A method for characterizing the dispersion coefficient of remaining oil in oil reservoirs

Abstract

The present invention belongs to the technical field of oil production engineering in petroleum and natural gas, and specifically relates to a method for characterizing the dispersion coefficient of the remaining oil in an oil reservoir. The method of the present invention uses the calculation results obtained by numerical simulation to screen the properties of the remaining oil patches, such as the area, perimeter, and number, and uses the entropy weight method to determine the weight of each factor. The remaining oil patches are identified and labeled using the 8-neighborhood boundary tracking algorithm, and a matrix composed of the properties of the remaining oil patches at different numerical simulation time steps is obtained, and the dispersion coefficient of the remaining oil at different time steps is calculated. The method quantitatively characterizes the changes in the dispersion of the remaining oil in the reservoir during the water injection development process, and guides the work of tapping the remaining oil potential and improving the recovery rate of the high water-cut period oil reservoir.

Description

Technical Field

[0001] The invention belongs to the technical field of oil production engineering in petroleum and natural gas, and in particular relates to a method for characterizing the dispersion coefficient of residual oil in an oil reservoir. Background Art

[0002] Oil and natural gas are important strategic resources for the country and the lifeblood of the national economy. With the expansion of oil and gas exploration, efficient development of high water-cut reservoirs has become one of the main battlefields for improving oil recovery in major oil fields in China.

[0003] At present, quantitative characterization and effective development of high water-cut reservoirs is a hot research topic. Domestic and foreign scholars have done some work in this area. Wang applied the random approximation SPSA algorithm to the research on reservoir production optimization and obtained the random approximation gradient; Oliver proposed the set-based EnOpt algorithm. The research results show that the method of characterizing the historical potential of the reservoir through the EnOpt algorithm has fast calculation efficiency and is convenient for actual field implementation, but its stability needs to be improved; Zhang Xiaodong solved the polymer production parameter control model and optimized the injection concentration and injection rate of the polymer.

[0004] Chinese invention patent CN110593863A discloses a method and system for identifying water-consuming zones in high-water-cut oil reservoirs. The identification method includes: based on the geological data of the target area in the high-water-cut oil reservoir, the production dynamic data of the oil wells and water wells in the target area, using a reservoir numerical simulator to fit the production dynamics of the oil wells and water wells to obtain a reservoir numerical simulation model; based on the reservoir numerical simulation model, calculating the identification coefficient of the water-consuming zone between each water well and the oil wells located around the water well; and based on the identification coefficient of the water-consuming zone, identifying the development level of the water-consuming zone. The invention can quickly identify the development level of the water-consuming zone and quantitatively characterize it, so as to effectively identify the development direction of the high-water-consuming zone and play an effective guiding role in the design of control schemes in the subsequent oilfield development stage.

[0005] However, the current high-water-cut reservoirs cannot use quantitative indicators to characterize the remaining oil dispersion coefficient of the reservoir, and apply it to the numerical simulation of the reservoir to propose corresponding well location and liquid volume optimization plans, resulting in problems such as poor injection-production correspondence rate of oil and water wells and low implementation rate of development plans. Summary of the invention

[0006] The main purpose of the present invention is to provide a method for characterizing the dispersion coefficient of the remaining oil in the reservoir. The method of the present invention uses the calculation results obtained by numerical simulation to screen the properties of the remaining oil patches such as area, perimeter, number, etc., and uses the entropy weight method to determine the weight of each factor. The remaining oil patches are identified and marked using the 8-neighborhood boundary tracking algorithm, and the matrix composed of the properties of the remaining oil patches at different numerical simulation time steps is obtained, and the dispersion coefficient of the remaining oil at different time steps is calculated. This method quantitatively characterizes the changes in the dispersion of the remaining oil in the reservoir during the water injection development process, and guides the work of tapping the potential of the remaining oil in the high water-cut period and improving the recovery rate.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] The present invention provides a method for characterizing the dispersion coefficient of remaining oil in an oil reservoir, comprising the following steps:

[0009] Residual oil dispersion coefficient index screening;

[0010] The entropy weight method is used to determine the weights of the parameters characterizing the residual oil dispersion coefficient;

[0011] Calculation of residual oil dispersion coefficient.

