Methods, identification methods, and devices for constructing microbial dolomite lithofacies identification plates.

Abstract

This invention discloses a method, identification method, and apparatus for constructing a microbial dolomite lithofacies identification chart. The construction method includes: determining the natural gamma ray parameters of the formation based on the natural gamma ray logging curve of a core calibration well; interpolating the grayscale image of the electro-imaging logging of the core calibration well to obtain a full-borehole coverage electro-imaging logging grayscale image of the core calibration well; binarizing the full-borehole coverage electro-imaging logging grayscale image to obtain an electro-imaging binary image of the core calibration well; determining the multifractal spectrum parameters of the core calibration well based on the electro-imaging binary image; wherein the multifractal spectrum parameters include: multifractal spectrum peak value, multifractal spectrum asymmetry index, and multifractal spectrum width; and constructing a microbial dolomite lithofacies identification chart based on the lithofacies, natural gamma ray parameters, and multifractal spectrum parameters of the core calibration well. This method combines the advantages of conventional logging and electro-imaging logging, enabling high-precision and high-efficiency identification of dolomite types.

Description

Technical Field

[0001] This invention relates to the field of petroleum geological exploration and well logging interpretation technology, and in particular to a method, identification method and device for constructing a microbial dolomite lithofacies identification chart. Background Technology

[0002] Because dolomite is modified by sedimentary and diagenetic processes (dolomization, dissolution, fracture development, etc.), the logging characteristics of dolomite in well logging data are very complex. In particular, when oil-bearing microorganisms are present in dolomite formations, it is even more difficult to identify different types of lithofacies. Therefore, identifying dolomite using well logging data has always been a technical problem that needs to be solved by those skilled in the art.

[0003] In existing technologies, the main methods for identifying dolomite facies through logging are neural networks, cluster analysis, and discriminant analysis. On the one hand, these methods primarily target coarser lithological classifications based on rock mineral composition (Liu Weifu, Fuzzy Mathematical Identification of Deep Buried Hill Dolomite Lithology [J], Xinjiang Petroleum Journal, 2003, 15(3): 35-39). On the other hand, they target lithological identification based on rock structural composition classification (Liu Hong, Tan Xiucheng, Zhou Yan, et al., Well Logging Facies Identification of Complex Dolomite Based on Grey Relations [J], Daqing Petroleum Geology and Development, 2008, 27(1): 122-125). However, there is relatively little research on well logging identification for special lithologies such as microbial rocks. The main method used is the electrical imaging logging chart method, for example, "Application of Imaging Logging in Facies Identification of Microbial Rocks in the Dengying Formation", Journal of Southwest Petroleum University, 2019. Summary of the Invention

[0004] To combine conventional logging and electrical imaging logging for accurate identification of microbial dolomite, thereby enriching the process routes and increasing the selection space, this invention provides a method for constructing a microbial dolomite lithofacies identification chart, an identification method, and an apparatus.

[0005] In a first aspect, embodiments of the present invention provide a method for constructing a microbial dolomite lithofacies identification plate, comprising:

[0006] Determine the natural gamma parameters of the formation based on the natural gamma logging curves of the core calibration well;

[0007] Interpolate the grayscale image of the electro-imaging logging of the core calibration well to obtain a grayscale image of the electro-imaging logging covering the entire wellbore of the core calibration well;

[0008] The grayscale image of the electrical imaging logging covering the entire wellbore is binarized to obtain the electrical imaging binary image of the core calibration well.

[0009] The multifractal spectrum parameters of the core calibration well are determined based on the binary image of the electrical imaging; wherein, the multifractal spectrum parameters include: multifractal spectrum peak value, multifractal spectrum asymmetry index, and multifractal spectrum width;

[0010] Based on the lithofacies of the core calibration well, the natural gamma parameters, and the multifractal spectrum parameters, a lithofacies identification chart of the microbial dolomite is constructed.

[0011] Optionally, after determining the multifractal spectrum parameters based on the electronic imaging binary image, the process may further include:

[0012] Based on the differences in the multifractal spectrum peak value, the multifractal spectrum asymmetry index, and the multifractal spectrum width, the multifractal spectrum difference parameters of the electronic imaging binary image are generated.

[0013] Accordingly, based on the lithofacies of the core calibration well, the natural gamma parameters, and the multifractal spectrum difference parameters, a lithofacies identification chart of the microbial dolomite is constructed.

[0014] Optionally, the multifractal spectrum difference parameter is F:

[0015]

[0016] Where a and b are constants;

[0017] a0 represents the peak value of the multifractal spectrum;

[0018] B is the multifractal spectrum asymmetry index;

[0019] W represents the width of the multifractal spectrum.

[0020] Optionally, constructing the microbial dolomite lithofacies identification chart based on the lithofacies of the core calibration well, the natural gamma parameters, and the multifractal spectrum parameters may include:

[0021] Based on the natural gamma parameters corresponding to micritic dolomite of the microbial dolomite lithofacies in the core calibration well, the correspondence between micritic dolomite and natural gamma parameters is determined.

[0022] Based on the multifractal spectrum parameters of algal stromatolite, algal clotted dolomite, algal lamellar dolomite, and algal sandstone dolomite in the microbial dolomite facies of the core calibration well, the correspondence between the algal stromatolite, algal clotted dolomite, algal lamellar dolomite, and algal sandstone dolomite and the multifractal spectrum parameters are determined respectively.

