CN107515425A - Suitable for the earthquake prediction method of actic region glutenite deposition connected component - Google Patents
Suitable for the earthquake prediction method of actic region glutenite deposition connected component Download PDFInfo
- Publication number
- CN107515425A CN107515425A CN201610430880.5A CN201610430880A CN107515425A CN 107515425 A CN107515425 A CN 107515425A CN 201610430880 A CN201610430880 A CN 201610430880A CN 107515425 A CN107515425 A CN 107515425A
- Authority
- CN
- China
- Prior art keywords
- connected component
- phased
- glutenite
- prediction method
- earthquake
- 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
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 present invention provides a kind of earthquake prediction method suitable for actic region glutenite deposition connected component, including：Individual well curvature correction and more real response characteristics of well consistency treatment recovery curve；The poststack determinated back analysis analysis deposition phase time, determine interface between the phase time；Seismic facies comprehensive study recognizes Conglomerate Body extension, primarily determines that the external boundary of small phase time；Determinated back analysis blends with earthquake and determines phased condition；The phased geostatistical inversion of high-resolution poststack portrays favourable Conglomerate Body；Favourable physical property development area is portrayed in the phased geostatistical inversion association simulation of high-resolution poststack；The phased geostatistical inversion of synthesis high-resolution and the simulation of geostatistics association portray to obtain the spatial distribution of effective connected component.This method is effectively portrayed for actic region glutenite deposition physical efficiency, is carried out physical property Favorable Areas and is predicted, and then obtains the spatial distribution of effective connected component, and the technical support of key is provided for actic region glutenite Efficient Development.
Description
Technical field
The present invention relates to oil field development technical field, especially relates to a kind of actic region glutenite that is applied to and deposits connected component
Earthquake prediction method.
Background technology
Glutenite lithosomic body not only vertically and horizontally change it is sufficiently complex, simultaneously as sand-conglomerate reservoir mutually accelerates, lithology is various,
The relationship between lithology and logging is complicated, reservoir heterogeneity is extremely strong, reservoir connected relation is in the heterogeneous change in space so as to causing, and causes to contain
Oily difference and production history are larger, have a strong impact on development effectiveness.New it is applied to actic region sand we have invented a kind of for this
Conglomerate deposits the earthquake prediction method of connected component, solves above technical problem.
The content of the invention
Physical property Favorable Areas can be carried out it is an object of the invention to provide one kind to be predicted, and then obtains the sky of effective connected component
Between be distributed be applied to actic region glutenite deposition connected component earthquake prediction method.
The purpose of the present invention can be achieved by the following technical measures：Suitable for the earthquake of actic region glutenite deposition connected component
Forecasting Methodology, should be applied to the earthquake prediction method of actic region glutenite deposition connected component includes：Step 1, individual well curve school
Just with more real response characteristics of well consistency treatment recovery curve；Step 2, the poststack determinated back analysis analysis deposition phase time,
Determine interface between phase time；Step 3, seismic facies comprehensive study understanding Conglomerate Body extension, the outside of small phase time is primarily determined that
Boundary；Step 4, determinated back analysis blends with earthquake and determines phased condition；Step 5, the phased geology system of high-resolution poststack
Meter learns inverting and portrays favourable Conglomerate Body；Step 6, favourable thing is portrayed in the phased geostatistical inversion association simulation of high-resolution poststack
Sexual development area；Step 7, the phased geostatistical inversion of comprehensive high-resolution and the simulation of geostatistics association portray to obtain effectively
The spatial distribution of connected component.
The purpose of the present invention can be also achieved by the following technical measures：
In step 1, less curve progress Multivariate Curve recurrence is influenceed with reference to by borehole environment, completes strict individual well
After curvature correction, more well consistency treatments are carried out to curve of the reservoir prediction compared with concern, eliminate due to different instruments series,
Different log-times systematic error and random error to caused by more borehole logging tool curves.
