CN117609741B - Shale oil reservoir thin interlayer logging identification method based on envelope curve algorithm - Google Patents
Shale oil reservoir thin interlayer logging identification method based on envelope curve algorithm Download PDFInfo
- Publication number
- CN117609741B CN117609741B CN202410089548.1A CN202410089548A CN117609741B CN 117609741 B CN117609741 B CN 117609741B CN 202410089548 A CN202410089548 A CN 202410089548A CN 117609741 B CN117609741 B CN 117609741B
- Authority
- CN
- China
- Prior art keywords
- logging
- oil reservoir
- shale oil
- algorithm
- envelope
- 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
- 239000011229 interlayer Substances 0.000 title claims abstract description 58
- 238000000034 method Methods 0.000 title claims abstract description 57
- 239000003079 shale oil Substances 0.000 title claims abstract description 43
- 238000007781 pre-processing Methods 0.000 claims abstract description 8
- 238000004458 analytical method Methods 0.000 claims abstract description 5
- 238000005553 drilling Methods 0.000 claims abstract description 5
- 238000001914 filtration Methods 0.000 claims abstract description 5
- 238000004364 calculation method Methods 0.000 claims description 7
- 238000010606 normalization Methods 0.000 claims description 5
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 239000011435 rock Substances 0.000 abstract description 2
- 239000010410 layer Substances 0.000 description 5
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 239000012634 fragment Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
-
- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
- E21B49/00—Testing the nature of borehole walls; Formation testing; Methods or apparatus for obtaining samples of soil or well fluids, specially adapted to earth drilling or wells
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/10—Pre-processing; Data cleansing
-
- H—ELECTRICITY
- H03—ELECTRONIC CIRCUITRY
- H03H—IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS; RESONATORS
- H03H17/00—Networks using digital techniques
- H03H17/02—Frequency selective networks
- H03H17/0283—Filters characterised by the filter structure
-
- 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
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/30—Assessment of water resources
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Mining & Mineral Resources (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- General Physics & Mathematics (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Geology (AREA)
- Environmental & Geological Engineering (AREA)
- Fluid Mechanics (AREA)
- Mathematical Physics (AREA)
- Computer Hardware Design (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geochemistry & Mineralogy (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention provides a shale oil reservoir thin interlayer logging identification method based on an envelope curve algorithm, which relates to the technical field of logging lithology identification and comprises the following steps: s1: drilling and coring a target horizon, deeply homing the rock core according to physical analysis data to obtain logging data, and preprocessing and normalizing the logging data; s2: obtaining an envelope curve of the deep lateral resistivity logging by using a difference quotient algorithm and a Savitzky-Golay filtering algorithm; s3: moving the envelope line to enable the envelope line to intersect with the deep lateral resistivity curve to obtain the aspect ratio of the intersection section; s4: comparing the interlayer section of the lithology of the coring section, and establishing a thin interlayer identification standard by continuously adjusting the left shift value of the envelope curve; s5: and respectively obtaining calcareous lithology and sandy lithology by using a density logging curve, and realizing quantitative identification of the lithology of the thin interlayer of the complex shale oil reservoir. The beneficial effects of the invention are as follows: and identifying sandy and calcareous thin interlayers in the complex shale oil reservoir based on lithologic density difference, and providing reliable dividing basis for identifying the shale oil reservoir thin interlayers.
Description
Technical Field
The invention relates to the technical field of logging lithology recognition, in particular to a shale oil reservoir thin interlayer logging recognition method based on an envelope curve algorithm.
Background
Shale oil reservoirs are very important unconventional oil and gas reservoirs, and sandy and calcareous thin interlayers in shale oil reservoirs are currently considered to play a key role in the production of oil and gas. However, in unconventional shale oil reservoirs, the response of the thin interlayer on logging is not as obvious as in conventional reservoirs, the conventional identification technology based on logging data cannot accurately identify the thin interlayer in the shale oil reservoirs, and the method of coring by drilling or logging by rock fragments is high in precision, but is high in cost, and cannot be studied in the whole basin range. Therefore, the thin interlayer in the shale oil reservoir can be finely identified based on the logging data, and the method has important guiding significance for the well position of the shale oil reservoir and the selection of the fracturing layer.
At present, the identification of shale is mainly based on conventional logging data, and is calibrated by using drilling coring and logging data according to the difference of logging response values and a cross map, so that a lithology identification plate based on logging response is established, but the method has more human factors and still cannot meet the requirement of identification precision.
Disclosure of Invention
In order to solve the problems of difficult identification of a thin interlayer, low lithology identification precision and the like in the prior art in a shale oil reservoir, the invention provides a shale oil reservoir thin interlayer logging identification method based on an envelope curve algorithm, which mainly comprises the following steps:
s1: the method comprises the steps of (1) drilling and coring a target horizon, namely, deeply homing the core according to physical analysis data in order to be close to the real situation of underground geology and enable the depth of the core to be consistent with that of logging data; reading a file in a standard logging format, preprocessing and normalizing logging data, wherein the logging data comprises: natural gamma GR, deep lateral RD, acoustic time difference AC, compensation density DEN;
s2: according to the logging data, a difference quotient algorithm and a Savitzky-Golay filtering algorithm are utilized to obtain an envelope curve of the deep lateral resistivity logging;
s3: moving the envelope curve to enable the envelope curve to intersect with the deep lateral resistivity curve to obtain the peak height and the peak width of the intersection section, and obtaining the height-width ratio by utilizing the peak height and the peak width;
s4: comparing the interlayer sections of the coring sections, continuously adjusting the left shift value of the envelope line to ensure that the peak width is consistent with the thickness of the coring sections, recording the aspect ratio at the moment, and establishing a thin interlayer identification standard by taking the area corresponding to the envelope line as a thin interlayer;
s5: based on the thin interlayer identification standard, the density logging curve value is utilized to distinguish calcareous lithology and sandy lithology, so that quantitative identification of the thin interlayer lithology of the reservoir shale oil reservoir is realized.
Further, in step S1, when the logging data is preprocessed, only the deep lateral direction RD needs to be preprocessed, where the preprocessing formula is as follows:
（1）
in the method, in the process of the invention,RD i for a deep lateral resistivity value at depth i,RD new taking the logarithmic calculated deep lateral resistivity value;
further, the preprocessed logging data is normalized, so that the logging data are in the same order of magnitude, and the normalization formula is as follows:
（2）
in the method, in the process of the invention,Xfor each of the preprocessed well log data,X max at the point of maximum value of the energy,X min at the level of the minimum value of the values,X new is calculated according to the formula (2).
Further, the specific implementation steps of the step S2 are as follows:
s21: and calculating the maximum value of the deep lateral resistivity in the depth direction by using a difference quotient algorithm, wherein the specific formula is as follows:
（3）
in the method, in the process of the invention,is deep lateral resistivity at depth +>Rate of change at; />And->Respectively at depth->Anddeep lateral resistivity values at;
s22: and taking the maximum value of the deep lateral resistivity in the depth direction as a control point, and carrying out interpolation by using a Savitzky-Golay filtering algorithm to obtain an envelope curve of the deep lateral resistivity logging.
Further, the calculation formula of the aspect ratio in step S3 is:
（4）
in the method, in the process of the invention,HWRrepresenting the aspect ratio of the height to the width,represents the distance from the peak top of a single intersection to the envelope,/->Representing the width of a single intersection segment on the envelope.
Further, in step S5, the density log has a value of
The storage device stores instructions and data for realizing a shale oil reservoir thin interlayer logging identification method based on an envelope curve algorithm.
A shale oil reservoir thin-sandwich logging identification device based on an envelope algorithm, comprising: a processor and the storage device; the processor loads and executes the instructions and the data in the storage device to realize the shale oil reservoir thin interlayer logging identification method based on the envelope curve algorithm.
The technical scheme provided by the invention has the beneficial effects that: based on logging data, the method calculates the envelope curve of the deep direction-finding resistivity through a difference quotient algorithm, calibrates the identification standard of the thin interlayer by using real coring data, and finally quantitatively identifies the sandy and calcareous thin interlayer in the shale oil reservoir based on lithologic density difference, thereby providing reliable division basis for lithologic quantitative identification of the thin interlayer of the shale oil reservoir.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow chart of a shale oil reservoir thin interlayer logging identification method based on an envelope algorithm in an embodiment of the invention;
FIG. 2 is a graph of X-well coring well lithology versus identifying lithology in an embodiment of the present invention;
FIG. 3 is a schematic diagram of the operation of a hardware device in an embodiment of the invention.
