ANTI-SLUGGING CONTROL METHOD USING SURFACE PRESSURE MEASUREMENTS IN OIL AND GAS PRODUCTION

Description

CROSS-REFERENCE

This application claims priority to Brazilian Patent Application No. 1020240275314, filed Dec. 30, 2024, which is incorporated herein in its entirety by reference thereto.

FIELD

The present disclosure is within the technical field of oil engineering, more specifically it refers to the control of multiphase flow in oil and gas production systems. Even more specifically, the present disclosure relates to a method for controlling oil and gas production wells to improve operation under slugging conditions when limited instrumentation is available, i.e., using solely surface pressure measurements (at the site where the well reaches the oil, water, and gas separation and treatment plant).

BACKGROUND

Stationary Production Units (SPUs) are offshore facilities 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 discarded, while the gas is compressed to be used in oil recovery, be exported, or burned for power generation. Some of the water and gas is recirculated to the reservoir after processing at the SPU plant.

In offshore operations, a significant challenge is the slugging phenomenon, which is characterized by cyclical instabilities in multiphase flow, which affect the efficiency of producing wells and operational continuity. These self-sustaining oscillations occur in the liquid and gaseous phases within the production lines and are influenced by factors such as bathymetry of the subsea lines, system pressure, and fluid properties.

Stabilization of oil wells to slugging has been initially studied by Schmidt et al. (Z. Schmidt, J. P. Brill, D. H. Beggs, Choking can eliminate severe pipeline slugging, Oil Gas J. 12 (1979) 230-238; and Z. Schmidt, J. P. Brill, D. H. Beggs, Experimental study of severe slugging in a two-phase flow pipeline-riser system, SPE Prod. Eng. 20 (1980) 407-414), which have shown that increasing the pressure by throttling the choke valve can eliminate the phenomenon.

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. These valves are typically installed at the Christmas tree or near the processing plant and adjust the fluid pressure and flow rate, ensuring operational stability and protecting downstream equipment from overpressure or wear caused by high flow velocities. Modular choke valves allow for fine, manual or automatic adjustments and are essential for optimizing production.

Taitel (Y. Taitel, Stability of severe slugging, J. Multiph. Flow. 12 (2) (1986) 203-2017) proposed alternatives such as increasing the pressure in the gravitational separator or implementing proportional feedback control. Subsequent works, including those by Blick and Boone (E. F. Blick, L. Boone, Stabilization of naturally flowing oil Wells using feedback control, in: 56^th California Regional Meeting of SPE Held in Oakland, 1986) further developed the concept of stabilization through feedback control using downhole pressure as the measured variable and the choke valve as the manipulated variable. Despite theoretical developments, these approaches had not been validated in the field until the late 1980s.

Active slug stabilization control has been studied since the 1980s, with a broad consensus regarding its significant potential to increase production. Traditionally, this approach requires subsurface sensors, which are typically installed on the seabed (at the wellhead) or at the bottom of the production tubing (a downhole 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.

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

An alternative to subsurface sensor control strategies is the use of pressure measurements available on the platforms (surface), particularly around the choke valve. These sensors provide greater durability and the possibility of immediate maintenance. However, the direct use of these surface pressures has limited performance due to the emergence of a dynamic pattern known as inverse response. Inverse response in process control refers to a scenario where the system output initially moves in the opposite direction to what is expected when an input is applied, resulting in severe performance limitation and significantly compromising the benefits from closed loop control.

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

More recent studies have explored control structures based on secondary variables, such as estimated flow rate, to overcome the lack of subsurface sensors. Although feasible in theory, these approaches lack practical validation in industrial settings. Furthermore, multiphase flow measurements, while eliminating the inverse response in surface pressure control, require expensive and complex equipment, limiting their application in offshore installations.

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

In other words, the implementation of the proposed method includes the steps of dynamic modeling, signal processing, and controller development, allowing for the stabilization of multiphase flow even in scenarios where limited instrumentation is available, ensuring greater production efficiency, increased oil flow rate, and a reduction in unscheduled shutdowns due to overpressure in the processing plant.

In view of this, and in order to solve the aforementioned technical problem, the present disclosure provides a significant improvement in slug management by enabling the use of active control strategies under challenging operational conditions. Among the main advantages are increased production due to an improved well efficiency and higher flow rates, reduced operational downtime with fewer unscheduled shutdowns, and improved accessibility by eliminating the dependence on subsurface sensors, thereby reducing costs and operational complexity.

