CN110656924B - Ultra-low permeability oil reservoir classification method - Google Patents
Ultra-low permeability oil reservoir classification method Download PDFInfo
- Publication number
- CN110656924B CN110656924B CN201910810191.0A CN201910810191A CN110656924B CN 110656924 B CN110656924 B CN 110656924B CN 201910810191 A CN201910810191 A CN 201910810191A CN 110656924 B CN110656924 B CN 110656924B
- Authority
- CN
- China
- Prior art keywords
- ultra
- low permeability
- oil reservoir
- ultralow
- reservoir
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000035699 permeability Effects 0.000 title claims abstract description 207
- 238000000034 method Methods 0.000 title claims abstract description 78
- 238000011161 development Methods 0.000 claims abstract description 69
- 238000000556 factor analysis Methods 0.000 claims abstract description 54
- 230000000694 effects Effects 0.000 claims abstract description 30
- 238000011156 evaluation Methods 0.000 claims abstract description 30
- 238000007621 cluster analysis Methods 0.000 claims abstract description 21
- 238000011084 recovery Methods 0.000 claims description 19
- 238000005553 drilling Methods 0.000 claims description 18
- 230000009467 reduction Effects 0.000 claims description 16
- 238000012847 principal component analysis method Methods 0.000 claims description 14
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 14
- 239000011159 matrix material Substances 0.000 claims description 13
- 238000010586 diagram Methods 0.000 claims description 10
- 238000004458 analytical method Methods 0.000 claims description 8
- 230000003247 decreasing effect Effects 0.000 claims description 8
- 238000004590 computer program Methods 0.000 claims description 7
- 238000012545 processing Methods 0.000 claims description 7
- 230000008859 change Effects 0.000 claims description 6
- 238000012937 correction Methods 0.000 claims description 5
- 238000003860 storage Methods 0.000 claims description 5
- 238000004519 manufacturing process Methods 0.000 abstract description 8
- 239000003921 oil Substances 0.000 description 135
- 208000010392 Bone Fractures Diseases 0.000 description 8
- 206010017076 Fracture Diseases 0.000 description 8
- 230000006870 function Effects 0.000 description 8
- 238000004891 communication Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 4
- 230000001186 cumulative effect Effects 0.000 description 3
- 230000007423 decrease Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 238000009933 burial Methods 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 239000010779 crude oil Substances 0.000 description 1
- 238000011549 displacement method Methods 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 239000003129 oil well Substances 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000000513 principal component analysis Methods 0.000 description 1
- 238000000638 solvent extraction Methods 0.000 description 1
- 230000000087 stabilizing effect Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 239000004575 stone Substances 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Classifications
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B47/00—Survey of boreholes or wells
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B47/00—Survey of boreholes or wells
- E21B47/06—Measuring temperature or pressure
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Mining & Mineral Resources (AREA)
- Geology (AREA)
- Geophysics (AREA)
- Environmental & Geological Engineering (AREA)
- Fluid Mechanics (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geochemistry & Mineralogy (AREA)
- Business, Economics & Management (AREA)
- Marine Sciences & Fisheries (AREA)
- Marketing (AREA)
- Agronomy & Crop Science (AREA)
- Health & Medical Sciences (AREA)
- Economics (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Animal Husbandry (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Investigation Of Foundation Soil And Reinforcement Of Foundation Soil By Compacting Or Drainage (AREA)
Abstract
The invention provides a method and a system for classifying an ultralow permeability oil reservoir, wherein the provided method comprises the following steps: acquiring preset parameter data of a plurality of ultralow permeability reservoirs, and acquiring main component information of each ultralow permeability reservoir based on a factor analysis method according to the preset parameter data; performing cluster analysis on the principal component information to obtain classification information of each of the ultra-low permeability reservoirs; and carrying out development effect evaluation on each type of ultra-low permeability oil reservoir in the classification information according to the classification information to obtain development effect evaluation information of each ultra-low permeability oil reservoir. The method provided by the embodiment of the invention can effectively distinguish the development characteristics of various ultralow-permeability oil reservoir blocks, the classification result is identical with the actual development characteristics, the classification basis can be provided for reasonable development of the ultralow-permeability oil reservoir, and the production and development of the ultralow-permeability oil reservoir can be guided correctly.
Description
Technical Field
The invention relates to the technical field of oil and gas exploration and development, in particular to an ultralow permeability oil reservoir classification method.
Background
Low permeability reservoirs are reservoirs with low permeability, low abundance, and low single well productivity.
The permeability is (0.1-50) x 10 according to the classification of the upper limit and the lower limit of the low-permeability oil layer -3 μm 2 Is known as a low permeability reservoir. According to actual production characteristics, low permeability oil reservoirs can be further divided into three types according to the average permeability of the oil layer, wherein the average permeability of the oil layer of the ultra-low permeability oil reservoir is (0.1-1.0) multiplied by 10 -3 μm 2 The oil layer is very compact, the irreducible water saturation is very high, the natural productivity is basically not provided, and the industrial development value is generally not provided. But if other conditions are favorable, such as thicker oil layer, shallower burial, better crude oil propertiesAnd the like, powerful measures which can improve the productivity of an oil well, reduce investment and cost are adopted, industrial development can be performed, and certain economic benefits are obtained, so that the ultra-low permeability oil reservoirs are classified, the main control factors influencing various ultra-low permeability oil reservoirs are found, and reasonable technical measures and methods can be provided for developing the ultra-low permeability oil reservoirs.
