Shale oil reservoir type identification method based on strata parameter extraction

Abstract

The invention provides a shale oil reservoir type identification method based on strata parameter extraction, and belongs to the field of unconventional oil and gas, and the method comprises the following steps: 1, carrying out strata identification based on electric imaging logging data; 2, extracting the thickness of the texture layer and determining the maximum thickness Lmax of the texture layer; 3, selecting a fixed window length WL and an initial step length SLj based on the maximum lamina thickness Lmax; 4, judging whether the fixed window length WL is covered with a fingerprint layer to change the initial step length SLj or not; step 5, calculating the density of the grain layer and the ratio of the thickness of the grain layer; and 6, determining the type of the shale oil reservoir based on the ratio of the density of the lamina to the thickness of the lamina. According to the method, based on the variable step size thought, more accurate grain density and grain thickness parameters can be extracted, and the terrestrial facies shale oil reservoir type can be automatically identified by integrating the grain density and grain thickness parameters, so that technical assistance is provided for cost reduction and efficiency improvement of shale oil exploration and development.

Description

Technical Field

[0001] The present invention relates to the field of unconventional oil and gas, and particularly to a method for identifying shale oil reservoir types based on the extraction of lamination parameters. Background Art

[0002] The potential of continental shale oil resources is huge and has become a strategic replacement area for oil and gas exploration in China. Preliminary evaluation results show that the technically recoverable resources of continental shale oil in China exceed 145×10 8 t (see the article "Global shale oil formation, distribution, potential and theoretical and technical progress of continental shale oil in China" published by Zou Caineng et al. in 2023). In recent years, through continuous technical research and development focusing on "sweet spot evaluation, horizontal wells and volume fracturing", a series of important progress has been made in the main continental shale formations of basins such as Junggar, Ordos, Songliao, Santanghu, Bohai Bay, and Beibu Gulf (see the article "Classification of continental shale oil in China and its significance" published by Jin Zhijun et al. in 2023).

[0003] According to the distribution states of source rocks and reservoir rocks, the medium- to high-maturity shale oil formations mainly develop three types of shale oil reservoirs: interlayer type, interbedded type, and pure shale type. There are certain differences in terms of hydrocarbon generation capacity, reservoir property, mobility, and fracturability, etc., and it is necessary to classify and evaluate them in well logging interpretation and evaluation. At present, the classification of these three types of shales mainly relies on manual observation and division, and no relatively convenient and fast automated method or model has been formed.

[0004] Laminae are the most basic units of sedimentary bedding and one of the distinguishable sedimentary structures visible to the naked eye in sediments or sedimentary rocks. As a unique fabric feature of shale, the development density and thickness of laminae have important implications for the evaluation of source rock quality, reservoir quality, and engineering quality, including organic matter abundance, petrological and mineralogical characteristics, as well as the fracture propagation law and fracturing effect of horizontal well volume fracturing. Currently, many researchers have conducted studies on shale laminae. CN116296946B discloses a method and device for characterizing the development degree of shale laminae based on fractal-wavelet theory, which characterizes the development degree of shale laminae based on fractal-wavelet theory. CN113808190B discloses a method for quantitatively extracting shale lamina information based on electrical imaging logging images, which quantitatively extracts the thickness and occurrence information of shale laminae based on electrical imaging logging images. CN116255130A discloses a method for evaluating the seepage characteristics of shale oil according to lamina density, screening key logging curves, and using the second derivative to determine the development density of laminae. CN111951347B is a method for extracting sand lamina parameters of shale oil and gas reservoirs. CN115761318A discloses a method, device, and storage medium for lamina identification. From the current research status of laminae, certain research results have been achieved in the identification and thickness extraction of laminae, but the quantitative characterization research of lamina development density is still insufficient. Conventional logging curves are not sufficient to accurately characterize laminae, and the accuracy of calculating lamina density based on logging curves is relatively low. Extracting lamina parameters based on electrical imaging logging images is the main method, but there will be an overlap between the window boundary and the laminae when extracting laminae with a fixed window length, resulting in an overestimated calculation of lamina density. This problem urgently needs to be solved.