[0012] Furthermore, the residual oil patch is used as an evaluation object to evaluate the dispersion degree of the residual oil; the residual oil patch is formed by extracting the area with higher potential abundance after evaluating the residual potential with the dominant potential abundance.

[0013] Furthermore, three indices, namely, average patch area, patch density and average shape index, were selected to evaluate the dispersion degree of residual oil.

[0014] Furthermore, the mean plaque area MPS: MPS = A / N

[0015] Plaque density PD: PD = N / A

[0016] Mean Shape Index MSI:

[0017]

[0018] Among them, A: total area of ​​remaining oil patches; N: total number of patches; E: total circumference of remaining oil patches.

[0019] Furthermore, the smaller the average patch area, the smaller the recoverable reserves of a single remaining oil block, and the higher the remaining oil dispersion coefficient; the greater the patch density, the more the number of remaining oil patches, and the higher the remaining oil dispersion coefficient; the larger the average shape index, the more complex the shape of the remaining oil block, and the higher the remaining oil dispersion coefficient.

[0020] Furthermore, the entropy weight method for determining the weight of the residual oil dispersion coefficient characterization parameter includes the following steps:

[0021] Each characterization parameter is standardized; after obtaining the parameter normalization data, the proportion of the j-th indicator in the sample is calculated to obtain the entropy weight of the indicator, and finally the score of each indicator is obtained to determine the weight of the characterization parameter.

[0022] Furthermore, the remaining oil dispersion coefficient calculation method is as follows: the results of reservoir numerical simulation are used to extract the result file, and the continuous oil phase, namely the remaining oil patches, are identified and labeled using the 8-neighborhood boundary tracing algorithm; the area and perimeter properties of the labeled patches are obtained through the regionprops function; the matrix composed of the properties of the remaining oil patches at different time steps is obtained, and the remaining oil dispersion coefficient at different time steps can be calculated using the entropy weight method.

[0023] Furthermore, the reservoir numerical simulation is to use a computer to solve the reservoir mathematical model, simulate the underground oil and water flow, and give the oil and water distribution at a certain moment to predict the reservoir dynamics.

[0024] Compared with the prior art, the present invention has the following advantages:

[0025] The method of the present invention can conveniently, accurately, quickly and effectively determine the remaining oil dispersion coefficient at different development stages of the oil reservoir, and plays an important economic role in increasing the recovery rate and improving the development effect of old oil fields.

[0026] This invention determines the location of the oil block with advantageous potential enrichment in the reservoir by the abundance of advantageous reserves, uses three indicators such as unit area, distribution density, and shape index to characterize the dispersion of remaining oil, and determines the calculation weight of each indicator by the entropy weight method, and finally forms a method for characterizing the dispersion of remaining oil that can describe the process of reservoir development. The timing of optimizing measures in the process of reservoir development can be optimized through a large number of numerical simulation results, and the dispersion of remaining oil has a staged characteristic. Therefore, the concept of residual oil dispersion has a certain guiding significance for the selection of the timing of optimizing measures. The actual blocks are evaluated by using the characterization parameters, and the degree of dispersion of remaining oil in the reservoir from the time of development to the present is quantified, providing a reference for development adjustments. This overcomes the problem that the existing technology cannot directly quantitatively describe the dispersion of remaining oil through numerical simulation of the reservoir, thereby guiding the subsequent work of tapping the potential of remaining oil, optimizing the well network and liquid volume, resulting in problems such as poor injection and production correspondence rate of oil and water wells and low implementation rate of development plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0028] Figure 1 This is the typical curve of residual oil dispersion coefficient;

[0029] Figure 2 This is a schematic diagram of a steady increase phase of the residual oil dispersion coefficient according to a specific embodiment of the present invention: a represents one year of production, and b represents two years of production;

