A method and system for calculating the content of pyrite in sedimentary rock considering the occurrence form
By acquiring conventional well logging and core data, the resistivity response parameters and volume ratio conversion parameters of pyrite are calculated, and an occurrence morphology index is established. This solves the problem of inaccurate calculation caused by differences in pyrite occurrence morphology, and achieves high-precision pyrite content calculation, which is applicable to shale reservoir resistivity correction and oil saturation calculation.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA UNIV OF PETROLEUM (EAST CHINA)
- Filing Date
- 2026-04-14
- Publication Date
- 2026-07-21
AI Technical Summary
Existing technologies fail to effectively consider the differences in pyrite occurrence when calculating the pyrite content in shale reservoirs, resulting in inaccurate calculations and high costs, making large-scale application difficult.
By acquiring conventional well logging data, core observation data, and scanning electron microscopy data, the resistivity response parameters and volume ratio conversion parameters of pyrite are calculated, a pyrite occurrence morphology index is established, morphology types are classified, and a quantitative calculation model is established based on this. The pyrite content is then continuously calculated using conventional well logging data.
It enables quantitative characterization and continuous identification of pyrite occurrence morphology, improves calculation accuracy, is suitable for large-scale oilfield field applications, reduces costs, and has wide adaptability.
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Figure CN122131422B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of oil and gas reservoir logging evaluation technology, specifically relating to a method and system for calculating pyrite content in sedimentary rocks considering the occurrence morphology. It is particularly suitable for reservoirs with well-developed pyrite and diverse occurrence morphologies, and can achieve high-precision calculation of pyrite content through conventional logging data. Background Technology
[0002] In the exploration and development of unconventional reservoirs such as shale oil and gas, resistivity is a key parameter for calculating oil saturation and identifying "sweet spots." Pyrite, as a typical conductive mineral in shale reservoirs, can lead to abnormally low reservoir resistivity even in low concentrations, severely interfering with the accuracy of oil saturation calculations and reservoir effectiveness assessments.
[0003] Existing technologies for calculating pyrite content mainly include laboratory analysis methods, multiple regression methods, rock physical volumetric methods, formation element logging methods, and artificial intelligence-based prediction methods. However, these methods generally suffer from the following shortcomings: experimental techniques such as XRD, scanning electron microscopy, and energy dispersive spectroscopy are costly, unsuitable for large-scale samples, and produce discontinuous and time-consuming results, making it difficult to meet the needs of regional reservoir evaluation; multiple regression methods are simple to operate but have limited applicability and do not consider the differences in pyrite occurrence morphology; rock physical volumetric models require high integrity and diversity of logging series, limiting their application; formation element logging methods offer high accuracy but are expensive, making large-scale promotion difficult; and artificial intelligence algorithms such as neural networks require a large number of training samples, and their physical mechanisms are unclear. Therefore, developing a pyrite content calculation method that considers the differentiated influence of complex pyrite occurrence morphology on electrical properties, relies on conventional logging data, has high calculation accuracy, and is widely applicable has become an urgent technical problem to be solved. Summary of the Invention
[0004] To address the shortcomings of existing technologies, this invention provides a method for calculating pyrite content in sedimentary rocks that considers the occurrence morphology. The main objective of this invention is to provide a conventional well logging interpretation model for pyrite content in sedimentary rocks that considers the occurrence morphology, thereby overcoming the inaccurate content calculations caused by neglecting the differences in the electrical characteristics of pyrite occurrence morphology in existing technologies, as well as the drawbacks of high cost and difficulty in widespread application.
[0005] The present invention also provides a system for calculating the pyrite content in sedimentary rocks that takes into account the occurrence morphology.