In the prior art, research on low-permeability oil deposit classification is focused on low-permeability oil deposits and ultra-low-permeability oil deposits, analysis on the ultra-low-permeability oil deposits is less, and meanwhile, reasonable classification basis for the ultra-low-permeability oil deposits is lacking, so that production and development of the ultra-low-permeability oil deposits are limited.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides an ultralow permeability oil reservoir classification method, which solves the problems that the analysis of the ultralow permeability oil reservoir is less and the reasonable classification basis of the ultralow permeability oil reservoir is lacking in the prior art, so that the production and development of the ultralow permeability oil reservoir are limited.
The technical scheme for solving the technical problems is as follows:
the invention provides an ultralow permeability oil reservoir classification method, which comprises the following steps:
acquiring preset parameter data of a plurality of ultralow permeability reservoirs, and acquiring main component information of each ultralow permeability reservoir based on a factor analysis method according to the preset parameter data;
performing cluster analysis on the principal component information to obtain classification information of each of the ultra-low permeability reservoirs;
according to the classification information, carrying out development effect evaluation on each type of ultra-low permeability oil reservoir in the classification information to obtain development effect evaluation information of each ultra-low permeability oil reservoir;
wherein, the preset parameter data comprises: the average drilling encounters a combination of one or more of effective thickness, porosity, original oil saturation, fluidity, volume coefficient, pressure coefficient, and fracture development.
Further, the preset parameter data includes: the average drilling encounters a combination of one or more of effective thickness, porosity, original oil saturation, fluidity, volume coefficient, pressure coefficient, and fracture development.
Further, the step of obtaining the main component information of each ultralow permeability reservoir based on the factor analysis method specifically includes: carrying out standardization processing on the preset parameter data of a plurality of ultra-low permeability reservoirs, and carrying out factor analysis on the standardized preset parameter data to obtain a factor analysis result; and determining the number of factors to be extracted by a principal component analysis method in combination with the factor analysis result to obtain a component matrix.
Further, the step of obtaining the component matrix by determining the number of factors to be extracted by the principal component analysis method in combination with the factor analysis result specifically includes: selecting a first main component, a second main component, a third main component and a fourth main component according to a preset total variance interpretation table and a lithotriptic diagram; wherein the first main component comprises parameters of original oil saturation and porosity; wherein the parameters contained in the second main component are volume coefficients; the parameters contained in the third main component are average drilling effective thickness and crack development degree; the fourth main component comprises parameters of pressure coefficient and fluidity.
Further, the step of performing cluster analysis on the principal component information to obtain classification information of each of the ultra-low permeability reservoirs in the plurality of ultra-low permeability reservoirs specifically includes: and carrying out cluster analysis on the principal component information, and dividing a plurality of ultra-low permeability oil reservoirs into three types to obtain classification information of each ultra-low permeability oil reservoir.
Further, according to the classification information, performing development effect evaluation on each type of ultra-low permeability oil reservoir in the classification information to obtain development effect evaluation information of each type of ultra-low permeability oil reservoir, which specifically comprises the following steps: correcting the standard Tong Shi template according to a preset low permeability reservoir Tong Shi template correction method to obtain an ultralow permeability reservoir block Tong Shi template; and predicting the recovery ratio of each type of ultra-low permeability oil deposit according to the ultra-low permeability oil deposit block Tong Shi plate to obtain the recovery ratio of each type of ultra-low permeability oil deposit.
Further, the method further comprises: and judging each type of ultralow permeability oil reservoir according to a diagramming method to obtain the attenuation characteristics of each type of ultralow permeability oil reservoir.
In a second aspect, the present invention provides an ultra-low permeability reservoir classification system comprising:
the factor analysis module is used for acquiring preset parameter data of a plurality of ultralow-permeability oil reservoirs, and acquiring main component information of each ultralow-permeability oil reservoir based on a factor analysis method according to the preset parameter data;
the clustering module is used for carrying out clustering analysis on the principal component information to obtain the classification information of each ultralow-permeability oil reservoir in a plurality of ultralow-permeability oil reservoirs;
the evaluation module is used for evaluating the development effect of each type of ultra-low permeability oil reservoir in the classification information according to the classification information to obtain development effect evaluation information of each ultra-low permeability oil reservoir;
wherein, the preset parameter data comprises: the average drilling encounters a combination of one or more of effective thickness, porosity, original oil saturation, fluidity, volume coefficient, pressure coefficient, and fracture development.
Further, the factor analysis module is specifically configured to:
carrying out standardization processing on the preset parameter data of a plurality of ultra-low permeability reservoirs, and carrying out factor analysis on the standardized preset parameter data to obtain a factor analysis result;
and determining the number of factors to be extracted by a principal component analysis method in combination with the factor analysis result to obtain a component matrix.
In a third aspect, the present invention provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the ultra low permeability reservoir classification method as provided in the first aspect above when the program is executed.
In a fourth aspect, embodiments of the present invention provide a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the ultra low permeability reservoir classification method as provided in the first aspect above.
