A mixed accumulation type shale reservoir lithology logging identification method

Abstract

The present application relates to a kind of mixed accumulation type shale reservoir lithology logging identification method, and the main steps of the method include lithology classification;Data arrangement statistics;Based on rock physics model forward optimization logging curve, construct the curve combination parameter sensitive to formation component, granularity, permeability information, and calculate sensitive combination parameter dL1, dL2 And dL3 using conventional logging curve, from dolomite component, granularity and permeability three dimensions, and with the aid of density curve DEN auxiliary discrimination, realize the quantitative discrimination of main lithology.The method uses rock physics forward means, carries out sensitive logging curve optimization, and constructs sensitive curve combination parameter, effectively solves the problem of low precision of single logging curve in mixed accumulation type shale lithology identification under the geological conditions of "multiple mineral components, different lithology frequent interaction, strong oil heterogeneity".

Description

Technical Field

[0001] This invention belongs to the field of petroleum geological reservoir lithology interpretation technology, and relates to a well logging method for identifying the lithology of mixed-sediment shale reservoirs. Background Technology

[0002] Fine-grained mixed-sediment reservoirs are important carriers of continental shale oil enrichment in my country. Influenced by multi-source mixing (including terrigenous clastic input and intra-basin chemical deposition), mixed-sediment reservoirs are characterized by diverse mineral compositions, complex lithology, rapid vertical variations, and thin thickness. The rock types of deltaic front-saline lacustrine mixed-sediment reservoirs can generally be divided into four main categories: siltstone, dolomite, mudstone, and dolomitic siltstone. These can be further subdivided into siltstone, calcareous siltstone, argillaceous siltstone, sandy dolomite, silty dolomite, micritic dolomite, dolomitic siltstone, dolomitic mudstone, and carbonaceous mudstone. Different lithologies exhibit significant differences in rock composition, pore structure, oil-bearing capacity, and mobility. Therefore, precise lithological identification of mixed-sediment reservoirs is crucial for guiding the evaluation and selection of shale oil sweet spots and is a prerequisite for conducting well logging interpretation of parameters in this type of reservoir. Currently, common lithological identification methods for continental mixed-sediment reservoirs include nuclear magnetic resonance logging and, for lacustrine fine-grained mixed-sediment reservoirs with underdeveloped silt particles, the "green pattern method" and composition-structure classification identification method. However, for multi-source mixed-sediment (dolomite, mud-scale particles, silt particles, and a small amount of tuff) strata in delta front-saline lacustrine backgrounds, these two methods are difficult to apply due to the large number of silt components and more complex lithology.

[0003] Well logging data continuously records the acoustic, electrical, magnetic, and radioactive physical information of subsurface rock formations. Different lithologies vary in grain size, material composition, pore space, and hydrocarbon potential, typically exhibiting different well logging response values. Therefore, fine interpretation of subsurface lithology can be achieved based on well logging curves. Fine interpretation of well logging lithology is widely used in conventional reservoirs, but it faces the following challenges for mixed-sedimentary shale reservoirs: 1) Mixed-sedimentary shale reservoirs have complex lithologies, thin single-layer thickness, and rapid vertical variations, resulting in limited well logging resolution and making it difficult to directly establish a connection between core scale and well logging curves; 2) Mixed-sedimentary shale reservoirs have fine grain size, diverse mineral components, and strong heterogeneity in physical properties, leading to frequent changes in rock composition and physical properties. The weak differences in well logging curve responses between different lithologies increase the difficulty of conventional well logging identification. Summary of the Invention

[0004] To address the problems existing in the prior art, this invention discloses a lithology logging identification method for mixed-sediment shale reservoirs. This method utilizes rock physics forward modeling to optimize sensitive logging curves and construct sensitive curve combination parameters. It effectively solves the problem that the low accuracy of a single logging curve in identifying mixed-sediment lithology is caused by the superposition of multiple factors such as "multiple mineral components, frequent interaction of different lithologies, and strong oil-bearing heterogeneity". It also achieves quantitative identification of the main lithologies.

