A method for fine characterization of theoretical model of flow field in high water-cut reservoirs

Abstract

The present invention provides a method for finely characterizing a theoretical model of a flow field in a high-water-cut reservoir. The method comprises the following steps: step 1, characterizing a flow field of streamline clusters; step 2, characterizing a dominant potential abundance; step 3, characterizing an instantaneous displacement intensity field; step 4, characterizing a flow heterogeneity field; and step 5, establishing a theoretical model of the flow field. Based on parameters characterizing the reservoir flow field, the weight distribution of each parameter in a prediction process is determined using an entropy weight method, thereby forming a theoretical formula for flow field reconstruction to enhance oil recovery. The method for finely characterizing a theoretical model of a flow field in a high-water-cut reservoir can conveniently, accurately, quickly, and effectively define the development effect of a high-water-cut reservoir, evaluate the flow field reconstruction capability based on a flow field reconstruction enhanced oil recovery evaluation index, and achieve important economic benefits in enhancing oil recovery and improving development effects in mature oil fields.

Description

Technical Field

[0001] The present invention relates to the technical field of oil production engineering in petroleum and natural gas, and in particular to a method for finely characterizing a theoretical model of a flow field in a high-water-content oil reservoir. Background Art

[0002] Oil and natural gas are important strategic resources for the nation and the lifeblood of the national economy. With the expansion of oil and gas exploration, the efficient development of high-water-cut reservoirs has become a key focus for improving oil recovery at China's major oil fields.

[0003] Currently, characterizing theoretical models of flow fields in high-water-cut reservoirs has become a hot topic of research, and scholars both domestically and internationally have conducted considerable research in this area. Hou Yupei categorized reservoir flow field adjustment into well pattern adjustment, formation adjustment, and production system adjustment. Based on this, he designed a flow field reconstruction scheme for a real-world block in the Chengdong East District, achieving promising results. Based on numerical simulations of the Weizhou A Oilfield, Yao Zheng proposed adjustment methods such as shutting down high-water-cut wells and their corresponding water injection wells, re-stratifying existing wells and drilling new wells, switching production wells to injection wells, rotating water injection, and formation adjustment. Feng Qihong considered reservoir flow field intensity as the primary indicator for well pattern optimization to optimize reservoir development outcomes. Jiang Ruizhong et al. developed a reservoir flow field evaluation system using a BP neural network and proposed corresponding flow field control methods. However, the flow field reconstruction proposed in current research is essentially based on production system adjustment, and the theoretical flow field model has not been defined from the perspective of flow field control.

[0004] The Chinese patent application with application number CN202010023702.7 involves a method for evaluating the heterogeneity of the seepage field in water-flooded reservoirs based on flow field diagnosis. The method establishes a geological model of the target reservoir and calculates the flow exchange between grids of the target reservoir geological model. The propagation time distribution of the seepage field is calculated based on the flow exchange between grids, and numerical tracer calculations are performed. Different injection well affected areas and production well control areas are divided according to the numerical tracer distribution. The grid nodes in different areas are sorted according to the propagation time, and a flow capacity-storage capacity diagnostic diagram is drawn to evaluate the flow heterogeneity in different areas. However, the patent is rather one-sided in its discussion of the flow field theoretical model, focusing on the instantaneous flow capacity in the oil and water well control area as the center of the flow field research, weakening the demonstration of the flow field cumulative parameters and their evolution process.

[0005] The Chinese patent application, application number CN201910369071.1, describes a new method for flow field characterization, including the establishment of a flow field characterization parameter set, determination of parameter weights, parameter standardization, establishment of a comprehensive flow field intensity characterization index, establishment of a flow field intensity classification standard, and study of flow field distribution characteristics. However, when establishing the flow field intensity index characterization, the patent uses a fuzzy comprehensive evaluation method to establish dimensionless flow field characterization parameters, weakening the meaning of each physical quantity and failing to reflect the flow field distribution from the perspective of seepage characteristics.

[0006] Chinese patent application number CN202010262704.1 describes a method for evaluating the flow field adaptability of high-water-cut reservoirs. The method includes the following steps: 1. Establishing a numerical simulation model for the reservoir and obtaining evaluation parameters; 2. Preprocessing the data; 3. Calculating flow field adaptability evaluation indicators for high-water-cut reservoirs; and 4. Finding the optimal reservoir control scheme. However, in demonstrating the flow field, the patent only considers reservoir potential and displacement intensity as the primary factors for flow field adaptation, ignoring the impact of positive and negative indicators on the flow field.

[0007] To this end, we invented a new method to finely characterize the theoretical model of flow field in high water-cut reservoirs, which solved the above technical problems. Summary of the Invention

[0008] The purpose of the present invention is to provide a method for finely characterizing a theoretical model of flow field in a high-water-cut reservoir, which is simple to operate, fast and effective, easy to promote and use, and can better guide the work of tapping the remaining oil potential of a high-water-cut reservoir during its development period.

[0009] The purpose of the present invention can be achieved through the following technical measures: a method for finely characterizing a theoretical model of the flow field of a high-water-cut oil reservoir, the method comprising: step 1, characterizing the flow field of a streamline cluster; step 2, characterizing the abundance of a dominant potential; step 3, characterizing the instantaneous displacement intensity field; step 4, characterizing the flow heterogeneity field; step 5, establishing a theoretical model of the flow field, and determining the weight distribution of each parameter in the prediction process by an entropy weight method based on the parameters characterizing the flow field of the oil reservoir, thereby forming a theoretical formula for flow field reconstruction to enhance oil recovery.