In step 2, by poststack determinated back analysis analyze impedance structure, further recognize sand-conglomerate body spatial shape and
Spread scale, and then gone out the boundary line delimitation of big phase time of the Conglomerate Body of more phases time of longitudinal direction according to impedance structure.
In step 3, first carry out fine well shake demarcation, on statistics well each substratum seismic waveform form, comparative analysis,
The signature waveform of each small phase time is obtained, carries out waveform clustering, primarily determines that the spatial extent scope of Conglomerate Body of the small phase time.
In step 4, the interface of determinated back analysis certainty several big phases time and deposition phase, seismic facies Preliminary Study are true
The external boundary of qualitative each small phase time, the two is organically blended, that is, has obtained the phased condition of geostatistical inversion,
It is normalized, then contrast test, suitable constraint weight is chosen, turns into constraints and is tied to statistical models
Among.
In steps of 5, geology, the multi-disciplinary information of well logging are organically blended in the form of geological statisticses with earthquake information,
Obtain predicting that achievement had both met geology prior information, but with earthquake posterior information energy perfect matching, with reference to the phased of characteristic
Matter statistics inverting, the sedimentary facies model in region is fused in inverting engine, and prediction achievement not only conforms with geophysics rule
Rule, has rational depositional model, obtains the high-resolution prediction achievement of each substratum again.
In step 6, after high-resolution phased statistics inverting is completed, the analog study of statistics association is carried out, by impedance
And lithographic model, it is mapping through Cloud transform and is mapped as physical property model, and constrained by well point, obtain the spatial distribution of physical property.
In step 7, shallow-layer sandy gravel materials are high resistant, and partially below mud stone, the two, which has, is necessarily stacked, deep for sandstone impedance
Layer impedance is low-resistance, and porosity can aid in further carrying out lithology and favourable oil gas reservoir area differentiates；It is general using petrofacies
Double cutoffs of rate and porosity, reflect with reference to real bore on well with later development water filling, effectively carve effective connected component
Spatial distribution.
The earthquake prediction method for being applied to actic region glutenite deposition connected component in the present invention, is united by the phased geology of poststack
Meter learns inverting, is effectively portrayed for actic region glutenite deposition physical efficiency, assists and simulate with reference to statistics, carrying out physical property has
Li Qu is predicted, and then obtains the spatial distribution of effective connected component, and key is provided for actic region glutenite Efficient Development
Technical support.
Brief description of the drawings
Fig. 1 is the specific embodiment for being applied to actic region glutenite and depositing the earthquake prediction method of connected component of the present invention
Flow chart；
Fig. 2 is sound wave statistic histogram before more well consistency treatments in the specific embodiment of the present invention；
Fig. 3 is Statistics of Density histogram before more well consistency treatments in the specific embodiment of the present invention；
Fig. 4 is sound wave statistic histogram after more well consistency treatments in the specific embodiment of the present invention；
Fig. 5 is Statistics of Density histogram after more well consistency treatments in the specific embodiment of the present invention；
Fig. 6 is the seismic facies wave character comparison diagram of the specific embodiment statistics of the present invention；
Fig. 7 is the geostatistics operational flowchart applied in the present invention；
Fig. 8 is the geostatistical inversion workflow diagram that phased constraint is utilized in the present invention；
Fig. 9 is a sand-conglomerate body spatial characteristic pattern specifically predicted in the present invention.
Embodiment
For enable the present invention above and other objects, features and advantages become apparent, it is cited below particularly go out preferred embodiment,
And coordinate shown in accompanying drawing, it is described in detail below.
As shown in figure 1, Fig. 1 is the flow for being applied to actic region glutenite and depositing the earthquake prediction method of connected component of the present invention
Figure.
Step 101, individual well curvature correction and more real response characteristics of well consistency treatment recovery curve.