[0005] The present invention proposes a method for identifying shale oil reservoir types based on the extraction of lamina parameters. By adopting the idea of variable step size, more accurate lamina density and lamina thickness parameters are extracted, and the types of continental shale oil reservoirs are automatically identified by integrating the lamina density and lamina thickness parameters, so as to provide technical support for reducing costs and increasing efficiency in shale oil exploration and development. Summary of the Invention

[0006] In view of the problems existing in the extraction of lamina parameters and the identification of shale oil reservoir types, considering that when extracting lamina density using a fixed window length based on electrical imaging logging data, the window boundary will overlap with the laminae, resulting in inaccurate calculation of lamina density, the present invention proposes a method for identifying shale oil reservoir types based on the extraction of lamina parameters. By adopting the idea of variable step size and moving the window by changing the step size, the laminae can be fully included in the window, accurately calculating the lamina density and thickness, and finally achieving the purpose of identifying shale oil reservoir types.

[0007] To achieve the above object, the technical solution of the present invention is realized as follows: A method for identifying shale oil reservoir types based on the extraction of lamina parameters, comprising the following steps:

[0008] Step 1: Conduct lamination identification based on electrical imaging logging data;

[0009] Step 2: Extract lamination thickness and determine the maximum lamination thickness L max ;

[0010] Step 3: Select a fixed window length WL and an initial step length SL based on the maximum lamination thickness L max ; j ;

[0011] Step 4: Determine whether the fixed window length WL covers the lamination to change the initial step length SL j ;

[0012] Step 5: Calculate the lamination density and the proportion of lamination thickness;

[0013] Step 6: Discriminate the shale oil reservoir type based on the lamination density and the proportion of lamination thickness.

[0014] Furthermore, Step 2 includes:

[0015] First, calculate the depth difference corresponding to the upper and lower boundaries of the lamination as the lamination thickness. The calculation formula for the lamination thickness is as follows:

[0016] L i+1 = Dep i+1 - Dep i (3)

[0017] In the formula, L i+1 is the lamination thickness of the lamination interface i, m; Dep i+1 is the depth corresponding to the lamination interface i + 1, m; Dep i is the depth corresponding to the lamination interface i, m;

[0018] Then, based on the lamination thickness, extract and determine the maximum lamination thickness L max .

[0019] Furthermore, in Step 3, taking the maximum lamination thickness L identified in Step 2 max as a reference, it is stipulated that the fixed window length WL = L max + 0.01, and the initial step length SL j = WL.

[0020] Furthermore, Step 4 includes using a variable step length method to move the window by changing the step length so that the lamination is fully included within the window length WL to achieve the purpose of accurately calculating the lamination density.

[0021] Furthermore, in Step 5, the lamination density refers to the ratio of the number of laminations developed within the fixed window length to the fixed window length. The formula is expressed as follows:

[0022] D = n / WL (4)

[0023] In the formula, D is the lamination density; n is the number of laminations developed within the fixed window length; WL is the fixed window length;

[0024] The proportion of lamination thickness refers to the proportion of the cumulative thickness of each lamination in a single window length in the fixed window length;

[0025] Among them, the cumulative thickness of each lamination in a single window length is expressed as: L i 1 + ··· + L i n , and the calculation formula for the proportion of lamination thickness is expressed as follows:

[0026] B = (L i 1 + ··· + L i n ) / WL (5)

[0027] In the formula, B is the proportion of lamination thickness; L i n is the thickness of the nth lamination in the ith window.

[0028] Furthermore, step 6 includes: determining the high and low thresholds D of lamination density and the large and small threshold B of the proportion of lamination thickness according to core observation or previous research experience f ; comprehensively discriminating the type of shale oil reservoir based on the lamination density and the proportion of lamination thickness. f ; comprehensively discriminating the type of shale oil reservoir based on the lamination density and the proportion of lamination thickness.

