CN111598440A - Multi-angle driven quantitative evaluation method and system for permeability of complex medium reservoir - Google Patents
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Abstract
The invention relates to a multi-angle driven quantitative evaluation method and a system for permeability of a complex medium reservoir, which are characterized by comprising the following contents: 1) acquiring physical parameters and logging data of the depth of a complex medium reservoir core sample in a logged well, and calculating oil-containing parameters, derived parameters and lithological parameters of the core sample; 2) determining a functional relation between the porosity of each category of the rock core sample and the permeability of the rock core; 3) selecting the acquired data and parameters, and carrying out sensitivity sequencing on the selected data or parameter set; 4) determining an optimal data or parameter subset and a complex medium reservoir classification model corresponding to the optimal data or parameter subset; 5) acquiring various data or parameters in the optimal data or parameter subset of the whole well section of the well to be evaluated; 6) and calculating the permeability of the complex medium reservoir in each category to finish quantitative evaluation of the permeability of the complex medium reservoir in the whole well section of the well to be evaluated.
Description
Technical Field
The invention relates to a permeability quantitative evaluation method and a system, in particular to a multi-angle driven complex medium reservoir permeability quantitative evaluation method and a system.
Background
In general, the reservoir permeability is calculated by adopting porosity to establish a permeability calculation formula based on a high-precision functional relationship between the porosity and the permeability. Thus, the reservoir permeability may be expressed as K ═ f (Φ), where K is the permeability and Φ is the porosity. In a complex medium reservoir, the lithology and the type of the pores of the formation are rich, and the heterogeneity is strong, so that the high-precision functional relation between the porosity and the permeability in the homogeneous reservoir does not exist. Therefore, it is not feasible to calculate the permeability of a complex media reservoir based on the above equation.
Based on nuclear magnetic logging data, in the free fluid model, the permeability is calculated as K ═ phi/C4(FFI/BVI)2Wherein C is an empirical constantWhere FFI is the pore volume of the free fluid and BVI is the pore volume of the bound water. At average T2In the model, the permeability is calculated by the formula K ═ aT2gm 2φ4Wherein a is an empirical constant, T2gmIs T2Geometric mean of the distribution. When complex media reservoir lacks nuclear magnetic log data, or the free fluid model and the mean T2When the important parameters of the complex medium reservoir in the calculation formula of the permeability of the model cannot be accurately determined, the permeability of the complex medium reservoir cannot be accurately calculated naturally.
Under the condition of lacking special logging information such as nuclear magnetism logging, imaging logging and the like, in order to accurately calculate the permeability of a complex medium reservoir stratum, the complex medium reservoir stratum must be classified finely according to different lithologies and different pore types, and then in each class, the permeability is calculated by adopting the porosity based on a high-precision functional relation between the porosity and the permeability.
Disclosure of Invention
In view of the above problems, the present invention aims to provide a method and a system for quantitatively evaluating the permeability of a multi-angle driven complex medium reservoir, which are accurate and simple in classification.
In order to achieve the purpose, the invention adopts the following technical scheme: the quantitative evaluation method for the permeability of the multi-angle driven complex medium reservoir comprises the following steps: 1) acquiring physical parameters and logging data of the depth of a complex medium reservoir core sample in the logged well, and calculating oil-containing parameters, derivative parameters and lithology parameters of the core sample according to the acquired logging data; 2) classifying the rock core samples, and determining a functional relation between the porosity and the permeability of each category of rock core of the rock core samples; 3) selecting the acquired data and parameters, and carrying out sensitivity sequencing on the selected data or parameter set; 4) adjusting the selected data or parameter set according to the sensitivity sequencing result, and determining an optimal data or parameter subset and a complex medium reservoir classification model corresponding to the optimal data or parameter subset; 5) acquiring various data or parameters in the optimal data or parameter subset of the whole well section of the well to be evaluated; 6) and inputting the acquired data or parameters into an optimal complex medium reservoir classification model, dividing the categories of complex medium reservoirs in the whole well section of the well to be evaluated, calculating the permeability of the complex medium reservoirs in each category according to the determined functional relationship, and finishing the quantitative evaluation of the permeability of the complex medium reservoirs in the whole well section of the well to be evaluated.
