Construction method, recognition method and device of microbial dolomite lithofacies recognition plate

Abstract

The invention discloses a construction method, a recognition method and a device of a microbial dolomite lithofacies recognition chart. The construction method comprises the following steps: determining natural gamma parameters of a stratum based on a natural gamma logging curve of a core calibration well; interpolating the electric imaging logging gray level image of the core calibration well to obtain an electric imaging logging gray level image covered by a full borehole of the core calibration well; carrying out binarization processing on the electric imaging logging gray level image covered by the whole borehole to obtain an electric imaging binary image of the core calibration well; determining a multi-fractal spectrum parameter of the core calibration well based on the electric imaging binary image; wherein the multi-fractal spectrum parameters comprise a multi-fractal spectrum peak value, a multi-fractal spectrum asymmetric index and a multi-fractal spectrum width; and constructing a microbial dolomite lithofacies recognition chart based on the lithofacies of the core calibration well, the natural gamma parameters and the multi-fractal spectrum parameters. According to the method, the advantages of conventional logging and electrical imaging logging are integrated, and the dolomite type can be identified with high precision and high efficiency.

Description

Technical Field

[0001] The invention relates to the technical field of petroleum geological exploration and well logging interpretation, and in particular to a construction method, an identification method and a device for a microbial dolomite lithofacies identification plate. Background Art

[0002] Since dolomite is transformed by sedimentation and diagenesis (dolomization, dissolution, fracture development, etc.), the logging characteristics of dolomite in logging data are very complex, especially when there are oil-containing microorganisms in the dolomite formation, which makes it more difficult to identify different types of lithofacies. Therefore, using logging data to identify dolomite has always been a technical problem that technicians in this field need to solve urgently.

[0003] In the prior art, the main method for identifying dolomite lithofacies by logging is to use neural networks, cluster analysis, discriminant analysis and other identification methods. On the one hand, it is mainly aimed at the coarse lithology classification at the level of rock mineral components (Liu Weifu, Identification of Deep Buried Hill Dolomite Lithology by Fuzzy Mathematics [J], Journal of Xinjiang Petroleum, 2003, 15(3): 35-39), and on the other hand, it is aimed at the lithology identification of rock structural component classification (Liu Hong, Tan Xiucheng, Zhou Yan, etc., Complex Dolomite Logging Lithofacies Identification Based on Grey Correlation [J], Daqing Petroleum Geology and Development, 2008, 27(1): 122-125). However, there are few studies on logging identification of special lithologies such as microbial rocks, which mainly use the electrical imaging logging plate method, such as "Application of Imaging Logging in Identification of Microbial Rock Lithofacies in Dengying Formation", Journal of Southwest Petroleum University, 2019. Summary of the invention

[0004] In order to use conventional logging and electrical imaging logging in combination to accurately identify microbial dolomite, enrich process routes and increase selection space, a method for constructing a microbial dolomite lithofacies identification plate, an identification method and a device are provided in an embodiment of the present invention.

[0005] In a first aspect, an embodiment of the present invention provides 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 curve of the core calibration well;

[0007] Interpolating the electrical imaging logging grayscale image of the core calibration well to obtain an electrical imaging logging grayscale image covering the entire borehole of the core calibration well;

[0008] Binarizing the electrical imaging logging grayscale image covering the entire borehole to obtain an electrical imaging binary image of the core calibration well;

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

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

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

[0012] generating a multifractal spectrum difference parameter of the electrographic binary image based on the difference between the multifractal spectrum peak, the multifractal spectrum asymmetry index and the multifractal spectrum width;

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

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

[0015]

[0016] Among them, a and b are constants;

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

[0018] B is the asymmetry index of the multifractal spectrum;

[0019] W is the multifractal spectrum width.

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

[0021] Based on the natural gamma parameters corresponding to the micrite dolomite of the microbial dolomite lithofacies in the core calibration well, determining the corresponding relationship between the micrite dolomite and the natural gamma parameters;

[0022] Based on the multi-fractal spectrum parameters of the algae stromatolite, algae clot dolomite, algae laminated dolomite and algae psammite of the microbial dolomite lithofacies in the core calibration well, respectively determine the corresponding relationship between the algae stromatolite, the algae clot dolomite, the algae laminated dolomite and the algae psammite and the multi-fractal spectrum parameters;

[0023] Based on the correspondence between the mud-crystal dolomite and the natural gamma parameters, and the correspondence between the algal stromatolite, the algal clot dolomite, the algal laminar dolomite and the algal psammite dolomite and the multi-fractal spectrum parameters, the microbial dolomite lithofacies identification plate is constructed.