[0006] To achieve the above objectives, the present invention proposes the following technical solution: A method for calculating the pyrite content in sedimentary rocks considering their occurrence morphology includes the following steps: Step 1: Obtain conventional logging data, core observation data, scanning electron microscopy data, micron-CT data, and core analysis data of the target formation; Step 2: Based on the deep lateral resistivity data from conventional well logging data, and combined with the pyrite content from core analysis data, calculate the pyrite resistivity response parameter PRR and the pyrite volume ratio conversion parameter V. PRYZ ; Step 3: Based on the pyrite resistivity response parameter PRR and the pyrite volume ratio conversion parameter V PRYZ The occurrence morphology index (PYI) of pyrite is calculated, and the occurrence morphology of pyrite is classified according to the preset PYI threshold. Step 4: Draw a cross plot between the pyrite occurrence morphology index (PYI) and at least one conventional logging response, and establish a PYI prediction model; Step 5: For pyrite of different occurrence forms, establish quantitative calculation models for pyrite content based on deep lateral resistivity; Step 6: Based on the PYI prediction model, determine the occurrence type of pyrite at each depth point in the target stratum, and then use the quantitative calculation model of the corresponding occurrence type, combined with the deep lateral resistivity at that depth point, to calculate the continuous pyrite content in the target stratum.
[0007] According to a preferred embodiment of the present invention, in step 1, the conventional logging data includes deep lateral resistivity and three-porosity logging data; The core observation data includes descriptions of the occurrence morphology of pyrite observed in the cores; The core analysis data includes pyrite content data from core experimental analysis.
[0008] According to a preferred embodiment of the present invention, in step 2, the formula for calculating the pyrite resistivity response parameter PRR is: ; The pyrite volume ratio conversion parameter V PRYZ The calculation formula is: ; Where RLLD is the deep lateral resistivity, in Ω·m and V. PRY For core analysis of pyrite content, %.
[0009] According to a preferred embodiment of the present invention, in step 3, the formula for calculating the pyrite occurrence morphology index (PYI) is as follows: .
[0010] According to a preferred embodiment of the present invention, in step 3, the preset PYI threshold includes at least a first threshold a and a second threshold b. When PYI is less than the first threshold a, the pyrite is determined to be dispersed; when PYI is greater than or equal to the first threshold a and less than the second threshold b, it is determined to be agglomerated; when PYI is greater than or equal to the second threshold b, it is determined to be banded.
[0011] Further preferred, the first and second thresholds are determined by statistical analysis of the pyrite occurrence morphology index (PYI) of known core samples in the study area.
[0012] The optimal approach involves determining a first threshold 'a' and a second threshold 'b' through cumulative frequency analysis of the pyrite occurrence modality index (PYI), resulting in samples with different occurrence modalities exhibiting different slope characteristics on the cumulative frequency plot. Furthermore, in the PRR and V... PRYZ In the intersection plot under logarithmic coordinates, data points of different occurrence morphologies are distributed on parallel lines with the same slope but different intercepts, and pyrite of different occurrence morphologies has clearly distinguishable features in SEM images. The specific methods for determining the first threshold a and the second threshold b are as follows: All core samples were sorted in ascending order of pyrite occurrence morphology index (PYI) from smallest to largest. The cumulative number of samples corresponding to each pyrite occurrence morphology index (PYI) was counted, and the cumulative frequency was calculated. A cumulative frequency distribution curve of the PYI index was plotted with the pyrite occurrence morphology index (PYI) as the x-axis and the cumulative frequency as the y-axis. The plotted cumulative frequency distribution curve of the PYI index was observed, and combined with the known core morphology identification results, two key points in the curve where the slope changed abruptly were identified. The pyrite occurrence morphology index (PYI) corresponding to these two key points are the values of the first threshold a and the second threshold b.
[0013] According to a preferred embodiment of the present invention, in step 4, the conventional logging response is density logging, and the PYI prediction model is a linear model: PYI=A*DEN+B, where DEN is the density logging value, and A and B are model coefficients obtained by fitting measured data from the study area.
[0014] According to a preferred embodiment of the present invention, in step 5, the quantitative calculation model is the pyrite content V. PRY A linear relationship model between the linear relationship and the natural logarithm of the deep lateral resistivity (RLLD); For dispersed pyrite, the quantitative calculation model is: V PRY1 = A1 * ln(RLLD1) + B1;V PRY1 It refers to the content of dispersed pyrite, and RLLD1 refers to the deep lateral resistivity of dispersed pyrite; For massive pyrite, the quantitative calculation model is: V PRY2 = A2 * ln(RLLD2) + B2;V PRY2 RLLD2 refers to the content of massive pyrite, while RLLD2 refers to the deep lateral resistivity of massive pyrite. For banded pyrite, the quantitative calculation model is: V PRY3 = A3 * ln(RLLD3) + B3;V PRY3It refers to the content of banded pyrite, and RLLD3 refers to the deep lateral resistivity of banded pyrite; Where A1, A2, A3, B1, B2, and B3 are model coefficients calibrated using core data.