Further, the logging data in the step 1) comprise natural gamma, natural potential, deep resistivity, medium resistivity, shallow resistivity, volume density, neutron porosity and longitudinal wave time difference; the physical property parameters are the porosity and permeability of the rock core; the oil-bearing parameters comprise oil saturation and water saturation; the derived parameters comprise three porosity indexes, depth resistivity ratio, porosity and saturation product; lithology parameters include argillaceous content, sandstone content, dolomitic content, and limestone content.
Further, the specific process of step 2) is as follows: 2.1) classifying the core samples according to the core porosity and the core permeability of the core samples; 2.2) establishing cross graphs of different types of core samples according to the classification result, the core porosity and the core permeability of the core samples; 2.3) determining the functional relation between the porosity and the permeability of the core of each category of the core sample according to the established cross plot:
K＝f(φ)
wherein K is the permeability of the rock core; phi is the core porosity.
Further, the specific process of step 2.1) is as follows: 2.1.1) calculating a reservoir quality factor RQI of the core sample according to the core porosity and the core permeability of the core sample:
2.1.2) calculating the ratio phi of the pore volume to the particle volume of the core sample according to the core porosity of the core samplez：
2.1.3) establishing a reservoir quality factor RQI and a ratio of pore volume to particle volume phi in the core samplezAccording to the distribution characteristics of the data in the cross map, the complex medium reservoir core samples are divided into corresponding categories.
Further, the specific process of step 3) is as follows: 3.1) adopting knowledge driving to select data or parameter sets which are beneficial to dividing the category of the rock core sample according to logging data, lithology parameters, physical parameters, oil-containing parameters and derivative parameters of the corresponding depth of the rock core sample; and 3.2) analyzing the contribution degree of the complex medium reservoir core sample category according to the selected data or parameter set by adopting a feature selection algorithm, and carrying out sensitivity sequencing on the selected data or parameter set according to the analyzed contribution degree.
Further, the specific process of the step 4) is as follows: 4.1) adjusting the selected data or parameter set according to the sensitivity sorting result to obtain a plurality of data or parameter subsets, and determining a complex medium reservoir classification model corresponding to each data or parameter subset; and 4.2) determining the optimal data or parameter subset and the corresponding complex medium reservoir classification model according to the complex medium reservoir classification model corresponding to each data or parameter subset by taking the model accuracy as a target.
Further, the specific process of the step 4.1) is as follows: 4.1.1) sequentially selecting n, n-1 and n-2 … data or parameters in the selected data or parameter set as data or parameter subsets according to the sensitivity sorting result, wherein n is the number of the data or parameters in the selected data or parameter set; 4.1.2) integrating the obtained data or parameter subsets through one or more functional relations by adopting a classification algorithm, and determining a complex medium reservoir classification model corresponding to each data or parameter subset.
Further, the optimal complex medium reservoir classification model in the step 4.2) is as follows:
in the formula, M is an optimal complex medium reservoir classification model; u is an optimal data or parameter subset; x is the depth of the complex medium reservoir classification model; and y is the model accuracy.
Further, the permeability of the complex medium reservoir of the whole well section of the well to be evaluated in the step 6) is as follows:
Kgeneral assembly＝KI+KII+KIII+…
Wherein, KGeneral assemblyAs the total permeability; kI、KII、KIIIRespectively the permeability of the I type complex medium reservoir, the II type complex medium reservoir and the III type complex medium reservoir.
A multi-angle driven quantitative evaluation system for permeability of a complex medium reservoir comprises: the logging data acquisition module is used for acquiring physical parameters and logging data of the depth of a complex medium reservoir core sample in the logged well, and calculating oil-bearing parameters, derived parameters and lithology parameters of the core sample according to the acquired logging data; the functional relation determining module is used for classifying the rock core samples and determining the functional relation between the porosity and the permeability of the rock core of each category of the rock core samples; the sensitivity sorting module is used for selecting the acquired data and parameters and carrying out sensitivity sorting on the selected data or parameter set; the optimal classification model determining module is used for adjusting the selected data or parameter set according to the sensitivity sequencing result and determining an optimal data or parameter subset and a complex medium reservoir classification model corresponding to the optimal data or parameter subset; the system comprises a to-be-evaluated well data acquisition module, a data acquisition module and a data acquisition module, wherein the to-be-evaluated well data acquisition module is used for acquiring optimal data or various data or parameters in a parameter subset of the whole well section of the to-be-evaluated well; and the permeability calculation module is used for inputting the acquired data or parameters into the optimal complex medium reservoir classification model, dividing the categories of the complex medium reservoirs in the whole well section of the well to be evaluated, and calculating the permeability of the complex medium reservoirs in each category according to the determined functional relationship.
