CN117365458A - Rock logging identification method for mixed-accumulation shale reservoir - Google Patents
Rock logging identification method for mixed-accumulation shale reservoir Download PDFInfo
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Abstract
The invention relates to a lithology logging identification method of a mixed-accumulation shale reservoir, which mainly comprises lithology classification; data arrangement statistics; based on forward optimization logging curve of petrophysical model, constructing curve combination parameters sensitive to stratum components, granularity and permeability information, calculating sensitive combination parameters dL1, dL2 and dL3 by using conventional logging curve, and quantitatively judging main lithology from three dimensions of dolomite components, granularity and permeability by means of auxiliary judgment of density curve DEN. The method utilizes petrophysical forward means to conduct sensitive logging curve optimization, and constructs sensitive curve combination parameters, so that the problem that under geological conditions of multiple mineral components, frequent interaction of different lithologies and strong oil-containing heterogeneity, the single logging curve has low accuracy in identifying the lithology of the mixed-accumulation shale is effectively solved.
Description
Technical Field
The invention belongs to the technical field of petroleum geological reservoir lithology interpretation, and relates to a method for identifying rock logging of a mixed-accumulation shale reservoir.
Background
Fine-grained mixed accumulation type reservoir is an important carrier for enriching land shale oil in China, is influenced by multi-source mixed accumulation (comprising land source debris input and in-basin chemical deposition), and has the characteristics of multiple mineral components, complex lithology, quick vertical change, thin thickness and the like. The rock types of delta front-salty lake-phase mixed reservoir are generally classified into four categories of siltstone, dolomite, mud rock and cloud siltstone, and further classified into siltstone, gritty siltstone, marshy dolomite, siltstone dolomite, mudstone dolomite, cloud siltstone, dolomite, and the like. Different lithology has large differences in rock components, pore structures, oiliness, mobility and the like, so that lithology fine identification of the mixed-accumulation reservoir is a key for guiding evaluation and optimization of shale oil desserts, and is also a precondition for developing logging interpretation of the reservoir parameters. At present, a common lithology recognition method aims at a land phase mixed accumulation type reservoir, one is nuclear magnetic logging, the other is a lake phase fine particle mixed accumulation type reservoir which does not develop powder sand particles, a green mode method, a component-structure classification recognition method and the like are adopted, and for a multi-source component mixed accumulation (dolomite, mud-level particles, powder sand particles and a small amount of curdling) layer under the front edge-salty lake phase background of a delta, the two methods are difficult to be applied due to the fact that the powder sand components are more and the lithology is more complex.
The logging data can continuously record physical information such as sound, electricity, magnetism, radioactivity and the like of underground rock formations, different lithology is different in granularity, material composition, pore space, oil-gas property and the like, and generally different logging response values are shown, so that fine interpretation of the underground lithology can be realized based on logging curves. The fine interpretation of logging lithology is common in conventional reservoirs, but faces the following problems for miscible shale reservoirs: 1) The mixed-accumulation shale reservoir is complex in lithology, thin in single-layer thickness, quick in vertical change, limited in logging resolution, and difficult to directly establish a connection between a core scale and a logging curve; 2) The mixed-accumulation shale reservoir has the advantages of fine granularity, multiple mineral components, strong physical property heterogeneity, frequent changes of rock components, physical properties and the like, weak response difference of different lithology logging curves and increased conventional logging identification difficulty.
Disclosure of Invention
Aiming at the problems in the prior art, the invention discloses a method for identifying the lithology logging of a mixed-accumulation shale reservoir, which utilizes a petrophysical forward modeling means to conduct sensitive logging curve optimization and construct sensitive curve combination parameters, so that the problem that the single logging curve has low accuracy in identifying the lithology of the mixed-accumulation shale and the quantitative discrimination of main lithology is realized due to multi-factor superposition of multi-mineral components, frequent interaction of different lithology and strong oil-containing heterogeneity is effectively solved.
