Anti-gulf control method using surface pressure measurements in oil and gas production.

Description

1 / 27 Anti-gulf control method using surface pressure measurements in oil and gas production. Field of invention

[001] The present invention falls within the technical field of petroleum engineering, more specifically in the control of multiphase flows in oil and gas production systems. More specifically still, the present invention relates to a method of controlling oil and gas producing wells to improve operation in slug scenarios with low instrumentation available, i.e., only with pressure measurements at the surface (well arrival at the oil, water and gas separation and treatment plant). Fundamentals of the invention

[002] Stationary Production Units (SPUs) are offshore installations designed to extract and process petroleum, separating it into oil, water, and gas. The oil can be stored, exported via pipelines, or transferred to other vessels. The water undergoes treatment and can be reused, reinjected into the reservoir, or discharged, while the gas is compressed for use in oil recovery, exported, or flared for power generation. Some of the water and gas is recirculated to the reservoir after processing at the SPU plant.

[003] In offshore operations, a significant challenge is the phenomenon of slugging, characterized by cyclical instabilities in multiphase flow, which impact the efficiency of producing wells and operational continuity. These self-sustaining oscillations occur in the liquid and gaseous phases within the production lines, being Petition 870240111083, dated 12 / 30 / 2024, page 34 / 72 2 / 27 influenced by factors such as bathymetry of submarine lines, system pressure, and fluid properties.

[004] The stabilization of oil wells against slugging was initially explored by Schmidt et al. (Z. Schmidt, JP Brill, DH Beggs, Choking can eliminate severe pipeline slugging, Oil Gas J. 12 (1979) 230-238; and Z. Schmidt, JP Brill, DH Beggs, Experimental study of severe slugging in a two-phase flow pipeline-riser system, SPE Prod. Eng. 20 (1980) 407-414), who demonstrated that increasing the pressure by throttling the choke valve can eliminate the phenomenon.

[005] A production choke valve is a modular device used to control and regulate the flow of fluids from an oil and gas production well, playing a critical role in the interface between production lines and the processing plant. Usually installed in the Christmas tree or near the processing plant, these valves adjust the pressure and flow rate of fluids, ensuring operational stability and protecting downstream equipment against overpressure or wear caused by high flow velocities. Modular choke valves allow for fine adjustments, manual or automatic, and are essential for optimizing production.

[006] Taitel (Y. Taitel, Stability of severe slugging, J. Multiph. Flow. 12 (2) (1986) 203-2017) proposed alternatives such as increasing the pressure in the gravity separator or implementing proportional feedback control. Subsequent work, including that of Blick and Boone (EF Blick, L. Boone, Stabilization of naturally flowing oil wells) Petition 870240111083, dated 12 / 30 / 2024, page 35 / 72 3 / 27 using feedback control, in: 56th California Regional Meeting of SPE Held in Oakland, 1986), advanced the idea of ​​stabilization via feedback control, using bottomhole pressure as the measured variable and the choke valve as the manipulated variable. Despite the theoretical advances, until the end of the 1980s, these approaches had not been validated in the field.

[007] Active slug stabilization control is a technique that has been studied since the 1980s, with a consensus on its significant potential to increase production. Traditionally, this approach requires subsurface sensors, which are typically installed on the seabed (wellhead) or at the bottom of the production string (bottom sensor, near the perforation). However, maintaining these sensors is complex and expensive due to the harsh environment in which they are located, resulting in a limited lifespan.

[008] In the 1990s, more advanced strategies emerged, including the use of fuzzy logic and cascaded PID controls. These approaches showed promising results in laboratories and field implementations, with gains in oil production and a reduction in the use of injected gas. However, the limitations of these techniques lay in the intensive use of subsurface sensors, such as pressure and temperature gauges, whose cost and maintenance hinder their widespread application.

[009] An alternative to subsurface sensor control strategies is the use of pressure measurements available on the (surface) platforms, particularly in Petition 870240111083, dated 12 / 30 / 2024, page 36 / 72 4 / 27 around the choke valve. These sensors offer greater durability and the possibility of immediate maintenance. However, the direct use of these surface pressures presents performance limitations due to the emergence of a dynamic pattern known as inverse response. Inverse response in process control refers to a situation where the system output initially moves in the opposite direction to that expected when an input is applied, resulting in severe performance limitations and significantly compromising the gains from the closed loop.

[010] Unlike surface pressure, well flow rate is a variable that does not exhibit an inverse response to choke valve actuation. Therefore, well flow rate is the ideal variable for achieving good slug suppression control performance with surface measurements. Unfortunately, flow meters are not normally found in offshore oil and gas production facilities. This is because well flow rate measurements require large multiphase equipment, with negative impacts on the FPSO (Operational Expenditure) and CAPEX (Capital Expenditure) of the FPSO.