[0010] The purpose of the present invention can also be achieved by the following technical measures:

[0011] In step 1, the streamline cluster flow rate refers to the sum of the flow rates of all streamlines on the streamline cluster, reflecting the size of the flow rate between the injection well and the oil production well. The calculation formula for the streamline cluster flow rate is:

[0012]

[0013] Where SF is the streamline cluster flow, m3 / d;

[0014] —Average flow rate on streamline under formation conditions, m 3 / d;

[0015] sl—streamlines belonging to a streamline cluster;

[0016] slb—streamline cluster between injection and production wells.

[0017] In step 2, the dominant potential abundance is calculated as:

[0018]

[0019] in:

[0020] Where, J O3 is the dominant reserve abundance, 10 4 t / km 2 ;h is the reservoir thickness, m; is the porosity; S o is the oil saturation; S or is the residual oil saturation; ρ o is the density of crude oil, g / cm 3 ; B o is the crude oil volume coefficient; α is the dominant potential abundance coefficient; K is the reservoir permeability, 10 -3 μm 2 ;K max is the maximum permeability in the reservoir, 10 -3 μm 2 ;K ro is the relative permeability of the oil phase; K rw is the relative permeability of water phase; μ o is the viscosity of crude oil, mPa.S; μ w is the viscosity of water, mPa.S.

[0021] In step 3, the result of logarithmic processing of the instantaneous excess liquid ratio is used to characterize the hydrodynamic strength;

[0022] The calculation formula of fluid flow in reservoir numerical simulation is:

[0023]

[0024] Where flow is the grid flow size, m 3 / d; FLOOIL I+ is the oil flow rate of the grid in the I+ direction, m 3 / d; FLOOIL J+ is the oil flow rate of the grid in the J+ direction, m 3 / d; FLOOIL K+ is the oil flow rate of the grid in the K+ direction, m 3 / d;FLOWAT I+ is the water flow rate of the grid in the I+ direction, m 3 / d;FLOWAT J+ is the water flow rate of the grid in the J+ direction, m 3 / d;FLOWAT K+ is the water flow rate of the grid in the K+ direction, m 3 / d;

[0025] The calculation formula for hydrodynamic strength is:

[0026]

[0027] In step 3, the grid flow size of each grid is calculated by extracting the oil and water flow in the I, J, and K directions of each grid in numerical simulation; the pore volume of each grid is extracted to calculate the hydrodynamic intensity field distribution of each grid. The hydrodynamic intensity is an instantaneous quantity that represents the fluid flow situation at the current moment.

[0028] In step 4, the flight time of the fluid flow in the grid is extracted through reservoir numerical simulation, where the forward flight time is the time required for the fluid to flow from the injection well or boundary to any position in the reservoir, and the backward flight time is the time required for the fluid to flow from any position in the reservoir to the production well or boundary. The flight time is obtained by solving the linear stability equation in the numerical simulation, and its mathematical expression is:

[0029]

[0030]

[0031] Where, is the fluid flow velocity, m / s; τ f is the forward flight time, s; τ b is the backward flight time; is the reservoir porosity; inflow is the internal boundary condition of the reservoir; outflow is the external boundary condition of the reservoir.

[0032] In step 4, after determining the flight time values from each grid to different oil wells in the numerical simulation, the backward flight time values are sorted, and the grid is assigned to the oil well with the lowest corresponding backward flight time value. This can obtain the control range of different oil wells in the reservoir at the current moment.

[0033] In step 4, the fluid flow capacity index within the control range of different oil wells is defined as:

[0034]

[0035] The fluid storage capacity index is:

[0036]

[0037] Where, F i is the cumulative flow capacity index of the grid; q i is the sum of the traffic in different grids; q n is the sum of the flow rates within the control range of a single well; Φ i is the cumulative storage capacity index of the grid; V i is the sum of the pore volumes in different grids; V n It is the sum of the pore volumes within the control range of a single well.

[0038] In step 4, the storage capacity index and flow capacity index are sorted according to the flight time corresponding to the location, the Lorentz curve is drawn, and the Lorentz coefficient representing the flow heterogeneity of the reservoir is obtained. The expression is:

[0039]

[0040] Where F is the cumulative flow capacity index of the grid; Φ is the cumulative storage capacity index of the grid; L c is the Lorentz coefficient;

[0041] This index is equivalent to the area contained in the F-Φ curve. The larger the value, the steeper the F-Φ curve, indicating that a larger volume flow rate will be contained in the same pore space and the flow field heterogeneity will be stronger. If the F-Φ curve is a straight line with a slope of 1, the volume flow rate contained in the same pore space in the flow field is smaller, and the flow field is in a completely homogeneous flow situation.

[0042] In step 5, the parameters affecting the flow field are classified into two categories: negative indicators and positive indicators. Negative indicators require lowering the values of these parameters during flow field regulation, while positive indicators require increasing the values of these parameters during flow field regulation, thereby achieving the purpose of improving the recovery rate through flow field regulation.

[0043] In step 5, the positive indicators screened are streamline cluster flow and instantaneous displacement intensity. The streamline cluster density represents the balance of the streamline field, and the instantaneous displacement intensity represents the balance of fluid flow during the historical development process of the reservoir. The higher the values of these indicators, the better the flow field improvement effect and the greater the recovery rate improvement.

[0044] In step 5, the negative indicators screened are flow heterogeneity and dominant potential abundance heterogeneity, where flow heterogeneity characterizes the heterogeneity of the flow field, and dominant potential abundance heterogeneity characterizes the heterogeneity of the dominant potential distribution in the reservoir; the lower the values of these two indicators, the better the flow field improvement effect and the greater the extent of increased recovery.

[0045] In step 5, the number of flow field parameters at different time steps is used as evaluation samples, and different characterization indicators are used as evaluation parameters. Different algorithms are used to normalize the data for positive and negative indicators. The processing method for positive indicators is as follows:

[0046]

[0047] The processing method of negative indicators is:

[0048]

[0049] Where P1 ij 、P2 ij is the evaluation index after standardization; x ij is the value of the jth indicator of the ith sample; n is the number of evaluation samples.