Due to the adoption of the technical scheme, the invention has the following advantages: 1. the method and the device provided by the invention are used for obtaining the well-logged core permeability and well logging data from the well-logged core analysis data of the core sample, calculating the permeability of the whole well section of the well to be evaluated without the core sample, avoiding the development of a large number of core experiments and effectively saving the cost. 2. The method adopts a data and knowledge multi-angle driving mode to divide the categories of the complex medium reservoir in the whole well section of the well to be evaluated and accurately calculate the permeability of the complex medium reservoir, so that the accuracy of the quantitative evaluation result of the permeability of the complex medium reservoir can be ensured, the method is simple and practical, and the method can be widely applied to the field of permeability quantitative evaluation.
Drawings
FIG. 1 is a schematic diagram of rock types of a G oil field complex medium reservoir in an embodiment of the invention;
FIG. 2 is a schematic diagram of pore types of a G oilfield complex media reservoir in an embodiment of the invention;
FIG. 3 is a cross plot of core porosity and core permeability of a G oil field complex medium reservoir in an example of the invention;
FIG. 4 is a cross plot of pore volume to particle volume ratio, reservoir quality factor for a G-field complex media reservoir in an example of the present invention;
FIG. 5 is a cross plot of core porosity and core permeability of a G oil field complex medium reservoir in an example of the invention;
FIG. 6 is a schematic diagram illustrating the results of parameter sensitivity analysis in a complex medium reservoir classification model of a G oilfield cored sample well in an embodiment of the present invention;
FIG. 7 is a schematic diagram of permeability evaluation results of a complex medium reservoir driven by multiple angles of a G oil field non-cored sample well in an embodiment of the invention;
FIG. 8 is a cross plot of permeability calculated by a conventional method for a complex medium reservoir of a G-field uncare sample well and core permeability in an embodiment of the invention;
FIG. 9 is a cross plot of permeability and core permeability calculated for a G field uncare sample well using the method of the present invention in an example of the present invention.
Detailed Description
The present invention is described in detail below with reference to the attached drawings. It is to be understood, however, that the drawings are provided solely for the purposes of promoting an understanding of the invention and that they are not to be construed as limiting the invention.
By utilizing Darcy's law and Poiseue's law, the quantitative relationship between the core porosity of the complex medium reservoir core sample and the core permeability of the core analysis can be established:
wherein K is the core permeability (unit is mD); phi is the core porosity; r ismhIs the mean flow radius; fsIs a form factor of the pore channel; τ is the tortuosity of the channel.
Surface area S per unit volume of rock particlesgvThe relationship between the core porosity and the mean flow radius of the core sample of the complex medium reservoir is as follows:
combining the above equations (1) and (2) yields the following equation:
equation (3), i.e., Kozeny-Carmen equation (conganin-camann equation), is transformed from equation (3) to obtain:
taking the logarithm of equation (4) yields:
lgRQI＝lgφz+lg FZI (5)
in the formula, RQI is reservoir quality factor of a complex medium reservoir core sample; FZI is a reservoir type index of the complex medium reservoir core sample; phi is azThe ratio of the pore volume to the particle volume of the complex medium reservoir core sample is shown.
Based on the above description, the multi-angle driven quantitative evaluation method for the permeability of the complex medium reservoir provided by the invention comprises the following steps:
1) obtaining physical parameters (core porosity and core permeability) of the core sample of the complex medium reservoir according to core analysis data of the core sample of the complex medium reservoir in the well; based on the depth of a complex medium reservoir core sample in a stratum, acquiring logging data of the depth of the complex medium reservoir core sample in the logging by a logging instrument, and calculating lithology parameters, oil-containing parameters (including oil saturation and water saturation) and derivative parameters (including three-porosity index, depth-shallow resistivity ratio, porosity, saturation product and the like) of the complex medium reservoir core sample according to the acquired logging data and core analysis data of the complex medium reservoir core sample in the logging, wherein the logging data comprises natural gamma, natural potential, deep resistivity, medium resistivity, shallow resistivity, volume density, neutron porosity, longitudinal wave time difference and the like, the lithology parameters comprise shale content, sandstone content, dolomite content, limestone content and the like, and each parameter is calculated by adopting a calculation method disclosed by the prior art, the detailed process is not described herein.