[0015] A system for calculating pyrite content in sedimentary rocks, taking into account the occurrence morphology, includes: The data acquisition module is configured to acquire conventional logging data, core observation data, scanning electron microscope data, micron CT data, and core analysis data of the target formation. The pyrite occurrence morphology classification module is configured to: calculate the pyrite resistivity response parameter PRR and the pyrite volume ratio conversion parameter V based on deep lateral resistivity data from conventional well logging data and the pyrite content from core analysis data. PRYZ Based on the pyrite resistivity response parameter PRR and the pyrite volume ratio conversion parameter V PRYZ The occurrence morphology index (PYI) of pyrite is calculated, and the occurrence morphology of pyrite is classified according to the preset PYI threshold. The PYI prediction model building module is configured to: draw a cross plot between the pyrite occurrence morphology index PYI and at least one conventional logging response, and build a PYI prediction model. The module for establishing a quantitative calculation model for pyrite content based on deep lateral resistivity is configured to: establish quantitative calculation models for pyrite content based on deep lateral resistivity for different types of pyrite occurrence. The pyrite content calculation module is configured to: determine the occurrence morphology of pyrite at each depth point of the target stratum based on the PYI prediction model, and then calculate the continuous pyrite content of the target stratum by using the quantitative calculation model of the corresponding occurrence morphology and combining the deep lateral resistivity at that depth point.
[0016] The beneficial effects of this invention are as follows: 1. This invention proposes for the first time a pyrite occurrence morphology index, which realizes the quantitative characterization of pyrite occurrence morphology and continuous identification based on conventional well logging, and determines the quantitative thresholds for different morphologies, thus solving the fundamental problem of traditional methods ignoring morphological differences.
[0017] 2. This invention establishes a content calculation model for pyrite in different occurrence forms, realizing accurate calculation of pyrite content by form. The calculation accuracy is high and can effectively support shale reservoir resistivity logging correction, accurate calculation of oil saturation, and evaluation of sweet spots.
[0018] 3. The PRR and V constructed in this invention PRYZ The parameters such as PYI have clear rock physics meanings, and the entire model is built on a solid physical mechanism.
[0019] 4. The core parameters and models of this invention are all based on conventional logging data, without the need for expensive formation element logging data or a large number of training samples. The logging data is easy to obtain, low in cost, and the calculation steps are simple, making it suitable for large-scale application in oilfields.
[0020] 5. The method of the present invention has good adaptability and scalability. It can be applied to shale reservoirs with different sedimentary environments simply by recalibrating the model parameters and coefficients. This solves the problem of weak regional applicability of existing methods and has broad industrial application value. Attached Figure Description
[0021] Figure 1 The flowchart illustrates the implementation of the method for calculating pyrite content in sedimentary rocks considering the occurrence morphology in this invention. Figure 2 In one embodiment of the present invention, PRR and V PRYZ Intersection plot in logarithmic coordinates; Figure 3 This is a cumulative frequency distribution curve of the PYI index in one embodiment of the present invention; Figure 4 This diagram illustrates the negative correlation between the PYI index and density logging values, and the established linear prediction model, in one embodiment of the present invention. Figure 5 This is a quantitative relationship model diagram of pyrite content and natural logarithm of deep lateral resistivity established for three different occurrence modes in one embodiment of the present invention. Figure 6 This is a diagram showing the application results of the method of the present invention in an actual well in the Jimsar Depression. Detailed Implementation
[0022] The implementation of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.
[0023] Terminology Explanation: 1. Dispersed pyrite: Pyrite is distributed in isolation in the rock matrix in the form of fine particles, with no obvious aggregation or connection between the particles, and it exists in a discrete state.
[0024] 2. Massive pyrite: Pyrite particles aggregate to form localized dense masses, which are only connected to each other within a small area and do not form a continuous extended structure.
[0025] 3. Banded pyrite: Pyrite is distributed in continuous bands and lines, forming long-distance interconnected low-resistivity conductive channels.
