Method, device and equipment for determining main control parameters of organic matter enrichment

Abstract

The embodiment of the invention relates to the field of shale oil and gas exploration and development, in particular to a method, device and equipment for determining main control parameters of organic matter enrichment, and the method comprises the following steps: acquiring first depth domain data of the total organic carbon content of a shale layer section and second depth domain data of various geological parameters; converting the first depth domain data and the second depth domain data into frequency domain data, and extracting a first main period of the first depth domain data and a second main period of the second depth domain data according to the frequency domain data; and according to the similarity of the first main period and the second main period, determining organic matter enrichment main control parameters of the shale layer section. According to the embodiment of the invention, the total organic carbon content of the shale layer section and the depth domain data of various geological parameters can be converted into the frequency domain so as to extract the corresponding main period, the association between the dynamic change period of the geological parameters and the organic matter enrichment main control parameter is fully considered, and the organic matter enrichment main control parameter can be more accurately determined.

Description

Technical Field

[0001] The embodiments of this specification relate to the field of shale oil and gas exploration and development, and particularly to a method, device, and equipment for determining the main controlling parameters of organic matter enrichment. Background Art

[0002] The enrichment of organic matter is controlled by various geological factors, including paleoclimate, paleoenvironment, paleoproductivity, etc. These factors interact with each other and jointly determine the enrichment degree and distribution characteristics of organic matter. Therefore, accurately identifying the main controlling parameters of organic matter enrichment is of great significance for shale oil and gas exploration and development.

[0003] The existing methods for determining the main controlling parameters of organic matter enrichment mainly include geochemical index analysis methods and statistical and machine learning methods. Geochemical index analysis infers the main controlling parameters of organic matter enrichment by measuring the element contents (such as Al, Si, Ca, Mg, Fe, V, Cr, Ni, etc.) and isotope ratios (such as δ13C, δ15N, etc.) in rocks. Statistical and machine learning methods process multi-source data through multiple regression analysis, principal component analysis (PCA), or machine learning models (such as random forest, support vector machine, etc.) and establish prediction models.

[0004] However, the above geochemical index analysis methods and statistical and machine learning methods mainly focus on static geochemical parameters in practical applications and lack consideration of dynamic environmental changes, resulting in one-sided discrimination of the main controlling parameters and making it difficult to accurately identify the main controlling parameters of organic matter enrichment under complex geological backgrounds. Specifically, factors such as paleoclimate conditions, sea-level changes, sedimentation rates, and redox conditions play key roles in the process of organic matter enrichment, but the dynamic changes of these factors are often simplified or ignored in the existing methods, thus making it difficult to comprehensively reflect the control mechanism of organic matter enrichment under complex geological backgrounds. For example, in the sedimentary environment of continental lacustrine basins, the dry-wet changes in climate will significantly affect lake productivity and organic matter preservation conditions; in the marine sedimentary environment, sea-level rise and fall will change the redox state of the sedimentary environment, thereby affecting the preservation and degradation of organic matter.

[0005] Therefore, how to solve the problems existing in the existing methods, such as ignoring dynamic environmental changes, one-sided identification of the main controlling parameters, and poor identification accuracy, and propose a simple, comprehensive evaluation, and high-accuracy identification method for determining the main controlling parameters of organic matter enrichment is a key problem to be solved urgently. Summary of the Invention

[0006] The purpose of the embodiments of this specification is to provide a method, device, and equipment for determining the main controlling parameters of organic matter enrichment to overcome the problems existing in the existing methods for determining the main controlling parameters of organic matter enrichment, such as ignoring dynamic environmental changes, one-sided identification of the main controlling parameters, and poor identification accuracy.

[0007] On the one hand, an embodiment of this specification provides a method for determining the main control parameters of organic matter enrichment, including: obtaining the first depth domain data of the total organic carbon content of the shale section and the second depth domain data of various geological parameters; converting the first depth domain data and the second depth domain data into frequency domain data to extract the first main period of the first depth domain data and the second main period of the second depth domain data according to the frequency domain data; determining the main control parameters of organic matter enrichment in the shale section according to the similarity between the first main period and the second main period.

[0008] On the other hand, an embodiment of this specification provides a device for determining the main control parameters of organic matter enrichment, including: an acquisition module for obtaining the first depth domain data of the total organic carbon content of the shale section and the second depth domain data of various geological parameters; an extraction module for converting the first depth domain data and the second depth domain data into frequency domain data to extract the first main period of the first depth domain data and the second main period of the second depth domain data according to the frequency domain data; a determination module for determining the main control parameters of organic matter enrichment in the shale section according to the similarity between the first main period and the second main period.

[0009] On yet another hand, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the above method for determining the main control parameters of organic matter enrichment.

[0010] As can be seen from the technical solutions provided by the embodiments of this specification above, the embodiments of this specification can obtain the first depth domain data of the total organic carbon content of the shale section and the second depth domain data of various geological parameters; convert the first depth domain data and the second depth domain data into frequency domain data to extract the first main period of the first depth domain data and the second main period of the second depth domain data according to the frequency domain data; determine the main control parameters of organic matter enrichment in the shale section according to the similarity between the first main period and the second main period. Compared with the existing methods, the embodiments of this specification can comprehensively determine the main control parameters of organic matter enrichment by combining the total organic carbon content of the shale section and the depth domain data of various geological parameters. In addition, by converting the depth domain data into the frequency domain to extract the corresponding main periods, the association between the dynamic change period of geological parameters and the main control parameters of organic matter enrichment is fully considered, and the main control parameters of organic matter enrichment can be determined more accurately. Description of the Drawings

[0011] In order to more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art.

[0012] Figure 1It is a flowchart of a method for determining the main control parameters of organic matter enrichment provided by an embodiment of this specification;

[0013] Figure 2 It is a schematic diagram of the main periodic analysis result of the total organic carbon content of a certain shale layer section in the Nanpu Sag area of Basin A provided by an embodiment of this specification;

[0014] Figure 3 It is a schematic diagram of the main periodic analysis result of the paleotemperature of a certain shale layer section in the Nanpu Sag area of Basin A provided by an embodiment of this specification;

[0015] Figure 4 It is a schematic diagram of the main periodic analysis result of the paleoprecipitation of a certain shale layer section in the Nanpu Sag area of Basin A provided by an embodiment of this specification;

[0016] Figure 5 It is a schematic diagram of the main periodic analysis result of the redox environment of a certain shale layer section in the Nanpu Sag area of Basin A provided by an embodiment of this specification;

[0017] Figure 6 It is a schematic diagram of the main periodic analysis result of the paleosalinity of a certain shale layer section in the Nanpu Sag area of Basin A provided by an embodiment of this specification;

[0018] Figure 7 It is a schematic diagram of the main periodic analysis result of the paleoproductivity of a certain shale layer section in the Nanpu Sag area of Basin A provided by an embodiment of this specification;

[0019] Figure 8 It is a schematic diagram of the structural composition of a device for determining the main control parameters of organic matter enrichment provided by an embodiment of this specification;

[0020] Figure 9 It is a schematic diagram of the structural composition of a computer device provided by an embodiment of this specification. Detailed implementation manners

[0021] Next, the technical solutions in the embodiments of this specification will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this specification without making creative efforts shall fall within the protection scope of this specification.

