A shale facies decision recognition method, device and electronic equipment

Abstract

The present application provides a kind of shale facies decision identification method, device and electronic equipment, wherein the method comprises: determining the sensitive attribute parameter of different facies of at least one shale structure, establishing the Y value calculation model corresponding to structure;Based on simulated annealing algorithm inversion Y value calculation model, obtain inversion coefficient;The inversion coefficient is input into Y value calculation model, and the Y value facies identification model corresponding to structure is generated, and the Y value facies identification model is used for the facies identification of shale reservoir.By the shale facies decision identification method, device and electronic equipment provided in the embodiment of the present application, the coefficient of sensitive attribute parameter in Y value calculation model is inverted by using simulated annealing algorithm, Y value facies identification model is established, high-precision, resolution, convenient and effective identification of shale reservoir facies is realized, so as to solve the problem of urgently exploring shale reservoir facies identification, provide scientific basis for discovering more unconventional oil and gas resources and improving development efficiency and saving cost.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration and development technology, and more specifically, to a method, apparatus, electronic device, and computer-readable storage medium for shale lithofacies decision identification. Background Technology

[0002] Shale oil and gas, as one of the main types of unconventional oil and gas resources, is destined to become a major alternative energy source in the future, based on its enormous resource potential. Lithofacies determines the quality of shale reservoirs and the differences in development strategies; it is the foundation for shale reservoir evaluation and oil and gas reservoir development, making lithofacies identification crucial.

[0003] Currently, there are roughly four categories of methods for identifying the lithofacies of shale reservoirs: 1) geological classification standard methods; 2) probabilistic statistical methods and chart methods; 3) well logging mineral and petrographic lithology identification methods; and 4) intelligent algorithms. However, these four methods still face certain difficulties in identifying and evaluating the lithofacies of shale reservoirs, which can be summarized into the following three problems: 1) insufficient matching between core scanning resolution and well logging resolution; 2) lack of analysis of influencing factors and attributes; and 3) algorithms that can achieve high accuracy may also suffer from problems such as poor global applicability, high cost, and high cost.

[0004] Therefore, how to use conventional logging data for rapid and effective identification of lithofacies, improve identification accuracy, and thus improve the efficiency of shale reservoir logging interpretation is a major challenge that urgently needs to be addressed. Summary of the Invention

[0005] To address the existing technical problems, embodiments of the present invention provide a shale lithofacies decision identification method, apparatus, electronic device, and computer-readable storage medium.

[0006] In a first aspect, embodiments of the present invention provide a shale lithofacies decision-making and identification method, comprising: determining sensitive attribute parameters of different lithofacies of at least one shale structure, establishing a Y-value calculation model corresponding to the structure; inverting the Y-value calculation model based on a simulated annealing algorithm to obtain inversion coefficients; inputting the inversion coefficients into the Y-value calculation model to generate a Y-value lithofacies identification model corresponding to the structure, and using the Y-value lithofacies identification model to identify the lithofacies of shale reservoirs.

[0007] Optionally, determining sensitive attribute parameters for different lithofacies of at least one shale structure includes: determining logging sensitive data for different lithofacies logging response relationship charts corresponding to each structure; processing the logging sensitive data to obtain the sensitive attribute parameters; the data processing includes: normalizing the logging sensitive data and performing reverse processing on the correlation between the logging sensitive data.

[0008] Optionally, the process of obtaining different lithofacies logging response relationship charts corresponding to each of the aforementioned structures includes: acquiring lithofacies logging response characteristics based on conventional logging data with core lithofacies scale; the lithofacies logging response characteristics include at least: natural gamma, sonic transit time, rock bulk density, compensated neutron, deep lateral resistivity, and shallow lateral resistivity; and establishing different lithofacies logging response relationship charts corresponding to each of the aforementioned lithofacies logging response characteristics.

[0009] Optionally, logging-sensitive data include: natural gamma, sonic transit time, rock bulk density, and multiple data in compensated neutrons.

[0010] Optionally, the inversion coefficients are obtained by inverting the Y-value calculation model based on the simulated annealing algorithm, including: inputting the sensitive attribute parameters, the Y-value calculation model, the maximum number of iterations, the initial temperature, the cooling rate, the core lithofacies and their labels, and the target matching rate into the simulated annealing algorithm; the simulated annealing algorithm randomly generates nearest neighbor solutions and generates corresponding Y-values ​​based on the nearest neighbor solutions, and inputs the Y-values ​​into the decision algorithm; in the decision algorithm, based on the core lithofacies, inversion coefficients that can satisfy the optimal matching rate in the current iteration are generated.

[0011] Optionally, in generating inversion coefficients that can achieve the best matching rate in the current iteration, the method further includes generating a threshold and a lithofacies decision identification scheme that can achieve the best matching rate in the current iteration.

