CN112796738A - Stratum permeability calculation method combining array acoustic logging and conventional logging - Google Patents
Stratum permeability calculation method combining array acoustic logging and conventional logging Download PDFInfo
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Abstract
The invention discloses a stratum permeability calculation method combining array acoustic logging and conventional logging, which comprises the following steps of (1) preparing data; (2) classifying the rock core hole seepage relation; (3) preferably, permeability sensitive parameters are selected; (4) establishing a permeability calculation model; (5) optimizing a permeability model; (6) permeability calculation and error calculation. The method is different from the conventional method for calculating the permeability by utilizing the porosity, mainly combines the array acoustic logging and the conventional logging, considers various influence factors, designs a corresponding permeability calculation model aiming at stratums with different pore permeation conditions, and improves the calculation precision of the stratum permeability; the calculation result can be used for quantitatively analyzing the permeability of the stratum in the oil-gas exploration stage, and the exploration and development of the subsequent oil-gas reservoir are facilitated.
Description
Technical Field
The invention belongs to the field of oil and gas exploration and development, and particularly relates to a stratum permeability calculation method combining array acoustic logging and conventional logging.
Background
The existing stratum permeability calculation model is mainly established based on microscopic parameters and a conventional logging curve, and comprises the establishment of a correlation relation with porosity, a large number of pore or particle microscopic parameters (pore radius, particle radius, specific surface area, tortuosity, bound water content, flow units and the like) are also used, nuclear magnetic resonance logging (T2 and nuclear magnetic resonance porosity) and resistivity logging (porosity and resistivity) are widely applied, and then natural gamma logging is carried out.
The array acoustic logging is an acoustic logging method with multi-probe acoustic system and multi-wave train measurement, and can effectively suppress interference and accurately extract various information of longitudinal waves, transverse waves and Stoneley waves by recording a plurality of curves to perform correlation and superposition processing. At present, few permeability calculation methods based on array acoustic logging are adopted, and the porosity is calculated only by using the transverse wave time difference or the longitudinal wave time difference, so that the formation permeability is calculated, and the precision is far insufficient for accurately and quantitatively describing the formation permeability. Formation permeability can only be qualitatively evaluated by using array acoustic logging before, and the formation permeability is determined quantitatively by using array acoustic logging through further research.
The invention provides a permeability calculation method combining array acoustic logging and conventional logging information, which is different from the conventional permeability calculation method, on the basis of summarizing conventional permeability calculation formulas and conclusions obtained by previous researches, namely, a relation model between permeability and various influence factors is established by the conventional logging, the array acoustic logging and core physical property analysis information, and the relation model is applied to stratums with different pore permeability relations, so that the stratum permeability calculation accuracy is greatly improved. The method can be used for quantitatively analyzing the permeability of the stratum in the oil-gas exploration stage, thereby helping the exploration and development of the subsequent oil-gas reservoir.
Disclosure of Invention
The invention mainly overcomes the defects in the prior art, and provides a stratum permeability calculation method combining array acoustic logging and conventional logging.
The invention solves the technical problems, and the provided technical scheme is as follows: a stratum permeability calculation method combining array acoustic logging and conventional logging comprises the steps of obtaining core porosity, core permeability, a natural gamma curve, a mud content curve, logging calculation porosity, transverse wave time difference, acoustic wave time difference and Stoneley wave time difference according to core physical property analysis data, conventional logging data and array acoustic logging data of a research area;
according to the core physical property analysis data of the research area, calculating the porosity by using the core permeability and well logging, and drawing a porosity-core permeability scatter diagram;
dividing the core stratum of the research area into stratums of different pore-permeability categories according to different characteristics of data point distribution in the porosity-core permeability scatter diagram;
according to the core permeability of each type of stratum, respectively establishing a fitting relation and a regression coefficient of wave time difference, porosity, natural gamma, shale content, transverse wave time difference and Stoneley wave time difference and the core permeability of each type of stratum; three permeability model sensitive parameters are preferably selected in each type of stratum according to the regression coefficient;
respectively establishing an exponential product form permeability calculation model and a power form permeability calculation model according to the three permeability model sensitive parameters selected from each type of stratum;
respectively calculating results according to an exponential product form permeability calculation model and a power form permeability calculation model established for each type of stratum, respectively performing regression analysis on two calculation results of each type of stratum and the core permeability of each type of stratum, comparing the sizes of regression coefficients, and selecting a model with a large regression coefficient as an optimal permeability calculation model for each type of stratum;
determining a stratum permeability calculation model combining the array acoustic waves and conventional well logging in a research area according to the optimal permeability calculation model of each type of stratum;
and calculating the porosity of the stratum according to a stratum permeability calculation model combining the array sound wave and conventional logging in the research area and any other stratum without core physical property analysis data in the research area, and obtaining the stratum permeability by applying a corresponding permeability calculation model.