[0024] Optionally, after constructing the microbial dolomite lithofacies identification plate, the following steps may also be included:

[0025] The microbial dolomite lithofacies identification chart is verified based on the lithofacies, natural gamma parameters, and multi-fractal spectrum parameters of other core calibration wells in the study area where the core calibration well is located.

[0026] In a second aspect, an embodiment of the present invention provides a microbial dolomite lithofacies logging identification method, which may include:

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

[0028] Interpolating the electrical imaging logging grayscale image of the target well to obtain an electrical imaging logging grayscale image covering the entire borehole of the target well;

[0029] Binarizing the electrical imaging logging grayscale image covering the entire wellbore to obtain an electrical imaging binary image of the target well;

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

[0031] Based on the natural gamma parameter and the multifractal spectrum parameter of the target well and a pre-constructed microbial dolomite lithofacies identification plate, identifying the target formation lithofacies of the target well;

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

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

[0034] In a third aspect, an embodiment of the present invention provides a device for constructing a microbial dolomite lithofacies identification plate, which may include:

[0035] A first 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 core calibration well;

[0036] A first interpolation module is used to interpolate the electrical imaging logging grayscale image of the core calibration well to obtain an electrical imaging logging grayscale image covering the entire borehole of the core calibration well;

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

[0038] A first multifractal spectrum parameter determination module is used to determine the multifractal spectrum parameters of the core calibration well based on the electrical 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 plate based on the lithofacies of the core calibration well, the natural gamma parameters and the multi-fractal spectrum parameters.

[0040] In a fourth aspect, an embodiment of the present invention provides a microbial dolomite lithofacies logging identification device, which may include:

[0041] A second natural gamma parameter determination module, used to determine the natural gamma parameters of the formation based on the natural gamma logging curve of the target well;

[0042] A second interpolation module is used to interpolate the electrical imaging logging grayscale image of the target well to obtain an electrical imaging logging grayscale image covering the entire borehole of the target well;

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

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

[0045] An identification module, for identifying the target stratigraphic lithofacies of the target well based on the natural gamma parameter and the multifractal spectrum parameter of the target well and a pre-constructed microbial dolomite lithofacies identification plate;

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

[0047] In a fifth aspect, an embodiment of the present invention provides 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 plate 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, an embodiment of the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for constructing a microbial dolomite lithofacies identification plate as described in the first aspect is implemented, or the method for microbial dolomite lithofacies well logging identification as described in the second aspect is implemented.

[0049] The beneficial effects of the above technical solution provided by the embodiment of the present invention include at least:

[0050] The embodiment of the present invention provides a method for constructing a microbial dolomite lithofacies identification map, an identification method and a device. The method combines the advantages of conventional logging and electrical imaging logging. The constructed microbial dolomite lithofacies identification map can identify the microbial dolomite rock type with high precision and high efficiency, and solves the problems of different experiences in artificial experience distinction and multiple solutions caused by different identification results, so that quantitative parameters can be standardized.

[0051] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0052] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

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

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

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

[0057] Figure 4 The microbial dolomite lithofacies identification plate constructed in Example 1 of the present invention;

[0058] Figure 5 The lithofacies logging identification result of Well MX108 provided in the first embodiment of the present invention;

[0059] Figure 6 The lithofacies logging identification result of Well MX105 provided in Example 1 of the present invention;

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

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

[0062] Fig. 9 This is a schematic diagram of the structure of the microbial dolomite lithofacies logging identification device provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0063] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0064] The inventors found that there is a lack of research on the quantitative parameters of electrical imaging logging. Conventional logging and electrical imaging logging are generally identified by two different plates, and there are few methods for establishing a unified identification plate for electrical imaging logging and conventional logging. In view of the above problems, the present invention is proposed to provide a method for constructing a microbial dolomite lithofacies identification plate, an identification method and a device that overcome the above problems or at least partially solve the above problems.

