An evaluation method and industrialized process for interlayer heterogeneity of reservoirs in old oilfields

Abstract

The present invention discloses a method for evaluating the interlayer heterogeneity of reservoirs in old oilfields and an industrialized process. The logging interpretation curves of porosity, permeability, and shale content are standardized. After the formation is subdivided, the reservoir parameter data sets are aggregated within the secondary formation units, and a data grid is obtained through planar interpolation of multi-well data. Using the RGB color system, with the porosity, permeability, and shale content values as the brightness values of red, green, and blue colors respectively, a three-parameter fusion image of the study area is obtained; a reservoir multifractal heterogeneity index is proposed, and this index is calculated using the multifractal analysis results of the fused image; after calculating the reservoir multifractal heterogeneity index layer by layer, the reservoir heterogeneity of the secondary formations within the target layer is ranked according to the magnitude of this index. The present invention provides a simple, reasonable, and effective method for comprehensively evaluating the differences in interlayer heterogeneity of reservoirs considering various factors.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas reservoir development, belonging to the category of oil and gas reservoir development geology. Specifically, it is a method for evaluating the interlayer heterogeneity of reservoirs in old oilfields and an industrialized process. Background Art

[0002] With the urgent need to reduce costs and increase efficiency in oil reservoir development and the requirement of accurate development to reduce energy consumption driven by the "dual carbon" goal, it is necessary to accurately characterize the reservoir heterogeneity characteristics of oil reservoirs, understand the distribution of remaining oil controlled by reservoir heterogeneity, guide the economic and efficient development of oil and gas reservoirs, reduce ineffective water injection, reduce high-energy consumption input, and reduce sewage treatment costs. Among them, for old oilfields that have experienced long-term development, the interlayer heterogeneity of reservoirs is an important basis for reasonably dividing development strata and designing water injection schemes, and is one of the key points in oil and gas reservoir development geological analysis. Some widely used methods for characterizing the interlayer heterogeneity of reservoirs have been formed in the academic and petroleum industries. However, there are still several problems in these characterization methods:

[0003] First, the parameters cover one-sided information. Commonly used characterization methods use porosity, permeability, and heterogeneity parameters of permeability as the main evaluation parameters. For example, the coefficient of variation of permeability, the permeability ratio, the permeability breakthrough coefficient, etc. These parameters mainly characterize the reservoir storage and seepage capabilities and their differential distributions. It is true that the seepage ability of the reservoir affects oil reservoir development. However, on the one hand, the accuracy of the logging interpretation results of permeability parameters in complex oil and gas reservoirs needs to be improved, and their errors are further transmitted to the characterization of reservoir heterogeneity, affecting the reliability of the characterization results. On the other hand, oil and gas reservoir development is affected by various factors and needs to be considered comprehensively.

[0004] Second, although there are literature reports on attempts to conduct multi-factor analysis in the analysis of interlayer heterogeneity of reservoirs, how to fuse parameters reflecting different geological factors is a challenging task. The quantitative evaluation method of multi-parameter weighted average is the main method used, but there are great problems in the rationality of determining the weights in such methods.

[0005] On the one hand, the present invention realizes the fusion of three-parameter information by using the method of color fusion, avoiding the problems of the aforementioned weighted average method. On the other hand, a new reservoir heterogeneity evaluation index is constructed by using two analysis indexes of multifractal to quantitatively evaluate the heterogeneity of single layers within the research strata, providing new ideas, specific methods, and work processes for the analysis of interlayer heterogeneity of reservoirs. Summary of the Invention

[0006] The present invention provides a method for evaluating the interlayer heterogeneity of reservoirs in old oilfields and an industrialized process. The specific technical solutions of this method include the following steps:

[0007] Step (1): Conduct logging interpretation of reservoir parameters to obtain the porosity curve, permeability curve, and shale content curve of the reservoir section to be studied. Standardize the porosity curve, permeability curve, and shale content curve of the reservoir section respectively. The data ranges of the three parameters after processing are all [0, 255]. The specific processing is as follows:

[0008]

[0009]

[0010]

[0011] Among them, Φ i 、K i 、Vsh i are the porosity, permeability, and shale content values of the i-th data point in the order from top to bottom according to depth on the logging interpretation parameter curve of the target interval respectively. Φ' i 、K' i 、Vsh' i are the porosity, permeability, and shale content values of the i-th data point after standardization respectively. Φ max 、K max 、Vsh max are the maximum porosity, maximum permeability, and maximum shale content of the target interval respectively. Φ min 、K min 、Vsh min are the minimum porosity, minimum permeability, and minimum shale content of the target interval respectively. i is a natural number;

[0012] Step (2): Conduct fine stratification of the formation. Denote the number of secondary layers in the target interval as m. Number the m secondary formations from top to bottom. Denote the serial number of the j-th secondary formation as j. j is a natural number, and j = 1, 2, 3, …, m;

[0013] Step (3): Select the j-th layer as the research object. Aggregate the standardized porosity parameters at each well point in the j-th layer to obtain the standardized porosity value at each well point in the j-th layer. Limit the data range within the closed interval [0, 255] for planar gridding and interpolation calculation of the data to obtain the standardized porosity plane distribution data grid of the j-th layer;

[0014] Step (4): Select the j-th layer as the research object. Aggregate the standardized permeability parameters at each well point in the j-th layer to obtain the standardized permeability value at each well point in the j-th layer. Limit the data range within the closed interval [0, 255] for planar gridding and interpolation calculation of the data to obtain the standardized permeability plane distribution data grid of the j-th layer;

[0015] Step (5): Select the j-th layer as the research object, aggregate the standardized shale content parameters at each well point within the j-th layer to obtain the standardized shale content values at each well point in the j-th layer, limit the data range to the closed interval [0, 255], perform planar gridding and interpolation calculations on the data, and obtain the standardized shale content planar distribution data grid for the j-th layer;

[0016] Step (6): Denote the position coordinates of any point on the plane of the study area as (x t , y p ). Obtain the three logging interpretation parameters at the point (x t , y p ) from the three parameter planar distribution data grids obtained in steps (3) - (5) to form an array, denoted as (Φ tp , K tp , Vsh tp ). The numerical range of each parameter in this array is [0, 255]. Using the RGB color system, take the three numerical values in the above array as the brightness values of the red, green, and blue colors in the RGB color system respectively, that is: Denote the RGB color value of this point as (R tp , G tp , B tp ), where:

[0017] Red component R tp = Φ tp

[0018] Green component G tp = K tp

[0019] Blue component B tp = Vsh tp

[0020] where t, p, and tp are all natural numbers,

[0021] Use the above method to achieve the color fusion display of the three logging parameters at any plane position point in the study area, and obtain the color fusion image of the planar distribution of the three logging parameters in the study area according to this method;

[0022] Step (7): Perform multifractal analysis on the three-parameter color fusion image of the j-th layer obtained in step (6) to obtain the multifractal singularity exponent and singularity spectrum of this image, denoted as α j and f j (α) respectively. Calculate the data distribution widths of the above two parameters, denoted as Δα j and Δf j (α), where Δα j is the difference between the maximum and minimum values of the multifractal singularity exponent of the image, and Δf j(α) is the difference between the maximum and minimum values of the image multifractal singularity spectrum;

[0023] Step (8): Calculate Δα obtained in step (7) j and Δf j (α) The root mean square of the two parameters, which is defined as the reservoir multifractal heterogeneity index of the j-th layer and denoted as T j , as the heterogeneity characterization parameter of the j-th layer, the expression is as follows:

[0024]

[0025] Step (9): According to the methods and processes of steps (3) to (8), sequentially obtain the reservoir multifractal heterogeneity index of each layer from the 1st layer to the m-th layer. The smaller this index is, the weaker the reservoir heterogeneity of the corresponding layer is, so as to evaluate and rank the heterogeneity of the secondary strata within the target layer for research.