The quality of individual well log includes curve matching, depth correction etc. first, is in addition log by borehole environment
Influence (hole glutenite undergauge, mud stone collapse expanding), main implementation process is smaller with reference to being influenceed by borehole environment
Curve, such as natural gamma (GR), resistivity (Rt) etc. carries out Multivariate Curve recurrence, bent completing strict individual well
After line correction, it is also necessary to curve (such as interval transit time curve (P-sonic), density curve of the reservoir prediction compared with concern
(Density) more well consistency treatments) are carried out, are eliminated because different instruments are serial, different log-times are to more borehole logging tools
Systematic error caused by curve and random error, it is interval transit time curve and the more wells one of density curve as shown in Fig. 2 to Fig. 5
Cause property treatment effect comparison diagram.
Step 102, poststack determinated back analysis analyzes the big deposition phase time, determines interface between the phase time.
Poststack determinated back analysis is the inverting based on earthquake, very little is influenceed by other factors (such as well), due to sparse
The intrinsic de-tuned advantage of pulsing algorithm, impedance structure is analyzed by poststack determinated back analysis, further recognizes sand-conglomerate body
Spatial shape and spread scale, and then according to impedance structure by the boundary line delimitation of big phase time of time Conglomerate Body of more phases of longitudinal direction
Go out.
Step 103, seismic facies comprehensive study understanding Conglomerate Body extension, the external boundary of small phase time is primarily determined that.
Seismic facies comprehensive study work is carried out, obtains the spatial dimension of the Conglomerate Body of each small phase time, its external boundary of certainty,
Support is provided accurately to portray conglomerate volume morphing of each small phase time.Its concrete implementation process be first carry out fine well shake demarcation,
The seismic waveform form of each substratum on statistics well, as shown in fig. 6, comparative analysis, obtains the signature waveform of each small phase time,
Waveform clustering is carried out, primarily determines that the spatial extent scope of Conglomerate Body of the small phase time.
Step 104, determinated back analysis blends with earthquake and determines phased condition.
The interface of determinated back analysis certainty several big phases time and deposition phase, seismic facies Preliminary Study certainty each small phase
Secondary external boundary, the two is organically blended, that is, has been obtained the phased condition of geostatistical inversion, be normalized
Processing, then contrast test, suitable constraint weight is chosen, turns into constraints and is tied among statistical models.
Step 105, the phased geostatistical inversion of high-resolution poststack portrays favourable Conglomerate Body.
The sharpest edges of poststack high-resolution geostatistics are i.e. by the multi-disciplinary information such as geology, well logging with geological statisticses
Form is organically blended with earthquake information, obtains predicting that achievement had both met geology prior information, but with earthquake posterior information
Energy perfect matching, with reference to the phased geostatistical inversion of characteristic, the sedimentary facies model in region is fused in inverting engine,
Prediction achievement not only conforms with geophysics rule, has rational depositional model again, obtains the high-resolution prediction of each substratum
Achievement, as shown in Figure 7.
Step 106, favourable physical property development area is portrayed in the phased geostatistical inversion association simulation of high-resolution poststack.
After high-resolution phased statistics inverting is completed, also need to carry out the analog study of statistics association, by impedance and petrofacies
Model, it is mapping through Cloud transform and is mapped as physical property model, and constrained by well point, obtains the spatial distribution of physical property, such as Fig. 8
It is shown.
Step 107, the comprehensive phased geostatistical inversion of high-resolution is assisted simulation to portray and effectively connected with geostatistics
The spatial distribution of body, as shown in Figure 9.