[0050] In step 5, the proportion of the jth indicator in the i-th sample is calculated using the formula:

[0051]

[0052] Where, P ij is the weight of the jth indicator in the i-th sample; r ij It is the evaluation index after standardization;

[0053] Calculate the entropy value e of the j-th indicator j , the formula is as follows:

[0054]

[0055] Where k = 1 / lnn;

[0056] According to the entropy value of each indicator, the entropy weight of each indicator can be calculated, thereby obtaining the comprehensive weight of each indicator. The expression of the comprehensive weight is:

[0057]

[0058] Where w j is the entropy weight of each indicator, S j is the weight of each indicator;

[0059] After determining the weights of each parameter, the flow field parameters at each time step of the numerical simulation can be calculated to quantify the changing patterns of the flow field parameters.

[0060] In step 5, the flow field reconstruction enhanced oil recovery evaluation index is calculated as follows:

[0061]

[0062] Where D is the density of streamline clusters, M is the degree of surface flux balance, flow is the instantaneous fluid flow rate, L is the c is the degree of flow heterogeneity; j is the degree of potential abundance heterogeneity; F is the cumulative flow capacity index of the grid; Φ is the cumulative storage capacity index of the grid; h is the reservoir thickness, m; is the porosity; S o is the oil saturation; S or is the residual oil saturation; ρ o is the density of crude oil, g / cm 3 ; B o is the crude oil volume coefficient; α is the dominant potential abundance coefficient; K is the reservoir permeability, 10 -3 μm 2 ;K max is the maximum permeability in the reservoir, 10 -3 μm 2 ;K ro is the relative permeability of the oil phase; K rw is the relative permeability of water phase; μ o is the viscosity of crude oil, mPa.S; μ w is the viscosity of water, mPa.S. slb is the flow line cluster between the injection and production wells. Δt is the interval time; Dx is the horizontal grid step size; Dy is the vertical grid step size;

[0063] To improve the recovery rate by flow field reconstruction, we need to start from two aspects: improving the flow field and reducing the heterogeneity of potential distribution. ij Positive indicator evaluation improvement coefficient, for P2 ij The negative indicator is used to evaluate the heterogeneity reduction coefficient, and the comprehensive calculation results are used to obtain the flow field reconstruction EOR evaluation effect; the flow field reconstruction EOR evaluation index is established by jointly using the standardized positive and negative indicators, and the entropy weight method is used to determine the change weights of each parameter in the prediction process to obtain the final evaluation index. By comparing the evaluation indices, the changes before and after the flow field index regulation can be determined, that is, the EOR effect of the flow field regulation can be evaluated.

[0064] The method for finely characterizing the theoretical model of the flow field of a high-water-cut reservoir in the present invention first determines the characterization method of the streamline cluster flow field, the instantaneous displacement intensity field, the flow heterogeneity field and the dominant potential abundance field, determines the weight of each indicator based on the entropy weight method, and finally determines the theoretical model for flow field regulation evaluation to obtain a flow field regulation score. The method for finely characterizing the theoretical model of the flow field of a high-water-cut reservoir starts from indicators such as the streamline cluster flow field, the instantaneous displacement intensity field, the flow heterogeneity field and the dominant potential abundance field, determines the weights of different indicators based on the entropy weight method, clarifies the theoretical model of the flow field, and finally forms a quantitative evaluation coefficient of the flow field of a high-water-cut reservoir to guide the evaluation of the flow field regulation effect in the high-water-cut period. The method involved in the present invention can conveniently, accurately, quickly and effectively define the development effect of a high-water-cut reservoir, evaluate the flow field transformation capability based on the flow field reconstruction enhanced recovery evaluation index, and play an important economic role in improving the recovery rate and development effect of old oil fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A flowchart of a specific embodiment of the method for finely characterizing a high-water-cut reservoir flow field theoretical model of the present invention;

[0066] Figure 2 Schematic diagram of the F-Φ curve in a specific embodiment of the present invention;

[0067] Figure 3 A diagram showing a calculation model for flow field distribution before regulation in a specific embodiment of the present invention;

[0068] Figure 4 This is a calculation model diagram of the flow field distribution after regulation in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0069] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0070] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations and / or combinations thereof.

[0071] like Figure 1 As shown, Figure 1 The flowchart of the method for finely characterizing the theoretical model of flow field in high water-cut reservoirs of the present invention is shown.

[0072] Step 101: Characterization of flow field of streamline cluster

[0073] The streamline cluster flow rate refers to the sum of the flow rates of all streamlines on the streamline cluster, which reflects the size of the flow rate between the injection well and the oil production well. The calculation formula of the streamline cluster flow rate is:

[0074]

[0075] Where SF is the streamline cluster flow, m 3 / d;

[0076] —Average flow rate on streamline under formation conditions, m 3 / d;

[0077] sl—streamlines belonging to a streamline cluster;

[0078] slb—streamline cluster between injection and production wells.

[0079] Step 102: Characterization of dominant potential abundance

[0080] When the reservoir enters the high water content development stage, the conventional method to characterize the remaining potential is to calculate the remaining oil reserves abundance or the remaining oil recoverable reserves abundance. The calculation formula for the remaining oil recoverable reserves abundance is:

[0081]

[0082] The calculation formula for the abundance of remaining oil recoverable reserves is:

[0083]

[0084] Where, J O1 is the abundance of remaining oil reserves, 10 4 t / km 2 ; J O1 is the remaining oil recoverable reserves abundance, 10 4 t / km 2 ;h is the reservoir thickness, m; is the porosity; S o is the oil saturation; S or is the residual oil saturation; ρ o is the density of crude oil, g / cm 3 ; B o is the volume coefficient of crude oil.