[0026] Example 1 A method for calculating the pyrite content in sedimentary rocks considering their occurrence morphology includes the following steps: Step 1: Obtain conventional logging data, core observation data, scanning electron microscopy (SEM) data, micron-CT data, and core analysis data of the target formation; Step 2: Based on the deep lateral resistivity data from conventional well logging data, and combined with the pyrite content from core analysis data, calculate the pyrite resistivity response parameter PRR and the pyrite volume ratio conversion parameter V. PRYZ ; Step 3: Based on the pyrite resistivity response parameter PRR and the pyrite volume ratio conversion parameter V PRYZ The occurrence morphology index (PYI) of pyrite is calculated, and the occurrence morphology of pyrite is classified according to the preset PYI threshold. Step 4: Draw a cross plot between the pyrite occurrence morphology index (PYI) and at least one conventional logging response, and establish a PYI prediction model; Step 5: For pyrite of different occurrence forms, establish quantitative calculation models for pyrite content based on deep lateral resistivity; Step 6: Based on the PYI prediction model, determine the occurrence type of pyrite at each depth point in the target stratum, and then use the quantitative calculation model of the corresponding occurrence type, combined with the deep lateral resistivity at that depth point, to calculate the continuous pyrite content in the target stratum.
[0027] Example 2 The method for calculating pyrite content in sedimentary rocks considering the occurrence morphology, as described in Example 1, differs in that: In step 1, conventional logging data includes deep lateral resistivity and three-porosity logging data; both deep lateral resistivity logging data and three-porosity logging data (density logging, neutron logging, and sonic logging) are standard data obtained from conventional logging series in oilfield exploration and development. Core observation data includes descriptions of pyrite occurrence morphology observed in cores; scanning electron microscopy (SEM) data and micron-CT data are microscopic test data that characterize the occurrence morphology of pyrite (core observation data is obtained through direct observation and description of cores in the field; scanning electron microscopy (SEM) data and micron-CT data are obtained through laboratory microscopic test analysis), used to intuitively characterize the occurrence morphology and spatial distribution characteristics of pyrite; Core analysis data includes pyrite content data from core experiments. Determination is based on XRD whole-rock diffraction experiments; this method is a standard experimental technique for mineral content calibration in the current field of well logging interpretation.
[0028] In step 2, the formula for calculating the pyrite resistivity response parameter PRR is: ; pyrite volume ratio conversion parameter V PRYZ The calculation formula is: ; Where RLLD is the deep lateral resistivity, in Ω·m and V. PRY For core analysis of pyrite content, %.
[0029] In step 3, the formula for calculating the pyrite occurrence morphology index (PYI) is: .
[0030] In step 3, the preset PYI thresholds include at least a first threshold a and a second threshold b. When PYI is less than the first threshold a, the pyrite is determined to be dispersed; when PYI is greater than or equal to the first threshold a and less than the second threshold b, it is determined to be agglomerated; when PYI is greater than or equal to the second threshold b, it is determined to be banded. The specific classification of the occurrence morphology can be adjusted according to the actual development characteristics of pyrite in the study area.
[0031] The first and second thresholds were determined by statistical analysis of the pyrite occurrence morphology index (PYI) of known core samples from the study area.
[0032] By performing cumulative frequency analysis on the pyrite occurrence morphology index (PYI), a first threshold 'a' and a second threshold 'b' were determined, resulting in samples with different occurrence morphologies exhibiting different slope characteristics on the cumulative frequency plot. Furthermore, in the PRR and V... PRYZ In the intersection plot under logarithmic coordinates, data points of different occurrence morphologies are distributed on parallel lines with the same slope but different intercepts, and pyrite of different occurrence morphologies has clearly distinguishable features in SEM images. The specific methods for determining the first threshold a and the second threshold b are as follows: All core samples were sorted in ascending order of pyrite occurrence morphology index (PYI). The cumulative number of samples corresponding to each pyrite occurrence morphology index (PYI) was counted, and the cumulative frequency was calculated (cumulative frequency is the ratio of the current cumulative number of samples to the total number of samples, multiplied by 100%). A cumulative frequency distribution curve of the PYI index was plotted with the pyrite occurrence morphology index (PYI) as the x-axis and the cumulative frequency as the y-axis. The plotted cumulative frequency distribution curve of the PYI index was observed, and combined with the known core morphology identification results, two key points in the curve where the slope changed significantly were identified. The pyrite occurrence morphology index (PYI) corresponding to these two key points are the values of the first threshold a and the second threshold b. The core basis for judgment is that pyrite samples with the same occurrence form have a single fixed slope on the cumulative frequency curve because the PYI index is concentrated and the variation pattern is consistent. However, pyrite samples with different occurrence forms have a significant difference in the distribution range of the PYI index, which will cause the cumulative frequency curve to have a sudden change in slope at the boundary between the two forms. This sudden change point is the boundary threshold between the two forms.