Furthermore, an increased well production of between 5 and 15% is observed, along with a reduction in unscheduled shutdowns, a decreased risk of overpressure in surface and subsea equipment, and a reduction in the fatigue in subsea and surface pipelines and drill heads, since it reduces the vibration and cycling caused by water slugging.

STATE OF THE ART

In the state of the art, there are methods for controlling slugging; however, no method in the state of the art combines pressure measurements with choke valve opening to virtually estimate the flow rate, without the need for subsurface sensors.

Patent document BR1020190193506, for example, refers to an anti-slug 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.

However, document BR1020190193506 differs from the present disclosure, as the present disclosure solves the problem of the inverse response of surface pressure measurements (upstream of the choke valve or inlet pressure) by using the flow rate variable instead of pressure as in BR1020190193506.

Document BR1020130305715 in turn refers to an advanced control system to automatically eliminate or minimize the occurrence of the phenomenon known as slug flow in deepwater oil production wells. The main object of BR1020130305715 is to provide a system that automatically controls and ensures the operation of a deepwater oil production well without slugging by using pressure gauges at various alternative spots along the pipeline and continuous actuation of the production choke valves, employing aggregated computational algorithms that monitor a set of operational variables.

However, document BR1020130305715 differs from the present disclosure because the present disclosure stands out for its simplicity, requiring significantly fewer measurement spots and instrumentation to solve the slugging problem than BR1020130305715. By using only surface pressure measurements and choke valve opening, the present disclosure provides a more affordable and effective solution, in particular in resource-limited environments.

Furthermore, document U.S. Pat. No. 9,982,846 refers to a method and a control system for reducing the size and/or the frequency of hydrodynamic slugging in a fluid processing system. The fluid processing system includes a pipeline for conveying produced fluids and a vessel for receiving 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.

However, document U.S. Pat. No. 9,982,846 differs from the present disclosure since the present disclosure presents a more direct approach, employing a less complex control logic (single PID instead of a cascaded strategy) for slug suppression, thereby requiring lower installation and maintenance costs and allowing for faster and more effective implementation compared to the more complicated approach of U.S. Pat. No. 9,982,846.

BRIEF DESCRIPTION

The present disclosure relates to an anti-slugging control method 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) fitting a virtual flow rate model; (e) validating a process gain; (f) estimating a well liquid flow rate 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 an estimated flow rate in step (f) and a set point. The data from step (a) comprise an upstream and downstream pressure, a choke valve position (opening variables), and flow rate data estimated by a Multiphase Virtual Meter (MVM). In step (d) an optimization is performed to obtain accurate flow rate estimates, including least-squares methods and considering correlations between variables. Furthermore, in step (e) the flow response to the choke valve opening is evaluated by removing nonlinear behaviors. Fitting in step (d) is performed in real time. In step (a) operational data is acquired continuously minute by minute. Furthermore, step (a) excludes shutdown and startup periods from a dataset reflecting only normal operational states of a well. Step (b) further 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) provides 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 fitting the flow rate 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 a bias factor (Fbias); (d.4) smoothing a flow rate estimation (F) and a set point (SP) using stability filters in the control; (d.5) calculating an error between the estimated flow rate (F) and the set point (SP); and (d.6) applying the error calculated in step (d.5) to a PID controller. Furthermore, in step (d.2) the flow rate coefficient (Cv(z)) is fitted based on parametric models represented by equations (2), (3), (4) and (5). In step (d.1) upstream and downstream pressure measurements (Pup and Pdown) 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 set point (SP) is smoothed by a first-order filter, without abrupt changes in the operating point. In step (d) the bias factor (Fbias) is parameterized by correcting discrepancies between the model and the actual well data. 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. Finally, in step (d.5) the calculation of the error between the estimated flow rate (F) and the set point (SP) uses the smoothed values of both variables.

BRIEF DESCRIPTION OF THE FIGURES

To complement the present description and provide a better understanding of the features of the present disclosure, in accordance with a preferred embodiment thereof, a set of figures is attached, which, by way of example and without limitation, illustrates its preferred embodiment.

FIG. 1 shows a graph of the inverse response of the surface pressure to a step change in the choke valve.

FIG. 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 disclosure.

FIG. 3 shows a schematics of the control strategy, where dP is the pressure drop across the choke valve, F is the virtual flow rate, SP is the desired set point, and C is the PID controller. Moreover, WCT is the wet Christmas tree, located 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 disclosure.

FIG. 4 shows the (CEO) filters of well pressure signals, the flow rate inference and the PID algorithm of the claimed method, according to a preferred embodiment of the present disclosure.

FIG. 5 shows a comparative graph of different filter time constants for the well flow rate response inferred by a step model of variation in the choke valve, according to a preferred embodiment of the present disclosure.

