METHOD FOR MAXIMIZING WATER INJECTION AND ENSURING THE INTEGRITY OF OIL RESERVOIRS, AND COMPUTER-READABLE STORAGE MEDIUM

Description

/ 17 METHOD FOR MAXIMIZING WATER INJECTION AND ENSURING THE INTEGRITY OF OIL RESERVOIRS, AND COMPUTER-READABLE STORAGE MEDIUM TECHNICAL FIELD

[001] The present invention is situated within the scope of offshore oil exploration and production (E&P) and operational decision support and automation technology in water injection wells. Thus, the present invention focuses on the description of a method and decision support system based on models and algorithms that enable the optimization of the efficiency of injection wells in oil and gas reservoirs and the guarantee of the integrity of these reservoirs. BACKGROUND OF THE INVENTION

[002] Stationary Production Units (SPUs) are offshore platforms responsible for extracting and separating petroleum into three components: oil, water, and gas. The oil can be stored, exported via pipelines, or transferred to other vessels. Water taken from the sea or produced by oil wells undergoes treatment and can be injected / reinjected, used in other applications, or disposed of in accordance with regulatory requirements. The gas is compressed in the SPU's processing plant and can be exported, used in oil recovery, or flared for power generation. Some of the water and gas return to the reservoir after passing through equipment in the SPU's processing plants.

[003] Water injection in oil reservoirs is a widely used technique in the oil industry and plays a crucial role in various stages of oil production. This practice offers several benefits that help maximize oil recovery and extend the lifespan of oil fields.

[004] Injecting water into oil reservoirs is a technique that helps maintain or increase internal pressure. Petition 870240111068, dated 12 / 30 / 2024, page 34 / 63 / 17 of the reservoir, ensuring more efficient extraction and extending the lifespan of the fields. By acting as a pusher, the water displaces oil from hard-to-reach areas, increasing the amount of recoverable oil, especially in low-permeability reservoirs. Furthermore, the technique allows control of the water-to-oil ratio produced, optimizing production and reducing costs associated with water treatment and disposal. It is also used in secondary and tertiary recovery, combining with other methods to maximize extraction in mature fields. From an environmental perspective, produced water reinjection minimizes the environmental impacts of disposal at sea and can reduce the need for natural gas flaring, decreasing greenhouse gas emissions.Economically, although it has operational costs, water injection increases the value of the field by maximizing the extraction of reserves, more than offsetting the initial investment.

[005] Water injection systems face several operational problems, such as fouling, corrosion, biological contamination, and chemical incompatibility, which can cause blockages and damage to equipment. Fouling and biofouling, resulting from the interaction of waters with different chemical compositions, can block pores and pipes, reducing injection efficiency. In addition, corrosion, caused by elements such as oxygen and sulfides, compromises the integrity of metallic materials, requiring constant maintenance and the use of resistant materials. Water compatibility is crucial to avoid the formation of precipitates and emulsions that obstruct flow in the reservoir.

[006] Another significant problem is the loss of injectivity, which occurs when the ability to inject water into the reservoir decreases due to blockages in the interface region between the injection well and the reservoir.Injection efficiency can be compromised by wells operating in an open loop, i.e., without automatic flow control systems. Petition 870240111068, dated 12 / 30 / 2024, page 35 / 63 / 17 and pressure, which leads to suboptimal operations and loss of opportunities to maximize oil recovery.

[007] One point that must be taken into consideration in the operation of injection wells is the geomechanical limit pressure of the reservoir. The geomechanical limit pressure of a reservoir is a fundamental concept in reservoir engineering and refers to the maximum pressure that the reservoir can withstand without mechanical failure occurring in its geological structure. This pressure is determined by the strength of the rocks that make up the reservoir and by the stresses acting on them, including the pressure of fluids (oil, gas and water) and tectonic stresses.

[008] Maintaining reservoir pressure within safe geomechanical limits is essential to preserve its structural integrity and optimize oil production. This involves continuous monitoring of fluid pressure and balancing water or gas injection rates with oil production, avoiding both over-pressurization and under-pressurization that can compromise reservoir stability and integrity. The use of advanced technology, such as pressure sensors and real-time data analysis systems, can allow for the detection of pressure changes or approaches to the maximum allowable pressure limit. Furthermore, it is important to control flow rates to prevent unwanted fractures.

