WO2025158170A1

WO2025158170A1 - Closed-loop production optimization for wells

Info

Publication number: WO2025158170A1
Application number: PCT/IB2024/050585
Authority: WO
Inventors: Carlos Mata; Cristina HERNANDEZ; Thabet CHELLIGUE; Erismar RUBIO; Amine EL KHAIR; Umar Asif; Wael ALMADHOUN; Luigi Saputelli; Abdullah ALHAMMO
Original assignee: Matrix Jvco Ltd Trading Aiq AS
Current assignee: Matrix Jvco Ltd Trading Aiq AS
Priority date: 2024-01-22
Filing date: 2024-01-22
Publication date: 2025-07-31
Anticipated expiration: 2026-07-22

Abstract

An integrated system (1000) and a computer-implemented method for production optimization in upstream oil and/or gas production, the integrated system (1000) comprising a first system (100) comprising a first tool packet (110) and a second tool packet (120); a second system (300) for monitoring and controlling operation of one or more wells, wherein the first tool packet (110) is configured to continuously provide adjustment information to the second system (300) to improve well performance within a permissible operating envelope, and wherein the second system (300) is configured to continuously provide feedback about real-time operational data and/or information related to well status to the first tool packet (110); and a third system (400) comprising one or more tools for analyzing operational data associated with the one or more wells, the operational data being received from the second system (300), wherein the third system (400) is configured to provide analytical results to the second tool packet (120) of the first system (100).

Description

Closed-Loop Production Optimization for Wells

Field

[oooi] The present invention generally relates to production optimization for oil/gas wells, more specifically, to systems and computer-implemented methods for production optimization for oil/gas wells.

Background

[0002] Stabilizing production rates in oil/gas wells is an important task because stability leads to improved field recovery and reduced costs per unit of oil/gas. On the other hand, instability of wells can have ripple effects on both downstream and upstream operations, impacting lifting and production networks. Traditionally, the operation of oil/gas wells necessitates continuous monitoring and manual intervention by field personnel to make frequent adjustments in control variables such as choke positions for natural flow wells and/ or lift parameters for artificially lifted wells. For example, to counteract instability in gas lift wells, operations personnel may resort to increasing the gas lift rate beyond requirements or throttling well production. While these measures do stabilize the production rate, they simultaneously diminish well deliverability, resulting in e.g., wastage of gas lift and associated energy required to provide it. Furthermore, these corrective activities consume a considerable amount of time, and manual adjustments cannot always be applied in time.

[0003] It is known from the prior art that advanced process control (APC) and/or model predictive control (MPC) techniques may generally be suitable for production optimization in the oil/gas industry. For example, US 2023/0083389 Al provides a method for optimizing production of a natural gas liquefaction process (i.e., a downstream process) by using an APC system.

[0004] However, there is still a need for a comprehensive solution for systematic and automatic production optimization. Thus, it is an aim of the present invention to provide improved methods and systems for production optimization. In particular, it is an aim of the present invention to provide a solution that is directly applicable to control the production rates of the wells (i.e., focusing on the upstream production).

Summary

[0005] The invention is defined by the independent claims. Preferred embodiments are defined in the dependent claims.

[0006] The various embodiments detailed herein relate to integrated systems and computer-implemented methods for production optimization (e.g., for upstream production). The embodiments and aspects of the present invention provide other benefits that will become clear to those skilled in the art from the foregoing description.

[0007] An example integrated system for production optimization in upstream oil and/or gas production may comprise a first system comprising a first tool packet and a second tool packet; a second system for monitoring and controlling the operation of one or more wells, wherein the first tool packet is configured to continuously provide adjustment information to the second system to improve well performance within a permissible operating envelope, and wherein the second system is configured to continuously provide feedback about real-time operational data and/or information related to well status to the first tool packet; and a third system comprising one or more tools for analyzing operational data associated with the one or more wells, the operational data being received from the second system, wherein the third system is configured to provide analytical results to the second tool packet of the first system.

[ooo8]An example computer-implemented method for production optimization in upstream oil and/or gas production, performed by a first tool packet of a first system of an integrated system, may comprise determining adjustment information for one or more parameters; upon determining the adjustment information, sending first information including the adjustment information to a monitor & control system of the integrated system; receiving feedback from the monitor & control system, the feedback including real-time operational data and/ or information related to well status of one or more wells; determining new adjustment information for the one or more parameters; and upon determining the new adjustment information, sending second information including the new adjustment information to the monitor & control system. Brief description of the drawings

[0009] The accompanying drawings are included to provide a further understanding of the invention. The drawings illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.

[0010] In the drawings:

[0011] Fig. 1 illustrates an integrated system for production optimization according to aspects of the disclosure.

[0012] Fig. 2a illustrates an example optimization task performed for natural flowing wells by the supervisory controller of the integrated system according to aspects of the disclosure.

[0013] Fig. 2b illustrates an example optimization task performed for gas lift wells by the supervisory controller of the integrated system according to aspects of the disclosure.

[0014] Fig. 2c illustrates an example optimization task performed for ESP (Electric Submersible Pump) wells by the supervisory controller of the integrated system according to aspects of the disclosure.

[0015] Fig. 3 illustrates an example control scheme for a natural flowing well according to aspects of the disclosure.

[0016] Fig. 4 illustrates an example supervisory controller comprising two components according to aspects of the disclosure.

[0017] Fig. 5 illustrates an example smart wellhead.

[0018] Fig. 6 illustrates a more detailed example integrated system for production optimization according to aspects of the disclosure.