[011] More recent studies have explored control structures based on secondary variables, such as estimated flow rate, to circumvent the lack of subsurface sensors. While feasible in theory, these approaches lack practical validation in industrial environments. Furthermore, multiphase flow rate measurements, while eliminating the inverse response in surface pressure control, require Petition 870240111083, dated 12 / 30 / 2024, page 37 / 72 5 / 27 expensive and complex equipment, limiting its application in offshore installations.

[012] In view of the above, an alternative to the lack of measurement is the use of virtual variables, that is, variables estimated through measurements available at the plant. Thus, the present invention proposes a method for controlling slug flow, using only surface measurements, which is based on a well flow inference system coupled with feedback control, that is, with models to stabilize the oil and gas production of a producing well. The proposed methodology is based on the integration of pressure and choke valve opening measurements to estimate the flow rate virtually, without the need for subsurface sensors. This estimate is then processed by PID-type feedback control algorithms.

[013] In other words, the implementation of the proposed method includes the steps of dynamic modeling, signal processing and controller development, allowing the stabilization of multiphase flow even in scenarios with instrumentation limitations, ensuring greater productive efficiency, increased oil flow and reduction of unscheduled shutdowns due to overpressure in the processing plant.

[014] In view of this, and in order to solve the technical problem described above, the present invention makes it possible to achieve a significant advance in slug management, enabling the use of active control strategies in challenging operational scenarios. Among the main advantages are increased production. Petition 870240111083, dated 12 / 30 / 2024, page 38 / 72 6 / 27 due to improved well efficiency and increased flow rate, reduced operational interruptions with fewer unscheduled shutdowns, and accessibility by eliminating reliance on subsurface sensors, reducing costs and operational complexity.

[015] Furthermore, an increase in production of between 5-15% of the well is observed, a reduction in unscheduled shutdowns, a reduction in the risk of overpressure in surface and subsea equipment, and a reduction in fatigue in subsea and surface lines and heads, since it reduces the vibration and cycling caused by slugging. State of the art

[016] In the state of the art, there are methods for slug control, however, no state-of-the-art method integrates pressure measurements and choke valve opening to estimate flow virtually, without the need for subsurface sensors.

[017] Patent document BR1020190193506, for example, refers to an anti-choke controller capable of stabilizing the flow using only an easily obtainable surface measurement, such as the pressure upstream of the choke valve. To compensate for the unfavorable dynamics of this type of measurement, a hybrid fuzzy-PID control algorithm was used, in which the fuzzy portion of the algorithm compensates for the limitations of the PID controller through heuristic interventions.

[018] However, document BR1020190193506 differs from the present invention, as the present invention solves the problem of the inverse response of pressure measurements of Petition 870240111083, dated 12 / 30 / 2024, p. 39 / 72 7 / 27 surface (cough valve upstream or inlet pressure) using the flow rate variable instead of pressure as in BR1020190193506.

[019] In turn, document BR1020130305715 applies to an advanced control system to automatically promote the elimination or minimization of the occurrence of the phenomenon called slug flow in a deepwater oil production well. The main objective of BR1020130305715 is to provide a system that automatically controls and ensures the operation of a deepwater oil production well without the occurrence of slug flow, through the use of pressure gauges at various alternative points in the pipeline and continuous actuation of the production choke valves, employing aggregated computational algorithms that monitor a set of operational variables.

[020] However, document BR1020130305715 differs from the present invention, as the present invention stands out for its simplicity, requiring significantly fewer measurement points and instrumentation to solve the choke problem than BR1020130305715. By using only surface pressure and choke valve opening measurements, the present invention offers a more accessible and effective solution, especially in resource-limited environments.

[021] Furthermore, document US9982846 refers to a method and a control system that are provided for reducing the size and / or frequency of hydrodynamic shocks in a fluid processing system. The system of Petition 870240111083, dated 12 / 30 / 2024, page 40 / 72 8 / 27 Fluid processing includes a pipeline to transport produced fluids and a vessel to receive the produced fluids from the pipeline. A control valve is provided in the pipeline upstream of the vessel. A pressure sensor is provided upstream of the control valve.

[022] However, document US9982846 differs from the present invention, as the present invention presents a more direct approach, employing a less complex control logic (single PID instead of a cascade strategy) for slug suppression, therefore requiring less installation and maintenance costs and allowing for faster and more effective implementation compared to the more complicated approach of US9982846. Brief description of the invention