2) According to the core porosity and the core permeability of the core sample of the complex medium reservoir, the core sample of the complex medium reservoir is classified, and the method specifically comprises the following steps:
2.1) calculating a reservoir quality factor RQI of the core sample of the complex medium reservoir by adopting the following formula (6) according to the core porosity and the core permeability of the core sample of the complex medium reservoir:
2.2) calculating the ratio phi of the pore volume to the particle volume of the core sample of the complex medium reservoir by adopting the following formula (7) according to the core porosity of the core sample of the complex medium reservoirz：
2.3) establishment ofReservoir quality factor RQI and pore volume to particle volume ratio phi in complex medium reservoir core samplezAnd classifying the complex medium reservoir core samples according to the cross graphs of the two parameters and the distribution characteristics of the data in the cross graphs, namely classifying the complex medium reservoir core samples into one class when the precision of the functional relation between the core porosity and the core permeability of each class of complex medium reservoir core samples in the cross graphs is more than 0.8.
3) Establishing an intersection graph of different categories of the complex medium reservoir core samples according to the classification result, the core porosity and the core permeability of the complex medium reservoir core samples, and obtaining a functional relation between the core porosity and the core permeability of each category of the complex medium reservoir core samples according to the established intersection graph:
K＝f(φ) (8)
4) and (3) adopting knowledge driving, namely selecting a data or parameter set which is beneficial to dividing the categories of the complex medium reservoir rock core samples such as reservoir lithology, pore type and the like according to logging data, lithology parameters, physical parameters, oil-containing parameters and derivative parameters of the corresponding depth of the complex medium reservoir rock core samples and considering the physical meaning of each data or parameter.
5) And analyzing the contribution degree of the complex medium reservoir core sample category according to the selected data or parameter set by adopting a feature selection algorithm, and carrying out sensitivity sequencing on the selected data or parameter set according to the analyzed contribution degree, wherein the feature selection algorithm can adopt an embedded, filtering or packaging type feature selection algorithm, and the specific process is not repeated herein.
6) Adopting data driving, namely adjusting the selected data or parameter set according to the obtained sensitivity sequencing result to obtain a plurality of data or parameter subsets, and determining a complex medium reservoir classification model corresponding to each data or parameter subset by adopting a classification algorithm, wherein the method specifically comprises the following steps:
6.1) sequentially selecting n, n-1 and n-2 … data or parameters in the selected data or parameter set as data or parameter subsets according to the sensitivity sorting result, wherein n is the number of the data or parameters in the selected data or parameter set.
And 6.2) performing complex-form integration on the obtained data or parameter subsets through one or more functional relations by adopting a classification algorithm, and determining a complex medium reservoir classification model corresponding to each data or parameter subset, wherein the classification algorithm can adopt algorithms such as a decision tree, a random forest, a support vector machine, an artificial neural network or a rough set, and the specific process is not repeated.
7) And with the model accuracy as a target, determining the optimal data or parameter subset and the corresponding complex medium reservoir classification model M according to the complex medium reservoir classification model corresponding to each data or parameter subset:
in the formula, M is a complex medium reservoir classification model; u is an optimal data or parameter subset; x is the depth (in layers) of the complex media reservoir classification model; and y is the model accuracy.
8) And acquiring the optimal data or parameters in the optimal data or parameter subset of the whole well section of the well to be evaluated.
9) Inputting the acquired data or parameters into the optimal complex medium reservoir classification model M obtained in the step 7), dividing the categories of complex medium reservoirs in the whole well section of the well to be evaluated, calculating the permeability of the complex medium reservoir in each category according to the divided categories and the functional relationship obtained in the step 3), and completing the quantitative evaluation of the permeability of the complex medium reservoir in the whole well section of the well to be evaluated, wherein the permeability of the complex medium reservoir in the whole well section of the well to be evaluated is as follows:
Kgeneral assembly＝KI+KII+KIII+…(10)
In the formula, KGeneral assemblyAs the total permeability; kI、KII、KIIIRespectively the permeability of the I type complex medium reservoir, the II type complex medium reservoir and the III type complex medium reservoir.