[0005] This invention discloses a well logging method for identifying the lithology of mixed-sediment shale reservoirs, which includes the following steps:

[0006] S1. Lithological Classification: The main lithological classification is carried out by core observation, oil-bearing description and thin section identification, combined with rock grain size, relative dolomite content and composition;

[0007] S2. Data processing and statistics: Based on historical core test results, the range parameters of mineral composition, physical properties and organic matter abundance under different lithologies are statistically analyzed, and the conventional logging curves of the corresponding lithologies are statistically analyzed.

[0008] S3. Well logging curve selection: Construct corresponding rock physics models according to different rock components, and perform linear superposition of the rock physics models of different rock components to establish a forward model of the well logging response of the corresponding rock. Fit the theoretical well logging response values ​​of different rock components according to different lithologies, compare the correlation between the theoretical well logging response values ​​and the actual well logging curve values, and select well logging curves with forward modeling accuracy higher than the correlation threshold P to participate in lithology identification.

[0009] S4. Using the forward modeling model of logging response, a series of variable porosity logging curves for different lithologies were obtained. The logging curves sensitive to dolomite / non-dolomite and coarse / fine grained lithologies were identified respectively. The logging curves sensitive to the corresponding lithologies were combined to obtain the combined curve parameters dL1 and dL2 that are sensitive to dolomite composition and grain size. Using the resistivity RXO of the flushed zone and the resistivity RI of the intrusive zone in the conventional logging curves, the combined curve parameter dL3 reflecting the permeability of the mixed sedimentary formation was constructed.

[0010] S5. Lithology discrimination is performed based on rock composition, grain size, permeability, and the density curve DEN of the corresponding core: First, the DL2 index, which reflects grain size, and the DL3 index, which reflects permeability, are used for primary discrimination to classify the rock lithology into coarse-grained lithology group A with good permeability and fine-grained lithology group B with poor physical properties; then, the DL1 index, which reflects dolomite composition, and the density curve DEN are used for secondary discrimination of lithology group A and lithology group B respectively.

[0011] Furthermore, the lithological classification in step S1 includes siltstone, dolomitic siltstone, micritic dolomite, sandy dolomite, dolomitic mudstone, and carbonaceous mudstone.

[0012] Furthermore, the rock components in step S3 include feldspar, quartz, carbonates, organic matter, clay, and pore fluids.

[0013] Furthermore, the specific method for establishing a forward model of well logging response by linearly superimposing rock physics models of different rock components in step S3 is as follows:

[0014] ;

[0015] ;

[0016] ;

[0017] ;

[0018] ;

[0019] Where i represents a single rock component, i = 1, 2, 3, 4, 5 or 6, where 1-6 represent feldspar, quartz, carbonate, organic matter, clay and pore fluid in the rock component, respectively; Vi represents the relative component content of rock component i; GR represents the natural gamma ray logging curve value, GRi represents the natural gamma ray logging curve value of rock component i; DT represents the sonic transit time logging curve value, DTi represents the sonic transit time logging curve value of rock component i; CNL represents the neutron logging curve value, CNLi represents the neutron logging curve value of rock component i; DEN represents the density logging curve value, DENi represents the density logging curve value of rock component i.

[0020] Furthermore, in step S3, sample cores are collected, and the sample cores are analyzed to obtain the corresponding rock components, organic matter abundance, and porosity test results, and the relative component content Vi of rock component i in different rocks is calculated.

[0021] Furthermore, the specific method for combining the response-sensitive logging curves in the corresponding lithology to obtain the combined curve parameters dL1 and dL2 in step S4 is as follows:

[0022] S41. Based on the mean values ​​of rock components and organic matter abundance under different lithologies in step S2, the porosity is changed sequentially, and the relative content of the corresponding rock components is recalculated; and the sonic transit time logging curve value DT, density logging curve value DEN, and neutron logging curve value CNL under different porosities are calculated using the forward model of logging response to obtain a series of logging curve values ​​for the corresponding lithology under variable porosity conditions;

[0023] S42. Construct the combined curve parameter dL1 by combining the logging values ​​of acoustic transit time, density, and neutron:

[0024] dL1=[(CNL-CNLmax) / (CNLmin-CNLmax)+(DT-DTmin) / (DTmax-DTmin)] / 2-(DEN-DENmin) / (DENmax-DENmin);

[0025] Where CNLmax and CNLmin represent the maximum and minimum values ​​of the neutron logging curve; DTmax and DTmin represent the maximum and minimum values ​​of the sonic transit time logging curve; and DENmax and DENmin represent the maximum and minimum values ​​of the density logging curve.