[0029] Compared with the prior art, the method for identifying the type of shale oil reservoir based on the extraction of lamination parameters according to the present invention has the following advantages:

[0030] (1) The present invention realizes the automation of lamination parameter extraction and shale oil reservoir type identification;

[0031] (2) The present invention takes into account the phenomenon that when extracting the lamination density using a fixed window length based on resistivity imaging logging data, the window boundary will overlap with the lamination, which will lead to inaccurate calculation of the lamination density. It proposes to adopt the idea of variable step length, move the window by changing the step length, so that the lamination is fully contained within the window, and can accurately calculate the lamination density and thickness;

[0032] (3) On the basis of accurately extracting the lamination parameters, the present invention realizes the purpose of identifying the type of shale oil reservoir based on the lamination parameters, which also provides a new method reference for shale oil identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0034] Figure 1 This is the technical roadmap of the shale oil reservoir type identification method based on lamination parameter extraction described in the embodiments of the present invention;

[0035] Figure 2 This is the filling effect diagram of the blank band in the electrical imaging;

[0036] Figure 3 This is to pick up the lamination by segmenting the electrical imaging image;

[0037] Figure 4 This is the schematic diagram of the variable step-size moving window;

[0038] Figure 5 This is the discrimination pattern diagram of the shale oil reservoir type;

[0039] Figure 6 This is the calculation example diagram of the lamination development density of a single well. Specific implementation manners

[0040] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0041] The present invention will be described in detail below with reference to the drawings and in combination with the embodiments.

[0042] The present invention is a shale oil reservoir type identification method based on lamination parameter extraction, including the following steps:

[0043] Step 1: Identify the lamination based on the electrical imaging logging data;

[0044] Based on the logging data processing and interpretation software EGPS and the electrical imaging logging data, identify the sandy lamination. The method for identifying the sandy lamination is a prior art, and the specific process of identification is as follows:

[0045] 1) 360-degree filling of the electrical imaging logging image

[0046] The imaging logging can obtain the two-dimensional image around the well, which can more intuitively and clearly reflect the structure and characteristics of the wellbore. Using the visibility and intuitiveness of the logging image, geological problems that are difficult to solve by conventional logging can be solved. However, due to the reasons of the wellbore structure and the structure of the electrical imaging logging instrument, when measuring, the instrument is in an open state, resulting in part of the wellbore not being measured during the scanning along the wellbore, and the coverage rate cannot reach 100%, and white bands are generated in the electrical logging image, affecting the quality of the image. Use the gated convolution method to fill the electrical imaging logging image by 360 degrees, and the effect is shown in Figure 2 (the fifth track).

[0047] 2) Identify the lamination by the watershed algorithm

[0048] The watershed segmentation method is a segmentation method based on the topological theory of mathematical morphology. Its basic idea is to regard the image as a topological landform in geodesy. The gray value of each pixel in the image represents the altitude of that point. Each local minimum value and its influence area are called catchment basins, and the boundaries of the catchment basins form watersheds. The concept and formation of watersheds can be illustrated by simulating the immersion process. On the surface of each local minimum value, a small hole is pierced, and then the whole model is slowly immersed in water. As the immersion deepens, the influence domain of each local minimum value slowly expands outwards. A dam is built at the confluence of two catchment basins, that is, the watershed is formed.

[0049] The calculation process of the watershed is an iterative labeling process. The more classic calculation method of the watershed was proposed by L Vincent. In this algorithm, the watershed calculation is divided into two steps, one is the sorting process, and the other is the flooding process. First, the gray levels of each pixel are sorted from low to high. Then, during the flooding process from low to high, the influence domain of each local minimum value at the h-order height is judged and labeled using the first-in-first-out (FIFO) structure.