[0022] The shale layer section is an important target layer section in oil and gas exploration, especially in the development of unconventional oil and gas resources (such as shale gas and shale oil). Shale has the characteristics of low porosity and low permeability, but it is rich in organic matter and can be used as a source rock and a reservoir rock. The geological parameters of the shale layer section can at least include paleotemperature, paleoprecipitation, redox conditions, paleosalinity, and paleoproductivity.

[0023] Paleotemperature can represent the temperature conditions in the sedimentary environment during the geological history period and can be reconstructed through geochemical indicators (such as oxygen isotopes of carbonate rocks, organic matter biomarkers, etc.). Higher paleotemperatures may accelerate the degradation of organic matter, while lower temperatures are favorable for the preservation of organic matter. Temperature changes will affect the dissolution and precipitation of minerals, and thus affect the pore structure and reservoir properties of source rocks. Oxygen isotopes (δ 18 O) can be used to analyze carbonate rocks or biological shells to reconstruct paleotemperatures. Paleotemperature changes can also be inferred through the temperature sensitivity indicators of biomarkers (such as long-chain alkenones).

[0024] Paleoprecipitation can represent the precipitation amount and precipitation pattern during the geological history period and can be inferred through geochemical indicators in sediments (such as hydrogen isotopes, clay mineral assemblages, etc.). Changes in precipitation amount will affect the input of terrigenous materials, and thus affect the composition and thickness of sediments. An increase in precipitation amount may lead to water body dilution, while a decrease in precipitation may lead to an increase in water body salinity. Hydrogen isotopes (δD) can be used to analyze sedimentary organic matter or clay minerals to reconstruct paleoprecipitation patterns. The dry-wet changes of the paleoclimate can also be inferred through the assemblages of clay minerals (such as montmorillonite, kaolinite) in sediments.

[0025] Redox conditions can represent the oxygen content and redox state in the sedimentary environment and can be characterized through geochemical indicators (such as trace elements, sulfur isotopes, organic matter types, etc.). Anoxic environments are favorable for the preservation of organic matter, while oxygen-rich environments may lead to the oxidative degradation of organic matter. Changes in redox conditions will affect the occurrence states of elements such as iron, manganese, and sulfur, and thus affect the geochemical characteristics of sediments. The ratios of trace elements (such as V / Cr, Ni / Co, U / Th) can be used to judge redox conditions. The redox state of the ancient environment can also be inferred by analyzing sulfides such as pyrite through sulfur isotopes (δ 34 S).

[0026] Paleosalinity can represent the salinity conditions of water bodies during the geological history period and can be reconstructed through geochemical indicators in sediments (such as boron content, strontium isotopes, organic matter biomarkers, etc.). Changes in salinity will affect the distribution and productivity of aquatic organisms, and thus affect the source and enrichment of organic matter. Changes in salinity can also affect the formation and dissolution of evaporites (such as gypsum, halite). Boron content or boron isotopes (δ 11 B) can be used to analyze clay minerals to reconstruct paleosalinity. Paleosalinity changes can also be inferred through the distribution characteristics of biomarkers (such as steranes, hopanes).

[0027] Paleoproductivity can represent the production capacity of organisms in water bodies during the geological history period and can be characterized by indicators such as the organic matter content, biomarkers, and trace elements in sediments. High-productivity environments are accompanied by the input and preservation of a large amount of organic matter. Changes in productivity will affect the cycling of elements such as carbon, nitrogen, and phosphorus, and thus affect the composition of sediments. The distribution of organic carbon content (TOC) and biomarkers (such as algal steranes) can be used to evaluate paleoproductivity. The change of paleoproductivity can also be inferred by the content of trace elements (such as Ba, P).

[0028] By comprehensively analyzing parameters such as paleotemperature, paleoprecipitation, redox conditions, paleosalinity, and paleoproductivity, it is helpful to comprehensively reveal the sedimentary environmental characteristics and organic matter enrichment mechanism of the shale interval.

[0029] Figure 1 This is a flowchart of a method for determining the main controlling parameters of organic matter enrichment provided by the embodiments of this specification. Specifically, when implemented, it includes the following steps:

[0030] S101: Obtain the first-depth-domain data of the total organic carbon content and the second-depth-domain data of various geological parameters of the shale interval.

[0031] In some embodiments, the log resistivity difference data can be calculated based on the acoustic logging data and the log resistivity data of the shale interval; based on the log resistivity difference data, the first-depth-domain data of the total organic carbon content is determined through a preset TOC calculation model.

[0032] By determining the first-depth-domain data of the total organic carbon content based on the log resistivity difference data, the abundance of organic matter and its vertical distribution characteristics in the shale interval can be effectively obtained, laying a good data foundation for the determination of the main controlling parameters of organic matter enrichment in the shale interval.

[0033] The first-depth-domain data of the total organic carbon content (Total Organic Carbon, TOC) can represent the distribution data that characterizes the change of organic carbon content with depth in the form of continuous or discrete points within the vertical depth range of the formation. These data can intuitively reflect the abundance of organic matter and its vertical distribution characteristics in the shale interval. By analyzing the vertical change characteristics of the TOC depth-domain data, the distribution law of organic matter in the formation and its controlling factors, such as sedimentary environment, paleoclimate, and tectonic activities, can be revealed.

[0034] The acoustic logging data and log resistivity data of the shale interval can be obtained. The acoustic logging data and log resistivity data can be acquired by conventional logging methods. Among them, the acoustic logging data is used to measure the time difference of acoustic wave propagation in the formation (i.e., acoustic transit time) by an acoustic logging tool, and then the propagation speed of the acoustic wave in the formation is calculated. The acoustic wave propagation speed is closely related to the lithology, porosity and compaction degree of the formation. Therefore, the acoustic logging data can effectively reflect the physical properties of the formation. Based on the acoustic logging data, the baseline resistivity value of the shale interval can be obtained. For example, the porosity data of the shale interval can be calculated according to the acoustic logging data. Combining the porosity data and water saturation data, the Archie formula can be used to calculate the baseline resistivity value of the shale interval. For example, the baseline resistivity value of the shale interval can also be directly calculated through a preset statistical model of acoustic transit time and resistivity, which will not be elaborated here. The log resistivity data can be obtained by resistivity logging methods. A deep investigation resistivity logging tool (such as lateral logging or induction logging) can be used to measure the resistivity value of the formation and convert it into a logarithmic form. The log resistivity data can characterize the electrical properties of the formation. Due to its special electrical response, the organic-rich shale shows a relatively high resistivity value.

[0035] Based on the obtained acoustic logging data and log resistivity data, the log resistivity difference data can be calculated. Specifically, through the correlation analysis between the log resistivity data and the acoustic logging data, a baseline resistivity model can be established. The baseline resistivity model reflects the resistivity characteristics of the formation without organic matter or with extremely low organic matter content, and can be derived from the empirical relationship between the acoustic logging data and the resistivity data. For example, the following formula can be used to calculate the log resistivity difference data:

[0036] △logR=logR - logR 基线 ;

[0037] In the formula, logR is the measured log resistivity data, and logR 基线 is the baseline resistivity value. Since the resistivity change of the organic-rich shale is mainly controlled by the organic matter content, by calculating the log resistivity difference data, the influence of non-organic components (such as mineral components, pore fluids, etc.) in the formation on the resistivity can be eliminated, so as to more accurately reflect the contribution of the organic matter content to the resistivity. Based on the calculated log resistivity difference data, the first depth domain data of the total organic carbon content can be determined through a preset TOC calculation model.