[0019] Fig. 7 illustrates a computer-implemented method for production optimization according to aspects of the disclosure. Detailed description

[0020]The following detailed description describes integrated systems, computer- implemented methods, and associated techniques for production optimization (e.g., for upstream production of oil/gas wells). The present disclosure is not intended to be limited to the described or illustrated examples, but to be accorded the scope consistent with the described principles and features.

[0021] The present invention addresses a critical problem in the field of oil/gas production — namely, inefficiencies and challenges associated with manual well operations. The techniques provided herein can be used for systematically stabilizing well production by providing (almost) real-time production optimization of the wells.

[0022] For example, production optimization according to aspects of the disclosure maybe based on critical lifting objectives such as optimizing lift consumption, and adhering to essential operational constraints (e.g., minimum bottom hole pressure, maximum flow line pressure, maximum annulus pressure, and others.).

[0023] The techniques disclosed herein will be better understood with reference to the implementation examples discussed below.

[0024] Fig. 1 illustrates an integrated system for production optimization according to aspects of the disclosure.

[0025] As illustrated in Fig. 1, an integrated system 1000 for production optimization according to aspects of the disclosure comprises a first system too (e.g., an optimization system too) comprising a first tool packet 110 (e.g., can be named as a supervisory controller 110 or a dynamic optimizer 110) and a second tool packet 120, a second system 300 (e.g., can be named as a monitor & control system 300), and a third system 400 (e.g., can be named as an analytics system 400).

[0026] The supervisory controller 110 is an essential tool packet provided by the present invention to provide (almost) real-time production optimization of the wells. Based on the techniques of the disclosure herein, configurations/settings used by the monitor & control system 300 for controlling the well(s) can be modified based on adjustment information generated by the supervisory controller 110. In some implementations, the adjustment information generated by the supervisory controller 110 may be in a form of setpoints. The setpoints may be directly applied by the monitor & control system 300 for final control elements, such as valve actuators and/or motor frequencies. Moreover, the monitor & control system 300 may be configured to provide feedback on real-time operational data and/or information related to well status to the supervisory controller 110. The realtime operational data may include pressure, temperature, choke position, lifting parameters, etc., which could e.g., be (at least partly) measured by sensors connected to the well(s).

[0027] In some implementations, the supervisoiy controller 110 maybe configured to operate at an interval of a certain duration, so that adjustment information can be continuously updated to the monitor & control system 300 at said interval. For example, the certain duration can be one, two, three ... fifteen minutes. In some implementations, the interval used by the supervisoiy controller 110 may be configurable, either manually or automatically. Moreover, the monitor & control system 300 may be configured to provide feedback on real-time operational data and/or information related to well status to the supervisory controller 110 at the same interval.

[0028] In some implementations, the monitor & control system 300 may be based on one or more of the following technologies: Distributed Control Systems (DCS), Edge computing, and Remote Terminal Units (RTU). DCS technology provides a centralized platform for monitoring and orchestrating the well’s processes, offering a holistic view of well performance. Edge computing facilitates on-site data processing and decision-making, enhancing responsiveness and reducing latency. RTUs further amplify the system’s capabilities, enabling the collection of critical data from wellsite equipment and the execution of control actions when necessary.

[0029] In some implementations, the system 1000 may comprise one monitor & control system 300 for multiple wells, or the system 1000 may comprise multiple monitor & control systems 300, each responsible for a perspective well.

[0030] During production process, the monitor & control system 300 may collect realtime operational data from the well(s) and forward the data to the third system 400 via a first communication link, labeled as Li in Fig. 1. The collected real-time operational data can be stored either directly in the third system 400 or in a separate component (not shown in Fig. 1). [0031] In some implementations, the third system 400 maybe configured to retrieve the collected real-time operational data (from its own storage or an external component) and perform engineering analysis: e.g., production allocation, well model update, production optimization workflows, etc. The system 400 may generate outputs such as business requirements, well guidelines, troubleshooting tickets, steady state models, engineering limits, theoretical capacities, safety envelope, etc. and forward those outputs to the second tool packet 120.

[0032] The system 400 may be configured to send analytical results to the second tool packet 120 via a second communication link, labeled as L2 in Fig. 1. Both the second tool packet 120 and the system 400 are located within the same network environment, labeled as 450 in Fig. 1. The network environment 450 can be understood as an office business network, which may be located at a remote location (normally far away from the actual production site).

[0033] The second tool packet 120 may be configured to generate target(s) and/or requirement(s) for the well(s). The target(s) and/or requirement(s) maybe passed on to the supervisory controller 110 via a third communication link, labeled as L3 in Fig. 1.

[0034] In some implementations, the second tool packet 120 may include a tool for well requirements management. For example, the well requirements management tool may be configured to perform a continuous comparison between what the business requires to be accomplished (either manually generated by an engineer or automatically by a tool) vs. what the operator has implemented in the field. In some other examples, the well requirements management tool may be configured to automatically delete activities that are no longer needed (e.g., because either they have been fulfilled by the operator or the conditions have changed).

[0035] In some implementations, the well requirements management tool may include a subsystem functioning as a ticketing system or an action prioritization and tracker update system. Through this subsystem, requirements to adjust control targets or limits can be presented to the operator in a ranked and organized way. Thus, this subsystem can ensure continuous and organized tracking and management of well requirements, contributing to safer and more efficient operations.

[0036] The supervisory controller 110 may be configured to analyze the target(s) and/ or requirement(s) for the well(s) and generate adjustment information for the monitor & control system 300. The adjustment information maybe passed on to the monitor & control system 300 via a fourth communication link, labeled as L4 in Fig. 1.