[009] It is very common in the offshore oil production industry to operate water injection wells in an open loop, that is, without a control system monitoring and acting automatically. Operating wells without an adequate flow and pressure control system can lead to significant inefficiencies and potentially compromise the geomechanical integrity of the reservoir. The lack of control allows fluid pressure and flow to fluctuate significantly, increasing the risk of over-pressurization or under-pressurization of the reservoir. This not only reduces the Petition 870240111068, dated 12 / 30 / 2024, page 36 / 63 / 17 The efficiency of oil recovery can also lead to unwanted fractures, as well as reduced permeability and connectivity of pores within the rock formation. As a result, production becomes less predictable and more prone to sudden drops, with risks of frequent and costly interventions to correct problems that could have been avoided with adequate control. Furthermore, without control, there is a greater possibility of exceeding the geomechanical limit pressure, which can cause irreparable structural damage to the reservoir, compromising the longevity and profitability of the oil field.

[0010] Document CN115099539A discloses a method and system for injecting water into oil wells using big data and artificial intelligence (AI), aiming to optimize the water injection process and improve oil recovery. This system operates using advanced mathematical models, deep neural networks, and machine learning models to predict oil production and water injection in each well. Furthermore, the residual neural network is one of the system components used to predict water absorption capacity and adjust the injection process. The system also adopts a control approach that avoids overpressure, preventing damage to the reservoir.The algorithm continuously measures interference factors between layers and adjusts water injection, ensuring that each layer receives the ideal amount of water without overloading the geological structures. It also acts as a decision support tool, providing critical real-time information based on AI models, neural networks, and mathematical algorithms to support operational decisions, even preventing potential damage. Its ability to predict, monitor, and automatically adjust water injection and oil production parameters ensures that operators have accurate data.

[0011] Document CN105676633A discloses a method and system for controlling a production well to acquire Petition 870240111068, dated 12 / 30 / 2024, page 37 / 63 / 17 real-time status information from the production well injection system and transmit it to a controller that performs closed-loop control on the production injection system. The disclosed production well control method and system can meet the production well control requirements at the point of optimal working condition, achieve optimal system control, and improve system efficiency.

[0012] Document US11821289B2 discloses techniques that include real-time nodal analysis to automate production optimization for smart well completions. It describes modeling techniques that combine segment production data and downhole parameter estimates to provide optimal flow control valve (ICV) settings to improve the performance of multisegment wells. Optimal settings can be defined, such as settings that maximize the production of a multisegment well over a period of time. In some implementations, automated model optimization can be used to perform real-time optimizations, bringing a production optimization approach using real-time data and nodal modeling to multisegment wells. In some implementations, optimization algorithms can be used to help determine optimal production scenarios for complex wells, such as multisegment wells.For example, based on data from a well's current production conditions, an algorithm can recommend changes to the downhole valve settings that allow for optimized production.

[0013] Document US2023114088A1 discloses a production control system that can be used to optimize hydrocarbon production from a well using a nonlinear, data-driven Artificial Intelligence (AI) model. An analysis of the relationship between the dynamics of flow variables (e.g., multiphase flow rates at the wellhead, well pressures, and temperatures) associated with hydrocarbon production from a well and wellhead pressure values ​​is presented. Petition 870240111068, dated 12 / 30 / 2024, page 38 / 63 / 17. Analyzing well data at various times can facilitate a more accurate prediction of future multiphase well flow rates. Such analysis can be represented in a data-driven model that can be used by the production control system to generate ideal control settings for a throttling valve for enhanced well production control. The data-driven model can be defined based on available production data and sensor data using system identification techniques such as a dynamic mode decomposition (DMD) algorithm. Furthermore, the data-driven model can be corrected using data assimilation methods.

[0014] Thus, despite technological developments, the state of the art does not reveal a solution through a method of controlling water injection wells in oil reservoirs using algorithms, which would enable improved efficiency and reduced risks to the integrity of the reservoir rock.