[0038] In some embodiments, third-depth domain data of multiple key chemical elements can be obtained; key chemical elements corresponding to each key geological parameter are determined among the multiple key chemical elements; and second-depth domain data of each key geological parameter are calculated based on the third-depth domain data of the key chemical elements corresponding to each key geological parameter.

[0039] By systematically converting the third-depth domain data of key chemical elements into the second-depth domain data of key geological parameters, important data support is provided for determining the main controlling parameters of organic matter enrichment in the shale interval.

[0040] The key chemical elements in the shale interval can at least include strontium, barium, magnesium, calcium, vanadium, chromium, copper, rubidium, zirconium, iron, and manganese. Each key geological parameter (such as paleotemperature, paleoprecipitation, redox conditions, paleosalinity, and paleoproductivity) is associated with specific chemical elements or geochemical indicators. These key chemical elements can reflect the variation characteristics of key geological parameters.

[0041] For paleotemperature, the relevant key chemical elements or geochemical indicators can include oxygen isotope (δ 18 O), magnesium-calcium ratio (Mg / Ca), and biomarkers. Specifically, the oxygen isotope (δ 18 O) ratio in carbonate rocks or biological shells is closely related to paleotemperature, the Mg / Ca ratio in certain biological shells (such as foraminifera) can be used to reconstruct paleotemperature, and the distribution characteristics of long-chain alkenones are related to paleotemperature.

[0042] For paleoprecipitation, the relevant key chemical elements or geochemical indicators can include hydrogen isotope (δD) and clay mineral assemblage. Specifically, the hydrogen isotope ratio in sedimentary organic matter or clay minerals can reflect the paleoprecipitation pattern, and the ratio of kaolinite to montmorillonite is related to the wet-dry changes of paleoprecipitation.

[0043] For redox conditions, the relevant key chemical elements or geochemical indicators can include trace element ratios, sulfur isotope (δ 34 S), and organic matter type. Specifically, trace element ratios such as V / Cr, Ni / Co, U / Th, etc. can be used to judge redox conditions. The sulfur isotope ratio in sulfides such as pyrite can reflect the redox state of the ancient environment. The organic matter type such as the Pr / Ph (pristane / phytane) ratio is related to redox conditions.

[0044] For paleosalinity, the relevant key chemical elements or geochemical indicators can include boron content or boron isotope (δ 11 B), strontium isotope ( 87 Sr / 86Sr), and biomarkers. Specifically, the boron content or isotope ratio in clay minerals can reflect paleosalinity. The strontium isotope ratio in carbonate rocks is related to paleosalinity. The distribution characteristics of biomarkers such as steranes and hopanes can be used to infer paleosalinity.

[0045] For paleoproductivity, the relevant key chemical elements or geochemical indicators can include biomarkers and trace elements. Specifically, the distribution characteristics of biomarkers such as algal steranes are related to paleoproductivity. The contents of trace elements such as Ba and P can be used to infer changes in paleoproductivity.

[0046] Based on the third-depth-domain data of the chemical elements corresponding to each key geological parameter, the second-depth-domain data of the key geological parameter are calculated through a mathematical model or empirical formula. A mathematical model can be established according to the quantitative relationship between the chemical element and the geological parameter. For example, the paleotemperature model can be defined by the following formula:

[0047] MAT = -18.516(S) + 17.298;

[0048] In the formula, S is the molar ratio of Na2O* and K2O* to Al2O3*.

[0049] Again, for example, the paleoprecipitation model can be defined by the following formula:

[0050] MAP = 147.75 × exp(0.0232 × CIA-K);

[0051] In the formula, CIA can be defined as CIA = [Al2O3 / (Al2O3 + CaO* + Na2O + K2O)] × 100, and CIA-K is the CIA without potassium.

[0052] In some embodiments, the key chemical element data for each depth point can be calculated to obtain the values of the corresponding key geological parameters. Combining the calculation results of each depth point with the depth values can generate the second depth domain data of the key geological parameters. Specifically, according to the standardized element contents, key geological parameters (such as V / Cr ratio, Sr / Ba ratio, etc.) can be calculated. The calculation results of the key geological parameters for each depth point can be combined with the corresponding depth values (such as meters or feet) to form the initial depth domain data of the key geological parameters. The initial depth domain data of the key geological parameters can be fitted to obtain the second depth domain data. The fitting methods can be the least squares method, polynomial regression, Laplace smoothing, and other methods. For example, Laplace smoothing can be used to convert the initial depth domain data into the second depth domain data. Laplace smoothing can eliminate the noise in the data and generate a continuous curve to better reflect the overall trend of the data. Laplace smoothing can adjust the value of each data point to gradually approach the average value of its neighboring points, thereby removing local fluctuations and noise while retaining the main features of the data. The following formula can be used to convert the initial depth domain data into the second depth domain data:

[0053] x i k+1 =x i k +λΔx i k ;

[0054] In the formula, x i k represents the value corresponding to the i-th data point at the k-th iteration. λ is the smoothing coefficient (for example, 0 < λ < 1), which is used to control the intensity of smoothing. Δx i k represents the difference between the i-th data point and its neighboring points. Through multiple iterations, the data points gradually become smooth, and finally a continuous curve is generated. The second depth domain data of the key geological parameters generated by fitting methods such as Laplace smoothing can, on the one hand, provide a reliable data basis for subsequent quantitative research (such as the extraction of the main period), and on the other hand, can intuitively display the variation law of the key geological parameters.

[0055] S102: Convert the first depth domain data and the second depth domain data into frequency domain data to extract the first main period of the first depth domain data and the second main period of the second depth domain data according to the frequency domain data.

[0056] In some embodiments, equidistant interpolation processing may be performed on the first depth-domain data and the second depth-domain data; Fourier transform is performed on the interpolated first depth-domain data and the interpolated second depth-domain data to obtain frequency-domain data; based on the frequency-domain data, the first main period and the second main period are extracted.

[0057] By performing equidistant interpolation processing on the second depth-domain data of each geological parameter and the first depth-domain data of the total organic carbon content (TOC), the sampling intervals can be made consistent, eliminating the bias caused by uneven sampling depths or data gaps in the original data, and ensuring the accuracy and reliability of subsequent analyses. In addition, converting the depth-domain data into frequency-domain data to extract the main period of the data provides important periodic information for the study of sedimentary environment evolution and organic matter enrichment mechanisms.

[0058] The main period may refer to the periodic component with the most concentrated energy and the largest amplitude in the data change sequence (depth-domain data) of geological parameters, which represents the most important characteristics of the geological parameter changes. Specifically, spectral analysis can be performed on the data change sequence of geological parameters, and then the main period can be obtained based on the peak of the energy spectrum obtained from the spectral analysis.