[0023] Based on the correspondence between the micritic dolomite and the natural gamma parameters, and the correspondence between the algal stromatolite, the algal clotted dolomite, the algal lamellar dolomite and the algal sandstone dolomite and the multifractal spectrum parameters, a lithofacies identification chart of the microbial dolomite is constructed.

[0024] Optionally, after constructing the microbial dolomite lithofacies identification map, it may further include:

[0025] The lithofacies, natural gamma parameters, and multifractal spectrum parameters of other core calibration wells in the study area where the core calibration well is located are used to verify the lithofacies identification chart of the microbial dolomite.

[0026] Secondly, embodiments of the present invention provide a method for identifying microbial dolomite lithofacies through well logging, which may include:

[0027] Determine the formation's natural gamma parameters based on the natural gamma logging curve of the target well;

[0028] Interpolate the grayscale image of the electrical imaging logging of the target well to obtain a grayscale image of the electrical imaging logging covering the entire wellbore of the target well;

[0029] The grayscale image of the electrical imaging logging covering the entire wellbore is binarized to obtain the electrical imaging binary image of the target well.

[0030] The multifractal spectrum parameters of the target well are determined based on the binary image of the electrical imaging; wherein, the multifractal spectrum parameters include: multifractal spectrum peak value, multifractal spectrum asymmetry index, and multifractal spectrum width;

[0031] Based on the natural gamma parameters and multifractal spectrum parameters of the target well, as well as the pre-constructed microbial dolomite lithofacies identification chart, the target stratigraphic lithofacies of the target well are identified;

[0032] The microbial dolomite facies identification plate is pre-constructed according to the construction method of the microbial dolomite facies identification plate described in the first aspect.

[0033] Optionally, the method may further include: performing a well-connected profile of the target facies identified by multiple target wells to obtain the sedimentary facies distribution of different microbial dolomite facies in the study area where the target wells are located.

[0034] Thirdly, embodiments of the present invention provide a device for constructing a microbial dolomite lithofacies identification plate, which may include:

[0035] The first natural gamma parameter determination module is used to determine the natural gamma parameters of the formation based on the natural gamma logging curves of the core calibration well.

[0036] The first interpolation module is used to interpolate the grayscale image of the electro-imaging logging of the core calibration well to obtain the full-bore coverage grayscale image of the electro-imaging logging of the core calibration well.

[0037] The first binarization module is used to perform binarization processing on the grayscale image of the electrical imaging logging covering the entire wellbore to obtain the electrical imaging binary image of the core calibration well.

[0038] The first multifractal spectrum parameter determination module is used to determine the multifractal spectrum parameters of the core calibration well based on the electronic imaging binary image; wherein, the multifractal spectrum parameters include: multifractal spectrum peak value, multifractal spectrum asymmetry index, and multifractal spectrum width;

[0039] A construction module is used to construct the microbial dolomite lithofacies identification chart based on the lithofacies of the core calibration well, the natural gamma parameters, and the multifractal spectrum parameters.

[0040] Fourthly, embodiments of the present invention provide a microbial dolomite lithofacies logging identification device, which may include:

[0041] The second natural gamma parameter determination module is used to determine the natural gamma parameters of the formation based on the natural gamma logging curve of the target well.

[0042] The second interpolation module is used to interpolate the grayscale image of the electrical imaging logging of the target well to obtain a grayscale image of the electrical imaging logging covering the entire wellbore of the target well.

[0043] The second binarization module is used to perform binarization processing on the grayscale image of the electrical imaging logging covering the entire wellbore to obtain the electrical imaging binary image of the target well.

[0044] The second multifractal spectrum parameter determination module is used to determine the multifractal spectrum parameters of the target well based on the electronic imaging binary image; wherein, the multifractal spectrum parameters include: multifractal spectrum peak value, multifractal spectrum asymmetry index, and multifractal spectrum width;

[0045] The identification module is used to identify the target stratigraphic lithofacies of the target well based on the natural gamma parameters and multifractal spectrum parameters of the target well, as well as a pre-constructed microbial dolomite lithofacies identification chart.

[0046] The microbial dolomite facies identification plate is pre-constructed according to the construction method of the microbial dolomite facies identification plate described in the first aspect.

[0047] Fifthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for constructing a microbial dolomite lithofacies identification chart as described in the first aspect, or implements the microbial dolomite lithofacies logging identification method as described in the second aspect.

[0048] In a sixth aspect, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method for constructing a microbial dolomite lithofacies identification chart as described in the first aspect, or the method for well logging identification of microbial dolomite lithofacies as described in the second aspect.

[0049] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0050] This invention provides a method, identification method, and device for constructing a microbial dolomite lithofacies identification chart. This method combines the advantages of conventional logging and electrical imaging logging. The constructed microbial dolomite lithofacies identification chart can identify the rock type of microbial dolomite with high accuracy and efficiency. It solves the problems of ambiguity caused by different experience and identification results in manual identification, and enables the quantitative parameters to be standardized.