[0037] The supervisory controller 110 and monitor & control system(s) 300 may be located within the same environment, labeled as 200 in Fig. 1. The environment 200 can be understood as a control environment that is responsible for controlling the actual operation of the well(s) and which may be located directly at the production site. Furthermore, the supervisory controller 110, the monitor & control system(s) 300, field(s)/well(s) (not shown), and actuators/instruments (not shown) for the field(s)/well(s) may be comprised in the same network, labeled as 440 in Fig. 1. The network 440 can be understood as the control environment for the field production.

[0038] In some implementations, the supervisory controller 110 may be configured with one or more inferential models, such as dynamic inferential models and/or static inferential models. The inferential model(s) provide a physics-based estimation of non-measured variables (e.g., oil rates and bottomhole pressure) as a function of real-time measured variables (e.g., wellhead pressure, choke position temperature, etc.). According to the techniques disclosed herein, the inferential model(s) can bridge the gap between real-time measured data and the need for comprehensive datasets. This can enable faster model training, adaptability to changing conditions and improved accuracy in predicting non-measured variables. Moreover, the techniques disclosed herein may accelerate sampling requirements of the physics-based model in order to generate a well-informed and effectively trained data driven model, in a short amount of time, with a higher degree of confidence for changing well operating conditions. The resulting data driven model outputs can be linear, non-linear with respect to its inputs.

[0039] In some implementations, the inferential model used by the supervisory controller 110A may be trained in expected (future) operating ranges in such a way that the model remains valid (i.e., more robust to changes) despite minor changes in the measured parameters, e.g., water cut and gas-oil-ratio. Hence, the inferential model(s) included in the supervisory controller 110A may not require frequent parameter updates.

[0040] In some implementations, the supervisory controller 110 maybe configured with a multivariable predictive control (MPC) algorithm to perform the optimization task, i.e., maximum well efficiency. The optimization task is performed to ensure that wells meet production targets without violating wellbore, downstream, and upstream constraints. In some implementations, the optimization task maybe performed based on a given/computed target production rate for oil or gas production well.

[0041] In some implementations, the optimization task may be performed at a per- well level. Hence, the adjustment information output by the supervisory controller 110 may comprise individual well-level control parameters.

[0042] In some implementations, the supervisory controller 110 maybe configured with a control scheme that is adaptable based on a type of the well (e.g., natural flowing well or a well employing an artificial lift method). Moreover, the control scheme maybe further adaptable based on available actuators and/or instruments for the well(s).

[0043] In some implementations, the supervisory controller 110 maybe configured to account for various constraints such as bottom hole pressure (BHP), lift parameters, annulus pressure, flow line pressure and others. Moreover, the adjustment information generated by the supervisory controller 110 maybe based on target production rate(s) established by reservoir engineer(s). For example, the reservoir engineer(s) provide operator(s) with a monthly “Allowable Rate,” which suggests a proposed production rate per well for a specific field. The production rate adheres to reservoir management guidelines and operational constraints. However, daily operations often necessitate adjustments to well performance due to various field activities, resulting in deviations between the actual well production and the “Allowable Rate.” Hence, the supervisory controller 110 can play an essential role in estimating well production based on field targets while minimizing discrepancies from the “Allowable Rate.”

[0044] In some implementations, the supervisory controller 110 may be configured to determine a target per-well production rate based on a field production target. Then, based on the determined target per-well production rate, the supervisory controller 110 may be configured to determine the optimal artificial lift and/or choke parameters for each respective well.

[0045] In some implementations, the supervisory controller 110 maybe located on a dedicated server. Hosting on a dedicated server may improve security when compared with shared environments. Moreover, the dedicated server could be individually managed and may provide more flexibility to maintain it (e.g., so that a user can have full control over the software stack, configurations, and settings, etc.).

[0046] In some implementations, the communication link L4 between the supervisory controller 110 and the monitor & control system 300 may be enhanced by implementing an Open Platform Communication (OPC) gateway or server. The OPC gateway or server ensures seamless interaction and data exchange between the systems. Additional hardware and software deployment can be considered when necessary. In some scenarios, the implementation process might necessitate the incorporation of supplementary hardware components, such as additional computers, to accommodate and host specialized software elements. Depending on the existing infrastructure and the level of sophistication already embedded in the system, the inclusion of additional software might not always be imperative.

[0047] In some implementations, the communication link Li between the monitor & control system 300 and system 400 (or the external component for storing the measurement data/operational data) and the communication link L3 between the second tool packet 120 and the supervisory controller 110 maybe enhanced by implementing a protocol that allows devices to communicate with and synchronize data.

[0048] In some implementations, for security reasons, the communication link Li and L3 can be configured to work only in one direction: data can be only passed on from the monitor & control system 300 to the system 400 and from the second tool packet 120 to the supervisory controller 110, not the other way around. Consequently, information from the network 450 is to be exclusively passed to the supervisory controller 110 through the second tool packet 120. Thus, the layout of the different components/systems in the networks 440 and 450 ensures data security and at the same time makes it possible to perform the optimization task based on operational data (e.g., measurement data).

[0049] According to techniques disclosed herein, the system too is able to identify correlations between multiple input variables and multiple output variables. Moreover, the system too can analyze historical data and real-time measurements, which can help to identify complex relationships that might not be apparent when looking at individual variables. By using inferential model(s) (e.g., dynamic inferential models and/or static inferential models) and/or predictive control or model predictive control (MPC) model(s), the system too can also predict the model of the process. By considering interactions between different variables, the system too may be able to forecast how changes in one variable might affect others over time.