[0026] S43. Construct the combined curve parameter dL2 by combining the logging values ​​of sonic transit time and density:

[0027] dL2=(DEN-DENmax) / (DENmin-DENmax)-(DT-DTmin) / (DTmax-DTmin);

[0028] Where DEN represents the density logging curve value, DENmax and DENmin represent the maximum and minimum values ​​of the density logging curve value; DTmax and DTmin represent the maximum and minimum values ​​of the sonic transit time logging curve value.

[0029] Furthermore, the specific method for constructing the curve combination parameter dL3 using the resistivity RXO of the flushing zone and the resistivity RI of the intrusion zone in step S4 is as follows: when the mud is non-oil-based, dL3 = 1 - RXO / RI; when the mud is oil-based, dL3 = 1 - RI / RXO.

[0030] Furthermore, the specific steps in step S5 for making a judgment using the DL2 index, which reflects particle size, and the DL3 index, which reflects permeability, are as follows:

[0031] S51. Construct a cross-sectional diagram A using the DL2 index, which reflects particle size, and the DL3 index, which reflects permeability. Determine the classification boundaries a and b using the constructed cross-sectional diagram A, where a is the classification boundary of the DL2 index and b is the classification boundary of the DL3 index.

[0032] S52. When DL2>a and DL3>b, the rock's lithology is determined to belong to lithology group A, which is coarse-grained and has good permeability; when DL2≤a or DL3≤b, the rock's lithology is determined to belong to lithology group B, which is fine-grained and has poor permeability.

[0033] Furthermore, the specific steps for secondary discrimination using the DL1 index and density curve DEN, which reflect the composition of dolomite, in step S5 are as follows:

[0034] S53. For the rocks in lithology group A, construct the intersection diagram B by using the DL1 index reflecting the dolomite composition and the density curve DEN, and determine the classification boundaries ci and dj by using the intersection diagram B, where ci is the classification boundary of the DL1 index, i is the number of classification boundaries of the DL1 index, dj is the classification boundary of the density curve DEN, and j is the number of classification boundaries of the density curve DEN.

[0035] S54. Based on the distribution characteristics of the DL1 index and density curve DEN corresponding to different lithologies in lithology set A in the cross-sectional diagram, determine the lithology type of the corresponding rocks in lithology set A;

[0036] S55. For the rocks in lithology group B, construct the intersection diagram C by using the DL1 index reflecting the dolomite component and the density curve DEN, and determine the classification boundaries ei and fj by using the intersection diagram C, where ei is the classification boundary of the DL1 index, i is the number of classification boundaries of the DL1 index, fj is the classification boundary of the density curve DEN, and j is the number of classification boundaries of the density curve DEN.

[0037] S56. Based on the distribution characteristics of the DL1 index and density curve DEN corresponding to different lithologies in lithology set B in the cross-sectional diagram, determine the lithology type of the corresponding rocks in lithology set B.

[0038] 1) The lithology identification method for mixed-sediment shale reservoirs of the present invention establishes a rock physical model based on rock composition and constructs a forward model of the corresponding rock logging response using linear superposition. The method optimizes sensitive logging curves through model forward modeling and constructs a combination parameter of sensitive curves using the optimized sensitive logging curves. This effectively solves the problem of low accuracy of single logging curve identification for mixed-sediment lithology caused by the superposition of multiple factors such as "multiple mineral components, frequent interaction of different lithologies, and strong oil-bearing heterogeneity". At the same time, by constructing a combination parameter of sensitive curves that reflects grain size and the content of special formation components, and combining it with plate intersection, the method achieves quantitative discrimination of the main lithologies. The discrimination accuracy is high, reaching more than 75%, and only conventional curves such as density, neutron, sonic transit time and resistivity are required, which effectively reduces the interpretation cost and cycle. Attached Figure Description

[0039] Figure 1 This is a flowchart illustrating the lithological logging identification method for mixed-sediment shale reservoirs in this embodiment;

[0040] Figure 2 This is a graph showing the intersection of the DL1 index and skeleton density in this embodiment;

[0041] Figure 3 This is a graph showing the intersection of the acoustic time difference (DT) and the density curve (DEN) in this embodiment.