[0050] What the watershed transformation obtains is the catchment basin image of the input image. The boundary points between the catchment basins are the watersheds. The watershed represents the maximum value points of the input image. Therefore, to obtain the edge information of the image, usually the gradient image is used as the input image, that is:

[0051] g(x,y) = grad(f(x,y)) = {[f(x,y) - f(x - 1,y)] 2 [f(x,y) - f(x,y - 1)] 2} 0.5 (1)

[0052] In the formula, f(x, y) represents the original image, and grad{.} represents the gradient operation.

[0053] The watershed algorithm has a good response to weak edges. Noise in the image and subtle gray level changes on the object surface will both produce the phenomenon of over-segmentation. However, it should also be noted that the good response of the watershed algorithm to weak edges is a guarantee for obtaining closed and continuous edges. In addition, the closed catchment basins obtained by the watershed algorithm provide the possibility for analyzing the regional characteristics of the image.

[0054] To eliminate the over-segmentation generated by the watershed algorithm, usually two processing methods can be adopted. One is to use prior knowledge to remove irrelevant edge information; the other is to modify the gradient function so that the catchment basins only respond to the targets to be detected.

[0055] To reduce the over-segmentation caused by the watershed algorithm, it is usually necessary to modify the gradient function. A simple method is to perform threshold processing on the gradient image to eliminate the over-segmentation caused by small changes in gray level. That is:

[0056] g(x, y) = max(grad(f(x, y)), gθ) (2)

[0057] In the formula, gθ represents the threshold.

[0058] As Figure 3 shown, Figure 3 The left is the image of the electrical imaging logging data after the preliminary data, and then the blank is filled 360° by the gated convolution method. Figure 3 The right is the lamination obtained by the watershed algorithm.

[0059] Step 2, extract the lamination thickness and determine the maximum lamination thickness L max ;

[0060] First, calculate the depth difference corresponding to the upper and lower boundaries of the lamination as the lamination thickness. Among them, the calculation formula of the lamination thickness is as follows:

[0061] L i+1 = Dep i+1 -Dep i (3)

[0062] In the formula, L i+1 is the lamination thickness of the lamination interface i, m; Dep i+1 is the depth corresponding to the lamination interface i + 1, m; Dep i is the depth corresponding to the lamination interface i, m;

[0063] Then, based on the lamination thickness extraction, determine the maximum lamination thickness L max (Compare all the calculated lamination thickness values, take the maximum value of the lamination thickness, and determine it as the maximum lamination thickness L max ).

[0064] Step 3, select a fixed window length WL and an initial step length SL based on the maximum lamination thickness L max ; j ;

[0065] According to the distribution state of the source rock and the reservoir rock, there are mainly three types of shale oil developed in the medium-high maturity shale formation: interlayer type, interbedded type, and pure shale type. The interlayer type means that the single-layer thickness of siltstone is more than 3.0 m and it is the interlayer of the source rock; the interbedded type means that the single-layer thickness of siltstone is 0.2 m - 3.0 m and it is interbedded with the source rock; the pure shale type means that the thickness of laminated shale, laminated shale and mudstone siltstone stripes is less than 0.2 m, as shown in Table 1.

[0066] Table 1 Classification Criteria for Shale Oil Reservoir Types

[0067]

[0068] The laminations and interlayers identified in Step 1 and Step 2 of the present invention are collectively referred to as laminations (including interlayers with a single-layer thickness of more than 3.0 m, laminations less than 3 m, and thin laminations less than 0.2 m). The present invention uses the maximum lamination thickness L identified in Step 2 max as a reference, and stipulates that the fixed window length WL = L max + 0.01 (the purpose of adding 0.01 is to ensure that the maximum lamination can be completely included within the window to obtain a more accurate lamination density), and stipulates the initial step length SL j = WL.