[0015] The invention described below stems from ongoing research in this segment, which aims to develop a solution capable of enabling the implementation of decisions based on models and algorithms to increase the efficiency of water injection in reservoirs.

[0016] The algorithm of the present invention is called WAVE, an acronym for Waterflow Autonomy and Verification Expert, and it works based on mathematical models and feedback control logic.

[0017] The WAVE methodology can be applied to wells with injection / control valves (choke valves) with automatic actuation or to manual valves where the field operator makes the valve opening changes.

[0018] When a remotely operated choke valve is available, WAVE performs continuous operational diagnostics to verify that the water injection flow rate is being met. Petition 870240111068, dated 12 / 30 / 2024, page 39 / 63 / 17 fulfilled and if the pressure at the bottom of the injection column is within the geomechanical limits of the reservoir for the well in question. Based on this diagnosis, WAVE calculates and adjusts the action on the choke valve to maximize water injection without the bottomhole pressure exceeding the geomechanical limit. This continuous process allows the injection well to operate efficiently and safely, remaining within production targets for longer and increasing operational efficiency.

[0019] If remote choke valve actuation is not available, i.e., if manual actuation of this valve is necessary, WAVE enters decision support mode. Through artificial intelligence models, WAVE estimates the opening or surface pressure at the top to which the well must be brought so that the desired flow rate is met, considering, in this decision, the geomechanical pressure limit of the reservoir.Therefore, WAVE allows you to work on any type of choke valve to optimize the injection well. OBJECTIVES.

[0020] The objective of the present invention is to provide a method for monitoring the geomechanical boundary of a reservoir and controlling the injection of water into the reservoir and maximizing production.

[0021] Another objective of the present invention is to provide a computer-readable storage medium capable of implementing model- and algorithm-based decisions to increase the efficiency of water injection into reservoirs.

[0022] Thus, the present invention describes a method for generating the necessary models and the design of automatic feedback control. Therefore, the expected benefits of using this technology range from improved well efficiency, allowing an increase in production flow rate, as well as reducing the risk to reservoir rock integrity. SUMMARY OF THE INVENTION Petition 870240111068, dated 12 / 30 / 2024, page 40 / 63 / 17

[0023] The present invention relates to a method for maximizing water injection and ensuring the integrity of an oil reservoir, comprising the steps of: receive well information; to estimate the limit value of geomechanical pressure; Define the reference values ​​for the water injection quota; Define the reference values ​​for bottomhole pressure; To control the water injection flow rate; to monitor the reservoir status; and to control the opening value of the control valve.

[0024] Additionally, the present invention also relates to a computer-readable storage medium which, upon receiving a set of instructions, will perform the steps of the method of the present invention. BRIEF DESCRIPTION OF THE FIGURES

[0025] The present invention will be better understood from the detailed description and figures described below that refer to it.

[0026] Figure 1 is a flowchart representation of the WAVE methodology operating in closed-loop mode in an overlay strategy.

[0027] Figure 2 is a representation of the WAVE methodology operating in closed-loop mode based on an overlap strategy between injection quotas and limit pressure.

[0028] Figure 3 is an example of a screen from the methodology. WAVE for operator interface.

[0029] Figure 4 is an illustration of the performance of a decision support mode model of the WAVE methodology based on MLP (Multilayer Perceptron).

[0030] Figure 5 is an illustration of the operational decision support model of the WAVE methodology. Petition 870240111068, dated 12 / 30 / 2024, page 41 / 63 / 17

[0031] Figure 6 is an example of the use of the WAVE methodology.

[0032] Figure 7 shows the sum of time above the geomechanical limit of 5 injection wells of the same unit before and after the implementation of WAVE.

[0033] Figure 8 is a flowchart illustrating the steps for maximizing water injection and ensuring the integrity of the oil reservoir, according to the method of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0034] The following description constitutes only a preferred embodiment within the scope of the present invention.

[0035] The present invention aims to control water injection / reinjection wells in oil reservoirs through an algorithm-based operating methodology called WAVE, an acronym for Waterflow Autonomy and Verification Expert.