[0012] Secondly, embodiments of the present invention also provide a shale lithofacies decision-making and identification device, comprising: a model construction module, a model inversion module, and a lithofacies identification module; the model construction module is used to determine the sensitive attribute parameters of different lithofacies of at least one shale structure and establish a Y-value calculation model corresponding to the structure; the model inversion module is used to invert the Y-value calculation model based on a simulated annealing algorithm to obtain inversion coefficients; the lithofacies identification module is used to input the inversion coefficients into the Y-value calculation model to generate a Y-value lithofacies identification model corresponding to the structure, and use the Y-value lithofacies identification model to identify the lithofacies of shale reservoirs.

[0013] Thirdly, embodiments of the present invention provide an electronic device, including a processor and a memory, wherein the memory stores a computer program, characterized in that the processor executes the computer program stored in the memory, and the computer program, when executed by the processor, implements the shale lithofacies decision identification method described in the first aspect above.

[0014] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the shale lithofacies decision identification method described in the first aspect above.

[0015] Fifthly, this application also provides a computer program product, including a computer program that, when executed, can implement the shale lithofacies decision identification method described in the first aspect or any possible design of the first aspect.

[0016] The shale facies decision-making identification method, device, electronic equipment, and computer-readable storage medium provided in this invention establish a Y-value facies identification model by using the simulated annealing algorithm to invert the coefficients of parameters (sensitive attribute parameters) in the Y-value calculation model. This achieves high-precision, high-resolution, convenient, and effective identification of shale reservoir facies, thereby solving the urgent problem of shale reservoir facies identification and providing a scientific basis for discovering more unconventional oil and gas resources, improving development efficiency, and saving costs. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the background art, the accompanying drawings used in the embodiments of the present invention or the background art will be described below.

[0018] Figure 1 A flowchart of a shale lithofacies decision identification method provided by an embodiment of the present invention is shown;

[0019] Figure 2 This invention illustrates a graph showing the response relationship of logging curves for different facies in lamellar structures in the shale facies decision-making and identification method provided in this embodiment of the invention.

[0020] Figure 3 This invention illustrates a graph showing the response relationship of well logging curves for different facies in a layered structure within the shale facies decision-making and identification method provided in this embodiment of the invention.

[0021] Figure 4 This invention illustrates a graph showing the response relationship of logging curves for different lithofacies in a massive structure within the shale lithofacies decision-making and identification method provided in this embodiment of the invention.

[0022] Figure 5 This invention illustrates a graph showing the results of sensitive attribute parameters in the shale lithofacies decision-making and identification device provided in an embodiment of the invention.

[0023] Figure 6 The diagram shows a comparison between the identification results of the lamellar, layered, and massive rock facies applicable to the embodiments of the present invention and the rock facies of the core sample.

[0024] Figures 7(a) and (b) show the shale facies identification effect of a target section in Well 2-1 provided by the embodiment of the present invention;

[0025] Figure 8 This diagram illustrates the structure of a shale lithofacies decision-making and identification device according to an embodiment of the present invention.

[0026] Figure 9 The diagram shows a schematic representation of an electronic device for performing a shale lithofacies decision identification method, as provided in an embodiment of the present invention. Detailed Implementation

[0027] The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0028] Figure 1 A flowchart of a shale lithofacies decision identification method provided by an embodiment of the present invention is shown. Figure 1 As shown, the method includes the following steps 101-103.

[0029] Step 101: Determine the sensitive property parameters of different lithofacies of at least one shale structure, and establish a Y-value calculation model corresponding to the structure.

[0030] Specifically, the lithofacies of shale can include laminated mixed sedimentary shale, laminated felsic mixed sedimentary shale, laminated argillaceous mudstone shale, layered mixed sedimentary shale, layered felsic mixed sedimentary shale, layered argillaceous mudstone shale, massive sandstone, massive argillaceous mudstone, massive dolomite, etc. In embodiments of the present invention, a Y-value calculation model can be established for a single shale structure to identify the various lithofacies corresponding to that structure; alternatively, multiple Y-value calculation models can be established for different shale structures to identify the various lithofacies corresponding to different structures. For example, embodiments of the present invention can establish a Y-value calculation model for three types of lamellar structures (laminated mixed shale, lamellar felsic mixed shale, and lamellar argillaceous mudstone); establish a Y-value calculation model for three types of layered structures (laminated mixed shale, layered argillaceous mudstone, and layered felsic mixed shale); and establish a Y-value calculation model for three types of massive structures (massive sandstone, massive mudstone, and massive dolomite).

[0031] The Y-value calculation model is a model used to quantitatively describe statistical relationships, typically for discovering causal relationships between variables. Y-value calculation models can be divided into univariate function models and multivariate function models; this embodiment of the invention employs a multivariate function model. To establish a Y-value calculation model corresponding to a given structure, it is necessary to determine sensitive attribute parameters related to different lithofacies of that structure. It can be understood that the determined sensitive attribute parameters can better characterize the different lithofacies of the structure, thereby enabling accurate lithofacies identification through the relationships between variables.

[0032] For example, the established Y-value calculation model for laminated structures can be: Y = a × X1 + b × X2 + c × X3 + d; where X1, X2, and X3 are sensitive attribute parameters used to characterize and identify different lithofacies of laminated structures.