[0026] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0027] (1) The present invention realizes the three-parameter information fusion by using the color fusion method, and at the same time maintains the independence of the three-parameter information, avoiding the problem of multi-parameter weighted average in the existing methods;

[0028] (2) A new reservoir heterogeneity evaluation index is constructed by using two analysis indexes of multifractal to quantitatively evaluate the heterogeneity of a single layer within the research series, providing new ideas, specific methods and working processes for the analysis of reservoir interlayer heterogeneity. Specific embodiments

[0029] Implement according to the steps in the above-mentioned invention content, specifically including the following steps:

[0030] Step (1): Carry out logging interpretation of reservoir parameters to obtain the porosity curve, permeability curve and shale content curve of the research reservoir section, and perform standardization processing on the porosity curve, permeability curve and shale content curve of the reservoir section respectively. The data ranges of the three processed parameters are all [0, 255], and the specific processing is as follows:

[0031]

[0032]

[0033]

[0034] Among them, Φ i , K i , Vsh iThey are the porosity, permeability, and shale content values of the i-th data point in the log interpretation parameter curve of the target interval in the order from top to bottom according to depth, Φ' i , K' i , Vsh' i They are the porosity, permeability, and shale content values of the i-th data point after standardization processing, Φ max , K max , Vsh max They are the maximum porosity, maximum permeability, and maximum shale content in the target interval, Φ min , K min , Vsh min They are the minimum porosity, minimum permeability, and minimum shale content in the target interval, and i is a natural number;

[0035] Step (2): Denote the number of secondary layers in the target interval as m, number the m secondary formations from top to bottom, and denote the serial number of the j-th secondary formation as j, where j is a natural number and j = 1, 2, 3, …, m;

[0036] Step (3): Select the j-th layer as the research object, aggregate the standardized porosity parameters at each well point in the j-th layer, obtain the standardized porosity value at each well point in the j-th layer, limit the data range within the closed interval [0, 255], perform planar gridding and interpolation calculation on the data, and obtain the standardized porosity planar distribution data grid of the j-th layer;

[0037] Step (4): Select the j-th layer as the research object, aggregate the standardized permeability parameters at each well point in the j-th layer, obtain the standardized permeability value at each well point in the j-th layer, limit the data range within the closed interval [0, 255], perform planar gridding and interpolation calculation on the data, and obtain the standardized permeability planar distribution data grid of the j-th layer;

[0038] Step (5): Select the j-th layer as the research object, aggregate the standardized shale content parameters at each well point in the j-th layer, obtain the standardized shale content value at each well point in the j-th layer, limit the data range within the closed interval [0, 255], perform planar gridding and interpolation calculation on the data, and obtain the standardized shale content planar distribution data grid of the j-th layer;

[0039] Step (6): Denote the position coordinates of any point on the plane of the study area as (x t , y p ), and obtain an array composed of the three log interpretation parameters at the point (x t , y p ) from the three parameter planar distribution data grids obtained in steps (3) to (5), denoted as (Φ tp , Ktp , Vsh tp ), the numerical range of each parameter in the array is [0, 255]. Using the RGB color system, the three numerical values in the above array are respectively used as the brightness values of red, green, and blue in the RGB color system, that is: the RGB color value of this point is denoted as (R tp , G tp , B tp ), where:

[0040] Red component R tp = Φ tp

[0041] Green component G tp = K tp

[0042] Blue component B tp = Vsh tp

[0043] where t, p, and tp are all natural numbers,

[0044] Using the above method to achieve the color fusion display of well logging three parameters at any plane position point in the study area, and obtaining the color fusion image of the well logging three parameter plane distribution in the study area according to this method;