[0036] The WAVE methodology can be applied in closed loop or open loop, depending on the configuration desired by the user and the instrumentation available in the wells and in the UEP water plant.

[0037] The closed-loop mode uses algorithms that allow injecting the maximum possible amount of water, respecting the desired injection level and the geomechanical pressure limit of the reservoir. Thus, the well operates fully in automatic mode, with the actions on the choke-type injection control valve being coordinated by the algorithm's actions. Figures 1 and 2 schematically illustrate WAVE operating in closed loop.

[0038] In Figure 1, element 10 represents the water injection pump, while element 11 corresponds to the water distribution head for the wells. Elements 22a and 22b Petition 870240111068, dated 12 / 30 / 2024, page 42 / 63 / 17 are the choke valves of wells 18 and 17, respectively. In this configuration, the choke valve of well 18 is manipulated by the WAVE algorithm, represented by block 16. The WAVE signal inputs, indicated by arrow 19, include: bottomhole pressure, well flow rate, downstream pressure of the choke valve, as well as the set points for the desired injection level and the maximum pressure allowed by the geomechanical limit. Figure 2 illustrates the closed-loop actuation logic of WAVE, which uses an override strategy to alternate control between two algorithms, which can be purely I (integral) and / or PI (proportional-integral), represented by blocks 1 and 2. This approach ensures that both variables remain within the desired operating ranges. The controller inputs correspond to the calculated error between the variables measured in the field and the operational references provided to the algorithm by the operators.This configuration ensures that the pressure does not exceed the geomechanical limit, even when reaching the injection level, and that the injection level is not compromised in order to achieve pressures close to the geomechanical limit.

[0039] In closed-loop operating mode, WAVE uses as input variables the information received S100 from well 17, 18 as flow rate data and pressure data. It is necessary that at least real-time measurement information be provided on the flow rate of water injected into the well and the pressure at the bottom of the well where the geomechanical limit pressure S200 19 is estimated.

[0040] This bottomhole pressure is called PWF. These WAVE input variables can be collected directly from field sensors or estimated by inferences, which are models that seek to infer process variables through secondary measurements, i.e., those that have some degree of correlation with the inferred variable. Typically, the input variables of a controller are called process variables (PV). Petition 870240111068, dated 12 / 30 / 2024, page 43 / 63 / 17

[0041] Thus, an injection / reinjection well operation team needs to define S300 reference values ​​for the water injection level and S400 reference values ​​for the PWF pressure. The water injection level is the desired water flow rate, i.e., a value normally defined by reservoir engineering teams to maximize field production. The PWF pressure values ​​should be an injection pressure at the point where the reservoir integrity limit pressure (geomechanical limit) is calculated, and should be defined as a value close to this limit, but slightly lower. This margin between the geomechanical limit pressure and the desired PWF pressure should be defined in S301 by the engineering and operation teams (for example, a reasonable value would be 5 bar less than the geomechanical limit pressure). These desired values ​​for the level and pressure are called the set point (SP).

[0042] WAVE's closed-loop control strategy is based on an override algorithm. Override control is a technique used in industrial control systems to ensure that critical variables remain within safe limits, even if this means temporarily overriding (or overriding) the normal control of a process. In a system with override control, there are usually multiple controllers 1, 2 acting on the same manipulated variable 22a, 22b (like a valve). Each controller 1, 2 has a specific objective, such as controlling pressure, temperature, or flow. The different controllers have different priorities. The controller with the highest priority can override the control of the others if the variable it is monitoring approaches a critical limit. Override control is fundamental in situations where process safety is paramount.Furthermore, overlap control helps maintain process stability by preventing critical variables from spiraling out of control. In short, the... Petition 870240111068, dated 12 / 30 / 2024, page 44 / 63 / 17. Overlap control is an approach that ensures, in any situation where process conditions may become dangerous or unstable, the system will prioritize the most critical corrective action, even if this means temporarily deviating from normal control objectives. The WAVE methodology uses this overlap strategy to control the injection flow rate at the desired elevation (S500), overriding this objective when the PWF pressure reaches the SP value (a value close to, but lower than, the geomechanical limit pressure of the reservoir), through reservoir state monitoring (S600). The opening value (S700) of choke valves 22a, 22b, controlled by the WAVE methodology, is always the lowest output of the two parallel controllers 1, 2 that handle the PWF pressure and well flow rate. Figure 2 illustrates this strategy of the WAVE methodology.