The invention discloses a method for identifying rock logging of a mixed-accumulation shale reservoir, which comprises the following steps:
s1, lithology classification: adopting a rock core observation, oiliness description and sheet identification method, and carrying out main lithology classification by combining rock granularity and dolomite relative content and composition;
s2, data arrangement statistics: according to the historical core experiment result, counting the range parameters of mineral composition, physical property parameters and organic matter abundance under different lithology, and counting the conventional logging curves of the corresponding lithology;
s3, logging curves are preferably: respectively constructing corresponding rock physical models according to different rock components, respectively carrying out linear superposition according to the rock physical models of the different rock components, establishing a logging response forward model of the corresponding rock, respectively fitting logging theoretical response values of the different rock components according to different lithologies, comparing the correlation between the logging theoretical response values and actual logging curve values, and selecting a logging curve with forward result precision higher than a correlation threshold value P to participate in lithology recognition;
s4, obtaining variable porosity logging curve series values of different lithologies by using a logging response forward model, respectively judging logging curves sensitive to dolomite/non-dolomite and coarse grain/fine grain lithology responses, and respectively combining the logging curves sensitive to the responses in the corresponding lithologies to obtain combination curve parameters dL1 and dL2 sensitive to dolomite components and granularity; utilizing flushing zone resistivity RXO and invasion zone resistivity RI in a conventional logging curve to construct a curve combination parameter dL3 reflecting the permeability of the mixed rock stratum;
s5, judging lithology according to rock components, granularity and permeability and combining a density curve DEN of a corresponding rock core, namely firstly judging the lithology of the rock into a lithology set A with good coarse-grain permeability and a lithology set B with poor fine-grain physical property by utilizing a DL2 index of reaction granularity and a DL3 index of reaction permeability for one time; and then, respectively carrying out secondary discrimination on the lithology set A and the lithology set B by using the DL1 index and the density curve DEN of the reactive dolomite component.
Further, the lithology classification and division in the step S1 comprises siltstone, cloud siltstone, mud crystal dolomite, sand chip dolomite, dolomite mudstone and carbonaceous mudstone.
Further, the rock component in the step S3 includes feldspar, quartz, carbonate, organic matter, clay and pore fluid.
Further, in the step S3, the petrophysical models of different rock components are used for linear superposition, and the specific method for building the logging response forward model is as follows:
where i is a single rock component of the rock, i = 1,2,3,4,5 or 6, where 1-6 represent feldspar, quartz, carbonate, organic matter, clay and pore fluid, respectively, in the rock component; vi represents the relative component content of rock component i; GR represents a natural gamma log value, GRi represents a natural gamma log value for the rock component i; DT represents the acoustic time difference log values, and DTi represents the acoustic time difference log values for the rock component i; CNL represents neutron log values, CNLi represents neutron log values for rock component i; DEN represents the density log value and DENi represents the density log value of the rock constituent i.
Further, in the step S3, a sample core is collected, and is subjected to detection and analysis to obtain a corresponding rock component, organic matter abundance and porosity test result, and the relative component content Vi of the rock component i in different rocks is calculated.
Further, in the step S4, the specific method for respectively combining the response-sensitive logging curves in the corresponding lithology to obtain the combination curve parameters dL1 and dL2 includes:
s41, changing the porosity successively according to the average value of rock components and organic matter abundance under different lithology in the step S2, and recalculating the relative content of the corresponding rock components; calculating a sound wave time difference logging curve value DT, a density logging curve value DEN and a neutron logging curve value CNL under different porosities by using a logging response forward model to obtain a logging curve series value of the corresponding lithology under the condition of pore change;
s42, constructing a combination curve parameter dL1 by using logging curve values of simultaneous sound wave time difference, density and neutrons:
dL1＝[(CNL-CNLmax)/(CNLmin-CNLmax)+(DT-DTmin)/(DTmax-DTmin)]/2-(DEN-DENmin)/(DENmax-DENmin)；
wherein CNLmax and CNLmin represent the maximum and minimum values of neutron log values; DTmax and DTmin represent the maximum and minimum values of the sonic jet lag log values; denmax and Denmin represent the maximum and minimum values of the density log values;
s43, constructing a combination curve parameter dL2 by using log curve values of simultaneous sound wave time difference and density:
dL2＝(DEN-DENmax)/(DENmin-DENmax)-(DT-DTmin)/(DTmax-DTmin)；
wherein DEN represents a density log value, and DENmax and DENmin represent maximum and minimum values of the density log value; DTmax and DTmin represent the maximum and minimum values of the sonic jet lag log values.