[0033] In step 4, the conventional logging response is density logging (which varies in different study areas), and the PYI prediction model is a linear model: PYI = A * DEN + B, where DEN is the density logging value. Density logging is a conventional logging project in the oil and gas exploration field. By analyzing the pyrite content in the core, the logging response is calibrated (this method is commonly used in the field of logging interpretation). This can accurately obtain the response law between density logging and pyrite development characteristics, thus providing a reliable basis for constructing the PYI prediction model. A and B are model coefficients obtained by fitting the measured data of the study area. Both A and B are obtained by linear regression fitting of the core pyrite occurrence morphology index calculated in the study area with the density logging value using the least squares method. This method is a mature and conventional method in the field of logging interpretation. All the above coefficients are real numbers, without a unified fixed value range. Their specific values are determined by comprehensively considering the geological characteristics of the target strata in the study area, the pyrite occurrence characteristics, and the characteristics of measured logging and core data. They need to be calibrated separately in conjunction with the actual data of the study area.
[0034] In step 5, the quantitative calculation model is based on the pyrite content V. PRY A linear relationship model between the linear relationship and the natural logarithm of the deep lateral resistivity (RLLD); For dispersed pyrite, the quantitative calculation model is: V PRY1 = A1 * ln(RLLD1) + B1;V PRY1 It refers to the content of dispersed pyrite, and RLLD1 refers to the deep lateral resistivity of dispersed pyrite; For massive pyrite, the quantitative calculation model is: V PRY2 = A2 * ln(RLLD2) + B2;V PRY2 RLLD2 refers to the content of massive pyrite, while RLLD2 refers to the deep lateral resistivity of massive pyrite. For banded pyrite, the quantitative calculation model is: V PRY3 = A3 * ln(RLLD3) + B3;V PRY3 It refers to the content of banded pyrite, and RLLD3 refers to the deep lateral resistivity of banded pyrite; Among them, A1, A2, A3, B1, B2, and B3 are model coefficients calibrated using core data. A1, A2, A3, B1, B2, and B3 were all obtained by linear regression fitting of the measured pyrite content and deep lateral resistivity logging values in the core samples of the study area using the least squares method. This method is a mature and conventional method in the field of well logging interpretation. All the above coefficients are real numbers, and their specific values are determined by comprehensively considering the geological characteristics of the target strata in the study area, the occurrence characteristics of pyrite, and the characteristics of measured well logging and core data. They need to be calibrated separately in conjunction with the actual data of the study area.
[0035] This invention achieves quantitative identification of pyrite occurrence morphology for the first time and establishes a calculation model for pyrite content in different occurrence morphologies, effectively overcoming the problem of insufficient calculation accuracy caused by neglecting the differences in pyrite occurrence morphology in traditional methods. Moreover, this method only requires conventional well logging data for calculation, making the data easy to obtain, low in cost, and widely applicable. It can provide reliable technical support for resistivity correction, accurate calculation of oil saturation, and evaluation of reservoir sweet spots in pyrite-bearing reservoirs such as shale oil, and has significant application value and promising prospects for promotion.
[0036] Example 3 The method for calculating pyrite content in sedimentary rocks considering the occurrence morphology, as described in Example 1 or 2, differs in that: The following example of the Permian Lucaogou Formation shale oil reservoir in the Jimsar Depression of the Junggar Basin is used to explain the present invention, but the scope of protection of the present invention is not limited thereto.