The method and the system for classifying the ultralow-permeability oil deposit can effectively distinguish the development characteristics of various ultralow-permeability oil deposit blocks, the classification result is identical with the actual development characteristics, the classification basis can be provided for reasonable development of the ultralow-permeability oil deposit, and the production development of the ultralow-permeability oil deposit can be guided correctly.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an ultralow permeability reservoir classification method according to an embodiment of the present invention;
FIG. 2 is a crushed stone diagram in an ultralow permeability reservoir classification method according to an embodiment of the present invention;
FIG. 3 is a Tong Shi chart of an ultralow permeability reservoir type I ultralow permeability reservoir in an ultralow permeability reservoir classification method according to an embodiment of the present invention;
FIG. 4 is a Tong Shi chart of an ultralow permeability reservoir type II ultralow permeability reservoir in the method for classifying an ultralow permeability reservoir according to the embodiment of the present invention;
FIG. 5 is a Tong Shi chart of an ultralow permeability reservoir III class ultralow permeability reservoir in an ultralow permeability reservoir classification method according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a decreasing rate curve of an ultralow permeability reservoir in the method for classifying an ultralow permeability reservoir according to the embodiment of the present invention;
FIG. 7 is a schematic structural diagram of an ultralow permeability reservoir classification system according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the prior art, research on low-permeability oil deposit classification is focused on low-permeability oil deposit and ultra-low-permeability oil deposit, analysis on the ultra-low-permeability oil deposit is less, and meanwhile reasonable classification basis for the ultra-low-permeability oil deposit is lacking, so that production and development of the ultra-low-permeability oil deposit are limited. In order to solve the problem, an embodiment of the present invention provides an ultralow permeability oil reservoir classification method, and fig. 1 is a schematic flow chart of the ultralow permeability oil reservoir classification method provided by an embodiment of the present invention, as shown in fig. 1, where the method includes:
s1, acquiring preset parameter data of a plurality of ultra-low permeability reservoirs, and acquiring main component information of each ultra-low permeability reservoir based on a factor analysis method according to the preset parameter data.
S2, carrying out cluster analysis on the principal component information to obtain classification information of each ultra-low permeability oil reservoir in a plurality of ultra-low permeability oil reservoirs.
And S3, evaluating the development effect of each type of ultra-low permeability oil deposit in the classification information according to the classification information, and obtaining development effect evaluation information of each ultra-low permeability oil deposit.
Wherein, the preset parameter data includes but is not limited to: the average drilling encounters a combination of one or more of effective thickness, porosity, original oil saturation, fluidity, volume coefficient, pressure coefficient, and fracture development.
Specifically, in the present embodiment, the parameter data of 100 oil-bearing zones are obtained, where the parameter data includes, but is not limited to, a combination of one or more of average drilling effective thickness, porosity, original oil saturation, fluidity, volume coefficient, pressure coefficient, and crack development degree.
Combining the geology and development characteristics of the ultralow permeability oil reservoir, carrying out correlation analysis on dynamic and static parameters of the oil reservoir, and optimizing seven classification indexes; analyzing 100 ultra-low permeability blocks by combining a factor analysis method, extracting four main components, classifying by using a cluster analysis method, and carrying out discriminant analysis on the basis of classification; finally, dividing 100 oil reservoir blocks into three types, obtaining a discriminant function of each type of block, and carrying out dynamic analysis and development effect evaluation on the three types of oil reservoir blocks.
And (3) performing factor analysis on parameters of 100 ultralow permeability oil reservoir blocks, and selecting seven parameters including average drilling effective thickness, porosity, original oil saturation, fluidity, volume coefficient, pressure coefficient, crack development degree and the like for factor analysis. The number of factors to be extracted is determined using principal component analysis.
The factor analysis is to find out several factors capable of integrating the main information of all the variables from covariance matrix or correlation matrix of the variables as the entry point. The variable correlation in the same factor group is higher, and the variable correlation among different factor groups is poorer. The objective of factor analysis is to reduce the number of variables, replacing all variables with a few factors to analyze the entire problem.
In factor analysis, the statistics information includes: feature root and cumulative contribution.
Characteristic root: to describe how many original variables the main factor interprets on average. If the feature root of a certain factor is not greater than 1, it is interpreted that the factor cannot be interpreted even with an original variable, and thus the feature root is greater than 1 as a criterion for whether to extract the factor.
Cumulative contribution rate: the ratio of the sum of the extracted factor eigenvalues to the sum of all factor eigenvalues in the factor analysis is used to describe the representativeness of the extracted factors to all variables. The factor analysis aims at simplifying the process, and representing as many variables as possible by using as few factors as possible, so that the meaning of the variables is more definite.
The cluster analysis is a multi-element statistical method for researching 'physical aggregation', and divides data into a plurality of classes according to the Euclidean distance, so that the data difference in each class group is smaller, and the data difference between each class group is larger.
The discriminant analysis is to establish corresponding discriminant functions for each class according to known discriminant criteria, so as to judge which class the unknown object belongs to. The discriminant function is a linear combination of variables, and when each variable is distinguished, the variable indexes are brought into the discriminant function to obtain a discriminant score so as to judge the category number of the variable.
In this embodiment, 7 parameters of 100 blocks are normalized and factor-analyzed, and the number of factors to be extracted is determined by a principal component analysis method. The number of the finally selected main components is 4, the contribution rate of the main component 1 is 21.446%, the contribution rate of the main component 2 is 19.654%, the contribution rate of the main component 3 is 18.822%, the contribution rate of the main component 4 is 18.310%, and the cumulative contribution rate of the first four main components is 78.233%. And determining the number of the principal component factors to be 4 according to a principle of selecting a principal component analysis method of factor analysis.
