CN112505154A - Shale reservoir mineral component content analysis and lithofacies identification characterization method - Google Patents
Shale reservoir mineral component content analysis and lithofacies identification characterization method Download PDFInfo
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Abstract
The embodiment of the invention provides a shale reservoir mineral component content analysis and lithofacies identification characterization method, which comprises the following steps: establishing an inversion analytical model of the content of the mineral components in the shale reservoir, and solving the inversion analytical model to obtain analytical calculation results of all minerals; and obtaining a lithofacies recognition result through a mineral component decision tree based on the analysis calculation result of each mineral. According to the embodiment of the invention, the conventional logging data is used for establishing the shale reservoir mineral component content inversion analysis model, the program compiling is realized by the blind source simulated annealing method for matching iterative solution, the relative volume content of each mineral is analyzed, and the comparison with the core analysis result is carried out.
Description
Technical Field
The invention relates to the field of oil and gas exploration and development, in particular to a shale reservoir mineral component content analysis and lithofacies identification characterization method.
Background
For a shale reservoir, the analysis of the content of mineral components can not only help to divide lithology, but also identify good reservoir and oil-gas reservoir and judge the brittleness of the reservoir, so that the method is an effective method for searching for the 'sweet spot' of the reservoir. And identifying and representing lithofacies, judging reservoir development layer sections and making an effective oil extraction scheme.
At present, there are roughly three types of methods for analyzing the content of mineral components: one is that a multivariate statistical regression method is utilized to construct a multi-parameter calculation model for mineral component content logging in a research area, but for a reservoir with strong formation heterogeneity, logging data multi-parameter values are used to represent a certain mineral, which inevitably brings large errors and is poor in correlation; the other type is based on the main mineral types of the research area, a volume model of three main minerals is established by utilizing logging data, the volume model is solved by utilizing an optimized mathematical method, and then the mineral content of the research area is obtained, and the mineral components analyzed by the method are limited at present; the last type is an experimental method, mainly using X-ray diffraction to measure mineral content, the method is only suitable for core intervals, and the applicability is poor.
The methods for identifying and dividing lithofacies mainly include three types: one is that lithofacies are carved according to rock core data, the method is only suitable for a rock core section, and the applicability is poor; the other is to divide various sedimentary facies through a logging curve, namely to identify and divide lithofacies according to logging facies; the last category is that the well logging response value of the known lithofacies is used as sample data, neural network lithofacies training is carried out, and lithofacies identification and division are carried out.
Disclosure of Invention
In order to solve the above problems, an embodiment of the present invention provides a shale reservoir mineral component content analysis and lithofacies identification characterization method, which overcomes or at least partially solves the above problems, and the method includes: establishing an inversion analytical model of the content of the mineral components in the shale reservoir, and solving the inversion analytical model to obtain analytical calculation results of all minerals; and obtaining a lithofacies recognition result through a mineral component decision tree based on the analysis calculation result of each mineral.
The method for establishing the inversion analysis model of the shale reservoir mineral component content comprises the following steps: selecting a sensitive logging curve to establish an inversion analysis model of the content of the mineral components in the shale reservoir; wherein the sensitive log comprises acoustic moveout AC, compensated density DEN, and compensated neutron CNL.
Wherein solving the inversion analytic model comprises: and according to the constructed objective function, performing matching iterative solution by adopting a blind source simulated annealing method.
Wherein the objective function is:
wherein F (v) is an error index curve; pjIs a weight factor coefficient; k is a norm; a. thejiLogging response values for each mineral skeleton and pore fluid; y isjMeasuring the logging value of the actual stratum; viIs the relative volume percentage of each mineral and pore, namely the volume content and the porosity of each mineral to be analyzed.
Wherein, the matching iterative solution is carried out by adopting a 'blind source' simulated annealing method, and the method further comprises the following steps: and for the logging response value of each mineral, increasing the limiting conditions of the maximum value and the minimum value in the iterative solution.
Wherein, before the analyzing the calculation result based on each mineral, the method further comprises: and counting the maximum value, the minimum value and the average value of the clay mineral volume relative content, the long-English mass volume relative content and the gray matter volume relative content based on the volume content of each mineral, and meanwhile, counting the frequency of each mineral content distribution interval to construct the mineral component decision tree.