FIG. 6 shows a graph of the top measurement slug control strategy, wherein at spot 1, the claimed method is enabled and begins to control choke valve 2. Next, at spot 3, the operator raises the virtual flow rate setpoint resulting in increased production and, accordingly, reduced inlet pressure, as observed at spot 4, according to a preferred embodiment of the present disclosure.

FIG. 7 shows a graph of the virtual flow rate showing the open-loop and closed-loop configurations of the tests performed, illustrating the attenuation of the oscillatory behavior of the well and, accordingly, the operational improvement achieved in closed-loop operation (enabled algorithm), according to a preferred embodiment of the present disclosure.

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

FIG. 9 shows graphs demonstrating how the stationary production unit deals with the slugging problem by restricting its production. Production restriction is achieved by partially closing the choke valve, which tends to reduce well oscillations; however, as a side effect, the well operates at higher pressure and produces less oil and gas.

FIG. 10 shows a graph of the flow rate of an ultra-deepwater well showing the open loop (control off) and closed loop (control on) of tests performed in the field, such that the well flow rate is increased with no oscillations caused by slugging, demonstrating that the control strategy of the claimed method performs well in containing operational instabilities, according to a preferred embodiment of the present disclosure.

DETAILED DESCRIPTION

The present disclosure falls within the field of offshore oil exploration and production (E&P) and automated process operation technology, focusing on the description of a method for controlling oil and gas producing wells to improve operation under slugging conditions, as seen in FIGS. 8 and 9, where there is limited instrumentation available, i.e., only using pressure measurements at the surface (at the site where the well reaches the oil, water and gas separation and treatment plant).

In general, using pressure gauges available on the (surface) platforms, particularly around the choke valve, offers greater durability and the possibility of immediate maintenance. However, the direct use of these surface pressures has limited performance due to the emergence of a dynamic pattern known as inverse response, as seen in FIG. 1. Inverse response in process control refers to a scenario where the system output initially moves in the opposite direction to what is expected when an input is applied, resulting in severe performance limitation and significantly compromising the benefits from closed loop control.

Unlike surface pressure, well flow rate is a variable that does not exhibit an inverse response to the choke valve operation. Therefore, well flow rate is the ideal variable for achieving good slug suppression control performance when using surface measurements. Unfortunately, flow meters are not typically found in offshore oil and gas production facilities. This is because well flow measurements require large multiphase equipment, with negative impacts on the OPEX and CAPEX of the SPU.

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

That being said, the present disclosure infers the well liquid flow rate using the following variables: (1) opening of the choke valve, (2) upstream pressure and (3) downstream pressure of the surface choke valve, thereby there is no inverse response, as seen in FIG. 2. These variables are used in a custom single-phase flow model for the valve and allow capturing the flow dynamics of the liquid through the well choke valve. Such virtual flow rate measurement is used as a control variable for a PID that manipulates the choke valve to operate the well and control slugging. Thus, the flow rate inference of the present disclosure allows for the estimation of the flow rate dynamics of a slugging well, and the signal processing allows the removal of measurement noise and the filtering of dynamics so that their responses have speeds compatible with the field equipment. Furthermore, the controller used is fed by inference and processed signals, responding with actions that are applied to the production choke valve. The inference set, signal processing filters, and controller are orchestrated together, generating a computer-implemented method for anti-slugging control with surface pressure measurements in oil and gas production systems.

In other words, the present disclosure makes use of a customized model to infer the virtual flow rate based on surface pressure measurements and choke valve opening. This provides for flow stabilization under conditions where multiphase flow meters and/or subsea sensors are not available, significantly reducing implementation and maintenance costs. Therefore, 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 control systems and tends to reduce the achievable performance of the control strategy. Virtual flow inference eliminates this problem by providing a more robust and efficient control strategy. In other words, the present disclosure is distinguished by its ability to address the problem of inverse response in surface measurements, resulting in greater efficiency. Since the inverse response in producing wells can have different characteristics under different conditions, the design complexity of a control system using pressure directly increases. In general terms, the claimed method is simpler, as it requires significantly fewer measurement spots and instrumentation to solve the slugging problem. By using only surface pressure measurements and choke valve opening, the present disclosure provides a more affordable and effective solution, in particular in resource-limited environments. This approach makes the technology easier to implement and maintain, increasing its applicability in a variety of scenarios.