[0065] Embodiment 1

[0066] In the first embodiment of the present invention, a method for constructing a microbial dolomite lithofacies identification plate is provided, referring to Figure 1 As shown, the method may include the following steps:

[0067] Step S11, determining the natural gamma parameters of the formation based on the natural gamma logging curve of the core calibration well.

[0068] Step S12: interpolate the electrical imaging logging grayscale image of the core calibration well to obtain an electrical imaging logging grayscale image covering the entire borehole of the core calibration well.

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

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

[0071] Step S15: construct a microbial dolomite lithofacies identification chart based on the lithofacies, natural gamma parameters and multi-fractal spectrum parameters of the core calibration well.

[0072] The method for constructing the above-mentioned microbial dolomite lithofacies identification map provided in an embodiment of the present invention is based on the overall inventive concept that the mudstone dolomite phase and other lithofacies can be distinguished in conventional logging characteristics, especially in natural gamma. The mudstone dolomite exhibits high natural gamma, while the natural gamma of lithofacies other than the mudstone dolomite is generally medium and low. Therefore, natural gamma is selected as an identification parameter to complete the division of mudstone dolomite and other lithofacies; while other lithofacies are difficult to distinguish in conventional logging characteristics, especially lithofacies with microbial structural characteristics, algal stromatolite, algal clot dolomite, algal laminar dolomite and algal sand-clastic dolomite. These types of lithofacies have obvious distinctions in electrical imaging. After research, it was found that different lithofacies have large differences in the multifractal spectrum parameters (a0, W, B) of the electrical imaging image. Therefore, a microbial dolomite lithofacies identification map 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 precision and efficiency, solving the problems of different experiences in manual distinction and multiple solutions caused by different identification results, so that quantitative parameters can be standardized.

[0074] In a detailed embodiment, as shown in FIG2, the method for constructing the microbial dolomite petrographic identification plate may include the following steps:

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

[0076] The study area of ​​the embodiment of the present invention takes the Dengying Formation as an example. The formation is a set of microbial dolomite deposits, which can be roughly divided into two categories: crystalline dolomite and microbial dolomite. Microbial dolomite includes algal stratovolcanic dolomite, algal stromatolite, algal sand-clast dolomite, and algal clotted dolomite. The lithology of high-quality reservoirs is mainly algal stromatolite, algal clotted dolomite, and algal sand-clast dolomite. Therefore, logging to identify microbial dolomite is crucial for finding high-quality reservoirs. According to core description and thin section identification, it is divided into 5 types of lithofacies: mud crystal dolomite phase, algal stratovolcanic dolomite phase, algal stromatolite phase, algal sand-clast dolomite phase, and algal clotted dolomite phase.

[0077] The well logging data of the study area, as well as the core description data and thin-section identification results of the core from the MX51 well in the study area (i.e., the dolomite identified by grinding the core into thin slices under a microscope) are obtained as core calibration wells, and the core data from the MX105 well and the MX108 well are used for effect verification; wherein, the natural gamma GR of the logging data can be obtained by using existing technical equipment, such as the CLS-3700 logging instrument, the CLS-5700 logging instrument, etc. The electrical imaging logging images are obtained using the MAX500 imaging logging series. In this step, the core data includes the thin-section identification results of the core and / or the direct observation description results of the core. Among them, the thin-section identification results of the core can be obtained by grinding the core into thin slices and observing and identifying under a microscope.

[0078] Step S21, determining the natural gamma parameters of the formation based on the natural gamma logging curve of the core calibration well.

[0079] In this step, the core data is compared with the natural gamma parameters in depth to determine the positions of different lithofacies such as micrite dolomite and other microbial dolomite on the map.

[0080] Step S22: interpolate the electrical imaging logging grayscale image of the core calibration well to obtain an electrical imaging logging grayscale image covering the entire borehole of the core calibration well.