[0050] Moreover, the system 100 may include an optimization workflow for optimizing multiple variables simultaneously and continuously updating the models according to evolving process dynamics of the well(s) (e.g., via adaptive control method). This adaptability helps in predicting future trends by adjusting to changing conditions or variations in the process.

[0051] Advantageously, the system 300 may be configured to continuously monitor and receive information (e.g., real-time operational data and/or information related to well status) from multiple sensors and actuators connected to the well(s) and continuously forward the information to the supervisory controller 110. This allows for a quick real-time feedback loop, thus allowing for quick adjustments. Moreover, the information can be further passed on to the analytics system 400. This could help in forecasting future trends based on current observations.

[0052] Fig. 2a - 2c illustrate example optimization tasks performed by the supervisory controller 110 of the integrated system 1000 according to aspects of the disclosure. Fig. 2a illustrates an example optimization task performed for natural flowing wells by the supervisory controller 110. Fig. 2b illustrates an example optimization task performed for gas lift wells by the supervisory controller 110. Fig. 2c illustrates an example optimization task performed for ESP (Electric Submersible Pump) wells by the supervisory controller 110. According to aspects of the disclosure, the supervisory controller 110 is configured with adaptable control scheme to perform the different optimization tasks.

[0053] As shown in Fig. 2a - 2c, the example optimization task involves three types of variables: Control Variables (CV), Manipulated Variables (MV), and Disturbance Variables (DV). These variables operate within predefined boundaries. These boundaries/constraints encompass factors such as equipment limitations, safety thresholds, and environmental considerations.

[0054] The MVs are variables controlled or manipulated by the first tool packet 110, MVs are typically adjusted by control algorithms to achieve desired setpoints or targets for the controlled variables (CVs). Examples of MVs include valve positions, pump speeds, heater temperatures, or any other parameter that the system can adjust to regulate the process.

[0055] The CVs are key variables that the first tool packet 110 aims to maintain or control within specific ranges or setpoints. Examples of CVs are temperature, pressure, flow rates, or any other measurable parameter that needs to be regulated during the process.

[0056] The DVs are influences or factors that can affect the behavior of the process. These variables can impact the CVs potentially leading to deviations from desired operating conditions. DVs could include raw material variations, equipment faults or other external factors that introduce disturbances to the process.

[0057] The supervisory controller 110 may be configured with an internal model that captures the intricate relationship between the MVs, and CVs, facilitating predictions of future behavior and enabling optimization, along with the calculation of MV adjustments.

[0058] Fig. 2a - 2c show main parameters collected from sensors and instruments of the well. These parameters are associated with operating conditions of the well such as pressure and temperature from downhole sensors (bottom hole measurements), pressure and temperature form surface (wellhead, flowline), status of the valves (such as master vale, wing vale, subsurface safety valve, e.g., shown in Fig. 5), and pressure and temperature from the production manifold.

[0059] Further as shown in Fig. 2a, the main output from the supervisory controller 110 is control adjustments for automatic control of the choke position of the natural flowing well. As shown in Fig. 2b, the main output from the supervisory controller 110 is control adjustments for choke and the gas lift injection rate of the gas lifted well. As shown in Fig. 2c, the main output from the supervisory controller 110 is control adjustments for choke, frequency, and voltage to speed ratio for the ESP well.

[0060] The application of MIMO to a process culminates in the stabilization of the system, leading to a notable reduction in the variability of the CVs. Once the process achieves stability, operational targets can be nudged closer to equipment limits or product specifications. This strategic adjustment has the potential to amplify profitability, whether through revenue maximization, energy consumption minimization, or a combination of both.

[0061] It is important to note that a MIMO controller is particularly well-suited for the control of multivariable processes, such as those found in the realm of oil wells, in comparison to conventional PID (Proportional Integral Derivative) strategies. The conventional control approach often necessitates the use of intricate combinations of interconnected PID controllers, lag/lead compensators, and high/low selector blocks. This complexity makes maintenance and tuning a tough challenge. In contrast, the supervisory controller no streamlines this complexity into a singular block, rendering it comparatively easier to maintain and tune, although requiring specialized expertise for its design and implementation. One exceptional aspect of the supervisory controller no is its adaptability in the face of uncertainties. The supervisory controller no incorporates a learned model that anticipates well behavior by making assumptions based on the most likely scenarios, effectively minimizing overall losses. This learned model accounts for a spectrum of uncertainties, including variations in well conditions, equipment behavior, and external factors, allowing the supervisory controller no to make informed decisions in (almost) real-time.

[0062] The present invention provides a flexible solution to be applicable to all types of wells such as gas-lifted and pumped wells. The solution can be adapted depending on the well schematic and the instrumentation available. For example, if there is a lack of PID control for gas lift rate, lack of downhole gauges, etc., the supervisory controller 110 can be adapted accordingly.

[0063] According to aspects of the invention, the supervisory controller 110 maybe configured with one or more of the following models:

[0064] Dynamic Inferential Model

[0065] Dynamic Inferential Models are data driven models representing dynamic relationships between dependent and independent variables, which are trained using Artificial Intelligence machine learning models and known physical relationships. These models can capture many-to-many relationships between dependent and independent variables. In the context of the control schemes, dependent variables are referred to as Controlled Variables (CV), and independent variables may be Manipulated Variables (MV) or Disturbance Variables (DV). Historical data with statistical significance is required to generate these models. Step tests of the modelled processes are necessary in most cases to obtain data with the needed statistical significance. These models can be stored and managed in the supervisory controller no.