[0051] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0052] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0053] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0054] Figure 1 This is a flowchart of the method for constructing a microbial dolomite lithofacies identification plate provided in Embodiment 1 of the present invention;

[0055] Figure 2 This is a flowchart illustrating the detailed method for constructing a microbial dolomite lithofacies identification plate provided in Embodiment 1 of the present invention;

[0056] Figure 3 This is an example of a grayscale image of microbial dolomite lithofacies electrical imaging logging provided in Embodiment 1 of the present invention;

[0057] Figure 4 This refers to the microbial dolomite lithofacies identification plate constructed in Embodiment 1 of the present invention;

[0058] Figure 5 The lithological logging identification results of well MX108 provided in Embodiment 1 of the present invention;

[0059] Figure 6 The lithological logging identification results of well MX105 provided in Embodiment 1 of the present invention;

[0060] Figure 7 This is a schematic diagram of the structure of the device for constructing a microbial dolomite lithofacies identification plate provided in Embodiment 1 of the present invention;

[0061] Figure 8 This is a flowchart of the microbial dolomite lithofacies logging identification method provided in Embodiment 2 of the present invention;

[0062] Figure 9 This is a schematic diagram of the microbial dolomite lithofacies logging identification device provided in Embodiment 2 of the present invention. Detailed Implementation

[0063] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0064] The inventors discovered a lack of research on quantitative parameters in electro-imaging logging. Integrated identification of conventional and electro-imaging logging typically involves separate identification using two different charts, and there are few methods for establishing a unified identification chart for electro-imaging logging and conventional logging. In view of these problems, this invention is proposed to provide a method, identification method, and apparatus for constructing a microbial dolomite lithofacies identification chart that overcomes or at least partially solves the aforementioned problems.

[0065] Example 1

[0066] Embodiment 1 of this invention provides a method for constructing a microbial dolomite lithofacies identification plate, referring to... Figure 1 As shown, the method may include the following steps:

[0067] Step S11: Determine the natural gamma parameters of the formation based on the natural gamma logging curves of the core calibration well.

[0068] Step S12: Interpolate the grayscale image of the electro-imaging logging of the core calibration well to obtain a grayscale image of the electro-imaging logging covering the entire wellbore of the core calibration well.

[0069] Step S13: Binarize the grayscale image of the electrical imaging logging covering the entire wellbore to obtain the electrical imaging binary image of the core calibration well.

[0070] Step S14: Determine the multifractal spectrum parameters of the core calibration well based on the binary image of the electrical imaging; wherein, the multifractal spectrum parameters include: multifractal spectrum peak value, multifractal spectrum asymmetry index, and multifractal spectrum width.

[0071] Step S15: Based on the lithofacies, natural gamma parameters, and multifractal spectrum parameters of the core calibration well, construct a lithofacies identification chart for microbial dolomite.

[0072] The method for constructing the microbial dolomite lithofacies identification chart provided in this invention is based on the fact that micritic dolomite lithofacies can be distinguished from other lithofacies in conventional logging characteristics, especially in terms of natural gamma. Micritic dolomite exhibits high natural gamma, while lithofacies other than micritic dolomite generally have medium to low natural gamma values. Therefore, natural gamma is selected as the identification parameter to distinguish micritic dolomite from other lithofacies. Other lithofacies are difficult to distinguish in conventional logging characteristics, especially lithofacies with microbial structural characteristics, such as algal stromatolites, algal clotted dolomite, algal lamellar dolomite, and algal sandstone dolomite. These lithofacies are clearly distinguishable in electrical imaging. Studies have found that there are significant differences in the multifractal spectrum parameters (a0, W, B) of electrical imaging images among different lithofacies. Therefore, a microbial dolomite lithofacies identification chart is constructed based on the above-mentioned natural gamma parameters and multifractal spectrum parameters.

[0073] This method combines the advantages of conventional logging and electrical imaging logging. The constructed microbial dolomite lithofacies identification chart can identify the rock type of microbial dolomite with high accuracy and efficiency. It solves the problem of multiple interpretations caused by different experience in manual identification, which leads to different identification results, and enables the quantitative parameters to be standardized.

[0074] In a detailed embodiment, referring to Figure 2, the method for constructing the microbial dolomite lithofacies identification plate may include the following steps:

[0075] Step S20: Obtain conventional logging data and electrical imaging logging data from several core calibration wells in the study area.

[0076] The study area of ​​this invention takes the Dengying Formation as an example. This formation is a set of microbial dolomite deposits, which can be broadly divided into two categories: crystalline dolomite and microbial dolomite. Microbial dolomite includes algal-veined dolomite, algal stromatolite, algal-sandyl dolomite, and algal-massive dolomite. The lithology of high-quality reservoirs is mainly algal stromatolite, algal-massive dolomite, and algal-sandyl dolomite. Therefore, well logging identification of microbial dolomite is crucial for finding high-quality reservoirs. Based on core description and thin section identification, it is divided into five lithofacies: micritic dolomite facies, algal-veined dolomite facies, algal stromatolite facies, algal-sandyl dolomite facies, and algal-massive dolomite facies.

[0077] Well logging data from the study area, along with core description data and thin-section identification results (i.e., dolomite identified by grinding the core into thin sections and examining them under a microscope), were obtained as core calibration wells. Core data from wells MX105 and MX108 were used for verification. Natural gamma ray gradation (GR) logging data can be obtained using existing technology equipment, such as the CLS-3700 logging tool and the CLS-5700 logging tool. Electrical imaging logging images were obtained using the MAX500 imaging logging series. In this step, the core data includes thin-section identification results and / or direct observation description results. The thin-section identification results can be obtained by grinding the core into thin sections and observing them under a microscope.

[0078] Step S21: Determine the natural gamma parameters of the formation based on the natural gamma logging curves of the core calibration well.