[0081] The above-mentioned electrical imaging logging grayscale image in the embodiment of the present invention is obtained by pre-processing with digital image processing technology. The image obtained after electrical imaging logging is a color bitmap, which is composed of pixels. Each pixel is a single color, and these colors are a mixture of three primary colors: red (R), green (G), and blue (B). There are many bitmap file formats. This article mainly uses the JPEG (Joint Photographic Experts Group) format, which is the full name of "digital compression and encoding of continuous-tone static images". The file suffix is ​​".jpg" or ".jpeg", which is the most commonly used image file format. Color image is converted to grayscale image: Grayscale image is usually divided into 256 levels from 0 to 255, where 0 is the darkest (completely black), 255 is the brightest (completely white), and the middle is transitional gray. Each pixel in the color image has three components R, G, and B, so each pixel is converted into a grayscale value Grey by the following formula. For example, the formula for converting a color image into a grayscale image is as follows:

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

[0083] Among them, Gray is the gray value, R, G, B are colors Figure 3 The converted grayscale value is between 0 and 255.

[0084] In this step S22, the electrical imaging processing module of the Ciflog software can be used to perform geostatistical interpolation on the imaging data to obtain an electrical imaging logging grayscale image with full borehole coverage (wellbore coverage rate of 100%).

[0085] Step S23, binarizing the electrical imaging logging grayscale image covering the entire borehole to obtain an electrical imaging binary image of the core calibration well.

[0086] In this step, the Maltab software can be used to perform binary data processing on the electrical imaging logging grayscale image covering the entire borehole, and the im2bw() function can be used to process the data to obtain an electrical imaging binary image, that is, an image of the distribution of fractures and holes. The grayscale image is converted into a binary image, and a binary image is obtained by setting a segmentation threshold. The binary image has two values, namely 0 and 1. 0 is black, representing fractures and dissolution holes, and 1 is white, which is the background value. Through binarization, effective fracture and hole information can be extracted, that is, a fracture and hole image can be obtained.

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

[0088] In the embodiments of the present invention, fractal theory is widely used to study the distribution characteristics of self-similar or statistically self-similar fields of objects. For non-uniform complex fractal geometric bodies, a single fractal dimension cannot accurately characterize the differences in the fractal structure. Multifractal theory uses multiple fractal dimension sets to more accurately characterize the complex structural distribution of fractals.

[0089] The process of performing multifractal calculation on a binary image and obtaining multifractal spectrum parameters a0, W and B is as follows:

[0090] The multifractal method decomposes the fractal into a series of single fractal domains through the singularity of a certain metric value, and each single fractal domain is characterized by its singularity and fractal dimension. The embodiment of the present invention divides the fractal into grids of scale r. In the fractal domains with the same singularity, the probability of the fractal falling into the grid of scale r satisfies the following relationship:

[0091] P(r)=r α (1)

[0092] Among them, P(r) represents the probability that the fractal falls into the scale r square; α represents the singularity index, and the area with the same singularity index satisfies the single fractal theory. According to the fractal theory, the number of squares with the same singularity index satisfies the power exponential 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 range of singular index [α, dα]; f(α) is the fractal dimension spectrum, which characterizes the differences in different singular index regions.

[0095] In the same singular index region, the sum of the probabilities of the fractal falling into the scale r grid is recorded as the partition function:

[0096]

[0097] Among them, X(q,r) is the partition function; q is the weight coefficient, and q can take any value. Different weight coefficients can highlight the proportion of the fractal probability of different squares in the partition function. When q>1, the square with a high probability dominates the contribution of the partition function. When q<-1, the square with a small probability dominates the partition function. In addition, for each given weight coefficient q, the partition function and the scale r satisfy the following relationship:

[0098]

[0099] Among them, τ(q) is the quality index, which can be obtained from the partition function and scale relationship in double logarithmic coordinates. The quality index can be used to obtain the generalized dimension of the fractal:

[0100]

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

[0102] f(α)=αq-τ(q) (6)

[0103]

[0104] 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 formula shows that, by qD q The relationship can be used to obtain the α-f(α) relationship, and the complexity, irregularity and non-uniformity of the fractal structure can be measured based on the characteristics of the α-f(α) curve.

[0108] In order to quantitatively describe the multifractal characteristics, Shimizu (2002) proposed to use a quadratic function with (α0, f(α0)) as the vertex to fit the multifractal spectrum, which is in the form of:

[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, where the α value corresponding to the peak value of f(α) is recorded as α0, and α0 is used to characterize the regularity of the underlying process. The larger the α0, the more violent and irregular the fluctuation. B is the asymmetry coefficient. When B=0, the shape of the spectrum is symmetrical; when B>0, the spectrum is right-biased, and the higher fractal index dominates; when B<0, the spectrum is left-biased, and the lower fractal index dominates. Furthermore, in order to estimate the range of fractal indices, the width of the multifractal spectrum is obtained, W=α1-α2, where f(α1)=f(α2)=0. The width of the multifractal spectrum is a measure of the multifractal intensity. The larger W is, the more violent the fluctuation.