The multi-angle driven quantitative evaluation method for the permeability of the complex medium reservoir is described in detail below by taking the G oil field complex medium reservoir as a specific embodiment.
1) Obtaining the core porosity and the core permeability of a core sample according to core analysis data of the complex medium reservoir core sample in the core sample well in the G oil field; based on the depth of the complex medium reservoir core sample of the cored sample well in the stratum of the G oil field, logging data of the core sample corresponding to the depth is obtained through a logging instrument, and lithology parameters, oil content parameters and derivative parameters of the complex medium reservoir core sample are calculated according to the obtained logging data and core analysis data of the complex medium reservoir core sample, and the lithology parameters of the complex medium reservoir core sample of the cored sample well are shown in figure 1. As shown in fig. 2, according to the core analysis data of the core sample, the pore types of the complex medium reservoir in the G oil field are determined to include intergranular pores, biological pores, solution pores and solution cavities.
2) Calculating a reservoir quality factor RQI of the core sample by adopting a formula (6) according to the core porosity and the core permeability (shown in figure 3) of the core sample; calculating the ratio phi of the pore volume to the particle volume of the core sample by adopting a formula (7) according to the core porosity of the core samplez(ii) a Establishing a reservoir quality factor RQI and a pore volume to particle volume ratio phi in a core samplezThe cross plot of the two parameters is shown in fig. 4, and the complex medium reservoir of the cored sample well of the G oil field is subdivided into five types according to the distribution characteristics of the data in the cross plot.
3) According to the classification result, the core porosity and the core permeability, cross graphs of different classes of the complex medium reservoir core sample are established, and are shown in FIG. 5; and determining a functional relation between the porosity and the permeability of each category of the complex medium reservoir core sample according to the established intersection graph, as shown in the following table 1:
table 1: functional relation and precision between porosity and permeability of five-type complex medium reservoir in G oil field
| Type (B) | Porosity as a function of permeability | Function accuracy R2 |
| Ⅰ | K＝207.1φ3.1038 | R2＝0.84 |
| Ⅱ | K＝1078.5φ3.2515 | R2＝0.90 |
| Ⅲ | K＝3399.5φ3.1775 | R2＝0.94 |
| Ⅳ | K＝17487φ3.2185 | R2＝0.95 |
| Ⅴ | K＝44980φ2.6986 | R2＝0.91 |
4) And (3) adopting knowledge driving, selecting a data or parameter set which is beneficial to dividing the rock property and the pore type of the rock core sample of the G oil field complex medium reservoir according to logging data, lithology parameters, physical parameters, oil content parameters and derivative parameters of the corresponding depth of the rock core sample of the complex medium reservoir, considering the physical meaning of each data or parameter, namely the deep resistivity, the argillaceous content, the sandstone content, the dolomite content, the limestone content, the porosity and the water saturation.
5) And analyzing the contribution degree of the core sample category of the complex medium reservoir by adopting a characteristic selection algorithm according to the selected data or parameter set, and performing sensitivity sequencing on the selected data or parameter set according to the analyzed contribution degree, wherein the result is shown in figure 6, namely when five complex medium reservoirs are divided in the G oil field, the sensitivity of the data or parameters is in a strong and weak relationship of deep resistivity > sandstone content > water saturation > porosity > dolomite content > shale content > limestone content.
6) And adjusting the selected data or parameter set according to the obtained sensitivity sequencing result by adopting data driving to obtain a plurality of data or parameter subsets, and determining the complex medium reservoir classification model corresponding to each data or parameter subset by adopting a classification algorithm.
7) With the model accuracy as a target, determining the optimal data or parameter combination and the corresponding classification model thereof according to the complex medium reservoir classification model corresponding to each data or parameter subset as follows:
the optimal data or parameters of the G oil field complex medium reservoir are as follows: the depth resistivity, the sandstone content, the water saturation, the porosity, the dolomite content, the argillaceous content and the limestone content, the depth of the model is 24 layers, and the accuracy of the model is 81.98%.