[0059] The second depth-domain data of each geological parameter (such as paleotemperature, paleoprecipitation, redox conditions, paleosalinity, paleoproductivity) in the shale interval and the first depth-domain data of the total organic carbon content (TOC) can be sorted out. These data are presented in the form of discrete points, and the depth intervals may be uneven. According to the data resolution, a unified sampling interval (such as one data point per 0.1 meter or per 1 meter) can be determined. Through interpolation methods, equidistant depth-domain data of each geological parameter and the total organic carbon content can be generated. These data have a consistent sampling interval in depth, facilitating subsequent comparative analysis and periodic research.

[0060] Fast Fourier transform (FFT) can be performed on the interpolated depth-domain data to obtain a frequency-domain spectrum. The peaks in the frequency-domain spectrum correspond to the main periodic components in the depth-domain data. By analyzing the frequency-domain spectrum, the frequency component with the strongest energy can be identified and converted into the corresponding main period. For example, if the frequency corresponding to the peak in the frequency-domain spectrum is f, the main period T can be calculated by the formula T = 1 / f. By analyzing the main periods of the TOC content and geological parameters, the relationship between organic matter enrichment and sedimentary environment changes can be revealed.

[0061] S103: Determine the main controlling parameters for organic matter enrichment in the shale interval according to the similarity between the first main period and the second main period.

[0062] In some embodiments, the period error between the first main period and the second main period can be calculated; based on the period error and a preset error threshold, a main control parameter for organic matter enrichment in the shale interval can be selected from multiple geological parameters.

[0063] TOC is a direct indicator for measuring the organic matter content in sediments and can intuitively reflect the enrichment degree of organic matter in shale. TOC is also one of the most commonly used standardized indicators in geochemical research. Its data is easy to obtain and has high comparability. TOC can comprehensively reflect the influence of various geological processes (such as biological productivity and organic matter preservation conditions). Therefore, TOC can be used as a benchmark for selecting the main control parameter for organic matter enrichment from multiple geological parameters. The main period can also reflect the influence of periodic changes (such as Milankovitch cycles and sedimentary cycles) in geological processes. The main period obtained by frequency domain analysis can effectively remove high-frequency noise in depth domain data and highlight the main periodic characteristics. Therefore, by calculating the period error between the second main period of a geological parameter and the first main period of the total organic carbon content (TOC), the main control parameter for organic matter enrichment can be determined from multiple geological parameters more comprehensively and accurately by integrating the amplitude and phase information of frequency domain data.

[0064] After respectively extracting the first main period and the second main period of a geological parameter (such as paleotemperature, paleoprecipitation, redox condition, paleosalinity, paleoproductivity) and the total organic carbon content (TOC), the absolute difference between the first main period of the geological parameter and the second main period of the TOC content can be calculated as the period error. The period error can reflect the consistency of the periodic characteristics between the geological parameter and the TOC content. The error threshold can be used to determine whether there is a significant consistency between the second main period of the geological parameter and the first main period of the TOC content. The error threshold can be determined by, but not limited to, the following methods: such as based on empirical values in relevant literature, determined by statistical analysis (such as standard deviation or confidence interval), and set artificially according to research objectives, etc., which will not be elaborated here. The error threshold can be, for example, a positive number less than or equal to 0.2. The period error can be compared with the preset error threshold. If the period error is less than or equal to the preset error threshold, it is considered that there is a significant consistency between the main period of the geological parameter and the main period of the TOC content; otherwise, it is considered that there is no consistency between the main period of the geological parameter and the main period of the TOC content. For each geological parameter, the above steps can be repeated to calculate the period error and compare it with the error threshold, and all geological parameters that meet the consistency condition can be selected. Each geological parameter that meets the consistency condition can be used as the main control parameter for organic matter enrichment in the shale interval.

[0065] In some embodiments, the following formula can be used to construct a TOC calculation model: TOC = △logR × 10 a+△TOC; where TOC is the total organic carbon content at a certain depth point in the shale interval, △logR is the difference in logarithmic resistivity at a certain depth point in the shale interval, and a and △TOC are parameters obtained by least-squares fitting of some measured total organic carbon content data in the shale interval; based on the fitted parameters, the first-depth domain data of the total organic carbon content in the shale interval is calculated using the TOC calculation model.

[0066] By optimizing the parameters of the constructed TOC calculation model through the least-squares method, the TOC calculation model can be efficiently and accurately constructed, improving the accuracy and reliability of the TOC calculation model, and thus accurately obtaining the first-depth domain data of the total organic carbon content in the shale interval.

[0067] The measured TOC data and the corresponding difference data of logarithmic resistivity can be fitted, and the model parameters a and △TOC can be optimized using the least-squares method to minimize the error between the TOC value calculated by the model and the measured TOC value, which is the core step in constructing the TOC calculation model. Specifically, the error function E(a, △TOC) can be defined, and E(a, △TOC) can be defined as the sum of the squares of the errors between the model calculated value and the measured value. The optimal model parameters a and △TOC can be solved by minimizing the error function E(a, △TOC). The gradient descent method can be used to iteratively adjust the parameter values to gradually reduce the error function. The equation method can also be used to solve the partial derivatives of the error function with respect to the parameters and set them to zero to directly calculate the optimal parameter values, which will not be elaborated here. The goodness of fit of the model can be evaluated by calculating indicators such as the coefficient of determination (R 2 ) or the root mean square error (RMSE). The closer the coefficient of determination (R 2 ) is to 1, the better the model fitting effect; the smaller the RMSE, the higher the model prediction accuracy. Based on the fitted model parameters a and △TOC, the first-depth domain data of the total organic carbon content in the shale interval can be calculated point by point using the TOC calculation model.

[0068] In some embodiments, the following formula can be used to standardize the contents of key chemical elements in the shale interval at multiple depth points in the shale interval: XO* = (XO 实测 / Al2O 3实测 ) × Al2O 3UCC ; where XO* is the content of the key chemical element at a certain depth point in the shale interval after standardization, XO 实测 is the measured content of the key chemical element at a certain depth point in the shale interval, Al2O 3实测 is the measured content of alumina at a certain depth point in the shale interval, and Al2O 3UCCis the average content of alumina in the upper crust; according to the contents of each key chemical element at multiple depth points in the shale section after standardization, the third depth domain data of each key chemical element in the shale section is calculated.

[0069] The fine-grained part in shale (such as clay minerals) is rich in Al2O3. By using the formula XO* = (XO 实测 / Al2O 3实测 ) × Al2O 3UCC , the contents of each chemical element in the shale section can be standardized based on the ratio of the measured content of Al2O3 in shale to the average content of Al2O3 in the upper crust, effectively eliminating the variation of the background value of chemical element content caused by sediment composition differences or diagenesis, so as to more accurately reflect the enrichment or depletion characteristics of chemical elements.

[0070] The key chemical elements can include strontium (Sr), barium (Ba), magnesium (Mg), calcium (Ca), vanadium (V), chromium (Cr), copper (Cu), rubidium (Rb), zirconium (Zr), iron (Fe), manganese (Mn), etc. These elements have important indicative significance in geochemical research and can reflect the characteristics of organic matter enrichment. For example, the contents of strontium and barium can indicate the changes in paleosalinity and paleoproductivity, the ratio of vanadium and chromium can reflect the redox conditions, and the contents of iron and manganese are closely related to the diagenesis of sediments.