[0033] Step 102: Based on the simulated annealing algorithm, invert the Y-value calculation model and obtain the inversion coefficients.

[0034] This invention combines simulated annealing algorithm to iteratively optimize the construction of the corresponding Y-value calculation model, thereby obtaining the inversion coefficients of the Y-value calculation model. These inversion coefficients are the coefficients a, b, c, and d in the Y-value calculation model formula for the layered structure described above. It should be noted that the Y-value and the coefficients a, b, c, and d in the Y-value calculation models for different structures are independent and do not interfere with each other.

[0035] Step 103: Input the inversion coefficients into the Y-value calculation model to generate the Y-value lithofacies identification model corresponding to the structure, and use the Y-value lithofacies identification model to identify the lithofacies of the shale reservoir.

[0036] After obtaining the inversion coefficients of the Y-value calculation model corresponding to the structure, these inversion coefficients are input into the Y-value calculation model to generate the Y-value lithofacies identification model corresponding to the structure (i.e., the model generated after iterative training of the original Y-value calculation model). For example, if the inversion coefficients a, b, c, and d of the Y-value calculation model for a laminated structure obtained by simulated annealing are 0.9118, -0.8005, 0.7128, and -0.0224, respectively, then the final Y-value lithofacies identification model for the laminated structure is: Y = 0.9118 × X1 - 0.8005 × X2 + 0.7128 × X3 - 0.0224. Furthermore, this Y-value lithofacies identification model can be used for lithofacies identification of shale reservoirs.

[0037] The shale facies decision identification method provided in this invention establishes a Y-value facies identification model by using the simulated annealing algorithm to invert the coefficients of parameters (sensitive attribute parameters) in the Y-value calculation model. This achieves high-precision, high-resolution, convenient, and effective identification of shale reservoir facies, thereby solving the urgent problem of shale reservoir facies identification and providing a scientific basis for discovering more unconventional oil and gas resources, improving development efficiency, and saving costs.

[0038] Optionally, step 101 above, "determining sensitive property parameters of different lithofacies of at least one shale structure", may include the following steps A1-A2.

[0039] Step A1: For each type of structure, determine the logging sensitive data based on the different lithofacies logging response charts.

[0040] To obtain sensitive attribute parameters that better characterize different lithofacies, it is necessary to select sensitive logging data from the logging response relationship charts of different lithofacies corresponding to each structure to generate the sensitive attribute parameters. In other words, it is necessary to select sensitive logging data from the charts and obtain the sensitive attribute parameters based on the sensitive logging data. Optionally, the sensitive logging data may include multiple parameters such as natural gamma, sonic transit time, rock bulk density, and compensated neutrons.

[0041] For the different lithofacies logging response charts corresponding to each structure, please refer to [link / reference needed]. Figures 2 to 4 As shown. Figure 2 As shown, Figure 2 A chart showing the response relationship of well logging curves for different lithofacies of laminated structures is presented. Figure 2 The diagram uses dots to represent laminated argillaceous mudstone and shale, rhomboids to represent laminated mixed sedimentary shale, and triangles to represent laminated felsic mixed sedimentary shale. It is evident that the three types of sample points overlap to varying degrees, and the sample points are relatively dispersed. Furthermore, natural gamma (GR) shows a good positive correlation with acoustic transit time (AC) and compensated neutron (CNL) (e.g., ...). Figure 2 As shown in (a) and (b), natural gamma (GR) and rock bulk density (DEN) show a good negative correlation (e.g., Figure 2 (c) shows that the acoustic transit time (AC) and the compensated neutron (CNL) exhibit a good positive correlation (e.g., Figure 2 (d) Therefore, natural gamma (GR), sonic transit time (AC), compensated neutron (CNL), and rock bulk density (DEN) are preferred as logging sensitive data for different lithofacies with layered structures.

[0042] like Figure 3 As shown, Figure 3 This diagram shows the relationship between logging curves for different lithofacies in layered structures. Figure 3 The diagram uses dots to represent layered argillaceous mudstone and shale, triangles to represent layered felsic mixed shale, and rhombuses to represent layered mixed shale. It is evident that there is varying degrees of overlap among the four sample points. Furthermore, natural gamma (GR) and acoustic transit time (AC) show a strong positive correlation (e.g., Figure 3 (a) shows that rock bulk density (DEN) and acoustic transit time (AC) exhibit a good negative correlation (e.g., Figure 3 (f) shows that the sample points are in the GR-AC and DEN-AC response charts (as shown in f). Figure 3 As shown in (a) and (f), the response plots show relatively independent intervals compared to other response plots. Therefore, natural gamma (GR), sonic transit time (AC), and rock bulk density (DEN) are preferred as logging sensitive data for different lithofacies of layered structures.

[0043] like Figure 4 As shown, Figure 4 A chart showing the response relationship of logging curves for different lithofacies in massive structures is presented. Figure 4 In the diagram, triangular patterns represent massive sandstone, dotted patterns represent massive mudstone, and rhomboid patterns represent massive dolomite. It is easy to see that these three types of sample points are represented in the GR-AC and GR-CNL intersection plots (e.g., Figure 4 Although there is a small overlap between (a) and (b), most of them have their own distinct response ranges. Therefore, natural gamma (GR), sonic transit time (AC), and compensated neutron (CNL) are preferred as logging sensitive data for different lithofacies of massive structures.