And 4 main components are subjected to cluster analysis, and 100 ultralow permeability oil reservoir blocks are divided into 3 types. The division result is that the number of the ultra-low permeability oil reservoir blocks of the ultra-low permeability oil reservoir type I is 30, and the discriminant function is as follows:
the number of the III-class ultra-low permeability oil reservoir blocks of the ultra-low permeability oil reservoir is 25, and the discriminant function is as follows:
the number of the II-type ultralow permeability oil reservoirs of the ultralow permeability oil reservoirs is 45, and the discriminant function is
Wherein H is the average effective thickness when drilling;is porosity; s is S O Is the original oil saturation; k is permeability; mu is viscosity; c (C) r Is a crack; b (B) O Is the volume coefficient; η (eta) o Is the pressure coefficient.
And finally, predicting the ultra-low permeability oil reservoirs of each type according to the Tong Shi chart to obtain the final recovery ratio of the ultra-low permeability oil reservoirs of each type.
By the method, development characteristics of various ultralow-permeability oil reservoir blocks can be effectively distinguished, classification results are identical with actual development characteristics, classification basis can be provided for reasonable development of the ultralow-permeability oil reservoir, and production and development of the ultralow-permeability oil reservoir can be correctly guided.
Based on the foregoing embodiment, as an optional embodiment, the step of obtaining the principal component information of each of the ultralow permeability reservoirs based on the factor analysis method specifically includes: carrying out standardization processing on the preset parameter data of a plurality of ultra-low permeability reservoirs, and carrying out factor analysis on the standardized preset parameter data to obtain a factor analysis result; and determining the number of factors to be extracted by a principal component analysis method in combination with the factor analysis result to obtain a component matrix.
The step of obtaining the component matrix by determining the number of factors to be extracted by a principal component analysis method in combination with the factor analysis result specifically comprises the following steps: selecting a first main component, a second main component, a third main component and a fourth main component according to a preset total variance interpretation table and a lithotriptic diagram; wherein the first main component comprises parameters of original oil saturation and porosity; wherein the parameters contained in the second main component are volume coefficients; the parameters contained in the third main component are average drilling effective thickness and crack development degree; the fourth main component comprises parameters of pressure coefficient and fluidity.
The step of performing cluster analysis on the principal component information to obtain classification information of each of the ultra-low permeability reservoirs in the plurality of ultra-low permeability reservoirs specifically includes: and carrying out cluster analysis on the principal component information, and dividing a plurality of ultra-low permeability oil reservoirs into three types to obtain classification information of each ultra-low permeability oil reservoir.
According to the classification information, carrying out development effect evaluation on each type of ultra-low permeability oil reservoir in the classification information to obtain development effect evaluation information of each ultra-low permeability oil reservoir, wherein the method specifically comprises the following steps of: correcting the standard Tong Shi template according to a preset low permeability reservoir Tong Shi template correction method to obtain an ultralow permeability reservoir block Tong Shi template; and predicting the recovery ratio of each type of ultra-low permeability oil deposit according to the ultra-low permeability oil deposit block Tong Shi plate to obtain the recovery ratio of each type of ultra-low permeability oil deposit.
Based on the foregoing embodiment, as an optional embodiment, the method for classifying an ultralow permeability oil reservoir provided in this embodiment further includes: and judging each type of ultralow permeability oil reservoir according to a diagramming method to obtain the attenuation characteristics of each type of ultralow permeability oil reservoir.
Specifically, in this embodiment, the normalization processing is performed on 7 parameters of 100 blocks, and factor analysis is performed on the parameters, and the number of factors to be extracted is determined by using a principal component analysis method. As can be seen from the total variance interpretation table (table 1) and fig. 2, the number of selected principal components is 4, the principal component 1 contribution rate is 21.446%, the principal component 2 contribution rate is 19.654%, the principal component 3 contribution rate is 18.822%, the principal component 4 contribution rate is 18.310%, and the total contribution rate of the first four principal components is 78.233%. And determining the number of the principal component factors to be 4 according to a principle of selecting a principal component analysis method of factor analysis.
TABLE 1
From the rotated component matrix (table 2), the primary component 1 contains parameters of original oil saturation and porosity, the primary component 2 contains parameters of volume coefficient, the primary component 3 contains parameters of average effective drilling thickness and crack development degree, and the primary component 4 contains parameters of pressure coefficient and fluidity.
TABLE 2
And 4 main components are subjected to cluster analysis, and 100 ultralow permeability oil reservoir blocks are divided into 3 types. The partitioning results (table 3, table 4) are 30 ultra-low permeability reservoir class I ultra-low permeability reservoir blocks, 25 ultra-low permeability reservoir class III ultra-low permeability reservoir blocks, and 45 ultra-low permeability reservoir class II ultra-low permeability reservoirs.
TABLE 3 Table 3
TABLE 4 Table 4
Tong Shi plate can be used to predict the final recovery in the development process based on the relationship between water cut and recovery. And correcting the standard Tong Shi template by using a Yang Yandi permeable oil reservoir Tong Shi template correction method to obtain a template suitable for the ultralow permeability oil reservoir block Tong Shi, so as to predict the final recovery ratio of the ultralow permeability oil reservoir block.
As shown in fig. 3, the ultra-low permeability reservoir type I reservoir is high in abundance, water drive control is high, and the final recovery rate of the reservoir is predicted to be 25% according to the corrected ultra-low permeability reservoir Tong Shi plate.
As shown in fig. 4, the ultra-low permeability reservoir type II reservoir has large depth, small pressure coefficient, central water drive control, and centered geological reserves, and the final recovery rate of the reservoir is predicted to be 20% according to the corrected ultra-low permeability reservoir Tong Shi chart.