Further, the specific method for constructing the curve combination parameter dL3 by using the flushing band resistivity RXO and the invaded band resistivity RI in the 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.
Further, in the step S5, the specific step of performing the primary discrimination by using the DL2 index of the reaction granularity and the DL3 index of the reaction permeability is as follows:
s51, constructing a junction graph A through a DL2 index of reaction granularity and a DL3 index of reaction permeability, and determining classification boundaries a and b through the constructed junction graph A, wherein 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 are met, judging that the lithology of the rock belongs to lithology set A with good coarse grain permeability; when DL 2. Ltoreq.a or DL 3. Ltoreq.b is satisfied, it is determined that the lithology of the rock belongs to lithology set B having poor permeability of fine particles.
Further, in the step S5, the specific steps of performing the secondary discrimination by using the DL1 index of the reactive dolomite component and the density curve DEN are as follows:
s53, constructing a junction graph B according to DL1 indexes of reactive dolomite components and a density curve DEN aiming at rocks in the lithology set A, and determining classification boundaries ci and dj according to the junction graph B, wherein ci is the classification boundary of the DL1 indexes, i is the number of the classification boundaries of the DL1 indexes, dj is the classification boundary of the density curve DEN, and j is the number of the classification boundaries of the density curve DEN;
s54, judging the lithology type of the corresponding rock in the lithology set A according to the DL1 index corresponding to different lithology in the lithology set A and the distribution characteristics of the density curve DEN in the intersection graph;
s55, constructing a junction graph C according to DL1 indexes of reactive dolomite components and a density curve DEN aiming at rocks in the lithology set B, and determining classification boundaries ei and fj according to the junction graph C, wherein ei is the classification boundary of the DL1 indexes, i is the number of the classification boundaries of the DL1 indexes, fj is the classification boundary of the density curve DEN, and j is the number of the classification boundaries of the density curve DEN;
s56, judging the lithology type of the corresponding rock in the lithology set B according to the DL1 index corresponding to different lithology in the lithology set B and the distribution characteristics of the density curve DEN in the intersection graph.
1) According to the method for identifying the lithology logging of the mixed-accumulation shale reservoir, a petrophysical model is built according to rock components, a logging response forward model corresponding to rock is built by utilizing linear superposition, a forward evolution sensitive logging curve is optimized through the model, a sensitive curve combination parameter is built by utilizing the optimized sensitive logging curve, and the problem that the single logging curve has low accuracy in identifying the lithology of the mixed-accumulation shale due to multi-factor superposition of multi-mineral components, frequent interactions of different lithology and strong oil-containing heterogeneity is effectively solved; meanwhile, the quantitative discrimination of main lithology is realized by constructing sensitive curve combination parameters reflecting granularity and content of special stratum components and combining a pattern intersection means, the discrimination precision is high and can reach more than 75%, and only conventional curves such as density, neutrons, acoustic time difference, resistivity and the like are needed, so that the interpretation cost and period are effectively reduced.