[0037] This embodiment considers the well logging interpretation method for pyrite content based on the occurrence mode of pyrite, such as... Figure 1 As shown, the specific steps are as follows: Step 1: Acquisition of well logging and core experimental data: Conventional well logging data and experimental analysis data of 17 core samples were collected from the Permian Lucaogou Formation shale reservoir in the Jimsar Depression. Well logging data included deep resistivity logging (RLLD) and density logging (DEN). Core experimental data included: pyrite content analysis of the cores and the results of pyrite occurrence morphology (dispersed, massive, and banded) identified by SEM and core thin sections.
[0038] Step 2: Calculate the pyrite resistivity response parameter PRR and the pyrite volume ratio conversion parameter V. PRYZ And the pyrite occurrence speciation index (PYI): based on the pyrite content and corresponding deep lateral resistivity of the collected core samples, the PRR and V were calculated. PRYZ Taking a sample with a pyrite content of 3.74% and a corresponding depth resistivity logging response of 40.08 Ω·m as an example: ① Pyrite resistivity response parameter PRR: ②Pyrite volume ratio conversion parameter V PRYZ : ③Pyrite volume ratio conversion parameter PYI: Step 3: Classification of Pyrite Occurrence Modes: Correlation analysis was performed on the PYI values of all core samples and their observed occurrence modes. PRR and V were plotted. PRYZ Intersection plot in logarithmic coordinates ( Figure 2) and the cumulative frequency curve of the PYI index ( Figure 3 ), Figure 2 The data points with different memory configurations are distributed on parallel lines with the same slope but different intercepts. Figure 3 The study showed that pyrite with the same occurrence form has the same slope; it was found that data points of different occurrence forms are distributed on parallel lines with the same slope but different intercepts, and the cumulative frequency curve of the PYI index exhibits a clear three-segment characteristic. Based on this, the PYI thresholds for distinguishing the three occurrence forms of pyrite were determined, with PYI=9.595 and PYI=15.641 as the boundaries. PYI values less than 9.595 indicate a dispersed pattern, values between these two are considered a lumpy pattern, and values greater than or equal to 15.641 indicate a banded pattern.
[0039] Step 4: Establish the PYI prediction model: Plot the cross-plot between each PYI value and the corresponding well logging response at the depth. A significant negative correlation can be observed between PYI and DEN. Figure 4 A predictive model is established using linear regression:
[0040] PYI = -33.582 * DEN + 92.867 Step 5: Establish calculation models for pyrite content in different occurrence types: Based on the pyrite occurrence type classification determined by the PYI threshold, classify the core data points according to their occurrence type. For each type of data, establish a linear relationship between pyrite content and the natural logarithm of deep lateral resistivity (RLLD). Figure 5 (As shown). Specific model coefficients are obtained through regression analysis:
[0041] Dispersed pyrite: V PRY = -1.8×ln(RLLD)+10.429 R 2 =0.9481; Massive pyrite: V PRY = -1.755×ln(RLLD)+8.7419 R 2 =0.7467; Banded pyrite: V PRY = -1.35×ln(RLLD)+16.1162 R 2 =0.9884; Step 6: Continuous processing and interpretation of the entire well section: For the target well section requiring interpretation, processing is performed across the entire well depth. ① Read the DEN and RLLD logging values for each sampling point.
[0042] ② Substitute the DEN value into the established PYI prediction model to calculate the PYI index at that depth point, forming a continuous PYI curve.
[0043] ③ Based on the PYI thresholds for different occurrence types determined in step 3, the main occurrence type of pyrite at each depth point is determined using continuous PYI curves.
[0044] ④ Based on the identified morphology, the pyrite content calculation model corresponding to the occurrence morphology established in step 5 is used, combined with the RLLD value at that point, to calculate the final pyrite content and form a continuous pyrite content curve.
[0045] Results Verification: The calculated continuous pyrite content curve is compared with data available for verification. For example... Figure 6 As shown, Figure 6 This paper demonstrates the process from input logging curves to output continuous PYI curves, identification of occurrence morphologies, and final calculation of pyrite content curves. The results are compared and verified with those from formation element logging (ECS). In the well of this embodiment, the model's calculation results show a high degree of agreement in both morphology and numerical values with high-precision formation element logging (ECS) curves. Furthermore, the occurrence morphologies identified by the model are consistent with the core observation descriptions. The calculation results fully demonstrate the reliability and high accuracy of this method.