[023] The present invention relates to a method of controlling anti-slip with surface pressure measurements in oil and gas production comprising: (a) acquiring data; (b) pre-processing the data from step (a) by standardizing them; (c) designing and applying a low-pass filter at an inlet pressure; (d) adjusting a virtual flow model; (e) validating a process gain; (f) estimating a liquid flow rate from a well based on the data from step (b), considering the type and wear of a choke valve; and (g) controlling the choke valve, using a PID controller, based on an error between an estimated flow rate in step (f) and a reference value (set point). The data from step (a) comprise an upstream and downstream pressure, a position (opening variables) of the choke valve, and flow data estimated by a Multiphase Virtual Meter (MVM). In step Petition 870240111083, dated 12 / 30 / 2024, page 41 / 72 9 / 27 (d) an optimization is performed for accurate flow rate estimates, including least squares and considering correlations between variables. Furthermore, in step (e) a flow rate response to the opening of the choke valve is evaluated, removing non-linear behaviors. The adjustment of step (d) is performed in real time. In step (a) operational data are continuously acquired minute by minute. In addition, step (a) excludes shutdown and startup periods from a dataset reflecting only normal operational states of a well. Furthermore, step (b) also includes: (b.1) adjusting units for dimensional consistency; (b.2) removing outliers; and (b.3) excluding non-representative operational periods. In step (c) the low-pass filter design is applied to the upstream and downstream pressure signals of the choke valve, attenuating high-frequency noise, with the cutoff frequency and filter order defined based on the desired signal characteristics.In step (f), least squares optimization strategies are applied to estimate the virtual flow rate, using correlations such as the objective function and the parameters of the choke valve characteristic curve as decision variables. Furthermore, the execution of steps (a) to (g) generates a virtual flow rate measurement that is coupled to a signal processing system and used in a PID-type feedback controller. The virtual flow rate model of step (d) comprises: (d.1) calculating a pressure difference (AP) from the filtered upstream and downstream pressures of step (c); (d.2) dynamically adjusting the flow coefficient (Cv(z)) of the choke valve using... Petition 870240111083, dated 12 / 30 / 2024, page 42 / 72 10 / 27 a specific parametric model; (d.3) estimate a liquid flow rate (F) based on AP, Cv(z), and an adjustment factor (Fbias); (d.4) smooth an estimated flow rate (F) and a reference point (SP) using stability filters in the control; (d.5) calculate an error between the estimated flow rate (F) and the reference point (SP); and (d.6) apply the error calculated in step (d.5) to a PID controller. Furthermore, in step (d.2) the adjustment of the flow coefficient (Cv(z)) is performed based on parametric models represented by equations (2), (3), (4) and (5). In step (d.1) upstream and downstream pressure measurements (Pmon and Pjus) are smoothed using first-order low-pass filters with adjustable cutoff frequency. In step (d.4) the flow rate estimate (F) is smoothed by a first-order filter with a time constant adjustable between 3 and 10 times a sampling time.Furthermore, the reference point (SP) is smoothed by a first-order filter, without abrupt changes in the operating point. In step (d) the adjustment factor (Fbias) is parameterized correcting discrepancies between the model and the actual well data. In step (d.6) the PID controller output is added to the current choke valve position determining a new opening position. Finally, in step (d.5) the calculation of the error between the estimated flow rate (F) and the reference point (SP) uses the smoothed values ​​of both variables. Brief description of the figures

[024] In order to complement the present description and obtain a better understanding of the characteristics of the present invention, and in accordance with a preferred embodiment of Petition 870240111083, dated 12 / 30 / 2024, page 43 / 72 11 / 27 The same, attached, presents a set of figures, where, in an exemplary, though not limiting, manner, its preferred embodiment is represented.

[025] Figure 1 shows an inverse response graph of surface pressure to a step change in the choke valve.

[026] Figure 2 shows a graph of the well flow rate response inferred by a step model of variation in the choke valve - there is no inverse response in the observed dynamics, according to a preferred embodiment of the present invention.

[027] Figure 3 shows a schematic of the control strategy, where dP is the pressure drop in the choke valve, F is the virtual flow rate, SP is the desired set point, and C is the PID controller. Additionally, ANM is the wet Christmas tree at the wellhead, and the production header is the surface equipment that receives the oil, water, and gas produced by the well, according to a preferred embodiment of the present invention.

[028] Figure 4 shows the filters (CEO) of the well pressure signals, the flow inference and the PID algorithm of the claimed method, according to a preferred embodiment of the present invention.

[029] Figure 5 shows a comparative graph of different filter time constants for well flow rate response inferred by a step model of variation in the choke valve, according to a preferred embodiment of the present invention. Petition 870240111083, dated 12 / 30 / 2024, page 44 / 72 12 / 27

[030] Figure 6 shows a graph of the choke control strategy with top measurement, in which at point 1, the claimed method is enabled and begins to control choke valve 2. Then, at point 3, the operator raises the virtual flow setpoint, which results in an increase in production and, consequently, a reduction in inlet pressure, as observed at point 4, according to a preferred embodiment of the present invention.

[031] Figure 7 shows a graph of the virtual flow rate displaying the open-loop and closed-loop tests performed, in which it is possible to verify the attenuation of the oscillatory behavior of the well and, consequently, operational improvement obtained in closed-loop (algorithm on), according to a preferred embodiment of the present invention.