[0042] Figure 4 This is a cross-plot of the DL2 and DL3 indices in this embodiment;

[0043] Figure 5 This is the intersection diagram of DL1 and DEN for secondary discrimination of lithology set A in this embodiment;

[0044] Figure 6 This is a diagram showing the intersection of DL1 and DEN in the secondary discrimination of lithology set B in this embodiment;

[0045] Figure 7 This is a schematic diagram comparing the lithological logging identification results with thin section identification in this embodiment. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0048] Example 1:

[0049] This embodiment takes the Lucaogou Formation in the Jimsar Depression of Xinjiang Oilfield as an example. The Lucaogou Formation was formed in a delta front-saline lacustrine sedimentary environment. The sediments were influenced by extra-basin terrigenous clastic input, intra-basin chemical deposition, and volcanic activity. Fine-grained mixed sedimentary rocks are well-developed. The overall lithological assemblage is dominated by dark mudstone, interbedded with siltstone, carbonate rocks, and transitional lithologies. The Lucaogou Formation has a diverse mineral composition and complex lithology. Based on grain size and carbonate mineral content, it can be divided into siltstone, dolomitic siltstone, carbonate rocks, and mudstone. It can be further subdivided into siltstone, argillaceous siltstone, calcareous siltstone, dolomitic siltstone, sandy dolomite, micritic dolomite, silty dolomite, dolomitic mudstone, calcareous mudstone, silty mudstone, and carbonaceous mudstone. The dominant reservoirs mainly correspond to siltstone, dolomitic siltstone, and sandy dolomite.

[0050] Combination Figure 1 As shown in this embodiment, the lithological logging identification method for mixed-sediment shale reservoirs specifically disclosed includes the following steps:

[0051] S1. Lithological Classification: Core observation, oil-bearing characteristics description, and thin-section identification methods, combined with rock grain size, relative dolomite content, and composition, are used to classify the main lithologies. This step aims to preliminarily classify lithological types through core sampling and other means, considering factors such as the development scale, oil-bearing properties, and differences in well logging response characteristics of the main lithologies in the formation. In this embodiment, the lithological classification includes siltstone, dolomitic siltstone, micritic dolomite, sandy dolomite, dolomitic mudstone, and carbonaceous mudstone.

[0052] S2. Data Processing and Statistics: Based on historical core test results, the range parameters of mineral composition, physical properties, and organic matter abundance under different lithologies are statistically analyzed, and the corresponding conventional logging curves are statistically analyzed. This step requires more than 50 data points collected for each lithology to ensure the representativeness of the statistical data. If the sample quantity is insufficient, targeted sampling and relevant experimental tests are required. Generally, whole-rock mineral analysis, total organic carbon (TOC), and porosity testing can be conducted according to SY / T5163-2010 "Clay Minerals and Common Non-Clay Minerals in Sedimentary Rocks", GB / T19145-2003 "Determination of Total Organic Carbon in Sedimentary Rocks", and GB / T 34533-2017 "Determination of Porosity and Permeability of Shale", respectively. This step collects conventional logging curves including wellbore diameter, natural gamma ray, resistivity, sonic transit time, density, and neutron data. After data collection, the experimental data points are first assigned to depths, and the corresponding logging curve values ​​are statistically analyzed. Table 1 shows the distribution range of TOC (Total Organic Carbon) for different lithologies, including minimum and maximum values, with values ​​in parentheses representing the distribution mean. Table 1 indicates that sandy dolomite, dolomitic siltstone, and siltstone are the dominant reservoirs, exhibiting high porosity, low clay content, and low organic matter abundance. Sandy dolomite has the highest dolomite content. Micritic dolomite, dolomitic mudstone, and carbonaceous mudstone have poor physical properties, with dolomitic mudstone and carbonaceous mudstone exhibiting high organic matter abundance and clay content.