[0085] Reserve abundance reflects the remaining oil accumulation on the block plane to a certain extent, but it ignores the flow capacity of the remaining oil. Factors affecting flow capacity include the absolute permeability of the reservoir, the relative permeability of the oil and water phases, and the viscosity of the oil and water phases. Based on these factors, the formula for calculating the dominant potential abundance is proposed:

[0086]

[0087] in:

[0088] Where, J O3 is the dominant reserve abundance, 10 4 t / km 2 ; α is the dominant potential abundance coefficient; K is the reservoir permeability, 10 -3 μm 2 ;K max is the maximum permeability in the reservoir, 10 -3 μm 2 ;K ro is the relative permeability of the oil phase; K rw is the relative permeability of water phase; μ o is the viscosity of crude oil, mPa.S; μ w is the viscosity of water, mPa.S.

[0089] The dominant potential abundance characterizes the potential abundance of areas with better physical properties within the reservoir. It can eliminate the remaining oil in the low permeability area, making the adjustment area clearer. It is more targeted than the remaining recoverable reserves abundance when implementing residual oil potential tapping measures. This law can better reflect the relationship between reservoir physical properties and remaining oil. Therefore, the dominant potential abundance is selected as the characterization indicator of reservoir potential.

[0090] Step 103: Characterization of the instantaneous displacement intensity field

[0091] The logarithmic results of the instantaneous excess liquid ratio were used to characterize the hydrodynamic strength.

[0092] The calculation formula of fluid flow in reservoir numerical simulation is:

[0093]

[0094] Where flow is the grid flow size, m 3 / d; FLOOIL I+ is the oil flow rate of the grid in the I+ direction, m 3 / d; FLOOIL J+ is the oil flow rate of the grid in the J+ direction, m 3 / d; FLOOIL K+ is the oil flow rate of the grid in the K+ direction, m 3 / d;FLOWAT I+ is the water flow rate of the grid in the I+ direction, m 3 / d;FLOWAT J+ is the water flow rate of the grid in the J+ direction, m 3 / d;FLOWAT K+is the water flow rate of the grid in the K+ direction, m 3 / d;

[0095] The calculation formula for hydrodynamic strength is:

[0096]

[0097] By extracting the oil and water flow in the I, J, and K directions of each grid through numerical simulation, the grid flow size of each grid is calculated; the pore volume of each grid is extracted, so that the hydrodynamic intensity field distribution of each grid can be calculated. The hydrodynamic intensity is an instantaneous quantity that reflects the fluid flow situation at the current moment.

[0098] Step 104: Characterization of flow heterogeneity field

[0099] This study introduced the Lorenz curve to evaluate the heterogeneity of reservoir flow. It was first used in the field of economics to describe phenomena such as uneven distribution. The flight time of fluid flow in the grid is extracted through numerical simulation of the reservoir, where the forward flight time is the time required for the fluid to flow from the injection well or boundary to any position in the reservoir, and the backward flight time is the time required for the fluid to flow from any position in the reservoir to the production well or boundary. Theoretically, the flight time can be calculated for any point in the reservoir. If a region has a higher flight time value, it means that the fluid in this region is difficult to be affected. The flight time can be obtained by solving the linear stability equation in numerical simulation, and its mathematical expression is:

[0100]

[0101]

[0102] Where, is the fluid flow velocity, m / s; τ f is the forward flight time, s; τ b is the backward flight time; is the reservoir porosity; inflow is the internal boundary condition of the reservoir; outflow is the external boundary condition of the reservoir.

[0103] After determining the flight time values from each grid to different oil wells in the numerical simulation, the backward flight time values are sorted, and the grid is assigned to the oil well with the lowest corresponding backward flight time value. This can obtain the control range of different oil wells in the reservoir at the current moment.

[0104] The fluid flow capacity index within the control range of different oil wells is defined as:

[0105]

[0106] The fluid storage capacity index is:

[0107]

[0108] Where, F i is the cumulative flow capacity index of the grid; q i is the sum of the traffic in different grids; q n is the sum of the flow rates within the control range of a single well; Φ i is the cumulative storage capacity index of the grid; V i is the sum of the pore volumes in different grids; V n It is the sum of the pore volumes within the control range of a single well.

[0109] The storage capacity index and flow capacity index are sorted according to the flight time corresponding to the position, the Lorentz curve is drawn, and the Lorentz coefficient representing the flow heterogeneity of the reservoir is obtained. The expression is:

[0110]

[0111] This index is equivalent to the area contained in the F-Φ curve. The larger the value, the steeper the F-Φ curve, indicating that a larger volume flow rate will be contained in the same pore space and the flow field heterogeneity will be stronger. If the F-Φ curve is a straight line with a slope of 1, the volume flow rate contained in the same pore space in the flow field is smaller, and the flow field is in a completely homogeneous flow situation.

[0112] Step 105: Establishing a theoretical flow model

[0113] The parameters that affect the flow field are classified into two categories: negative parameters and positive parameters. Negative parameters need to reduce their values during the process of flow field regulation, while positive parameters need to increase their values during the process of flow field regulation, so as to achieve the purpose of improving the recovery rate through flow field regulation.

[0114] The positive indicators screened are streamline cluster flow and instantaneous displacement intensity. The streamline cluster density represents the balance of the streamline field, and the instantaneous displacement intensity represents the balance of fluid flow during the historical development process of the reservoir. The higher the values of these indicators, the better the flow field improvement effect and the greater the recovery rate improvement.