[0069] Step 4, determine whether the fixed window length WL covers the lamination to change the initial step length SL j ;

[0070] When extracting the lamination density using a fixed window length based on the electrical image logging data, the window boundary will overlap with the lamination, which will lead to inaccurate calculation of the lamination density and will wrongly split a lamination into two laminations, resulting in an increase in the lamination development density. The present invention adopts the means of variable step length, and moves the window by changing the step length (adding 0.01 m to the initial step length SL j as the new step length SL j+1 , but the step length automatically becomes the initial step length SL j when measuring the next window), so that the lamination is completely included within the window length WL to achieve the purpose of accurately calculating the lamination density. The schematic diagram of moving the window with variable step length is as shown in Figure 4 . As can be seen from Figure 4 , the window boundary of the initial window length WL overlaps with the lamination boundary, which will lead to inaccurate calculation of the lamination density. By changing the initial step length SL j , the window is caused to move, so that the lamination is completely included within the window length WL to achieve the purpose of accurately calculating the lamination density. Since the window length is fixed, the moved window can ensure that all the extracted lamination densities and lamination thicknesses are compared under the same standard.

[0071] Step 5, calculate the lamination density and the proportion of the lamination thickness;

[0072] The lamination density refers to the ratio of the number of laminations developed within the fixed window length to the fixed window length, and the formula is expressed as follows:

[0073] D = n / WL (4)

[0074] In the formula, D is the lamination density; n is the number of laminations developed within the fixed window length; WL is the fixed window length;

[0075] The proportion of lamination thickness refers to the proportion of the cumulative thickness of each lamination in a single window length in the fixed window length;

[0076] Among them, the cumulative thickness of each lamination in a single window length is expressed as: L i 1+···+L i n , and the calculation formula for the proportion of lamination thickness is expressed as follows:

[0077] B = (L i 1+···+L i n ) / WL (5)

[0078] In the formula, B is the proportion of lamination thickness; L i n is the thickness of the nth lamination in the ith window.

[0079] Step 6: Identify the shale oil reservoir type based on the lamination density and the proportion of lamination thickness.

[0080] As Figure 5 shown, comprehensively identify the shale oil reservoir type (interbedded type, interlayered type, pure shale type) based on the lamination density and the proportion of lamination thickness. Before identification, it is necessary to determine the high and low thresholds D f of the lamination density and the large and small threshold B f of the proportion of lamination thickness according to core observation or previous research experience, and identify the shale oil type according to the discrimination mode shown by the high and low threshold D f of the lamination density, the large and small threshold B f of the proportion of lamination thickness and Figure 5 . According to the general judgment principle, the interbedded type is characterized by high lamination density and large proportion of lamination thickness; the pure shale type is characterized by high or low lamination density but small proportion of lamination thickness; the interlayered type is characterized by low lamination density but large proportion of lamination thickness. Different basins and different oil and gas fields conduct type discrimination in combination with the actual situation of the region. However, it should be noted that although the thresholds in different regions may vary, the general judgment principle still needs to be followed, that is, the interbedded type is when the lamination density is higher than the threshold D f and the proportion of lamination thickness is greater than the threshold B f ; the pure shale type is when the lamination density is higher than the threshold D f or lower than the threshold D f , but the proportion of lamination thickness is less than the threshold B f ; the interlayered type is when the lamination density is lower than the threshold D f but the proportion of lamination thickness is greater than the threshold B f .

[0081] Next, take Well X in the X Oilfield in the Beibu Gulf Basin of China as an example.