Drawings
FIG. 1 is a flow chart of a method for identifying a lithology logging of a mixed-formation shale reservoir in the present embodiment;
FIG. 2 is a diagram showing the intersection of DL1 index and skeleton density in the present embodiment;
FIG. 3 is a diagram showing the intersection of the acoustic time difference DT and the density curve DEN according to the present embodiment;
fig. 4 is a diagram showing the intersection of DL2 and DL3 indexes in the present embodiment;
FIG. 5 is a diagram showing the intersection of DL1 and DEN for the lithology set A secondary discrimination in this example;
FIG. 6 is a diagram showing the intersection of DL1 and DEN for the lithology set B secondary discrimination in this example;
FIG. 7 is a diagram showing the comparison between the lithologic log recognition result and the slice identification in the present embodiment.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, based on the embodiments of the invention, which are apparent to those of ordinary skill in the art without inventive faculty, are intended to be within the scope of the invention.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, based on the embodiments of the invention, which are apparent to those of ordinary skill in the art without inventive faculty, are intended to be within the scope of the invention.
Example 1:
in the embodiment, taking a formation of a Gissage concave reed canary group in a Xinjiang oilfield as an example, the reed canary group is formed in a delta front edge phase-salty lake phase deposition environment, sediment is subjected to the actions of external land source debris input, chemical deposition in a basin, volcanic action and the like, fine-grained mixed rock is very developed, and lithology combination is mainly composed of dark mudstone, siltstone, carbonate rock and transitional lithology. The reed canary-grass ditch group has multiple mineral components and complex lithology, and can be divided into silty rocks, cloud silty rocks, carbonate rocks and mudstones according to granularity and carbonate mineral content, and can be further divided into silty rocks, argillite silty rocks, cloud silty rocks, sand dust dolomite, mud crystal dolomite, silty dolomite, dolomite mudstones, limestone mudstones, silty mudstones, carbonaceous mudstones and the like, and the dominant reservoir mainly corresponds to the silty rocks, cloud silty rocks and sand dust dolomite.
Referring to fig. 1, the method specifically disclosed in this embodiment for identifying a lithology logging of a mixed-formation shale reservoir includes the following steps:
s1, lithology classification: adopting a rock core observation, oiliness description and sheet identification method, and carrying out main lithology classification by combining rock granularity and dolomite relative content and composition; the method aims at primarily dividing lithology types by means of core sampling and the like, and considering factors such as development scale, oiliness, logging response characteristic difference and the like of main lithology in stratum during classification. Lithology classification in this embodiment includes silty sandstone, cloud silty sandstone, mudstone dolomite, sand dust dolomite, dolomite mudstone and carbonaceous mudstone.
S2, data arrangement statistics: according to the historical core experiment result, counting the range parameters of mineral composition, physical property parameters and organic matter abundance under different lithology, and counting the conventional logging curves of the corresponding lithology; the collected data points of each lithology are required to be more than 50 samples in the step, so that the data statistics are guaranteed to be representative. If the number of samples is insufficient, targeted sampling is required, and relevant experimental tests are carried out, and generally, all-rock mineral analysis, organic carbon content TOC and porosity tests can be carried out according to SY/T5163-2010 clay minerals and common non-clay minerals in sedimentary rocks, GB/T19145-2003 total organic carbon determination in sedimentary rocks, GB/T34533-2017 shale porosity and permeability determination. The step of collecting conventional logging data including logging data such as well diameter, natural gamma, resistivity, acoustic time difference, density, neutrons and the like, and after the data is collected, firstly carrying out deep homing on experimental data points and counting logging curve values corresponding to the experimental data points. As shown in table 1, the distribution ranges of TOC of different lithologies, mineral components and organic abundance are shown, wherein the numerical values in the table are respectively the minimum value-maximum value, and the numerical values in brackets are the distribution mean values. As can be seen from table 1, the sandy dolomite, cloud siltstone and siltstone are dominant reservoirs, and the three lithology has high porosity, low clay and low organic matter abundance, wherein the sandy dolomite has the highest dolomite content; the physical properties of the dolomitic mudstone, the dolomitic mudstone and the carbonaceous mudstone are poor, wherein the organic matter abundance and the clay content of the dolomitic mudstone and the carbonaceous mudstone are high.