[0043] Controllers 1 and 2 are, respectively, the PWF pressure control algorithm and the well water injection flow control algorithm. These controllers can be of the PID (Proportional-Integral-Derivative) type; however, a recommendation, but not a limitation, is the use of pure I controllers. A pure I controller (or pure integral controller) is a type of controller in control engineering that bases its action on the integral term of the error over time. It is part of the PID control strategies, but in the case of a pure I controller, the proportional and derivative terms are omitted. One of the main advantages of the pure I controller is its ability to eliminate permanent error, also known as steady-state error. Because it accumulates the error over time, it adjusts the output until the error is completely zeroed. It is useful in systems where the response may be slow, but where complete error elimination is crucial.This is common in temperature control processes, level control, or in flow systems that require high precision. Tuning a pure I-controller involves properly defining its single control parameter, which is... Petition 870240111068, dated 12 / 30 / 2024, page 45 / 63 / 17 the integral time (or integral constant). This parameter determines how quickly the controller reacts to the accumulated error over time. Conventional tuning adjustment methods can be used to define the integral time of the controllers.

[0044] An alternative that improves the performance of the WAVE methodology, but is optional and not limiting, is the use of a pure event-based controller. An event-based controller is a type of controller that adjusts the system output not at regular time intervals, as in traditional time-based controllers, but in response to specific events that occur in the system. These events can be changes in the state of a process variable, specific conditions that are met, or the arrival of new data indicating the need for corrective action. Events can be configured in the WAVE methodology according to the needs of each industrial installation, such as a deadband error between the desired value (SP) for the injection flow rate and the measured flow rate (PV). This reduces the number of algorithm actuations as needed, which may eventually result in increased choke valve lifespan.

[0045] In open-loop operating mode, the WAVE methodology functions as an operational decision support algorithm. This means that the operator enters the desired SP values ​​for elevation and pressure on the WAVE methodology screen, as shown in Figure 3, and the algorithm calculates the opening of choke valves 22a, 22b that should be applied in the field to achieve the desired flow-pressure conditions.

[0046] Alternatively, the WAVE methodology delivers the downstream pressure value for choke valve 22a, 22b that should occur in the field. This is valid for injection wells where choke valve 22a, 22b does not have a position reader, or where this reader is not returning reliable values ​​to the operation. In decision support mode, the actions indicated by Petition 870240111068, dated 12 / 30 / 2024, page 46 / 63 / 17: WAVE methodology must be applied in the field by an area operator.

[0047] The models used in decision support mode are different from those used in closed-loop mode. In open-loop mode, four models are used in series to generate recommendations for actuation on choke valves 22a, 22b. The first model, block 1 of Figure 5, aims to estimate the downstream pressure of the choke valve, using as input variables the pump discharge pressure 10 (or header pressure 11, as illustrated in Figure 1), the injection water flow rate (desired set point), and the PWF pressure (desired set point). This model can be designed based on neural networks, using historical well operation data for its training. Models based on MLP (Multilayer Perceptron) are a good option and show good performance in reconstructing the downstream pressure of the choke valve, as illustrated in Figure 5.It is worth noting that this is one modeling option, but there are no limitations in choosing another type of neural network or even in using other techniques, such as algebraic models, for example.

[0048] Model 2, block 2 of Figure 5, of the decision support mode is a single-phase flow model in a valve and uses the following variables as input: water flow rate F(z) and the difference between the choke inlet and outlet pressures AP. The model output is the valve flow capacity Cv(z), as per Equation 01. where: Cv(z) = flow capacity F(z) = water flow rate AP = difference between the inlet and outlet pressures of the control valve.

[0049] The value of Cv(z) is then used by a third model, block 3 of Figure 5, which is based on the choke valve characteristic curve provided by the manufacturer of Petition 870240111068, dated 12 / 30 / 2024, page 47 / 63 / 17 equipment. The valve manufacturer provides a table with Cv values ​​and shutter opening, which can be correlated in an algebraic equation to generate an estimate of the choke valve position for the desired PWF and injection quota conditions.