[0115] The negative screening indicators are flow heterogeneity and dominant potential abundance heterogeneity. The flow heterogeneity represents the heterogeneity of the flow field, and the dominant potential abundance heterogeneity represents the heterogeneity of the dominant potential distribution in the reservoir. The lower the values of these two indicators, the better the flow field improvement effect and the greater the increase in oil recovery.

[0116] The evaluation of EOR efficiency through multi-factor flow field manipulation can be based on parameters characterizing the reservoir flow field. The entropy weight method is used to determine the weight distribution of each parameter during the prediction process, thereby forming a theoretical formula for EOR efficiency through flow field reconstruction. The entropy weight method is an objective weighting method that relies solely on the discreteness of the data itself. The smaller the variation in the indicator during the evaluation process, the less information it provides, and the smaller its weight. Conversely, the smaller the variation in the indicator during the evaluation process, the greater its weight.

[0117] The number of flow field parameters at different time steps is used as the evaluation sample, and different characterization indicators are used as evaluation parameters. Since the measurement units of various indicators are not uniform, they need to be standardized before they are used to calculate the comprehensive indicators. Different algorithms are used for data standardization for positive and negative indicators. The processing method for positive indicators is as follows:

[0118]

[0119] The processing method of negative indicators is:

[0120]

[0121] Where P1 ij 、P2 ij is the evaluation index after standardization; x ij is the value of the jth indicator of the i-th sample. n is the number of evaluation samples.

[0122] Calculate the proportion of the jth indicator in the i-th sample. The formula is:

[0123]

[0124] Where, P ij is the weight of the j-th indicator in the i-th sample.

[0125] Calculate the entropy value e of the j-th indicator j , the formula is as follows:

[0126]

[0127] Where k = 1 / lnn.

[0128] According to the entropy value of each indicator, the entropy weight of each indicator can be calculated, thereby obtaining the comprehensive weight of each indicator. The expression of the comprehensive weight is:

[0129]

[0130] Where w j is the entropy weight of each indicator, S j is the weight of each indicator.

[0131] After determining the weights of each parameter, the flow field parameters at each time step of the numerical simulation can be calculated to quantify the changing patterns of the flow field parameters.

[0132] The calculation formula of flow field reconstruction enhanced oil recovery evaluation index is proposed:

[0133]

[0134] Where D is the density of streamline clusters, M is the degree of surface flux balance, flow is the instantaneous fluid flow rate, L is the c is the degree of flow heterogeneity; j is the degree of potential abundance heterogeneity.

[0135] The technical connotation of formula (18) is: for the recovery enhancement of flow field reconstruction, it is necessary to start from two aspects: improving the flow field and reducing the heterogeneity of potential distribution, that is, for P1 ij Positive indicator evaluation improvement coefficient, for P2 ij Negative indicators evaluate the heterogeneity reduction coefficient, and the comprehensive calculation results are used to evaluate the EOR effect of flow field reconstruction. The flow field reconstruction EOR evaluation index is established by combining the standardized positive and negative indicators. The entropy weight method is used to determine the weight of each parameter change during the prediction process, and the final evaluation index is obtained. By comparing the evaluation indices, the changes before and after flow field control can be determined, that is, the EOR effect of flow field control can be evaluated.

[0136] In a specific embodiment 1 of the present invention, the following steps are included:

[0137] Step 1: Characterization of streamline cluster flow field

[0138] The streamline cluster flow rate refers to the sum of the flow rates of all streamlines on the streamline cluster, which reflects the size of the flow rate between the injection well and the oil production well. The calculation formula of the streamline cluster flow rate is:

[0139]

[0140] Where SF is the streamline cluster flow, m 3 / d;

[0141] —Average flow rate on streamline under formation conditions, m 3 / d;

[0142] sl—streamlines belonging to a streamline cluster;

[0143] slb—streamline cluster between injection and production wells.

[0144] Step 2, Characterization of Dominant Potential Abundance

[0145] The calculation formula of dominant potential abundance is:

[0146]

[0147] in:

[0148] Where, J O3 is the dominant reserve abundance, 10 4 t / km 2 ;h is the reservoir thickness, m; is the porosity; S o is the oil saturation; S or is the residual oil saturation; ρ o is the density of crude oil, g / cm 3 ; B o is the crude oil volume coefficient; α is the dominant potential abundance coefficient; K is the reservoir permeability, 10 -3 μm 2 ;K max is the maximum permeability in the reservoir, 10 -3 μm 2 ;K ro is the relative permeability of the oil phase; K rw is the relative permeability of water phase; μ o is the viscosity of crude oil, mPa.S; μ w is the viscosity of water, mPa.S.

[0149] The dominant potential abundance characterizes the potential abundance of areas with better physical properties within the reservoir. It can eliminate the remaining oil in the low permeability area, making the adjustment area clearer. It is more targeted than the remaining recoverable reserves abundance when implementing residual oil potential tapping measures. This law can better reflect the relationship between reservoir physical properties and remaining oil. Therefore, the dominant potential abundance is selected as the characterization indicator of reservoir potential.

[0150] Step 3: Characterization of the transient displacement intensity field

[0151] The logarithmic results of the instantaneous excess liquid ratio were used to characterize the hydrodynamic strength.

[0152] The calculation formula of fluid flow in reservoir numerical simulation is:

[0153]

[0154] Where flow is the grid flow size, m 3 / d; FLOOIL I+ is the oil flow rate of the grid in the I+ direction, m 3 / d; FLOOIL J+ is the oil flow rate of the grid in the J+ direction, m 3 / d; FLOOIL K+ is the oil flow rate of the grid in the K+ direction, m 3 / d;FLOWATI+ is the water flow rate of the grid in the I+ direction, m 3 / d;FLOWAT J+ is the water flow rate of the grid in the J+ direction, m 3 / d;FLOWAT K+ is the water flow rate of the grid in the K+ direction, m 3 / d;

[0155] The calculation formula for hydrodynamic strength is:

[0156]

[0157] By extracting the oil and water flow in the I, J, and K directions of each grid through numerical simulation, the grid flow size of each grid is calculated; the pore volume of each grid is extracted, so that the hydrodynamic intensity field distribution of each grid can be calculated. The hydrodynamic intensity is an instantaneous quantity that reflects the fluid flow situation at the current moment.

