Oil reservoir recovery ratio prediction method based on spontaneous imbibition of surfactant

Abstract

The invention provides an oil reservoir recovery ratio prediction method based on spontaneous imbibition of a surfactant, and relates to the technical field of oilfield development recovery prediction.The method comprises the steps that based on a spontaneous imbibition oil displacement experiment of the surfactant, the putting amount of the surfactant and the crude oil production amount in unit imbibition time are recorded; an imbibition recovery curve is delimited in combination with oil reservoir reserves and exploitation duration, a surfactant adsorption migration model is established, oil reservoir parameters and surfactant concentration changes are associated, the actual and ideal concentration difference of each subinterval is calculated, a recovery coefficient is generated, the recovery curve of each subinterval is corrected according to the recovery coefficient, and the corrected petroleum pump output is obtained. And accumulating the petroleum pump-out quantities of all the subintervals, and calculating the overall petroleum pump-out quantity and the recovery ratio in combination with the oil reservoir reserves. According to the method, the surfactant adsorption migration model is established, and experimental data are combined, so that accurate prediction of the spontaneous imbibition oil displacement process of the oil reservoir surfactant and effective evaluation of the recovery ratio are realized.

Description

Technical Field

[0001] This invention relates to the field of oilfield development and recovery prediction technology, specifically to a method for predicting reservoir recovery rate based on the spontaneous adsorption of surfactants. Background Technology

[0002] The method for predicting reservoir recovery based on the spontaneous infiltration of surfactants aims to improve oil recovery by utilizing surfactants to reduce oil-water interfacial tension and improve oil-water flow characteristics, thereby promoting the spontaneous infiltration of the aqueous phase into reservoir pores. This technology helps to expel residual crude oil by altering capillary action and oil-water interactions within the reservoir. To optimize the application of this technology, researchers have established various prediction methods, including experimental data regression models, numerical simulation models, and mechanistic analysis models, combined with reservoir rock and fluid characteristics, to predict the optimal surfactant application strategy for achieving more efficient reservoir recovery enhancement.

[0003] Existing oil recovery prediction methods often rely on empirical formulas or single oil displacement mechanisms, making it difficult to accurately reflect the dynamic adsorption behavior of surfactants in complex reservoirs and their impact on reservoir oil displacement efficiency. In particular, under the interaction of multiple factors such as reservoir wettability, porous media characteristics, and surfactant adsorption and migration, traditional methods struggle to accurately predict crude oil production and recovery rates. Furthermore, the recovery curves used in traditional oil recovery methods often ignore the effects of reservoir heterogeneity and dynamic changes in oil displacement, resulting in inaccurate values ​​of oil pumped out, which in turn affects the oil recovery rate.

[0004] Therefore, it is necessary to provide a reservoir recovery prediction method based on the spontaneous adsorption of surfactants to solve the aforementioned problem.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for predicting oil recovery based on the spontaneous adsorption of surfactants, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A method for predicting reservoir recovery based on spontaneous adsorption of surfactants, comprising the following steps: Step 1: Obtain the geological parameters, wettability parameters, reservoir crude oil physical property parameters, and core samples of the reservoir to be predicted. Use the core samples to conduct a spontaneous adsorption oil displacement experiment with surfactants. Record the amount of surfactant added and the amount of crude oil extracted per unit adsorption recovery time under the same pressure. The geological parameters include the porosity and permeability of the core, the wettability parameters include the contact angle, and the reservoir crude oil physical property parameters include the crude oil density and crude oil viscosity. Step 2: Determine the initial concentration of the surfactant, and delineate the spontaneous seepage recovery curve based on the reservoir geological reserves and the crude oil production time requirements. Divide the crude oil production time into sub-intervals of integer units of seepage recovery time to obtain the seepage recovery curve for each sub-interval. Step 3: Establish a surfactant adsorption and migration model, correlate the relevant parameters of the reservoir to be predicted with the indicators affecting surfactant concentration, analyze the difference between the surfactant concentration change and the ideal surfactant concentration change in each sub-interval and generate the recovery coefficient, and perform coefficient correction on the percolation recovery curve of each sub-interval based on the recovery coefficient in each sub-interval to obtain the oil pumping rate of each interval. Step 4: Calculate the total oil pumping volume under the requirements of reservoir geological reserves and crude oil production time based on the oil pumping volume of each divided interval, and determine the reservoir recovery rate of spontaneous adsorption of surfactants in combination with the reservoir geological reserves.

