CN111046585B - Shale gas dessert prediction method based on multiple linear regression analysis - Google Patents
Shale gas dessert prediction method based on multiple linear regression analysis Download PDFInfo
- Publication number
- CN111046585B CN111046585B CN201911383648.0A CN201911383648A CN111046585B CN 111046585 B CN111046585 B CN 111046585B CN 201911383648 A CN201911383648 A CN 201911383648A CN 111046585 B CN111046585 B CN 111046585B
- Authority
- CN
- China
- Prior art keywords
- shale
- dessert
- geological
- gas
- area
- 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
- 235000021185 dessert Nutrition 0.000 title claims abstract description 70
- 238000000034 method Methods 0.000 title claims abstract description 32
- 238000012417 linear regression Methods 0.000 title claims abstract description 26
- 235000011850 desserts Nutrition 0.000 claims abstract description 33
- 238000009826 distribution Methods 0.000 claims abstract description 28
- 238000005259 measurement Methods 0.000 claims abstract description 6
- 238000009933 burial Methods 0.000 claims description 17
- 229910052500 inorganic mineral Inorganic materials 0.000 claims description 15
- 239000011707 mineral Substances 0.000 claims description 15
- 230000035699 permeability Effects 0.000 claims description 11
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 10
- 229910052799 carbon Inorganic materials 0.000 claims description 10
- 238000012360 testing method Methods 0.000 claims description 5
- 230000000007 visual effect Effects 0.000 abstract description 3
- 206010017076 Fracture Diseases 0.000 description 12
- 238000011160 research Methods 0.000 description 12
- 208000010392 Bone Fractures Diseases 0.000 description 11
- 238000004321 preservation Methods 0.000 description 11
- 230000008021 deposition Effects 0.000 description 10
- 238000005553 drilling Methods 0.000 description 10
- 230000002093 peripheral effect Effects 0.000 description 10
- 230000002349 favourable effect Effects 0.000 description 7
- 239000005416 organic matter Substances 0.000 description 6
- 230000008859 change Effects 0.000 description 5
- 238000011161 development Methods 0.000 description 5
- 238000011156 evaluation Methods 0.000 description 5
- 230000015572 biosynthetic process Effects 0.000 description 4
- 238000010276 construction Methods 0.000 description 4
- 238000005755 formation reaction Methods 0.000 description 4
- 239000004215 Carbon black (E152) Substances 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 3
- 229930195733 hydrocarbon Natural products 0.000 description 3
- 150000002430 hydrocarbons Chemical class 0.000 description 3
- 238000011835 investigation Methods 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 208000035126 Facies Diseases 0.000 description 2
- XUIMIQQOPSSXEZ-UHFFFAOYSA-N Silicon Chemical compound [Si] XUIMIQQOPSSXEZ-UHFFFAOYSA-N 0.000 description 2
- 238000010219 correlation analysis Methods 0.000 description 2
- 230000003247 decreasing effect Effects 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 239000011148 porous material Substances 0.000 description 2
- 239000011435 rock Substances 0.000 description 2
- 239000013049 sediment Substances 0.000 description 2
- 229910052710 silicon Inorganic materials 0.000 description 2
- 239000010703 silicon Substances 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000003860 storage Methods 0.000 description 2
- 230000000035 biogenic effect Effects 0.000 description 1
- 239000003153 chemical reaction reagent Substances 0.000 description 1
- 239000002734 clay mineral Substances 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 235000009508 confectionery Nutrition 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000003921 oil Substances 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 239000003208 petroleum Substances 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000013139 quantization Methods 0.000 description 1
- 238000000611 regression analysis Methods 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Classifications
-
- 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
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- 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
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Animal Husbandry (AREA)
- Life Sciences & Earth Sciences (AREA)
- Agronomy & Crop Science (AREA)
- Mining & Mineral Resources (AREA)
- General Health & Medical Sciences (AREA)
- Marine Sciences & Fisheries (AREA)
- Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The application provides a shale gas dessert prediction method based on multiple linear regression analysis, and belongs to the field of shale gas. It comprises the following steps: obtaining reservoir desserts, storing various geological parameters in desserts and pressure coefficient desserts, evaluating geological feature parameters related to shale gas content, quantifying the geological feature parameters into a plane distribution map, performing linear regression fitting on each geological feature parameter and the gas content to obtain regression equations of each geological feature parameter and the gas content, wherein the weight of each geological feature parameter is a correlation coefficient of the regression equation corresponding to the geological feature parameter, and quantifying and superposing according to actual measurement values of each geological feature parameter and the weight corresponding to each geological feature parameter to obtain the shale gas dessert prediction model of the shale reservoir in the region to be explored. The dessert distribution position obtained by the prediction method is finer and more accurate, and the sequential area display of desserts is more visual, so that subjective judgment errors caused by simple graph layer stacking can be avoided.
Description
Technical Field
The application relates to the field of shale gas, in particular to a shale gas dessert prediction method based on multiple linear regression analysis.
Background
Shale gas is rich in resources, and is hopeful to relieve the facing energy crisis. However, due to the complexity of shale formations, the exploration and development difficulties are great, and the shale gas drilling cost is far higher than that of conventional petroleum drilling. This requires accurate prediction and identification of "sweet spot" potential areas of future exploration when shale gas reservoirs are being developed.
According to Jarvie et al (2007), reference indexes given to North American shale desserts are mainly static parameters, and comprise three types of shale distribution (burial depth, shale thickness and transverse distribution and fracture distribution); shale composition (organic type and abundance, clay minerals, brittle minerals); shale properties (thermal maturity, porosity, permeability). However, the four-river basin and the peripheral sea shale layer in China have high thermal evolution degree, long burying time and complex geological conditions due to the fact that the construction period is too long. The shale gas content influencing factors are influenced by the early-stage static deposition indexes and further by the preservation parameter change caused by the later-stage construction movement. It is known that existing north american shale evaluation methods are difficult to meet predictions of sea shale desserts.
In addition, the existing methods for evaluating the geological parameters of the dessert and optimizing the dessert area mainly comprise qualitative description, simple subjective assignment scoring, multi-parameter comprehensive evaluation, single evaluation by simple superposition and preservation condition indexes, and the like. In the prediction process, the geological characteristic parameters are difficult to quantify, and the result of the superposition method prediction has poor comparability; meanwhile, the weight of each geological characteristic parameter on the influence of the air content is not considered, and the change of the dessert area or trend in the research area cannot be judged by the prediction plan obtained by the superposition method.
Disclosure of Invention
The application aims to provide a shale gas dessert prediction method based on multiple linear regression analysis.
In order to achieve the above object of the present application, the following technical solutions are specifically adopted:
a shale gas dessert prediction method based on multiple linear regression analysis comprises the following steps: obtaining reservoir desserts, storing various geological parameters in desserts and pressure coefficient desserts, evaluating geological feature parameters related to shale gas content, quantifying the geological feature parameters into a plane distribution map, performing linear regression fitting on each geological feature parameter and the gas content to obtain regression equations of each geological parameter and the gas content, wherein the weight of each geological parameter is a correlation coefficient of the regression equation corresponding to the geological parameter, and obtaining a shale gas dessert prediction model of the shale reservoir in the area to be explored after quantized superposition according to actual measurement values of the geological parameters and the weights corresponding to the geological parameters.
Further, in a preferred embodiment of the present application, after obtaining the regression equation, the method further includes performing a significance test on the regression equation and the regression coefficient.
Further, in a preferred embodiment of the present application, the geological parameters in the above-mentioned reservoir dessert include: organic carbon content, degree of thermal evolution, porosity, permeability and brittle mineral content; preserving geologic parameters in the dessert includes: the burial depth and outcrop distance; the geological parameters in the pressure coefficient dessert include pressure coefficients.
Further, in a preferred embodiment of the present application, the above-mentioned predictive model of shale gas desserts is as follows:
Gas＝a·Dep+b·TOC+c·R o +d·φ+e·K+f·BM+g·P+h·Dist
wherein, dep is the burial depth of the shale reservoir in the area to be explored; TOC is the organic carbon content of shale reservoirs in the area to be explored; r is R o The thermal evolution degree of the shale reservoir in the area to be explored; phi is the porosity of the shale reservoir in the area to be explored; k is the permeability of the shale reservoir in the area to be explored; BM is the brittle mineral content in shale reservoirs in the area to be explored; p is the pressure coefficient of the shale reservoir in the area to be explored; dist is the shale outcrop distance in the shale reservoir of the area to be explored; a. b, c, d, e, f, g and h are correlation coefficients in regression equations obtained by linear regression fitting of the corresponding geological parameters and the gas content.
Further, in the preferred embodiment of the application, the gas content of the shale gas dessert is more than or equal to 2m 3 /t。
Further, in the preferred embodiment of the application, the organic carbon content of the shale gas dessert is more than or equal to 2.5%.
Further, in a preferred embodiment of the present application, the shale gas dessert has a thermal evolution degree of 1.1-3.0%.
Further, in a preferred embodiment of the application, the brittle mineral content of the shale gas dessert is greater than or equal to 40%.
Compared with the prior art, the application has the beneficial effects that:
according to the shale gas dessert prediction method based on multiple linear regression analysis, provided by the application, the influence of various geological parameters in reservoir desserts, stored desserts and pressure coefficient desserts on shale gas content is systematically analyzed, a relation model between each geological parameter and gas content is established, and geological parameter weights of desserts in different structural areas are combined with multiple regression analysis to quantitatively predict the distribution of shale gas desserts in shale reservoirs in areas to be explored. The dessert area dividing position obtained by the prediction method is more refined and accurate, and the sequential area display of desserts is more visual, so that subjective judgment errors caused by simple graph layer stacking are avoided, and missing desserts are searched for future establishment of a mature detection area. The method is favorable for quantitatively selecting shale gas desserts in complex-structure areas under low exploration degree.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below.
FIG. 1 is a graph showing the quantitative distribution of the deposition phases of the Lommax group in the research area according to the embodiment of the present application.
FIG. 2 is a graph of gas content in a study area versus shale thickness in a Loumari group according to an embodiment of the present application.
Fig. 3 is a graph showing the thickness distribution of organic-rich mudstone in the longmaxi group of the research area according to the embodiment of the present application.
Fig. 4 is a graph of the relationship between gas content in a research area and organic carbon content in shale of the longmaxi group according to an embodiment of the present application.
FIG. 5 is a graph of the organic carbon content profile of the Lommaxi group of the research area provided by the example of the present application.
Fig. 6 is a graph of the relationship between gas content in a research area and the degree of thermal evolution of shale in a longmaxi group according to an embodiment of the present application.
Fig. 7 is a graph of the degree of thermal evolution of the longmaxi group in the research area according to the embodiment of the present application.
FIG. 8 is a graph of the porosity profile of the Lommaxi group of the study area provided in the example of the present application.
FIG. 9 is a graph of gas content versus porosity for a study area provided in an embodiment of the present application.
FIG. 10 is a graph showing the relationship between gas content and specific surface area in a study area according to an embodiment of the present application.
FIG. 11 is a graph of gas content versus permeability for a study area provided in an embodiment of the present application.
Fig. 12 is a graph of the triangular relationship of mineral components in a study area provided by an embodiment of the present application.
FIG. 13 is a graph showing the relationship between the silicon content and the organic carbon content of the biological origin of the research area according to the embodiment of the present application.
FIG. 14 is a graph of brittle minerals of the Lommaxi group of the study area provided by the example of the present application.
FIG. 15 is a graph of the distance outcrop of the residual syncline Drama stream at the rim of a Sichuan basin provided by an embodiment of the present application.
FIG. 16 is a graph of gas content in a study area versus shale outcrop exposure distance provided by an embodiment of the application.
FIG. 17 is a contour plot of the burial depths of the Loumari group of the research area provided by an embodiment of the present application.
FIG. 18 is a graph showing the slope of the formation of the Lommax group of the study area according to an embodiment of the present application.
FIG. 19 is a contour plot of the burial depths of the Loumari group of the research area provided by an embodiment of the present application.
FIG. 20 is a plot of a fit of the influence factors of each dessert provided in an embodiment of the present application.
FIG. 21 is a graph of multiple regression parameters and test results provided by an embodiment of the present application.
Fig. 22 is a graph of predicted dessert distribution for a complex region of a Sichuan basin and surrounding sea shale, provided by an embodiment of the present application.
Detailed Description
Embodiments of the present application will be described in detail below with reference to examples, but it will be understood by those skilled in the art that the following examples are only for illustrating the present application and should not be construed as limiting the scope of the present application. The specific conditions are not noted in the examples and are carried out according to conventional conditions or conditions recommended by the manufacturer. The reagents or apparatus used were conventional products commercially available without the manufacturer's attention.
The embodiment provides a shale gas dessert prediction method based on multiple linear regression analysis.
Since the definition of "dessert" in the prior art is mostly a standardized description, cannot be quantified, and the definition of "dessert" by different scholars is different. In the present application, "dessert" refers to hydrocarbon-rich intervals and zones that can be effectively developed under current economic and technical conditions, considering wide applicability to gas content>2m 3 And/t is used as a reference index. That is, in this patent, the lower limit of shale gas dessert gas content is 2m 3 /t。
In addition, the method of the present application will be described in detail with respect to the selection of the Sichuan basin and the peripheral area as the application area of the method of the present embodiment. In other words, the methods of embodiments of the present application may also be applied to dessert predictions for other beneficial regions of shale gas.
The shale gas dessert prediction method based on multiple linear regression analysis comprises the following steps:
step 1: shale gas geological feature parameter information of a plurality of shale gas drilling wells similar to geological environments of shale reservoirs in areas to be explored is analyzed, so that each geological parameter in gas content, reservoir desserts, storage desserts and pressure coefficient desserts of each shale gas drilling well is obtained, and the geological parameters are quantized into a plane distribution map.
Wherein, the parameters of the reservoir dessert, the preservation dessert and the pressure coefficient dessert are as follows:
1. the reservoir dessert comprises three aspects of a high-quality facies zone, a geochemical dessert and a reservoir physical dessert, wherein the high-quality facies zone evaluation parameters comprise high-quality shale thickness and lithology paleogeographic spread; the selection of the evaluation parameters of the geochemical dessert mainly considers the abundance and maturity of organic matters; reservoir desserts include shale porosity, permeability, specific surface and brittle mineral distribution.
2. The preservation dessert contains construction style (pleat style, break style, pleat-break combination style, burial history type), lid type (lid layer combination type, construction lid type, top-bottom panel feature) analysis.
3. The pressure coefficient dessert mainly considers the pressure coefficient distribution.
More specifically, the main geological factors affecting shale gas desserts include:
(1) Deposition phase and thickness profile:
research on deposition characteristics, stratum distribution characteristics and the like of Sichuan basin and peripheral area shows that: the development of the sea-phase high-quality hydrocarbon source rock is controlled by factors such as biological productivity, deposition rate, submarine deep fluid activity, deposition environment, water environment and the like in the water body, and the deposition environment can comprehensively reflect factors such as original productivity, deposition rate, organic matter preservation conditions and the like. The deep water land canopy sediment mainly forms anoxic and detention environment sediment, is favorable for preserving shale organic matters, and is favorable for shale gas generation; the organic matter is reduced when the shale is excessively on the shore, and the shale generation substance basis is weakened. Therefore, in the numerical processing of the contour map of the deposition phase distribution and the thickness distribution in the investigation region, the deposition phase is assigned with >2, >1 and <0 respectively according to the deep land canopy, the shallow land canopy and the shore system, as shown in fig. 1.
The statistics of the drilling results of the complex shale removal of the Sichuan basin and the peripheral structure shows that the gas content and the thickness of the organic mudstone have obvious positive correlation (shown in figure 2), the black shale of the Sichuan basin five-peak-Loumaxi group is stable in distribution and large in thickness, and a favorable support is provided for the formation of shale dessert areas (shown in figure 3).
(2) A localized dessert:
the abundance and type of organic matter in sedimentary rock are the material basis for generating oil and gas, but the organic matter can only start to generate a large amount of hydrocarbon with a certain amount of organic matter and a certain degree of thermal evolution. By analyzing the relationship between the thermal evolution degree and the gas content index, the thermal evolution degree (Ro) which is most favorable for shale gas formation and enrichment is considered to be 1.1-3.0%.
Referring to fig. 4 and 5, the drilling data in the Sichuan basin show that the TOC and the gas content have positive correlation. If the air content is 2m 3 And/t is the lower limit of the dessert, the lower limit of the TOC dessert is about 2.5%, and the high-value zone in the zone is mainly distributed in Changning, fuling-Wu Long and Wuxi zone.
Referring to fig. 6 and 7, the drilling Ro and the gas content in and out of the Sichuan basin are in a trend of increasing and decreasing. The shale in the basin is buried for a long time, the thermal evolution degree is higher, a large amount of organic matter pores in the dry gas stage develop and gradually collapse, and the reservoir space is affected, so that the gas content is reduced to a certain degree; the main body is still in a high-overmaturity evolution stage due to early lifting, and the thermal evolution degree is lower than that in the basin, so that the gas content is higher in the region with lower thermal evolution and moderate thermal evolution, and the gas content is also gradually reduced in the high evolution region.
(3) A reservoir dessert:
referring to fig. 8, the porosity and gas content of the study area volunteer line longmaxi group were counted in the range of 0.5-7.5%. Referring to fig. 9, the correlation analysis result shows that the gas content increases linearly with the increase of the porosity. In addition, partition characteristics appear in the relation between the porosity and the gas content: extra-basin complex area: two low (low porosity low gas content); high steep band in basin: two high (high porosity and high gas content).
Referring to fig. 8, the porosity characteristics of the five peak-longmaxi group in the study area were generally high in the wedney, yibin-luzhou, with progressively decreasing in-basin to out-of-basin in the plane distribution characteristics.
Referring to fig. 10, statistics of the specific surface area and the air content in the study area show that the specific surface area and the air content have a positive correlation.
Referring to fig. 11 and 12, correlation analysis is performed on the gas content and permeability data of the campaigns, the correlation between the gas content and permeability of the shale of the campaigns of the Sichuan basin and the peripheral campaigns is not obvious, and the correlation coefficients inside and outside the basin are respectively negative correlation and positive correlation, which indicates that the control factors may have large differences. If the storage condition in the basin is good, cracks are relatively not developed, the lower the permeability, the higher the gas content after being modified, the overall gas content outside the basin is lower, the correlation is not obvious, the poor positive correlation possibly reflects the poor connectivity among the pores of the reservoir, and the gas content is slightly increased due to the communication of the cracks.
The content of the brittle minerals is closely related to the gas content, and the later fracturing of shale gas is directly influenced. The brittle mineral content is high, and the compressibility is good. The brittle mineral content in the research area is generally more than 40%, mainly contains biogenic silicon, and has a certain positive correlation with the gas content. Therefore, the gas generation material base is increased, the preservation condition is improved, and the gas content is increased along with the increase of the biological content. In the plane, siliceous mineral content showed a trend of increasing from southwest in the basin to southeast outside the basin (as shown in fig. 12, 13 and 14), and overall matters were fractured.
(4) Preserving the dessert:
generally, the preservation conditions in the basin are better, and only the high-steep structure and the preservation conditions near the Chuan nan Changning anticline main body are moderate, and the influence of burial depth or local deep fracture is mainly considered; the extra-basin fold area needs to consider fold width, inclination angle, burial depth and fracture distribution, and the position with high yield (or larger curvature) and larger curvature is generally stronger in structural deformation, fracture and crack development and influences shale gas-bearing property.
Referring to fig. 15, the in-basin structure evolves relatively simply, but the depth of burial varies greatly, and there are hidden fractures, so the fracture distribution and depth of burial influence are much more serious; the external complex area of basin is different in form of syncline unit, the structure evolution is complex, the multi-stage fracture superposition and the burial depth change are large, so the outcrop distance, the structure curvature (dip angle), the fracture distribution and the burial depth all need to be considered.
Investigation of the relationship between the exposure distance of the well from the outcrop and the gas content shows that the gas content is higher as the well is farther from the outcrop, and the relationship shows a clear positive correlation (shown in fig. 16 and table 1).
TABLE 1 statistics of shale gas drilling for Sichuan basin
Referring to fig. 17, 18 and 19, the change of the dip angle of the stratum is relatively large in the high and steep folds in the southwest and the eastern of the basin, the occurrence of other areas is generally gentle, and the burying depth of the stratum is larger except for the shallower dip depth of the high and steep dip; the external dorsal inclined distribution area breaks and develops, the Longmaxi group stratum is broken by faults and folds, the stratum burial depth is smaller, the continuous distribution area of the stratum is smaller, and the stratum burial depth changes greatly in the syncline development area, and the continuous distribution stratum area integrally presents the characteristics of slower folds and nuclear stratum and steep two-wing production. The fault is mainly considered to be primary and secondary fracture, the distribution is mainly positioned at the basin edge and the outer part of the basin, and the preservation condition is better at least 10km away from the primary fracture; the preservation condition is better at a distance of more than 3-5km from the secondary sliding fracture; the preservation condition is better than 1-3 km from the three-stage fracture.
Step 2: and performing linear regression fitting on each geological parameter and the gas content to obtain a regression equation of each geological parameter and the gas content, wherein the weight of each geological parameter is a correlation coefficient of a regression equation corresponding to the geological parameter, and a shale gas dessert prediction model of the shale reservoir in the region to be explored is obtained after quantitative superposition according to the actual measurement value of each geological parameter and the corresponding weight of each geological parameter.
Preferably, after the regression equation is obtained, the method further comprises performing significance test on the regression equation and the regression coefficient.
Further, the prediction model of the shale gas dessert is as follows:
Gas＝a·Dep+b·TOC+c·R o +d·φ+e·K+f·BM+g·P+h·Dist
wherein, dep is the burial depth of the shale reservoir in the area to be explored; TOC is the organic carbon content of shale reservoirs in the area to be explored; r is R o The thermal evolution degree of the shale reservoir in the area to be explored; phi is the porosity of the shale reservoir in the area to be explored; k is the permeability of the shale reservoir in the area to be explored; BM is the brittle mineral content in shale reservoirs in the area to be explored; p is the pressure coefficient of the shale reservoir in the area to be explored; dist is the shale outcrop distance in the shale reservoir of the area to be explored; a. b, c, d, e, f, g and h are regression obtained by linear regression fitting of the geological parameters and the air contentCorrelation coefficients in the equation.
The features and capabilities of the present application are described in further detail below in connection with the following examples:
examples
The embodiment provides a shale gas dessert prediction method based on multiple linear regression analysis, which is used for identifying and predicting shale gas desserts by comparing multiple linear regression analysis according to basic characteristics of shale gas reservoirs and combining with geological parameter characteristics of Sichuan basin and peripheral geological characteristics.
Which comprises the following steps:
A. collecting shale gas geological feature parameter information of the Sichuan basin and the peripheral shale gas well drilling to obtain the Sichuan basin and the peripheral shale geological parameters, and quantifying each geological parameter into a plane distribution map as shown in table 2;
B. performing linear regression fitting on each geological parameter and the gas content by taking the gas content as a dependent variable and other geological parameters as independent variables to obtain a regression equation between each geological parameter and the gas content, wherein the weight of each geological parameter is the correlation coefficient of the corresponding regression equation, as shown in fig. 20;
C. the regression equation and regression coefficient obtained in step B were subjected to the significance test, and the result is shown in fig. 21.
D. According to the actual measurement values of the geological parameters and the corresponding weights thereof, the shale gas dessert prediction model of the shale reservoir in the area to be explored is obtained after quantitative superposition:
Gas＝0.0019*Depth+0.466*TOC+0.9594*Ro+0.034*φ-0.007*K+0.03*BM-0.63*Pressure-0.084*Dist.
and obtaining dessert distribution diagrams (shown in figure 22) of the Sichuan basin and the peripheral sea shale complex region by combining the dessert prediction model shown in the above with a quantization parameter plane graph (selected actual measurement value). As can be seen, the portions of the whole investigation region that are closer to the deeper grey levels in the Sichuan basin and the portions of the split bands inside and outside the basin are the shale gas dessert predicted positions. And moreover, the change trend of the positions of desserts in the basin can be effectively predicted, and the shale gas development in a low exploration area is facilitated. Therefore, the predicted result by the method has higher comparability, and the dessert region and the non-dessert region in the favorable block can be intuitively judged. On the other hand, the weights of all influence parameters are analyzed through multiple linear regression, and the final prediction of the shale gas dessert areas and the optimized sequencing are performed through quantitative superposition, so that the method has clear guiding significance on dessert areas in the unexplored beneficial blocks.
TABLE 2 Sichuan basin and peripheral drilling parameter table
Therefore, the shale gas dessert prediction method based on the multiple linear regression analysis provided by the application has the advantages that the prediction of the dessert area dividing position is more refined and accurate, the display of the dessert sequential area is more visual, the subjective judgment error caused by simple graph layer stacking is avoided, and missing desserts are searched for building a mature detection area in the future. The method is favorable for quantitatively selecting shale gas desserts in complex-structure areas under low exploration degree.
While particular embodiments of the present application have been illustrated and described, it will be appreciated that various other changes and modifications can be made without departing from the spirit and scope of the application. It is therefore intended to cover in the appended claims all such changes and modifications that are within the scope of this application.
Claims (5)
1. A shale gas dessert prediction method based on multiple linear regression analysis is characterized by comprising the following steps:
obtaining reservoir desserts, storing various geological parameters in desserts and pressure coefficient desserts, evaluating geological feature parameters related to shale gas content, quantifying the geological feature parameters into a plane distribution map, performing linear regression fitting on each geological feature parameter and the gas content to obtain regression equations of each geological feature parameter and the gas content, wherein the weight of each geological feature parameter is a correlation coefficient of the regression equation corresponding to the geological feature parameter, and obtaining a shale gas dessert prediction model of the shale reservoir in the area to be explored after quantized superposition according to actual measurement values of each geological feature parameter and the weight corresponding to each geological feature parameter; after the regression equation is obtained, the method further comprises the step of performing significance test on the regression equation and the correlation coefficient, wherein geological parameters in the reservoir dessert comprise: organic carbon content, degree of thermal evolution, porosity, permeability and brittle mineral content, the geological parameters in the preserved dessert comprising: burial depth and outcrop distance, the geological parameters in the pressure coefficient dessert comprise pressure coefficients, and the predictive model of the shale gas dessert is as follows:
Gas＝a·Dep+b·TOC+c·R o +d·φ+e·K+f·BM+g·P+h·Dist
wherein, dep is the burial depth of the shale reservoir in the area to be explored; TOC is the organic carbon content of shale reservoirs in the area to be explored; r is R o The thermal evolution degree of the shale reservoir in the area to be explored; phi is the porosity of the shale reservoir in the area to be explored; k is the permeability of the shale reservoir in the area to be explored; BM is the brittle mineral content in shale reservoirs in the area to be explored; p is the pressure coefficient of the shale reservoir in the area to be explored; dist is the shale outcrop distance in the shale reservoir of the area to be explored; a. b, c, d, e, f, g and h are correlation coefficients in regression equations obtained by linear regression fitting of the corresponding geological parameters and the gas content.
2. The method for predicting shale gas dessert based on multiple linear regression analysis according to claim 1, wherein the gas content of the shale gas dessert is not less than 2m 3 /t。
3. The method for predicting shale gas desserts based on multiple linear regression analysis according to claim 1, wherein the organic carbon content of the shale gas dessert is not less than 2.5%.
4. The method for predicting shale gas desserts based on multiple linear regression analysis according to claim 1, wherein the degree of thermal evolution of the shale gas dessert is 1.1-3.0%.
5. The method for predicting shale gas desserts based on multiple linear regression analysis according to claim 1, wherein the brittle mineral content of the shale gas dessert is not less than 40%.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911383648.0A CN111046585B (en) | 2019-12-27 | 2019-12-27 | Shale gas dessert prediction method based on multiple linear regression analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911383648.0A CN111046585B (en) | 2019-12-27 | 2019-12-27 | Shale gas dessert prediction method based on multiple linear regression analysis |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111046585A CN111046585A (en) | 2020-04-21 |
| CN111046585B true CN111046585B (en) | 2023-08-22 |
Family
ID=70240974
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911383648.0A Active CN111046585B (en) | 2019-12-27 | 2019-12-27 | Shale gas dessert prediction method based on multiple linear regression analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111046585B (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111411947B (en) * | 2020-05-14 | 2023-09-15 | 中国石油天然气集团有限公司 | Method for predicting formation pressure based on HTL parameters |
| CN113762660B (en) * | 2020-06-04 | 2024-05-10 | 中国石油化工股份有限公司 | Sea shale adsorption gas content evaluation method and device, electronic equipment and medium |
| CN113944460B (en) * | 2020-07-16 | 2024-08-27 | 中国石油天然气股份有限公司 | Method and device for predicting condensate oil content of shale gas in different development stages |
| CN112730198B (en) * | 2021-01-15 | 2022-04-01 | 中国地质大学（武汉） | Shale natural fracture permeability calculation method |
| CN114240212B (en) * | 2021-12-22 | 2024-09-24 | 中国地质大学（北京） | Method and equipment for determining influence weight of geological parameter on resource quantity |
| CN115291279B (en) * | 2022-07-21 | 2024-05-07 | 中国地质调查局成都地质调查中心（西南地质科技创新中心） | Method, device, storage medium and apparatus for characterizing deformation strength of out-of-basin construction |
| CN115856267B (en) * | 2023-02-23 | 2023-06-23 | 中国石油天然气股份有限公司 | Method for analyzing properties of shale oil in different connectivity pores of shale and application |
| CN116168172B (en) * | 2023-04-19 | 2023-07-11 | 武汉中旺亿能科技发展有限公司 | Shale oil gas dessert prediction method, device, equipment and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104597490A (en) * | 2015-01-28 | 2015-05-06 | 中国石油大学(北京) | Multi-wave AVO reservoir elastic parameter inversion method based on precise Zoeppritz equation |
| CN105528522A (en) * | 2015-12-15 | 2016-04-27 | 中国石油天然气股份有限公司 | Method and device for calculating resource quantity of continuous oil and gas reservoir based on spatial grid |
| CN106168685A (en) * | 2015-09-08 | 2016-11-30 | 西南石油大学 | A kind of shale gas individual well geological syntheses evaluation methodology |
| CN106568675A (en) * | 2016-10-17 | 2017-04-19 | 铜仁中能天然气有限公司 | Novel desorption pressure tester for shale gas |
| CN108376295A (en) * | 2018-01-31 | 2018-08-07 | 北京博达瑞恒科技有限公司 | A kind of oil gas dessert prediction technique and storage medium |
| CN108661630A (en) * | 2017-03-31 | 2018-10-16 | 中国石油化工股份有限公司 | One kind being based on the preferred geology dessert quantitative evaluation method of parameter |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170002630A1 (en) * | 2015-07-02 | 2017-01-05 | Schlumberger Technology Corporation | Method of performing additional oilfield operations on existing wells |
| US20180118972A1 (en) * | 2016-11-02 | 2018-05-03 | Covestro Llc | Reducing erosion of oil field pumping and transfer equipment |
-
2019
- 2019-12-27 CN CN201911383648.0A patent/CN111046585B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104597490A (en) * | 2015-01-28 | 2015-05-06 | 中国石油大学(北京) | Multi-wave AVO reservoir elastic parameter inversion method based on precise Zoeppritz equation |
| CN106168685A (en) * | 2015-09-08 | 2016-11-30 | 西南石油大学 | A kind of shale gas individual well geological syntheses evaluation methodology |
| CN105528522A (en) * | 2015-12-15 | 2016-04-27 | 中国石油天然气股份有限公司 | Method and device for calculating resource quantity of continuous oil and gas reservoir based on spatial grid |
| CN106568675A (en) * | 2016-10-17 | 2017-04-19 | 铜仁中能天然气有限公司 | Novel desorption pressure tester for shale gas |
| CN108661630A (en) * | 2017-03-31 | 2018-10-16 | 中国石油化工股份有限公司 | One kind being based on the preferred geology dessert quantitative evaluation method of parameter |
| CN108376295A (en) * | 2018-01-31 | 2018-08-07 | 北京博达瑞恒科技有限公司 | A kind of oil gas dessert prediction technique and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 准南上二叠统芦草沟组非常规油气储层特征;王正和等;《岩性油气藏》;第29卷(第05期);30-37 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111046585A (en) | 2020-04-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111046585B (en) | Shale gas dessert prediction method based on multiple linear regression analysis | |
| CN111461386B (en) | Shale gas dessert prediction method based on BP neural network | |
| CN107703560A (en) | A kind of fine recognition methods of mud shale petrofacies based on triple information | |
| CN105528522A (en) | Method and device for calculating resource quantity of continuous oil and gas reservoir based on spatial grid | |
| Pi-long et al. | Forming mechanism of subtle oil pools in fault basins-taking the Jiyang depression of the Bohaiwan basin as an example | |
| CN113887078B (en) | Deep-ultra-deep sea-facies carbonate reservoir occurrence depth lower limit prediction method | |
| CN104834006B (en) | The treating method and apparatus of structure origin cause of formation deposition parameter figure | |
| CN117031545B (en) | Method for identifying hydrate and free gas coexistence layer of sandy reservoir based on sensitive parameters | |
| CN114462237A (en) | Shale oil gas dessert prediction method | |
| Chen et al. | Geological risk mapping and prospect evaluation using multivariate and Bayesian statistical methods, western Sverdrup Basin of Canada | |
| CN108319743A (en) | Method and device for predicting abundance of oil-gas resources of ancient marine carbonate rock | |
| CN111460725B (en) | Shale gas dessert prediction based on multi-level fuzzy recognition | |
| CN112185469B (en) | Method for predicting favorable gathering area of sea natural gas hydrate | |
| Sabea et al. | Geological model of the Khabour Reservoir for studying the gas condensate blockage effect on gas production, Akkas Gas Field, Western Iraq | |
| CN113407899A (en) | Evaluation method for hydrocarbon discharge amount of marine high-over-mature hydrocarbon source rock | |
| CN116047602B (en) | Type II hydrate saturation prediction method based on hydrocarbon production numerical simulation | |
| Kunka et al. | The Nelson Field, Blocks 22/11, 22/6a, 22/7, 22/12a, UK North Sea | |
| Camp | Basin-centered gas or subtle conventional traps? | |
| Chunhua et al. | Depression and apophysis structure type and its controls on deposition in the Jiyang Depression, Bohai Bay Basin | |
| RU2298816C2 (en) | Method for geo-chemical testing of local objects during prognosis of oil content | |
| Ren et al. | Faults and gas chimneys jointly dominate the gas hydrate accumulation in the Shenhu Area, northern South China Sea | |
| CN110930020B (en) | Method for determining economic recoverable resource amount of unconventional oil and gas resources | |
| CN110764161B (en) | Comprehensive method for judging and identifying fractured and broken zone of carbonate rock through drilling data | |
| CN115773106B (en) | Method for identifying evolution process of overpressure seal box by utilizing ancient pressure decay gradient | |
| Wardhana et al. | Exploring the overlooked seurula potential in Arun field; a preliminary study from outcrops and subsurface data in North Sumatra Basin |
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 |