[0071] The contents of each key chemical element at multiple depth points in the shale section can be standardized. The purpose of standardization is to eliminate the background value variation caused by sediment composition differences or diagenesis, so as to more accurately reflect the enrichment or depletion characteristics of chemical elements. By the formula XO* = (XO 实测 / Al2O 3实测 ) × Al2O 3UCC , the contents of key chemical elements at each depth point in the target section can be standardized, thus eliminating the background value fluctuation caused by the change of alumina content and making the chemical element contents at different depth points comparable.

[0072] According to the contents of each key chemical element at multiple depth points in the shale section after standardization, the third depth domain data of each key chemical element in the shale section can be calculated. The standardized contents of each key chemical element can be combined with the corresponding depth points to form a depth domain data set of each key chemical element. Through interpolation or fitting methods, the depth domain data of key chemical elements can be transformed into continuous depth domain data.

[0073] In some embodiments, equidistant interpolation processing can be performed on the first depth-domain data and the second depth-domain data based on a locally adjustable Markov window.

[0074] By dynamically adjusting the interpolation window size and weight through a locally adjustable Markov window, the interpolation accuracy can be improved while retaining the details of the depth-domain data. The Markov window can effectively suppress the noise in the data through weighted averaging or local fitting, improving the reliability and computational efficiency of the interpolation result.

[0075] The Markov window is based on the Markov property, that is, the value of the current data point depends only on its neighboring data points and is independent of the data points far away. This property enables the Markov window to effectively capture the local features of the depth-domain data. The locally adjustable Markov window adapts to the changing characteristics of the depth-domain data by dynamically adjusting the interpolation window size and weight. For example, in regions where the depth-domain data changes violently, the window size will be reduced to retain details; while in regions where the depth-domain data changes smoothly, the window size will be enlarged to improve the smoothing effect.

[0076] The second depth-domain data of each geological parameter (such as paleotemperature, paleoprecipitation, redox conditions, paleosalinity, paleoproductivity) in the shale section and the first depth-domain data of the total organic carbon content (TOC) can be sorted out. For each target interpolation point, the range of its neighboring data points (i.e., the interpolation window size) can be determined. The interpolation window size can be dynamically adjusted according to the local gradient of the depth-domain data: a smaller window is used in regions with a larger gradient, and a larger window is used in regions with a smaller gradient. Weights can be assigned to each data point within the window. The weights can be calculated based on the distance or similarity between the data point and the target interpolation point, which will not be elaborated here. For each target interpolation point, the interpolation result can be calculated based on the neighboring data points within the window and their weights. Common interpolation methods include the weighted average method, Kriging interpolation method, or local polynomial fitting. For example, the weighted average method can be used to calculate the interpolation, that is, the mean value of all the data within the Markov window corresponding to the target interpolation point is used to determine the value of the target interpolation point. The locally adjustable Markov window can dynamically adjust the window size and weight according to the local features of the data, thereby improving the interpolation accuracy while retaining the data details. Through weighted averaging or local fitting, this method can effectively suppress the noise in the data and improve the reliability of the interpolation result.

[0077] In some embodiments, the amplitude spectrum corresponding to the frequency-domain data can be obtained; the peak frequency corresponding to the maximum amplitude value in the amplitude spectrum can be determined; and based on the peak frequency, the first main period and the second main period can be calculated.

[0078] By performing Fourier transform on the interpolated first depth-domain data and the interpolated second depth-domain data, frequency-domain data can be obtained, which can clearly reveal the periodic components in the depth-domain data. Furthermore, the main period can be accurately extracted based on the peak frequency in the amplitude spectrum corresponding to the frequency-domain data, providing important scientific basis for the evolution of sedimentary environment and the mechanism of organic matter enrichment.

[0079] Through Fourier transform, the hidden periodic components in the depth-domain data can be revealed, and the frequencies and amplitudes of these components can be quantified. The interpolated depth-domain data can be preprocessed, including detrending (such as subtracting a linear trend or a polynomial trend) and standardization (such as normalization), to eliminate the interference of dimensional differences in the data on periodic analysis. The preprocessed depth-domain data can be subjected to a fast Fourier transform (FFT) to transform it from the depth domain to the frequency domain. Based on the frequency-domain data, its amplitude spectrum can be calculated, and the amplitude spectrum reflects the energy distribution of different frequency components in the depth-domain data. By analyzing the amplitude spectrum, the frequency components with the strongest energy can be identified. These frequency components correspond to the main periodic features in the data. In the amplitude spectrum, the peak frequency corresponding to the maximum amplitude value can be found. The peak frequency is the main manifestation of the periodic components in the data. According to the relationship between frequency f and period T, T = 1 / f, the peak frequency f peak is converted to the corresponding main period T peak = 1 / f peak . The main period T peak reflects the characteristic scale of the periodic changes in the depth-domain data. For example, the main period may be related to Milankovitch cycles (such as eccentricity cycle, slope cycle, and precession cycle), revealing the periodic changes in paleoclimate and paleoenvironment. Fourier transform is performed on the interpolated first depth-domain data to obtain frequency-domain data, and then the first main period is calculated. Similarly, Fourier transform is performed on the interpolated second depth-domain data to obtain frequency-domain data, and then the second main period is calculated.

[0080] In some embodiments, based on the frequency-domain data, the first secondary period of the first depth-domain data and the second secondary period of the second depth-domain data can be extracted; the similarity between the first secondary period and the second secondary period can be calculated; based on the similarity between the first main period and the second main period, and the similarity between the first secondary period and the second secondary period, the main controlling parameters for organic matter enrichment in the shale layer section can be determined.

[0081] Frequency domain data can reflect the cyclic characteristics at different scales in the shale formation, including the main period and secondary periods. The main period corresponds to sedimentary cycles at a larger time scale, while secondary periods can reflect secondary cycles at a smaller time scale. By identifying and decomposing the spectral characteristics, the secondary cycle information representing different sedimentary cycles can be separated. The first secondary period and the second secondary period respectively reflect the secondary sedimentary cycle characteristics of the shale formation in the first depth domain data and the second depth domain data. These secondary periods are closely related to factors such as local sedimentary environment changes, climate fluctuations, or tectonic activities. For example, secondary periods may record the impact of short-term sea level changes, seasonal climate fluctuations, or local tectonic activities on the sedimentation process.

[0082] After extracting the first secondary period and the second secondary period, the similarity between them can be calculated by mathematical methods (such as correlation coefficient analysis, Fourier transform, or wavelet transform, etc.). By calculating the similarity, the matching degree of the two secondary periods in terms of waveform, amplitude, and phase can be quantified, thereby revealing the similarity or difference in the sedimentation process between the two depth domains. In addition, the similarity of the first main period and the second main period can be combined for comprehensive analysis. The main period reflects the sedimentary cycle characteristics at a larger time scale, and its similarity can macroscopically reveal the correlation in the sedimentary background between the two depth domain data. For example, the similarity of the main period can indicate the consistency of the two depth domain data in terms of regional tectonic evolution or long-term climate change, while the difference in secondary periods can reflect the influence of local sedimentary environment or short-term geological events.