[0008] Furthermore, the spontaneous seepage recovery curve was defined, dividing the crude oil extraction time into sub-intervals of integer units of seepage recovery time. The method used was as follows: Based on the reservoir geological reserves and the required crude oil production duration, a spontaneous seepage recovery curve is defined. The horizontal axis of the spontaneous seepage recovery curve represents time, and the vertical axis represents the real-time crude oil production at the wellhead. The expression for the defined spontaneous seepage recovery curve is as follows: ; in, This represents the spontaneous infiltration harvesting curve. Indicates the geological reserves of the oil reservoir. Indicates the required duration of crude oil extraction. This indicates the maximum crude oil production at the wellhead. Indicates the curve steepness coefficient. For time variables, .

[0009] Furthermore, the crude oil extraction time is adjusted by dividing it into sub-intervals of integer units of infiltration recovery time. The method used is as follows: When the crude oil extraction time is an integer multiple of the unit seepage recovery time, no adjustment to the extraction time is needed; the extraction time is simply divided equally. When the crude oil extraction time is not an integer multiple of the unit seepage recovery time, the number of integer unit operating time periods is calculated and rounded up. Then, the adjusted total time is calculated by dividing the number of integer time periods by the unit seepage recovery time period. The formula used is as follows: ; in, Indicates rounding up. This represents the number of unit running time segments after rounding up. This indicates the set unit infiltration harvesting time. Let be the index of the sub-interval after partitioning, and .

[0010] Furthermore, a surfactant adsorption and migration model was established based on the following method: Based on multimodal feature correlation, the geological parameters, wettability parameters, and reservoir crude oil physical properties of the reservoir to be predicted are correlated with indicators affecting surfactant concentration. These indicators include effective diffusion coefficient, average percolation velocity, and adsorption coefficient. The geological parameters include core porosity and permeability. The wettability parameters include contact angle. The reservoir crude oil physical properties include crude oil density and viscosity. The formula used for multimodal feature correlation is: ; in, , , The indicators that affect surfactant concentration are the effective diffusion coefficient, average percolation velocity, and adsorption coefficient. For penetration rate, For crude oil viscosity, This is an exponential effect function of porosity. Porosity Contact angle, For crude oil density, The density of water, It is the acceleration due to gravity; The partial differential equation of surfactant concentration under the influence of effective diffusion coefficient, average percolation velocity, and adsorption coefficient is established based on the Advagesion-Dispersion-Adsorption model. The formula used is as follows: ; in, This represents the partial differential equation for surfactant concentration established based on the model. This indicates the instantaneous concentration of the surfactant in the core pores. These are spatial coordinates, representing the location within the rock core. This represents the density of the rock skeleton.

[0011] Furthermore, the variation in surfactant concentration within each sub-interval was analyzed, and the corresponding percolation recovery curves for each sub-interval were corrected based on the recovery coefficient within each sub-interval. The method used was as follows: Based on the surfactant experiment from the initial entry into the core to its effluent, the initial experimental condition was set as follows: the core initially contained no surfactant. The inlet boundary is where the surfactant is continuously input at a concentration of [missing information]. The export boundary condition is set to a finite concentration at the far end, i.e. ; Solving the partial differential equation for surfactant concentration using the Laplace transform, the analytical expression for the change of surfactant concentration over time due to the combined effects of diffusion, convection, and adsorption is: ; in, An analytical expression representing the surfactant concentration, used to calculate... The concentration of surfactant at any given time, Represents the complementary error function; The concentration variation of surfactant in each sub-interval after division is analyzed using a functional analytical expression based on surfactant concentration, and the coefficients of the percolation recovery curves in each sub-interval are corrected. The formula used is as follows: ; in, Indicates the first The change in surfactant concentration in each sub-interval Indicates the first Crude oil production in each sub-region; Define the ideal concentration change of surfactant in each sub-interval The difference between the ideal concentration change and the calculated concentration change in each sub-interval is analyzed to generate the recovery factor. The corresponding crude oil extraction volume is then corrected based on the recovery factor for each sub-interval to obtain the oil pumping volume for each interval. The formula used is as follows: ; ; in, Indicates the first Harvesting coefficient of each sub-interval, Indicates the first Oil pump output of each sub-section.