[0111] Step S25: generating multifractal spectrum difference parameters of the electrographic binary image based on the differences in the multifractal spectrum peak value, the multifractal spectrum asymmetry index and the multifractal spectrum width.

[0112] Reference Figure 4 As shown, the electrical imaging logging images of the four microbial lithofacies are calibrated by the microbial rock types observed in the core, which are representative and typical. The multifractal spectrum parameters of the electrical imaging images are calculated and the multifractal spectrum difference parameters are used to amplify the differences.

[0113] Referring to the following Table 1, the peak value a0 of the multi-fractal spectrum is algal stromatolite < algal clot dolomite < algal sand-clastic dolomite < algal laminae dolomite, and the asymmetry index B of the multi-fractal spectrum is similarly algal stromatolite < algal clot dolomite < algal sand-clastic dolomite < algal laminae dolomite. However, the width W of the multi-fractal spectrum is greatly different between algal laminae and algal sand-clastics, and between algal clots and algal stromatolites. Through long-term research and development, the inventors have determined the following multi-fractal spectrum difference parameter F to amplify the difference and make it easier to distinguish different lithofacies.

[0114] Table 1 Comparison of the multi-fractal spectrum parameters and the new parameter F of four typical microbial lithofacies electrical imaging

[0115]

[0116]

[0117] The multifractal spectrum difference parameter is F:

[0118]

[0119] Among them, a and b are constants;

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

[0121] B is the asymmetry index of the multifractal spectrum;

[0122] W is the multifractal spectrum width.

[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] The above steps S22 to S25 perform grayscale conversion, full-bore interpolation, binarization, multi-fractal spectrum calculation and fitting of spectrum parameters a0, W, B on the electrical imaging logging data, and construct a new parameter F based on the spectrum parameters. The microbial dolomite lithofacies are distinguished by the constructed new multi-fractal spectrum difference parameter F.

[0125] Step S26: construct a microbial dolomite lithofacies identification chart based on the lithofacies, natural gamma parameters and multi-fractal spectrum difference parameters of the core calibration well.

[0126] This step may specifically include: determining the correspondence between the mud-crystal dolomite and the natural gamma parameters based on the natural gamma parameters corresponding to the mud-crystal dolomite of the microbial dolomite lithofacies in the core calibration well; determining the correspondence between the algal stromatolite, algal clot dolomite, algal laminated dolomite and algal psammitic dolomite and the multifractal spectrum parameters based on the multifractal spectrum parameters of the algal stromatolite, algal clot dolomite, algal laminated dolomite and algal psammitic dolomite of the microbial dolomite lithofacies in the core calibration well; constructing a microbial dolomite lithofacies identification chart based on the correspondence between the mud-crystal dolomite and the natural gamma parameters, as well as the correspondence between the algal stromatolite, algal clot dolomite, algal laminated dolomite and algal psammitic dolomite and the multifractal spectrum parameters.

[0127] That is, based on the correspondence between natural gamma parameters, multi-fractal spectrum difference parameters F and lithology, a microbial dolomite lithofacies identification chart is established, as follows:

[0128] P x =GR

[0129]

[0130] In formula 1 and 2, P x and P y Represent the x-axis and y-axis of the plate respectively;

[0131] GR is natural gamma, unit is API;

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

[0133] B is the asymmetry index of the multifractal spectrum;

[0134] W is the multifractal spectrum width.

[0135] This step obtains the plate recognition parameter P x and P y , it is necessary to classify different dolomite facies according to core calibration to achieve the purpose of identification; wherein, the position of the different facies division lines on the plate can be obtained by combining the core data and the identification parameter P x and P y The position of the dividing line is determined by comparing the samples at the same depth, that is, by core calibration.

[0136] Reference Figure 4 As shown in the figure, the core description and core thin section identification results of Well MX51 are compared with the identification parameter P x and P yCompare and calibrate at the same depth to determine the position of the dividing line for different lithofacies on the identification plate. After comparing and calibrating a large number of core data points, the identification plate is finally obtained.