[0066] Static Inferential Model

[0067] Static Inferential Models provide estimates of operational variables that are not measured in real time or are not measured with the needed frequency.

[0068] For the inferential models, different modeling approaches can be considered:

[0069] First Principles

[0070] This approach employs actual physics calculations. The models can either encompass full-scale physics or be tailored to reduced-order physics. This approach ensures a strong foundation rooted in the fundamental principles governing well operations.

[0071] Data Driven Models

[0072] Data Driven Models harness the power of machine learning and artificial intelligence, deriving insights from the sensitivity of full physics models and well related data (e.g., reservoir, facilities, operational, and others). Examples include neural networks and gradient-boosting techniques. This approach combines empirical data and computational efficiency to enhance predictive capabilities.

[0073] Hybrid Models

[0074] Hybrid Models recognize the strengths of both First Principles and Data Driven models. This amalgamation aims to leverage the precision of physics-based calculations while benefiting from the adaptability and learning capabilities of machine-learning models, thereby providing a comprehensive and versatile modeling solution.

[0075] Calculations from the inferential models can consider oil rate, bottomhole pressure, potential, artificial lift optimization, and/or others.

[0076] The main controlled variable for the well is the production rate (oil, gas, water), and this variable is not measured continuously. Usually, wells are routed to a testing unit (multiphase meter or test separator) regularly to measure these variables, but sometimes the quality of such measurements is poor. Therefore, the Oil rate is calculated in real time using the inferential models.

[0077] In addition, most of the wells do not have a downhole gauge, hence the flowing bottomhole pressure (PWF) needs to be calculated. If the well has a working downhole gauge, the downhole gauge (BHP) is enabled and the PWF calculated by the inferential model is disabled. If there is no working gauge, the PWF calculated by the inferential model is enabled.

[0078] For artificial wells for example Gas Lift or Electric Submersible Pump, the key manipulated variable to minimize energy consumption is calculated in (almost) real-time (e.g., Optimum Gas Lift rate, frequency, Voltage/speed ratio); these variables can be directly determined by the dynamic control models, or by inferential models.

[0079] Moreover, the supervisory controller 110 may further comprise well models based on nodal analysis techniques. These models provide a full responsive output of the well performance for a wide range of operating conditions, even though these models have been calibrated using few recent well tests. Physics models can be deployed in the supervisory controller 110 by means of an inferential model, for which parameters can be continuously created and updated.

[oo8o]The techniques provided for the supervisory controller 110 may help to avoid frequent inferential model parameter updates. The inferential model prediction may be adjusted by continuous reading of real-time operational data (e.g., feedback via the system 300 of Fig. 1). This provides a high degree of confidence for changing well operating conditions. When required, new model may be generated and its parameters may be transferred to the supervisory controller 110, either electronically or physically. Physical data transfer can involve more tangible methods like using CDs or other physical storage devices to transfer information between the components. This approach might be employed in situations where network connectivity is restricted due to security concerns or when the systems involved cannot directly communicate. Electronic update involves the continuous integration of incoming real-time data streams into the inferential model to refine its predictions. When necessary, a new model can be created and its updated parameters transferred to the supervisory controller 110 through digital means, ensuring that the system stays up to date and is capable of making accurate calculations based on the most current information available.

[0081] In addition, once a model is mapped to a well, the operator can have the option to specify if they want the model to self-tune, by disturbing the MVs and to what degree they allow disturbance of the MVs by the self-tuning mechanisms.

[0082] By embracing a diverse array of modeling strategies, the present invention ensures a robust and adaptable framework capable of handling the intricacies of well operations, enabling accurate predictions and informed decision-making in real-time scenarios.

[0083] Fig. 3 illustrates an example control scheme for a natural flowing well according to aspects of the disclosure. In this example, the primary objective of the supervisory controller 110 is to facilitate dynamic adjustment of setpoints, which can be used by e.g., the monitor & control system 300 to directly adjust the choke value parameter.

[0084] As shown in Fig. 3, the supervisory controller 110 is configured with a Well Production and Stabilization Control Scheme specially adapted for optimizing the operation of natural flowing wells. The control scheme is designed to achieve one or more of the following objectives:

(1) Maintain a specified oil production target as set by an operator (e.g., Operator 210 of Fig. 3).

(2) Ensure wellhead pressure remains within defined limits.

(3) Adhere to the high limit of flow line pressure.

(4) Comply with the low limit of wellhead temperature.

(5) Honor the low limit of bottomhole pressure.

[0085] In the example of Fig. 3, the supervisory controller 110 is configured to continuously monitor controlled and constrained variables (CV), which may include one or more of the following: oil rate, wellhead pressure, wellhead temperature, Annulus pressure, flowline pressure, bottom hole pressure, as well as limit(s) and/or target(s). Moreover, the supervisory controller 110 is configured to adjust a manipulated variable (MV) - in this case: production choke - to maintain the CV(s) within associated limits.

[0086] In some other implementations, the Well Production and Stabilization Control Scheme may be adapted to be used for other types of wells, such as gas-lifted and pumped wells.

[0087] Fig. 4 illustrates an example supervisory controller 110 of the integrated system 1000 according to aspects of the disclosure. In this example, the supervisory controller 110 comprises two components: a first component 111 and a second component 112. Maintaining the key functionalities of the supervisory controller 110 in two separate components can facilitate implementation and troubleshooting, preventing potential complexities that might arise from having all functions integrated into a single component.

[0088] Even though, in this example, the supervisory controller 110 of the integrated system is configured with two components, the supervisory controller 110 can be configured with any number of components as long as the desired functionalities of the supervisory controller 110 can be achieved.