[0030] Figure 3 This is a schematic diagram of the rapid increase of the residual oil dispersion coefficient in a specific embodiment of the present invention: a represents 3 years of production, b represents 4 years of production, c represents 5 years of production, and d represents 6 years of production;

[0031] Figure 4 This is a schematic diagram of the fluctuation and decline stages of the residual oil dispersion coefficient according to a specific embodiment of the present invention: a represents 7 years of production, b represents 8 years of production, c represents 9 years of production, and d represents 10 years of production;

[0032] Figure 5 This is a schematic diagram of the secondary steady increase stage of the residual oil dispersion coefficient according to a specific embodiment of the present invention: a is 11 years of production, b is 12 years of production, c is 13 years of production, and d is 14 years of production. DETAILED DESCRIPTION

[0033] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0034] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations and / or combinations thereof.

[0035] In order to enable those skilled in the art to more clearly understand the technical solution of the present invention, the technical solution of the present invention will be described in detail below in conjunction with specific embodiments.

[0036] Example

[0037] The method for characterizing the dispersion coefficient of the remaining oil in the reservoir comprises the following steps:

[0038] Step 1. Screening of residual oil dispersion coefficient index;

[0039] The residual oil dispersion coefficient is an indicator to measure the spatial dispersion of residual potential. After evaluating the residual potential with the dominant potential abundance, the areas with higher potential abundance are extracted to form residual oil patches, referred to as patches. Residual oil patches have attributes such as area, perimeter, and number. The evaluation of the dispersion of residual oil is also the evaluation of the dispersion of residual oil patches.

[0040] Based on the relevant achievements of landscape science and the characteristics of the plane distribution of residual oil, this paper selects three indicators, namely mean patch area (MPS), patch density (PD) and mean shape index (MSI), to evaluate the dispersion degree of residual oil. The specific description of each parameter is shown in Table 1.

[0041] Table 1 Characterization table of residual oil dispersion coefficient index

[0042]

[0043] In the table, A is the total area of ​​remaining oil; N is the number of remaining patches; and E is the total circumference of the remaining oil patches.

[0044] It can be seen from Table 1 that the smaller the average patch area, the smaller the recoverable reserves of a single remaining oil block, and the higher the remaining oil dispersion coefficient; the larger the patch density, the more the number of remaining oil patches, and the higher the remaining oil dispersion coefficient; the larger the average shape index, the more complex the shape of the remaining oil block, and the higher the remaining oil dispersion coefficient.

[0045] Step 2. Determine the weight of the parameter characterizing the residual oil dispersion coefficient by using the entropy weight method;

[0046] The basic idea of ​​the entropy weight method is to determine the objective weight according to the variability of the indicator. Generally speaking, if the information entropy of an indicator is smaller, it means that the variability of the indicator value is greater, the amount of information provided is more, the role it can play in the comprehensive evaluation is greater, and its weight is greater. On the contrary, the greater the information entropy of an indicator, the smaller the variability of the indicator value, the less information provided, the smaller the role it plays in the comprehensive evaluation, and the smaller its weight.

[0047] Since the measurement units of various indicators are not uniform, they must be standardized before they are used to calculate the comprehensive indicators, that is, the absolute values ​​of the indicators are converted into relative values, so as to solve the homogeneity problem of the values ​​of various indicators of different qualities. In addition, the values ​​of positive indicators and negative indicators have different meanings. The higher the value of positive indicators, the better, and the lower the value of negative indicators, the better. Therefore, different algorithms need to be used for data standardization for positive and negative indicators.

[0048] After obtaining the parameter normalization data, the proportion of the jth indicator in the sample is calculated to obtain the entropy weight of the indicator, and finally the score of each indicator is obtained to determine the weight of the characterization parameter.

[0049] Step 3. Calculation of residual oil dispersion coefficient.