8) And obtaining the optimal data or parameters in the whole well section of the G oil field non-coring sample well.
9) Inputting the acquired data or parameters into the optimal complex medium reservoir classification model M obtained in the step 7), dividing the classes of the complex medium reservoirs in the whole well section of the G oil field non-coring sample well, and calculating the permeability of the complex medium reservoirs in each class according to the divided classes and the functional relation obtained in the step 3), as shown in FIG. 7, thereby completing the quantitative evaluation of the permeability of the complex medium reservoirs in the whole well section of the G oil field non-coring sample well.
As shown in fig. 7, in the permeability quantitative evaluation result diagram of the complex medium reservoir of the uncased sample well, the 1 st channel is the formation measurement depth; the 2 nd channel is natural gamma and represents the lithology characteristics of the stratum; the 3 rd path is a deep resistivity logging curve which represents the electrical characteristics of the stratum; lane 4 is the bulk density and neutron porosity, which represent the physical properties of the formation; the 5 th path is the porosity calculated based on the logging curve and the porosity of core analysis, and the average absolute error is 2.69%; the 6 th path is the permeability calculated by the conventional method and the permeability of the core analysis, and the correlation coefficient R between the permeability and the permeability20.65 as shown in FIG. 8; the 7 th path is the permeability calculated by the method of the invention and the permeability of the core analysis, and the correlation coefficient R between the permeability and the core analysis20.84 as shown in fig. 9.
Based on the multi-angle driven quantitative evaluation method for the permeability of the complex medium reservoir, the invention also provides a multi-angle driven quantitative evaluation system for the permeability of the complex medium reservoir, which comprises the following steps:
the logging data acquisition module is used for acquiring physical parameters and logging data of the depth of a complex medium reservoir core sample in the logged well, and calculating oil-bearing parameters, derived parameters and lithology parameters of the core sample according to the acquired logging data;
the functional relation determining module is used for classifying the rock core samples and determining the functional relation between the porosity and the permeability of the rock core of each category of the rock core samples;
the sensitivity sorting module is used for selecting the acquired data and parameters and carrying out sensitivity sorting on the selected data or parameter set;
the optimal classification model determining module is used for adjusting the selected data or parameter set according to the sensitivity sequencing result and determining an optimal data or parameter subset and a complex medium reservoir classification model corresponding to the optimal data or parameter subset;
the system comprises a to-be-evaluated well data acquisition module, a data acquisition module and a data acquisition module, wherein the to-be-evaluated well data acquisition module is used for acquiring optimal data or various data or parameters in a parameter subset of the whole well section of the to-be-evaluated well;
and the permeability calculation module is used for inputting the acquired data or parameters into the optimal complex medium reservoir classification model, dividing the categories of the complex medium reservoirs in the whole well section of the well to be evaluated, and calculating the permeability of the complex medium reservoirs in each category according to the determined functional relationship.
The above embodiments are only used for illustrating the present invention, and the structure, connection mode, manufacturing process, etc. of the components may be changed, and all equivalent changes and modifications performed on the basis of the technical solution of the present invention should not be excluded from the protection scope of the present invention.
Claims (10)
1. The quantitative evaluation method for the permeability of the multi-angle driven complex medium reservoir is characterized by comprising the following steps:
1) acquiring physical parameters and logging data of the depth of a complex medium reservoir core sample in the logged well, and calculating oil-containing parameters, derivative parameters and lithology parameters of the core sample according to the acquired logging data;
2) classifying the rock core samples, and determining a functional relation between the porosity and the permeability of each category of rock core of the rock core samples;
3) selecting the acquired data and parameters, and carrying out sensitivity sequencing on the selected data or parameter set;
4) adjusting the selected data or parameter set according to the sensitivity sequencing result, and determining an optimal data or parameter subset and a complex medium reservoir classification model corresponding to the optimal data or parameter subset;
5) acquiring various data or parameters in the optimal data or parameter subset of the whole well section of the well to be evaluated;
6) and inputting the acquired data or parameters into an optimal complex medium reservoir classification model, dividing the categories of complex medium reservoirs in the whole well section of the well to be evaluated, calculating the permeability of the complex medium reservoirs in each category according to the determined functional relationship, and finishing the quantitative evaluation of the permeability of the complex medium reservoirs in the whole well section of the well to be evaluated.