[0044] Step A2: Process the logging sensitive data to obtain sensitive attribute parameters; the data processing includes: normalizing the logging sensitive data and performing reverse processing on the correlation between logging sensitive data.

[0045] In this embodiment of the invention, different data processing methods can be used to process the sensitive logging data according to the specific circumstances of different lithofacies logging response relationship charts corresponding to different structures, thereby obtaining the required sensitive attribute parameters. For example, for... Figure 2 In the case of the lamellar structure shown, given the previously dispersed distribution of sample points in the well logging sensitive data, the correlation between the well logging sensitive data points can be reversed after normalization for the lamellar structure. This process generates sensitive attribute parameters for different lithofacies of the lamellar structure, expanding the inverse relationship and thus achieving a convergence of sample point distribution. The final sensitive attribute parameter results are shown in the figure below. Figure 5 As shown in (a) and (b), the sensitive property parameters of different lithofacies in the determined laminated structure are: Among them, GR n AC n CNL n These are the normalized values ​​for natural gamma, acoustic time difference, and compensated neutron, respectively.

[0046] For example, targeting Figure 3 In the case of the layered structure shown, the above-mentioned well logging sensitive data can be normalized to determine the sensitive attribute parameters of different lithofacies of the layered structure as GR. n AC n DEN n Among them, DEN n The normalized value of rock bulk density is used to obtain the sensitive attribute parameter results in the following figure. Figure 5 As shown in (c) and (d).

[0047] For example, targeting Figure 4In the case of the massive structure shown, the above-mentioned logging sensitive data can be normalized to determine the sensitive attribute parameters of different lithofacies of the massive structure as GR. n AC n CNL n The resulting graph of the sensitive attribute parameters is shown below. Figure 5 As shown in (e) and (f).

[0048] It should be noted that, Figure 5 The sample points in each subgraph still use the methods described above. Figures 2 to 4 The diagram is shown in the image.

[0049] Optionally, the process of obtaining different lithofacies logging response charts corresponding to each structure in step A1 above may include the following steps A11-A12.

[0050] Step A11: Obtain the facies logging response characteristics based on conventional logging data from the core facies scale.

[0051] Specifically, lithofacies logging response features can be extracted from conventional logging data with core facies calibration. The lithofacies logging response features extracted in this embodiment of the invention include at least: natural gamma (GR), sonic transit time (AC), rock bulk density (DEN), compensated neutron (CNL), deep lateral resistivity (RT), and shallow lateral resistivity (RS). It should be noted that the process of acquiring conventional logging data with core facies calibration and the process of extracting lithofacies logging response features are both conventional techniques in this field and will not be elaborated upon here.

[0052] Step A12: Based on the lithofacies logging response characteristics, establish different lithofacies logging response relationship charts for each type of structure (e.g., Figures 2 to 4 (See attached figure). This allows us to clearly filter out the logging sensitive data from the figure and obtain the sensitive attribute parameters.

[0053] Optionally, step 102 above, "inverting the Y-value calculation model based on the simulated annealing algorithm and obtaining the inversion coefficients", may include the following steps B1-B2.

[0054] Step B1: Input the sensitive attribute parameters, Y-value calculation model, maximum number of iterations, initial temperature, cooling rate, core lithofacies and their labels, and target matching rate into the simulated annealing algorithm.

[0055] It should be noted that the sensitive attribute parameters and the Y-value calculation model are parameters and models corresponding to a specific structure. For example, using the three structures mentioned above (lamellar, layered, and blocky), we can see that the sensitive attribute parameters for the lamellar structure are... The model for calculating the Y-value of lamellar structures is as follows: The sensitive property parameter for layered structures is GR n AC n DEN n The model for calculating the Y value of layered structures is: Y = a × GR n +b×AC n +c×DEN n +d; The sensitive property parameter for blocky construction is GR n AC n CNL n The Y-value calculation model for blocky structures is: Y = a × GR n +b×AC n +c×CNL n +d.

[0056] Secondly, the maximum number of iterations, initial temperature, cooling rate, and target matching rate are all initial configuration parameters required by the simulated annealing algorithm. Specifically, the matching rate calculation formula used to calculate the target matching rate is: Where, N s 1 is the total number of samples; lables[i]＝＝0 Let `labels[i]` be an indicator function; when `labels[i] = 0`, 1 lables[i]＝＝0 =1, otherwise 0. Specifically, 1 lables[i]＝＝0 =abs(label) import -lable distribute ); where label import For input labels, distribute Labels are assigned. The matching rate is calculated using the matching rate calculation formula, and it is determined whether the pre-configured target matching rate is met. If it is met, the result is output; otherwise, the process is iterated until the target matching rate is met or the preset maximum number of iterations is reached.