[0089] The example shown in Fig. 4 concerns an oil well. However, the supervisory controller 110 can be configured for other types of wells, e.g., by adapting the control scheme (see related description with reference to Fig. 3).

[0090] As shown in Fig. 4, the first component 111 may be configured to receive information related to the system management (e.g., targets/requirements from the tool packet 120 of Fig. 1 or from an operator 210 as shown in Fig. 3). The first component 111 may be configured to determine a target production rate for the well(s) and send this information to the second component 112. Example information related to the system management is shown in Fig. 4, which may include targets/requirements and can be categorized into well inputs, field inputs, facility inputs, and reservoir inputs.

[0091] In some implementations, the parameter “target production rate” maybe determined per well. For example, the per-well target production rate can be determined based on a field production target.

[0092] In some implementations, the first component 111 may be configured with out- of-the-box linear and non-linear programming algorithms. [0093] The second component 112 (e.g., can be named as a “robotic controller”) maybe configured to perform a set of calculations that are translated into a series of multivariable actions, which are used to control the wells. For example, the second component 112 may be configured to determine optimal artificial lift and/ or choke parameters based on the “target production rate” determined by the first component 111. In some implementations, the determination of optimal artificial lift and choke parameters may be repeated at an interval of a certain duration to continually optimize the performance of the well. For example, the certain duration can be one, two, three minutes, etc. In some implementations, the interval used by the supervisory controller 110 may be configurable, either manually or automatically.

[0094] As shown in Fig. 4, the second component 112 is configured to calculate setpoints and send those setpoints to e.g., system 300 of Fig. 1. For example, the setpoints may be applied directly to final control elements, such as valve actuators and motor variable speed drive (e.g., for voltage and frequency adjustment). In some implementations, the determination of the setpoints may be based on maximizing well efficiency.

[0095] In some implementations, the calculation by the second component 112 may be further based on the following parameters/variables:

(1) Engineering limits (including Minimum and Maximum values). The following are some examples: bottomhole pressure, wellhead pressure, flow line pressure, wellhead temperature, high limit of choke percentage or rate of change.

(2) Well inputs (e.g., from source instruments and/or from tests). The following are some examples: water cut, GOR (gas/oil ratio), reservoir pressure, productivity index, bottomhole pressure and temperature, wellhead pressure and temperature, flow line pressure, readings from valve(s) (e.g., master vale, wing valve, subsurface safety valve, etc. Example valves are shown in more detail in Fig. 5.).

(3) Calculated values from the inferential models, such as oil rate, bottomhole pressure, etc.

(4) Reservoir, field, and facility inputs (e.g., inputs from the second tool packet 120 of Fig. 1). [0096] Based on one or more parameters/variables from the above, the second component 112 can be used to determine e.g., choke setting to operate the well at optimum conditions. The determined choke setting can be automatically updated to the monitor & control system 300 and the monitor & control system 300 can automatically adjust the well to achieve a desired well production rate.

[0097] Fig. 5 illustrates an example smart wellhead. Smart wellheads are advanced systems that enhance the monitoring, control, and management of the oil and gas wells. It integrates various sensors and remotely actuated chokes to optimize production, increase efficiency and improve safety. Here are some key components of a smart wellhead:

[0098] Sensors: incorporate a multitude of sensors placed at the wellhead and downhole. These sensors monitor crucial parameters, such as pressure, temperature, flow rates, fluid composition, and others. Real time data from these sensors provides a comprehensive understanding of the well’s conditions and behavior.

[0099] Automation and Control: these systems often feature automated control mechanisms that respond to the data collected by sensors. Automated valves, chokes and other equipment adjust in real time based on the data received.

[0100] Fig. 6 illustrates a more detailed example integrated system for production optimization according to aspects of the disclosure.

[0101] The example integrated system 1000A corresponds to the integrated system 1000 of Fig. 1 with more implementation details. As shown in Fig. 6, the first system 100A corresponds to the first system too of Fig. 1 with more implementation details; the supervisory controller 110A corresponds to the supervisory controller 110 of Fig. 1 to Fig. 4 (renamed as “RoboWell(OT)” in this example); the tool packet 120A corresponds to the tool packet 120 of Fig. 1 with more implementation details; and the system 400A (or together with the component 420) corresponds to the system 400 of Fig. 1 with more implementation details.

[0102] As shown in Fig. 6, the tool packet 120A is configured with five subsystems 121, 122, 123, 124, and 125. [0103] The first subsystem, subsystem 121, is configured for managing daily targets (including rates and/or pressures). If any change is detected, the subsystem 121 is configured to communicate this change immediately to the supervisory controller 110A. For example, daily targets maybe updated by a user or by changing certain well conditions or by other embedded number of rules which may be passed by other business engineering and support tools.

[0104] The second subsystem, subsystem 122, is configured for managing well requirements. Examples of these requirements include adjusting the gas-lift minimum value, reducing maximum working pressure, and adjusting the threshold of flowing bottom hole pressure.

[0105] In some implementations, the subsystem 122 may be configured to determine which actions and changes must be implemented in the field by the operator(s) 210. The operator’s role includes the approval of system parameters and production targets, updating parameters, and activating controllers. The operator interacts with the supervisoiy controller 110, working on setting the targets of the wells.

[0106] In some implementations, the subsystem 122 may be configured to provide a ranking of all the tasks that the field operator(a) 210 may have to look at.

[0107] In some implementations, the subsystem 122 may be configured to provide recommendations of all the tasks that the field operator(a) 210 may have to execute during the day.