As shown in fig. 5, the ultra-low permeability reservoir type III reservoir has small abundance, large pressure coefficient, small water drive control, small reserve of geology, and 15% of final recovery of the reservoir is predicted according to the corrected ultra-low permeability reservoir Tong Shi plate.
In the development process of an oil reservoir, the oil reservoir must undergo a yield decreasing process, and the decreasing period is far longer than the yield increasing period and the yield stabilizing period, so that the research of the decreasing curve has important significance for the development of the oil reservoir. The existing judgment method of the decreasing curve mainly comprises a drawing method, a trial-and-error method, a curve displacement method, a typical curve fitting method and a binary regression method. The three types of ultralow permeability oil reservoirs are judged to belong to attenuation decrements through a graphical method, the decrementing indexes are 0.5, the initial decrementing rate of the ultralow permeability oil reservoir type I is 1.28%, the initial decrementing yield is 45.47 tons/month, the initial decrementing rate of the ultralow permeability oil reservoir type II is 2.33%, the initial decrementing yield is 63.64 tons/month, the initial decrementing rate of the ultralow permeability oil reservoir type III is 19.55%, and the initial decrementing yield is 54.88 tons/month.
The relation of the attenuation decreasing rate is that
a＝a i (1+0.5a i t) -1
Wherein a is the reduction rate; ai is the initial taper rate; t is time.
As shown in fig. 6, the ultra-low permeability reservoir class I decline rate is less over time, the ultra-low permeability reservoir class II decline rate is centered over time, and the ultra-low permeability reservoir class III decline rate is greater over time.
In summary, the method provided by the embodiment of the invention can extract the common factors of the oil reservoir characteristics, and the influence of the artificial subjective factors can be reduced by carrying out cluster analysis on the common factors. The classification method can effectively identify the main characteristics of the ultralow permeability reservoir blocks of different categories. The ultra-low permeability oil reservoir type I oil reservoir has large abundance, large water drive control degree and large used geological reserves; the depth of the ultra-low permeability oil reservoir II type oil reservoir is large, the pressure coefficient is small, the water drive control degree is centered, and the geological reserve is centered; the ultra-low permeability oil reservoir III-type oil reservoir has small abundance, large pressure coefficient, small water drive control degree and small used geological reserves.
And various oil reservoir blocks are matched with actual development and production dynamic data. The final recovery ratio of the type I ultra-low permeability oil reservoir is 25%; the final recovery ratio of the type II ultra-low permeability oil reservoir is 20%; the final recovery ratio of the III class of the ultralow permeability oil reservoir is 15%.
Each type of reservoir block has different decrementing characteristics. The initial reduction rate of the type I of the ultralow-permeability oil reservoir is 1.28%, and the change rate of the reduction rate curve is small; the initial reduction rate of the type II oil deposit with ultralow permeability is 2.33%; the initial reduction rate of the III class of the ultralow-permeability oil reservoir is 19.55%, and the change rate of the reduction rate curve is large. Therefore, the classification method can provide scientific basis for the classification of the ultralow-permeability oil reservoirs, and has reference significance for researching the exploitation effects of different types of ultralow-permeability oil reservoirs and improving the recovery ratio of the ultralow-permeability oil reservoirs.
Referring to fig. 7, fig. 7 is a schematic structural diagram of an ultralow permeability reservoir classification system according to an embodiment of the present invention, where the system includes: factor analysis module 71, clustering module 72 and evaluation module 73.
The factor analysis module 71 is configured to obtain preset parameter data of a plurality of ultra-low permeability reservoirs, and obtain principal component information of each of the ultra-low permeability reservoirs based on a factor analysis method according to the preset parameter data;
the factor analysis module 71 is specifically configured to:
carrying out standardization processing on the preset parameter data of a plurality of ultra-low permeability reservoirs, and carrying out factor analysis on the standardized preset parameter data to obtain a factor analysis result;
and determining the number of factors to be extracted by a principal component analysis method in combination with the factor analysis result to obtain a component matrix.
The clustering module 72 is configured to perform cluster analysis on the principal component information to obtain classification information of each of the ultra-low permeability reservoirs;
the evaluation module 73 is configured to evaluate the development effect of each type of the ultra-low permeability oil reservoir in the classification information according to the classification information, so as to obtain development effect evaluation information of each type of the ultra-low permeability oil reservoir;
wherein, the preset parameter data includes but is not limited to: the average drilling encounters a combination of one or more of effective thickness, porosity, original oil saturation, fluidity, volume coefficient, pressure coefficient, and fracture development.
It should be noted that, the factor analysis module 71, the clustering module 72 and the evaluation module 73 cooperate to execute the method for classifying an ultralow permeability reservoir in the above embodiment, and specific functions of the system are referred to the embodiment of the method for classifying an ultralow permeability reservoir described above, and are not repeated herein.
Fig. 8 illustrates a schematic structural diagram of an electronic device, and as shown in fig. 8, the server may include: processor 810, communication interface (Communications Interface) 820, memory 830, and bus 840, wherein processor 810, communication interface 820, memory 830 accomplish communication with each other through bus 840. The communication interface 820 may be used for information transfer between a server and a smart television. The processor 810 may invoke logic instructions in the memory 830 to perform the following ultra-low permeability reservoir classification method: acquiring preset parameter data of a plurality of ultralow permeability reservoirs, and acquiring main component information of each ultralow permeability reservoir based on a factor analysis method according to the preset parameter data; performing cluster analysis on the principal component information to obtain classification information of each of the ultra-low permeability reservoirs; according to the classification information, carrying out development effect evaluation on each type of ultra-low permeability oil reservoir in the classification information to obtain development effect evaluation information of each ultra-low permeability oil reservoir; wherein, the preset parameter data includes but is not limited to: the average drilling encounters a combination of one or more of effective thickness, porosity, original oil saturation, fluidity, volume coefficient, pressure coefficient, and fracture development.