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

In particular, the present disclosure 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 ) ⁢ Δ ⁢ P + Fbias ( 1 )

• • wherein:
  • F(z) is the liquid flow rate of the well;
  • Cv(z) is the flow rate coefficient of the choke valve;
  • ΔP is the pressure drop at the choke valve; which is the difference between the upstream pressure (P_up) and the downstream pressure (P_down) of the valve; and Fbias is a bias factor that allows the model to be adjusted to the well field data.

The choke valve flow coefficient Cv(z) is a characteristic that depends on the type of valve being installed and the wearing the valve has suffered over the years of operation. Therefore, Cv(z) should be parameterized on a case-by-case basis. The recommended models for the 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 ) ( d + ez + 1 ) ( 4 ) v ⁡ ( z ) = ( a + bz + cz 2 ) ( d + ez + 1 ) ( 5 )

The estimated flow rate F is then compared with a reference flow rate value designated as the set point (SP), generating an error that is fed into a PID controller (C). Such PID algorithm, commonly used in the industry, calculates a control action to minimize the error between F and SP, generating a new position in the choke valve opening, which is a simplified control strategy, as seen in FIG. 3.

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

Also, the control strategy includes a set of pressure signal filters to mitigate inherent measurement noise and smooth control actions through virtual flow rate filtering. The well flow rate 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.

The cited filters consist of four filters, as shown in FIG. 4. In this context, two filters at the measured pressures P_up and P_down are intended to remove measurement noise from the sensors. Filters for these variables are designated as CEO1 and CEO2 and can be first-order filters, which are also designated as low-pass filters. Higher-order filters or filters based on other techniques can also be used to clean the signal used to calculate ΔP, losing as little information as possible about the prevailing dynamics.

After filtering P_up and P_down, the ΔP calculation is performed, which corresponds to the difference between the filtered P_up and P_down. This value is inserted in Equation (1) along with the calculation of Cv(z) which is carried out by one of the variables from Equations (2), (3), (4) and (5). In these calculations, z is the opening of the chole valve measured in the field at the same time as P_up and P_down are measured.

Once the flow rate F is calculated, its value passes through the algorithm's third filter, block 1/TF, as seen in FIG. 4. The behavior of the estimated flow rate shows a significant instantaneous abrupt variation when the choke valve opening changes, resulting from equation (1), as seen in FIG. 5. Such behavior also poses difficulties to the controller and should be mitigated. Here again, a first-order filter is recommended, aiming to make the response smoother. Thus, a filter with a time constant of 3 to 10 sampling times is sufficient, as seen in FIG. 5.

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

The error between the filtered values of SP and F feeds a PID controller that calculates an output Au which, in addition to the z-position of the choke valve, generates a new opening position for this valve, as seen in FIG. 4.

Specifically, the present disclosure relates to a method of anti-slugging control using surface pressure measurements in oil and gas production comprising performing the following general steps of (a) acquiring data, wherein information is collected from the operation of the sampling well at least minute by minute. The variables that must be acquired are the choke valve opening, and the upstream and downstream pressure of this valve. Additionally, for the parameterization of Equation (1), well flow rate data must be collected. This data could come from a multiphase meter from another more rigorous dynamic model, or well testing. The data acquisition period should represent the largest number of normal operational states of the well, with shutdowns and start-ups being removed from the dataset; Step (b), Data Pre-processing, in this step the variable units 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 stage (b) of data pre-processing, outliers and non-representative operating periods are removed; step (c) of designing the filters and flow inference, wherein the objective is to apply a low-pass filter to the pressure signal (Pup and Pdown), so that high-frequency measurement noise is minimized. To this end, the cutoff frequency must be defined, which is the frequency above which the filter will begin to attenuate the signal. This should be determined based on the signal frequencies to be preserved and the noise frequencies to be removed. Furthermore, the filter order must be defined so that the cutoff degree to be applied in the transition between the pass-through and the attenuation can be chosen. Higher-order filters have faster transitions, but are more complex. After designing the pressure filters, the virtual flow rate model must be fitted. The estimate can be carried out using a least squares optimization strategy, using correlation as the profit variable and the CV function parameters as decision variables. Finally, it is important to validate the flow rate estimation model behavior to assess consistency of the result, the phase of oscillation in relation to the actual behavior, and to ensure monotonic flow behavior in relation to the opening of the choke valve. Finally, the SP filter can be first order according to the accepted velocity in the well status transition; step (g) of designing the PID, wherein the virtual flow rate response to stimuli in the choke valve is modeled. To achieve this, a transfer function or a space representation of the well dynamics status can be identified. Next, 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, fitting parameters as required to handle unmodeled dynamics or changes in the operating conditions.