TABLE 1
S3, logging curves are preferably: respectively constructing corresponding rock physical models according to different rock components, wherein the rock components comprise feldspar, quartz, carbonate, organic matters, clay and pore fluid, respectively carrying out linear superposition according to the rock physical models of the different rock components, establishing a logging response forward model of the corresponding rock, respectively fitting logging theoretical response values of the different rock components according to different lithologies, comparing the correlation between the logging theoretical response values and actual logging curve values, and selecting a logging curve with the forward result precision higher than a correlation threshold value P to participate in lithology recognition;
the specific method for building the logging response forward model by utilizing the petrophysical models of different rock components to carry out linear superposition comprises the following steps:
where i is a single rock component of the rock, i = 1,2,3,4,5 or 6, where 1-6 represent feldspar, quartz, carbonate, organic matter, clay and pore fluid, respectively, in the rock component; vi represents the relative component content of rock component i; GR represents a natural gamma log value, GRi represents a natural gamma log value for the rock component i; DT represents the acoustic time difference log values, and DTi represents the acoustic time difference log values for the rock component i; CNL represents neutron log values, CNLi represents neutron log values for rock component i; DEN represents the density log value and DENi represents the density log value of the rock constituent i.
It should be noted that, in this embodiment, the relative component content Vi of the rock component i in different rocks is calculated by collecting a sample core, and performing detection analysis on the sample core to obtain test results of the corresponding rock component, the corresponding organic matter abundance and the corresponding porosity.
S4, obtaining variable porosity logging curve series values of different lithologies by using a logging response forward model, respectively judging logging curves sensitive to dolomite/non-dolomite and coarse grain/fine grain lithology responses, and respectively combining the logging curves sensitive to the responses in the corresponding lithologies to obtain combination curve parameters dL1 and dL2 sensitive to dolomite components and granularity; utilizing flushing zone resistivity RXO and invasion zone resistivity RI in a conventional logging curve to construct a curve combination parameter dL3 reflecting the permeability of the mixed rock stratum;
in this embodiment, the specific method for respectively combining the response-sensitive logging curves in the corresponding lithology to obtain the combination curve parameters dL1 and dL2 is as follows:
s41, changing the porosity successively according to the average value of rock components and organic matter abundance under different lithology in the step S2, and recalculating the relative content of the corresponding rock components; calculating a sound wave time difference logging curve value DT, a density logging curve value DEN and a neutron logging curve value CNL under different porosities by using a logging response forward model to obtain a logging curve series value of the corresponding lithology under the condition of pore change;
s42, constructing a combination curve parameter dL1 by using logging curve values of simultaneous sound wave time difference, density and neutrons:
dL1＝[(CNL-CNLmax)/(CNLmin-CNLmax)+(DT-DTmin)/(DTmax-DTmin)]/2-(DEN-DENmin)/(DENmax-DENmin)；
wherein CNLmax and CNLmin represent the maximum and minimum values of neutron log values; DTmax and DTmin represent the maximum and minimum values of the sonic jet lag log values; denmax and Denmin represent the maximum and minimum values of the density log values; as shown in fig. 2, the present embodiment can effectively reflect the variation of the dolomite component according to the constructed dL1 parameter, and for dolomite, dolomite mudstone, cloud siltstone, mudstone, etc., as the dolomite component decreases, dL1 gradually changes from a negative value to a positive value.
S43, constructing a combination curve parameter dL2 by using log curve values of simultaneous sound wave time difference and density:
dL2＝(DEN-DENmax)/(DENmin-DENmax)-(DT-DTmin)/(DTmax-DTmin)；
wherein DEN represents a density log value, and DENmax and DENmin represent maximum and minimum values of the density log value; DTmax and DTmin represent the maximum and minimum values of the sonic jet lag log values; referring to fig. 3, in this embodiment, when the log values DEN are the same, coarse lithologies such as sand dolomite, cloud siltstone and siltstone have relatively low sonic time difference log values, so that the log values of simultaneous sonic time difference and density can effectively reflect the granularity.