According to the further technical scheme, according to different characteristics of data point distribution in the porosity-core permeability scatter diagram, dividing the core stratum of the research area into strata of different pore-permeability categories comprises the following steps:
determining a rock core permeability classification result and a classification standard according to different characteristics distributed in the porosity-rock core permeability scatter diagram;
and dividing the core stratum of the research area into strata of different pore permeation categories according to the core pore permeation classification result and the classification standard.
According to the further technical scheme, according to the core permeability of each type of stratum, a fitting relation and a regression coefficient of wave time difference, porosity, natural gamma, shale content, transverse wave time difference and Stoneley wave time difference and the core permeability are respectively established for each type of stratum; and preferably selecting three permeability model sensitive parameters according to the regression coefficient for each type of stratum, wherein the three permeability model sensitive parameters comprise:
according to the core permeability of each type of stratum, drawing a core permeability-acoustic wave time difference scatter diagram, a core permeability-porosity scatter diagram, a core permeability-natural gamma scatter diagram, a core permeability-shale content scatter diagram, a core permeability-transverse wave time difference scatter diagram and a core permeability-Stoneley wave time difference scatter diagram respectively for each type of stratum; respectively fitting and calculating to obtain a core permeability-sound wave time difference regression coefficient, a core permeability-porosity regression coefficient, a core permeability-natural gamma regression coefficient, a core permeability-shale content regression coefficient, a core permeability-transverse wave time difference regression coefficient and a core permeability-Stoneley wave time difference regression coefficient;
comparing the two regression coefficients according to the core permeability-sound wave time difference regression coefficient and the core permeability-porosity regression coefficient, and selecting a factor with a larger regression coefficient as one of the sensitive parameters of the permeability model;
comparing the two regression coefficients according to the core permeability-natural gamma regression coefficient and the core permeability-shale content regression coefficient, and selecting a factor with a larger regression coefficient as one of the sensitive parameters of the permeability model;
and comparing the two regression coefficients according to the core permeability-transverse wave time difference regression coefficient and the core permeability-Stoneley wave time difference regression coefficient, and selecting the factor with the larger regression coefficient as one of the sensitive parameters of the permeability model.
The further technical scheme is that the exponential product form permeability calculation model is as follows:
in the formula: kComputingTo calculate the permeability; x1、X2、X3The values of the three permeability model sensitive parameters are respectively; a is1、b1、c1、d1Are fitting coefficients.
The further technical scheme is that the power form permeability calculation model is as follows:
in the formula: kComputingTo calculate the permeability; x1、X2、X3The values of the three permeability model sensitive parameters are respectively; a is2、b2、c2、d2Are fitting coefficients.
The further technical scheme is that the establishment process of the exponential product form permeability calculation model comprises the following steps:
firstly, calculating the logarithm value of the permeability of the rock core and the logarithm values of the sensitive parameters of the three permeability models, and then establishing a multiple regression equation of the four parameters;
lgKcore＝a1+b1×lgX1+c1×lgX2+d1×lgX3
In the formula: kCoreThe core permeability is taken as the core permeability; x1、X2、X3The values of the three permeability model sensitive parameters are respectively; a is1、b1、c1、d1Is a fitting coefficient;
taking the indexes of 10 at the left side and the right side of the equal sign of the multiple regression equation at the same time to obtain a permeability model in the form of an exponential product;
in the formula: kComputingTo calculate the permeability; x1、X2、X3The values of the three permeability model sensitive parameters are respectively; a is1、b1、c1、d1Are fitting coefficients.