[0079] This step involves comparing core data with natural gamma parameters to determine the location of micritic dolomite and other different lithofacies such as microbial dolomite on the plate.

[0080] Step S22: Interpolate the grayscale image of the electro-imaging logging of the core calibration well to obtain a grayscale image of the electro-imaging logging covering the entire wellbore of the core calibration well.

[0081] The grayscale image of the electro-imaging logging described in this embodiment of the invention is obtained through preprocessing using digital image processing technology. The image obtained after electro-imaging logging is a color bitmap, composed of pixels, each pixel being a single color. These colors are mixed from the three primary colors: red (R), green (G), and blue (B). There are many bitmap file formats; this paper mainly uses the JPEG (Joint Photographic Experts Group) format, which stands for "Digital Compression and Encoding of Continuous Tone Static Images," with the file extension ".jpg" or ".jpeg," and is the most commonly used image file format. The color image is converted to grayscale: Grayscale images are typically divided into 256 levels, from 0 to 255, where 0 is the darkest (pure black), 255 is the brightest (pure white), and the intermediate grays are transitional shades. Each pixel in a color image has three components: R, G, and B. Therefore, each pixel is converted to a grayscale value using the following formula. For example, the formula for converting a color image to a grayscale image is as follows:

[0082] Gray=R*0.299+G*0.587+B*0.114

[0083] Where Gray represents the grayscale value, and R, G, and B represent the color values. Figure 3 Each component pixel value, the converted grayscale value Gray is between 0 and 255.

[0084] In step S22, the electro-imaging processing module of the Ciflog software can be used to interpolate the imaging data using geostatistical methods to obtain an electro-imaging logging grayscale image covering the entire wellbore (100% wellbore coverage).

[0085] Step S23: Binarize the grayscale image of the electrical imaging logging covering the entire wellbore to obtain the electrical imaging binary image of the core calibration well.

[0086] This step utilizes the Maltab software to binarize the grayscale images of the entire wellbore covered by electro-imaging logging. The `im2bw()` function is used to process the data, obtaining a binary electro-imaging image, specifically an image showing the distribution of fractures and voids. The grayscale image is converted to a binary image, and a segmentation threshold is set to obtain the binary image. The binary image uses only two values: 0 and 1. 0 represents black, indicating fractures and dissolution voids, while 1 represents white, the background value. Binarization allows for the extraction of effective fracture and void information, thus obtaining the fracture and void image.

[0087] Step S24: Determine the multifractal spectrum parameters of the core calibration well based on the binary image of the electrical imaging; wherein, the multifractal spectrum parameters include: multifractal spectrum peak value, multifractal spectrum asymmetry index and multifractal spectrum width.

[0088] In this invention, fractal theory is widely used to study the distribution characteristics of self-similarity or statistical self-similarity fields of objects. For non-uniform complex fractal geometries, a single fractal dimension cannot accurately characterize the differences in the structure of the fractal body. However, multifractal theory can more precisely characterize the complex structural distribution of the fractal body by using multiple sets of fractal dimensions.

[0089] The process of performing multifractal calculations on a binary image to obtain the multifractal spectrum parameters a0, W, and B is as follows:

[0090] Multifractal methods decompose a fractal into a series of single fractal domains based on the singularity of a certain metric. Each single fractal domain is characterized by its singularity and fractal dimension. In this embodiment of the invention, the fractal is divided into squares of scale r. Within fractal domains with the same singularity, the probability of the fractal falling into a square of scale r satisfies the following relationship:

[0091] P(r) = r α (1)

[0092] Where P(r) represents the probability that the fractal falls within a grid of scale r; α represents the singularity index, and regions with the same singularity index satisfy the single fractal theory. According to fractal theory, the number of grids with the same singularity index satisfies a power-law relationship (Halsey and Jensen, 1986; Chhabra and Jensen, 1989):

[0093] N(α)∝r -f(α) (2)

[0094] Where N(α) is the number of squares in the singularity index range [α, dα]; f(α) is the fractal dimension spectrum, which characterizes the differences in different singularity index regions.

[0095] Within the same singularity index region, the sum of the probabilities that a fractal falls into a grid of scale r is denoted as the partition function:

[0096]

[0097] Where X(q,r) is the partition function; q is the weighting coefficient, and q can take any value. Different weighting coefficients can highlight the proportion of fractal probabilities of different squares in the partition function. When q > 1, squares with high probability dominate the contribution of the partition function; when q < -1, squares with low probability dominate the partition function. Furthermore, for each given weighting coefficient q, the partition function and the scale r satisfy the following relationship:

[0098]

[0099] Here, τ(q) is the mass index, which can be obtained from the relationship between the partition function and the scale in double logarithmic coordinates. The generalized dimension of the fractal can be obtained using the mass index:

[0100]

[0101] According to multifractal theory, a fractal is characterized by a series of singularity indices and the fractal dimension of the fractal domain it scales. Using the Legendre transformation, the fractal dimension spectrum and singularity indices of a fractal satisfy the following relationship:

[0102] f(α)=αq-τ(q) (6)

[0103]

[0104] By combining equations (5), (6), and (7), we can obtain the relationship between the fractal dimension spectrum and the generalized dimension:

[0105] f(α)=αq-(q-1)D q (8)

[0106]

[0107] The above equation shows that, by qD q The relationship can be used to obtain the α-f(α) relationship. Based on the characteristics of the α-f(α) curve, the complexity, irregularity and non-uniformity of the fractal structure can be measured.