[0083] In some embodiments, the secondary period can be a sequence of periods. The periods in the period sequence can be arranged in ascending order of period size, and each period can correspond to an impact of a type of local sedimentary environment or short-term geological event. For the first secondary period and the second secondary period, the corresponding first period sequence and second period sequence can be obtained respectively. The period error between the first period sequence and the second period sequence can be calculated. The period error of the first period sequence and the second period sequence, as well as the period error of the first main period and the second main period, can be combined to select the main controlling parameters for organic matter enrichment in the shale formation from multiple geological parameters. Specifically, the period matching degree between the geological parameter and TOC can be calculated by the formula where T Pri represents the period error between the first main period and the second main period, represents the period error between the first period sequence and the second period sequence, represents the period error between the i-th secondary period in the first period sequence and the i-th secondary period in the second period sequence, and ω0 and ω iis a weight coefficient used to balance the influence of different periodic errors. Based on the calculated periodic matching degrees of each geological parameter and TOC, geological parameters with a periodic matching degree greater than or equal to a preset matching degree threshold can be selected as the main controlling parameters for organic matter enrichment in shale intervals.

[0084] In some embodiments, according to the variance corresponding to each period and the significance corresponding to each period in the depth-domain data, the weight coefficient corresponding to each period can be determined. The variance corresponding to each period in the depth-domain data is an important indicator for measuring the contribution degree of this period to the overall change of the data. The variance can reflect the fluctuation degree of the data around its mean value. The larger the variance, the greater the contribution of this period to the data change. Therefore, when determining the weight, a period with a larger variance can be assigned a higher weight to reflect its significant influence on the overall change of the data. The significance corresponding to each period in the depth-domain data can characterize the average numerical level of this period in the data. The higher the significance, the more prominent the performance of this period in the data and the higher its importance. Therefore, a period with a higher significance can also be assigned a larger weight to ensure that it is fully emphasized in data analysis. For the main period and multiple secondary periods in the depth-domain data, the weight factor of each period can be obtained by multiplying the variance of each period by the significance. In this way, the dual influence of variance and significance on the importance of the period can be comprehensively considered, so as to more comprehensively evaluate the contribution of each period. To ensure the rationality and comparability of the weight coefficients, the weight factors of all periods can be normalized. Normalization is the process of converting the weight factors of each period into relative proportions, making the sum of all weight coefficients equal to 1. Through normalization, the weight coefficients of different periods can be intuitively compared to ensure that their weight distributions are balanced and reasonable. By combining the two key indicators of variance and significance, and through weight factor calculation and normalization processing, the weight coefficient of each period in the depth-domain data can be scientifically determined. It can not only quantify the contribution of each period to the data change, but also provide a reliable basis for the matching degree analysis of depth-domain data of different parameters. For the first depth-domain data and the second depth-domain data, the weight coefficients of the main period and multiple secondary periods in the period sequence can be obtained respectively. According to the weight coefficient of the main period corresponding to the first depth-domain data and the weight coefficient of the main period corresponding to the second depth-domain data, the weighted average method can be used to determine ω0 in formula Similarly, according to the weight coefficients of each secondary period corresponding to the first depth-domain data and the weight coefficients of each secondary period corresponding to the second depth-domain data, ω in formula can be determined. i .

[0085] By comprehensively analyzing the matching degree of the main cycle and the secondary cycle, the main controlling parameters for organic matter enrichment in the shale section can be more comprehensively determined by integrating the change characteristics on multiple different time scales. For example, if the similarities of the main cycle and the secondary cycle are both relatively high, it indicates that the sedimentary environments and organic matter preservation conditions in the two depth domains have strong consistency and may be controlled by similar paleoclimate, paleotectonics, or paleogeography conditions. In this case, organic matter enrichment may be mainly controlled by regional factors, such as a long-term stable anoxic environment or continuous organic matter input. Conversely, if the similarity is low, it may imply that local factors (such as sedimentation rate, water body redox conditions, or organic matter source) play a dominant role in organic matter enrichment. For example, local water stratification, microbial activities, or changes in sediment supply rate may lead to differences in organic matter enrichment.

[0086] In some embodiments, a preset similarity transfer attention model can be used to determine the weights of the main controlling parameters for organic matter enrichment in the shale section; based on the main controlling parameters for organic matter enrichment in the shale section and the corresponding weights, the sweet spots in the shale section can be determined.

[0087] The similarity transfer attention mechanism can offset the irrelevant local features in different main controlling parameters, eliminate noise and irrelevant non-local information, so as to highlight the important main controlling parameters for organic matter enrichment related to the sweet spots.

[0088] The sweet spot refers to the area with relatively high development potential in the shale oil and gas reservoir. These areas have characteristics such as relatively good physical properties, high total organic carbon (TOC) content, relatively developed fractures, good rock brittleness, and weak in-situ stress anisotropy. The sweet spots have relatively thick shale layers, high organic matter abundance, good reservoir performance and permeability. These characteristics make the sweet spots the main areas for shale oil and gas enrichment. The rocks in the sweet spots have relatively high brittleness and are easy to form fracture networks through engineering means such as hydraulic fracturing, thereby increasing oil and gas production. In addition, the in-situ stress conditions in the sweet spots are also relatively favorable, which is conducive to the expansion of fractures and the flow of oil and gas.

[0089] The depth domain data of multiple main controlling parameters for organic matter enrichment in the shale section can be normalized to eliminate the influence of different parameter dimensions. The preset similarity transfer attention model can calculate the similarity matrix between the depth domain data of different main controlling parameters, enhance the transfer of features through the similarity matrix, and highlight the role of similar main controlling parameters in sweet spot prediction. The similarity transfer attention mechanism can use a soft threshold to offset the irrelevant non-local features in different main controlling parameters to obtain sparse feature representations of different main controlling parameters. According to the sparse feature representations of different main controlling parameters, larger weights are assigned to similar main controlling parameters. For example, the following formula can be used to offset the irrelevant non-local features in different main controlling parameters:

[0090] SoftThreshold(x,k(x)) = sign(x)max(|x| - k(x),0);

[0091] In the formula, k(x) represents the soft threshold function. By introducing the soft threshold technology, the performance of the high-similarity transfer attention mechanism can be significantly improved, generating a more compact feature representation and providing more reliable support for sweet spot prediction. The similarity (such as cosine similarity, Euclidean distance) between the sparse feature representations of multiple master parameters after offset can be calculated, and a similarity matrix can be constructed based on the similarity between the sparse feature representations. After constructing the similarity matrix, a weight determination method (such as factor analysis method, principal component analysis method, AHP hierarchical analysis method, entropy value method, etc.) can be used to calculate the weights of each master parameter. For example, according to the similarity matrix, the information entropy of different master parameters can be calculated, and the corresponding redundancy can be obtained according to the information entropy of each master parameter. Normalize the redundancy of each master parameter, that is, obtain the weight of each master parameter. After determining the weights of each master parameter, a sweet spot prediction model can be constructed. The sweet spot prediction model can be based on pre-trained deep learning models such as LSTM, CNN, and ResNet. The sweet spot prediction model takes the weights and sparse feature representations of all master parameters as inputs and outputs the sweet spot index or score of the shale interval. The shale interval can be classified according to the level of the sweet spot index or score to determine the location and range of the sweet spot area.