Wherein, the lithofacies recognition result is obtained through a mineral composition decision tree, which comprises the following steps: dividing and determining two intervals according to the maximum value, the minimum value, the average value and a distribution interval frequency diagram of the clay mineral volume relative content, wherein the two differences respectively correspond to the clay mineral volume relative content of 14% -48% and 48% -72%; identifying as a mudstone phase for an interval with the relative clay volume content of 48-72%; for the interval with the clay relative volume content of 14% -48%, combining the average value and the frequency distribution characteristics, and dividing the interval into two intervals of 14% -40% and 40% -48% according to the clay relative volume content by taking the clay relative volume content of 40% as a subdivision limit; identifying an interval with 14-40% of clay content, and dividing two intervals with 0-18% and 18-31% of gray matter relative volume content according to the maximum value, the minimum value and the average value of the gray matter relative volume content, wherein the clay is identified as a argillaceous limestone phase when the gray matter content is 18-31%; when the content of the gray matter is 0-18%, according to the maximum value, the minimum value and the average value of the long-English matter content, the clay is identified as argillaceous silty sandstone phase when the clay content is 14-40%, the gray matter content is 0-18% and the long-English matter content is 6-38%, and the clay is identified as silty sandstone phase when the clay content is 14-40%, the gray matter content is 0-18% and the long-English matter content is 38-60%; identifying an interval with the clay content of 40% -48%, subdividing the interval into three intervals of 6% -30%, 30% -38% and 38% -60% according to the maximum value, the minimum value and the average value of the long-English content, identifying the interval as silty shale phase when the long-English content is 30% -38%, and identifying the interval as silty sandstone phase when the long-English content is 38% -60%; when the content of the longing matter is 6-30%, the argillaceous limestone is needed to be subdivided by combining the content of the gray matter, when the content of the clay is 40-48%, the content of the longing matter is 6-30%, and the content of the gray matter is 0-18%, the argillaceous lithofacies are identified, when the content of the clay is 40-48%, the content of the longing matter is 6-30%, and the content of the gray matter is 18-31%.
Wherein the inverse analytical model comprises:
where DEN is the compensated density log of the formation; AC is the sonic time difference log of the stratum; CNL is the compensated neutron log value of the stratum; vQuartz、VFeldspar、VCalcite、VClay、VThe rest of the minerals、VPores ofQuartz, feldspar, calcite, clay, other minerals and pores respectively account for volume percentage; ACQuartz、DENQuartz、CNLQuartzThe acoustic time difference, density and neutron logging response value of quartz; ACFeldspar、DENFeldspar、CNLFeldsparThe acoustic time difference, density and neutron logging response value of the feldspar are obtained; ACCalcite、DENCalcite、CNLCalciteThe acoustic time difference, density and neutron logging response value of calcite are obtained; ACClay、DENClay、CNLClayAcoustic moveout, density, neutron log response, AC, for clayThe rest of the minerals、DENThe rest of the minerals、CNLThe rest of the mineralsIs the acoustic time difference, density and neutron logging response value, AC of other mineralsPores of、DENPores of、CNLPores ofThe acoustic moveout, density and neutron logging response values of the pore fluid are shown.
The method for analyzing the content of the mineral components in the shale reservoir and identifying and characterizing the lithofacies, provided by the embodiment of the invention, has at least the following effects:
(1) the method is effective and accurate, greatly improves the efficiency of well logging interpretation and reduces the cost of the well logging interpretation.
(2) The mineral components are analyzed by using the acoustic wave time difference, the compensation density and the compensation neutrons, so that the coring cost can be saved, accurate mineral component content data can be provided for a non-coring well, and the method provides guidance and basis for efficient field exploration and development.
(3) The method for establishing the mineral component decision tree realizes the rapid identification and accurate division of the logging lithofacies, and provides reliable theoretical basis and guidance for field exploration and production.
(4) The method has wide application prospect, and the mineral component analysis and lithofacies identification characterization method can be widely applied to evaluation and dessert prediction of compact reservoirs such as shale and compact sandstone, and brings economic benefit for guiding efficient operation of field exploration and development.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from these without inventive effort.