The further technical scheme is that the establishment process of the power form permeability calculation model comprises the following steps:
firstly, calculating a rock core permeability logarithm value, and then establishing a multivariate regression equation of the rock core permeability logarithm value and three permeability model sensitive parameters;
lgKcore＝a2+b2×X1+c2×X2+d2×X3
In the formula: kCoreThe core permeability is taken as the core permeability; x1、X2、X3The values of the three permeability model sensitive parameters are respectively; a is2、b2、c2、d2Is a fitting coefficient;
taking the left side and the right side of the equal sign of the multiple regression equation as 10 indexes at the same time to obtain a permeability model in a power form as follows;
in the formula: kComputingTo calculate the permeability; x1、X2、X3The values of the three permeability model sensitive parameters are respectively; a is2、b2、c2、d2Are fitting coefficients.
The invention has the following beneficial effects: the method is different from the conventional method for calculating the permeability by utilizing the porosity, mainly combines the array acoustic logging and the conventional logging, considers various influence factors, designs a corresponding permeability calculation model aiming at stratums with different pore permeation conditions, and improves the calculation precision of the stratum permeability; the calculation result can be used for quantitatively analyzing the permeability of the stratum in the oil-gas exploration stage, and the exploration and development of the subsequent oil-gas reservoir are facilitated.
Drawings
FIG. 1 is a block flow diagram of the present invention;
FIG. 2 is a diagram showing the result of classification of the core hole permeability relationship;
FIG. 3 is a graph showing the result of classification of the core hole permeability relationship in example 1;
FIG. 4 is a comparison graph of the fit relationship between the core permeability, the acoustic moveout, and the porosity of example 1;
FIG. 5 is a comparison graph of the fitting relationship between the core permeability and the natural gamma and shale contents of example 1;
FIG. 6 is a comparison graph of the fit relationship between the core permeability and the shear wave time difference and Stoneley wave of example 1;
FIG. 7 is a graph comparing permeability calculation results and core permeability regression results of different models in a high permeability formation
FIG. 8 is a graph comparing permeability calculation results for different models in a low permeability formation with regression analysis of core permeability;
FIG. 9 is a graph comparing permeability results calculated by the method of the present invention (combined array sonic logging and conventional logging) and conventional methods (using porosity) in low permeability formations;
FIG. 10 is a graph comparing the permeability results calculated by the method of the present invention (combined array sonic logging and conventional logging) and conventional methods (using porosity) in highly porous formations.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, 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.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
As shown in FIG. 1, the formation permeability calculation method combining the array acoustic logging and the conventional logging comprises the following steps:
(1) preparing data: preparing core physical property analysis data, conventional logging data and array acoustic logging data of a research area, and obtaining parameters such as core porosity, core permeability, a natural gamma curve, a mud value content curve, logging calculation porosity, transverse wave time difference, Stoneley wave time difference and the like according to the data;
(2) and (3) classifying the rock core hole permeability relation: selecting the stratum with the core physical property analysis data in the step (1), drawing a porosity-core permeability scatter diagram by using the core permeability and the porosity calculated by logging, dividing the stratum into different pore-permeability categories according to different characteristics of data point distribution (such as obvious difference of pore-permeability fitting curves of two different porosity areas), and obtaining a core pore-permeability classification result and determining a classification standard as shown in figure 2: the porosity of the low porosity zone is less than or equal to A%, the porosity of the medium porosity zone is less than or equal to B% and the porosity of the high porosity zone is less than B% (wherein A is less than B); in fig. 2, only three categories of pore-permeation relationships are divided, and in practical application, more or fewer categories of pore-permeation relationships may be divided according to specific situations;
(3) the permeability model sensitive parameters are preferably: selecting a stratum with a certain type of pore-permeability relation based on the pore-permeability relation classification result in the step (2), respectively establishing a fitting relation and a regression coefficient of the acoustic time difference, the porosity, the natural gamma, the shale content, the transverse wave time difference and the Stoneley wave time difference with the core permeability, and preferably selecting three permeability model sensitive parameters:
specifically, the method comprises the following steps: drawing a core permeability-acoustic time difference scatter diagram, performing fitting calculation to obtain a core permeability-acoustic time difference regression coefficient, drawing a core permeability-porosity scatter diagram, performing fitting calculation to obtain a core permeability-porosity regression coefficient, comparing the two regression coefficients, and selecting a factor with a larger regression coefficient as one of permeability model sensitive parameters;