[0158] Step 4: Characterization of flow heterogeneity

[0159] This study introduced the Lorenz curve to evaluate the heterogeneity of reservoir flow. It was first used in the field of economics to describe phenomena such as uneven distribution. The flight time of fluid flow in the grid is extracted through numerical simulation of the reservoir, where the forward flight time is the time required for the fluid to flow from the injection well or boundary to any position in the reservoir, and the backward flight time is the time required for the fluid to flow from any position in the reservoir to the production well or boundary. Theoretically, the flight time can be calculated for any point in the reservoir. If a region has a higher flight time value, it means that the fluid in this region is difficult to be affected. The flight time can be obtained by solving the linear stability equation in numerical simulation, and its mathematical expression is:

[0160]

[0161]

[0162] Where, is the fluid flow velocity, m / s; τ f is the forward flight time, s; τ b is the backward flight time; is the reservoir porosity; inflow is the internal boundary condition of the reservoir; outflow is the external boundary condition of the reservoir.

[0163] After determining the flight time values from each grid to different oil wells in the numerical simulation, the backward flight time values are sorted, and the grid is assigned to the oil well with the lowest corresponding backward flight time value. This can obtain the control range of different oil wells in the reservoir at the current moment.

[0164] The fluid flow capacity index within the control range of different oil wells is defined as:

[0165]

[0166] The fluid storage capacity index is:

[0167]

[0168] Where, F i is the cumulative flow capacity index of the grid; q i is the sum of the traffic in different grids; q n is the sum of the flow rates within the control range of a single well; Φ i is the cumulative storage capacity index of the grid; V i is the sum of the pore volumes in different grids; V n It is the sum of the pore volumes within the control range of a single well.

[0169] The storage capacity index and flow capacity index are sorted according to the flight time corresponding to the position, the Lorentz curve is drawn, and the Lorentz coefficient representing the flow heterogeneity of the reservoir is obtained. The expression is:

[0170]

[0171] This index is equivalent to the area contained in the F-Φ curve. The larger the value, the steeper the F-Φ curve, indicating that a larger volume flow rate will be contained in the same pore space and the flow field heterogeneity will be stronger. If the F-Φ curve is a straight line with a slope of 1, the volume flow rate contained in the same pore space in the flow field is smaller, and the flow field is in a completely homogeneous flow situation.

[0172] Figure 2 Three typical F-Φ curves are shown. Among them, the strong heterogeneous flow curve has a storage capacity index of 25% when the flow capacity index is 99%. This means that 99% of the flow in the numerical simulation model comes from 25% of the pore volume. Its Lorentz coefficient can be calculated as 0.98 by formula (12). The flow heterogeneity of the reservoir represented by this curve is strong at the current moment. The Lorentz coefficient of the heterogeneous flow curve calculated by the same method is 0.7, indicating that the fluid flow in the reservoir represented by this curve is heterogeneous. The Lorentz coefficient of the F-Φ curve of completely balanced flow is 0, which only exists in the ideal model.

[0173] Step 5: Establishment of flow field theoretical model

[0174] The parameters that affect the flow field are classified into two categories: negative parameters and positive parameters. Negative parameters need to reduce their values during the process of flow field regulation, while positive parameters need to increase their values during the process of flow field regulation, so as to achieve the purpose of improving the recovery rate through flow field regulation.

[0175] The positive indicators screened are streamline cluster flow and instantaneous displacement intensity. The streamline cluster density represents the balance of the streamline field, and the instantaneous displacement intensity represents the balance of fluid flow during the historical development process of the reservoir. The higher the values of these indicators, the better the flow field improvement effect and the greater the recovery rate improvement.

[0176] The negative screening indicators are flow heterogeneity and dominant potential abundance heterogeneity. The flow heterogeneity represents the heterogeneity of the flow field, and the dominant potential abundance heterogeneity represents the heterogeneity of the dominant potential distribution in the reservoir. The lower the values of these two indicators, the better the flow field improvement effect and the greater the increase in oil recovery.

[0177] The evaluation of EOR efficiency through multi-factor flow field manipulation can be based on parameters characterizing the reservoir flow field. The entropy weight method is used to determine the weight distribution of each parameter during the prediction process, thereby forming a theoretical formula for EOR efficiency through flow field reconstruction. The entropy weight method is an objective weighting method that relies solely on the discreteness of the data itself. The smaller the variation in the indicator during the evaluation process, the less information it provides, and the smaller its weight. Conversely, the smaller the variation in the indicator during the evaluation process, the greater its weight.

[0178] The number of flow field parameters at different time steps is used as the evaluation sample, and different characterization indicators are used as evaluation parameters. Since the measurement units of various indicators are not uniform, they need to be standardized before they are used to calculate the comprehensive indicators. Different algorithms are used for data standardization for positive and negative indicators. The processing method for positive indicators is as follows:

[0179]

[0180] The processing method of negative indicators is:

[0181]

[0182] Where P1 ij 、P2 ij is the evaluation index after standardization; x ij is the value of the jth indicator of the i-th sample. n is the number of evaluation samples.

[0183] Calculate the proportion of the jth indicator in the i-th sample. The formula is:

[0184]

[0185] Where, Pij is the weight of the j-th indicator in the i-th sample.

[0186] Calculate the entropy value e of the j-th indicator j , the formula is as follows:

[0187]

[0188] Where k = 1 / lnn.

