Macro-micro combined quantitative evaluation method for anisotropism of oil reservoir

Abstract

The invention discloses a macroscopic-microscopic combined quantitative evaluation method for oil reservoir heterogeneity, which comprises the following steps: 1) on the basis of macroscopic heterogeneity quantitative evaluation, acquiring parameters representing the heterogeneity degree of all oil reservoir groups in a research area; 2) based on microscopic heterogeneity quantitative evaluation, acquiring parameters representing heterogeneity degrees of all oil reservoir groups in the research area; 3) determining a set of m wells and a set of n index parameters in the research area, constructing an index parameter evaluation matrix, and performing normalization processing; and 4) realizing macroscopic and microscopic combined heterogeneity quantitative characterization by using an entropy weight method. The reasonable macro-micro heterogeneity quantitative evaluation method is established, through the macro-micro combined method, the influence of subjective factors is reduced, and the real heterogeneous characteristics of the reservoir stratum are restored to the maximum extent.

Description

Technical Field

[0001] This invention relates to reservoir development technology, and more particularly to a quantitative evaluation method for reservoir heterogeneity that combines macro- and micro-level analysis. Background Technology

[0002] Oil reservoir heterogeneity is closely related to the flow, distribution, accumulation, and exploitation of oil and gas. Due to the influence of sedimentation, tectonic activity, and changes in diagenetic environment, the internal structure and porosity-permeability of oil reservoirs will change to varying degrees during their formation, making oil reservoir development and exploitation more difficult. Therefore, oil reservoir heterogeneity is a key factor restricting oil reservoir development and affecting recovery rate. The study and evaluation of oil reservoir heterogeneity is an extremely necessary part of the oil reservoir geological development research process.

[0003] In general, the quantitative characterization of macroscopic heterogeneity refers to the fact that when evaluating the overall reservoir of a study area, many factors generally influence reservoir heterogeneity. Therefore, to determine whether a reservoir is conducive to oil and gas accumulation, the overall evaluation typically integrates various factors affecting its degree. This allows for a fair and accurate assessment of the heterogeneity of the studied reservoir. The quantitative characterization method involves selecting parameters that can represent the degree of heterogeneity of all oil-bearing formations in the study area, including the permeability variation coefficient, gradient, surge coefficient, sandstone density, and the permeability of all wells in the study area. Then, the proportion of these parameters in each sub-layer is obtained. A comprehensive heterogeneity coefficient is introduced using cluster analysis, thus providing an accurate and intuitive description of heterogeneity.

[0004] Microscopic heterogeneity has been extensively studied by scholars. Reservoir microscopic heterogeneity generally refers to geological factors affecting the continuous flow of fluids within micropores or throats. This is not only due to the relatively uneven distribution of pores generated during sedimentation or diagenesis, but may also be related to the uneven distribution of authigenic minerals produced during these processes. Generally, microscopic heterogeneity mainly includes the size, distribution pattern, and internal structural characteristics of pores or throats. The determining factors of microscopic heterogeneity include pore type and pore-throat structure characteristics, with pore type playing a decisive role in determining reservoir properties. Generally, the basic pore types present in sandstone are intergranular pores and dissolution pores. Reservoir pore structure refers to the geometry, size, distribution, and interconnection of pores and throats in the rock. It is an important indicator for reservoir evaluation, a crucial factor influencing reservoir space, and can reveal the reservoir surface of reservoir rocks relatively accurately. Currently, there is very little research combining macroscopic heterogeneity with microscopic heterogeneity, but combining the two has a significant effect on improving oilfield recovery. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a quantitative evaluation method for reservoir heterogeneity that combines macro- and micro-level analysis, addressing the deficiencies in the existing technology.

[0006] The technical solution adopted by this invention to solve its technical problem is: a quantitative evaluation method for reservoir heterogeneity combining macro- and micro-level analysis, comprising the following steps:

[0007] 1) Based on the quantitative evaluation of macroscopic heterogeneity, obtain parameters representing the degree of heterogeneity of all oil-bearing formations in the study area;

[0008] Based on the permeability changes in the oil reservoir, obtain the permeability variation coefficient, permeability surge coefficient, and permeability gradient coefficient within and between layers;

[0009] Obtain the porosity and sandstone density parameters of the oil reservoir;

[0010] 2) Based on the quantitative evaluation of microscopic heterogeneity, obtain parameters representing the degree of heterogeneity of all oil-bearing formations in the study area;

[0011] Obtain parameters such as discharge pressure, maximum connecting orifice throat radius, and porosity.