[032] Figure 8 shows a slug plot, demonstrating the cyclical pattern of multiphase flow that arises from the combination of certain factors (bathymetry, BSW, RGO, reservoir pressure, emulsion, etc.).

[033] Figure 9 shows graphs demonstrating how the stationary production unit deals with the problem of slugging by restricting its production, where the restriction of production is done through the partial closure of the choke valve and tends to reduce well oscillations, however, as a side effect, the well starts to work more pressurized and with lower oil and gas production. Petition 870240111083, dated 12 / 30 / 2024, page 45 / 72 13 / 27

[034] Figure 10 shows a graph of the flow rate of an ultra-deepwater well displaying the open loop (control off) and the closed loop (control on) of tests carried out in the field, in such a way that the well flow rate is increased without the oscillations caused by slugging occurring, demonstrating that the control strategy of the claimed method has good performance in containing operational instabilities, according to a preferred embodiment of the present invention. Detailed description of the invention

[035] The present invention falls within the field of offshore oil exploration and production (E&P) and automatic process operation technology, focusing on the description of a method for controlling oil and gas producing wells to improve operation in slug scenarios, as observed in Figures 8 and 9, with low instrumentation available, i.e., only with pressure measurements at the surface (arrival of the well at the oil, water and gas separation and treatment plant).

[036] In general, the use of pressure measurements available on the platforms (surface), particularly around the choke valve, offers greater durability and the possibility of immediate maintenance. However, the direct use of these surface pressures presents performance limitations due to the emergence of a dynamic pattern known as inverse response, as seen in Figure 1. Inverse response in process control refers to a situation where the system output initially moves in the opposite direction to that expected when Petition 870240111083, dated 12 / 30 / 2024, page 46 / 72 14 / 27 an input is applied, resulting in severe performance limitations, significantly compromising the gains from the closed loop.

[037] Unlike surface pressure, well flow rate is a variable that does not exhibit an inverse response to choke valve actuation. Therefore, well flow rate is the ideal variable to achieve good slug suppression control performance with surface measurements. Unfortunately, flow meters are not normally found in offshore oil and gas production facilities. This is because well flow rate measurements require large multiphase equipment, with negative impacts on the FPSO and CAPEX of the FPSO.

[038] In view of the aforementioned technical problem, an alternative to the lack of measurement is the use of virtual variables, that is, variables estimated through measurements available at the plant.

[039] That being said, the present invention infers the liquid flow rate from the well through the following variables: (1) choke valve opening, (2) upstream pressure and (3) downstream pressure of the surface choke valve, thus perceiving the disappearance of the inverse response, as observed in Figure 2. These variables are used in a customized single-phase flow model in a valve and allow the capture of the flow dynamics of the liquid flow through the well choke valve. This virtual flow measurement is used as a control variable of a PID that manipulates the choke valve to operate the well and control slugging. Thus, the flow inference of the present Petition 870240111083, dated 12 / 30 / 2024, page 47 / 72 The invention (15 / 27) allows for the estimation of the flow dynamics of a well with slugs. Signal processing removes measurement noise and filters the dynamics so that their responses have speeds compatible with field equipment. Furthermore, the controller used is fed by inference and processed signals, responding with actions applied to the production choke valve. The inference, signal processing filters, and controller are orchestrated together, generating a computer-implemented method for anti-slug control with surface pressure measurements in oil and gas production systems.

[040] In other words, the present invention uses a customized model to infer virtual flow rate based on surface pressure and choke valve opening measurements. This allows for flow stabilization in scenarios where multiphase flow meters and / or underwater sensors are not available, significantly reducing implementation and maintenance costs. Thus, the main advantage of using the flow rate variable instead of pressure is that it solves the problem of the inverse response of surface pressure measurements (upstream of the choke valve or inlet pressure), which is a challenge in slug flow control systems and tends to reduce the achievable performance of the control strategy. Virtual flow rate inference eliminates this problem, providing a more robust and efficient control strategy. That is, the present invention is distinguished by its ability to handle the problem of the inverse response in Petition 870240111083, dated 12 / 30 / 2024, pp. 48 / 72 16 / 27 surface measurements, resulting in greater efficiency. However, the inverse response in producing wells can have different characteristics in distinct scenarios, increasing the design complexity of a control system that directly utilizes pressure. In general, the proposed method presents greater simplicity, as it requires significantly fewer measurement points and instrumentation to solve the slugging problem. By using only surface pressure measurements and choke valve opening, the present invention offers a more accessible and effective solution, especially in resource-limited environments. This approach makes the technology easier to implement and maintain, increasing its applicability in a variety of scenarios.