[0108] The third subsystem, subsystem 123, is configured for updating the inferential model(s) included in the supervisory controller 110A. When required, a new model can be generated and its parameters can be transferred to the supervisory controller 110A, e.g., via the subsystem 123.

[0109] The fourth subsystem, subsystem 124, is configured for updating system operating envelope. In some implementations, the resulting operating envelope outputs can be linear or non-linear with respect to its inputs. The operating envelope refers to the range of operational conditions within which the supervisoiy controller 110 can function safely and effectively. It encompasses various parameters, including but not limited to temperature, pressure, flow rates, and other relevant factors specific to the system. The need to update the operating envelope arises due to changes in conditions that affect the system’s performance or safety. For example, the subsystem 124 may be configured to update the operating envelope when there are significant changes that affect the system’s operational limits or when there is a need to adapt to new conditions.

[0110] The fifth subsystem, subsystem 125, is configured for tracking system conditions and/ or ranking actions. For example, the subsystem 125 may be configured to keep track of new conditions (such as abnormal conditions e.g., sensor calibration, actuator malfunction, model mismatch, etc.). Information about new conditions is delivered promptly by the system 400A.

[0111] In some implementations, the subsystem 125 may include a ticketing system. For example, when information of a new event is received, the ticketing system generates a respective ticket. Furthermore, the ticketing system may be configured to prioritize and rank different tickets.

[0112] In some implementations, the subsystem 125 maybe configured to track the status of the operational conditions of the well(s). For example, the subsystem 125 may be configured to track which actions and changes must be implemented by the operator(s) 210 or other user(s) 430 and the priority of each item. Moreover, the subsystem 125 may be configured to track the resolution of such issues by continuously comparing to triggering data. Triggering data refers to the data that created the exception so that a new ticket was generated.

[0113] In some implementations, the subsystem 125 maybe configured to provide recommendations of all the actions that a user may have to perform in each action ticket.

[oii4]Further as shown in Fig. 6, a web interface 115 is provided on the side of the network 440 and a web interface 126 is provided on the side of the network 450.

[0115] In some implementations, the web interface 115 maybe used by operator(s) 210 to access the supervisoiy controller 110A. For example, the web interface 115 may contain one or more of the following capabilities:

(1) User interface for managing parameters for different production system entities (e.g., well, facility equipment, reservoir, reservoir sector, field, etc.). (2) User interface for managing and maintaining quality control (QC) of the production system “topology” (e.g., which well flows from which sector to which facility, etc.).

(3) User interface for managing the optimization process, allowing for automatic transmission of new targets to the Robotic Controller 112.

[on6]In some implementations, the web interface 115 maybe provided on one or more screens. These screens may provide general overviews of the system operation and allow the operations personnel easy access to activate and deactivate control schemes or variables and to manage the supervisory controller 110A.

[0117] In some implementations, the web interface 126 may be used by operator(s) 210 in the field and/or engineer(s) in the office. For example, the web interface 126 can be used for generation and analysis of various scenarios related to the field performance. These scenarios encompass analyses of tuning parameters, controlled variables, manipulated variable constraints, and changes in optimization priorities.

[on8]Further as shown in Fig. 6, component 420 is provided between the monitor & control system 300 and the system 400A. The component 420 serves as a centralized repository that captures, stores, and organizes vast volumes of real-time operational data. In some implementations, the component 420 maybe configured to provide a comprehensive and historical record of all critical processes and equipment, enabling organizations to monitor, analyze, and optimize their operations with unparalleled precision. Furthermore, the component 420 maybe (seamlessly) integrated with various systems (e.g., SCADA (Supervisory Control and Data Acquisition), loT (Internet of Things) devices, etc.) to provide a holistic view of operations.

[oii9]The system 400A may comprise business & engineering support tools. These tools can be used to ensure efficient and profitable operations. Example outputs generated by the system 400A are shown as outputs 410 in Fig. 6. In some examples, the outputs maybe e.g., new well guidelines, which serve as essential directives for efficient well operation, offering guidance for maintaining optimal performance. In some other examples, the outputs maybe e.g., troubleshooting tickets, which provide a structured approach to addressing any issues that may arise, ensuring swift and effective problem resolution. The troubleshooting tickets may be understood as an example of the analytical results described in association with Fig. 1.

[0120] Moreover, Fig. 6 shows advanced instrumentation 500 for the well infrastructure 600. In some implementations, the instrumentation suite 500 may comprise a diverse array of sensors, encompassing pressure gauges, temperature sensors and flow meters all of which constitute indispensable components. For example, these sensors may be strategically positioned along the wellbore at specific intervals to systematically acquire critical data pertaining to wellbore conditions, fluid properties, and the intricate dynamics of fluid flow.

[0121] In some implementations, the instrumentation suite 500 may comprise smart well instrumentation and control devices. Every well is a link between the subsurface reservoir and the surface facilities. The remote instrumentation may include pressure and temperature, both at surface and downhole, allowing continuous monitoring of well conditions. The control devices, including motor operated actuators, allow remote adjustment of chokes and valves, for changing flow rates and pressure targets, based on reservoir conditions.

[0122] In some implementations, the instrumentation suite 500 may comprise actuators 510. For example, the actuators 510 may be distributed across the wellhead and associated equipment. These actuators 510 are configured for precise and remotely controlled adjustments, specifically tailored to valves, chokes, and other flow regulating mechanisms. These actuators 510 can be helpful in effecting operational changes in real-time in response to insights derived from real-time data.