[0108] To quantitatively describe the characteristics of multifractals, Shimizu (2002) proposed using a quadratic function with vertices (α0, f(α0)) to fit the multifractal spectrum, which takes the form:

[0109] f(α)=A(α-α0) 2 +B(α-α0)+C

[0110] Generally, parameters α0, W, and B are used to describe the complexity of the system. The value of α corresponding to the peak value of f(α) is denoted as α0, which characterizes the regularity of the underlying process. A larger α0 indicates more severe and irregular fluctuations. B is an asymmetric coefficient. When B = 0, the spectrum shape is symmetrical; when B > 0, the spectrum is right-skewed, with higher fractal indices dominating; when B < 0, the spectrum is left-skewed, with lower fractal indices dominating. Further, to estimate the range of fractal indices, the width of the multifractal spectrum is obtained as W = α1 - α2, where f(α1) = f(α2) = 0. This width of the multifractal spectrum is a measure of the multifractal intensity; a larger W indicates more severe fluctuations.

[0111] Step S25: Based on the differences in multifractal spectrum peak value, multifractal spectrum asymmetry index, and multifractal spectrum width, generate multifractal spectrum difference parameters for the binary image of the electro-imaging system.

[0112] Reference Figure 4 As shown, the electrical imaging logging images of the four microbial rock facies were calibrated by observing the rock types of microbial rocks in the core, and are representative and typical. By calculating the multifractal spectrum parameters of the electrical imaging images, the differences were amplified by using the multifractal spectrum difference parameters.

[0113] Referring to Table 1 below, the peak value a0 of the multifractal spectrum is: algal stromatolite < algal clotted shale < algal sandstone < algal stratiform shale. Similarly, the asymmetry index B of the multifractal spectrum is: algal stromatolite < algal clotted shale < algal sandstone < algal stratiform shale. However, the width W of the multifractal spectrum varies greatly between algal stratiform and algal sandstone, and between algal clotted and algal stromatolite. Through long-term research and development, the inventors have determined the following multifractal spectrum difference parameter F to amplify the differences and make it easier to distinguish different lithofacies.

[0114] Table 1 Comparison of multifractal spectral parameters and new parameter F for electro-imaging of four typical microbial lithofacies.

[0115]

[0116]

[0117] The multifractal spectrum difference parameter is F:

[0118]

[0119] Where a and b are constants;

[0120] a0 represents the peak value of the multifractal spectrum;

[0121] B is the multifractal spectrum asymmetry index;

[0122] W represents the width of the multifractal spectrum.

[0123] In the above parameters, a can be 10 and b can be 0.1, that is: F = 10 × W + 0.1 × B a0 .

[0124] Steps S22 to S25 above describe a complete process for performing grayscale conversion, whole-bore interpolation, binarization, multifractal spectrum calculation and fitting of spectral parameters a0, W, B on electrical imaging logging data, and constructing a new parameter F based on the spectral parameters. The constructed new multifractal spectrum difference parameter F has been used to distinguish the lithofacies of microbial dolomite.

[0125] Step S26: Based on the lithofacies, natural gamma parameters, and multifractal spectrum difference parameters of the core calibration well, construct a lithofacies identification chart for microbial dolomite.

[0126] This step may specifically include: determining the correspondence between micritic dolomite and natural gamma parameters based on the natural gamma parameters of micritic dolomite in the core calibration well; determining the correspondence between algal stromatolite, algal clotted dolomite, algal lamellar dolomite, and algal sandstone dolomite and multifractal spectrum parameters based on the multifractal spectrum parameters of algal stromatolite, algal clotted dolomite, algal lamellar dolomite, and algal sandstone dolomite in the core calibration well; and constructing a microbial dolomite facies identification chart based on the correspondence between micritic dolomite and natural gamma parameters, as well as the correspondence between algal stromatolite, algal clotted dolomite, algal lamellar dolomite, and algal sandstone dolomite and multifractal spectrum parameters.

[0127] Based on the correspondence between natural gamma parameters, multifractal spectrum difference parameters F, and lithology, a lithofacies identification chart for microbial dolomite is established, as follows:

[0128] P x =GR

[0129]

[0130] In equations 1 and 2, P x and P y These represent the x-axis and y-axis of the graph, respectively.

[0131] GR stands for natural gamma, measured in API.

[0132] a0 represents the peak value of the multifractal spectrum;

[0133] B is the multifractal spectrum asymmetry index;

[0134] W represents the width of the multifractal spectrum.

[0135] This step obtains the image recognition parameter P. x and P y Different dolomite lithofacies need to be classified based on core calibration to achieve identification; the positions of the lithofacies division lines on the chart can be determined by combining the core data with the identification parameter P. x and P y The dividing line is determined by comparing it at the same depth, that is, by using core calibration to determine the position of the dividing line.

[0136] Reference Figure 4 As shown, the core description and thin section identification results from well MX51 are compared with the identification parameter P. x and P yBy comparing and calibrating at the same depth, the positions of the dividing lines for different lithofacies on the identification chart were determined. After comparing and calibrating a large number of core data points, the final identification chart was obtained.

[0137] Step S27: Verify the microbial dolomite lithofacies identification chart based on the lithofacies of other core calibration wells in the study area where the core calibration well is located, as well as the natural gamma parameters and multifractal spectrum parameters.