Finally, constructing curve combination parameters dL3 according to mud properties and by using the flushing zone resistivity RXO and the invaded zone resistivity RI obtained by logging in the step S2: when the mud is non-oil based, dL3=1-RXO/RI; when the mud is oil-based, dL3=1-RI/RXO.
S5, judging lithology according to rock components, granularity and permeability and combining a density curve DEN of a corresponding rock core, namely firstly judging the lithology of the rock into a lithology set A with good coarse-grain permeability and a lithology set B with poor fine-grain physical property by utilizing a DL2 index of reaction granularity and a DL3 index of reaction permeability for one time; and then, respectively carrying out secondary discrimination on the lithology set A and the lithology set B by using the DL1 index and the density curve DEN of the reactive dolomite component.
Specifically, the specific step of performing the primary discrimination by using the DL2 index of the reaction granularity and the DL3 index of the reaction permeability in the step S5 is as follows:
s51, constructing a junction graph A through a DL2 index of reaction granularity and a DL3 index of reaction permeability, and determining classification boundaries a and b through the constructed junction graph A, wherein 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 are met, judging that the lithology of the rock belongs to lithology set A with good coarse grain permeability; when DL 2. Ltoreq.a or DL 3. Ltoreq.b is satisfied, it is determined that the lithology of the rock belongs to lithology set B having poor permeability of fine particles.
Further, in the step S5, the specific steps of performing the secondary discrimination by using the DL1 index of the reactive dolomite component and the density curve DEN are as follows:
s53, constructing a junction graph B according to DL1 indexes of reactive dolomite components and a density curve DEN aiming at rocks in the lithology set A, and determining classification boundaries ci and dj according to the junction graph B, wherein ci is the classification boundary of the DL1 indexes, i is the number of the classification boundaries of the DL1 indexes, dj is the classification boundary of the density curve DEN, and j is the number of the classification boundaries of the density curve DEN;
s54, judging the lithology type of the corresponding rock in the lithology set A according to the DL1 index corresponding to different lithology in the lithology set A and the distribution characteristics of the density curve DEN in the intersection graph;
s55, constructing a junction graph C according to DL1 indexes of reactive dolomite components and a density curve DEN aiming at rocks in the lithology set B, and determining classification boundaries ei and fj according to the junction graph C, wherein ei is the classification boundary of the DL1 indexes, i is the number of the classification boundaries of the DL1 indexes, fj is the classification boundary of the density curve DEN, and j is the number of the classification boundaries of the density curve DEN;
s56, judging the lithology type of the corresponding rock in the lithology set B according to the DL1 index corresponding to different lithology in the lithology set B and the distribution characteristics of the density curve DEN in the intersection graph.
For example, in the lithology recognition process for the delta front phase formed by the reed canary grass group in the salty lake phase deposition environment:
lithology is first discriminated into two major categories using the indicators dL2 and dL3 reflecting granularity and permeability: a lithology set A (mainly comprising siltstone and sand dolomite) with good coarse grain permeability and a lithology set B (mainly comprising mud rock and mud crystal dolomite) with poor fine grain physical properties are determined to be a lithology set B through dL2 and dL3 intersection graphs, as shown in figure 4, wherein the lithology set A meets dL2>0 and dL3>0.05, and other lithology types which do not meet the lithology set A are judged to be the lithology set B;
the dL1 and density log DEN reflecting dolomite component indices are then further subdivided. Boundaries for each type of lithology in lithology set a and lithology set B are determined in dL1-DEN junction graphs, respectively, as shown in fig. 5 and 6. In lithology set A, the sand dolomite meets dL1<0, cloud siltstone satisfies 0<dL1<0.2 and DEN>2.35g/cm 3 The balance of siltstone. In lithology set B, the mudstone dolomite meets dL1<0 and DEN>2.45g/cm 3 Cloud mudstone satisfies dL1<0.15 and DEN>2.4g/cm 3 The rest is carbonaceous mudstone.