[041] Furthermore, the claimed method uses a Multiphase Virtual Meter (MVM) for flow inference training and subsequent control of a choke valve, ensuring stability and optimization in the flow. The claimed methodology comprises a virtual flow model adjusted in real time, and presents a control structure with filters and tuning that adapt the performance according to the non-linear dynamics of the well. Consequently, the present invention is distinguished by using a system that adjusts the flow-valve curve for non-linear behaviors.

[042] In particular, the present invention has a model that allows the liquid flow rate of the production well to be inferred through secondary variables as presented in Equation (1) below. F(z) = Cv(z)JKP + Fbias (1) Petition 870240111083, dated 12 / 30 / 2024, page 49 / 72 17 / 27 In which: F(z) is the liquid flow rate of the well; Cv(z) is the flow coefficient of the choke valve; ΔP is the pressure drop across the choke valve; which is the difference between the upstream pressure (Pmon) and the downstream pressure (Pjus) of the valve; and Fbias is a bias factor that allows the model to be adjusted to the well field data.

[043] The choke valve flow coefficient Cv(z) is a characteristic that depends on the type of valve installed and the wear that the valve has suffered over the years of operation. Therefore, it is necessary to parameterize Cv(z) on a case-by-case basis. The recommended models for Cv(z) adjustments are presented in Equations (2), (3), (4) and (5) below. Cv(z) = a + bz (2) Cv(z) = (a+bz) d (3) Cv(z) = (a+bz) (4) (d+ez+1) p(z) = / ' (5) (d+ez+1)

[044] The estimated flow rate F is then compared with a reference flow rate value called the set point (SP), generating an error that is fed into a PID controller (C). This PID algorithm, commonly used in industry, calculates a control action to minimize the error between F and SP, generating a new position in the choke valve opening, this being a simplified mode control strategy, as seen in Figure 3. Petition 870240111083, dated 12 / 30 / 2024, pp. 50 / 72 18 / 27

[045] These equations can be chosen according to the desired fit quality and acceptable complexity. In these algebraic equations, z is the choke valve position, while a, b, c, d, and e are equation adjustment parameters to adapt the valve's Cv(z) to the actual behavior of the installed equipment.

[046] Additionally, the control strategy includes a set of pressure signal filters to mitigate inherent measurement noise and smooth control actions through the virtual flow filter. The well flow responds very quickly to changes in the choke valve opening, therefore a filter in the control action improves the controller's performance by preventing abrupt actions in the final control element.

[047] The filters mentioned constitute four filters, as observed in Figure 4. In which, two filters in the measured pressures Pmon and Pjus aim to remove measurement noise from the sensors. The filters for these variables are called CEO1 and CEO2 and can be first-order filters, also called low-pass filters. Higher-order filters or filters based on other techniques can also be used in order to clean the signal that calculates ΔP, losing the minimum amount of information about the prevailing dynamics.

[048] After filtering Pmon and Pjus, ΔP is calculated, which corresponds to the difference between filtered Pmon and Pjus. This value is fed into Equation (1) along with the calculation of Cv(z), which is done using one of the variables from Equations (2), (3), (4), and (5). In these calculations, Petition 870240111083, dated 12 / 30 / 2024, pp. 51 / 72 19 / 27 z is the choke valve opening measured in the field at the same time as the Pmon and Pjus measurements.

[049] Once the flow rate F is calculated, its value passes through the third filter of the algorithm, the 1 / tF block, as seen in Figure 4. The behavior of the estimated flow rate shows a significant instantaneous abrupt variation when there is a change in the choke valve opening, resulting from equation (1), as seen in Figure 5. This behavior also causes difficulties for the controller and should be mitigated. Here again, a first-order filter is recommended, aiming to make this response smoother. Thus, a filter with a time constant of 3 to 10 sampling times is sufficient, as seen in Figure 5.

[050] Then, the filtered flow rate F is sent to a calculation block that subtracts it from the filtered SP. SP is filtered by the 1 / tSP block, which aims to smooth out changes in the operating point through new SP values, making the controller's action more parsimonious.

[051] The error between the filtered values ​​of SP and F feeds a PID controller that calculates an output Δu which, added to the position z of the choke valve, generates a new opening position for this valve, as seen in Figure 4.