[0082] Figure 1This is the specific implementation flowchart of the present invention. According to the process, the laminations of a sample well are identified and the thickness is extracted to obtain L max is 3.9m, so SL j = L max + 0.01 = 3.91m. The high and low thresholds D f of the lamination density and the threshold B f of the proportion of the lamination thickness are determined through core observation. That is, for the X Oilfield in the Beibu Gulf Basin, those with a lamination density greater than 4 strips / m and a proportion of the lamination thickness greater than 0.6 are interbedded types; those with a proportion of the lamination thickness less than 0.6 are pure shale types; those with a lamination density less than 4 strips / m but a proportion of the lamination thickness greater than 0.6 are interlayer types. Then, the lamination density and the proportion of the lamination thickness are calculated. The calculation results of some local sections are as shown in Figure 6 . By comparing with the lamination density and the proportion of the lamination thickness obtained from the actual observation of the core by artificial means, the calculated lamination density and lamination thickness are basically consistent with the lamination density observed in the core, and the correlation coefficients are as high as 0.87 and 8.4 respectively (the scatter plot intersection analysis is carried out on the lamination density and lamination thickness obtained from the actual observation of the core and the calculated lamination density and lamination thickness to determine the correlation coefficient). The determined shale oil reservoir type is also consistent with the conclusion of the core observation. That is, the shale oil reservoir type identification method based on the extraction of lamination parameters of the present invention can better extract the lamination density and lamination thickness parameters, and can distinguish the shale oil reservoir type, and can provide certain technical support for the evaluation of the quality and engineering quality of shale oil reservoirs

[0083] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention

Claims

1. A shale oil reservoir type identification method based on laminae parameter extraction, characterized in that: The following steps are involved: Step 1, lamination identification based on electrical imaging logging data; Step 2: Extract the laminae thickness and determine the maximum laminae thickness L max ; Step 3, based on the maximum laminae thickness L max Select a fixed window length WL and initial step length SL j ; Step 4: Determine whether the fixed window length WL covers the lamination to change the initial step length SL j ; Step 5, calculate the laminae density and laminae thickness ratio; Step 6: Identify the shale oil reservoir type based on the ratio of lamina density to lamina thickness.

2. The shale oil reservoir type identification method based on laminae parameter extraction according to claim 1 is characterized in that ,Step 2 includes: First, the depth difference between the upper and lower boundaries of the laminae is calculated as the thickness of the laminae. The calculation formula for the laminae thickness is as follows: IT i+1 =Department i+1 -Dep i (3) Where, L i+1 is the thickness of the laminae at laminae interface i, m; Dep i+1 Dep is the depth corresponding to the laminae interface i+1, m; i is the depth corresponding to the laminae interface i, m; Then, based on the laminae thickness, the maximum laminae thickness L is extracted and determined. max .

3. The shale oil reservoir type identification method based on laminae parameter extraction according to claim 2 is characterized in that: In step 3, the maximum laminae thickness L identified in step 2 is max For reference, a fixed window length WL=L is specified. max +0.01, and specify the initial step length SL j =WL.

4. The shale oil reservoir type identification method based on laminae parameter extraction according to claim 3 is characterized in that: Step 4 includes using a variable step size to move the window by changing the step size so that the laminae are all contained within the window length WL, so as to achieve the purpose of accurately calculating the laminae density.

5. The shale oil reservoir type identification method based on laminae parameter extraction according to claim 1 is characterized in that: In step 5, the lamina density refers to the ratio of the number of lamina developed within a fixed window length to the fixed window length, and the formula is expressed as follows: D=n / WL (4) Where D is the density of laminae; n is the number of laminae developed within a fixed window length; WL is the fixed window length; The proportion of lamina thickness refers to the proportion of the cumulative thickness of each lamina in a single window length to the fixed window length; The cumulative thickness of each layer in a single window length is expressed as: L i 1+···+L i n , the calculation formula of the laminae thickness ratio is expressed as follows: B=(L i 1+···+L i n ) / WL (5) Where, B is the thickness ratio of the laminae; L i n is the thickness of the nth layer in the ith window.

6. The shale oil reservoir type identification method based on laminae parameter extraction according to claim 1 is characterized in that: Step 6 includes: determining the high and low thresholds of laminae density D based on core observations or previous research experience f And the threshold value B of the laminae thickness ratio f ; The shale oil reservoir type is identified based on the comprehensive ratio of lamina density and lamina thickness.

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