[0138] To verify the reliability of the identification results, taking wells MX108 and MX105 in the study area as examples, following the steps described above, the GR and F values ​​were calculated for each depth segment with a thickness of 0.5m. GR is the average value for the depth segment, and the F value is the spectral parameter calculated based on the electro-optical imaging image of the 0.5m depth segment, using Formula 2. Refer to the figure for the calculation. Figure 4 By projecting the two parameter values ​​of this depth segment onto the chart, the lithofacies can be identified layer by layer. For the identification results of wells MX108 and MX105, such as... Figure 5 , Figure 6 The core description thickness of well MX108 is 48.8m, and the identified lithofacies thickness is 42.08m, with an identification accuracy of 86.22%. The core description thickness of well MX105 is 11m, and the identified lithofacies thickness is 9.68m, with an accuracy of 88%. It can be seen that the above-mentioned method provided in this embodiment of the invention achieves a lithofacies identification accuracy of over 85% when quantitatively identifying microbial dolomite formations, which fully meets production requirements.

[0139] The method for constructing the microbial dolomite lithofacies identification chart provided in this embodiment of the invention can be applied to any microbial dolomite formation. As long as the natural gamma logging curve, FMI electrical imaging logging image, and sufficient core data are available, a microbial dolomite lithofacies identification chart can be constructed. Furthermore, this chart has the advantages of high precision, high efficiency, and quantitative identification, which can further meet production needs.

[0140] Based on the same inventive concept, this invention also provides a device for constructing a microbial dolomite lithofacies identification plate, referring to... Figure 7 As shown, the device may include:

[0141] The first natural gamma parameter determination module 71 is used to determine the natural gamma parameters of the formation based on the natural gamma logging curves of the core calibration well.

[0142] The first interpolation module 72 is used to interpolate the grayscale image of the electro-imaging logging of the core calibration well to obtain the full-bore coverage grayscale image of the electro-imaging logging of the core calibration well.

[0143] The first binarization module 73 is used to binarize the grayscale image of the electrical imaging logging covering the entire wellbore to obtain the electrical imaging binary image of the core calibration well.

[0144] The first multifractal spectrum parameter determination module 74 is used to determine the multifractal spectrum parameters of the core calibration well based on the binary image of the electrical imaging; wherein, the multifractal spectrum parameters include: multifractal spectrum peak value, multifractal spectrum asymmetry index and multifractal spectrum width;

[0145] Module 76 is used to construct a lithofacies identification chart for microbial dolomite based on lithofacies, natural gamma parameters, and multifractal spectrum parameters of core calibration wells.

[0146] In an optional embodiment, refer to Figure 7 As shown, the device may also include: an acquisition module 70, which is used to acquire conventional logging data and electrical imaging logging data of several core calibration wells in the study area.

[0147] In another alternative embodiment, refer to Figure 7 As shown, the device may further include: a multifractal spectrum difference parameter generation module 75, which generates multifractal spectrum difference parameters of the electro-imaging binary image based on the differences between the multifractal spectrum peak value, the multifractal spectrum asymmetry index and the multifractal spectrum width.

[0148] Accordingly, the construction module 76 is used to construct the microbial dolomite lithofacies identification chart based on the lithofacies of the core calibration well, the natural gamma parameters, and the multifractal spectrum difference parameters.

[0149] In another optional embodiment, the multifractal spectrum difference parameter generated by the multifractal spectrum difference parameter generation module 75 is F:

[0150]

[0151] Where a and b are constants;

[0152] a0 represents the peak value of the multifractal spectrum;

[0153] B is the multifractal spectrum asymmetry index;

[0154] W represents the width of the multifractal spectrum.

[0155] In another alternative embodiment, the building module 76 is specifically used for:

[0156] Based on the natural gamma parameters corresponding to micritic dolomite of the microbial dolomite lithofacies in the core calibration well, the correspondence between micritic dolomite and natural gamma parameters is determined.

[0157] Based on the multifractal spectrum parameters of algal stromatolite, algal clotted dolomite, algal lamellar dolomite, and algal sandstone dolomite in the microbial dolomite facies of the core calibration well, the correspondence between the algal stromatolite, algal clotted dolomite, algal lamellar dolomite, and algal sandstone dolomite and the multifractal spectrum parameters are determined respectively.

[0158] Based on the correspondence between the micritic dolomite and the natural gamma parameters, and the correspondence between the algal stromatolite, the algal clotted dolomite, the algal lamellar dolomite and the algal sandstone dolomite and the multifractal spectrum parameters, a lithofacies identification chart of the microbial dolomite is constructed.

[0159] In another alternative embodiment, refer to Figure 7 As shown, the device may further include: a verification module 77, which is used to verify the microbial dolomite lithofacies identification chart based on the lithofacies of other core calibration wells in the study area where the core calibration well is located, as well as natural gamma parameters and multifractal spectrum parameters.

[0160] Based on the same inventive concept, this embodiment of the invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-mentioned method for constructing a microbial dolomite lithofacies identification plate.

[0161] Based on the same inventive concept, this embodiment of the invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-mentioned method for constructing a microbial dolomite lithofacies identification plate.

[0162] The principles by which the above-mentioned devices, media, and related equipment in the embodiments of the present invention solve the problem are similar to those of the aforementioned methods. Therefore, their implementation can refer to the implementation of the aforementioned methods, and repeated details will not be repeated.