drawing a core permeability-natural gamma scatter diagram, performing fitting calculation to obtain a core permeability-natural gamma regression coefficient, drawing a core permeability-shale content scatter diagram, performing fitting calculation to obtain a core permeability-shale content regression coefficient, comparing the two regression coefficients, and selecting a factor with a larger regression coefficient as one of permeability model sensitive parameters;
drawing a core permeability-transverse wave time difference scatter diagram, performing fitting calculation to obtain a core permeability-transverse wave time difference regression coefficient, drawing a core permeability-Stoneley wave time difference scatter diagram, performing fitting calculation to obtain a core permeability-Stoneley wave time difference regression coefficient, comparing the two regression coefficients, and selecting a factor with a larger regression coefficient as one of the permeability model sensitive parameters;
(4) and (3) establishing a permeability model: aiming at the stratum with a certain pore-permeability relation selected in the step (3), establishing a permeability calculation model of the stratum by utilizing the three permeability model sensitive parameters selected in the step (3);
the invention adopts two forms of permeability calculation models: the operation steps of establishing the exponential product form permeability calculation model and the power form permeability calculation model are respectively explained as follows:
calculating model of permeability in form of exponential product
The operation steps for establishing the permeability calculation model in the form of exponential product are as follows:
firstly, calculating the logarithm value of the permeability of the rock core and the logarithm values of the three permeability model sensitive parameters determined in the step (3), and then establishing a multivariate regression equation (1) of the four parameters:
lgKcore＝a1+b1×lgX1+c1×lgX2+d1×lgX3 (1)
In the formula: kCoreThe core permeability is taken as the core permeability; x1、X2、X3The values of the three permeability model sensitive parameters are respectively; a is1、b1、c1、d1Is a fitting coefficient;
taking the index of 10 on the left side and the right side of the equal sign of the formula (1) at the same time to obtain an exponential product form permeability calculation model (formula 2):
in the formula: kComputingTo calculate the permeability; x1、X2、X3The values of the three permeability model sensitive parameters are respectively; a is1、b1、c1、d1Is a fitting coefficient;
② power form permeability calculation model
The operation steps for establishing the power form permeability model are as follows:
firstly, calculating a core permeability logarithm value, and then establishing a multivariate regression equation (3) of the core permeability logarithm value and the three permeability model sensitive parameters determined in the step (3):
lgKcore＝a2+b2×X1+c2×X2+d2×X3 (3)
In the formula: kCoreThe core permeability is taken as the core permeability; x1、X2、X3The values of the three permeability model sensitive parameters are respectively; a is2、b2、c2、d2Is a fitting coefficient;
taking the left side and the right side of the equal sign of the formula (3) as 10 exponents at the same time, and obtaining a power form permeability calculation model (4) as follows:
in the formula: kComputingTo calculate the permeability; x1、X2、X3The values of the three permeability model sensitive parameters are respectively; a is2、b2、c2、d2Is a fitting coefficient;
(5) the permeability model is preferably: respectively calculating the calculation results of the permeability models in the exponential product form and the power form of the formation in a certain type of pore-permeability relationship by using the two permeability calculation models (formula 2 and formula 4) established in the step (4), performing regression analysis on the calculation results and the permeability of the rock core respectively, comparing the sizes of regression coefficients, and selecting a model with a larger regression coefficient as an optimal permeability calculation model of the formation in the type of pore-permeability relationship;
the establishment of the optimal permeability model of the stratum with the hole-permeability relation in the step (2) is completed, and the operations from the step (3) to the step (5) are repeated only until the optimal permeability model is established for all the types of hole-permeability relations, so that a stratum permeability calculation model combining the array sound wave and the conventional well logging is obtained;
(6) and (3) permeability and error calculation: selecting any other stratum without core physical property analysis data in the research area to calculate the porosity of the stratum, and calculating the stratum permeability by using a corresponding permeability model according to the permeability calculation models of different pore-permeability relations obtained in the step (5);
and (3) error calculation: calculating the formula (5) by using the absolute error average value, and comparing the calculation precision of the model of the invention with the calculation precision of the conventional model;
in the formula:
the absolute error average of the permeability calculation results; kCalculate iCalculating permeability for the ith; kCore iThe permeability of the ith core is taken as the permeability of the ith core; and n is the number of the core permeability data points of the selected stratum.