The lithology fine recognition is carried out on the mixed accumulation type stratum of the Gissary reed canary ditch group by utilizing the steps, as shown in fig. 7, the lithology of the well logging recognition is better consistent with the observation and description result of the rock core, the 9 positions of siltstone and cloud siltstone are identified by the thin sheet, and the recognition accuracy of the 7 positions of the well logging recognition is 78%; the sand and mud crystal dolomite are identified by the thin sheet to be 3 and 7 respectively, and the 3 and 5 positions are identified by the well logging of the method, so that the coincidence rate reaches 80 percent. Overall log identification is more consistent with slice identification.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.
Claims (9)
1. The method for identifying the rock logging of the mixed-accumulation shale reservoir is characterized by comprising the following steps of:
s1, lithology classification: adopting a rock core observation, oiliness description and sheet identification method, and carrying out main lithology classification by combining rock granularity and dolomite relative content and composition;
s2, data arrangement statistics: according to the historical core experiment result, counting the range parameters of mineral composition, physical property parameters and organic matter abundance under different lithology, and counting the conventional logging curves of the corresponding lithology;
s3, logging curves are preferably: respectively constructing corresponding rock physical models according to different rock components, respectively carrying out linear superposition according to the rock physical models of the different rock components, establishing a logging response forward model of the corresponding rock, respectively fitting logging theoretical response values of the different rock components according to different lithologies, comparing the correlation between the logging theoretical response values and actual logging curve values, and selecting a logging curve with forward result precision higher than a correlation threshold value P to participate in lithology recognition;
s4, obtaining variable porosity logging curve series values of different lithologies by using a logging response forward model, respectively judging logging curves sensitive to dolomite/non-dolomite and coarse grain/fine grain lithology responses, and respectively combining the logging curves sensitive to the responses in the corresponding lithologies to obtain combination curve parameters dL1 and dL2 sensitive to dolomite components and granularity; utilizing flushing zone resistivity RXO and invasion zone resistivity RI in a conventional logging curve to construct a curve combination parameter dL3 reflecting the permeability of the mixed rock stratum;
s5, judging lithology according to rock components, granularity and permeability and combining a density curve DEN of a corresponding rock core, namely firstly judging the lithology of the rock into a lithology set A with good coarse-grain permeability and a lithology set B with poor fine-grain physical property by utilizing a DL2 index of reaction granularity and a DL3 index of reaction permeability for one time; and then, respectively carrying out secondary discrimination on the lithology set A and the lithology set B by using the DL1 index and the density curve DEN of the reactive dolomite component.
2. The method for identifying the lithology logging of the mixed-accumulation shale reservoir according to claim 1, which is characterized in that: the lithology classification and division in the step S1 comprises siltstone, cloud siltstone, mud crystal dolomite, sand chip dolomite, dolomite mudstone and carbonaceous mudstone.
3. The method for identifying the lithology logging of the mixed-accumulation shale reservoir according to claim 1, which is characterized in that: the rock components in the step S3 comprise feldspar, quartz, carbonate, organic matters, clay and pore fluid.
4. The method for identifying the lithology logging of the mixed-accumulation shale reservoir according to claim 1, which is characterized in that: in the step S3, rock physical models of different rock components are utilized for linear superposition, and the specific method for building the logging response forward model is as follows:
where i is a single rock component of the rock, i = 1,2,3,4,5 or 6, where 1-6 represent feldspar, quartz, carbonate, organic matter, clay and pore fluid, respectively, in the rock component; vi represents the relative component content of rock component i; GR represents a natural gamma log value, GRi represents a natural gamma log value for the rock component i; DT represents the acoustic time difference log values, and DTi represents the acoustic time difference log values for the rock component i; CNL represents neutron log values, CNLi represents neutron log values for rock component i; DEN represents the density log value and DENi represents the density log value of the rock constituent i.
5. The method for identifying the lithology logging of the mixed-formation shale reservoir according to claim 4, which is characterized by comprising the following steps: and in the step S3, a sample rock core is acquired, the sample rock core is detected and analyzed, rock components, organic matter abundance and porosity test results of the rock core are obtained, and the relative component content Vi of the rock component i in different rocks is calculated.