[0057] Furthermore, the input core facies and their labels in the embodiments of the present invention can be: layered mixed shale 1, layered felsic mixed shale 2, layered calcareous mudstone 3, layered mixed shale 4, layered felsic mixed shale 5, layered calcareous mudstone 6, massive sandstone 7, massive mudstone 8, massive dolomite 9.

[0058] Step B2: The simulated annealing algorithm randomly generates nearest neighbor solutions and generates corresponding Y values ​​based on the nearest neighbor solutions. The Y values ​​are then input into the decision algorithm. In the decision algorithm, based on the core facies, inversion coefficients that can satisfy the best matching rate in the current iteration are generated.

[0059] As shown in Table 1 below, Table 1 presents the inversion results of the Y-value calculation model using the example above.

[0060]

[0061] Table 1: Inversion Results of the Y-value Calculation Model

[0062] Based on Table 1 above, the inversion coefficients a, b, c, and d of the Y-value calculation model for layered structures are 0.9118, -0.8005, 0.7128, and -0.0224, respectively; the inversion coefficients a, b, c, and d of the Y-value calculation model for layered structures are 0.3461, -1.2047, -1.6346, and 0.6615, respectively; and the inversion coefficients a, b, c, and d of the Y-value calculation model for massive structures are -4.4445, -2.5617, 1.9662, and -10.3428, respectively.

[0063] Optionally, in step B3 above, "generating inversion coefficients that can satisfy the optimal matching rate in the current iteration", the method may further include: generating a threshold that can satisfy the optimal matching rate in the current iteration and a lithofacies decision identification scheme.

[0064] It is understandable that using the simulated annealing algorithm to invert various Y-value calculation models can not only yield the inversion coefficients corresponding to the models, but also the threshold that satisfies the optimal fit rate in the current iteration and the lithofacies decision-making identification scheme. For example, according to Table 1 above, the Y-value calculation model for laminated structures is as follows: The decision-making scheme for lamellar lithofacies is as follows: when Y ≤ 0.6955, it is lamellar mixed sedimentary shale 1; when 0.6955 ≤ Y ≤ 0.7507, it is lamellar felsic mixed sedimentary shale 2; when Y ≥ 0.6955, it is lamellar argillaceous mudstone shale 3. The lamellar Y-value lithofacies identification model is: Y = 0.3461 × GR n -1.2047×AC n -1.6346×DEN n +0.6615; The layered lithofacies decision identification scheme is as follows: when Y≤-1.481, it is layered mixed sedimentary shale 4; when -1.481≤Y≤-1.3824, it is layered argillaceous mudstone shale 6; when Y≥-1.3824, it is layered felsic mixed sedimentary shale 5. The massive Y-value lithofacies identification model is: Y=-4.4445×GR n -2.5617×AC n +1.9662×CNL n -10.3428; The decision identification scheme for massive lithofacies is as follows: when Y≤-14.372, it is massive sandstone 7; when -14.372≤Y≤-13.158, it is massive mudstone 8; when Y≥-13.158, it is massive dolomite 9.

[0065] The matching rates of the lamellar, layered, and massive structures were 70.35%, 60.06%, and 75.53%, respectively.

[0066] like Figure 6 As shown, Figure 6 Images (a), (b), and (c) respectively show comparison diagrams between the identification results of lamellar, layered, and massive lithofacies structures applicable to the embodiments of the present invention and the lithofacies of the core samples. Figure 6 As shown, the horizontal axis represents the number of sample points, and the vertical axis represents the lithofacies label. The dark circular sample points represent the lithofacies of the core, obtained from conventional logging based on the core description results. The light square sample points represent the Y-value division results. The light square sample points are stacked on top of the dark circular sample points. Therefore, when they completely cover each other, it is considered that the identification results match. When the dark circular sample points appear alone, it is considered that there is an error in the identification results. This demonstrates the applicability of the model in this embodiment of the invention.

[0067] Furthermore, the Y-value lithofacies identification model constructed above was applied to the entire section of a certain 2-1 well, and the results are shown in Figures 7(a) and (b). Figures 7(a) and (b) are the lithofacies identification effect diagrams of the target shale section of a certain 2-1 well provided by the embodiment of the present invention. The first channel from the left is the lithology logging channel, which includes natural gamma (GR); the second channel from the left is the depth channel; the third channel from the left is the resistivity logging channel, which includes deep lateral resistivity (RT) and shallow lateral resistivity (RS) in sequence; the fourth channel from the left is the porosity logging channel, which includes acoustic transit time (AC), compensated neutron (CNL), and rock bulk density (DEN) in sequence; the fifth and sixth channels from the left are the core lithofacies map and the Y-value lithofacies identification result map, respectively. Among them, the larger the lithofacies label value, the wider it is displayed in the map, and different lithofacies have different color systems. Thus, a lithofacies identification characterization map is established to intuitively identify and distinguish different lithofacies of shale reservoirs. As shown in Figure 7, the Y-value lithofacies identification model has high accuracy and resolution, and can form a good correspondence with the lithofacies of the core.

[0068] The shale facies decision identification method provided by the embodiments of the present invention has been described in detail above. This method can also be implemented by a corresponding device. The shale facies decision identification device provided by the embodiments of the present invention will be described in detail below.