The present embodiment also provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, are capable of performing the method of classifying an ultra-low permeability reservoir provided by the above method embodiments, for example comprising: acquiring preset parameter data of a plurality of ultralow permeability reservoirs, and acquiring main component information of each ultralow permeability reservoir based on a factor analysis method according to the preset parameter data; performing cluster analysis on the principal component information to obtain classification information of each of the ultra-low permeability reservoirs; according to the classification information, carrying out development effect evaluation on each type of ultra-low permeability oil reservoir in the classification information to obtain development effect evaluation information of each ultra-low permeability oil reservoir; wherein, the preset parameter data includes but is not limited to: the average drilling encounters a combination of one or more of effective thickness, porosity, original oil saturation, fluidity, volume coefficient, pressure coefficient, and fracture development.
The present embodiment provides a non-transitory computer-readable storage medium storing computer instructions that cause a computer to perform the methods provided by the above-described method embodiments, for example, including: acquiring preset parameter data of a plurality of ultralow permeability reservoirs, and acquiring main component information of each ultralow permeability reservoir based on a factor analysis method according to the preset parameter data; performing cluster analysis on the principal component information to obtain classification information of each of the ultra-low permeability reservoirs; according to the classification information, carrying out development effect evaluation on each type of ultra-low permeability oil reservoir in the classification information to obtain development effect evaluation information of each ultra-low permeability oil reservoir; wherein, the preset parameter data includes but is not limited to: the average drilling encounters a combination of one or more of effective thickness, porosity, original oil saturation, fluidity, volume coefficient, pressure coefficient, and fracture development.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (4)
1. An ultra-low permeability reservoir classification method, comprising:
acquiring preset parameter data of a plurality of ultralow permeability reservoirs, and acquiring main component information of each ultralow permeability reservoir based on a factor analysis method according to the preset parameter data;
performing cluster analysis on the principal component information to obtain classification information of each of the ultra-low permeability reservoirs;
according to the classification information, carrying out development effect evaluation on each type of ultra-low permeability oil reservoir in the classification information to obtain development effect evaluation information of each ultra-low permeability oil reservoir;
wherein, the preset parameter data comprises: average drilling effective thickness, porosity, original oil saturation, fluidity, volume coefficient, pressure coefficient and crack development degree;
the step of obtaining the main component information of each ultralow permeability reservoir based on the factor analysis method specifically comprises the following steps:
carrying out standardization processing on the preset parameter data of a plurality of ultra-low permeability reservoirs, and carrying out factor analysis on the standardized preset parameter data to obtain a factor analysis result;
determining the number of factors to be extracted by a principal component analysis method in combination with the factor analysis result to obtain a component matrix;
the step of obtaining the component matrix by determining the number of factors to be extracted by a principal component analysis method in combination with the factor analysis result specifically comprises the following steps:
selecting a first main component, a second main component, a third main component and a fourth main component according to a preset total variance interpretation table and a lithotriptic diagram;
wherein the first main component comprises parameters of original oil saturation and porosity; the parameters contained in the second main component are volume coefficients; the parameters contained in the third main component are the average drilling effective thickness and the crack development degree; the fourth main component comprises parameters of pressure coefficient and fluidity;
the step of performing cluster analysis on the principal component information to obtain classification information of each of the ultra-low permeability reservoirs in the plurality of ultra-low permeability reservoirs specifically includes:
performing cluster analysis on the principal component information, and dividing a plurality of ultra-low permeability reservoirs into three types to obtain classification information of each ultra-low permeability reservoir;
the three types are specifically: the ultra-low permeability oil reservoir type I oil reservoir has large abundance, large water drive control degree and large used geological reserves; the depth of the ultra-low permeability oil reservoir II type oil reservoir is large, the pressure coefficient is small, the water drive control degree is centered, and the geological reserve is centered; the ultra-low permeability oil reservoir III-type oil reservoir has small abundance, large pressure coefficient, small water drive control degree and small used geological reserves;
according to the classification information, carrying out development effect evaluation on each type of ultra-low permeability oil reservoir in the classification information to obtain development effect evaluation information of each ultra-low permeability oil reservoir, wherein the method specifically comprises the following steps of:
correcting the standard Tong Shi template according to a preset low permeability reservoir Tong Shi template correction method to obtain an ultralow permeability reservoir block Tong Shi template;
predicting the recovery ratio of each type of ultra-low permeability oil deposit according to the Tong Shi plate of the ultra-low permeability oil deposit block to obtain the recovery ratio of each type of ultra-low permeability oil deposit;
the method further comprises the steps of: judging each type of ultralow permeability oil reservoir according to a diagramming method to obtain attenuation characteristics of each type of ultralow permeability oil reservoir;
the three types of oil reservoir blocks have different decreasing characteristics; the initial reduction rate of the type I of the ultralow-permeability oil reservoir is 1.28%, and the change rate of the reduction rate curve is small; the initial reduction rate of the type II oil deposit with ultralow permeability is 2.33%; the initial reduction rate of the III class of the ultralow-permeability oil reservoir is 19.55%, and the change rate of the reduction rate curve is large.