[0050] The choke valve opening value must be corrected since the Cv (flow coefficient) and the opening provided by the manufacturer may deviate from the original values ​​over time due to natural wear and tear of the equipment. This wear can have several origins such as: corrosion; internal valve components, such as the body, seat, and obturator, may suffer wear and corrosion over time, altering the internal geometry, which may reduce the effective flow area and, consequently, the actual Cv of the valve; accumulation of material deposits such as scale, sediment, or chemicals on the internal surfaces of the valve, restricting flow and changing the opening behavior; maintenance and repairs may cause changes in the valve's characteristics, especially if the replacement parts are not identical to the originals.To correct the valve value, or to adjust the opening estimate to the current installed characteristics, a fourth model is needed, block 4 of Figure 5, and the last model in the WAVE decision support mode. This model can be designed based on neural networks, using historical well and valve data for its training, provided that the equipment has a position gauge that indicates valve opening. Models based on MLP (Multilayer Perceptron) are a good option and perform well in capturing valve wear. It is important to emphasize that the MLP network is a modeling option, but there are no limitations in choosing another type of neural network or even in using other techniques, such as algebraic models.

[0051] The WAVE operational decision support model is schematized in Figure 5, where Ppwf is the pressure set point. Petition 870240111068, dated 12 / 30 / 2024, page 48 / 63 / 17 of the PWF, Ftop is the well water flow set point, Pd is the pump discharge pressure or water header of the injection wells, Ptop is the downstream pressure of the choke valve, Cv is the estimated flow coefficient for the valve, z is the estimated valve opening, and ez* is the estimated valve opening considering equipment wear.

[0052] WAVE's two operational modes, closed loop and open loop (decision support), allow the methodology to be applied at different instrumentation levels of actuation valves in the field, as well as providing flexibility for operational teams to choose the most appropriate way to use the algorithms. Thus, WAVE enables the optimization of injection well operation, in addition to protecting the integrity of the reservoir rock through the use of automatic control algorithms, models, and artificial intelligence.

[0053] In this way, the WAVE methodology performs the steps illustrated in Figure 8 to achieve the maximization of water injection and ensure the integrity of the oil reservoir. EXAMPLE

[0054] The examples in this section are intended to illustrate one of the numerous embodiments of the invention, however without limiting its scope of protection.

[0055] In this example, as illustrated in figure 6, the operational decision-making assistance method will be presented with the support of the WAVE methodology in a real injection well of a FEA operating in ultra-deep waters.

[0056] The demonstration test was initiated at 10:00 when the team decided to activate the WAVE methodology to evaluate the quality of decision-making in water injection operations in wells. Injection well X was selected by the reservoir and lift engineers.

[0057] Before the test, well X was operating with a bottomhole pressure (PWF) of 368 kgf / cm2 and an injection flow rate of 175 m3 / h. The pressure downstream of the choke valve at the surface Petition 870240111068, dated 12 / 30 / 2024, page 49 / 63 / 17, was 6930 kPa. With the scenario ready, the engineering team established new set points along with the operation: the bottom pressure PWF should be raised to 375 kgf / cm2 and the injection flow rate adjusted to 200 m3 / h.

[0058] At approximately 10:30, operations reported that the set points had been changed according to the guidelines. WAVE then estimated that, with these new settings, the downstream pressure of the choke valve should be increased to 8070 kPa. The teams reviewed the estimates provided by WAVE and, after validating the results, operations were instructed to proceed with the change in the injection plant.

[0059] The field operator was then called upon to manually adjust the well choke valve. At 11:02, he performed the task, and the results began to unfold. The downstream pressure of the choke valve in the field was manipulated by the operator to 7691 kPa, resulting in an increase in the bottomhole pressure to 372 kgf / cm2. The injected water flow rate, in turn, rose to 193 m3 / h, representing an increase of 18 m3 / h, or 432 m3 / day, in the water injection from well X.