[0045] Step (7): Perform multifractal analysis on the three-parameter color fusion image of the jth layer obtained in step (6) to obtain the multifractal singularity index and singularity spectrum of the image, denoted as α j and f j (α), respectively calculate the data distribution widths of the above two parameters, denoted as Δα j and Δf j (α), where Δα j is the difference between the maximum and minimum values of the multifractal singularity index of the image, and Δf j (α) is the difference between the maximum and minimum values of the multifractal singularity spectrum of the image;

[0046] Step (8): Calculate the root mean square of the two parameters Δα j and Δf j (α) obtained in step (7), and define it as the reservoir multifractal heterogeneity index of the jth layer, denoted as T j , as the heterogeneity characterization parameter of the jth layer, and the expression is as follows:

[0047]

[0048] Step (9): According to the methods and processes of steps (3) to (8), the reservoir multifractal heterogeneity indices of each layer from layer 1 to layer m are obtained in sequence. The smaller this index is, the weaker the reservoir heterogeneity of the corresponding layer is, thereby evaluating and ranking the heterogeneity of the secondary strata within the target layer for the research purpose.

[0049] Example

[0050] The research area of this example is located in a certain block of Dagang Oilfield, and the target interval is a certain oil zone in the lower part of the Zaoxian Formation of the Paleogene Kongdian Formation. There are 160 various types of wells in this area.

[0051] Implement according to the steps in the above invention content, specifically including the following steps:

[0052] Step (1): Obtain the porosity curve, permeability curve, and shale content curve of the target interval through well logging interpretation, and perform standardization processing on the above three curves respectively. The data ranges of the three processed parameters are all [0, 255];

[0053] Step (2): Subdivide the strata within the oil zone. There are 6 sub-layers developed in this oil zone, and the 6 sub-layers are numbered from top to bottom;

[0054] Step (3): Aggregate the standardized porosity parameters at each well point in layer 1 to obtain the standardized porosity values of 160 wells in layer 1. Limit the data range within the closed interval [0, 255] for data plane gridding and interpolation calculation to obtain the standardized porosity plane distribution data grid of layer 1;

[0055] Step (4): According to the method of step (3), use the standardized permeability curve to obtain the permeability plane distribution data grid of layer 1;

[0056] Step (5): According to the method of step (3), use the standardized shale content parameter to obtain the shale content plane distribution data grid of layer 1;

[0057] Step (6): Make a color fusion image of the plane distribution of the three well logging parameters in the research area. The image uses the RGB color system. The numerical values of red, green, and blue in the color of each position point are respectively the standardized porosity, permeability, and shale content numerical values at that point. These numerical values are from the three parameter plane distribution data grids obtained in steps (3) to (5). Taking one point as an example, if the values in the plane distribution data grids of porosity, permeability, and shale content are 200, 185, and 86 respectively, then the RGB color numerical value of this point is (200, 185, 86);

[0058] Step (7): Perform multifractal analysis on the three-parameter color fusion image of Layer 1 obtained in step (6) to obtain the multifractal singular values and singular spectrum of the image, and calculate the data distribution span of the multifractal singular values and the data distribution span of the singular spectrum of the image respectively;

[0059] Step (8): Use the data distribution span 0.7233 of the multifractal singular values of the image and the data distribution span 0.9191 of the singular spectrum obtained in step (7), and the root mean square of the two parameters as the multifractal heterogeneity index 1.1696 of the reservoir of Layer 1;

[0060] Step (9): According to the methods and procedures of steps (3) to (8), sequentially obtain the multifractal heterogeneity index of the reservoir for each layer from Layer 1 to Layer 6. The smaller this index is, the weaker the reservoir heterogeneity of the corresponding layer. Sort the heterogeneity of the 6 sub-layers. The sub-layer numbers with heterogeneity from weak to strong are 1, 3, 4, 2, 5, 6 in sequence.