The Rock physical analysis of early stage shows, p-wave impedance has certain separating capacity to analysis, but with the increase of buried depth,
Stratum is gradually compacted, mineral dehydration, and the impedance of glutenite and mud stone has one substantially to become big trend, but the two changes
Speed is otherwise varied, is less than mud stone in the impedance of shallow-layer sandstone, and DEEP SANDSTONE impedance is higher than mud stone, and fine and close conglomerate impedance
Higher always, in actic region glutenite deposition, bottom often covers in conglomerate for thick set conglomerate, bottom reservoir development in thickness
The preferable trend of fragility, physical property, generally speaking, shallow-layer sandy gravel materials are high resistant, sandstone impedance partially below mud stone,
The two, which has, is necessarily stacked, and deep layer impedance is low-resistance, and porosity can aid in further carrying out lithology and favourable oil gas reservoir
Area differentiates.
Using petrofacies probability and double cutoffs of porosity, reflect with reference to real bore on well with later development water filling, can be effective
The effective connected component of engraving spatial distribution.
Claims (8)
1. suitable for the earthquake prediction method of actic region glutenite deposition connected component, it is characterised in that actic region gravel should be applied to
The earthquake prediction method of rock deposition connected component includes：
Step 1, individual well curvature correction and more real response characteristics of well consistency treatment recovery curve；
Step 2, the poststack determinated back analysis analysis deposition phase time, interface between the phase time is determined；
Step 3, seismic facies comprehensive study understanding Conglomerate Body extension, the external boundary of small phase time is primarily determined that；
Step 4, determinated back analysis blends with earthquake and determines phased condition；
Step 5, the phased geostatistical inversion of high-resolution poststack portrays favourable Conglomerate Body；
Step 6, favourable physical property development area is portrayed in the phased geostatistical inversion association simulation of high-resolution poststack；
Step 7, the phased geostatistical inversion of comprehensive high-resolution and the simulation of geostatistics association portray to obtain the sky of effective connected component
Between be distributed.
2. the earthquake prediction method according to claim 1 suitable for actic region glutenite deposition connected component, it is characterised in that
In step 1, less curve progress Multivariate Curve recurrence is influenceed with reference to by borehole environment, completes strict individual well curve
After correction, more well consistency treatments are carried out to curve of the reservoir prediction compared with concern, eliminated due to different instruments series, difference
Log-time systematic error and random error to caused by more borehole logging tool curves.
3. the earthquake prediction method according to claim 1 suitable for actic region glutenite deposition connected component, it is characterised in that
In step 2, impedance structure is analyzed by poststack determinated back analysis, further recognizes the spatial shape and spread of sand-conglomerate body
Scale, and then gone out the boundary line delimitation of big phase time of the Conglomerate Body of more phases time of longitudinal direction according to impedance structure.
4. the earthquake prediction method according to claim 1 suitable for actic region glutenite deposition connected component, it is characterised in that
In step 3, first carry out fine well shake demarcation, on statistics well each substratum seismic waveform form, comparative analysis, obtain
To the signature waveform of each small phase time, waveform clustering is carried out, primarily determines that the spatial extent scope of Conglomerate Body of the small phase time.
5. the earthquake prediction method according to claim 1 suitable for actic region glutenite deposition connected component, it is characterised in that
In step 4, the interface of determinated back analysis certainty several big phases time and deposition phase, seismic facies Preliminary Study certainty
The external boundary of each small phase time, the two is organically blended, that is, has obtained the phased condition of geostatistical inversion, is carried out
Normalized, then contrast test, suitable constraint weight is chosen, turns into constraints and is tied among statistical models.
6. the earthquake prediction method according to claim 1 suitable for actic region glutenite deposition connected component, it is characterised in that
In steps of 5, geology, the multi-disciplinary information of well logging are organically blended in the form of geological statisticses with earthquake information, obtained
Both met geology prior information to prediction achievement, but with earthquake posterior information energy perfect matching, with reference to the phased geology of characteristic
Statistics inverting, the sedimentary facies model in region being fused in inverting engine, prediction achievement not only conforms with geophysics rule,
There is rational depositional model again, obtain the high-resolution prediction achievement of each substratum.
7. the earthquake prediction method according to claim 1 suitable for actic region glutenite deposition connected component, it is characterised in that
In step 6, after high-resolution phased statistics inverting is completed, the analog study of statistics association is carried out, by impedance and rock
Phase model, it is mapping through Cloud transform and is mapped as physical property model, and constrained by well point, obtains the spatial distribution of physical property.
8. the earthquake prediction method according to claim 1 suitable for actic region glutenite deposition connected component, its feature
Be, in step 7, shallow-layer sandy gravel materials are high resistant, sandstone impedance partially below mud stone, the two have it is certain stacked,
Deep layer impedance is low-resistance, and porosity can aid in further carrying out lithology and favourable oil gas reservoir area differentiates；Using petrofacies
Double cutoffs of probability and porosity, effectively connected with later development water filling reflection, effective engraving with reference to real bore on well
The spatial distribution of body.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610430880.5A CN107515425A (en) | 2016-06-16 | 2016-06-16 | Suitable for the earthquake prediction method of actic region glutenite deposition connected component |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610430880.5A CN107515425A (en) | 2016-06-16 | 2016-06-16 | Suitable for the earthquake prediction method of actic region glutenite deposition connected component |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107515425A true CN107515425A (en) | 2017-12-26 |
Family
ID=60720982
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610430880.5A Pending CN107515425A (en) | 2016-06-16 | 2016-06-16 | Suitable for the earthquake prediction method of actic region glutenite deposition connected component |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107515425A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110007344A (en) * | 2019-04-08 | 2019-07-12 | 中国石油化工股份有限公司 | A kind of seismic identification and device of disconnected solution reservoir communication |
| CN112987095A (en) * | 2021-02-25 | 2021-06-18 | 中国科学院地理科学与资源研究所 | Geological fault detection method and device |
| CN114200524A (en) * | 2021-10-29 | 2022-03-18 | 五季数据科技(北京)有限公司 | Logging density curve correction method based on artificial intelligence deep learning |
| CN116755153A (en) * | 2023-07-13 | 2023-09-15 | 山东石油化工学院 | Seismic waveform identification method for steep slope belt sandstone structure interface |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104657523A (en) * | 2013-11-20 | 2015-05-27 | 中国石油化工股份有限公司 | Glutenite comprehensive geologic modeling method |
| US20150219793A1 (en) * | 2012-09-26 | 2015-08-06 | Hongmei Li | Conditional Process-Aided Multiple-Points Statistics Modeling |
| CN105044770A (en) * | 2015-07-06 | 2015-11-11 | 成都理工大学 | Compact glutenite gas reservoir quantificational prediction method |
-
2016
- 2016-06-16 CN CN201610430880.5A patent/CN107515425A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150219793A1 (en) * | 2012-09-26 | 2015-08-06 | Hongmei Li | Conditional Process-Aided Multiple-Points Statistics Modeling |
| CN104657523A (en) * | 2013-11-20 | 2015-05-27 | 中国石油化工股份有限公司 | Glutenite comprehensive geologic modeling method |
| CN105044770A (en) * | 2015-07-06 | 2015-11-11 | 成都理工大学 | Compact glutenite gas reservoir quantificational prediction method |
Non-Patent Citations (7)
| Title |
|---|
| 刘豪等: "《油气储层地震综合预测技术及应用》", 31 October 2013, 武汉：中国地质大学出版社有限责任公司 * |
| 吕世超等: "基于地震地层学的砂砾岩期次划分方法研究", 《油气藏评价与开发》 * |
| 惠长松等: "沉积期次控制下的砂砾岩体有效储层地球物理综合预测", 《石油物探》 * |
| 王延光等: "《胜利油田地球物理技术进展与实践（2005-2011年）上册》", 30 November 2012, 北京：石油工业出版社 * |
| 钱玉贵等: "叠前地质统计学反演技术在复杂储层量化预测中的应用", 《石油与天然气地质》 * |
| 韩东等: "基于地质统计学反演的缝洞储集体物性定量评价", 《地球物理学进展》 * |
| 韩东等: "基于马蒙算法地质统计学反演的缝洞储集体预测", 《物探与化探》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110007344A (en) * | 2019-04-08 | 2019-07-12 | 中国石油化工股份有限公司 | A kind of seismic identification and device of disconnected solution reservoir communication |
| CN112987095A (en) * | 2021-02-25 | 2021-06-18 | 中国科学院地理科学与资源研究所 | Geological fault detection method and device |
| CN112987095B (en) * | 2021-02-25 | 2021-10-15 | 中国科学院地理科学与资源研究所 | Geological fault detection method and device |
| CN114200524A (en) * | 2021-10-29 | 2022-03-18 | 五季数据科技(北京)有限公司 | Logging density curve correction method based on artificial intelligence deep learning |
| CN116755153A (en) * | 2023-07-13 | 2023-09-15 | 山东石油化工学院 | Seismic waveform identification method for steep slope belt sandstone structure interface |
| CN116755153B (en) * | 2023-07-13 | 2023-10-31 | 山东石油化工学院 | Seismic waveform identification method for steep slope belt sandstone structure interface |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Jolley et al. | Faulting and fault sealing in production simulation models: Brent Province, northern North Sea | |
| CN101158724B (en) | Reservoir thickness prediction method based on dipolar wavelet | |
| CN104047597B (en) | Oily mud shale stratum log standardized method | |
| CN109541685B (en) | River channel sand body identification method | |
| CN105044770A (en) | Compact glutenite gas reservoir quantificational prediction method | |
| CN110056346B (en) | Oil reservoir three-dimensional original water saturation simulation method based on trend change function | |
| WO2013176799A1 (en) | System and method for predicting rock strength | |
| CN105527653A (en) | Virtual well construction method based on geological information | |
| CN107515425A (en) | Suitable for the earthquake prediction method of actic region glutenite deposition connected component | |
| CN109143399B (en) | Method for identifying carbonate rock sequence interface | |
| Cabello et al. | An outcrop-based comparison of facies modelling strategies in fan-delta reservoir analogues from the Eocene Sant Llorenç del Munt fan-delta (NE Spain) | |
| CN104122581A (en) | Poststack acoustic wave impedance inversion method | |
| CN106285642A (en) | A kind of formation pore pressure Forecasting Methodology based on seismic data | |
| CN107831540A (en) | The direct new method for extracting of reservoir physical parameter | |
| CN110688728B (en) | Method for quantitatively analyzing sedimentation characteristics of one-dimensional sediments in time domain and water environment | |
| Iltaf et al. | Facies and petrophysical modeling of Triassic Chang 6 tight sandstone reservoir, Heshui oil field, Ordos basin, China | |
| CUI et al. | A volumetric model for evaluating tight sandstone gas reserves in the Permian Sulige gas field, Ordos Basin, Central China | |
| CN105589098B (en) | Carbonate rock removes the reservoir inversion method and system that deposition shale laterally influences | |
| CN113534263B (en) | Oil-gas saturation prediction method independent of logging information | |
| Abdolahi et al. | Improving the 3D facies model with the seismic-derived log volumes: a case study from the Asmari Formation in the Hendijan Field, southwest Iran | |
| CN109765621B (en) | Mapping method for sedimentary facies fine carving | |
| Zene et al. | Volumetric estimation and OOIP calculation of the Ronier4 block of Ronier oilfield in the Bongor basin, Chad | |
| Bárbara et al. | Quantifying the impact of the structural uncertainty on the gross rock volume in the Lubina and Montanazo oil fields (Western Mediterranean) | |
| CN112782780A (en) | Reservoir evaluation method, device and equipment based on rock physical facies | |
| CN117345208B (en) | Quantitative characterization method and device for fracturing advantage area, electronic equipment and medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20171226 |