In particular, the claimed 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 rate model; (e) validating a process gain; (f) estimating a well liquid flow rate 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 an estimated flow rate in step (f) and a set point. The data from step (a) comprise an upstream and downstream pressure, a choke valve position (opening variables), and flow rate data estimated by a Multiphase Virtual Meter (MVM). In step (d) an optimization is performed to obtain accurate flow rate estimates, including least-squares methods and considering correlations between variables. Furthermore, in step (e) the flow response to the choke valve opening is evaluated by removing nonlinear behaviors. Fitting in step (d) is performed in real time. In step (a) operational data is acquired continuously minute by minute. Furthermore, step (a) excludes shutdown and startup periods from a dataset reflecting only normal operational states of a well. Step (b) further 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) provides 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 rate 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 a bias factor (Fbias); (d.4) smoothing a flow rate estimation (F) and a set point (SP) using stability filters in the control; (d.5) calculating an error between the estimated flow rate (F) and the set point (SP); and (d.6) applying the error calculated in step (d.5) to a PID controller.

Furthermore, in step (d.2) the flow rate coefficient (Cv(z)) is fitted based on parametric models represented by equations (2), (3), (4) and (5). In step (d.1) upstream and downstream pressure measurements (P_up and P_down) 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 set point (SP) is smoothed by a first-order filter, without abrupt changes in the operating point. In step (d) the bias factor (Fbias) is parameterized by correcting discrepancies between the model and the actual well data. 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. Finally, in step (d.5) the calculation of the error between the estimated flow rate (F) and the set point (SP) uses the smoothed values of both variables.

Also, 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 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 that are relevant to the system's behavior are considered. Furthermore, the filter order is adjusted to balance the smoothness and speed of the transition between preserved and attenuated frequencies, taking specific characteristics of the signal and the control system requirements into account. This ensures that the filtered signals are suitable for inference and control, improving the accuracy and stability of the method.

Furthermore, in step (f) the virtual flow rate estimate is made using optimization strategies that use the least squares method. This approach fits the flow rate model parameters, such as the choke valve flow rate coefficient and a bias factor, to minimize the difference between the flow rate estimated by the model and reference values observed during operation of the well. The process considers variables such as the upstream and downstream pressure of the choke valve, as well as its opening, ensuring that the model accurately reflects the actual behavior of the system. Such fitting is validated to ensure that the estimated flow rate is consistent and representative, even in the face of operational variations and noise, allowing for a robust and efficient real-time application.

Example of Implementation/Tests/Results

Several tests were carried out using the claimed method to assess the strategic efficiency of the model, as shown in FIG. 6.

Initially, the tests were validated in an ultra-deepwater offshore unit, where 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 testing, it was validated that the method of the present disclosure filled this gap, using only topside measurements, as observed in FIG. 7.

In one instance of applying the claimed method to the operation of a producing well, as observed in FIG. 6, 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. Prior to controller activation, the well in question was operating with a yield of about 420 cubic meters of oil per day. After actuation of the claimed 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. Such an increase of about 7% was achieved without causing significant fluctuations in the system, highlighting the efficiency and stability provided by the application of the technology.

In another application of the proposed method to stabilize a well exhibiting strong oscillations resulting from slugging, as seen in FIG. 7, the robustness of the methodology in challenging scenarios was evident. Unlike the previous case, this example highlights an essential feature of the algorithm: its ability to mitigate instabilities already formed in the system. In this context, the claimed 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 the technology in optimizing and ensuring the safety of production operations.

Furthermore, in another example of the application of the claimed method in a real operational environment, as seen in FIG. 10, the field efficiency of the present disclosure was demonstrated. In such an instance, the result was an approximately 5% increase in production, which was achieved in a stable and controlled manner. These three examples clearly illustrate the robustness and effectiveness of the technology discussed in the present disclosure. The claimed method stands out not only for its ability to mitigate slugs, but also for enabling increased production and optimized operational efficiency, all in a safe and stable manner. This combination of benefits reinforces the technology's potential as an advanced and reliable solution for the oil and gas industry.

Furthermore, the proposed method was exhaustively validated in virtual scenarios through rigorous transient simulations performed with the LedaFlow and Olga multiphase simulators that are widely recognized for their accuracy in modeling multiphase flows under dynamic conditions. The control strategy was developed and implemented using the Python programming language and integrated into the simulators via a specialized plugin, which allowed for efficient communication between the control algorithm and the system's dynamic model. These validations demonstrated the effectiveness of the proposed method in handling complex flow variations, ensuring not only operational stability but also optimized performance under simulated conditions that fully replicate the challenges found in the field. This approach confirms the robustness and practical applicability of the technology in industrial settings.