[0053] Table 1

[0054]

[0055] S3. Well logging curve selection: Construct corresponding rock physics models based on different rock components, including feldspar, quartz, carbonate, organic matter, clay, and pore fluid. Linearly superimpose the rock physics models of different rock components to establish a forward model of the well logging response for the corresponding rock. Fit the theoretical well logging response values ​​of different rock components according to different lithologies. Compare the correlation between the theoretical well logging response values ​​and the actual well logging curve values. Select well logging curves with a forward modeling accuracy higher than the correlation threshold P to participate in lithology identification.

[0056] The specific method for establishing a forward model of well logging response by linearly superimposing rock physics models of different rock compositions is as follows:

[0057] ;

[0058] ;

[0059] ;

[0060] ;

[0061] ;

[0062] Where i represents a single rock component, i = 1, 2, 3, 4, 5 or 6, where 1-6 represent feldspar, quartz, carbonate, organic matter, clay and pore fluid in the rock component, respectively; Vi represents the relative component content of rock component i; GR represents the natural gamma ray logging curve value, GRi represents the natural gamma ray logging curve value of rock component i; DT represents the sonic transit time logging curve value, DTi represents the sonic transit time logging curve value of rock component i; CNL represents the neutron logging curve value, CNLi represents the neutron logging curve value of rock component i; DEN represents the density logging curve value, DENi represents the density logging curve value of rock component i.

[0063] It should be noted that in this embodiment, the relative component content Vi of rock component i in different rocks is calculated by collecting sample cores and analyzing the sample cores to obtain the corresponding rock components, organic matter abundance and porosity test results.

[0064] S4. Using the forward modeling model of logging response, a series of variable porosity logging curves for different lithologies were obtained. The logging curves sensitive to dolomite / non-dolomite and coarse / fine grained lithologies were identified respectively. The logging curves sensitive to the corresponding lithologies were combined to obtain the combined curve parameters dL1 and dL2 that are sensitive to dolomite composition and grain size. Using the resistivity RXO of the flushed zone and the resistivity RI of the intrusive zone in the conventional logging curves, the combined curve parameter dL3 reflecting the permeability of the mixed sedimentary formation was constructed.

[0065] In this embodiment, the specific method for combining the response-sensitive logging curves in the corresponding lithology to obtain the combined curve parameters dL1 and dL2 is as follows:

[0066] S41. Based on the mean values ​​of rock components and organic matter abundance under different lithologies in step S2, the porosity is changed sequentially, and the relative content of the corresponding rock components is recalculated; and the sonic transit time logging curve value DT, density logging curve value DEN, and neutron logging curve value CNL under different porosities are calculated using the forward model of logging response to obtain a series of logging curve values ​​for the corresponding lithology under variable porosity conditions;

[0067] S42. Construct the combined curve parameter dL1 by combining the logging values ​​of acoustic transit time, density, and neutron:

[0068] dL1=[(CNL-CNLmax) / (CNLmin-CNLmax)+(DT-DTmin) / (DTmax-DTmin)] / 2-(DEN-DENmin) / (DENmax-DENmin);

[0069] Where CNLmax and CNLmin represent the maximum and minimum values ​​of the neutron logging curve; DTmax and DTmin represent the maximum and minimum values ​​of the sonic transit time logging curve; DENmax and DENmin represent the maximum and minimum values ​​of the density logging curve; such as Figure 2 As shown in the figure, the constructed dL1 parameter in this embodiment can effectively reflect the changes in dolomite composition. For dolomite, dolomitic mudstone, dolomitic siltstone, siltstone and mudstone, as the dolomite composition decreases, dL1 gradually changes from a negative value to a positive value.

[0070] S43. Construct the combined curve parameter dL2 by combining the logging values ​​of sonic transit time and density:

[0071] dL2=(DEN-DENmax) / (DENmin-DENmax)-(DT-DTmin) / (DTmax-DTmin);

[0072] Where DEN represents the density logging curve value, DENmax and DENmin represent the maximum and minimum density logging curve values; DTmax and DTmin represent the maximum and minimum sonic transit time logging curve values; combined with Figure 3 As shown in this embodiment, when the density logging curve value DEN is the same, coarse-grained lithologies such as sandy dolomite, dolomitic siltstone and siltstone have relatively low sonic transit time logging curve values. Therefore, logging curve values ​​that combine sonic transit time and density can effectively reflect grain size.