[0137] Step S27: verify the microbial dolomite lithofacies identification chart based on 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.

[0138] In order to verify the reliability of the identification results, taking the MX108 well and the MX105 well in the study area as examples, referring to the above steps, for each depth segment with a thickness of 0.5m, the GR and F values ​​are calculated, where GR is the average value of the depth segment, and the F value is the spectral parameter calculated based on the electrical imaging image of the 0.5m depth segment. The value calculated according to formula 2 is shown in Figure 2. Figure 4 , projecting the two parameter values ​​of the depth section onto the chart, the lithofacies can be identified layer by layer. For the identification results of Well MX108 and Well MX105, Figure 5 , Figure 6 The core description thickness of Well MX108 is 48.8m, the identified lithofacies thickness is 42.08m, and the identification conformity rate is 86.22%. The core description thickness of Well MX105 is 11m, and the logging identified lithofacies thickness is 9.68m, and the conformity rate is 88%. It can be seen that when the above method provided in the embodiment of the present invention quantitatively identifies the microbial dolomite formation, the conformity rate of lithofacies identification is more than 85%, which fully meets the production requirements.

[0139] The method for constructing the above-mentioned microbial dolomite lithofacies identification map provided by the embodiment of the present invention can be applied to any microbial dolomite formation. As long as the above-mentioned natural gamma logging curve, FMI electrical imaging logging image and sufficient core data of the coring well are available, a microbial dolomite lithofacies identification map can be constructed; and the map has the advantages of high precision, high efficiency, and quantitative identification, and can further meet production needs.

[0140] Based on the same inventive concept, the present 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 curve of the core calibration well;

[0142] The first interpolation module 72 is used to interpolate the electrical imaging logging grayscale image of the core calibration well to obtain the electrical imaging logging grayscale image covering the entire borehole of the core calibration well;

[0143] The first binarization module 73 is used to perform binarization processing on the electrical imaging logging grayscale image 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 electrical imaging binary image; wherein the multifractal spectrum parameters include: multifractal spectrum peak value, multifractal spectrum asymmetry index and multifractal spectrum width;

[0145] The construction module 76 is used to construct a microbial dolomite lithofacies identification chart based on the lithofacies, natural gamma parameters and multi-fractal spectrum parameters of the core calibration well.

[0146] In an alternative embodiment, referring to Figure 7 As shown, the device may further 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, referring to Figure 7 As shown, the device may further include: a multifractal spectrum difference parameter generating module 75, which generates a multifractal spectrum difference parameter of the electro-imaging binary image based on the difference between the multifractal spectrum peak value, the multifractal spectrum asymmetry index and the multifractal spectrum width;

[0148] Correspondingly, the construction module 76 is used to construct the microbial dolomite lithofacies identification plate based on the lithofacies of the core calibration well, the natural gamma parameter and the multi-fractal spectrum difference parameter.

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

[0150]

[0151] Among them, a and b are constants;

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

[0153] B is the asymmetry index of the multifractal spectrum;

[0154] W is the multifractal spectrum width.

[0155] In another optional embodiment, the construction module 76 is specifically used for:

[0156] Based on the natural gamma parameters corresponding to the micrite dolomite of the microbial dolomite lithofacies in the core calibration well, determining the corresponding relationship between the micrite dolomite and the natural gamma parameters;

[0157] Based on the multi-fractal spectrum parameters of the algae stromatolite, algae clot dolomite, algae laminated dolomite and algae psammite of the microbial dolomite lithofacies in the core calibration well, respectively determine the corresponding relationship between the algae stromatolite, the algae clot dolomite, the algae laminated dolomite and the algae psammite and the multi-fractal spectrum parameters;

[0158] Based on the correspondence between the mud-crystal dolomite and the natural gamma parameters, and the correspondence between the algal stromatolite, the algal clot dolomite, the algal laminar dolomite and the algal psammite dolomite and the multi-fractal spectrum parameters, the microbial dolomite lithofacies identification plate is constructed.

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

[0160] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the method for constructing the above-mentioned microbial dolomite lithofacies identification plate is implemented.

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

[0162] The principles of the problems solved by the above-mentioned devices, media, and related equipment in the embodiments of the present invention are similar to those of the above-mentioned methods, so their implementation can refer to the implementation of the above-mentioned methods, and the repeated parts will not be repeated.