[0069] Figure 8 A schematic diagram of a shale facies decision-making and identification device provided in an embodiment of the present invention is shown. Figure 8 As shown, the shale lithofacies decision-making and identification device includes a processor. The processor includes: a model construction module 81, a model inversion module 82, and a lithofacies identification module 83.

[0070] Model construction module 81 is used to determine the sensitive attribute parameters of different lithofacies of at least one shale structure and establish a Y-value calculation model corresponding to the structure.

[0071] The model inversion module 82 is used to invert the Y-value calculation model based on the simulated annealing algorithm and obtain the inversion coefficients.

[0072] The lithofacies identification module 83 is used to input the inversion coefficients into the Y-value calculation model to generate the Y-value lithofacies identification model corresponding to the structure, and to use the Y-value lithofacies identification model to identify the lithofacies of the shale reservoir.

[0073] Optionally, the model construction module 81 includes: a first construction submodule and a second construction submodule.

[0074] The first structural submodule is used to determine the logging sensitive data for different lithofacies logging response charts corresponding to each structural type.

[0075] The second construction submodule is used to process the well logging sensitive data to obtain the sensitive attribute parameters; the data processing includes: normalizing the well logging sensitive data and performing reverse processing on the correlation between the well logging sensitive data.

[0076] Optionally, the first construction submodule includes: a feature acquisition unit and a map creation unit.

[0077] The feature acquisition unit is used to acquire lithofacies logging response features based on conventional logging data with core lithofacies scale; the lithofacies logging response features include at least: natural gamma, sonic transit time, rock bulk density, compensated neutron, deep lateral resistivity and shallow lateral resistivity.

[0078] The chart establishment unit is used to establish different lithofacies logging response relationship charts for each type of structure based on the lithofacies logging response characteristics.

[0079] Optionally, logging-sensitive data include: natural gamma, sonic transit time, rock bulk density, and multiple data in compensated neutrons.

[0080] Optionally, the model inversion module 82 includes: a first inversion submodule and a second inversion submodule.

[0081] The first inversion submodule is used to input the sensitive attribute parameters, the Y-value calculation model, the maximum number of iterations, the initial temperature, the cooling rate, the core lithofacies and their labels, and the target matching rate into the simulated annealing algorithm.

[0082] The second inversion submodule is used to randomly generate nearest neighbor solutions in the simulated annealing algorithm, generate corresponding Y values ​​based on the nearest neighbor solutions, and input the Y values ​​into the decision algorithm; in the decision algorithm, based on the core lithofacies, inversion coefficients that can satisfy the best matching rate in the current iteration are generated.

[0083] Optionally, the second inversion submodule also includes a result unit.

[0084] The results unit is used to generate the threshold and lithofacies decision identification scheme that can meet the optimal matching rate in the current iteration.

[0085] The apparatus provided in this invention establishes a Y-value facies identification model by using a simulated annealing algorithm to invert the coefficients of parameters (sensitive attribute parameters) in the Y-value calculation model. This achieves high-precision, high-resolution, convenient, and effective identification of shale reservoir facies, thereby solving the urgent problem of shale reservoir facies identification and providing a scientific basis for discovering more unconventional oil and gas resources, improving development efficiency, and saving costs.

[0086] It should be noted that the shale facies decision-making and identification device provided in the above embodiments is only illustrated by the division of the above functional modules when implementing the corresponding functions. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the shale facies decision-making and identification device and the shale facies decision-making and identification method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0087] According to one aspect of this application, embodiments of the present invention also provide a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication component. When the computer program is executed by a processor, the shale lithofacies decision identification method provided in embodiments of this application is performed.

[0088] In addition, embodiments of the present invention also provide an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor. The transceiver, the memory, and the processor are connected via a bus. When the computer program is executed by the processor, it implements the various processes of the above-described shale lithofacies decision identification method embodiments and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0089] For details, see Figure 9 As shown, the electronic device includes a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, and a user interface 1160.

[0090] In this embodiment of the invention, the electronic device further includes a computer program stored in a memory 1150 and executable on a processor 1120. When the computer program is executed by the processor 1120, it implements the various processes of the above-described shale lithofacies decision identification method embodiment.

[0091] Transceiver 1130 is used to receive and send data under the control of processor 1120.

[0092] In this embodiment of the invention, a bus architecture (represented by bus 1110) is used. Bus 1110 may include any number of interconnected buses and bridges. Bus 1110 connects various circuits, including one or more processors represented by processor 1120 and memory represented by memory 1150.

[0093] Bus 1110 represents one or more of several types of bus architectures, including memory buses and memory controllers, peripheral buses, Accelerated Graphics Port (AGP), processors, or local buses using any bus architecture from various bus architectures. As an example and not a limitation, such architectures include: Industry Standard Architecture (ISA) buses, Micro Channel Architecture (MCA) buses, Enhanced ISA (EISA) buses, Video Electronics Standards Association (VESA) buses, and Peripheral Component Interconnect (PCI) buses.