[0050] Using the results of reservoir numerical simulation, the result files are extracted, and the continuous oil phase, i.e., the remaining oil patches, are identified and labeled using the 8-neighborhood boundary tracking algorithm. The area, perimeter and other properties of the labeled patches are obtained using the regionprops function. The matrix composed of the remaining oil patch properties at different time steps is obtained, and the remaining oil dispersion coefficient at different time steps can be calculated using the entropy weight method.

[0051] like Figure 1 The typical curve of the residual oil dispersion coefficient shown generally includes four stages: a first steadily rising stage, a rapid rising stage, a fluctuating falling stage and a second steadily rising stage.

[0052] The method described in the above embodiment is used to characterize the dispersion coefficient of the remaining oil in the reservoir. In the first stage, the area of ​​the remaining oil patch is continuously reduced and the shape index is continuously increased. Figure 2 As shown; in the second stage, the number of remaining oil patches increases and the area decreases, as shown Figure 3 As shown; the third stage: the number of remaining oil patches decreases, and the area of ​​remaining oil patches decreases, as shown Figure 4 As shown in Figure 2; in the fourth stage, the area of ​​the residual oil patch decreases and the shape index increases. Figure 5 shown.

[0053] Comparing the above four stages with the typical curve of the traditional remaining oil dispersion coefficient, it is found that the changes in each stage described in the present invention are the reasons for the change trends of each stage of the traditional typical curve. Therefore, the method of the present invention can accurately characterize the dispersion coefficient of the remaining oil in the reservoir.

[0054] The traditional optimization timing of measures is obtained through a large number of numerical simulation optimizations, and the residual oil dispersion coefficient has a stage-by-stage characteristic. Therefore, the proposal of the residual oil dispersion coefficient has certain guiding significance for the selection of the timing of measures.

[0055] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be equivalent replacement methods and are included in the protection scope of the present invention.

Claims

1. A method for characterizing the dispersion coefficient of remaining oil in an oil reservoir, characterized in that: The following steps are involved: Residual oil dispersion coefficient index screening; The entropy weight method is used to determine the weights of the parameters characterizing the residual oil dispersion coefficient; Calculation of residual oil dispersion coefficient; The residual oil patch is used as an evaluation object to evaluate the dispersion degree of the residual oil; the residual oil patch is formed by extracting the area with higher potential abundance after evaluating the residual potential with the dominant potential abundance; The average patch area, patch density and average shape index were selected to evaluate the dispersion of residual oil. Mean plaque area MPS: Plaque density PD: Mean Shape Index MSI: Where, A: total area of ​​remaining oil patches; N: total number of plaques; E is the total circumference of remaining oil plaques.

2. The method according to claim 1, characterized in that: The smaller the average patch area, the smaller the recoverable reserves of a single remaining oil block, and the higher the remaining oil dispersion coefficient; the greater the patch density, the more the number of remaining oil patches, and the higher the remaining oil dispersion coefficient; the larger the average shape index, the more complex the shape of the remaining oil block, and the higher the remaining oil dispersion coefficient.

3. The method according to claim 1, characterized in that: The entropy weight method for determining the weight of the residual oil dispersion coefficient characterization parameter includes the following steps: Each characterization parameter is standardized; after obtaining the parameter normalization data, the proportion of the j-th indicator in the sample is calculated to obtain the entropy weight of the indicator, and finally the score of each indicator is obtained to determine the weight of the characterization parameter.

4. The method according to claim 1, characterized in that: Calculation method of remaining oil dispersion coefficient: Use the calculation results of reservoir numerical simulation to extract the result file, use the 8-neighborhood boundary tracing algorithm to identify the continuous oil phase, namely the remaining oil patches, and mark them; use the regionprops function to obtain the area and perimeter properties of the marked patches; obtain the matrix composed of the properties of the remaining oil patches at different time steps, and calculate the remaining oil dispersion coefficient at different time steps by the entropy weight method.

5. The method according to claim 4, characterized in that: The reservoir numerical simulation is to use a computer to solve the reservoir mathematical model, simulate the underground oil and water flow, give the oil and water distribution at a certain moment, and predict the reservoir dynamics.

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