CN105785446A - Oil shale earthquake identification and evaluation method - Google Patents
Oil shale earthquake identification and evaluation method Download PDFInfo
- Publication number
- CN105785446A CN105785446A CN201610151741.9A CN201610151741A CN105785446A CN 105785446 A CN105785446 A CN 105785446A CN 201610151741 A CN201610151741 A CN 201610151741A CN 105785446 A CN105785446 A CN 105785446A
- Authority
- CN
- China
- Prior art keywords
- oil shale
- seismic
- oil
- shale
- content
- 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.)
- Pending
Links
- 239000004058 oil shale Substances 0.000 title claims abstract description 69
- 238000011156 evaluation Methods 0.000 title claims abstract description 21
- 238000000034 method Methods 0.000 claims abstract description 28
- 239000011435 rock Substances 0.000 claims abstract description 27
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims abstract description 6
- 229910052799 carbon Inorganic materials 0.000 claims abstract description 6
- 239000011159 matrix material Substances 0.000 claims description 24
- 229910052500 inorganic mineral Inorganic materials 0.000 claims description 22
- 239000011707 mineral Substances 0.000 claims description 22
- 238000010008 shearing Methods 0.000 claims description 12
- 238000012549 training Methods 0.000 claims description 9
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 8
- 238000000605 extraction Methods 0.000 claims description 6
- 230000001537 neural effect Effects 0.000 claims description 6
- 239000005416 organic matter Substances 0.000 claims description 5
- 238000012935 Averaging Methods 0.000 claims description 3
- 238000012417 linear regression Methods 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 230000007935 neutral effect Effects 0.000 claims description 3
- 229920006395 saturated elastomer Polymers 0.000 claims description 3
- 238000011160 research Methods 0.000 description 4
- 235000015076 Shorea robusta Nutrition 0.000 description 2
- 244000166071 Shorea robusta Species 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000005553 drilling Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000005065 mining Methods 0.000 description 2
- 238000002360 preparation method Methods 0.000 description 2
- 208000035126 Facies Diseases 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- ORTYMGHCFWKXHO-UHFFFAOYSA-N diethadione Chemical compound CCC1(CC)COC(=O)NC1=O ORTYMGHCFWKXHO-UHFFFAOYSA-N 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
- 239000003208 petroleum Substances 0.000 description 1
- 239000003079 shale oil Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/40—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging
- G01V1/44—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging using generators and receivers in the same well
- G01V1/48—Processing data
- G01V1/50—Analysing data
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/61—Analysis by combining or comparing a seismic data set with other data
- G01V2210/616—Data from specific type of measurement
- G01V2210/6169—Data from specific type of measurement using well-logging
Landscapes
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Engineering & Computer Science (AREA)
- Acoustics & Sound (AREA)
- Environmental & Geological Engineering (AREA)
- Geology (AREA)
- Remote Sensing (AREA)
- General Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Geophysics (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention discloses an oil shale earthquake identification and evaluation method, which comprises the steps: a) calculating shale gas organic carbon content TOC through a wave impedance delta logR superposition method; b) the TOC value calculated in the step a) being same with the actually-measured TOC value of samples; c) establishing a rock physical model of an oil shale; and d) establishing a logging curve of logging information and seismic attribute longitudinal wave velocity by utilizing seismic channels beside a shale gas well. The rock physical model is established, and organic content of the oil shale is obtained through velocity inversion; and through the earthquake multi-attribute prediction technique, oil content information is inverted and extrapolated to seismic data, so that earthquake oil content inversion data volume of a work area is obtained, and a foundation is laid for identification and evaluation of the oil shale.
Description
Technical field
The present invention relates to oil shale logging technique field, be specifically related to seismic recognition and the evaluation method of a kind of oil shale.
Background technology
Oil shale belongs to unconventional petroleum resources, with its aboundresources and have exploitation feasibility, is listed in 21 century very important alternative energy source.Oil Shale Resources in China enriches, and resource potential is big, but exploration Chengdu is relatively low, all only exists in detailed survey and survey stage containing mining area, and exploration engineering falls behind, and exploration method is single.Logging technique comparative maturity in terms of identification with evaluation oil shale at present, and it is widely used in each big oil shale containing mining area, but little in the research of seismic recognition and the theoretical foundation of evaluation oil shale and correlation technique, the research of this aspect is currently in space state.