[0094] The processor 1120 can be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method embodiments can be completed by integrated logic circuits in the processor hardware or by instructions in software form. The processors mentioned above include: general-purpose processors, central processing units (CPUs), network processors (NPs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), complex programmable logic devices (CPLDs), programmable logic arrays (PLAs), microcontroller units (MCUs) or other programmable logic devices, discrete gates, transistor logic devices, and discrete hardware components. They can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. For example, the processor can be a single-core processor or a multi-core processor, and the processor can be integrated on a single chip or located on multiple different chips.

[0095] Processor 1120 can be a microprocessor or any conventional processor. The method steps disclosed in the embodiments of the present invention can be directly executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in readable storage media known in the art, such as Random Access Memory (RAM), Flash Memory, Read-Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), registers, etc. The readable storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0096] Bus 1110 can also connect various other circuits, such as peripheral devices, voltage regulators, or power management circuits. Bus interface 1140 provides an interface between bus 1110 and transceiver 1130, all of which are well known in the art. Therefore, the embodiments of the present invention will not be described further.

[0097] Transceiver 1130 can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. For example, transceiver 1130 receives external data from other devices, and transceiver 1130 is used to send data processed by processor 1120 to other devices. Depending on the nature of the computer system, a user interface 1160 may also be provided, such as a touchscreen, physical keyboard, monitor, mouse, speaker, microphone, trackball, joystick, or stylus.

[0098] It should be understood that, in embodiments of the present invention, memory 1150 may further include memory remotely configured relative to processor 1120, and such remotely configured memory can be connected to a server via a network. One or more portions of the aforementioned network may be an ad hoc network, intranet, extranet, virtual private network (VPN), local area network (LAN), wireless local area network (WLAN), wide area network (WAN), wireless wide area network (WWAN), metropolitan area network (MAN), Internet, public switched telephone network (PSTN), ordinary old-style telephone service (POTS), cellular telephone network, wireless network, Wi-Fi network, and combinations of two or more of the aforementioned networks. For example, cellular telephone networks and wireless networks can be Global System for Mobile Communications (GSM), Code Division Multiple Access (CDMA), WiMAX, General Packet Radio Service (GPRS), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), LTE Frequency Division Duplex (FDD), LTE Time Division Duplex (TDD), Advanced Long Term Evolution (LTE-A), Universal Mobile Telecommunications System (UMTS), Enhanced Mobile Broadband (eMBB), Massive Machine Type Communication (mMTC), Ultra Reliable Low Latency Communications (uRLLC), etc.

[0099] It should be understood that the memory 1150 in the embodiments of the present invention may be volatile memory or non-volatile memory, or may include both volatile memory and non-volatile memory. Non-volatile memory includes: read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory.

[0100] Volatile memory includes random access memory (RAM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM). The memory 1150 of the electronic device described in this embodiment includes, but is not limited to, the above-described and any other suitable types of memory.

[0101] In this embodiment of the invention, the memory 1150 stores the following elements of the operating system 1151 and the application 1152: executable modules, data structures, or subsets thereof, or extended sets thereof.

[0102] Specifically, the operating system 1151 includes various system programs, such as a framework layer, a core library layer, and a driver layer, used to implement various basic business functions and handle hardware-based tasks. The application program 1152 includes various applications, such as a media player and a browser, used to implement various application functions. Programs implementing the methods of this embodiment of the invention can be included in the application program 1152. The application program 1152 includes applets, objects, components, logic, data structures, and other computer system executable instructions that perform specific tasks or implement specific abstract data types.

[0103] Furthermore, this embodiment of the invention also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the various processes of the above-described shale lithofacies decision identification method embodiment and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0104] Computer-readable storage media include: permanent and non-permanent, removable and non-removable media, which are tangible devices capable of retaining and storing instructions for use by an instruction execution device. Computer-readable storage media include: electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, and any suitable combination thereof. Computer-readable storage media include: phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, optical disc read-only memory (CD-ROM), digital versatile optical disc (DVD) or other optical storage, magnetic tape storage, magnetic disk storage or other magnetic storage devices, memory sticks, mechanical encoding devices (e.g., punched cards or raised structures in grooves on which instructions are recorded), or any other non-transfer medium that can be used to store information accessible by a computing device. As defined in the embodiments of the present invention, computer-readable storage media do not include temporary signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses passing through fiber optic cables), or electrical signals transmitted through wires.

[0105] In the several embodiments provided in this application, it should be understood that the disclosed apparatus, electronic devices, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, or it may be an electrical, mechanical, or other form of connection.

[0106] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to solve the problems addressed by the embodiments of the present invention, depending on actual needs.

[0107] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0108] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (including: a personal computer, a server, a data center, or other network device) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media listed above that can store program code.

[0109] In the description of the embodiments of the present invention, those skilled in the art should understand that the embodiments of the present invention can be implemented as methods, apparatuses, electronic devices, and computer-readable storage media. Therefore, the embodiments of the present invention can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software. Furthermore, in some embodiments, the embodiments of the present invention can also be implemented as a computer program product in one or more computer-readable storage media, the computer-readable storage media containing computer program code.