2. An ultra-low permeability reservoir classification system, comprising:
the factor analysis module is used for acquiring preset parameter data of a plurality of ultralow-permeability oil reservoirs, and acquiring main component information of each ultralow-permeability oil reservoir based on a factor analysis method according to the preset parameter data; the clustering module is used for carrying out clustering analysis on the principal component information to obtain the classification information of each ultralow-permeability oil reservoir in a plurality of ultralow-permeability oil reservoirs;
the evaluation module is used for evaluating the development effect of each type of ultra-low permeability oil reservoir in the classification information according to the classification information to obtain development effect evaluation information of each ultra-low permeability oil reservoir;
wherein, the preset parameter data comprises: average drilling effective thickness, porosity, original oil saturation, fluidity, volume coefficient, pressure coefficient and crack development degree;
the step of obtaining the main component information of each ultralow permeability reservoir based on the factor analysis method specifically comprises the following steps:
carrying out standardization processing on the preset parameter data of a plurality of ultra-low permeability reservoirs, and carrying out factor analysis on the standardized preset parameter data to obtain a factor analysis result;
determining the number of factors to be extracted by a principal component analysis method in combination with the factor analysis result to obtain a component matrix;
the step of obtaining the component matrix by determining the number of factors to be extracted by a principal component analysis method in combination with the factor analysis result specifically comprises the following steps:
selecting a first main component, a second main component, a third main component and a fourth main component according to a preset total variance interpretation table and a lithotriptic diagram;
wherein the first main component comprises parameters of original oil saturation and porosity; the parameters contained in the second main component are volume coefficients; the parameters contained in the third main component are the average drilling effective thickness and the crack development degree; the fourth main component comprises parameters of pressure coefficient and fluidity;
the step of performing cluster analysis on the principal component information to obtain classification information of each of the ultra-low permeability reservoirs in the plurality of ultra-low permeability reservoirs specifically includes:
performing cluster analysis on the principal component information, and dividing a plurality of ultra-low permeability reservoirs into three types to obtain classification information of each ultra-low permeability reservoir;
the three types are specifically: the ultra-low permeability oil reservoir type I oil reservoir has large abundance, large water drive control degree and large used geological reserves; the depth of the ultra-low permeability oil reservoir II type oil reservoir is large, the pressure coefficient is small, the water drive control degree is centered, and the geological reserve is centered; the ultra-low permeability oil reservoir III-type oil reservoir has small abundance, large pressure coefficient, small water drive control degree and small used geological reserves;
according to the classification information, carrying out development effect evaluation on each type of ultra-low permeability oil reservoir in the classification information to obtain development effect evaluation information of each ultra-low permeability oil reservoir, wherein the method specifically comprises the following steps of:
correcting the standard Tong Shi template according to a preset low permeability reservoir Tong Shi template correction method to obtain an ultralow permeability reservoir block Tong Shi template;
predicting the recovery ratio of each type of ultra-low permeability oil deposit according to the Tong Shi plate of the ultra-low permeability oil deposit block to obtain the recovery ratio of each type of ultra-low permeability oil deposit;
the method further comprises the steps of: judging each type of ultralow permeability oil reservoir according to a diagramming method to obtain attenuation characteristics of each type of ultralow permeability oil reservoir;
the three types of oil reservoir blocks have different decreasing characteristics; the initial reduction rate of the type I of the ultralow-permeability oil reservoir is 1.28%, and the change rate of the reduction rate curve is small; the initial reduction rate of the type II oil deposit with ultralow permeability is 2.33%; the initial reduction rate of the III class of the ultralow-permeability oil reservoir is 19.55%, and the change rate of the reduction rate curve is large.
3. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor performs the steps of the ultra-low permeability reservoir classification method according to claim 1.
4. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of the ultra-low permeability reservoir classification method according to claim 1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910810191.0A CN110656924B (en) | 2019-08-29 | 2019-08-29 | Ultra-low permeability oil reservoir classification method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910810191.0A CN110656924B (en) | 2019-08-29 | 2019-08-29 | Ultra-low permeability oil reservoir classification method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110656924A CN110656924A (en) | 2020-01-07 |
| CN110656924B true CN110656924B (en) | 2023-08-22 |
Family
ID=69036543
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910810191.0A Active CN110656924B (en) | 2019-08-29 | 2019-08-29 | Ultra-low permeability oil reservoir classification method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110656924B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111242490A (en) * | 2020-01-15 | 2020-06-05 | 长江大学 | Low-permeability reservoir characteristic master control factor extraction method and device and readable storage medium |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7963327B1 (en) * | 2008-02-25 | 2011-06-21 | QRI Group, LLC | Method for dynamically assessing petroleum reservoir competency and increasing production and recovery through asymmetric analysis of performance metrics |