[0012] Furthermore, the method used to determine the reservoir recovery rate based on spontaneous surfactant adsorption is as follows: The total oil pumping output is calculated based on the total number of sub-intervals and the corresponding oil pumping output of each sub-interval, using the following formula: ; ; in, This indicates the total oil pump output. This represents the predicted reservoir recovery rate.

[0013] Compared with the prior art, the beneficial effects of the present invention are: This invention addresses the common problem in existing oil recovery prediction methods that it is difficult to accurately reflect the spontaneous adsorption behavior of surfactants in the reservoir and their impact on oil displacement. By introducing a mathematical description of the surfactant adsorption and migration process, combined with recovery curves obtained from oil displacement experiments and concentration correction techniques, this invention solves the shortcomings of traditional models in considering the dynamic distribution and adsorption effects of surfactants, thereby improving the accuracy and reliability of oil recovery prediction. Furthermore, this invention effectively addresses the complexities caused by reservoir heterogeneity and dynamic changes in oil displacement through interval division and segmented recovery coefficient calculation, enabling refined prediction of recovery rates for different reservoir intervals. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the overall method flow of the present invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0016] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0017] Example: Please see Figure 1 A method for predicting reservoir recovery based on spontaneous adsorption of surfactants, comprising the following steps: Step 1: Obtain the geological parameters, wettability parameters, reservoir crude oil physical properties, and core samples of the reservoir to be predicted. Conduct spontaneous adsorption oil displacement experiments using the core samples. Record the amount of surfactant added and the amount of crude oil extracted per unit adsorption recovery time under the same pressure. The geological parameters include the porosity and permeability of the core sample, the wettability parameters include the contact angle, and the reservoir crude oil physical properties include the crude oil density and crude oil viscosity.

[0018] In a specific embodiment of this invention, representative core samples are collected from the reservoir to be predicted to ensure that the samples can better reflect the geological characteristics of the reservoir. Detailed physical parameter measurements are performed on these core samples, including porosity and permeability tests, typically using a helium porosimeter and permeability meter for precise measurements. Simultaneously, the wettability parameters of the core samples are measured, primarily by measuring the contact angle between the rock surface and the water-oil interface using a contact angle meter to assess the wettability of the reservoir. Next, crude oil samples from the reservoir are collected, and their physical parameters, including density and viscosity, are measured using a dedicated densitometer and rotational viscometer to ensure the accuracy and representativeness of the data. Subsequently, using the obtained core samples, a spontaneous adsorption oil displacement experimental device with surfactants is constructed in the laboratory. During the experiment, constant pressure conditions similar to those of the reservoir are maintained, and a surfactant solution of predetermined concentration and dosage is injected to simulate the actual oil displacement process. Within a set unit adsorption recovery time, the amount of surfactant added and the corresponding crude oil recovery are monitored and recorded in real time.

[0019] Step 2: Determine the initial concentration of the surfactant, and determine the spontaneous seepage recovery curve based on the reservoir geological reserves and the required crude oil production time. Divide the crude oil production time into sub-intervals of integer units of seepage recovery time to obtain the seepage recovery curve for each sub-interval.

[0020] In a specific embodiment of the present invention, the initial concentration of the surfactant is first determined to ensure the accuracy of the concentration parameters of the oil displacement agent used in the experiment and prediction process. This lays the foundation for simulating the spontaneous adsorption behavior of surfactants in the reservoir. Furthermore, combined with the reservoir geological reserves and the requirements of crude oil production time, the overall oil displacement process is divided into several sub-intervals of integer units of adsorption and recovery time, which can refine the dynamic changes of the oil displacement process. This segmented processing method enables the model to capture the changing law of surfactant oil displacement performance at different time stages. By defining the spontaneous adsorption and recovery curve and subdividing the sub-intervals, the model can not only reflect the dynamic evolution of the recovery rate over time during the oil displacement process, but also adjust the oil displacement scheme for different time periods, optimize the surfactant injection strategy, and achieve more efficient reservoir development and management.