Example 1
The invention relates to a stratum permeability calculation method combining array acoustic logging and conventional logging, which specifically comprises the following steps:
(1) preparing data: preparing core physical property analysis data, conventional logging data and array acoustic logging data of a research area, and acquiring core porosity, core permeability, a natural gamma curve, a mud value content curve, logging and calculating porosity, transverse wave time difference and Stoneley wave time difference according to the data;
(2) and (3) classifying the rock core hole permeability relation: selecting all stratums with core physical property analysis data in the step (1), drawing a porosity-core permeability scatter diagram by using the core permeability and the porosity calculated by logging, dividing the porosity-core permeability scatter diagram into different pore-permeability categories according to different distribution characteristics (such as obvious difference of pore-permeability fitting curves of two different porosity areas), and determining classification standards as shown in figure 3: the porosity of the low porosity zone is less than or equal to 9 percent, and the porosity of the high porosity zone is more than 9 percent;
(3) the permeability model sensitive parameters are preferably: selecting a stratum with high porosity in the pore-permeability area based on the pore-permeability relation classification result in the step (2);
(31) by drawing a core permeability-acoustic time difference scatter diagram (as shown in fig. 4 (a)), fitting and calculating to obtain a core permeability-acoustic time difference regression coefficient RACWhen the core permeability is equal to 0.433, a core permeability-porosity scatter diagram is drawn (as shown in fig. 4 (b)), and a regression coefficient R of the core permeability-porosity is obtained through fitting calculationφ0.462, apparently Rφ＞RACI.e. the correlation of porosity to core permeability is inferior to acoustic time differencePreferably, porosity is selected as one of the sensitivity parameters of the permeability model in this embodiment;
(32) the core permeability-natural gamma regression coefficient R is obtained through fitting calculation by drawing a core permeability-natural gamma scatter diagram (as shown in figure 5 (a))GRAnd (5) drawing a core permeability-shale content scatter diagram (as shown in fig. 5 (b)) when the core permeability is 0.178, and performing fitting calculation to obtain a core permeability-shale content regression coefficient RSH0.182, apparently RSH＞RGRNamely, the correlation between the shale content and the core permeability is better than that of natural gamma, so the shale content is selected as one of the permeability model sensitive parameters of the embodiment;
(33) fitting and calculating to obtain a regression coefficient R of the core permeability-transverse wave time difference through drawing a core permeability-transverse wave time difference scatter diagram (as shown in figure 6 (a))DTDAnd (3) drawing a core permeability-stoneley wave time difference scatter diagram (as shown in fig. 6 (b)) when the value is 0.395, and fitting and calculating to obtain a core permeability-stoneley wave time difference regression coefficient RDTSTWhen equal to 0.627, it is clear that RDTST＞RDTDNamely, the correlation between the stoneley wave time difference and the core permeability is better than that of the transverse wave time difference, so that the stoneley wave time difference is selected as one of the permeability model sensitive parameters of the embodiment;
(34) in conclusion, three permeability model sensitive parameters of the permeability calculation model of the high-permeability stratum in the embodiment are determined to comprise the porosity, the shale content and the Stoneley wave time difference;
(4) and (3) establishing a permeability model: aiming at the stratum with the high pore-permeability relation selected in the step (3), establishing a permeability calculation model by utilizing the three permeability model sensitive parameters selected in the step (3); an exponential product form and a power form, the following respectively describes the operation steps of establishing an exponential product form model and a power form model:
(ii) form of index
The operation steps for establishing the permeability model in the form of exponential product are as follows:
firstly, calculating a permeability logarithm value, a porosity logarithm value, a Stoneley cycle difference logarithm value and a shale content logarithm value of a rock core, and then establishing a multivariate regression equation (6) of the four values:
lgKcore＝-174.502+7.826×lgφ+73.085×lgDTST+2.268×lgSH (6)
In the formula: kCoreThe core permeability is taken as the core permeability; phi is porosity; DTST is Stoneley wave time difference; SH is the argillaceous content;
finally, the permeability calculation model (7) in the form of exponential product is obtained as follows:
Kcomputing＝10-174.502×φ7.826×DTST73.085×SH-2.268 (7)
In the formula: kComputingTo calculate the permeability; phi is porosity; DTST is Stoneley wave time difference; SH is the argillaceous content;
form of power-
The operation steps for establishing the permeability model in the form of power are as follows:
firstly, calculating the logarithmic permeability value, porosity, Stoneley wave time difference and shale content of the rock core, and then establishing a multiple regression equation (8) of the four values:
lgKcore＝-34.461+0.229φ+0.16DTST+0.054SH (8)
In the formula: kCoreThe core permeability is taken as the core permeability; phi is porosity; DTST is Stoneley wave time difference; SH is the argillaceous content;
finally, a permeability calculation model (9) in a power form is obtained as follows:
Kcomputing＝10-34.461+0.229φ+0.16DTST+0.054SH (9)
In the formula: kComputingTo calculate the permeability; phi is porosity; DTST is Stoneley wave time difference; SH is the argillaceous content;
(5) the permeability model is preferably: calculating the permeability calculation results of the two models of the exponential product form and the power form of the selected high pore permeability-related stratum by using the two permeability calculation models (formula 7 and formula 9) established in the step (4), performing regression analysis on the permeability calculation results and the core permeability of the selected stratum respectively, and comparing the obtained core permeability with regression coefficients of the permeability calculation of the two models as shown in fig. 7, so that the calculation model of the exponential product form (formula 7) with a larger linear regression coefficient is selected as the optimal permeability calculation model of the high pore permeability relationship in the embodiment;
thus, the establishment of one of the pore-permeability relationships divided in the step (2), namely the optimal permeability calculation model of the stratum with the pore-permeability relationship, is completed, only the operations from the step (3) to the step (5) are needed to be repeated to obtain two permeability models of the stratum with the pore-permeability relationship, as shown in fig. 8, the optimal permeability model of the stratum with the pore-permeability relationship, namely the permeability calculation model in the power form, is selected by comparing regression coefficients of the two models, and then the permeability models of all the stratum with the pore-permeability relationship are obtained as shown in table 1:
TABLE 1 model for calculating permeability of different pore permeability relationships
| Pore-permeability relationship classes | Permeability calculation model |
| Low pore permeability (phi < 9%) | KComputing＝1011.041+0.713φ-0.068DTST+0.0097SH |
| High porosity (9% less than phi) | KComputing＝10-174.502×φ7.826×DTST73.085×SH-2.268 |
(6) Permeability and error calculation
And (3) selecting any other stratum without core physical property analysis data in the research area to calculate the porosity of the stratum, and calculating the permeability of the stratum by applying a corresponding permeability model according to the permeability calculation models with different pore-permeability relations obtained in the step (5), namely obtaining a high-precision permeability calculation result of the target stratum by applying the established permeability calculation model according to the specific porosity condition of the selected stratum.
As shown in fig. 9 and 10, the permeability results are calculated by comparing the method of the invention (combining the array acoustic logging and the conventional logging) with the conventional method (using the porosity) under the stratum with different pore-permeability relations;
and (3) error calculation: the relative error of the calculated permeability of the model of the present invention and the relative error of the calculated permeability of the conventional model were obtained using the absolute error average calculation formula (5) as shown in table 2.
TABLE 2 comparison of relative errors for different model calculated permeabilities under different pore permeation conditions
| Design model of the invention | Conventional permeability calculation model | |
| Absolute error of high-porosity permeability stratum | 0.36 | 0.47 |
| Absolute error of low-porosity permeability stratum | 0.23 | 0.58 |
By combining the comparison results of the average absolute error (the average absolute error is calculated by using the formula 5) calculated by different models under different pore permeability conditions shown in table 1 and comparing the results of the permeability calculation by the method of the present invention (combining array sonic logging and conventional logging data) and the conventional method (using porosity) shown in fig. 9 and 10, it can be found that the formation permeability calculated by using the model of the present invention has further improved accuracy, and the permeability of the formation can be quantitatively analyzed in the oil and gas exploration phase by using the calculation results, so as to help the exploration and development of the subsequent oil and gas reservoir.
Although the present invention has been described with reference to the above embodiments, it should be understood that the present invention is not limited to the above embodiments, and those skilled in the art can make various changes and modifications without departing from the scope of the present invention.
Claims (7)
1. A stratum permeability calculation method combining array acoustic logging and conventional logging is characterized by comprising the following steps:
obtaining core porosity, core permeability, a natural gamma curve, a mud content curve, logging calculation porosity, transverse wave time difference, acoustic wave time difference and Stoneley wave time difference according to core physical property analysis data, conventional logging data and array acoustic logging data of a research area;
calculating porosity according to the core permeability and well logging of the research area to draw a porosity-core permeability scatter diagram;
dividing the stratum of the research area into different pore-permeability categories according to different characteristics of data point distribution in the porosity-core permeability scatter diagram;
according to the core permeability of each type of stratum, respectively establishing a fitting relation and a regression coefficient of the acoustic time difference, the porosity, the natural gamma, the shale content, the transverse wave time difference and the Stoneley wave time difference with the core permeability of each type of stratum; preferably selecting three permeability model sensitive parameters for each type of stratum according to the regression coefficient;
respectively establishing an exponential product form permeability calculation model and a power form permeability calculation model according to three permeability models which are preferably selected for each type of stratum;
respectively calculating results according to an exponential product form permeability calculation model and a power form permeability calculation model established for each type of stratum, respectively performing regression analysis on two calculation results of each type of stratum and the core permeability of each type of stratum, comparing the sizes of regression coefficients, and selecting a model with a large regression coefficient as an optimal permeability calculation model for each type of stratum;
determining a stratum permeability calculation model combining the array acoustic waves and conventional well logging in a research area according to the optimal permeability calculation model of each type of stratum;
and calculating the porosity of the stratum according to a stratum permeability calculation model combining the array sound wave and conventional logging in the research area and any other stratum without core physical property analysis data in the research area, and obtaining the stratum permeability by applying a corresponding permeability calculation model.
2. The method of claim 1, wherein the dividing of the core formation of the study area into formations of different pore-permeability categories according to different characteristics of distribution in a porosity-core permeability scattergram comprises:
determining a rock core permeability classification result and a classification standard according to different characteristics of data point distribution in the porosity-rock core permeability scatter diagram;
and dividing the core stratum of the research area into strata of different pore permeation categories according to the core pore permeation classification result and the classification standard.
3. The formation permeability calculation method combining the array acoustic logging and the conventional logging according to claim 1, wherein fitting relations and regression coefficients of acoustic time difference, porosity, natural gamma, shale content, transverse wave time difference and Stoneley wave time difference with the core permeability are respectively established for each type of formation according to the core permeability of each type of formation; and preferably selecting three permeability model sensitive parameters according to the regression coefficient for each type of stratum, wherein the three permeability model sensitive parameters comprise:
according to the core permeability of each type of stratum, drawing a core permeability-acoustic wave time difference scatter diagram, a core permeability-porosity scatter diagram, a core permeability-natural gamma scatter diagram, a core permeability-shale content scatter diagram, a core permeability-transverse wave time difference scatter diagram and a core permeability-Stoneley wave time difference scatter diagram respectively for each type of stratum; respectively fitting and calculating to obtain a core permeability-sound wave time difference regression coefficient, a core permeability-porosity regression coefficient, a core permeability-natural gamma regression coefficient, a core permeability-shale content regression coefficient, a core permeability-transverse wave time difference regression coefficient and a core permeability-Stoneley wave time difference regression coefficient;
comparing the two regression coefficients according to the core permeability-sound wave time difference regression coefficient and the core permeability-porosity regression coefficient, and selecting a factor with a larger regression coefficient as one of the sensitive parameters of the permeability model;
comparing the two regression coefficients according to the core permeability-natural gamma regression coefficient and the core permeability-shale content regression coefficient, and selecting a factor with a larger regression coefficient as one of the sensitive parameters of the permeability model;
and comparing the two regression coefficients according to the core permeability-transverse wave time difference regression coefficient and the core permeability-Stoneley wave time difference regression coefficient, and selecting the factor with the larger regression coefficient as one of the sensitive parameters of the permeability model.
4. The method for calculating formation permeability by combining array acoustic logging and conventional logging according to claim 1, wherein the exponential product form permeability calculation model is:
in the formula: kComputingTo calculate the permeability; x1、X2、X3Three permeability models respectivelyA value of a sensitive parameter; a is1、b1、c1、d1Are fitting coefficients.
5. The method of claim 1, wherein the power-form permeability calculation model is:
in the formula: kComputingTo calculate the permeability; x1、X2、X3The values of the three permeability model sensitive parameters are respectively; a is2、b2、c2、d2Are fitting coefficients.
6. The method for calculating the formation permeability by combining the array acoustic logging and the conventional logging according to claim 4, wherein the establishing process of the exponential product form permeability calculation model comprises the following steps:
firstly, calculating the logarithm value of the permeability of the rock core and the logarithm values of the sensitive parameters of the three permeability models, and then establishing a multiple regression equation of the four parameters;
lgKcore＝a1+b1×lgX1+c1×lgX2+d1×lgX3
In the formula: kCoreThe core permeability is taken as the core permeability; x1、X2、X3The values of the three permeability model sensitive parameters are respectively; a is1、b1、c1、d1Is a fitting coefficient;
taking the indexes of 10 at the left side and the right side of the equal sign of the multiple regression equation at the same time to obtain a permeability model in the form of an exponential product;
in the formula: kComputingTo calculate the permeability; x1、X2、X3The values of the three permeability model sensitive parameters are respectively; a is1、b1、c1、d1Are fitting coefficients.
7. The method for calculating the formation permeability by combining the array acoustic logging and the conventional logging according to claim 5, wherein the power-form permeability calculation model is established by the following steps:
firstly, calculating a rock core permeability logarithm value, and then establishing a multiple regression equation of the rock core permeability logarithm value and three permeability sensitive parameters;
lgKcore＝a2+b2×X1+c2×X2+d2×X3
In the formula: kCoreThe core permeability is taken as the core permeability; x1、X2、X3The values of the three permeability model sensitive parameters are respectively; a is2、b2、c2、d2Is a fitting coefficient;
taking the left side and the right side of the equal sign of the multiple regression equation as 10 indexes at the same time to obtain a permeability model in a power form as follows;
in the formula: kComputingTo calculate the permeability; x1、X2、X3The values of the three permeability model sensitive parameters are respectively; a is2、b2、c2、d2Are fitting coefficients.
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