[052] Specifically, the present invention relates to a method of controlling anti-choke pressure with surface pressure measurements in oil and gas production comprising performing the following general steps of (a) data acquisition, in which information on the operation of the sampling well is collected at least minute by minute. The variables to be acquired are the choke valve opening, Petition 870240111083, dated 12 / 30 / 2024, pp. 52 / 72 20 / 27 pressure upstream and downstream of this valve. Additionally, for the parameterization of Equation (1), well flow data must be collected. This data can be from a multiphase meter, another more rigorous dynamic model, or well tests. The data acquisition period should represent the largest number of normal operational states of the well, and shutdowns and startups should be removed from the data set; Step (b) Data Pre-processing, in this step the units of the variables must be adjusted so that there is dimensional consistency in the calculations of Equations (1), (2), (3), (4) and (5). Any unit system can be used, provided there is dimensional consistency.Still in step (b) of data pre-processing, outliers and unrepresentative operating periods are removed; Step (c) of designing the filters and flow inference, in which the objective is to apply a low-pass filter to the pressure signals (Pmon and Pjus), so that high-frequency measurement noise is minimized. For this, the cutoff frequency must be defined, which is the frequency above which the filter will begin to attenuate the signal. This must be determined based on the signal frequencies that are to be preserved and the noise frequencies that are to be removed. Furthermore, the filter order must be defined so that the degree of cutoff to be applied in the transition between pass-through and attenuation can be chosen. Higher-order filters have faster transitions but are more complex. After designing the pressure filters, the virtual flow model must be adjusted. The estimation can be done through a minimum optimization strategy. Petition 870240111083, dated 12 / 30 / 2024, pp. 53 / 72 21 / 27 squares, using correlation as the profit variable and the CV function parameters as decision variables. Finally, it is important to validate the flow estimation model's behavior to verify the consistency of the results, the phase of the oscillation in relation to the real behavior, and the guarantee of monotonic flow behavior in relation to the choke valve opening. Finally, the SP filter can be first-order according to the accepted velocity in the well state transition; Step (g) of designing the PID, in which the virtual flow response to stimuli in the choke valve is modeled. For this, it is possible to identify a transfer function or state-space representation of the well dynamics. Then, a PID tuning method must be chosen, such as Ziegler-Nichols, Frequency Response Method, or Manual Tuning.The controller should be validated in the field through experimental testing, adjusting parameters as needed to handle unmodeled dynamics or changes in operating conditions.

[053] In particular, the proposed method comprises: (a) acquiring data; (b) pre-processing the data from step (a) by standardizing them; (c) designing and applying a low-pass filter at an inlet pressure; (d) fitting a virtual flow model; (e) validating a process gain; (f) estimating a liquid flow rate from a well based on the data from step (b), considering a type and wear of a choke valve; and (g) controlling the choke valve, using a PID controller, based on an error between a flow rate estimated in step (f) and a reference value (set point). The data from step Petition 870240111083, dated 12 / 30 / 2024, pp. 54 / 72 22 / 27 (a) comprise an upstream and downstream pressure, a choke valve position (opening variables), and flow data estimated by a Multiphase Virtual Meter (MVM). In step (d), an optimization is performed for accurate flow estimates, including least squares and considering correlations between variables. Furthermore, in step (e), a flow response to choke valve opening is evaluated, removing non-linear behaviors. The adjustment of step (d) is performed in real time. Where in step (a), operational data are continuously acquired minute by minute. In addition, step (a) excludes shutdown and startup periods from a dataset reflecting only normal operational states of a well. Furthermore, step (b) also comprises: (b.1) adjusting units for dimensional consistency; (b.2) removing outliers; and (b.3) excluding unrepresentative operational periods.In step (c) the low-pass filter design is applied to the upstream and downstream pressure signals of the choke valve, attenuating high-frequency noise, with the cutoff frequency and filter order defined based on the desired signal characteristics. In step (f) least squares optimization strategies are applied to estimate the virtual flow rate, using correlations such as the objective function and the parameters of the choke valve characteristic curve as decision variables. Furthermore, the execution of steps (a) to (g) generates a virtual flow rate measurement that is coupled to a signal processing system and used in a PID-type feedback controller. The virtual flow rate model of step (d) comprises: (d.1) calculating a. Petition 870240111083, dated 12 / 30 / 2024, pp. 55 / 72 23 / 27 pressure difference (ΔP) from the filtered upstream and downstream pressures of step (c); (d.2) dynamically adjust the flow coefficient (Cv(z)) of the choke valve using a specific parametric model; (d.3) estimate a liquid flow rate (F) based on ΔP, Cv(z), and an adjustment factor (Fbias); (d.4) smooth an estimated flow rate (F) and a reference point (SP) using stability filters in the control; (d.5) calculate an error between the estimated flow rate (F) and the reference point (SP); and (d.6) apply the error calculated in step (d.5) to a PID controller.

[054] Furthermore, in step (d.2) the adjustment of the flow coefficient (Cv(z)) is performed based on parametric models represented by equations (2), (3), (4) and (5). In step (d.1) upstream and downstream pressure measurements (Pmon and Pjus) are smoothed using first-order low-pass filters with adjustable cutoff frequency. In step (d.4) the flow estimate (F) is smoothed by a first-order filter with an adjustable time constant between 3 and 10 times a sampling time. In addition, the reference point (SP) is smoothed by a first-order filter, without abrupt changes in the operating point. In step (d) the adjustment factor (Fbias) is parameterized correcting discrepancies between the model and the actual well data. In step (d.6) the PID controller output is added to the current choke valve position determining a new opening position. Finally, in step (d.5) the calculation of the error between the estimated flow rate (F) and the point Petition 870240111083, dated 12 / 30 / 2024, pp. 56 / 72 The 24 / 27 reference (SP) uses the smoothed values ​​of both variables.