[0021] Furthermore, the spontaneous seepage recovery curve was defined, dividing the crude oil extraction time into sub-intervals of integer units of seepage recovery time. The method used was as follows: Based on the reservoir geological reserves and the required crude oil production duration, a spontaneous seepage recovery curve is defined. The horizontal axis of the spontaneous seepage recovery curve represents time, and the vertical axis represents the real-time crude oil production at the wellhead. The expression for the defined spontaneous seepage recovery curve is as follows: ; in, This represents the spontaneous infiltration harvesting curve. Indicates the geological reserves of the oil reservoir. Indicates the required duration of crude oil extraction. This indicates the maximum crude oil production at the wellhead. Indicates the curve steepness coefficient. For time variables, ; In the above-described spontaneous seepage recovery curve, the logistic function is used. Because the logistic function has an "S"-shaped curve characteristic, it can well describe the dynamic changes in crude oil production during the oil displacement process: slow initial growth, rapid increase in the middle stage, and stabilization in the later stage. This basically matches the actual oil reservoir development and recovery process, avoiding the neglect of the phased effects of increasing and decreasing displacement agents by linear or simple exponential models. The reservoir geological reserves in the formula... and mining duration requirements Using these two parameters, the formula directly links the reservoir's resource situation with the production cycle, ensuring that the total recovery rate does not exceed the reservoir's maximum recoverable reserves. It also allows for adjustments to the pace and intensity of the seepage process based on the development plan, while setting the maximum crude oil production rate at the wellhead. This is a practical constraint in oilfield development. The formula uses the curve steepness coefficient to... Incorporating the harvest curve ensures that the curve does not exhibit unrealistic instantaneous high yields, reflecting the capacity limits of the actual production system; In curve steepness coefficient In its design, this expression integrates core factors such as maximum production, geological reserves, and duration to determine the steepness of the curve's ascent and descent. This makes the recovery process more closely resemble the actual oilfield's production capacity utilization rate, avoiding unreasonable situations where the curve is too flat or too steep. This is achieved by adjusting the curve steepness coefficient. The control mechanism can flexibly adapt to the oil displacement process under different reservoir and wellhead conditions.

[0022] It should be noted that the reason for adjusting the crude oil extraction time is primarily to standardize the time scale and the unit percolation recovery time. The total production time is typically determined based on the optimal time scale established by reservoir characteristics, oil displacement mechanisms, and engineering practices. Dividing the time into integer multiples helps ensure that the time length of each sub-interval is completely consistent, facilitating the analysis and control of the recovery process at a uniform pace. Furthermore, reservoir development simulation and real-time monitoring often use discrete time steps to handle problems. If the production time is not an integer multiple of the unit time, the inconsistent length of the last time interval will increase the computational complexity and the difficulty of handling boundary conditions, and may even affect numerical stability. This rounding-up division method ensures that the spontaneous seepage recovery curves between each sub-interval are continuous, avoiding abrupt changes or discontinuities in recovery caused by non-integer multiple division.

[0023] Therefore, it is necessary to adjust the crude oil extraction time by dividing it into sub-intervals of integer units of infiltration recovery time. The method used is as follows: When the crude oil extraction time is an integer multiple of the unit seepage recovery time, no adjustment to the extraction time is needed; the extraction time is simply divided equally. When the crude oil extraction time is not an integer multiple of the unit seepage recovery time, the number of integer unit operating time periods is calculated and rounded up. Then, the adjusted total time is calculated by dividing the number of integer time periods by the unit seepage recovery time period. The formula used is as follows: ; in, Indicates rounding up. This represents the number of unit running time segments after rounding up. This indicates the set unit infiltration harvesting time. Let be the index of the sub-interval after partitioning, and .

[0024] Step 3: Establish a surfactant adsorption and migration model, correlate the relevant parameters of the reservoir to be predicted with the indicators affecting surfactant concentration, analyze the difference between the surfactant concentration change and the ideal surfactant concentration change in each sub-interval, and generate the recovery coefficient. Based on the recovery coefficient in each sub-interval, perform coefficient correction on the percolation recovery curve of each corresponding sub-interval to obtain the oil pumping rate of each interval.