Summary of the invention
The technical problem to be solved is that in prior art, oil shale is less in seismic recognition and study on evaluation way, purpose be to provide seismic recognition and the evaluation method of a kind of oil shale.Solve the problem that current oil shale is in the blank stage at seismic recognition and evaluation study.
The present invention is achieved through the following technical solutions:
The seismic recognition of a kind of oil shale and evaluation method, comprise the following steps:
Step a, employing wave impedance Δ logR overlay method calculate shale gas organic carbon content TOC；
Step b, described step a calculated TOC value is consistent with actual measurement print gained TOC value；
Step c, oil shale is made up of organic matter, mineral matter, moisture, from oil shale rock structure, sets up the petrophysical model of oil shale, farther includes
C.1, organic and mineral matter forms bedded rock matrix according to Reuss averaging method to step；
Wherein, KmatrixAnd umatrixIt is respectively bulk modulus and modulus of shearing, the GPa of Rock Matrix；KmineralAnd umineralIt is respectively bulk modulus and modulus of shearing, the f of mineral mattermineralAnd fkerofenIt is respectively mineral matter and organic volume content, %；
C.2 step, adds micropore, builds rock matrix according to Krief relational expression；
C.3 step, calculates the elastic modelling quantity of saturated rock according to Gassmann equation；
C.4 step, calculates the velocity of longitudinal wave of oil shale according to oil shale rock density and elastic modelling quantity；
Step d, is combined with shale gas seismic trace near well and sets up well logging information and the extrapolated log of seismic properties velocity of longitudinal wave.
Existing oil shale identification and evaluation method are all to utilize the logging response character of oil shale, but earthquake aspect there is presently no ripe effective method.Utilizing earthquake to identify and evaluating oil shale is fully to use various seismic characters, as seimic wave velocity, walk time, wave impedance, amplitude etc., find out the seismic response features of oil shale, thus it be identified and evaluate.
Oil shale has the praetersonic time difference and low-density feature, uses wave impedance and conventional logging response characteristic to come overlapping, and spacing can become apparent from.Wherein, the preparation method of wave impedance has two kinds: 1) it is directly to be converted to by log, 2) it is to utilize seismic inversion to obtain, by analysis with sample tentative calculation, obtain wave impedance Δ logR overlay method computing formula.
Further, in order to preferably realize the present invention, described step d is further comprising the steps of:
D.1, seismic attribute abstraction, from shale gas seismic trace near well extraction seismic properties, and from oil content curve extraction oil content parameter for step；
D.2 step, optimizes seismic properties, utilizes the linear regression of many attributes or validation-cross method to realize seismic properties sequence, selects best attributes combination；
D.3, neural metwork training, the seismic properties that application selects is as training sample, and oil content is that predicted target values carries out neural metwork training, it is thus achieved that what neutral net was overall reflects weight coefficient matrix, sets up the non-linear relation of seismic properties and oil content for step.
Further, in order to preferably realize the present invention, described step is c.2 middle uses below equation component rock matrix,
Wherein, KframeAnd uframeIt is respectively bulk modulus and modulus of shearing, the GPa of rock matrix；fwaterFor the volume content of moisture, %, Φ are porosity, %.
Further, in order to preferably realize the present invention, described step c.3 in, use below equation calculate elastic modelling quantity
Wherein, KsaturedAnd usaturedIt is respectively bulk modulus and modulus of shearing, the GPa of oil shale；KwaterFor the bulk modulus of moisture, GPa.
The present invention compared with prior art, has such advantages as and beneficial effect:
The method of the invention is by setting up petrophysical model by the velocity inversion oil shale content of organic matter, seismic multi-attribute Predicting Technique is utilized oil content information inverting to be extrapolated on geological data, thus obtaining the earthquake oil content inverting data volume in work area, the identification for earthquake lays the foundation with evaluation.
On the basis of using existing oil shale well logging individual features and Logging Evaluation Method Δ logR overlay method, Δ logR overlay method is expanded, introduce seismic impedance and carry out overlapping evaluation oil shale with conventional logging response characteristic；Establish oil shale rock physical model simultaneously, propose a kind of based on petrophysical model by the method for the velocity inversion oil shale content of organic matter；Finally utilize seismic multi-attribute technology the oil content information inverting of drilling well to be extrapolated on geological data, thus obtain oil content inverting data volume, thus predict the spatial feature of oil content.Utilize seismic recognition and evaluate the seismic response features that oil shale is exactly the fully various oil shales of utilization, thus it be identified and evaluate.
Detailed description of the invention
For making the object, technical solutions and advantages of the present invention clearer, below in conjunction with embodiment, the present invention is described in further detail, and the exemplary embodiment of the present invention and explanation thereof are only used for explaining the present invention, not as a limitation of the invention.
Embodiment:
The seismic recognition of a kind of oil shale and evaluation method, comprise the following steps:
Step a, employing wave impedance Δ logR overlay method calculate shale gas organic carbon content TOC；
Step b, described step a calculated TOC value is consistent with actual measurement print gained TOC value；
Step c, oil shale is made up of organic matter, mineral matter, moisture, from oil shale rock structure, sets up the petrophysical model of oil shale, farther includes
C.1, organic and mineral matter forms bedded rock matrix according to Reuss averaging method to step；
Wherein, KmatrixAnd umatrixIt is respectively bulk modulus and modulus of shearing, the GPa of Rock Matrix；KmineralAnd umineralIt is respectively bulk modulus and modulus of shearing, the f of mineral mattermineralAnd fkerofenIt is respectively mineral matter and organic volume content, %；
C.2 step, adds micropore, builds rock matrix according to Krief relational expression；
Described step is c.2 middle uses below equation component rock matrix,
Wherein, KframeAnd uframeIt is respectively bulk modulus and modulus of shearing, the GPa of rock matrix；fwaterFor the volume content of moisture, %, Φ are porosity, %.
C.3 step, calculates the elastic modelling quantity of saturated rock according to Gassmann equation；
Wherein, KsaturedAnd usaturedIt is respectively bulk modulus and modulus of shearing, the GPa of oil shale；KwaterFor the bulk modulus of moisture, GPa.
C.4 step, calculates the velocity of longitudinal wave of oil shale according to oil shale rock density and elastic modelling quantity；
Step d, is combined with shale gas seismic trace near well and sets up well logging information and the extrapolated log of seismic properties velocity of longitudinal wave.
Described step d farther includes:
D.1, seismic attribute abstraction, from shale gas seismic trace near well extraction seismic properties, and from oil content curve extraction oil content parameter for step；
D.2 step, optimizes seismic properties, utilizes the linear regression of many attributes or validation-cross method to realize seismic properties sequence, selects best attributes combination；
D.3, neural metwork training, the seismic properties that application selects is as training sample, and oil content is that predicted target values carries out neural metwork training, it is thus achieved that what neutral net was overall reflects weight coefficient matrix, sets up the non-linear relation of seismic properties and oil content for step.
Existing oil shale identification and evaluation method are all to utilize the logging response character of oil shale, but earthquake aspect there is presently no ripe effective method.Utilizing earthquake to identify and evaluating oil shale is fully to use various seismic characters, as seimic wave velocity, walk time, wave impedance, amplitude etc., find out the seismic response features of oil shale, thus it be identified and evaluate.
Oil shale has the praetersonic time difference and low-density feature, uses wave impedance and conventional logging response characteristic to come overlapping, and spacing can become apparent from.Wherein, the preparation method of wave impedance has two kinds: 1) it is directly to be converted to by log, 2) it is to utilize seismic inversion to obtain, by analysis with sample tentative calculation, obtain wave impedance Δ logR overlay method computing formula.
Research work area is positioned at Song-liao basin south In The Central Depression and settles in an area Hong Gang terrace, and in work area, oil shale major developmental is in two sections of bottoms of Nenjiang group and Nenjiang group one section, mainly utilizes aforesaid side be identified oil shale and evaluate.
Utilize well logging Δ logR overlay method that by resistivity and interval transit time, the D45 well in work area is calculated oil shale organic carbon content TOC and oil content curve, wherein it is based on the relation TOC of the prior art and oil content by the transformational relation of organic carbon content TOC to oil content, owing to research work area and document belong to Song-liao basin south, but industrial area oil shale buries deeper, utilize TOCE and the relation of oil content in prior art, can directly carry out earthquake prediction oil shale.
Utilize seismic multi-attribute predicted method that in work area six mouthfuls of wells are carried out inverting and obtain oil content of oil shale section, from crossing well oil content, can to obtain the calculating oil content corresponding relation of oil content of oil shale and well logging with inverting fine, and can be seen that oil shale be interconnected, contact, overlapping and high-order bit oil content ground, feature that low position oil content is high.It follows that method described in employing the present embodiment can truly, reliably reflect the product shale oil the most do not surveyed in current work area.
Use seismic multi-attribute to predict the oil content of oil shale obtained, it can be seen that oil content higher position is north-south distribution strip, thus provide reliable foundation for finding the favorable facies belt of oil shale.
Application well logging and seismic method hillock red to Song-liao basin district two sections of oil shales of Nenjiang group are predicted and evaluate, result shows that it is effective for utilizing seismic recognition and evaluating oil shale, drilling well in work area can be supplemented and take the deficiency of new data, the oil shale of the whole district can also be carried out quantitative assessment simultaneously, strong foundation can be provided for the resource assessment of oil shale, have a good application prospect.
Above-described detailed description of the invention; the purpose of the present invention, technical scheme and beneficial effect are further described; it is it should be understood that; the foregoing is only the detailed description of the invention of the present invention; the protection domain being not intended to limit the present invention; all within the spirit and principles in the present invention, any modification, equivalent substitution and improvement etc. done, should be included within the scope of the present invention.
Claims (4)
1. the seismic recognition of an oil shale and evaluation method, it is characterised in that comprise the following steps:
Step a, employing wave impedance Δ logR overlay method calculate shale gas organic carbon content TOC；
Step b, described step a calculated TOC value is consistent with actual measurement print gained TOC value；
Step c, oil shale is made up of organic matter, mineral matter, moisture, from oil shale rock structure, sets up the petrophysical model of oil shale, farther includes
C.1, organic and mineral matter forms bedded rock matrix according to Reuss averaging method to step；
Wherein, KmatrixAnd umatrixIt is respectively bulk modulus and modulus of shearing, the GPa of Rock Matrix；KmineralAnd umineralIt is respectively bulk modulus and modulus of shearing, the f of mineral mattermineralAnd fkerofenIt is respectively mineral matter and organic volume content, %；
C.2 step, adds micropore, builds rock matrix according to Krief relational expression；
C.3 step, calculates the elastic modelling quantity of saturated rock according to Gassmann equation；
C.4 step, calculates the velocity of longitudinal wave of oil shale according to oil shale rock density and elastic modelling quantity；
Step d, is combined with shale gas seismic trace near well and sets up well logging information and the extrapolated log of seismic properties velocity of longitudinal wave.
The seismic recognition of a kind of oil shale the most according to claim 1 and evaluation method, it is characterised in that: described step d is further comprising the steps of:
D.1, seismic attribute abstraction, from shale gas seismic trace near well extraction seismic properties, and from oil content curve extraction oil content parameter for step；
D.2 step, optimizes seismic properties, utilizes the linear regression of many attributes or validation-cross method to realize seismic properties sequence, selects best attributes combination；
D.3, neural metwork training, the seismic properties that application selects is as training sample, and oil content is that predicted target values carries out neural metwork training, it is thus achieved that what neutral net was overall reflects weight coefficient matrix, sets up the non-linear relation of seismic properties and oil content for step.
The seismic recognition of a kind of oil shale the most according to claim 2 and evaluation method, it is characterised in that: described step is c.2 middle uses below equation component rock matrix,
fwater=1-fkerogen-fmineral
Φ=fwater
Wherein, KframeAnd uframeIt is respectively bulk modulus and modulus of shearing, the GPa of rock matrix；fwaterFor the volume content of moisture, %, Φ are porosity, %.
The seismic recognition of a kind of oil shale the most according to claim 3 and evaluation method, it is characterised in that: described step c.3 in, use below equation calculate elastic modelling quantity
usatured=uframe
Wherein, KsaturedAnd usaturedIt is respectively bulk modulus and modulus of shearing, the GPa of oil shale；KwaterFor the bulk modulus of moisture, GPa.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610151741.9A CN105785446A (en) | 2016-03-17 | 2016-03-17 | Oil shale earthquake identification and evaluation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610151741.9A CN105785446A (en) | 2016-03-17 | 2016-03-17 | Oil shale earthquake identification and evaluation method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105785446A true CN105785446A (en) | 2016-07-20 |
Family
ID=56393878
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610151741.9A Pending CN105785446A (en) | 2016-03-17 | 2016-03-17 | Oil shale earthquake identification and evaluation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105785446A (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106324674A (en) * | 2016-08-23 | 2017-01-11 | 中国石油大学(华东) | Shale gas TOC pre-stack seismic inversion prediction method |
| CN106338778A (en) * | 2016-08-25 | 2017-01-18 | 西南石油大学 | Shale lithofacies continuity prediction method based on logging information |
| CN106707345A (en) * | 2016-12-13 | 2017-05-24 | 中国石油天然气股份有限公司 | Method and device for identifying lithology of angle elastic parameter |
| CN107102354A (en) * | 2016-12-21 | 2017-08-29 | 中国石油化工股份有限公司江汉油田分公司物探研究院 | A kind of shale dessert seismic Integrated Evaluation method |
| CN107766597A (en) * | 2016-08-18 | 2018-03-06 | 中国石油化工股份有限公司 | Mud shale fine-grained sediment petrofacies research method |
| CN107833144A (en) * | 2016-09-14 | 2018-03-23 | 中国石油化工股份有限公司 | A kind of method for characterizing mining area oil content of oil shale state |
| 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 |
| CN110568150A (en) * | 2019-04-28 | 2019-12-13 | 中国石油天然气股份有限公司 | Oil shale identification method and device |
| CN111101935A (en) * | 2019-12-25 | 2020-05-05 | 中海石油(中国)有限公司 | Oil shale prediction method under few-well condition |
| CN115126480A (en) * | 2021-03-29 | 2022-09-30 | 中国石油天然气股份有限公司 | Method, device, equipment and storage medium for identifying shale oil sweet spot |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104007484A (en) * | 2014-06-06 | 2014-08-27 | 董春梅 | Shale classification method |
| CN104237965A (en) * | 2014-09-02 | 2014-12-24 | 中国石油天然气股份有限公司 | shale oil resource evaluation method and device |
| CN105158803A (en) * | 2015-07-27 | 2015-12-16 | 中国石油天然气集团公司 | Method for selecting advantaged reservoir stratum |
-
2016
- 2016-03-17 CN CN201610151741.9A patent/CN105785446A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104007484A (en) * | 2014-06-06 | 2014-08-27 | 董春梅 | Shale classification method |
| CN104237965A (en) * | 2014-09-02 | 2014-12-24 | 中国石油天然气股份有限公司 | shale oil resource evaluation method and device |
| CN105158803A (en) * | 2015-07-27 | 2015-12-16 | 中国石油天然气集团公司 | Method for selecting advantaged reservoir stratum |
Non-Patent Citations (1)
| Title |
|---|
| 张佳佳 等: "油页岩的地球物理识别和评价方法", 《石油学报》 * |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107766597A (en) * | 2016-08-18 | 2018-03-06 | 中国石油化工股份有限公司 | Mud shale fine-grained sediment petrofacies research method |
| CN106324674A (en) * | 2016-08-23 | 2017-01-11 | 中国石油大学(华东) | Shale gas TOC pre-stack seismic inversion prediction method |
| CN106324674B (en) * | 2016-08-23 | 2018-06-12 | 中国石油大学(华东) | A kind of shale gas TOC pre-stack seismic inversion Forecasting Methodologies |
| CN106338778A (en) * | 2016-08-25 | 2017-01-18 | 西南石油大学 | Shale lithofacies continuity prediction method based on logging information |
| CN106338778B (en) * | 2016-08-25 | 2017-11-24 | 西南石油大学 | A kind of shale petrofacies continuous prediction method based on well logging information |
| CN107833144B (en) * | 2016-09-14 | 2021-11-30 | 中国石油化工股份有限公司 | Method for representing oil content state of oil shale in mining area |
| CN107833144A (en) * | 2016-09-14 | 2018-03-23 | 中国石油化工股份有限公司 | A kind of method for characterizing mining area oil content of oil shale state |
| CN106707345A (en) * | 2016-12-13 | 2017-05-24 | 中国石油天然气股份有限公司 | Method and device for identifying lithology of angle elastic parameter |
| CN107102354A (en) * | 2016-12-21 | 2017-08-29 | 中国石油化工股份有限公司江汉油田分公司物探研究院 | A kind of shale dessert seismic Integrated Evaluation method |
| CN107102354B (en) * | 2016-12-21 | 2019-04-02 | 中国石油化工股份有限公司江汉油田分公司物探研究院 | A kind of shale dessert seismic Integrated Evaluation method |
| 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 |
| CN110568150A (en) * | 2019-04-28 | 2019-12-13 | 中国石油天然气股份有限公司 | Oil shale identification method and device |
| CN110568150B (en) * | 2019-04-28 | 2022-03-01 | 中国石油天然气股份有限公司 | Oil shale identification method and device |
| CN111101935A (en) * | 2019-12-25 | 2020-05-05 | 中海石油(中国)有限公司 | Oil shale prediction method under few-well condition |
| CN111101935B (en) * | 2019-12-25 | 2023-01-03 | 中海石油(中国)有限公司 | Oil shale prediction method under few-well condition |
| CN115126480A (en) * | 2021-03-29 | 2022-09-30 | 中国石油天然气股份有限公司 | Method, device, equipment and storage medium for identifying shale oil sweet spot |
| CN115126480B (en) * | 2021-03-29 | 2024-12-31 | 中国石油天然气股份有限公司 | Method, device, equipment and storage medium for identifying shale oil sweet spot |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105785446A (en) | Oil shale earthquake identification and evaluation method | |
| Kirar et al. | Correlation between shear wave velocity (Vs) and SPT resistance (N) for Roorkee region | |
| Li et al. | Evaluation of the reservoir permeability of anthracite coals by geophysical logging data | |
| Razack et al. | Geostatistical estimation of the transmissivity in a highly fractured metamorphic and crystalline aquifer (Man-Danane Region, Western Ivory Coast) | |
| CN105134195A (en) | Shale gas reservoir quality evaluation method based on logging information | |
| CN103670383A (en) | Method and equipment for identifying effective reservoir of shale oil reservoir | |
| CN101887132A (en) | Method for quantitatively predicting sandstone reservoir fluid saturation by combining well and seismic | |
| Kosari et al. | An integrated approach to study the impact of fractures distribution on the Ilam-Sarvak carbonate reservoirs: A case study from the Strait of Hormuz, the Persian Gulf | |
| CN105426650A (en) | Method for optimizing fracturing reconstruction well section of shale gas well by using logging information | |
| Huang et al. | Spatial prediction of the exchangeable sodium percentage at multiple depths using electromagnetic inversion modelling | |
| Al-Jawad et al. | Geological model of Khasib reservoir-central area/East Baghdad Field | |
| CN107870368A (en) | A kind of total content of organic carbon spatial distribution Forecasting Methodology based on seismic properties | |
| Shazly et al. | Using of Dual Laterolog to detect fracture parameters for Nubia sandstone formation in Rudeis-Sidri area, Gulf of Suez, Egypt | |
| Sarkheil et al. | Fracture analysis in Tabnak hydrocarbon field of Iran by using fractal geometry and multi-fractal analysis | |
| Deng et al. | Machine learning based stereoscopic triple sweet spot evaluation method for shale reservoirs | |
| Nazarpour et al. | Geostatistical modeling of spatial distribution of porosity in the Asmari Reservoir of Mansuri oil field in Iran | |
| Abdizadeh et al. | Estimation of Total Organic Carbon from well logs and seismic sections via neural network and ant colony optimization approach: a case study from the Mansuri oil field, SW Iran | |
| CN105842734A (en) | Method of using seismic data to carry out mud shale gas content prediction | |
| Mohamed Ibrahem | Groundwater hydrology and characteristics of the tertiary aquifers, Northwest Cairo, Egypt | |
| CN112069682A (en) | Ore formation prediction method based on three-dimensional modeling | |
| Eladj et al. | Design of new model for water saturation based on neural network for low-resistivity phenomenon (Algeria) | |
| Liu et al. | Evaluation on an original resistivity inversion method of water flooding a conglomerate reservoir based on petrophysical analysis | |
| CN106568919A (en) | Shale brittleness prediction method based on rock physical analysis | |
| Brom et al. | Estimation of geotechnical parameters on the basis of geophysical methods and geostatistics | |
| Glad et al. | Geomechanical Modelling the Evolution of a Connected Natural Fracture Network to Explain Fluid Flow Variations Across a Fractured Chalk-Marl Reservoir |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20160720 |