Fig. 1 is a schematic flow chart of a shale reservoir mineral component content analysis and lithofacies identification characterization method according to an embodiment of the present invention;
FIG. 2 is a flow chart of a blind source simulated annealing method according to an embodiment of the present invention;
FIG. 3 is a cross-plot of the iterative analysis of the relative volume of quartz and the actual measurement of the core provided by an embodiment of the present invention;
fig. 4 is a cross-plot comparing the relative volume of the iteratively analyzed feldspar and the actual measurement of the core according to the embodiment of the present invention;
fig. 5 is a cross-plot comparing the relative calcite volume and the measured core volume by iterative analysis according to an embodiment of the present invention;
fig. 6 is a cross-plot comparing the relative volume of the iterative analytic clay with the actual measurement of the core according to the embodiment of the present invention;
FIG. 7 is a cross-plot comparing the relative volume of the remaining minerals with the actual measurement of the core by iterative analysis, provided by an embodiment of the present invention;
FIG. 8 is a frequency chart of clay mineral content distribution intervals according to an embodiment of the present invention;
FIG. 9 is a flow chart of a method for identifying a divided lithofacies using a mineral composition decision tree according to an embodiment of the present invention;
fig. 10 is a graph illustrating the effect of the method for analyzing mineral components and identifying lithofacies in the application of the method for characterizing rock phase identification in the actual well according to the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments, but not all embodiments, of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
At present, the well logging interpretation evaluation technology of a conventional reservoir is not suitable for a complex compact reservoir, particularly a shale reservoir, not only has complex lithology and variable skeleton minerals and strong heterogeneity, but also plays a crucial role in the evaluation of the reservoir and the identification of 'desserts' due to the content of mineral components, so that the key problems to be solved urgently in the oil and gas exploration and development of the current compact reservoir are solved by analyzing the content of the mineral components in the shale reservoir and identifying the type of a characteristic lithofacies. Since the study area often lacks imaging well log data, it is of great interest to use conventional well log data to study the interpretation of mineral constituent content and the identification characterization of lithofacies.
Based on the current situation, the embodiment of the invention provides a new method for analyzing the content of the mineral components, namely a 'blind source' simulated annealing analysis method, aiming at the problems of defects and lithofacies identification of the content analysis of the mineral components, i.e. the content of a plurality of (at least 5) mineral components is analyzed through iterative matching without sample data, and then the lithofacies type is judged.
Referring to fig. 1, an embodiment of the present invention provides a shale reservoir mineral composition content analysis and lithofacies identification characterization method, including but not limited to the following steps:
101, establishing an inversion analysis model of the content of the mineral components in the shale reservoir, and solving the inversion analysis model to obtain an analysis calculation result of each mineral;
and 102, analyzing and calculating results of all minerals, and obtaining lithofacies identification results through a mineral component decision tree.
The method for the step 101 may further include the following steps:
(1) in order to analyze the content of mineral components more accurately, firstly, a sensitive logging curve, namely a logging curve closely related to the content of mineral components is optimized, and through sample data analysis, as an optional embodiment, three logging data, namely acoustic moveout AC, compensation density DEN and compensation neutron CNL, are optimized, and an inversion analysis model of the content of mineral components in a reservoir is established as follows:
0≤Vi≤1 ∑iVi＝1 (1)
where DEN is the offset density log of the formation in g/cm3(ii) a AC is the acoustic time difference logging value of the stratum, and the unit is mu s/m; CNL is the compensated neutron log value of the stratum, and the unit is; vQuartz、VFeldspar、VCalcite、VClay、VThe rest of the minerals、VPores ofThe volume percentages of quartz, feldspar, calcite, clay, other minerals and pores are respectively, and the unit is; ACQuartz、DENQuartz、CNLQuartzThe acoustic time difference, density and neutron logging response value of quartz; ACFeldspar、DENFeldspar、CNLFeldsparThe acoustic time difference, density and neutron logging response value of the feldspar are obtained; ACCalcite、DENCalcite、CNLCalciteThe acoustic time difference, density and neutron logging response value of calcite are obtained; ACClay、DENClay、CNLClayAcoustic moveout, density, neutron log response, AC, for clayThe rest of the minerals、DENThe rest of the minerals、CNLThe rest of the mineralsIs the acoustic time difference, density and neutron logging response value, AC of other mineralsPores of、DENPores of、CNLPores ofThe acoustic moveout, density and neutron logging response values of the pore fluid are shown.
This equation is expressed in a simplified form of a matrix, namely: y isj＝Aji×Vi (2)
(2) And (3) solving the multi-mineral component content analysis model, wherein all parameters on the right side of the model equal sign are unknown quantities, the logging response value on the left side of the equal sign is known quantity, and the unknown quantity is too much, so that an objective function (formula 3) is established at first and is a more conventional original objective function.