[0025] In a specific embodiment of the present invention, a quantitative model with clear physical meaning and quantifiable parameters is constructed by combining reservoir geological parameters, wettability parameters, and reservoir crude oil physical property parameters with key influencing indicators of surfactant concentration through a multimodal feature correlation method. By establishing a partial differential equation based on convection-diffusion-adsorption, the transport and dynamic change process of surfactant in the reservoir pore space is accurately described. By quantifying different key influencing indicators, the changes in crude oil production in different sub-intervals are indirectly reflected.

[0026] Furthermore, a surfactant adsorption and migration model was established based on the following method: Based on multimodal feature correlation, the geological parameters, wettability parameters, and reservoir crude oil physical properties of the reservoir to be predicted are correlated with indicators affecting surfactant concentration. These indicators include effective diffusion coefficient, average percolation velocity, and adsorption coefficient. The geological parameters include core porosity and permeability. The wettability parameters include contact angle. The reservoir crude oil physical properties include crude oil density and viscosity. The formula used for multimodal feature correlation is: ; in, , , The indicators that affect surfactant concentration are the effective diffusion coefficient, average percolation velocity, and adsorption coefficient. For penetration rate, For crude oil viscosity, This is an exponential effect function of porosity. Porosity Contact angle, For crude oil density, The density of water, It is the acceleration due to gravity; In the above multimodal feature correlation formula, the effective diffusion coefficient This reflects the degree of diffusion and penetration of the surfactant. The larger, The larger the porosity, the more open the oil reservoir's pore structure, and the stronger the diffusion ability of surfactants; while porosity... The nonlinear effect of pore structure on diffusion was demonstrated by adjusting the exponential function. The larger, The larger the value, the more numerous and interconnected the rock pore spaces, which is conducive to the free diffusion of surfactant molecules in the rock pores; It's the viscosity of crude oil. The larger, The smaller the value, the better it conforms to the physical law of viscosity hindering diffusion in fluid dynamics. The formula as a whole conforms to Darcy's law and the basic principles of diffusion mechanism. Average Immersion Flow Rate Taking into account permeability, viscosity, porosity, contact angle, and gravity driving force generated by density difference, the flow velocity is proportional to permeability. The influence of viscosity and porosity reasonably reflects the coupling relationship between fluid flow resistance and reservoir porosity characteristics. The cosine function of contact angle reflects the regulating effect of wettability on oil displacement flow velocity. Density difference and gravitational acceleration reflect the natural driving force of infiltration and adsorption. The formula as a whole conforms to fluid mechanics and multiphase flow theory. Adsorption coefficient By contact angle To reflect the influence of wettability on surfactant adsorption behavior, the larger the contact angle, the more hydrophobic the rock surface, and the more significant the change in surfactant adsorption characteristics. The tangent function form shows the nonlinear relationship between the adsorption coefficient and the wettability parameter, which is consistent with the experimental and theoretical analysis results.

[0027] The partial differential equation of surfactant concentration under the influence of effective diffusion coefficient, average percolation velocity, and adsorption coefficient is established based on the Advagesion-Dispersion-Adsorption model. The formula used is as follows: ; in, This represents the partial differential equation for surfactant concentration established based on the model. This indicates the instantaneous concentration of the surfactant in the core pores. These are spatial coordinates, representing the location within the rock core. Density of the rock skeleton; In the partial differential equation for surfactant concentration established above, This part represents the diffusion term related to the concentration gradient and the effective diffusion coefficient. This value characterizes the diffusion ability of surfactants in the pores of the core. A higher value indicates a stronger diffusion ability of surfactant molecules through the pores. This contributes to a more uniform concentration distribution along the spatial direction, reducing the concentration gradient, i.e., the surfactant diffuses from high-concentration areas to low-concentration areas, while increasing... It will accelerate the diffusion rate of concentration, help the surfactant to be distributed more evenly in the pores, and promote the oil displacement effect; This part represents the convection term related to the average osmotic velocity and concentration gradient. This describes the migration rate of surfactants in porous media, reflecting the convection process. This item represents the transport of surfactants along the flow direction in the core. The higher the flow rate, the faster the surfactants migrate. This part plays a "transporting" role in the surfactant concentration distribution, causing the concentration peak to shift over time, affecting the spatial distribution of diffusion and adsorption. Furthermore, by adjusting the flow rate, the transport efficiency of surfactants to the depths of the reservoir can be controlled. This section describes the adsorption consumption term, which is related to the concentration gradient and describes the concentration decay of the surfactant due to adsorption onto the rock surface. It is the "consumption" term of concentration. The larger the size, the stronger the adsorption capacity. This increases the amount of surfactant adsorbed by the rock, reducing the effective concentration in the pores. The adsorption effect reduces the activity and effective concentration of surfactant in the mobile phase, affecting the actual utilization rate of the oil displacement agent.