[0092] The following provides a specific embodiment of this specification:

[0093] Taking the Nanpu Sag in Basin A as an example, multiple geological parameters of a shale interval are selected for analysis. Referring to Figure 2 、 Figure 3 、 Figure 4 、 Figure 5 、 Figure 6 and Figure 7 the schematic diagrams of the main cycle analysis results of total organic carbon content, paleotemperature, paleoprecipitation, redox environment, paleosalinity, and paleoproductivity in, it can be seen that the sedimentary main cycle of the total organic carbon content shows high consistency with paleotemperature, paleoprecipitation, redox conditions, and paleosalinity, indicating that the enrichment of organic matter is controlled by the coupling effect of climate environmental conditions. Therefore, it can be determined that paleotemperature, paleoprecipitation, redox conditions, and paleosalinity are the main control parameters for the enrichment of organic matter in the shale interval.

[0094] The method for determining the main control parameters of organic matter enrichment provided by the embodiments of this specification can obtain the first-depth-domain data of the total organic carbon content in the shale section and the second-depth-domain data of various geological parameters; convert the first-depth-domain data and the second-depth-domain data into frequency-domain data to extract the first main period of the first-depth-domain data and the second main period of the second-depth-domain data according to the frequency-domain data; determine the main control parameters of organic matter enrichment in the shale section according to the similarity between the first main period and the second main period. Compared with the existing methods, the embodiments of this specification can comprehensively determine the main control parameters of organic matter enrichment by combining the first-depth-domain data of the total organic carbon content in the shale section and the second-depth-domain data of various geological parameters. In addition, by converting the depth-domain data into the frequency domain to extract the corresponding main periods, the dynamic change periods of geological parameters and the association with the main control parameters of organic matter enrichment are fully considered, and the main control parameters of organic matter enrichment can be determined more accurately.

[0095] Based on the above method for determining the main control parameters of organic matter enrichment, the embodiments of a device for determining the main control parameters of organic matter enrichment are also proposed in this specification. As Figure 8 shown, the device 800 for determining the main control parameters of organic matter enrichment may specifically include the following modules:

[0096] An acquisition module 801, which can be used to acquire the first-depth-domain data of the total organic carbon content in the shale section and the second-depth-domain data of various geological parameters.

[0097] An extraction module 802, which can be used to convert the first-depth-domain data and the second-depth-domain data into frequency-domain data to extract the first main period of the first-depth-domain data and the second main period of the second-depth-domain data according to the frequency-domain data.

[0098] A determination module 803, which can be used to determine the main control parameters of organic matter enrichment in the shale section according to the similarity between the first main period and the second main period.

[0099] In some embodiments, the above acquisition module 801 may specifically be used to calculate the differential log resistivity data according to the acoustic logging data and the log resistivity data of the shale section; determine the first-depth-domain data of the total organic carbon content based on the differential log resistivity data through a preset TOC calculation model.

[0100] In some embodiments, the above acquisition module 801 may specifically also be used to construct a TOC calculation model using the following formula:

[0101] TOC = △logR × 10 a + △TOC;

[0102] In the formula, TOC is the total organic carbon content at a certain depth point in the shale section, △logR is the difference in logarithmic resistivity at a certain depth point in the shale section, and a and △TOC are parameters obtained by least squares fitting of some measured total organic carbon content data in the shale section; based on the fitted parameters, the first depth domain data of the total organic carbon content in the shale section is calculated using the TOC calculation model.

[0103] In some embodiments, the obtaining module 801 may specifically be further configured to obtain third depth domain data of multiple key chemical elements; determine the key chemical elements corresponding to each key geological parameter among the multiple key chemical elements; and calculate second depth domain data of each key geological parameter according to the third depth domain data of the key chemical elements corresponding to each key geological parameter.

[0104] In some embodiments, the obtaining module 801 may specifically be further configured to standardize the contents of the key chemical elements in the shale section at multiple depth points in the shale section using the following formula:

[0105] XO* = (XO 实测 / Al2O 3实测 ) × Al2O 3UCC ;

[0106] In the formula, XO* is the content of the key chemical element at a certain depth point in the shale section after standardization, XO 实测 is the measured content of the key chemical element at a certain depth point in the shale section, Al2O 3实测 is the measured content of alumina at a certain depth point in the shale section, and Al2O 3UCC is the average content of alumina in the upper crust; according to the contents of the key chemical elements after standardization at multiple depth points in the shale section, third depth domain data of the key chemical elements in the shale section is calculated.

[0107] In some embodiments, the extraction module 802 may specifically be configured to perform equally spaced interpolation processing on the first depth domain data and the second depth domain data; perform Fourier transform on the interpolated first depth domain data and the interpolated second depth domain data to obtain frequency domain data; and extract the first main period and the second main period according to the frequency domain data.

[0108] In some embodiments, the extraction module 802 may specifically be further configured to obtain the amplitude spectrum corresponding to the frequency domain data; determine the peak frequency corresponding to the maximum amplitude value in the amplitude spectrum; and calculate the first main period and the second main period according to the peak frequency.

[0109] In some embodiments, the above-mentioned determination module 803 may specifically be configured to calculate the period error between the first main period and the second main period; and select the main control parameters for organic matter enrichment in the shale interval from multiple geological parameters according to the period error and a preset error threshold.

[0110] As can be seen from the above, based on the apparatus for determining the main control parameters for organic matter enrichment provided in the embodiments of this specification, it is possible to obtain the first depth-domain data of the total organic carbon content in the shale interval and the second depth-domain data of multiple geological parameters; convert the first depth-domain data and the second depth-domain data into frequency-domain data to extract the first main period of the first depth-domain data and the second main period of the second depth-domain data according to the frequency-domain data; and determine the main control parameters for organic matter enrichment in the shale interval according to the similarity between the first main period and the second main period. Compared with the existing methods, the embodiments of this specification can comprehensively determine the main control parameters for organic matter enrichment by combining the depth-domain data of the total organic carbon content and multiple geological parameters in the shale interval. In addition, by converting the depth-domain data into the frequency domain to extract the corresponding main period, the association between the dynamic change period of the geological parameters and the main control parameters for organic matter enrichment is fully considered, and the main control parameters for organic matter enrichment can be determined more accurately.

[0111] It should be noted that the units, apparatuses, or modules described in the above embodiments may specifically be implemented by computer chips or entities, or by products with certain functions. For the convenience of description, when describing the above apparatuses, various modules are described separately according to their functions. Of course, when implementing this specification, the functions of each module may be implemented in one or more software and / or hardware, or the modules implementing the same function may be implemented by a combination of multiple sub-modules or sub-units, etc. The apparatus embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other may be through some interfaces, and the indirect couplings or communication connections of the apparatuses or units may be in electrical, mechanical, or other forms.