F(v)＝∑j(Yj-Aji×Vi)2 (3)
However, the original objective function has poor matching performance in iterative computation, global optimality is difficult to guarantee, and the computation result fluctuates, so the invention adopts the improved objective function as an optional embodiment, and the objective function is as follows:
wherein the norm K value is 1-15, and the superposition average is carried out to eliminate the calculation fluctuation PjAs a weight coefficient, performing overdetermined iteration according to the statistical core analysis value to obtain a weight coefficient AjiLog response values, V, for each mineral skeleton and pore fluidiThe relative values of the volumes of the minerals and the porosity are shown.
It should be noted that improving the objective function has the following advantages: 1. introducing a weight factor coefficient Pj, adjusting contribution of each part to achieve optimal matching and ensure global optimization; 2. introducing a norm K, ensuring global optimization and eliminating fluctuation of a calculation result; 3. sensitive effective logging data can be selected to be input, and non-sensitive logging data can not be input; 4. and setting an error indication curve F (v) to indicate the quality of the calculation result at any time.
(3) Designing a better objective function (formula 4) for a multi-mineral component content analytical model (formula 1), carrying out matching iterative solution on the objective function by adopting a blind source simulated annealing method, and writing a corresponding MATLAB analytical program to realize the solution process, wherein the specific calculation flow is shown in figure 2.
In order to ensure that the analytic result is more accurate and optimal, for the logging response value of each mineral, a limiting condition of a maximum value and a minimum value is added during iterative analytic calculation, as shown in table 1.
TABLE 1 log response interval values for each mineral
(4) And comparing the analysis calculation result of each mineral with the analysis result of the rock core experiment, and verifying the effectiveness of the method. And (3) respectively establishing intersection graphs of quartz, feldspar, calcite, clay, other minerals and pores by taking the analytic calculation result as an x axis and the actual core analysis result as a y axis as shown in figures 3-7. Analysis of FIGS. 3-7 shows that: all mineral calculation results and core analysis results are distributed on a diagonal line of y ═ x, which shows that mineral analysis calculation results are good and are consistent with actual core analysis data.
The method for the step 102 may further include the following steps:
(5) and (3) calculating results by using mineral analysis, namely counting the volume relative content of clay minerals, the volume relative content of long and short quartz (quartz + feldspar) and the maximum value, the minimum value and the average value of the volume relative content of gray matter (calcite) based on the volume content of each mineral, and counting the frequency of distribution intervals of the content of each mineral, wherein the clay minerals are taken as an example, as shown in fig. 8, the method lays a foundation for constructing a mineral component decision tree to identify and characterize a lithofacies method.
(6) A method for identifying and characterizing lithofacies by constructing a mineral component decision tree by taking clay minerals as starting boundary points is shown in fig. 9, namely: firstly, according to the maximum value (75%), the minimum value (14%), the average value (48%) and a distribution interval frequency chart (figure 8) of the clay mineral volume relative content, two intervals are determined through division, wherein the two intervals are respectively 14% -48% and 48% -72% of the clay mineral volume relative content, and the clay mineral volume relative content of 48% -72% is a typical characteristic of a shale phase, namely the shale phase. Secondly, clay with 14-48% of relative volume content still needs to be subdivided. The clay relative volume content is 40% as a subdivision limit by combining the average value and the frequency distribution characteristics, and is subdivided into two intervals of 14% -40% and 40% -48% according to the clay relative volume content. Then, an interval with the clay content of 14% -40% is firstly identified, and two intervals with the gray matter relative volume content of 0% -18% and 18% -31% are distinguished according to the maximum value (31%), the minimum value (0%) and the average value (18%) of the gray matter relative volume content, wherein the argillaceous limestone phase is obtained when the gray matter content is 18% -31%. And when the content of the gray matter is 0-18%, subdividing according to the content of the long quartz matter, and according to the maximum value (60%), the minimum value (6%) and the average value (38%) of the content of the long quartz matter (quartz relative volume + feldspar relative volume), a argillaceous silty sandstone phase is formed when the content of the clay is 14-40%, the content of the gray matter is 0-18%, the content of the long quartz matter is 6-38%, and a silty sandstone phase is formed when the content of the clay is 14-40%, the content of the gray matter is 0-18%, and the content of the long quartz matter is 38-60%. Finally, identifying the interval with the clay content of 40% -48%, subdividing the interval into three intervals of 6% -30%, 30% -38% and 38% -60% according to the maximum value (60%), the minimum value (6%) and the average value (38%) of the content of long quartz (relative volume of quartz + relative volume of feldspar), a silty shale phase when the long-grained content is 30-38%, a silty fine shale phase when the long-grained content is 38-60%, when the content of the long bones is 6-30%, the fine division is carried out by combining the content of the gray matter, when the content of the clay is 40-48%, the content of the long bones is 6-30%, and the content of the gray matter is 0-18%, the fine division is the gray matter mud rock phase, argillaceous limestone phase when the clay content is 40% -48%, the longing-english content is 6% -30% and the gray matter content is 18% -31%.