[0073] Finally, based on the mud properties and using the resistivity RXO of the flushing zone and the resistivity RI of the intrusion zone obtained from logging in step S2, a curve combination parameter dL3 is constructed: when the mud is non-oil-based, dL3 = 1 - RXO / RI; when the mud is oil-based, dL3 = 1 - RI / RXO.

[0074] S5. Lithology discrimination is performed based on rock composition, grain size, permeability, and the density curve DEN of the corresponding core: First, the DL2 index, which reflects grain size, and the DL3 index, which reflects permeability, are used for primary discrimination to classify the rock lithology into coarse-grained lithology group A with good permeability and fine-grained lithology group B with poor physical properties; then, the DL1 index, which reflects dolomite composition, and the density curve DEN are used for secondary discrimination of lithology group A and lithology group B respectively.

[0075] Specifically, the steps in step S5 for making a judgment using the DL2 index, which reflects particle size, and the DL3 index, which reflects permeability, are as follows:

[0076] S51. Construct a cross-sectional diagram A using the DL2 index, which reflects particle size, and the DL3 index, which reflects permeability. Determine the classification boundaries a and b using the constructed cross-sectional diagram A, where a is the classification boundary of the DL2 index and b is the classification boundary of the DL3 index.

[0077] S52. When DL2>a and DL3>b, the rock's lithology is determined to belong to lithology group A, which is coarse-grained and has good permeability; when DL2≤a or DL3≤b, the rock's lithology is determined to belong to lithology group B, which is fine-grained and has poor permeability.

[0078] Furthermore, the specific steps for secondary discrimination using the DL1 index and density curve DEN, which reflect the composition of dolomite, in step S5 are as follows:

[0079] S53. For the rocks in lithology group A, construct the intersection diagram B by using the DL1 index reflecting the dolomite composition and the density curve DEN, and determine the classification boundaries ci and dj by using the intersection diagram B, where ci is the classification boundary of the DL1 index, i is the number of classification boundaries of the DL1 index, dj is the classification boundary of the density curve DEN, and j is the number of classification boundaries of the density curve DEN.

[0080] S54. Based on the distribution characteristics of the DL1 index and density curve DEN corresponding to different lithologies in lithology set A in the cross-sectional diagram, determine the lithology type of the corresponding rocks in lithology set A;

[0081] S55. For the rocks in lithology group B, construct the intersection diagram C by using the DL1 index reflecting the dolomite component and the density curve DEN, and determine the classification boundaries ei and fj by using the intersection diagram C, where ei is the classification boundary of the DL1 index, i is the number of classification boundaries of the DL1 index, fj is the classification boundary of the density curve DEN, and j is the number of classification boundaries of the density curve DEN.

[0082] S56. Based on the distribution characteristics of the DL1 index and density curve DEN corresponding to different lithologies in lithology set B in the cross-sectional diagram, determine the lithology type of the corresponding rocks in lithology set B.

[0083] For example, in this embodiment, during the lithological identification of the delta front facies-saline lacustrine sedimentary environment formed by the Lucaogou Formation:

[0084] First, using the grain size and permeability indicators dL2 and dL3, the lithology is classified into two major categories: the lithology set A with coarse grains and good permeability (mainly including siltstone and sand debris dolomite), and the lithology set B with fine grains and poor physical properties (mainly including mudstone and micritic dolomite). The boundary is determined through the dL2 - dL3 cross - plot, as Figure 4 shown. In the lithology set A, dL2 > 0 and dL3 > 0.05 are satisfied, and other lithology types that do not meet the conditions of the lithology set A are judged as the lithology set B;

[0085] Then, further subdivision is carried out using dL1, which reflects the dolomite component index, and the density logging curve DEN. In the dL1 - DEN cross - plot, the boundaries of various lithologies in the lithology set A and the lithology set B are determined respectively, as Figure 5 and 6 shown. In the lithology set A, sand debris dolomite satisfies dL1 < 0, and dolomitic siltstone satisfies 0 < dL1 < 0.2 and DEN > 2.35 g / cm 3 , and the rest are siltstone. In the lithology set B, micritic dolomite satisfies dL1 < 0 and DEN > 2.45 g / cm 3 , and dolomitic mudstone satisfies dL1 < 0.15 and DEN > 2.4 g / cm 3 , and the rest are carbonaceous mudstone.