[0112] The embodiments of this specification also provide a computer device for determining the main control parameters of organic matter enrichment, including a processor and a memory for storing processor-executable instructions. When specifically implemented, the processor can execute the following steps according to the instructions: obtaining the first depth-domain data of the total organic carbon content of the shale section and the second depth-domain data of various geological parameters; converting the first depth-domain data and the second depth-domain data into frequency-domain data to extract the first main period of the first depth-domain data and the second main period of the second depth-domain data according to the frequency-domain data; determining the main control parameters of organic matter enrichment in the shale section according to the similarity between the first main period and the second main period.

[0113] To be able to more accurately complete the above instructions, refer to Figure 9 As shown, the embodiments of this specification also provide another specific computer device 900. Among them, the computer device 900 includes a network communication port 901, a processor 902, and a memory 903. The above structures are connected by internal cables so that each structure can perform specific data interactions.

[0114] The processor 902 can specifically be used to obtain the first depth-domain data of the total organic carbon content of the shale section and the second depth-domain data of various geological parameters; convert the first depth-domain data and the second depth-domain data into frequency-domain data to extract the first main period of the first depth-domain data and the second main period of the second depth-domain data according to the frequency-domain data; determine the main control parameters of organic matter enrichment in the shale section according to the similarity between the first main period and the second main period.

[0115] The memory 903 can specifically be used to store the corresponding instruction programs.

[0116] In this embodiment, the network communication port 901 can be bound to different communication protocols, so as to send or receive different data virtual ports. For example, the network communication port can be a port responsible for web data communication, or a port responsible for FTP data communication, or a port responsible for mail data communication. In addition, the network communication port can also be a physical communication interface or communication chip. For example, it can be a wireless mobile network communication chip, such as GSM, CDMA, etc.; it can also be a Wifi chip; it can also be a Bluetooth chip.

[0117] In this embodiment, the processor 902 can be implemented in any suitable manner. For example, the processor can take the form of, for example, a microprocessor or a processor, a computer-readable medium storing computer-readable program code (such as software or firmware) executable by the (micro)processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, etc. This specification does not make any limitations.

[0118] In this embodiment, the memory 903 includes volatile memory and non-volatile memory. The memory 903 can include multiple levels. In a digital system, anything that can store binary data can be a memory; in an integrated circuit, a circuit with a storage function without a physical form is also called a memory, such as RAM, FIFO, etc.; in a system, a storage device with a physical form is also called a memory, such as a memory stick, a TF card, etc.

[0119] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.

[0120] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of multiple flows and / or blocks.

[0121] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing devices to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of multiple flows and / or blocks.

[0122] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to generate a computer-implemented process, thereby providing instructions for implementing the steps of the function specified in one process or a plurality of processes and / or one block or a plurality of blocks in the flow Figure 1 one process or a plurality of processes and / or Figure 1 the steps of the function specified in one block or a plurality of blocks.

[0123] The specific embodiments described above further elaborate on the object, technical solution and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for determining the main control parameters of organic matter enrichment, characterized in that The method includes: Obtaining first-depth-domain data of the total organic carbon content and second-depth-domain data of various geological parameters in the shale interval; Converting the first-depth-domain data and the second-depth-domain data into frequency-domain data to extract a first main period of the first-depth-domain data and a second main period of the second-depth-domain data according to the frequency-domain data; Determining the main controlling parameters for organic matter enrichment in the shale interval according to the similarity between the first main period and the second main period.

2. The method according to claim 1, characterized in that, The obtaining of the first-depth-domain data of the total organic carbon content in the shale interval includes: Calculating log resistivity difference data according to the acoustic logging data and log resistivity data in the shale interval; Based on the log resistivity difference data, determining the first-depth-domain data of the total organic carbon content through a preset TOC calculation model.

3. The method according to claim 2, wherein The determining of the first-depth-domain data of the total organic carbon content through a preset TOC calculation model based on the log resistivity difference data includes: Constructing a TOC calculation model using the following formula: TOC = △logR × 10 a + △TOC; In the formula, TOC is the total organic carbon content at a certain depth point in the shale interval, △logR is the log resistivity difference at a certain depth point in the shale interval, and a and △TOC are parameters obtained by least squares fitting of partial measured total organic carbon content data in the shale interval; Based on the fitted parameters, calculating the first-depth-domain data of the total organic carbon content in the shale interval using the TOC calculation model.

4. The method according to claim 1, wherein The obtaining of the second-depth-domain data of various geological parameters in the shale interval includes: Obtaining third-depth-domain data of various key chemical elements; Determining the key chemical element corresponding to each key geological parameter among the various key chemical elements; Calculating the second-depth-domain data of each key geological parameter according to the third-depth-domain data of the key chemical element corresponding to each key geological parameter.

5. The method according to claim 4, wherein The obtaining of the third-depth-domain data of various key chemical elements includes: Normalizing the contents of the key chemical elements at multiple depth points in the shale interval using the following formula: XO*=(XO 实测 / Al2O 3实测 )×Al2O 3UCC ; Where XO* is the content of the key chemical element at a certain depth point in the shale interval after standardization, and XO 实测 is the measured content of the key chemical element at a certain depth point in the shale interval, and Al2O 3实测 is the measured content of aluminum oxide at a certain depth point in the shale interval, and Al2O 3UCC is the average content of aluminum oxide in the upper crust; Calculating the third-depth-domain data of each key chemical element in the shale interval according to the normalized contents of the key chemical elements at multiple depth points in the shale interval.

6. The method according to claim 1, wherein The converting of the first-depth-domain data and the second-depth-domain data into frequency-domain data to extract a first main period of the first-depth-domain data and a second main period of the second-depth-domain data according to the frequency-domain data includes: Performing equally-spaced interpolation processing on the first-depth-domain data and the second-depth-domain data; Performing Fourier transform on the interpolated first-depth-domain data and the interpolated second-depth-domain data to obtain frequency-domain data; Extracting the first main period and the second main period according to the frequency-domain data.

7. The method according to claim 6, wherein The extracting of the first main period and the second main period according to the frequency-domain data includes: Obtaining the amplitude spectrum corresponding to the frequency-domain data; Determining the peak frequency corresponding to the maximum amplitude value in the amplitude spectrum; Calculating the first main period and the second main period according to the peak frequency.

8. The method according to claim 1, wherein Determining the main controlling parameters for organic matter enrichment in the shale interval according to the similarity between the first main period and the second main period includes: Calculating the period error between the first main period and the second main period; Selecting the main controlling parameters for organic matter enrichment in the shale interval from multiple geological parameters according to the period error and a preset error threshold.

9. An apparatus for determining the main control parameters of organic matter enrichment, characterized in that, The device includes: An acquisition module, configured to acquire first depth domain data of the total organic carbon content in the shale interval and second depth domain data of multiple geological parameters; An extraction module, configured to convert the first depth domain data and the second depth domain data into frequency domain data, so as to extract the first main period of the first depth domain data and the second main period of the second depth domain data according to the frequency domain data; A determination module, configured to determine the main controlling parameters for organic matter enrichment in the shale interval according to the similarity between the first main period and the second main period.

10. A computer device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, the method according to any one of claims 1-8 is implemented.

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