[0123] In some implementations, the instrumentation suite 500 may comprise flow meters. These meters may be designed to handle the complexities inherent in the measurement of multiphase flows, which often comprise a combination of oil, gas, and water.

[0124] Fig. 7 illustrates a computer-implemented method 2000 for production optimization according to aspects of the disclosure. The method can be implemented by any suitable means. For example, the method 2000 can be implemented by an optimization system comprising at least one processor and at least one memory. The at least one memory comprises instruction, which, when executed by the at least one processor, cause the at least one processor to carry out one or more steps of the method disclosed herein. The optimization system maybe the supervisory controller no/ noA as described in association with Fig. 1-4 and 6.

[0125] As shown in Fig. 7, at block 2100, the optimization system may determine adjustment information for one or more parameters. The one or more parameters may be control parameters associated with the operation of one or more wells.

[0126] At block 2200, the optimization system may, upon determining the adjustment information, send first information including the adjustment information to a monitor & control system (e.g., the monitor & control system as described in association with Fig. 1 and Fig. 6). The monitor & control system may adjust the operation of the one or more wells based on the first information.

[0127] In some implementations, the adjustment information may be in the form of setpoint(s). The monitor & control system may directly apply the setpoint(s) to the corresponding configuration to adjust the operation of the one or more wells.

[0128] At block 2300, the optimization system may receive feedback from the monitor & control system. The feedback may include real-time operational data and/or information related to well status of the one or more wells. The real-time operational data may include pressure, temperature, choke position, etc., which could e.g., be (at least partly) measured by sensors connected to the well(s).

[0129] At block 2400, the optimization system may determine new adjustment information for the one or more parameters. The determination at block 2400 may be at least partially based on the feedback received at block 2300.

[0130] At block 2500, the optimization system may, upon determining the new adjustment information, send second information including the new adjustment information to the monitor & control system. The monitor & control system may adjust the operation of the one or more wells based on the second information.

[oi3i]While specific examples have been described herein, it will be obvious to those skilled in the art that various changes and modifications may be aimed to in the specification. It will, therefore, be understood by those skilled in the art that the particular embodiments of the invention presented here are by way of illustration only and are not meant to be in any way restrictive; therefore, numerous changes and modifications may be made, and the full use of equivalents resorted to, without departing from the scope of the invention.

Claims

1. An integrated system (1000) for production optimization in upstream oil and/or gas production, comprising: a first system (too) comprising a first tool packet (no) and a second tool packet (120); a second system (300) for monitoring and controlling operation of one or more wells, wherein the first tool packet (no) is configured to continuously provide adjustment information to the second system (300) to improve well performance within a permissible operating envelope, and wherein the second system (300) is configured to continuously provide feedback about real-time operational data and/or information related to well status to the first tool packet (110); and a third system (400) comprising one or more tools for analyzing operational data associated with the one or more wells, the operational data being received from the second system (300), wherein the third system (400) is configured to provide analytical results to the second tool packet (120) of the first system (too).

2. The integrated system (1000) of the preceding claim, wherein the adjustment information comprises one or more setpoints, each setpoint usable for the second system (300) to directly adjust a respective control parameter for operating the one or more wells.

3. The integrated system (1000) of one of the preceding claims, wherein the first tool packet (110) is configured to determine the adjustment information at an interval of a fixed or configurable time duration.

4. The integrated system (1000) of the preceding claim, wherein the internal has a time duration of N minute(s), and wherein N is an integer between one and fifteen inclusive of one and fifteen.

5. The integrated system (1000) of one of the preceding claims, wherein the first tool packet (110) comprises a control scheme adaptable to different types of wells (6oo) and available instrumentation (500); and one or more analytical models for continuously determining the adjustment information.

6. The integrated system (1000) of the preceding claim, wherein the one or more analytical models of the first tool packet (110) use one or more of the following parameters as input: bottom hole pressure, gas lift, annulus pressure, and flow line pressure.

7. The integrated system (1000) of one of the preceding claims, wherein the first tool packet (110) comprises a first component (111) and a second component (112), wherein the first component (111) is configured to determine target oil and/or gas production rate(s) based on one or more targets and/ or requirements determined by the second tool packet (120), and wherein the second component (112) is configured to determine the adjustment information based on the target oil and/or gas production rate(s).

8. The integrated system (1000) of the preceding claim, wherein the second component (112) comprise a robotic controller.

9. The integrated system (1000) of one of the preceding claims, wherein the one or more wells comprises one or more natural flowing wells.

10. The integrated system (1000) of one of the preceding claims, wherein the one or more wells comprises one or more gas lift wells.

11. The integrated system (1000) of one of the preceding claims, wherein the one or more wells comprises one or more Electric Submersible Pump (ESP) wells.

12. The integrated system (1000) of one of the preceding claims, wherein the adjustment information comprises configuration information for a choke position associated with a respective well.

13. A computer-implemented method for production optimization in upstream oil and/or gas production, the method performed by a first tool packet (110) of a first system (100) of an integrated system (1000), the method comprising: determining (2100) adjustment information for one or more parameters; upon determining the adjustment information, sending (2200) first information including the adjustment information to a monitor & control system (300) of the integrated system (1000); receiving (2300) feedback from the monitor & control system (300), the feedback including real-time operational data and/or information related to well status of one or more wells; determining (2400) new adjustment information for the one or more parameters; and upon determining the new adjustment information, sending (2500) second information including the new adjustment information to the monitor & control system (300).

14. The computer-implemented method of claim 13, wherein the adjustment information comprises one or more setpoints, each setpoint usable for the monitor & control system (300) to directly adjust a respective control parameter for operating the one or more wells.