2. The multi-angle driven quantitative evaluation method for the permeability of a complex medium reservoir of claim 1, wherein the logging data in the step 1) comprises natural gamma, natural potential, deep resistivity, medium resistivity, shallow resistivity, bulk density, neutron porosity and longitudinal wave time difference; the physical property parameters are the porosity and permeability of the rock core; the oil-bearing parameters comprise oil saturation and water saturation; the derived parameters comprise three porosity indexes, depth resistivity ratio, porosity and saturation product; lithology parameters include argillaceous content, sandstone content, dolomitic content, and limestone content.
3. The multi-angle driven quantitative evaluation method for the permeability of the complex medium reservoir as claimed in claim 2, wherein the specific process of the step 2) is as follows:
2.1) classifying the core samples according to the core porosity and the core permeability of the core samples;
2.2) establishing cross graphs of different types of core samples according to the classification result, the core porosity and the core permeability of the core samples;
2.3) determining the functional relation between the porosity and the permeability of the core of each category of the core sample according to the established cross plot:
K＝f(φ)
wherein K is the permeability of the rock core; phi is the core porosity.
4. The multi-angle driven quantitative evaluation method for the permeability of the complex medium reservoir as claimed in claim 3, wherein the specific process of the step 2.1) is as follows:
2.1.1) calculating a reservoir quality factor RQI of the core sample according to the core porosity and the core permeability of the core sample:
2.1.2) calculating the ratio phi of the pore volume to the particle volume of the core sample according to the core porosity of the core samplez：
2.1.3) establishing a reservoir quality factor RQI and a ratio of pore volume to particle volume phi in the core samplezAccording to the distribution characteristics of the data in the cross map, the complex medium reservoir core samples are divided into corresponding categories.
5. The multi-angle driven quantitative evaluation method for the permeability of the complex medium reservoir as claimed in claim 1, wherein the specific process of the step 3) is as follows:
3.1) adopting knowledge driving to select data or parameter sets which are beneficial to dividing the category of the rock core sample according to logging data, lithology parameters, physical parameters, oil-containing parameters and derivative parameters of the corresponding depth of the rock core sample;
and 3.2) analyzing the contribution degree of the complex medium reservoir core sample category according to the selected data or parameter set by adopting a feature selection algorithm, and carrying out sensitivity sequencing on the selected data or parameter set according to the analyzed contribution degree.
6. The multi-angle driven quantitative evaluation method for the permeability of the complex medium reservoir as claimed in claim 1, wherein the specific process of the step 4) is as follows:
4.1) adjusting the selected data or parameter set according to the sensitivity sorting result to obtain a plurality of data or parameter subsets, and determining a complex medium reservoir classification model corresponding to each data or parameter subset;
and 4.2) determining the optimal data or parameter subset and the corresponding complex medium reservoir classification model according to the complex medium reservoir classification model corresponding to each data or parameter subset by taking the model accuracy as a target.
7. The multi-angle driven quantitative evaluation method for the permeability of the complex medium reservoir as claimed in claim 6, wherein the specific process of the step 4.1) is as follows:
4.1.1) sequentially selecting n, n-1 and n-2 … data or parameters in the selected data or parameter set as data or parameter subsets according to the sensitivity sorting result, wherein n is the number of the data or parameters in the selected data or parameter set;
4.1.2) integrating the obtained data or parameter subsets through one or more functional relations by adopting a classification algorithm, and determining a complex medium reservoir classification model corresponding to each data or parameter subset.
8. The multi-angle driven quantitative evaluation method for the permeability of the complex medium reservoir as claimed in claim 6, wherein the optimal classification model of the complex medium reservoir in the step 4.2) is as follows:
in the formula, M is an optimal complex medium reservoir classification model; u is an optimal data or parameter subset; x is the depth of the complex medium reservoir classification model; and y is the model accuracy.