[0012] 3) Determine the set of m wells and the set of n index parameters in the study area, construct the index parameter evaluation matrix, and perform normalization processing; the n index parameters include: parameters for quantitative evaluation of macroscopic heterogeneity and parameters for quantitative evaluation of microscopic heterogeneity;

[0013] 4) The entropy weight method is used to achieve a quantitative characterization of heterogeneity that combines macroscopic and microscopic perspectives.

[0014] 4.1) Entropy value and entropy weight calculation: Calculate a certain parameter y j entropy value e j Then, based on the relationship between entropy value and entropy weight, the entropy weight 'a' of the corresponding index parameter is calculated. j The formula used is:

[0015]

[0016]

[0017] in,

[0018] 4.2) Calculation of conflicting index parameters:

[0019] Calculate the index parameter x j The standard deviation σj is expressed by the formula:

[0020]

[0021] This represents the mean value of xij across m wells;

[0022] Calculate the index parameter x j With index parameter x l The correlation coefficient ρ between them il Formula used:

[0023]

[0024] 4.3) Weight Optimization and Composite Index Calculation:

[0025] The weights calculated using the entropy weight method are optimized based on the magnitude of index conflict to determine the final weight w. j The formula used is:

[0026]

[0027] Finally, the comprehensive index I of reservoir heterogeneity is calculated based on the weight matrix W = (W1, W2, W3, ..., Wn), using the formula:

[0028] WYT

[0029] The beneficial effects of this invention are:

[0030] This invention establishes a reasonable method for quantitative evaluation of macro-micro heterogeneity. By combining macro and micro methods, the influence of subjective factors is reduced, and the true heterogeneity characteristics of the reservoir are restored to the greatest extent. Attached Figure Description

[0031] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings:

[0032] Figure 1 This is a flowchart of a method according to an embodiment of the present invention;

[0033] Figure 2 These are the evaluation parameters of the embodiments of the present invention;

[0034] Figure 3 This is a schematic diagram illustrating the correlation between porosity and carbonate content in the quantitative evaluation of reservoir heterogeneity according to an embodiment of the present invention.

[0035] Figure 4 This is a schematic diagram illustrating the correlation between permeability and carbonate content in the quantitative evaluation of reservoir heterogeneity according to an embodiment of the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0037] like Figure 1As shown, a quantitative evaluation method for reservoir heterogeneity combining macro- and micro-level analysis includes the following steps:

[0038] 1) Based on the quantitative evaluation of macroscopic heterogeneity, obtain parameters representing the degree of heterogeneity of all oil-bearing formations in the study area;

[0039] Based on the permeability changes in the oil reservoir, obtain the permeability variation coefficient, permeability surge coefficient, and permeability gradient coefficient within and between layers;

[0040] Obtain the porosity and sandstone density parameters of the oil reservoir;

[0041] 2) Based on the quantitative evaluation of microscopic heterogeneity, obtain parameters representing the degree of heterogeneity of all oil-bearing formations in the study area;

[0042] Obtain parameters such as discharge pressure, maximum connecting orifice throat radius, and porosity.

[0043] 3) Determine the set of m wells and the set of n index parameters in the study area, construct the index parameter evaluation matrix, and perform normalization processing; the n index parameters include: parameters for quantitative evaluation of macroscopic heterogeneity and parameters for quantitative evaluation of microscopic heterogeneity;

[0044] 4) The entropy weight method is used to achieve a quantitative characterization of heterogeneity that combines macroscopic and microscopic perspectives.

[0045] 4.1) Entropy value and entropy weight calculation: Calculate a certain parameter y j entropy value e j Then, based on the relationship between entropy value and entropy weight, the entropy weight 'a' of the corresponding index parameter is calculated. j The formula used is:

[0046]

[0047] 4.2) Calculation of conflicting index parameters:

[0048] Calculate the index parameter x j The standard deviation σj is expressed by the formula:

[0049]

[0050] This represents the mean value of xij across m wells;

[0051] Calculate the index parameter x j With index parameter x l The correlation coefficient ρ between them il Formula used:

[0052]

[0053] 4.3) Weight Optimization and Composite Index Calculation:

[0054] The weights calculated using the entropy weight method are optimized based on the magnitude of index conflict, and the final weight w is determined. j The formula used is:

[0055]

[0056] Finally, the comprehensive index I of reservoir heterogeneity is calculated based on the weight matrix W = (W1, W2, W3, ..., Wn), using the formula:

[0057] WYT

[0058] To verify the effectiveness of the method of this invention, the upper part of the Shahejie Formation (Section 3) in the Dongying Depression was selected as the research object. The sedimentary environment is deltaic. The study area is located in the Bamianhe Oilfield, within Shouguang City, Shandong Province. Structurally, it lies on the Bamianhe Fault Zone on the southern slope of the Dongying Depression in the Bohai Bay Basin. It is connected to the Guangli Depression in the northeast direction, the Wangjiagang Fault Zone in the northwest direction, the Chunhuacaoqiao Fault Zone in the southwest direction, and the Guangrao Uplift in the south direction. This oilfield is rich in oil and gas resources. Currently, there are 750 wells drilled in the study area, with a high well density, providing a basis for studying heterogeneity. Figure 3 and Figure 4 A graph showing the correlation between porosity, permeability, and carbonate content in the quantitative evaluation of reservoir heterogeneity using a combination of macro- and micro-scale methods.

[0059] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A quantitative evaluation method for reservoir heterogeneity combining macro- and micro-level analysis, characterized in that, Includes the following steps: 1) Based on the quantitative evaluation of macroscopic heterogeneity, obtain parameters representing the degree of heterogeneity of all oil-bearing formations in the study area; 2) Based on the quantitative evaluation of microscopic heterogeneity, obtain parameters representing the degree of heterogeneity of all oil-bearing formations in the study area; 3) Determine the set of m wells and the set of n index parameters in the study area, construct the index parameter evaluation matrix, and perform normalization processing; The n indicator parameters include: parameters for quantitative evaluation of macroscopic heterogeneity and parameters for quantitative evaluation of microscopic heterogeneity; 4) The entropy weight method is used to achieve a quantitative characterization of heterogeneity that combines macroscopic and microscopic perspectives.

2. The method for quantitative evaluation of reservoir heterogeneity combining macro-microscopic analysis according to claim 1, characterized in that, In step 1), the parameters in the quantitative evaluation of macroscopic heterogeneity include: Based on the permeability changes in the oil reservoir, obtain the permeability variation coefficient, permeability surge coefficient, and permeability gradient coefficient within and between layers; Obtain the porosity and sandstone density parameters of the oil reservoir.

3. The method for quantitative evaluation of reservoir heterogeneity combining macro-microscopic analysis according to claim 1, characterized in that, In step 2), the parameters for quantitative evaluation of microscopic heterogeneity include: Obtain parameters such as exhaust pressure, maximum connecting orifice throat radius, and orifice porosity.

4. The method for quantitative evaluation of reservoir heterogeneity combining macro-microscopic analysis according to claim 1, characterized in that, In step 3), normalization is performed by dividing the parameter value by the maximum value among all parameter values ​​for the positive parameter. Conversely, for reverse parameters, normalization is achieved by subtracting each parameter value from the maximum value of the parameter and then dividing by the maximum value.

5. The method for quantitative evaluation of reservoir heterogeneity combining macro-microscopic analysis according to claim 1, characterized in that, Step 3) is as follows: 4.1) Entropy value and entropy weight calculation: Calculate a certain parameter y j entropy value e j Then, based on the relationship between entropy value and entropy weight, the entropy weight 'a' of the corresponding index parameter is calculated. j The formula used is: in, 4.2) Calculation of conflicting index parameters: Calculate the index parameter x j Standard deviation σ j Formula used: x represents ij The average value across m wells; Calculate the index parameter x j With index parameter x l The correlation coefficient ρ between them il Formula used: 4.3) Weight Optimization and Composite Index Calculation: The weights calculated using the entropy weight method are optimized based on the magnitude of index conflict to determine the final weight w. j The formula used is: Finally, the comprehensive index I of reservoir heterogeneity is calculated based on the weight matrix W = (W1, W2, W3, ..., Wn), using the formula: I=WY T 。 6. An electronic device, characterized in that, include: One or more processors; as well as Storage device for storing one or more programs. Wherein, when the one or more programs are executed by the one or more processors, the one or more processors perform the method according to any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1 to 5.

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