[0163] Example 2

[0164] Embodiment 1 of this invention provides a method for identifying the lithofacies of microbial dolomite through well logging, referring to... Figure 8 As shown, the method may include the following steps:

[0165] Step S81: Determine the natural gamma parameters of the formation based on the natural gamma logging curve of the target well.

[0166] Step S82: Interpolate the grayscale image of the electrical imaging logging of the target well to obtain a grayscale image of the electrical imaging logging covering the entire wellbore of the target well.

[0167] Step S83: Binarize the grayscale image of the electrical imaging logging covering the entire wellbore to obtain the electrical imaging binary image of the target well.

[0168] Step S84: Determine the multifractal spectrum parameters of the target well based on the binary image of the electrical imaging; wherein, the multifractal spectrum parameters include: multifractal spectrum peak value, multifractal spectrum asymmetry index, and multifractal spectrum width.

[0169] Step S85: Identify the target stratigraphic facies of the target well based on the natural gamma parameters and multifractal spectrum parameters of the target well, as well as the pre-constructed microbial dolomite facies identification chart; wherein, the microbial dolomite facies identification chart is pre-constructed according to the construction method of the microbial dolomite facies identification chart described in Example 1.

[0170] In another optional embodiment, the above method may further include: step S86, performing a well-to-well profile of the target strata facies identified by multiple target wells to obtain the sedimentary facies distribution of different microbial dolomite facies in the study area where the target wells are located.

[0171] This method can accurately identify the lithology of microbial dolomite formations using existing conventional logging and electrical imaging logging, as well as pre-constructed microbial dolomite facies identification charts, providing a basis for oil and gas exploration, saving the cost of core sampling and calibration, and further conserving resources.

[0172] Based on the same inventive concept, this invention also provides a microbial dolomite lithofacies logging identification device, referring to... Figure 9 As shown, the device may include:

[0173] The second natural gamma parameter determination module 91 is used to determine the natural gamma parameters of the formation based on the natural gamma logging curve of the target well.

[0174] The second interpolation module 92 is used to interpolate the grayscale image of the electrical imaging logging of the target well to obtain a grayscale image of the electrical imaging logging covering the entire wellbore of the target well.

[0175] The second binarization module 93 is used to binarize the grayscale image of the electrical imaging logging covering the entire wellbore to obtain the electrical imaging binary image of the target well.

[0176] The second multifractal spectrum parameter determination module 94 is used to determine the multifractal spectrum parameters of the target well based on the binary image of the electrical imaging; wherein, the multifractal spectrum parameters include: multifractal spectrum peak value, multifractal spectrum asymmetry index and multifractal spectrum width;

[0177] The identification module 95 is used to identify the target stratigraphic lithofacies of the target well based on the natural gamma parameters and multifractal spectrum parameters of the target well, as well as a pre-constructed microbial dolomite lithofacies identification chart.

[0178] Among them, the microbial dolomite lithofacies identification plate was pre-constructed according to the construction method of the microbial dolomite lithofacies identification plate described in Example 1.

[0179] Based on the same inventive concept, this embodiment of the invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for identifying microbial dolomite lithofacies through logging.

[0180] Based on the same inventive concept, this embodiment of the invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-mentioned microbial dolomite lithofacies logging identification method.

[0181] The principles by which the above-described apparatus, client, medium, related equipment, and system in this embodiment solve the problem are similar to those of the aforementioned method. Therefore, their implementation can refer to the implementation of the aforementioned method, and repeated details will not be repeated.

[0182] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0183] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0184] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0185] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0186] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for constructing a microbial dolomite lithofacies identification plate, characterized in that, include: Determine the natural gamma parameters of the formation based on the natural gamma logging curves of the core calibration well; Interpolate the grayscale image of the electro-imaging logging of the core calibration well to obtain a grayscale image of the electro-imaging logging covering the entire wellbore of the core calibration well; The grayscale image of the electrical imaging logging covering the entire wellbore is binarized to obtain the electrical imaging binary image of the core calibration well. The multifractal spectrum parameters of the core calibration well are determined based on the binary image of the electrical imaging; wherein, the multifractal spectrum parameters include: multifractal spectrum peak value, multifractal spectrum asymmetry index, and multifractal spectrum width; Based on the differences in the multifractal spectrum peak value, the multifractal spectrum asymmetry index, and the multifractal spectrum width, the multifractal spectrum difference parameters of the electronic imaging binary image are generated. Based on the lithofacies of the core calibration well, the natural gamma parameters, and the multifractal spectrum difference parameters, a lithofacies identification chart of the microbial dolomite is constructed. Wherein, the multifractal spectrum difference parameter is F: ； Where a and b are constants; a0 represents the peak value of the multifractal spectrum; B is the multifractal spectrum asymmetry index; W represents the width of the multifractal spectrum.

2. The method according to claim 1, characterized in that, Based on the lithofacies of the core calibration well, the natural gamma parameters, and the multifractal spectrum difference parameters, a lithofacies identification chart of the microbial dolomite is constructed, including: Based on the natural gamma parameters corresponding to micritic dolomite of the microbial dolomite lithofacies in the core calibration well, the correspondence between micritic dolomite and natural gamma parameters is determined. Based on the multifractal spectrum difference parameters of algal stromatolite, algal clotted dolomite, algal lamellar dolomite, and algal sandstone dolomite in the microbial dolomite facies of the core calibration well, the correspondence between the algal stromatolite, algal clotted dolomite, algal lamellar dolomite, and algal sandstone dolomite and the multifractal spectrum difference parameters is determined respectively. Based on the correspondence between the micritic dolomite and the natural gamma parameters, and the correspondence between the algal stromatolite, the algal clotted dolomite, the algal lamellar dolomite and the algal sandstone dolomite and the multifractal spectrum difference parameters, a lithofacies identification chart of the microbial dolomite is constructed.