6. The method for identifying the lithology logging of the mixed-accumulation shale reservoir according to claim 1, which is characterized in that: in the step S4, the specific method for respectively combining the response-sensitive logging curves in the corresponding lithology to obtain the combination curve parameters dL1 and dL2 is as follows:
s41, changing the porosity successively according to the average value of rock components and organic matter abundance under different lithology in the step S2, and recalculating the relative content of the corresponding rock components; calculating a sound wave time difference logging curve value DT, a density logging curve value DEN and a neutron logging curve value CNL under different porosities by using a logging response forward model to obtain a logging curve series value of the corresponding lithology under the condition of pore change;
s42, constructing a combination curve parameter dL1 by using logging curve values of simultaneous sound wave time difference, density and neutrons:
dL1＝[(CNL-CNLmax)/(CNLmin-CNLmax)+(DT-DTmin)/(DTmax-DTmin)]/2-(DEN-DENmin)/(DENmax-DENmin)；
wherein CNLmax and CNLmin represent the maximum and minimum values of neutron log values; DTmax and DTmin represent the maximum and minimum values of the sonic jet lag log values; denmax and Denmin represent the maximum and minimum values of the density log values;
s43, constructing a combination curve parameter dL2 by using log curve values of simultaneous sound wave time difference and density:
dL2＝(DEN-DENmax)/(DENmin-DENmax)-(DT-DTmin)/(DTmax-DTmin)；
wherein DEN represents a density log value, and DENmax and DENmin represent maximum and minimum values of the density log value; DTmax and DTmin represent the maximum and minimum values of the sonic jet lag log values.
7. The method for identifying the formation logging of the mixed-formation shale reservoir according to claim 1, wherein the specific method for constructing the curve combination parameter dL3 by using the flushing zone resistivity RXO and the invaded zone resistivity RI in the 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.
8. The method for identifying the lithology logging of the mixed-accumulation shale reservoir according to claim 1, wherein the specific step of performing the primary discrimination by using the DL2 index of the reaction granularity and the DL3 index of the reaction permeability in the step S5 is as follows:
s51, constructing a junction graph A through a DL2 index of reaction granularity and a DL3 index of reaction permeability, and determining classification boundaries a and b through the constructed junction graph A, wherein 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 are met, judging that the lithology of the rock belongs to lithology set A with good coarse grain permeability; when DL 2. Ltoreq.a or DL 3. Ltoreq.b is satisfied, it is determined that the lithology of the rock belongs to lithology set B having poor permeability of fine particles.
9. The method for identifying the lithology logging of the mixed-accumulation shale reservoir according to claim 8, wherein the specific steps of performing the secondary discrimination by using the DL1 index of the reaction dolomite component and the density curve DEN in the step S5 are as follows:
s53, constructing a junction diagram B aiming at the rocks in the lithology set A through the DL1 index of the reactive dolomite component and the density curve DEN, and determining a classification boundary c through the junction diagram B i And d j Wherein c i I is the number of the classification boundaries of the DL1 index and d is the classification boundary number of the DL1 index j The j is the number of the classification boundaries of the density curve DEN;
s54, judging the lithology type of the corresponding rock in the lithology set A according to the DL1 index corresponding to different lithology in the lithology set A and the distribution characteristics of the density curve DEN in the intersection graph;
s55, constructing an intersection graph C aiming at the rocks in the lithology set B through the DL1 index of the reactive dolomite component and the density curve DEN, and determining a classification boundary e through the intersection graph C i And f j Wherein e is i Is the classification boundary of the DL1 index, i is the number of the classification boundaries of the DL1 index, f j The j is the number of the classification boundaries of the density curve DEN;
s56, judging the lithology type of the corresponding rock in the lithology set B according to the DL1 index corresponding to different lithology in the lithology set B and the distribution characteristics of the density curve DEN in the intersection graph.
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