[0028] It should be noted that the reason for calculating the recovery coefficient of each sub-interval to correct the percolation recovery curve of the corresponding sub-interval is that the surfactant concentration directly affects its ability to reduce the interfacial tension between oil and water, change wettability, and improve the fluidity of oil and water, thereby affecting the oil displacement efficiency and recovery rate. In an ideal state, a surfactant concentration range should correspond to a certain recovery effect. However, in actual oil reservoirs, due to complex factors such as geological heterogeneity, fluid interaction, and adsorption loss, the actual concentration distribution deviates from the ideal distribution.

[0029] Therefore, it is necessary to analyze the variation of surfactant concentration in each sub-interval and to correct the coefficients of the percolation recovery curves for each sub-interval based on the recovery coefficients within each sub-interval. The method used is as follows: Based on the surfactant experiment from the initial entry into the core to its effluent, the initial experimental condition was set as follows: the core initially contained no surfactant. The inlet boundary is where the surfactant is continuously input at a concentration of [missing information]. The export boundary condition is set to a finite concentration at the far end, i.e. ; Solving the partial differential equation for surfactant concentration using the Laplace transform, the analytical expression for the change of surfactant concentration over time due to the combined effects of diffusion, convection, and adsorption is: ; in, An analytical expression representing the surfactant concentration, used to calculate... The concentration of surfactant at any given time, Represents the complementary error function; In the above analytical expression for the surfactant concentration, This section presents the classical analytical solution form of the standard one-dimensional convection-diffusion equation in the absence of adsorption, describing a velocity-dependent... The surfactant concentration wavefront moving in the positive direction It is a complementary error function, derived from the solution of the diffusion equation, reflecting the spatial diffusion expansion and smooth transition of concentration. This part represents the spatial broadening that balances the shift of convection and diffusion: when At this time, the surfactant concentration begins to rise significantly, reflecting the wavefront position; when When the surfactant concentration approaches zero, it meets the far-end boundary condition; when At this point, the surfactant concentration approaches the inlet concentration. Exponential decay term This mainly reflects the effect of adsorption on the time decay of concentration, as time... As the concentration of surfactant increases, adsorption gradually consumes it, leading to an exponential decrease in its concentration in the porous aqueous phase.

[0030] It should be noted that this expression is a known analytical solution combining the classical convection-diffusion equation with a first-order attenuation source. It satisfies the partial differential equation and the given initial boundary conditions. When solving based on the Laplace transform method, for linear equations with constant coefficients, this type of analytical solution can be obtained. Furthermore, the appearance of the complementary error function is a manifestation of the standard solution of diffusion problems, and its form conforms to the mathematical characteristics of the diffusion process.

[0031] The concentration variation of surfactant in each sub-interval after division is analyzed using a functional analytical expression based on surfactant concentration, and the coefficients of the percolation recovery curves in each sub-interval are corrected. The formula used is as follows: ; in, Indicates the first The change in surfactant concentration in each sub-interval Indicates the first Crude oil production in each sub-region; Define the ideal concentration change of surfactant in each sub-interval The difference between the ideal concentration change and the calculated concentration change in each sub-interval is analyzed to generate the recovery factor. The corresponding crude oil extraction volume is then corrected based on the recovery factor for each sub-interval to obtain the oil pumping volume for each interval. The formula used is as follows: ; ; in, Indicates the first Harvesting coefficient of each sub-interval, Indicates the first Oil pump output of each sub-section; in the above calculation of the recovery factor In the formula, by introducing the recovery factor, the actual change in surfactant concentration within a certain range can be quantitatively characterized. and The difference between the two values ​​reflects the degree of deviation of the oil displacement effect in that range. The closer the recovery coefficient is to 1, the closer the actual oil displacement effect is to the ideal state. The further the recovery coefficient deviates from 1, the more the oil displacement effect in that range is affected by factors such as insufficient concentration, adsorption loss, and seepage resistance, resulting in a decrease in actual recovery efficiency.