(7) The constructed mineral component decision tree recognition and characterization lithofacies method (figure 9) ensures that all intervals are not overlapped with each other and can not be omitted, and finally 6 lithofacies types and corresponding recognition and division standards are divided, namely argillaceous limestone facies, silty fine sandstone facies, gray argillaceous shale facies, argillaceous silty sandstone facies, silty sandstone facies and argillaceous lithofacies. The specific identification and classification results and criteria are shown in table 2.
TABLE 2 mineral component decision Tree method for identifying and dividing lithofacies results and criteria
In summary, the method for analyzing the content of the mineral components in the shale reservoir and identifying and characterizing the lithofacies provided by the embodiment of the invention at least has the following effects:
(4) the method is effective and accurate, greatly improves the efficiency of well logging interpretation and reduces the cost of the well logging interpretation.
(5) The mineral components are analyzed by using the acoustic wave time difference, the compensation density and the compensation neutrons, so that the coring cost can be saved, accurate mineral component content data can be provided for a non-coring well, and the method provides guidance and basis for efficient field exploration and development.
(6) The method for establishing the mineral component decision tree realizes the rapid identification and accurate division of the logging lithofacies, and provides reliable theoretical basis and guidance for field exploration and production.
(4) The method has wide application prospect, and the mineral component analysis and lithofacies identification characterization method can be widely applied to evaluation and dessert prediction of compact reservoirs such as shale and compact sandstone, and brings economic benefit for guiding efficient operation of field exploration and development.
A specific example is provided below to illustrate the above process:
the method applies the procedures of mineral component analysis (blind source simulated annealing method) and lithofacies identification and characterization method (mineral component decision tree method) to the shale reservoir sample well, and can calculate and obtain the content of each mineral component and the lithofacies identification result of the well, as shown in fig. 10. In the graph 10, the fifth column, the sixth column, the seventh column, the eighth column, the ninth column and the tenth column are respectively the relative volumes of the resolved quartz, feldspar, calcite, clay, other minerals and pores, the eleventh column is a mineral section, and the iterative analysis result is very consistent with the core analysis result (3520-. The twelfth column is a mineral composition decision tree method for quantitatively identifying divided lithofacies sections which are very consistent with mineral sections (the eleventh column), which shows that the mineral composition decision tree is also very effective.
In summary, the embodiment of the present invention is a method for analyzing mineral component content and identifying and characterizing facies based on conventional logging data, and is to perform mineral component content analysis and quantitative identifying and characterizing facies by using the conventional logging data in the absence of imaging logging data and core analysis data. Firstly, a sensitive curve of each mineral is optimized, an inversion analytical model of each mineral is established, iterative analysis is carried out on the inversion analytical model by adopting a blind source simulated annealing method, compiling of a program is realized, the relative volume of each mineral is analyzed, and the relative volume is compared with a core analysis result. On the basis, according to the analyzed content of each mineral component, counting the volume distribution interval of each mineral, constructing a mineral component decision tree method by taking clay minerals as an initial boundary point to identify and quantitatively divide lithofacies, and identifying and dividing six lithofacies types: a argillaceous limestone phase, a silty fine sandstone phase, a gray argillaceous shale phase, a argillaceous sillago sandstone phase, a silty argillaceous shale phase and a mudstone phase, and establishing a division standard. Compared with results such as core analysis, literature data and the like, the mineral component analysis method and the lithofacies identification characterization method are high in calculation accuracy and wide in application range, and provide reliable theoretical basis and guidance for efficient exploration and development of compact complex reservoirs such as shale.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. A shale reservoir mineral component content analysis and lithofacies identification characterization method is characterized by comprising the following steps:
establishing an inversion analytical model of the content of the mineral components in the shale reservoir, and solving the inversion analytical model to obtain analytical calculation results of all minerals;
and obtaining a lithofacies recognition result through a mineral component decision tree based on the analysis calculation result of each mineral.
2. The method of claim 1, wherein the establishing an inverse analytical model of shale reservoir mineral composition content comprises:
selecting a sensitive logging curve to establish an inversion analysis model of the content of the mineral components in the shale reservoir; wherein the sensitive log comprises acoustic moveout AC, compensated density DEN, and compensated neutron CNL.
