizers may have blades made of carbon fiber ceramic material that can be applied directly to a casing surface.

In some embodiments, well performance operations may also be performed at a well site. For example, well performance 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., hydraulic fracturing operations, coiled tubing, flow back, separator, pumping, wellhead and production tree maintenance, slickline, braided line, coiled tubing, snubbing, workover, subsea well performance, etc.). For example, well performance 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 performance operations are used to 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 operations, at a well site. For example, one or more service entities may be responsible for performing a cementing operation in the wellboreprior to delivering the well to a producing entity.

In some embodiments, well performance operations may include various operations carried out by one or more automatic systems such as control systems. For example, well performance activities may be similar to sliding sleeve choke indexing operations, automatic valve operations, etc., carried out by automatic control systems. The automatic control systems may be coupled to a reservoir simulator or other well control systems or software platforms.

Turning to the reservoir simulator (e.g., a reservoir simulator) may include hardware and/or software with functionality for performing a well simulation (e.g., well simulations of the wellbore of one or more wells) such as storing and analyzing well logs, production data, sensor data (e.g., from a wellhead, downhole sensor devices, or flow control valves, inflow control valves, inflow control devices), and/or other types of data to generate and/or update one or more geological models of one or more reservoir regions. Geological models may include geochemical or geomechanical models that describe structural relationships within a particular geological region. Likewise, the reservoir simulatormay also determine changes in reservoir pressure and other reservoir properties for a geological region of interest, e.g., in order to evaluate the health of a particular reservoir during the lifetime of one or more producing wells.

While the reservoir simulatoris shown at a well site, in some embodiments, the reservoir simulatoror other components inmay be remote from a well site. In some embodiments, the reservoir simulatoris implemented as part of a software platform for the well control system. The software platform may obtain data acquired by a control system as inputs, which may include multiple data types from multiple sources. The software platform may aggregate the data from these systems in real time for rapid analysis. In some embodiments, the well control systemand the reservoir simulator, and/or a user device coupled to one of these systems may include a computer system that is similar to the computer system (e.g., computer) described below with regard toand the accompanying description.

In some embodiments, the reservoir simulatormay include software configured with machine learning capabilities and artificial intelligence (AI) that learns from trends of the one or more parameters tracked by the well control system. In one or more embodiments, the AI and machine learning (ML) capabilities employed by the reservoir simulator may include any suitable algorithms and processes for predicting well behavior using historical data as input. For example, the ML models or machine-learning algorithms may include supervised algorithms, unsupervised algorithms, deep learning algorithms that use artificial neural networks (ANN), etc. More specifically, supervised ML models include classification, regression models, (support vector machines or support vector networks) etc. Unsupervised ML models include, for example, clustering models.

Turning to,shows a schematic diagram in accordance with one or more embodiments.illustrates a well siteat a surface location (e.g., a surface). The well site includes a hydrocarbon reservoir (e.g., a reservoir), located in a subsurface hydrocarbon-bearing formation (e.g., a formation), and a well system. The well system includes a sub-surface system (e.g., a well sub-surface system) and an electrical submersible pump (ESP) system (e.g., an ESP P) with an ESP flow inlet (e.g., an ESP Flow Inlet I). As known in the art, a well with one or more zones may include a packer above and/or below the zone to isolate the zone from other zones. The well may include one or more choke components in hydraulic communication with each of the respective zones.shows a formation with three zones, a lower zone (e.g., a Lower Zone L), a middle zone (e.g. a Middle Zone M), and an upper zone (e.g., an Upper Zone U). Each zone may have a choking component, in this example, an inflow control valve (ICV).

As known in the art, the flow control valve may be variously known as a flow control choke, an interval control valve (ICV), a flow control valve, an inflow control valve, a downhole choke, smart valve, smart control valve, a smart choke, etc. A flow control choke with only an on position and an off position may be considered an on/off valve. The Lower Zone Lmay have a first flow control choke (a choke L) disposed in the lower zone. The Middle Zone Mmay have a second flow control choke (e.g., a choke M) disposed in the middle zone. The Upper Zone Umay have a third flow control choke (e.g., a choke U) disposed in the upper zone. The zones are zones of interest such as zones that bear hydrocarbons.

Each choke may have a cross-sectional area that may change in increments from a closed position such as a fully-choked position to a first (1) choked position, to a second (2) choked position, to an nposition, and so on incrementally up to an nchoked position. Each choke position may be associated with a different cross-sectional flowpath area. In this manner the flowrate may be regulated by selecting different choke positions for different flowpath areas. The fully-choked position may be considered a fully-closed position. The nchoked position may be considered a fully-open position. Each choke may have an indexer device configured to change the cross-sectional area of the flowpath of each choke. Indexer devices have index settings. Index settings are associated with choke positions.

Each choke may be incremented between each choked position using an indexer. The indexer changes the choke position in the various increments by indexing the internal components of the chokes. In this manner the indexer indexes from one index setting to another index setting and each index setting corresponds with a choke position. The indexer indexes each choke from one choke index setting corresponding to a choke position to another choke index setting corresponding to another choke position. For example, the choke Lmay have an indexer L (e.g., an Indexer L), the choke Mmay have an indexer M (e.g., an Indexer M), and the choke Umay have an indexer U (e.g., an Indexer U).