[0032] Step 4: Calculate the total oil pumping volume under the requirements of reservoir geological reserves and crude oil production time based on the oil pumping volume of each divided interval, and determine the reservoir recovery rate of spontaneous adsorption of surfactants in combination with the reservoir geological reserves.

[0033] In a specific embodiment of the present invention, the significance of calculating and summing the oil pumping output by dividing the reservoir into multiple sub-regions lies in the fact that oil reservoirs often exhibit geological heterogeneity, including uneven distribution of porosity, permeability, displacement agent concentration, and adsorption characteristics. Dividing the reservoir or experimental core into multiple sub-regions and calculating the oil pumping output of each sub-region separately can capture the differences in oil displacement efficiency in different areas, and a recovery factor is introduced for each sub-region. Oil production after adjusting for the actual impact of surfactant concentration This ensures that the production forecast for each zone is more consistent with the actual oil displacement effect.

[0034] The method used to determine reservoir recovery from spontaneous surfactant adsorption is as follows: The total oil pumping output is calculated based on the total number of sub-intervals and the corresponding oil pumping output of each sub-interval, using the following formula: ; ; in, This indicates the total oil pump output. This represents the predicted reservoir recovery rate.

[0035] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0036] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0037] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0038] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for predicting reservoir recovery based on spontaneous adsorption of surfactants, characterized in that, The specific steps include: Step 1: Obtain the geological parameters, wettability parameters, reservoir crude oil physical property parameters, and core samples of the reservoir to be predicted. Use the core samples to conduct a spontaneous adsorption oil displacement experiment with surfactants. Record the amount of surfactant added and the amount of crude oil extracted per unit adsorption recovery time under the same pressure. The geological parameters include the porosity and permeability of the core, the wettability parameters include the contact angle, and the reservoir crude oil physical property parameters include the crude oil density and crude oil viscosity. Step 2: Determine the initial concentration of the surfactant, and determine the spontaneous seepage recovery curve based on the reservoir geological reserves and the crude oil production time requirements. Divide the crude oil production time into sub-intervals of integer units of seepage recovery time to obtain the seepage recovery curve for each sub-interval. Step 3: Establish a surfactant adsorption and migration model, correlate the relevant parameters of the reservoir to be predicted with the indicators affecting surfactant concentration, analyze the difference between the surfactant concentration change and the ideal surfactant concentration change in each sub-interval and generate the recovery coefficient, and perform coefficient correction on the percolation recovery curve of each sub-interval based on the recovery coefficient in each sub-interval to obtain the oil pumping rate of each interval. Step 4: Calculate the total oil pumping volume under the requirements of reservoir geological reserves and crude oil production time based on the oil pumping volume of each divided interval, and determine the reservoir recovery rate of spontaneous adsorption of surfactants in combination with the reservoir geological reserves.

2. The method for predicting reservoir recovery based on spontaneous adsorption of surfactants according to claim 1, characterized in that, The spontaneous seepage recovery curve is defined by dividing the crude oil extraction time into sub-intervals of integer units of seepage recovery time. The method used is as follows: Based on the reservoir geological reserves and the required crude oil production duration, a spontaneous seepage recovery curve is defined. The horizontal axis of the spontaneous seepage recovery curve represents time, and the vertical axis represents the real-time crude oil production at the wellhead. The expression for the defined spontaneous seepage recovery curve is as follows: ； in, This represents the spontaneous infiltration harvesting curve. Indicates the geological reserves of the oil reservoir. Indicates the required duration of crude oil extraction. This indicates the maximum crude oil production at the wellhead. Indicates the curve steepness coefficient. For time variables, .