3. The method of claim 1, wherein solving the inverse analytical model comprises:
and according to the constructed objective function, performing matching iterative solution by adopting a blind source simulated annealing method.
4. The method of claim 3, wherein the objective function is:
wherein F (v) is an error index curve; pjIs a weight factor coefficient; k is a norm; a. thejiLogging response values for each mineral skeleton and pore fluid; y isjMeasuring the logging value of the actual stratum; viIs the relative volume percentage of each mineral and pore, namely the volume content and the porosity of each mineral to be analyzed.
5. The method of claim 3, wherein the performing the iterative solution of matching using a "blind source" simulated annealing method further comprises:
and for the logging response value of each mineral, increasing the limiting conditions of the maximum value and the minimum value in the iterative solution.
6. The method of claim 1, wherein the calculating the results based on the analysis of each mineral further comprises:
and counting the maximum value, the minimum value and the average value of the clay mineral volume relative content, the long-English mass volume relative content and the gray matter volume relative content based on the volume content of each mineral, and meanwhile, counting the frequency of each mineral content distribution interval to construct the mineral component decision tree.
7. The method of claim 1, wherein obtaining facies recognition results via a mineralogical decision tree comprises:
dividing and determining two intervals according to the maximum value, the minimum value, the average value and a distribution interval frequency diagram of the clay mineral volume relative content, wherein the two differences respectively correspond to the clay mineral volume relative content of 14% -48% and 48% -72%;
identifying as a mudstone phase for an interval with the relative clay volume content of 48-72%;
for the interval with the clay relative volume content of 14% -48%, combining the average value and the frequency distribution characteristics, and dividing the interval into two intervals of 14% -40% and 40% -48% according to the clay relative volume content by taking the clay relative volume content of 40% as a subdivision limit;
identifying an interval with 14-40% of clay content, and dividing two intervals with 0-18% and 18-31% of gray matter relative volume content according to the maximum value, the minimum value and the average value of the gray matter relative volume content, wherein the clay is identified as a argillaceous limestone phase when the gray matter content is 18-31%; when the content of the gray matter is 0-18%, according to the maximum value, the minimum value and the average value of the long-English matter content, the clay is identified as argillaceous silty sandstone phase when the clay content is 14-40%, the gray matter content is 0-18% and the long-English matter content is 6-38%, and the clay is identified as silty sandstone phase when the clay content is 14-40%, the gray matter content is 0-18% and the long-English matter content is 38-60%;
identifying an interval with the clay content of 40% -48%, subdividing the interval into three intervals of 6% -30%, 30% -38% and 38% -60% according to the maximum value, the minimum value and the average value of the long-English content, identifying the interval as silty shale phase when the long-English content is 30% -38%, and identifying the interval as silty sandstone phase when the long-English content is 38% -60%; when the content of the longing matter is 6-30%, the argillaceous limestone is needed to be subdivided by combining the content of the gray matter, when the content of the clay is 40-48%, the content of the longing matter is 6-30%, and the content of the gray matter is 0-18%, the argillaceous lithofacies are identified, when the content of the clay is 40-48%, the content of the longing matter is 6-30%, and the content of the gray matter is 18-31%.
8. The method of claim 2, wherein the inverse analytical model comprises:
where DEN is the compensated density log of the formation; AC is the sonic time difference log of the stratum; CNL is the compensated neutron log value of the stratum; vQuartz、VFeldspar、VCalcite、VClay、VThe rest of the minerals、VPores ofQuartz, feldspar, calcite, clay, other minerals and pores respectively account for volume percentage; ACQuartz、DENQuartz、CNLQuartzThe acoustic time difference, density and neutron logging response value of quartz; ACFeldspar、DENFeldspar、CNLFeldsparThe acoustic time difference, density and neutron logging response value of the feldspar are obtained; ACCalcite、DENCalcite、CNLCalciteThe acoustic time difference, density and neutron logging response value of calcite are obtained; ACClay、DENClay、CNLClayIs stickyAcoustic time difference, density, neutron log response, AC, of the earthThe rest of the minerals、DENThe rest of the minerals、CNLThe rest of the mineralsIs the acoustic time difference, density and neutron logging response value, AC of other mineralsPores of、DENPores of、CNLPores ofThe acoustic moveout, density and neutron logging response values of the pore fluid are shown.
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