[0110] The aforementioned computer-readable storage medium may be any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, optical disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any combination thereof. In embodiments of the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0111] The computer program code contained in the aforementioned computer-readable storage medium may be transmitted using any suitable medium, including wireless, wire, optical fiber, radio frequency (RF), or any suitable combination thereof.

[0112] Computer program code for performing the operations of the embodiments of the present invention can be written in assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, integrated circuit configuration data, or in one or more programming languages ​​or combinations thereof. The programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The computer program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer or an external computer via any type of network, including a local area network (LAN) or a wide area network (WAN).

[0113] The embodiments of the present invention describe the provided methods, apparatus, and electronic devices through flowcharts and / or block diagrams.

[0114] It should be understood that each block of a flowchart and / or block diagram, as well as combinations of blocks in a flowchart and / or block diagram, can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine that, when executed by a computer or other programmable data processing apparatus, creates means for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0115] These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to function in a particular manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction set product that includes the functions / operations specified in the blocks of a flowchart and / or block diagram.

[0116] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus or other device to produce a computer-implemented process, such that the instructions that execute on the computer or other programmable data processing apparatus provide a process for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0117] The above description is merely a specific implementation of the embodiments of the present invention, but the protection scope of the embodiments of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the embodiments of the present invention should be included within the protection scope of the embodiments of the present invention. Therefore, the protection scope of the embodiments of the present invention should be determined by the protection scope of the claims.

Claims

1. A method of shale facies decision recognition, characterized in that, The method comprises the following steps: determining sensitive attribute parameters of different lithofacies of at least one shale structure, establishing a Y-value calculation model corresponding to the structure; inverting the Y-value calculation model based on a simulated annealing algorithm to obtain inversion coefficients; inputting the inversion coefficients into the Y-value calculation model to generate a Y-value lithofacies identification model corresponding to the structure, and using the Y-value lithofacies identification model to identify the lithofacies of a shale reservoir; wherein the determination of the sensitive attribute parameters of different lithofacies of at least one shale structure comprises: for each structure corresponding to different lithofacies, determining well logging sensitive data based on a well logging response relationship chart, wherein the well logging sensitive data comprises multiple types of natural gamma, acoustic time difference, rock volume density and compensated neutron; processing the well logging sensitive data to obtain the sensitive attribute parameters; the data processing comprises: normalizing the well logging sensitive data, and inversely processing the correlation between the well logging sensitive data to converge the sample point distribution, thereby obtaining the sensitive attribute parameters; wherein the inversion of the Y-value calculation model based on the simulated annealing algorithm to obtain the inversion coefficients comprises: inputting the sensitive attribute parameters, the Y-value calculation model, the maximum number of iterations, the initial temperature, the cooling rate, the core lithofacies and its label, and the target coincidence rate into the simulated annealing algorithm; the simulated annealing algorithm randomly generates a near neighbor solution, generates a corresponding Y-value based on the near neighbor solution, and inputs the Y-value into a decision algorithm; based on the core lithofacies, the decision algorithm generates inversion coefficients that can meet the best coincidence rate in the current iteration.

2. The method of claim 1, wherein, The process of obtaining the well logging response relationship chart of each structure corresponding to different lithofacies comprises: based on core lithofacies calibration of conventional well logging data, obtaining lithofacies well logging response characteristics; the lithofacies well logging response characteristics at least include: natural gamma, acoustic time difference, rock volume density, compensated neutron, deep lateral resistivity and shallow lateral resistivity; based on the lithofacies well logging response characteristics, establishing a well logging response relationship chart of each structure corresponding to different lithofacies.

3. The method of claim 1, wherein, In the case of generating inversion coefficients that can meet the best coincidence rate in the current iteration, it further comprises: generating a threshold value and a lithofacies decision identification scheme that can meet the best coincidence rate in the current iteration.

4. A shale facies decision recognition device based on the shale facies decision recognition method according to any one of claims 1 to 3, characterized by, The shale lithofacies decision identification device comprises a model construction module, a model inversion module and a lithofacies identification module; the model construction module is used to determine sensitive attribute parameters of different lithofacies of at least one shale structure, and establish a Y-value calculation model corresponding to the structure; the model inversion module is used to invert the Y-value calculation model based on a simulated annealing algorithm to obtain inversion coefficients; the lithofacies identification module is used to input the inversion coefficients into the Y-value calculation model to generate a Y-value lithofacies identification model corresponding to the structure, and use the Y-value lithofacies identification model to identify the lithofacies of a shale reservoir. 5.An electronic device, comprising a processor and a memory, the memory storing a computer program, characterized in that, The processor executes the computer program stored in the memory to realize the steps in the shale lithofacies decision identification method according to any one of claims 1 to 3.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, which is executed by a processor, implements the steps of the method for shale facies decision recognition according to any one of claims 1 to 3.

7. A computer program product comprising a computer program, characterized in that, When the computer program is executed, the steps of the method for shale facies decision recognition according to any one of claims 1 to 3 can be implemented.

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