[0189] According to the entropy value of each indicator, the entropy weight of each indicator can be calculated, thereby obtaining the comprehensive weight of each indicator. The expression of the comprehensive weight is:

[0190]

[0191] Where w j is the entropy weight of each indicator, S j is the weight of each indicator.

[0192] After determining the weights of each parameter, the flow field parameters at each time step of the numerical simulation can be calculated to quantify the changing patterns of the flow field parameters.

[0193] The calculation formula of flow field reconstruction enhanced oil recovery evaluation index is proposed:

[0194]

[0195] Where D is the density of streamline clusters, M is the degree of surface flux balance, flow is the instantaneous fluid flow rate, L is the c is the degree of flow heterogeneity; j is the degree of potential abundance heterogeneity.

[0196] The technical connotation of formula (18) is: for the recovery enhancement of flow field reconstruction, it is necessary to start from two aspects: improving the flow field and reducing the heterogeneity of potential distribution, that is, for P1 ij Positive indicator evaluation improvement coefficient, for P2 ij Negative indicators evaluate the heterogeneity reduction coefficient, and the comprehensive calculation results are used to evaluate the EOR effect of flow field reconstruction. The flow field reconstruction EOR evaluation index is established by combining the standardized positive and negative indicators. The entropy weight method is used to determine the weight of each parameter change during the prediction process, and the final evaluation index is obtained. By comparing the evaluation indices, the changes before and after flow field control can be determined, that is, the EOR effect of flow field control can be evaluated.

[0197] In a specific embodiment 2 of the present invention, taking an actual block oil reservoir as an example, the flow field distribution calculation model diagram before and after regulation is obtained by calculating the flow field theoretical model, as shown in FIG. Figure 34. Before regulation, the flow field distribution was unevenly calculated using the flow field theoretical model, and the evaluation coefficient score was low. After a series of well pattern optimization and injection-production structure adjustments, the flow field evaluation coefficient score was high after regulation. This method quantitatively evaluated the changes in the reservoir flow field.

[0198] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art may modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features therein. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

[0199] Except for the technical features described in the specification, all other technical features are known technologies to those skilled in the art.

Claims

1. A method for finely characterizing a theoretical model of flow field in a high water-cut reservoir, characterized in that: The method for finely characterizing the theoretical model of flow field in high water-cut reservoirs includes: Step 1, characterize the flow field of streamline cluster; Step 2, characterize the abundance of dominant potential; Step 3, characterize the instantaneous displacement intensity field; Step 4, characterize the flow heterogeneity field; Step 5: Establish a theoretical flow field model. Based on the parameters characterizing the reservoir flow field, the weight distribution of each parameter in the prediction process is determined by the entropy weight method, thereby forming a theoretical formula for flow field reconstruction to enhance oil recovery. In step 1, the streamline cluster flow rate refers to the sum of the flow rates of all streamlines on the streamline cluster, reflecting the size of the flow rate between the injection well and the oil production well. The calculation formula for the streamline cluster flow rate is: Where SF is the streamline cluster flow, m 3 / d; —Average flow rate on streamline under formation conditions, m 3 / d; sl—streamlines belonging to a streamline cluster; slb—streamline cluster between injection and production wells; In step 2, the dominant potential abundance is calculated as: in: Where, J O3 is the dominant reserve abundance, 10 4 t / km 2 ;h is the reservoir thickness, m; is the porosity; S o is the oil saturation; S or is the residual oil saturation; ρ o is the density of crude oil, g / cm 3 ; B o is the crude oil volume coefficient; α is the dominant potential abundance coefficient; K is the reservoir permeability, 10 -3 μm 2 ;K max is the maximum permeability in the reservoir, 10 -3 μm 2 ;K ro is the relative permeability of the oil phase; K rw is the relative permeability of water phase; μ o is the viscosity of crude oil, mPa.S; μ w is the viscosity of water, mPa.S; In step 3, the result of logarithmic processing of the instantaneous excess liquid ratio is used to characterize the hydrodynamic strength; The calculation formula of fluid flow in reservoir numerical simulation is: Where flow is the grid flow size, m 3 / d; FLOOIL I+ is the oil flow rate of the grid in the I+ direction, m 3 / d; FLOOIL J+ is the oil flow rate of the grid in the J+ direction, m 3 / d; FLOOIL K+ is the oil flow rate of the grid in the K+ direction, m 3 / d;FLOWAT I+ is the water flow rate of the grid in the I+ direction, m 3 / d;FLOWAT J+ is the water flow rate of the grid in the J+ direction, m 3 / d;FLOWAT K+ is the water flow rate of the grid in the K+ direction, m 3 / d; The calculation formula for hydrodynamic strength is: In step 3, the grid flow rate of each grid is calculated by extracting the oil and water flow in the I, J, and K directions of each grid through numerical simulation; the pore volume of each grid is extracted to calculate the hydrodynamic intensity field distribution of each grid. The hydrodynamic intensity is an instantaneous quantity that represents the fluid flow situation at the current moment; In step 4, the flight time of the fluid flow in the grid is extracted through reservoir numerical simulation, where the forward flight time is the time required for the fluid to flow from the injection well or boundary to any position in the reservoir, and the backward flight time is the time required for the fluid to flow from any position in the reservoir to the production well or boundary. The flight time is obtained by solving the linear stability equation in the numerical simulation, and its mathematical expression is: Where, is the fluid flow velocity, m / s; τ f is the forward flight time, s; τ b is the backward flight time; is the reservoir porosity; inflow is the reservoir internal boundary condition; outflow is the reservoir external boundary condition; In step 5, the parameters that affect the flow field are classified into two categories: negative indicators and positive indicators. Negative indicators require the values of these parameters to be reduced during the flow field regulation process, while positive indicators require the values of these parameters to be increased during the flow field regulation process, thereby achieving the purpose of flow field regulation to improve oil recovery. In step 5, the positive indicators selected are streamline cluster flow and instantaneous displacement intensity. The streamline cluster density represents the balance of the streamline field, and the instantaneous displacement intensity represents the balance of fluid flow during the historical development process of the reservoir. The higher the value of these indicators, the better the flow field improvement effect and the greater the recovery rate improvement. In step 5, the flow field reconstruction enhanced oil recovery evaluation index is calculated as follows: Where D is the density of streamline clusters, M is the degree of surface flux balance, flow is the instantaneous fluid flow rate, L is the c is the degree of flow heterogeneity; is the degree of potential abundance heterogeneity; F is the cumulative flow capacity index of the grid; Φ is the cumulative storage capacity index of the grid; h is the reservoir thickness, m; is the porosity; S o is the oil saturation; S or is the residual oil saturation; ρ o is the density of crude oil, g / cm 3 ; B o is the crude oil volume coefficient; α is the dominant potential abundance coefficient; K is the reservoir permeability, 10 -3 μm 2 ;K max is the maximum permeability in the reservoir, 10 -3 μm 2 ;K ro is the relative permeability of the oil phase; K rw is the relative permeability of water phase; μ o is the viscosity of crude oil, mPa.S; μ w is the viscosity of water, mPa.S; slb is the streamline cluster between injection and production wells; Δt is the interval time; Dx is the horizontal grid step size; Dy is the vertical grid step size; To improve the recovery rate by flow field reconstruction, we need to start from two aspects: improving the flow field and reducing the heterogeneity of potential distribution. ij Positive indicator evaluation improvement coefficient, for P2 ij The negative indicator is used to evaluate the heterogeneity reduction coefficient, and the comprehensive calculation results are used to obtain the flow field reconstruction EOR evaluation effect; the flow field reconstruction EOR evaluation index is established by jointly using the standardized positive and negative indicators, and the entropy weight method is used to determine the change weights of each parameter in the prediction process to obtain the final evaluation index. By comparing the evaluation indices, the changes before and after the flow field index regulation can be determined, that is, the EOR effect of the flow field regulation can be evaluated.