[0046] Example 4 A system for calculating pyrite content in sedimentary rocks, taking into account the occurrence morphology, includes: The data acquisition module is configured to acquire conventional logging data, core observation data, scanning electron microscope (SEM) data, micron CT data, and core analysis data of the target formation. The pyrite occurrence morphology classification module is configured to: calculate the pyrite resistivity response parameter PRR and the pyrite volume ratio conversion parameter V based on deep lateral resistivity data from conventional well logging data and the pyrite content from core analysis data. PRYZ Based on the pyrite resistivity response parameter PRR and the pyrite volume ratio conversion parameter V PRYZ The occurrence morphology index (PYI) of pyrite is calculated, and the occurrence morphology of pyrite is classified according to the preset PYI threshold. The PYI prediction model building module is configured to: draw a cross plot between the pyrite occurrence morphology index PYI and at least one conventional logging response, and build a PYI prediction model. The module for establishing a quantitative calculation model for pyrite content based on deep lateral resistivity is configured to: establish quantitative calculation models for pyrite content based on deep lateral resistivity for different types of pyrite occurrence. The pyrite content calculation module is configured to: determine the occurrence morphology of pyrite at each depth point of the target stratum based on the PYI prediction model, and then calculate the continuous pyrite content of the target stratum by using the quantitative calculation model of the corresponding occurrence morphology and combining the deep lateral resistivity at that depth point.
[0047] The above description is merely a specific embodiment of the present invention, but the scope of protection 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 present invention should be included within the scope of protection of the present invention. For example, the specific value of the PYI threshold may vary depending on the region and stratigraphic position; when establishing a PYI prediction model, it is also possible to try to establish a relationship with other sensitive logging curves; the specific coefficients of the content calculation model also need to be calibrated based on actual field data.
Claims
1. A method for calculating the pyrite content in sedimentary rocks considering their occurrence morphology, characterized in that, Includes the following steps: Step 1: Obtain conventional logging data, core observation data, scanning electron microscopy data, micron-CT data, and core analysis data of the target formation; Step 2: Based on the deep lateral resistivity data from conventional well logging data, and combined with the pyrite content from core analysis data, calculate the pyrite resistivity response parameter PRR and the pyrite volume ratio conversion parameter V. PRYZ ; Step 3: Based on the pyrite resistivity response parameter PRR and the pyrite volume ratio conversion parameter V PRYZ The occurrence morphology index (PYI) of pyrite is calculated, and the occurrence morphology of pyrite is classified according to the preset PYI threshold. Step 4: Draw a cross plot between the pyrite occurrence morphology index (PYI) and at least one conventional logging response, and establish a PYI prediction model; Step 5: For pyrite of different occurrence forms, establish quantitative calculation models for pyrite content based on deep lateral resistivity; Step 6: Based on the PYI prediction model, determine the occurrence type of pyrite at each depth point in the target stratum, and then use the quantitative calculation model of the corresponding occurrence type, combined with the deep lateral resistivity at that depth point, to calculate the continuous pyrite content in the target stratum. In step 2, the formula for calculating the pyrite resistivity response parameter PRR is: ; The pyrite volume ratio conversion parameter V PRYZ The calculation formula is: ; Where RLLD is the deep lateral resistivity, in Ω·m and V. PRY For core analysis, the pyrite content is % . In step 3, the formula for calculating the pyrite occurrence morphology index (PYI) is as follows: ; By performing cumulative frequency analysis on the pyrite occurrence morphology index (PYI), a first threshold 'a' and a second threshold 'b' were determined, resulting in samples with different occurrence morphologies exhibiting different slope characteristics on the cumulative frequency plot. Furthermore, in the PRR and V... PRYZ In the intersection plot under logarithmic coordinates, data points of different occurrence morphologies are distributed on parallel lines with the same slope but different intercepts, and pyrite of different occurrence morphologies has clearly distinguishable features in SEM images. The specific methods for determining the first threshold a and the second threshold b are as follows: All core samples were sorted in ascending order of pyrite occurrence morphology index (PYI) from smallest to largest. The cumulative number of samples corresponding to each pyrite occurrence morphology index (PYI) was counted, and the cumulative frequency was calculated. A cumulative frequency distribution curve of the PYI index was plotted with the pyrite occurrence morphology index (PYI) as the x-axis and the cumulative frequency as the y-axis. The plotted cumulative frequency distribution curve of the PYI index was observed, and combined with the known core morphology identification results, two key points in the curve where the slope changed abruptly were identified. The pyrite occurrence morphology index (PYI) corresponding to these two key points are the values of the first threshold a and the second threshold b.