In this manner choke positions refer to positions of the internal components such as a sliding sleeve within the choke. Choke locations refer to the location in the well, such as a total vertical depth or a measured depth of the choke in the wellbore. Index settings refer to the setting of choke internal components such as the indexer within the choke. An indexer has index settings to move the choke from one choke index setting to another setting. An index parameter refers to the specification of the index setting. The index parameters are the numbers of the choke index setting, e.g., “3” is a specific index parameter of a choke index setting. A choke index setting parameter of three indicates that the indexer is set at index setting three which sets the sliding sleeve of the choke in the choking position three. Choke index data may include data regarding one or more choke index settings, e.g., choke index parameters.

In accordance with one or more embodiments each choke may have two to twelve or more index settings. A twelve settings-position table (e.g., First Settings-Position Table) illustrated inshows, for example a ten-choking position choking valve may have twelve index settings (e.g., twelve index settings) corresponding to twelve positions (e.g., twelve choke positions) ranging from fully choked, i.e., fully closed (a first index setting pl corresponding to a choke position one) to fully open (a twelfth index setting p12 corresponding to a choke position twelve) with ten intermediate choking flow index settings between the first and the twelfth positions. E.g., a choke index setting of three configures the choke in the choke position three. The second of ten intermediate choking flow index settings may be a choke position three or 3choked position. The 3rd choked position may correspond to the second intermediate choking flow setting which may correspond to a cross-sectional area of the flowpath (e.g., Flowpath Areas) equivalent to 20% of the cross-sectional area of the production tubing to which the choke is hydraulically coupled. In a well with three zones, one choke per zone, twelve index settings per choke, the total is 1,9928 combinations of valve positions and zones (12{circumflex over ( )}3).

shows an example four settings-position table (e.g., Second Settings-Position Table) for another choke example. Inthe two-choking position choke Lmay have four index settings (e.g., four index settings) corresponding to four positions (e.g., four choke positions) ranging from fully choked, i.e., fully closed (a first index setting p1 corresponding to a choke position one) to fully open (a fourth index setting p4 corresponding to a choke position four) with two intermediate choking flow index settings between position one (the first position) and position four (the fourth position). The second of two intermediate choking flow index settings may be a choke position three or 3choked position. The 3rd choked position may correspond to the second intermediate choking flow setting which may correspond to a cross-sectional area of the flowpath (e.g., Flowpath Areas) equivalent to 66% of the cross-sectional area of the production tubing to which the choke is hydraulically coupled. The choke positions correspond with four index settings, e.g., choke index settings, such that each choke position is determined by a choke index setting. Each choke position corresponds with a predetermined cross-sectional flow area of the choke. In an example well with three zones, one choke per zone, four index settings per choke, the total is sixty-four combinations of valve positions and zones (4{circumflex over ( )}3). The example sixty-four combinations (e.g., a combinations matrix) are partially illustrated in.

Returning to, the choke Lmay have a first index setting (e.g., an indexer L first index setting) corresponding to the first (1) choked position (e.g., a choke L first position), a second index setting (e.g., an indexer L second index setting) corresponding to the second (2) choked position (e.g., a choke L second position), a third index setting (e.g., an indexer L third index setting) corresponding to the third (3) choked position (e.g., a choke L third position), and a fourth index setting (e.g., an indexer L fourth index setting) corresponding to the fourth (4) choked position (e.g., a choke L fourth position).

For example, choke Mmay include four index settings, a first index setting (e.g., an indexer M first index setting) corresponding to the first choked position (e.g., a choke M first position), a second index setting (e.g., an indexer M second index setting) corresponding to the second choked position (e.g., a choke M second position), a third index setting (e.g., an indexer M third index setting) corresponding to the third choked position (e.g., a choke M third position), and a fourth index setting (e.g., an indexer M fourth index setting) corresponding to the fourth choked position (e.g., a choke M fourth position).

For example, choke Umay include four index settings, a first index setting (e.g., an indexer U first index setting) corresponding to the first choked position (e.g., a choke U first position), a second index setting (e.g., an indexer U second index setting) corresponding to the second choked position (e.g., a choke U second position), a third index setting (e.g., an indexer U third index setting) corresponding to the third choked position (e.g., a choke U third position), and a fourth index setting (e.g., an indexer U fourth index setting) corresponding to the fourth choked position (e.g., a choke U fourth position).

Each indexer may be configured to move from their respective first index settings to their respective second index settings in response to an actuation pressure input. The actuation pressure input may be positive (e.g., pressurizing) or negative (e.g., depressurizing). For example, a negative actuation pressure input is depressurizing. Subsequent changes in actuation pressures may result in changes to pressure inputs to the internal components of the indexer. Pressure input changes may cause a shift of the indexer from a first position to a next position in response to a second actuation pressure input.