2. The method for finely characterizing the theoretical model of flow field in high water-cut reservoirs according to claim 1, characterized in that: In step 4, after determining the flight time values from each grid to different oil wells in the numerical simulation, the backward flight time values are sorted, and the grid is assigned to the oil well with the lowest corresponding backward flight time value. This can obtain the control range of different oil wells in the reservoir at the current moment.

3. The method for finely characterizing the theoretical model of flow field in high water-cut reservoirs according to claim 2, characterized in that: In step 4, the fluid flow capacity index within the control range of different oil wells is defined as: The fluid storage capacity index is: Where, F i is the cumulative flow capacity index of the grid; q i is the sum of the traffic in different grids; q n is the sum of the flow rates within the control range of a single well; Φ i is the cumulative storage capacity index of the grid; V i is the sum of the pore volumes in different grids; V n It is the sum of the pore volumes within the control range of a single well.

4. The method for finely characterizing the theoretical model of flow field in high water-cut reservoirs according to claim 3, characterized in that: In step 4, the storage capacity index and flow capacity index are sorted according to the flight time corresponding to the location, the Lorentz curve is drawn, and the Lorentz coefficient representing the flow heterogeneity of the reservoir is obtained. The expression is: Where F is the cumulative flow capacity index of the grid; Φ is the cumulative storage capacity index of the grid; L c is the Lorentz coefficient; This index is equivalent to the area contained by the F-Φ curve. The larger the value, the steeper the F-Φ curve, indicating that a larger volume flow rate will be contained in the same pore space and the flow field heterogeneity will be stronger. If the F-Φ curve is a straight line with a slope of 1, the smaller the volume flow contained in the same pore space in the flow field, the more likely the flow field is in a completely homogeneous flow situation.

5. The method for finely characterizing the theoretical model of flow field in high water-cut reservoirs according to claim 1, characterized in that: In step 5, the negative indicators screened are flow heterogeneity and dominant potential abundance heterogeneity, where flow heterogeneity characterizes the heterogeneity of the flow field, and dominant potential abundance heterogeneity characterizes the heterogeneity of the dominant potential distribution in the reservoir; the lower the values of these two indicators, the better the flow field improvement effect and the greater the extent of increased recovery.

6. The method for finely characterizing the theoretical model of flow field in high water-cut reservoirs according to claim 1, characterized in that: In step 5, the number of flow field parameters at different time steps is used as evaluation samples, and different characterization indicators are used as evaluation parameters. Different algorithms are used to normalize the data for positive and negative indicators. The processing method for positive indicators is as follows: The processing method of negative indicators is: Where P1 ij 、P2 ij is the evaluation index after standardization; x ij is the value of the jth indicator of the ith sample; n is the number of evaluation samples.

7. The method for finely characterizing the theoretical model of flow field in high water-cut reservoirs according to claim 6, characterized in that: In step 5, the proportion of the jth indicator in the i-th sample is calculated using the formula: Where, P ij is the weight of the jth indicator in the i-th sample; r ij It is the evaluation index after standardization; Calculate the entropy value e of the j-th indicator j , the formula is as follows: Where k = 1 / lnn; According to the entropy value of each indicator, the entropy weight of each indicator can be calculated, thereby obtaining the comprehensive weight of each indicator. The expression of the comprehensive weight is: Where w j is the entropy weight of each indicator, S j is the weight of each indicator; After determining the weights of each parameter, the flow field parameters at each time step of the numerical simulation can be calculated to quantify the changing patterns of the flow field parameters.

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