| CN103670384A (en) * | 2013-11-18 | 2014-03-26 | 中国石油天然气集团公司 | Reservoir classification method and system |
| CN104699954A (en) * | 2015-01-30 | 2015-06-10 | 中国地质大学（武汉） | Calibration method for oil recovery rate of karst-fissure-cavity carbonatite reservoir |
| 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 |
| US9946986B1 (en) * | 2011-10-26 | 2018-04-17 | QRI Group, LLC | Petroleum reservoir operation using geotechnical analysis |
| CN108229051A (en) * | 2018-01-18 | 2018-06-29 | 陕西延长石油(集团)有限责任公司研究院 | A kind of method of oil reservoir prediction air foam flooding shaft recovery ratio |
| CN108843286A (en) * | 2018-06-21 | 2018-11-20 | 中国石油大港油田勘探开发研究院 | The technical method of well is selected in a kind of Novel layered oil recovery |
| CN109636094A (en) * | 2018-10-30 | 2019-04-16 | 中国石油天然气股份有限公司 | Low-permeability oilfield reservoir type identification method and device |
| CN109711429A (en) * | 2018-11-22 | 2019-05-03 | 中国石油天然气股份有限公司 | Reservoir evaluation classification method and device |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9416647B2 (en) * | 2012-01-31 | 2016-08-16 | Schlumberger Technology Corporation | Methods and apparatus for characterization of hydrocarbon reservoirs |
| FR2998396B1 (en) * | 2012-11-20 | 2015-01-16 | Total Sa | METHOD FOR EVALUATING A QUANTITY OF HYDROCARBONS IN A STORAGE |
| US10101484B2 (en) * | 2014-03-03 | 2018-10-16 | Schlumberger Technology Corporation | Assessing risks of compartmentalization |
| US10884147B2 (en) * | 2017-12-08 | 2021-01-05 | Chevron U.S.A. Inc. | System and method for reservoir facies classification that segments both petrophysical and geophysical properties |
-
2019
- 2019-08-29 CN CN201910810191.0A patent/CN110656924B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7963327B1 (en) * | 2008-02-25 | 2011-06-21 | QRI Group, LLC | Method for dynamically assessing petroleum reservoir competency and increasing production and recovery through asymmetric analysis of performance metrics |
| US9946986B1 (en) * | 2011-10-26 | 2018-04-17 | QRI Group, LLC | Petroleum reservoir operation using geotechnical analysis |
| CN103670384A (en) * | 2013-11-18 | 2014-03-26 | 中国石油天然气集团公司 | Reservoir classification method and system |
| CN104699954A (en) * | 2015-01-30 | 2015-06-10 | 中国地质大学（武汉） | Calibration method for oil recovery rate of karst-fissure-cavity carbonatite reservoir |
| 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 |
| CN108229051A (en) * | 2018-01-18 | 2018-06-29 | 陕西延长石油(集团)有限责任公司研究院 | A kind of method of oil reservoir prediction air foam flooding shaft recovery ratio |
| CN108843286A (en) * | 2018-06-21 | 2018-11-20 | 中国石油大港油田勘探开发研究院 | The technical method of well is selected in a kind of Novel layered oil recovery |
| CN109636094A (en) * | 2018-10-30 | 2019-04-16 | 中国石油天然气股份有限公司 | Low-permeability oilfield reservoir type identification method and device |
| CN109711429A (en) * | 2018-11-22 | 2019-05-03 | 中国石油天然气股份有限公司 | Reservoir evaluation classification method and device |
Non-Patent Citations (1)
| Title |
|---|
| 低渗透油藏综合分类方法;陈新彬等;《大庆石油地质与开发》;20140201;第33卷(第1期);第58-61页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110656924A (en) | 2020-01-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108009716B (en) | Multi-level evaluation method for influence factors of horizontal well volume fracturing effect | |
| CN112228054B (en) | Method, device and equipment for determining shale gas yield based on convolutional neural network | |
| CN108181219B (en) | Method and device for predicting permeability based on carbonate rock pore structure | |
| CN108960651B (en) | Comprehensive evaluation method for completion efficiency of compact oil and gas reservoir multi-stage fractured horizontal well | |
| CN111046341A (en) | Unconventional natural gas fracturing effect evaluation and capacity prediction method based on principal component analysis | |
| CN115345378B (en) | Shale gas well yield evaluation method based on machine learning | |
| CN105089642B (en) | Method and device for identifying abnormal production well of oil and gas reservoir | |
| CN111242490A (en) | Low-permeability reservoir characteristic master control factor extraction method and device and readable storage medium | |
| CN110656924B (en) | Ultra-low permeability oil reservoir classification method | |
| CN111028095A (en) | Method for quantitatively identifying shale lithofacies based on well logging curve | |
| CN113052374B (en) | Data-driven intelligent prediction method for casing loss depth of oil well | |
| CN109031461A (en) | Carbonate porosity type oil and gas reservoir quantitative identification method and region quantitative method | |
| CN113762614A (en) | Shale gas well productivity prediction method based on static oil and gas field data | |
| CN110222981B (en) | Reservoir classification evaluation method based on parameter secondary selection | |
| CN112800590A (en) | Grid coarsening method for machine learning-assisted two-phase flow oil reservoir random modeling | |
| CN117332668A (en) | Shale gas well fracturing effect analysis method and system based on data driving | |
| CN117313919A (en) | Horizontal well casing variable working condition prediction method, system, equipment and storage medium | |
| CN114581666B (en) | Rock sample intelligent identification method and system based on image deep learning | |
| CN116562428A (en) | Fracturing construction parameter optimization method based on machine learning | |
| CN114895371A (en) | Method for dividing surface karst interface with bottom based on dynamic time sequence matching technology | |
| CN109254330B (en) | Identification method and device for fractured segments of igneous rock reservoir | |
| CN104462792A (en) | Log data lithological stratum numerical value reduction method | |
| CN110717528A (en) | Support vector machine-based sedimentary microfacies identification method using conventional logging information | |
| CN116226623B (en) | Mark layer division method and device based on SegNet segmentation model and computer equipment | |
| CN118154458B (en) | Filtering processing method of machine tool cutter image based on complex industrial environment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |