CN104678432A - Glutenite crack recognition method - Google Patents
Glutenite crack recognition method Download PDFInfo
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- CN104678432A CN104678432A CN201310610148.2A CN201310610148A CN104678432A CN 104678432 A CN104678432 A CN 104678432A CN 201310610148 A CN201310610148 A CN 201310610148A CN 104678432 A CN104678432 A CN 104678432A
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Abstract
The invention provides a glutenite crack recognition method. The method comprises steps: a strata thickness is determined through precise strata contrast and division; a conventional logging curve and core information are used for determining sedimentary facies; according to seismic data and regional background information, a regional tectonic stress is determined; a crack strength indication curve is built; a crack is recognized according to a crack strength indication value; crack feature parameters are determined; 3D geological modeling software is used to build a skeleton model and an attribute model for the crack; and dynamic data are used and developed to verify and correct the precision of the crack model. According to the glutenite crack recognition method, quantitative recognition is carried out on the glutenite crack, and on the basis, the 3D geological model is used for carrying out effective predication on crack development characteristics.
Description
Technical field
The present invention relates to oil-field development technical field, particularly relate to a kind of method of glutenite crack identification.
Background technology
Crack is one of key factor controlling particular lithologic effect of reservoir development, and identification and estimate of fracture has important more practical value for oil-gas exploration.How to utilize conventional data carry out crack identification and evaluate be always puzzlement geophysicist major issue.We have invented a kind of method of new glutenite crack identification for this reason, solve above technical matters.
Summary of the invention
The object of this invention is to provide and a kind ofly can carry out quantitative judge to glutenite crack and utilize three-dimensional geological modeling fracture development characteristics to carry out the method for the glutenite crack identification of effectively prediction.
Object of the present invention realizes by following technical measures: the method for glutenite crack identification, and the method for this glutenite crack identification comprises: step 1, divides layer thickness definitely by meticulous Strata Comparison; Step 2, uses Logging Curves, rock core information determination sedimentary facies; Step 3, according to seismic data, regional background data determination areal structure stress; Step 4, builds intensity indicative curve; Step 5, according to intensity indicated value crack identification; Step 6, determines FRACTURE CHARACTERISTICS parameter; Step 7, applying three-dimensional Geologic modeling software, sets up skeleton pattern and the attribute model in crack; And step 8, exploitation dynamic data fracture model accuracy carries out verifying and revising.
Object of the present invention also realizes by following technical measures:
In step 1, by Comprehensive Correlation logging trace, determine objective interval correlation marker, carry out meticulous Strata Comparison, divide stratigraphic section comparison diagram, layer thickness definitely.
In step 2, rock signature is analyzed, by petrographic thin section identification and analysis mineral characteristic, according to judging sedimentary facies above by rock core, log data.
In step 3, by seismic data, utilize composite traces and inversion technique, determine areal structure stress.
In step 4, fracture development section is shown as high-amplitude sinusoidal waveform on FMI imaging logging image, Using Conventional Logs shows resistivity and reduces, and when in stratum during agensis crack, dark, shallow resistivity is basic coincidence; And the stratum having crack to link up, because infiltrative impact causes mud intrusion in various degree, dark, shallow resistivity embodies amplitude difference and to educate all the more amplitude difference larger in crack, is reconstructed, obtain intensity indicative curve by dark, shallow resistivity.
The formula of this intensity indicative curve is:
RDS=[log（Rt）-log(Rxo)]*[L1/(L1+L2)]*K*P
Wherein: RDS-fracture development intensity, relative value, without unit;
Rt-dark resistance, ohm meter;
Rxo-shallow resistance, ohm meter;
L1, L2-objective interval lithology percentage composition, %;
K-experience factor, the relative value of reflection different lithology fracture development density;
P-other influence factor.
In steps of 5, intensity indicated value is larger, and crack is educated all the more.
In step 6, FRACTURE CHARACTERISTICS parameter comprises the orientation in crack, occurrence, density, width, factor of porosity, permeability, wherein the orientation in crack, occurrence, density are determined according to core observation, FMI Image Logging Data and areal structure stress, and the width in crack, factor of porosity, permeability are determined according to core analysis, FMI imaging logging and dual laterolog data.
In step 7, applying three-dimensional Geologic modeling software, the zone thickness, the sedimentary facies that utilize above-mentioned steps 1,2 to determine, set up the skeleton pattern in crack; The FRACTURE CHARACTERISTICS parameter utilizing step 6 to determine, sets up the attribute model in crack.
The method of the glutenite crack identification in the present invention, comprise definitely layer thickness, judge sedimentary facies, judging area tectonic stress, build intensity indicative curve, crack identification, determine FRACTURE CHARACTERISTICS parameter, set up crack skeleton pattern and attribute model, exploitation dynamic data test correction eight steps.The method carries out quantitative judge in conjunction with FMI data and Using Conventional Logs to glutenite crack, on this basis, utilizes three-dimensional geological modeling fracture development characteristics effectively to predict.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of a specific embodiment of the method for glutenite crack identification of the present invention;
Fig. 2 is the feature schematic diagram of fracture development section on Logging Curves.
Embodiment
For making above and other object of the present invention, feature and advantage can become apparent, cited below particularly go out preferred embodiment, and coordinate institute's accompanying drawings, be described in detail below.
As shown in Figure 1, Fig. 1 is the process flow diagram of a specific embodiment of the method for glutenite crack identification of the present invention.
In step 101, divide layer thickness definitely by meticulous Strata Comparison.By Comprehensive Correlation logging trace, determine objective interval correlation marker, carry out meticulous Strata Comparison successively, divide stratigraphic section comparison diagram, layer thickness definitely.Flow process enters into step 102.
In step 102, use Logging Curves, rock core information determination sedimentary facies.By rock core, log data, analyzing rock signature, being identified by petrographic thin section, assaying feature, according to judging sedimentary facies above.Flow process enters into step 103.
In step 103, according to rock core, log data, regional background data determination areal structure stress.By seismic data, utilize composite traces and inversion technique, determine tectonic characteristic and fracture system.Flow process enters into step 104.
In step 104, build intensity indicative curve.Fracture development section is at FMI(Formation MicroScanner Image, stratum micro resistor) imaging logging image is shown as high-amplitude sinusoidal waveform, Using Conventional Logs shows resistivity and reduces (as shown in Figure 2), deeply, there is amplitude difference in shallow resistivity, when in stratum during agensis crack, dark, shallow resistivity should be basic coincidence; And the stratum having crack to link up, because infiltrative impact causes mud intrusion in various degree, dark, shallow resistivity embodies amplitude difference and to educate all the more amplitude difference larger in crack.By dark, shallow resistivity reconstruct, obtain intensity indicative curve.
The formula of intensity indicative curve is:
RDS=[log（Rt）-log(Rxo)]*[L1/(L1+L2)]*K*P
Wherein: RDS-fracture development intensity, relative value, without unit;
Rt-dark resistance, ohm meter;
Rxo-shallow resistance, ohm meter;
L1, L2-objective interval lithology percentage composition, %;
K-experience factor, the relative value of reflection different lithology fracture development density;
P-other influence factor.
Flow process enters into step 105.
In step 105, according to intensity indicated value RDS crack identification, RDS is larger, and crack is educated all the more.Flow process enters into step 106.
In step 106, determine FRACTURE CHARACTERISTICS parameter.The characteristic parameter in crack comprises the parameter such as orientation, occurrence, density, width, factor of porosity, permeability in crack, wherein the orientation in crack, occurrence, density are determined according to core observation, FMI Image Logging Data and areal structure stress, and the width in crack, factor of porosity, permeability are determined according to core analysis, FMI imaging logging and dual laterolog data.Flow process enters into step 107.
In step 107, applying three-dimensional Geologic modeling software, the zone thickness, the sedimentary facies that utilize above-mentioned steps 101,102 to determine, set up the skeleton pattern in crack; The characteristic parameter in the crack utilizing above-mentioned steps 106 to determine, sets up the attribute model in crack.Flow process enters into step 108.
In step 108, exploitation dynamic data fracture model accuracy carries out verifying and revising.Flow process terminates.
Claims (9)
1. the method for glutenite crack identification, is characterized in that, the method for this glutenite crack identification comprises:
Step 1, divides layer thickness definitely by meticulous Strata Comparison;
Step 2, uses Logging Curves, rock core information determination sedimentary facies;
Step 3, according to seismic data, regional background data determination areal structure stress;
Step 4, builds intensity indicative curve;
Step 5, according to intensity indicated value crack identification;
Step 6, determines FRACTURE CHARACTERISTICS parameter;
Step 7, applying three-dimensional Geologic modeling software, sets up skeleton pattern and the attribute model in crack; And
Step 8, exploitation dynamic data fracture model accuracy carries out verifying and revising.
2. the method for glutenite crack identification according to claim 1, is characterized in that, in step 1, by Comprehensive Correlation logging trace, determines objective interval correlation marker, carries out meticulous Strata Comparison, divides stratigraphic section comparison diagram, layer thickness definitely.
3. the method for glutenite crack identification according to claim 1, is characterized in that, in step 2, analyzes rock signature, by petrographic thin section identification and analysis mineral characteristic, according to judging sedimentary facies above by rock core, log data.
4. the method for glutenite crack identification according to claim 1, is characterized in that, in step 3, by seismic data, utilizes composite traces and inversion technique, determines areal structure stress.
5. the method for glutenite crack identification according to claim 1, it is characterized in that, in step 4, fracture development section is shown as high-amplitude sinusoidal waveform on FMI imaging logging image, Using Conventional Logs shows resistivity reduce, when in stratum during agensis crack, dark, shallow resistivity is basic coincidence; And the stratum having crack to link up, because infiltrative impact causes mud intrusion in various degree, dark, shallow resistivity embodies amplitude difference and to educate all the more amplitude difference larger in crack, is reconstructed, obtain intensity indicative curve by dark, shallow resistivity.
6. the method for glutenite crack identification according to claim 1, is characterized in that, the formula of this intensity indicative curve is:
RDS=[log（Rt）-log(Rxo)]*[L1/(L1+L2)]*K*P
Wherein: RDS-fracture development intensity, relative value, without unit;
Rt-dark resistance, ohm meter;
Rxo-shallow resistance, ohm meter;
L1, L2-objective interval lithology percentage composition, %;
K-experience factor, the relative value of reflection different lithology fracture development density;
P-other influence factor.
7. the method for glutenite crack identification according to claim 1, is characterized in that, in steps of 5, intensity indicated value is larger, and crack is educated all the more.
8. the method for glutenite crack identification according to claim 1, it is characterized in that, in step 6, FRACTURE CHARACTERISTICS parameter comprises the orientation in crack, occurrence, density, width, factor of porosity, permeability, wherein the orientation in crack, occurrence, density are determined according to core observation, FMI Image Logging Data and areal structure stress, and the width in crack, factor of porosity, permeability are determined according to core analysis, FMI imaging logging and dual laterolog data.
9. the method for glutenite crack identification according to claim 1, is characterized in that, in step 7, applying three-dimensional Geologic modeling software, the zone thickness, the sedimentary facies that utilize above-mentioned steps 1,2 to determine, set up the skeleton pattern in crack; The FRACTURE CHARACTERISTICS parameter utilizing step 6 to determine, sets up the attribute model in crack.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105182423A (en) * | 2015-10-22 | 2015-12-23 | 中国石油大学(华东) | Integrated recognition method for overpressured crack |
| CN105332685A (en) * | 2015-11-13 | 2016-02-17 | 西南石油大学 | Propping agent multistage paving method for improving coal bed complicated crack supporting effect |
| CN106149771A (en) * | 2016-07-01 | 2016-11-23 | 中国电力科学研究院 | The construction method of basic crack figure and device for decomposed rock foundation |
| CN106569267A (en) * | 2016-10-14 | 2017-04-19 | 中国石油大学(北京) | Multi-scale crack model of compact low-penetration reservoir and modeling method of model |
| CN108375804A (en) * | 2018-01-22 | 2018-08-07 | 中国石油大港油田勘探开发研究院 | A kind of continental clastic " unification of four phases " sedimentary micro method of discrimination |
| CN108898286A (en) * | 2018-06-11 | 2018-11-27 | 中国石油大学(北京) | The evaluation method and device of reservoir fissure development degree |
| CN111257962A (en) * | 2018-12-03 | 2020-06-09 | 核工业二0八大队 | Method for positioning and predicting ore body by using three-dimensional geological modeling technology |
| CN113552638A (en) * | 2021-09-18 | 2021-10-26 | 数皮科技(湖北)有限公司 | Millimeter-level bedding joint method for identifying shale reservoir in-situ state |
| CN114135264A (en) * | 2020-08-14 | 2022-03-04 | 中国石油化工股份有限公司 | Method and device for determining development degree of microcracks of tight sandstone and storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020099504A1 (en) * | 1999-01-29 | 2002-07-25 | Cross Timothy A. | Method of predicting three-dimensional stratigraphy using inverse optimization techniques |
| CN101221250A (en) * | 2007-01-12 | 2008-07-16 | 大港油田集团有限责任公司 | Three-dimensional stratum characteristic decryption method based on geologic information acquisition data |
-
2013
- 2013-11-27 CN CN201310610148.2A patent/CN104678432A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020099504A1 (en) * | 1999-01-29 | 2002-07-25 | Cross Timothy A. | Method of predicting three-dimensional stratigraphy using inverse optimization techniques |
| CN101221250A (en) * | 2007-01-12 | 2008-07-16 | 大港油田集团有限责任公司 | Three-dimensional stratum characteristic decryption method based on geologic information acquisition data |
Non-Patent Citations (2)
| Title |
|---|
| 崔晓朵 等: "墨西哥EPC区碳酸盐储层裂缝特征和非均质性研究", 《海洋地质前沿》 * |
| 王伟: "哥伦比亚Ombu区Kc组裂缝描述和地质建模", 《现代地质》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105182423A (en) * | 2015-10-22 | 2015-12-23 | 中国石油大学(华东) | Integrated recognition method for overpressured crack |
| CN105332685A (en) * | 2015-11-13 | 2016-02-17 | 西南石油大学 | Propping agent multistage paving method for improving coal bed complicated crack supporting effect |
| CN105332685B (en) * | 2015-11-13 | 2018-05-25 | 西南石油大学 | A kind of proppant multistage spread method for improving coal seam complex fracture support effect |
| CN106149771A (en) * | 2016-07-01 | 2016-11-23 | 中国电力科学研究院 | The construction method of basic crack figure and device for decomposed rock foundation |
| CN106149771B (en) * | 2016-07-01 | 2020-04-24 | 中国电力科学研究院 | Method and device for constructing foundation crack diagram of weathered rock foundation |
| CN106569267A (en) * | 2016-10-14 | 2017-04-19 | 中国石油大学(北京) | Multi-scale crack model of compact low-penetration reservoir and modeling method of model |
| CN108375804A (en) * | 2018-01-22 | 2018-08-07 | 中国石油大港油田勘探开发研究院 | A kind of continental clastic " unification of four phases " sedimentary micro method of discrimination |
| CN108898286A (en) * | 2018-06-11 | 2018-11-27 | 中国石油大学(北京) | The evaluation method and device of reservoir fissure development degree |
| CN111257962A (en) * | 2018-12-03 | 2020-06-09 | 核工业二0八大队 | Method for positioning and predicting ore body by using three-dimensional geological modeling technology |
| CN114135264A (en) * | 2020-08-14 | 2022-03-04 | 中国石油化工股份有限公司 | Method and device for determining development degree of microcracks of tight sandstone and storage medium |
| CN114135264B (en) * | 2020-08-14 | 2024-04-02 | 中国石油化工股份有限公司 | Method, device and storage medium for determining development degree of microcracks of tight sandstone |
| CN113552638A (en) * | 2021-09-18 | 2021-10-26 | 数皮科技(湖北)有限公司 | Millimeter-level bedding joint method for identifying shale reservoir in-situ state |
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Application publication date: 20150603 |