[0163] Embodiment 2

[0164] In the first embodiment of the present invention, a microbial dolomite lithofacies logging identification method is provided, referring to Figure 8 As shown, the method may include the following steps:

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

[0166] Step S82: interpolate the electrical imaging logging grayscale image of the target well to obtain an electrical imaging logging grayscale image covering the entire borehole of the target well.

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

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

[0169] Step S85, identifying the target stratigraphic lithofacies of the target well based on the natural gamma parameters and multifractal spectrum parameters of the target well and a pre-constructed microbial dolomite lithofacies identification plate; wherein the microbial dolomite lithofacies identification plate is pre-constructed according to the method for constructing the microbial dolomite lithofacies identification plate described in Example 1.

[0170] In another optional embodiment, the above method may further include: step S86, connecting well profiles for the target formation lithofacies identified by multiple target wells to obtain the sedimentary phase distribution of different microbial dolomite lithofacies in the study area where the target wells are located.

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

[0172] Based on the same inventive concept, a microbial dolomite lithofacies logging identification device is also provided in the embodiment of the present invention. Fig. 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 electrical imaging logging grayscale image of the target well to obtain the electrical imaging logging grayscale image covering the entire borehole of the target well;

[0175] The second binarization module 93 is used to perform binarization processing on the electrical imaging logging grayscale image covering the entire wellbore to obtain an 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 electrical imaging binary image; 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 formation lithofacies of the target well based on the natural gamma parameters and multi-fractal spectrum parameters of the target well and the pre-constructed microbial dolomite lithofacies identification plate;

[0178] The microbial dolomite lithofacies identification plate is pre-constructed according to the method for constructing the microbial dolomite lithofacies identification plate described in Example 1.

[0179] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned microbial dolomite lithofacies logging identification method is implemented.

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

[0181] The principles of the problems solved by the above-mentioned devices, clients, media, related equipment and systems in the embodiments of the present invention are similar to those of the above-mentioned methods, so their implementation can refer to the implementation of the above-mentioned methods, and the repeated parts will not be repeated.

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

[0183] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0184] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0185] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0186] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended 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 curve of the core calibration well; Interpolating the electrical imaging logging grayscale image of the core calibration well to obtain an electrical imaging logging grayscale image covering the entire borehole of the core calibration well; Binarizing the electrical imaging logging grayscale image covering the entire borehole to obtain an electrical imaging binary image of the core calibration well; Determine the multifractal spectrum parameters of the core calibration well based on the electrical imaging binary image; wherein the multifractal spectrum parameters include: multifractal spectrum peak, multifractal spectrum asymmetry index and multifractal spectrum width; Based on the lithofacies of the core calibration well, the natural gamma parameters and the multi-fractal spectrum parameters, the microbial dolomite lithofacies identification chart is constructed.

2. The method according to claim 1, characterized in that After determining the multifractal spectrum parameters based on the electro-imaging binary image, the method further includes: generating a multifractal spectrum difference parameter of the electrographic binary image based on the difference between the multifractal spectrum peak, the multifractal spectrum asymmetry index and the multifractal spectrum width; Accordingly, based on the lithofacies of the core calibration well, the natural gamma parameters and the multi-fractal spectrum difference parameters, the microbial dolomite lithofacies identification chart is constructed.

3. The method according to claim 2, characterized in that The multifractal spectrum difference parameter is F: Among them, a and b are constants; a0 is the peak value of the multifractal spectrum; B is the asymmetry index of the multifractal spectrum; W is the multifractal spectrum width.

4. The method according to any one of claims 1 to 3, characterized in that Based on the lithofacies of the core calibration well, the natural gamma parameters and the multi-fractal spectrum parameters, the microbial dolomite lithofacies identification chart is constructed, including: Based on the natural gamma parameters corresponding to the micrite dolomite of the microbial dolomite lithofacies in the core calibration well, determining the corresponding relationship between the micrite dolomite and the natural gamma parameters; Based on the multi-fractal spectrum parameters of the algae stromatolite, algae clot dolomite, algae laminated dolomite and algae psammite of the microbial dolomite lithofacies in the core calibration well, respectively determine the corresponding relationship between the algae stromatolite, the algae clot dolomite, the algae laminated dolomite and the algae psammite and the multi-fractal spectrum parameters; Based on the correspondence between the mud-crystal dolomite and the natural gamma parameters, and the correspondence between the algal stromatolite, the algal clot dolomite, the algal laminar dolomite and the algal psammite dolomite and the multi-fractal spectrum parameters, the microbial dolomite lithofacies identification plate is constructed.

5. The method according to any one of claims 1 to 3, characterized in that: After constructing the microbial dolomite lithofacies identification plate, the method further includes: The microbial dolomite lithofacies identification chart is verified based on the lithofacies, natural gamma parameters, and multi-fractal spectrum parameters of other core calibration wells in the study area where the core calibration well is located.

6. A microbial dolomite lithofacies logging identification method, characterized in that: include: Determine the natural gamma parameters of the formation based on the natural gamma logging curve of the target well; Interpolating the electrical imaging logging grayscale image of the target well to obtain an electrical imaging logging grayscale image covering the entire borehole of the target well; Binarizing the electrical imaging logging grayscale image covering the entire wellbore to obtain an electrical imaging binary image of the target well; Determining the multifractal spectrum parameters of the target well based on the electrical imaging binary image; wherein the multifractal spectrum parameters include: multifractal spectrum peak, multifractal spectrum asymmetry index and multifractal spectrum width; Based on the natural gamma parameter and the multifractal spectrum parameter of the target well and a pre-constructed microbial dolomite lithofacies identification plate, identifying the target formation lithofacies of the target well; Wherein, the microbial dolomite lithofacies identification plate is pre-constructed according to the method for constructing a microbial dolomite lithofacies identification plate according to any one of claims 1 to 5.

7. The method according to claim 6, characterized in that Also includes: The target stratigraphic lithofacies identified by multiple target wells are connected to form well sections to obtain the sedimentary facies distribution of different microbial dolomite facies in the study area where the target wells are located.

8. A device for constructing a microbial dolomite lithofacies identification plate, characterized in that: include: A first 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 core calibration well; A first interpolation module is used to interpolate the electrical imaging logging grayscale image of the core calibration well to obtain an electrical imaging logging grayscale image covering the entire borehole of the core calibration well; A first binarization module is used to perform binarization processing on the electrical imaging logging grayscale image covering the entire wellbore to obtain the electrical imaging binary image of the core calibration well; A first multifractal spectrum parameter determination module is used to determine the multifractal spectrum parameters of the core calibration well based on the electrical imaging binary image; wherein the multifractal spectrum parameters include: multifractal spectrum peak value, multifractal spectrum asymmetry index and multifractal spectrum width; A construction module is used to construct the microbial dolomite lithofacies identification plate based on the lithofacies of the core calibration well, the natural gamma parameters and the multi-fractal spectrum parameters.

9. A microbial dolomite lithofacies logging identification device, characterized in that: include: A second natural gamma parameter determination module, used to determine the natural gamma parameters of the formation based on the natural gamma logging curve of the target well; A second interpolation module is used to interpolate the electrical imaging logging grayscale image of the target well to obtain an electrical imaging logging grayscale image covering the entire borehole of the target well; A second binarization module is used to perform binarization processing on the electrical imaging logging grayscale image covering the entire wellbore to obtain an electrical imaging binary image of the target well; A second multifractal spectrum parameter determination module is used to determine the multifractal spectrum parameters of the target well based on the electrical imaging binary image; wherein the multifractal spectrum parameters include: multifractal spectrum peak value, multifractal spectrum asymmetry index and multifractal spectrum width; An identification module, for identifying the target stratigraphic lithofacies of the target well based on the natural gamma parameter and the multifractal spectrum parameter of the target well and a pre-constructed microbial dolomite lithofacies identification plate; Wherein, the microbial dolomite lithofacies identification plate is pre-constructed according to the method for constructing a microbial dolomite lithofacies identification plate according to any one of claims 1 to 5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for constructing a microbial dolomite lithofacies identification plate according to any one of claims 1 to 5 is implemented, or the method for microbial dolomite lithofacies logging identification according to claim 6 or 7 is implemented.

11. 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, the method for constructing a microbial dolomite lithofacies identification plate according to any one of claims 1 to 5 is implemented, or the method for microbial dolomite lithofacies logging identification according to claim 6 or 7 is implemented.

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