9. The quantitative evaluation method for the permeability of the complex medium reservoir driven by multiple angles according to claim 1, characterized in that the permeability of the complex medium reservoir of the whole well section of the well to be evaluated in the step 6) is as follows:
Kgeneral assembly＝KI+KII+KIII+…
Wherein, KGeneral assemblyAs the total permeability; kI、KII、KIIIRespectively the permeability of the I type complex medium reservoir, the II type complex medium reservoir and the III type complex medium reservoir.
10. Multi-angle driven complex media reservoir permeability quantitative evaluation system, its characterized in that includes:
the logging data acquisition module is used for acquiring physical parameters and logging data of the depth of a complex medium reservoir core sample in the logged well, and calculating oil-bearing parameters, derived parameters and lithology parameters of the core sample according to the acquired logging data;
the functional relation determining module is used for classifying the rock core samples and determining the functional relation between the porosity and the permeability of the rock core of each category of the rock core samples;
the sensitivity sorting module is used for selecting the acquired data and parameters and carrying out sensitivity sorting on the selected data or parameter set;
the optimal classification model determining module is used for adjusting the selected data or parameter set according to the sensitivity sequencing result and determining an optimal data or parameter subset and a complex medium reservoir classification model corresponding to the optimal data or parameter subset;
the system comprises a to-be-evaluated well data acquisition module, a data acquisition module and a data acquisition module, wherein the to-be-evaluated well data acquisition module is used for acquiring optimal data or various data or parameters in a parameter subset of the whole well section of the to-be-evaluated well;
and the permeability calculation module is used for inputting the acquired data or parameters into the optimal complex medium reservoir classification model, dividing the categories of the complex medium reservoirs in the whole well section of the well to be evaluated, and calculating the permeability of the complex medium reservoirs in each category according to the determined functional relationship.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112559957A (en) * | 2020-11-17 | 2021-03-26 | 中海油田服务股份有限公司 | Permeability calculation method and device |
| CN113435066A (en) * | 2021-08-26 | 2021-09-24 | 北京润泽创新科技有限公司 | Logging interpretation reservoir evaluation method based on digital core technology |
| CN113504172A (en) * | 2021-09-10 | 2021-10-15 | 北京润泽创新科技有限公司 | Permeability evaluation method and device based on digital core |
| CN114109354A (en) * | 2021-11-16 | 2022-03-01 | 中海石油(中国)有限公司 | Method and system for evaluating low-permeability reservoir productivity, processing equipment and storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105069303A (en) * | 2015-08-17 | 2015-11-18 | 中国海洋石油总公司 | Quantitative evaluation method of low-permeability reservoir production capacity |
| CN106295113A (en) * | 2016-07-15 | 2017-01-04 | 中国海洋石油总公司 | A kind of method for quantitatively evaluating of complex oil and gas reservoir permeability |
-
2020
- 2020-05-14 CN CN202010407642.9A patent/CN111598440B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105069303A (en) * | 2015-08-17 | 2015-11-18 | 中国海洋石油总公司 | Quantitative evaluation method of low-permeability reservoir production capacity |
| CN106295113A (en) * | 2016-07-15 | 2017-01-04 | 中国海洋石油总公司 | A kind of method for quantitatively evaluating of complex oil and gas reservoir permeability |
Non-Patent Citations (2)
| Title |
|---|
| 王庆如等: "碳酸盐岩气藏储量参数测井评价方法", 《岩性油气藏》 * |
| 陈志强等: "基于流动单元的致密砂岩气储层渗透率测井评价――以川中广安地区须家河组为例", 《岩性油气藏》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112559957A (en) * | 2020-11-17 | 2021-03-26 | 中海油田服务股份有限公司 | Permeability calculation method and device |
| CN113435066A (en) * | 2021-08-26 | 2021-09-24 | 北京润泽创新科技有限公司 | Logging interpretation reservoir evaluation method based on digital core technology |
| CN113435066B (en) * | 2021-08-26 | 2021-11-12 | 北京润泽创新科技有限公司 | Logging interpretation reservoir evaluation method based on digital core technology |
| CN113504172A (en) * | 2021-09-10 | 2021-10-15 | 北京润泽创新科技有限公司 | Permeability evaluation method and device based on digital core |
| CN114109354A (en) * | 2021-11-16 | 2022-03-01 | 中海石油(中国)有限公司 | Method and system for evaluating low-permeability reservoir productivity, processing equipment and storage medium |
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