[0086] Using the above steps, fine lithology identification is carried out on the mixed - type formation of the Lucaogou Formation in Jimusa'er, as Figure 7 shown. The lithology identified by logging has good consistency with the results of core observation and description. Through thin - section identification, 9 siltstone and dolomitic siltstone are identified, while 7 are identified by the logging method in this paper, and the identification accuracy is 78%; through thin - section identification, 3 sand debris dolomite and 7 micritic dolomite are identified respectively, while 3 and 5 are identified by the logging method in this paper, and the coincidence rate reaches 80%. Overall, the logging identification and thin - section identification are in good agreement.

[0087] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are all included within the protection scope of the present invention.

Claims

1. A well logging method for identifying the lithology of mixed-sediment shale reservoirs, characterized in that, Includes the following steps: S1. Lithological Classification: The main lithological classification is carried out by core observation, oil-bearing description and thin section identification, combined with rock grain size, relative dolomite content and composition; S2. Data processing and statistics: Based on historical core test results, the range parameters of mineral composition, physical properties and organic matter abundance under different lithologies are statistically analyzed, and the conventional logging curves of the corresponding lithologies are statistically analyzed. S3. Well logging curve selection: Construct corresponding rock physics models according to different rock components, and perform linear superposition of the rock physics models of different rock components to establish a forward model of the well logging response of the corresponding rock. Fit the theoretical well logging response values ​​of different rock components according to different lithologies, compare the correlation between the theoretical well logging response values ​​and the actual well logging curve values, and select well logging curves with forward modeling accuracy higher than the correlation threshold P to participate in lithology identification. S4. Using the forward modeling model of logging response, a series of variable porosity logging curves for different lithologies were obtained. The logging curves sensitive to dolomite / non-dolomite and coarse / fine grained lithologies were identified respectively. The logging curves sensitive to the corresponding lithologies were combined to obtain the combined curve parameters dL1 and dL2 that are sensitive to dolomite composition and grain size. Using the resistivity RXO of the flushed zone and the resistivity RI of the intrusive zone in the conventional logging curves, the combined curve parameter dL3 reflecting the permeability of the mixed sedimentary formation was constructed. The specific method for combining the response-sensitive logging curves in the corresponding lithology to obtain the combined curve parameters dL1 and dL2 in step S4 is as follows: S41. Based on the mean values ​​of rock components and organic matter abundance under different lithologies in step S2, the porosity is changed sequentially, and the relative content of the corresponding rock components is recalculated; and the sonic transit time logging curve value DT, density logging curve value DEN, and neutron logging curve value CNL under different porosities are calculated using the forward model of logging response to obtain a series of logging curve values ​​for the corresponding lithology under variable porosity conditions; S42. Construct the combined curve parameter dL1 by combining the logging values ​​of acoustic transit time, density, and neutron: dL1=[(CNL-CNLmax) / (CNLmin-CNLmax)+(DT-DTmin) / (DTmax-DTmin)] / 2-(DEN-DENmin) / (DENmax-DENmin); CNLmax and CNLmin represent the maximum and minimum values ​​of the neutron logging curve; DTmax and DTmin represent the maximum and minimum values ​​of the sonic transit time logging curve; DENmax and DENmin represent the maximum and minimum values ​​of the density logging curve; S43. Construct the combined curve parameter dL2 by combining the logging values ​​of sonic transit time and density: dL2=(DEN-DENmax) / (DENmin-DENmax)-(DT-DTmin) / (DTmax-DTmin); Where DEN represents the density logging curve value, DENmax and DENmin represent the maximum and minimum values ​​of the density logging curve value; DTmax and DTmin represent the maximum and minimum values ​​of the sonic transit time logging curve value. The specific method for constructing the curve combination parameter dL3 using the resistivity RXO of the flushing zone and the resistivity RI of the intrusion zone in step S4 is as follows: when the mud is non-oil-based, dL3 = 1 - RXO / RI; when the mud is oil-based, dL3 = 1 - RI / RXO. S5. Lithology discrimination is performed based on rock composition, grain size, permeability, and the density curve DEN of the corresponding core: First, the DL2 index, which reflects grain size, and the DL3 index, which reflects permeability, are used for primary discrimination to classify the rock lithology into coarse-grained lithology group A with good permeability and fine-grained lithology group B with poor physical properties; then, the DL1 index, which reflects dolomite composition, and the density curve DEN are used for secondary discrimination of lithology group A and lithology group B respectively. The specific steps for making a judgment using the DL2 index, which reflects particle size, and the DL3 index, which reflects permeability, in step S5 are as follows: S51. Construct a cross-sectional diagram A using the DL2 index, which reflects particle size, and the DL3 index, which reflects permeability. Determine the classification boundaries a and b using the constructed cross-sectional diagram A, where a is the classification boundary of the DL2 index and b is the classification boundary of the DL3 index. S52. When DL2>a and DL3>b, the rock's lithology is determined to belong to lithology group A, which is coarse-grained and has good permeability; when DL2≤a or DL3≤b, the rock's lithology is determined to belong to lithology group B, which is fine-grained and has poor permeability.