[0061] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to the embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An evaluation method and industrialized process for interlayer heterogeneity of reservoir layers in old oilfields, characterized in that, Including the following steps: Step (1): Conduct logging interpretation of reservoir parameters to obtain the porosity curve, permeability curve, and shale content curve of the reservoir section to be studied. Standardize the porosity curve, permeability curve, and shale content curve of the reservoir section respectively. The data ranges of the three parameters after processing are all [0, 255]. The specific processing is as follows: Among them, Φ i , K i , Vsh i are the porosity, permeability, and shale content values of the i-th data point in the log interpretation parameter curve of the target interval in the order from top to bottom according to depth, Φ' i , K i ', Vsh' i are the porosity, permeability, and shale content values of the i-th data point after standardization, Φ max , K max , Vsh max are the maximum porosity, maximum permeability, and maximum shale content in the target interval, Φ min , K min , Vsh min are the minimum porosity, minimum permeability, and minimum shale content in the target interval, and i is a natural number; Step (2): Conduct fine stratification of the formation. Denote the number of secondary layers within the target layer section as m. Number the m secondary formations from top to bottom. Denote the serial number of the j-th secondary formation as j, where j is a natural number and j = 1, 2, 3, …, m; Step (3): Select the j-th layer as the research object. Aggregate the standardized porosity parameters at each well point within the j-th layer to obtain the standardized porosity value at each well point in the j-th layer. Limit the data range within the closed interval [0, 255] for planar gridding and interpolation calculation of the data to obtain the standardized porosity planar distribution data grid of the j-th layer; Step (4): Select the j-th layer as the research object. Aggregate the standardized permeability parameters at each well point within the j-th layer to obtain the standardized permeability value at each well point in the j-th layer. Limit the data range within the closed interval [0, 255] for planar gridding and interpolation calculation of the data to obtain the standardized permeability planar distribution data grid of the j-th layer; Step (5): Select the j-th layer as the research object. Aggregate the standardized shale content parameters at each well point within the j-th layer to obtain the standardized shale content value at each well point in the j-th layer. Limit the data range within the closed interval [0, 255] for planar gridding and interpolation calculation of the data to obtain the standardized shale content planar distribution data grid of the j-th layer; Step (6): Denote the position coordinates of any point on the plane of the study area as (x t , y p ). Obtain an array composed of three logging interpretation parameters at the point (x t , y p ) from the three parameter plane distribution data grids obtained in steps (3) to (5), and denote it as (Φ tp , K tp , Vsh tp ). The numerical range of each parameter in this array is [0, 255]. Using the RGB color system, take the three numerical values in the above array as the brightness values of the red, green, and blue colors in the RGB color system respectively, and denote the RGB color value of this point as (R tp , G tp , B tp ), where: Red component R tp = Φ tp Green component G tp = K tp Blue component B tp = Vsh tp where t, p, and tp are all natural numbers, Use the above method to achieve the color fusion display of the three logging parameters at any planar position point in the study area, and obtain the color fusion image of the planar distribution of the three logging parameters in the study area according to this method; Step (7): Perform multifractal analysis on the three-parameter color fusion image of the j-th layer obtained in step (6) to obtain the multifractal singularity exponent and singularity spectrum of the image, denoted as α j and f j (α), respectively calculate the data distribution widths of the above two parameters, denoted as Δα j and Δf j (α), where Δα j is the difference between the maximum and minimum values of the multifractal singularity exponent of the image, and Δf j (α) is the difference between the maximum and minimum values of the multifractal singularity spectrum of the image; Step (8): Calculate Δα obtained in step (7) j and Δf j (α) The root mean square of the two parameters, which is defined as the reservoir multifractal heterogeneity index of the j-th layer, denoted as T j , as the heterogeneity characterization parameter of the j-th layer, the expression is as follows: Step (9): According to the methods and processes of steps (3) to (8), sequentially obtain the reservoir multifractal heterogeneity index of each layer from the 1st layer to the m-th layer. The smaller the index, the weaker the reservoir heterogeneity of the corresponding layer, thereby evaluating and ranking the heterogeneity of the secondary formations within the study target layer.

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