2. The method for calculating pyrite content in sedimentary rocks considering occurrence morphology according to claim 1, characterized in that, In step 1, the conventional logging data includes deep lateral resistivity and three-porosity logging data; The core observation data includes descriptions of the occurrence morphology of pyrite observed in the cores; The core analysis data includes pyrite content data from core experimental analysis.
3. The method for calculating pyrite content in sedimentary rocks considering occurrence morphology according to claim 1, characterized in that, In step 3, the preset PYI threshold includes at least a first threshold a and a second threshold b. When PYI is less than the first threshold a, the pyrite is determined to be in a dispersed state. When PYI is greater than or equal to the first threshold a and less than the second threshold b, it is determined to be clumpy; when PYI is greater than or equal to the second threshold b, it is determined to be strip-like.
4. The method for calculating pyrite content in sedimentary rocks considering occurrence morphology according to claim 1, characterized in that, In step 4, the conventional logging response is density logging, and the PYI prediction model is a linear model: PYI=A*DEN+B, where DEN is the density logging value, and A and B are model coefficients obtained by fitting measured data from the study area.
5. A method for calculating pyrite content in sedimentary rocks considering occurrence morphology, as described in any one of claims 1-4, characterized in that, In step 5, the quantitative calculation model is based on the pyrite content V. PRY A linear relationship model between the linear relationship and the natural logarithm of the deep lateral resistivity (RLLD); For dispersed pyrite, the quantitative calculation model is: V PRY1 = A1 * ln(RLLD1) + B1;V PRY1 It refers to the content of dispersed pyrite, and RLLD1 refers to the deep lateral resistivity of dispersed pyrite; For massive pyrite, the quantitative calculation model is: V PRY2 = A2 * ln(RLLD2) + B2;V PRY2 RLLD2 refers to the content of massive pyrite, while RLLD2 refers to the deep lateral resistivity of massive pyrite. For banded pyrite, the quantitative calculation model is: V PRY3 = A3 * ln(RLLD3) + B3;V PRY3 It refers to the content of banded pyrite, and RLLD3 refers to the deep lateral resistivity of banded pyrite; Where A1, A2, A3, B1, B2, and B3 are model coefficients calibrated using core data.
6. A system for calculating pyrite content in sedimentary rocks considering their occurrence morphology, used to implement the method for calculating pyrite content in sedimentary rocks considering their occurrence morphology as described in any one of claims 1-5, characterized in that, include: The data acquisition module is configured to acquire conventional logging data, core observation data, scanning electron microscope data, micron CT data, and core analysis data of the target formation. The pyrite occurrence morphology classification module is configured to: calculate the pyrite resistivity response parameter PRR and the pyrite volume ratio conversion parameter V based on deep lateral resistivity data from conventional well logging data and the pyrite content from core analysis data. PRYZ Based on the pyrite resistivity response parameter PRR and the pyrite volume ratio conversion parameter V PRYZ The occurrence morphology index (PYI) of pyrite is calculated, and the occurrence morphology of pyrite is classified according to the preset PYI threshold. The PYI prediction model building module is configured to: draw a cross plot between the pyrite occurrence morphology index PYI and at least one conventional logging response, and build a PYI prediction model. The module for establishing a quantitative calculation model for pyrite content based on deep lateral resistivity is configured to: establish quantitative calculation models for pyrite content based on deep lateral resistivity for different types of pyrite occurrence. The pyrite content calculation module is configured to: determine the occurrence morphology of pyrite at each depth point of the target stratum based on the PYI prediction model, and then calculate the continuous pyrite content of the target stratum by using the quantitative calculation model of the corresponding occurrence morphology and combining the deep lateral resistivity at that depth point.