2. The lithological logging identification method for mixed-sediment shale reservoirs according to claim 1, characterized in that: The lithological classification in step S1 includes siltstone, dolomitic siltstone, micritic dolomite, sandy dolomite, dolomitic mudstone, and carbonaceous mudstone.

3. The lithological logging identification method for mixed-sediment shale reservoirs according to claim 1, characterized in that: The rock components in step S3 include feldspar, quartz, carbonates, organic matter, clay, and pore fluids.

4. The lithological logging identification method for mixed-sediment shale reservoirs according to claim 1, characterized in that: The specific method for establishing a forward model of well logging response by linearly superimposing rock physics models of different rock components in step S3 is as follows: ； ； ； ； ； Where i is a single rock component, i = 1, 2, 3, 4, 5 or 6, where 1-6 represent feldspar, quartz, carbonate, organic matter, clay and pore fluid in the rock component, respectively; Vi represents the relative composition content of rock component i; GR represents the natural gamma logging curve value, GRi represents the natural gamma logging curve value of rock component i; DT represents the sonic transit time logging curve value, and DTi represents the sonic transit time logging curve value of rock component i. CNL represents the neutron logging curve value, CNLi represents the neutron logging curve value of rock component i; DEN represents the density logging curve value, and DENi represents the density logging curve value of rock component i.

5. The lithological logging identification method for mixed-sediment shale reservoirs according to claim 4, characterized in that: In step S3, sample cores are collected and analyzed to obtain the rock composition, organic matter abundance, and porosity test results of the cores, and the relative component content Vi of rock component i in different rocks is calculated.

6. The lithological logging identification method for mixed-sediment shale reservoirs according to claim 1, characterized in that, The specific steps for secondary discrimination using the DL1 index and the density curve DEN, which reflect the composition of dolomite, in step S5 are as follows: S53. For the rocks in lithology group A, construct the intersection diagram B by using the DL1 index reflecting the dolomite composition and the density curve DEN, and determine the classification boundaries ci and dj by using the intersection diagram B, where ci is the classification boundary of the DL1 index, i is the number of classification boundaries of the DL1 index, dj is the classification boundary of the density curve DEN, and j is the number of classification boundaries of the density curve DEN. S54. Based on the distribution characteristics of the DL1 index and density curve DEN corresponding to different lithologies in lithology set A in the cross-sectional diagram, determine the lithology type of the corresponding rocks in lithology set A; S55. For the rocks in lithology group B, construct the intersection diagram C by using the DL1 index reflecting the dolomite component and the density curve DEN, and determine the classification boundaries ei and fj by using the intersection diagram C, where ei is the classification boundary of the DL1 index, i is the number of classification boundaries of the DL1 index, fj is the classification boundary of the density curve DEN, and j is the number of classification boundaries of the density curve DEN. S56. Based on the distribution characteristics of the DL1 index and density curve DEN corresponding to different lithologies in lithology set B in the cross-sectional diagram, determine the lithology type of the corresponding rocks in lithology set B.

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