3. The method according to claim 1, characterized in that, After constructing the microbial dolomite lithofacies identification plate, the following is also included: The lithofacies, natural gamma parameters, and multifractal spectrum difference parameters of other core calibration wells in the study area where the core calibration well is located are used to verify the lithofacies identification chart of the microbial dolomite.

4. A method for identifying microbial dolomite lithofacies through well logging, characterized in that, include: Determine the formation's natural gamma parameters based on the natural gamma logging curve of the target well; Interpolate the grayscale image of the electrical imaging logging of the target well to obtain a grayscale image of the electrical imaging logging covering the entire wellbore of the target well; The grayscale image of the electrical imaging logging covering the entire wellbore is binarized to obtain the electrical imaging binary image of the target well. The multifractal spectrum parameters of the target well are determined based on the binary image of the electrical imaging; wherein, the multifractal spectrum parameters include: multifractal spectrum peak value, multifractal spectrum asymmetry index, and multifractal spectrum width; Based on the differences in the multifractal spectrum peak value, the multifractal spectrum asymmetry index, and the multifractal spectrum width, the multifractal spectrum difference parameters of the electronic imaging binary image are generated. Based on the natural gamma parameters and multifractal spectrum difference parameters of the target well, as well as the pre-constructed microbial dolomite lithofacies identification chart, the target stratigraphic lithofacies of the target well are identified; The microbial dolomite facies identification plate is pre-constructed using the construction method of the microbial dolomite facies identification plate according to any one of claims 1 to 3.

5. The method according to claim 4, characterized in that, Also includes: By performing a well-connected profile of the target strata identified from multiple target wells, the sedimentary facies distribution of different microbial dolomite facies in the study area where the target wells are located can be obtained.

6. A device for constructing a microbial dolomite lithofacies identification plate, characterized in that, include: The first natural gamma parameter determination module is used to determine the natural gamma parameters of the formation based on the natural gamma logging curves of the core calibration well. The first interpolation module is used to interpolate the grayscale image of the electro-imaging logging of the core calibration well to obtain a grayscale image of the electro-imaging logging covering the entire wellbore of the core calibration well. The first binarization module is used to perform binarization processing on the grayscale image of the electrical imaging logging covering the entire wellbore to obtain the electrical imaging binary image of the core calibration well. The first multifractal spectrum parameter determination module is used to determine the multifractal spectrum parameters of the core calibration well based on the electronic imaging binary image; wherein, the multifractal spectrum parameters include: multifractal spectrum peak value, multifractal spectrum asymmetry index, and multifractal spectrum width; The first multifractal spectrum difference parameter generation module is used to generate multifractal spectrum difference parameters of the electronic imaging binary image based on the differences between the multifractal spectrum peak value, the multifractal spectrum asymmetry index and the multifractal spectrum width. The construction module is used to construct the microbial dolomite lithofacies identification chart based on the lithofacies of the core calibration well, the natural gamma parameters, and the multifractal spectrum difference parameters; Wherein, the multifractal spectrum difference parameter is F: ； Where a and b are constants; a0 represents the peak value of the multifractal spectrum; B is the multifractal spectrum asymmetry index; W represents the width of the multifractal spectrum.

7. A microbial dolomite lithofacies logging identification device, characterized in that, include: The second natural gamma parameter determination module is used to determine the natural gamma parameters of the formation based on the natural gamma logging curve of the target well. The second interpolation module is used to interpolate the grayscale image of the electrical imaging logging of the target well to obtain a grayscale image of the electrical imaging logging covering the entire wellbore of the target well. The second binarization module is used to perform binarization processing on the grayscale image of the electrical imaging logging covering the entire wellbore to obtain the electrical imaging binary image of the target well. The second multifractal spectrum parameter determination module is used to determine the multifractal spectrum parameters of the target well based on the electronic imaging binary image; wherein, the multifractal spectrum parameters include: multifractal spectrum peak value, multifractal spectrum asymmetry index, and multifractal spectrum width; The second multifractal spectrum difference parameter generation module is used to generate multifractal spectrum difference parameters of the electronic imaging binary image based on the differences between the multifractal spectrum peak value, the multifractal spectrum asymmetry index and the multifractal spectrum width. The identification module is used to identify the target stratigraphic lithofacies of the target well based on the natural gamma parameters and the multifractal spectrum difference parameters of the target well, as well as a pre-constructed microbial dolomite lithofacies identification chart. The microbial dolomite facies identification plate is pre-constructed using the construction method of the microbial dolomite facies identification plate according to any one of claims 1 to 3.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the method for constructing a microbial dolomite lithofacies identification chart as described in any one of claims 1 to 3, or the method for well logging identification of microbial dolomite lithofacies as described in claim 4 or 5.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for constructing a microbial dolomite lithofacies identification chart as described in any one of claims 1 to 3, or the method for well logging identification of microbial dolomite lithofacies as described in claim 4 or 5.

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