[055] Additionally, in step (c) low-pass filters are applied to the pressure signals measured upstream and downstream of the choke valve in order to attenuate high-frequency noise that could compromise the quality of the data used in the control. The process involves defining a cutoff frequency, which determines which signal components will be preserved and which will be attenuated, ensuring that only variations relevant to the system behavior are considered. Furthermore, the filter order is adjusted to balance the smoothness and speed of the transition between the preserved and attenuated frequencies, considering the specific characteristics of the signal and the requirements of the control system. This ensures that the filtered signals are suitable for inference and control, improving the accuracy and stability of the method.

[056] Furthermore, in step (f) the virtual flow rate estimate is made using optimization strategies that utilize the least squares method. This approach adjusts the flow rate model parameters, such as the choke valve flow coefficient and an adjustment factor, to minimize the difference between the flow rate estimated by the model and reference values ​​observed during well operation. The process considers variables such as upstream pressure and Petition 870240111083, dated 12 / 30 / 2024, pp. 57 / 72 25 / 27 downstream of the choke valve, in addition to its opening, ensuring that the model accurately reflects the real behavior of the system. This adjustment is validated to ensure that the estimated flow rate is consistent and representative, even in the face of operational variations and noise, allowing for robust and efficient real-time application. Example of implementation / Tests / Results

[057] Some tests were carried out with the proposed method to verify the strategic efficiency of the model, as observed in Figure 6.

[058] Initially, the tests were validated in an ultra-deepwater offshore unit, in which an average increase of 10% in production was observed, since subsurface measurement (PDG or TPT) was required, however, these measurements were not always available. During the tests, it was validated that the method of the present invention filled this gap, using only topside measurements, as observed in Figure 7.

[059] In a case of applying the proposed method to the operation of a producing well, as observed in Figure 6, the control is performed based on a variable that represents the virtualized flow of the well, directly manipulating the choke valve to adjust the flow rate. Before activating the controller, the well in question operated with a production of approximately 420 cubic meters of oil per day. After activating the proposed method, the operators gradually increased the flow rate setpoint, while the controller precisely adjusted the choke valve, bringing production to approximately 450 cubic meters per day. Petition 870240111083, dated 12 / 30 / 2024, pp. 58 / 72 26 / 27 This increase of approximately 7% was achieved without causing significant fluctuations in the system, highlighting the efficiency and stability provided by the application of the technology.

[060] In another application of the proposed method in stabilizing a well that exhibited strong oscillations resulting from slugging, as seen in Figure 7, the robustness of the methodology in challenging scenarios was evident. Unlike the previous case, this example highlights an essential characteristic of the algorithm: its ability to mitigate instabilities already formed in the system. In this context, the proposed method not only controls production but also acts directly to suppress abrupt variations and restore the operational stability of the well. This functionality is especially relevant in operations subject to adverse dynamic conditions, reinforcing the role of technology in optimizing and ensuring the safety of production operations.

[061] Moreover, in another example of applying the claimed method in a real operational environment, as seen in Figure 10, the field efficiency of the present invention was demonstrated. In this case, the result was an increase of approximately 5% in production, achieved in a stable and controlled manner. These three examples clearly illustrate the robustness and effectiveness of the technology addressed in the present invention. The claimed method not only stands out for its ability to mitigate swells, but also for enabling increased production and optimized operational efficiency, all in a safe and stable way. This Petition 870240111083, dated 12 / 30 / 2024, pp. 59 / 72 The 27 / 27 combination of benefits reinforces the technology's potential as an advanced and reliable solution for the oil and gas industry.

[062] Furthermore, the proposed method was exhaustively validated in virtual scenarios through rigorous transient simulations, performed with the LedaFlow and Olga multiphase simulators, widely recognized for their accuracy in modeling multiphase flows under dynamic conditions. The control strategy was developed and implemented using the Python language and integrated into the simulators through a specialized plugin, which allowed efficient communication between the control algorithm and the dynamic model of the system. These validations demonstrated the effectiveness of the proposed method in handling complex flow variations, ensuring not only operational stability but also performance optimization under simulated conditions that faithfully replicate challenges faced in the field. This approach confirms the robustness and practical applicability of the technology in industrial scenarios. Petition 870240111083, dated 12 / 30 / 2024, pages 60 / 72

Claims

1 / 6 CLAIMS 1. A method for controlling anti-choke leaks using surface pressure measurements in oil and gas production, characterized in that it comprises: (a) acquiring data; (b) pre-treating the data from step (a) by standardizing them; (c) designing and applying a low-pass filter at an inlet pressure; (d) adjusting a virtual flow model; (e) validating a process gain; (f) estimating a liquid flow rate from a well based on the data from step (b), considering a type and wear of a choke valve; and (g) controlling the choke valve, by means of a PID controller, based on an error between a flow rate estimated in step (f) and a reference value (set point).