3. The method for predicting reservoir recovery based on spontaneous adsorption of surfactants according to claim 2, characterized in that, The crude oil extraction time is adjusted by dividing it into sub-intervals of integer units of infiltration recovery time. The method used is as follows: When the crude oil extraction time is an integer multiple of the unit seepage recovery time, no adjustment to the extraction time is needed; the extraction time is simply divided equally. When the crude oil extraction time is not an integer multiple of the unit seepage recovery time, the number of integer unit operating time periods is calculated and rounded up. Then, the adjusted total time is calculated by dividing the number of integer time periods by the unit seepage recovery time period. The formula used is as follows: ； in, Indicates rounding up. This represents the number of unit running time segments after rounding up. This indicates the set unit infiltration harvesting time. Let be the index of the sub-interval after partitioning, and .

4. The method for predicting reservoir recovery based on spontaneous adsorption of surfactants according to claim 3, characterized in that, The method used to establish the surfactant adsorption and migration model is as follows: Based on multimodal feature correlation, the geological parameters, wettability parameters, and reservoir crude oil physical properties of the reservoir to be predicted are correlated with indicators affecting surfactant concentration. These indicators include effective diffusion coefficient, average percolation velocity, and adsorption coefficient. The geological parameters include core porosity and permeability. The wettability parameters include contact angle. The reservoir crude oil physical properties include crude oil density and viscosity. The formula used for multimodal feature correlation is: ； in, , , The indicators that affect surfactant concentration are the effective diffusion coefficient, average percolation velocity, and adsorption coefficient. For penetration rate, For crude oil viscosity, This is an exponential effect function of porosity. Porosity Contact angle, For crude oil density, The density of water, It is the acceleration due to gravity; The partial differential equation of surfactant concentration under the influence of effective diffusion coefficient, average percolation velocity, and adsorption coefficient is established based on the Advagesion-Dispersion-Adsorption model. The formula used is as follows: ； in, This represents the partial differential equation for surfactant concentration established based on the model. This indicates the instantaneous concentration of the surfactant in the core pores. These are spatial coordinates, representing the location within the rock core. This represents the density of the rock skeleton.

5. The method for predicting reservoir recovery based on spontaneous adsorption of surfactants according to claim 4, characterized in that, The variation of surfactant concentration in each sub-interval was analyzed, and the coefficient correction was applied to the percolation recovery curves of each sub-interval based on the recovery coefficient within each sub-interval. The method used was as follows: Based on the surfactant experiment from the initial entry into the core to its effluent, the initial experimental condition was set as follows: the core initially contained no surfactant. The inlet boundary is where the surfactant is continuously input at a concentration of [missing information]. The export boundary condition is set to a finite concentration at the far end, i.e. ; Solving the partial differential equation for surfactant concentration using the Laplace transform, the analytical expression for the change of surfactant concentration over time due to the combined effects of diffusion, convection, and adsorption is: ； in, An analytical expression representing the surfactant concentration, used to calculate... The concentration of surfactant at any given time, Represents the complementary error function; The concentration variation of surfactant in each sub-interval after division is analyzed using a functional analytical expression based on surfactant concentration, and the coefficients of the percolation recovery curves in each sub-interval are corrected. The formula used is as follows: ； in, Indicates the first The change in surfactant concentration in each sub-interval, Indicates the first Crude oil production in each sub-region; Define the ideal concentration change of surfactant in each sub-interval The difference between the ideal concentration change and the calculated concentration change in each sub-interval is analyzed to generate the recovery factor. The corresponding crude oil extraction volume is then corrected based on the recovery factor for each sub-interval to obtain the oil pumping volume for each interval. The formula used is as follows: ； ； in, Indicates the first Harvesting coefficient of each sub-interval, Indicates the first Oil pump output of each sub-section.

6. The method for predicting reservoir recovery based on spontaneous adsorption of surfactants according to claim 5, characterized in that, The method used to determine reservoir recovery from spontaneous surfactant adsorption is as follows: The total oil pumping output is calculated based on the total number of sub-intervals and the corresponding oil pumping output of each sub-interval, using the following formula: ； ； in, This indicates the total oil pump output. This represents the predicted reservoir recovery rate.

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