[0060] According to reservoir engineering teams, this increase in water injection results in an increase in production of approximately 540 barrels of oil per day.

[0061] Figure 7 illustrates a histogram showing the operating time above the geomechanical limit of five injection wells belonging to the same production unit. The data were organized into two distinct periods: three months before and three months after the implementation of WAVE. A significant reduction in the time of exposure to high pressures is observed, from 256.25 hours to only 60.83 hours, which corresponds to a decrease of more than 75% in the violation of the maximum recommended pressure limit. This result highlights the effectiveness of WAVE, demonstrating that the methodology not only improves the efficiency of the injection process, but also reinforces the guarantee of reservoir integrity. Petition 870240111068, dated 12 / 30 / 2024, pages 50 / 63

Claims

1 / 4 CLAIMS 1. A method for maximizing water injection and ensuring the integrity of an oil reservoir, characterized by comprising the steps of: receiving (S100) well information; estimating (S200) the limit value of geomechanical pressure; defining (S300) the reference values ​​for the water injection level; defining (S400) the reference values ​​for bottomhole pressure; controlling (S500) the water injection flow rate; monitoring (S600) the reservoir status; and controlling (S700) the opening value of the control valve.

2. Method for maximizing water injection and ensuring the integrity of an oil reservoir, according to claim 1, characterized in that the step of estimating (S200) the limit value of geomechanical pressure comprises the step of collecting (S201) information on the flow rate of water injected into the well and pressure at the bottom of the well.

3. Method for maximizing water injection and ensuring oil reservoir integrity, according to claim 1 or 2, characterized in that the step of defining (S400) the bottomhole pressure reference values ​​comprises defining (S401) a value at least 5 bar less than the geomechanical pressure limit value.

4. Method for maximizing water injection and ensuring the integrity of an oil reservoir, according to any one of claims 1 to 3, characterized by the fact that the step of controlling (S500) the water injection flow rate is performed by means of an overlapping algorithm.

5. Method for maximizing water injection and ensuring the integrity of an oil reservoir, according to any one of claims 1 to 4, characterized in that the step of monitoring (S600) the reservoir state is carried out by means of controllers (1,2).

6. Method for maximizing water injection and ensuring oil reservoir integrity, according to claim 5, characterized in that controller (1) is a bottomhole pressure control algorithm and controller (2) is a well flow control algorithm.

7. Method for maximizing water injection and ensuring the integrity of an oil reservoir, according to claim 5, characterized in that the controllers (1, 2) are PID type controllers.

8. Method for maximizing water injection and ensuring oil reservoir integrity, according to claim 5, characterized in that the controllers (1, 2) are pure integral controllers.

9. Method for maximizing water injection and ensuring oil reservoir integrity, according to claim 8, characterized in that the pure integral controllers (1, 2) are event-based controllers.

10. Method for maximizing water injection and ensuring the integrity of an oil reservoir, according to any one of claims 1 to 9, characterized in that the step of controlling (S700) the opening value of the control valve is performed autonomously by means of an algorithm based on reservoir state data.

11. Method for maximizing water injection and ensuring the integrity of an oil reservoir, according to any one of claims 1 to 9, characterized in that the step of controlling (S700) the opening value of the control valve is performed by means of a decision support model.

12. A method for maximizing water injection and ensuring the integrity of an oil reservoir, according to claim 11, characterized in that the series model estimates the downstream pressure of the control valve, the flow rate of the injection water, and the bottomhole pressure.

13. Method for maximizing water injection and ensuring the integrity of an oil reservoir, according to claim 11, characterized in that the series model is a single-phase flow model in a valve, according to the equation: Cv(z) = F(z) 4ΔΡ where: Cv(z) = flow capacity F(z) = water flow rate ΔP = difference between the inlet and outlet pressures of the control valve.

14. A method for maximizing water injection and ensuring the integrity of an oil reservoir, according to claim 11, characterized in that the series model is a model based on neural networks and / or based on historical data from the well and the control valve.

15. Computer-readable storage medium, characterized in that it comprises a set of instructions, wherein when the set of instructions is executed on a computer, it causes the computer to perform the method as defined in any one of claims 1 to 14.

Images