2. Method according to claim 1, characterized in that the data from step (a) comprise an upstream and a downstream pressure, a position (opening variables) of the choke valve and flow data estimated by a Multiphase Virtual Meter (MVM).

3. Method, according to claim 1, characterized in that in step (d) an optimization is performed for accurate flow estimates, including least squares and considering correlations between variables. Petition 870240111083, dated 12 / 30 / 2024, pp. 61 / 72 2 / 6 4. Method, according to claim 1, characterized in that in step (e) a flow response to the opening of the choke valve is evaluated by removing non-linear behaviors.

5. Method according to claim 1, characterized in that the adjustment of step (d) is performed in real time.

6. Method according to claim 1, characterized in that in step (a) operational data are continuously acquired minute by minute.

7. Method according to claim 1, characterized in that step (a) excludes shutdown and startup periods from a dataset reflecting only normal operational states of a well.

8. Method according to claim 1, characterized in that step (b) further comprises: (b.1) adjusting units for dimensional consistency; (b.2) removing outliers; and (b.3) excluding unrepresentative operating periods.

9. Method, according to claim 1, characterized in that in step (c) low-pass filters are applied to the pressure signals measured upstream and downstream of the choke valve, attenuating high-frequency noise; wherein a cutoff frequency is defined, which determines which signal components will be preserved and which will be attenuated, wherein only variations relevant to a system behavior are considered.

10. Method according to claim 9, characterized in that in step (c) an order of the filter is adjusted considering specific characteristics of the signal and requirements of the control system.

11. Method, according to claim 1, characterized in that in step (f), the virtual flow rate estimate is made by means of optimization strategies that use a least squares method.

12. Method according to claim 11, characterized in that in step (f), the least squares method adjusts the flow model parameters of step (d), such as a choke valve flow coefficient and an adjustment factor, minimizing a difference between the flow estimated by the model and reference values ​​observed during a well operation.

13. Method according to claim 12, characterized in that in step (f) variables such as the upstream and downstream pressure of the choke valve, as well as the choke valve opening, are considered.

14. Method, according to claim 1, characterized in that the execution of steps (a) to (g) generates a virtual flow measurement that is coupled to a Petition 870240111083, dated 12 / 30 / 2024, page 63 / 72 4 / 6 signal processing system and is used in a PID type feedback controller.

15. Method according to claim 1, characterized in that the virtual flow model of step (d) comprises: (d.1) calculating a pressure difference (AP) from the filtered upstream and downstream pressures of step (c); (d.2) dynamically adjusting the flow coefficient (Cv(z)) of the choke valve using a specific parametric model; (d.3) estimating a liquid flow rate (F) based on AP, Cv(z), and an adjustment factor (Fbias); (d.4) smoothing an estimated flow rate (F) and a reference point (SP) using stability filters in the control; (d.5) calculating an error between the estimated flow rate (F) and the reference point (SP); and (d.6) applying the error calculated in step (d.5) to a PID controller.

16. Method, according to claim 15, characterized in that in step (d.2) the adjustment of the flow coefficient (Cv(z)) is performed based on parametric models represented by the equations: Cv(z) = a + bz (2) rr \ (a+bz) Cv(z) = —— (3) Petition 870240111083, of 12 / 30 / 2024, page 64 / 72 5 / 6 ^)=-(^ J (d+ez+1) (4) (o+^z+ez^) v J (d+ez+1) (5) where z is the position of the choke valve; while a, b, c, d and e are adjustment parameters based on well conditions.

17. Method, according to claim 15, characterized in that in step (d.1) upstream and downstream pressure measurements (Pmon and Pjus) are smoothed using first-order low-pass filters with adjustable cutoff frequency.

18. Method according to claim 15, characterized in that in step (d.4) the flow rate estimate (F) is smoothed by a first-order filter with a time constant adjustable between 3 and 10 times a sampling time.

19. Method, according to claim 15, characterized in that the reference point (SP) is smoothed by a first-order filter, without abrupt changes in the operating point.

20. Method, according to claim 15, characterized in that in step (d) the adjustment factor (Fbias) is parameterized correcting discrepancies between the model and the actual well data.

21. Method, according to claim 15, characterized in that in step (d.6) the output of the PID controller is added to the current position of the choke valve, determining a new opening position.

22. Method, according to claim 15, characterized in that in step (d.5) the calculation of the error between the estimated flow rate (F) and the reference point (SP) uses the smoothed values ​​of both variables. Petition 870240111083, dated 12 / 30 / 2024, pp. 66 / 72

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