Detailed Description
The method for identifying the shale oil reservoir thin interlayer logging based on the envelope curve algorithm is further described below by combining specific embodiments so as to help a person skilled in the art to understand the inventive concept and technical scheme more completely, accurately and deeply; it should be noted that the description of the specific embodiments is illustrative and not meant to limit the scope of the invention, which is defined by the claims appended hereto. The lithology recognition is carried out on the X well by adopting the method described by the patent, as shown in fig. 1, the method for specifically recognizing the shale oil reservoir thin interlayer logging based on the envelope curve algorithm comprises the following steps:
s1: core homing is carried out on coring of a plurality of key wells in the block, logging data (namely logging curve values) with corresponding depths are read, and the depth homing is carried out on the cores according to physical analysis data for being close to the real situation of underground geology, so that the depths of the cores of the target layer are consistent with the depths of the logging data; reading a file in a standard logging format, preprocessing and normalizing logging data, wherein the logging data is recorded by the file in the format of the labs, and specifically comprises the following steps: natural gamma GR, deep lateral RD, sonic moveout AC, and compensation density DEN such that the core depth matches the logging depth;
in the step, the core is reset by utilizing core pore-permeation analysis data, and the reset logging curve value and lithology are read. Logging data of the X well are collated and recorded in an excel table, and the obtained table is shown in table 1:
table 1 logging data for X well
The column data in table 1 are depth, natural Gamma (GR), deep lateral (RD), sonic time difference (AC), compensation Density (DEN), depth segments corresponding to the shale oil reservoir layer top depth and layer bottom depth in order, for explaining that the data sources are true, and the results are true and reliable.
The data in table 1 is read by using the python program, and is subjected to preprocessing and maximum and minimum normalization processing, wherein the data preprocessing and normalization processing calculation process is as follows:
s1.1: the method comprises the steps of processing the deep lateral direction (RD) in logging data by adopting a logarithmic calculation mode, wherein the formula is as follows:
（1）
in the formula, RD i For a deep lateral resistivity value at depth i, RD new Taking the logarithmic calculated deep lateral resistivity value;
s1.2: normalization processing is carried out on natural Gamma (GR), deep lateral (RD), acoustic time difference (AC) and compensation Density (DEN) logging data by adopting a formula (2):
（2）
in the method, in the process of the invention,Xfor each of the well-log data,X max at the point of maximum value of the energy,X min mean @ minimum valueX) As an average value of the values,X （new） for each log calculated by equation (2), specifically GR new ，RD new ，AC new And DEN new ；
The normalized result of the log data for each depth obtained after calculation is recorded as a seven-dimensional vector (GR new ，CAL new ，RD new ，RS new ，AC new ，DEN new ，CNL new ）。
S2: according to the normalized logging data, the change rate of the deep lateral resistivity in depth is calculated by adopting a difference quotient calculation formula shown as follows:
（10）
in the method, in the process of the invention,is deep lateral resistivity at depth +>Rate of change at; />And->Respectively at depth->And->Deep lateral resistivity values at;
and taking the maximum value of the deep lateral resistivity in the depth direction as a control point, and interpolating by using a Savitzky-Golay filter algorithm to obtain an envelope curve of the deep lateral resistivity logging, wherein the window size of the Savitzky-Golay filter algorithm in the embodiment is set to be 51, and the polynomial order is set to be 2.
S3: moving the envelope curve leftwards, solving peak height and peak width after the envelope curve and the deep lateral resistivity are intersected, and defining the aspect ratio;
s4: on a logging histogram, comparing interlayer sections of the coring section, continuously shifting a wrapping line left to ensure that the thickness of the interlayer section is consistent with the peak width of a logging record, determining that the left shift value at the moment is reasonable, recording the aspect ratio at the moment, and establishing a thin interlayer identification standard, wherein the optimal left shift value of X-well calibration in the embodiment is 0.08;
s5: using the standard to identify thin interlayers, as shown in FIG. 2, the result of the envelope algorithm is obtained in the interlayer, the black area is the interlayer section obtained by prediction, and the accuracy of the thin interlayer prediction is 90% compared with the lithology profile on the left side. Using density log curve value [ ]) And the lithology of the thin interlayer of the complex shale oil reservoir is quantitatively identified by distinguishing the lithology of the calcareous and the sandy lithology for a threshold value.
Referring to fig. 3, fig. 3 is a schematic working diagram of a hardware device according to an embodiment of the present invention, where the hardware device specifically includes: a shale oil reservoir thin interlayer logging identification device 301, a processor 302 and a storage device 303 based on an envelope algorithm.
Shale oil reservoir thin-interlayer logging identification equipment 301 based on envelope algorithm: the shale oil reservoir thin interlayer logging identification equipment 301 based on the envelope algorithm realizes the shale oil reservoir thin interlayer logging identification method based on the envelope algorithm.
Processor 302: the processor 302 loads and executes the instructions and data in the storage device 303 for implementing the method for identifying shale oil reservoir thin-layer logging based on the envelope algorithm.
Storage device 303: the storage device 303 stores instructions and data; the storage device 303 is used for implementing the shale oil reservoir thin interlayer logging identification method based on the envelope curve algorithm.
The invention has the technical effects that: according to the method, the shale oil reservoir thin interlayer lithology recognition plate based on envelope curve calculation parameters is established according to the conventional logging curve, sandy and calcareous thin interlayers can be well and accurately recognized and distinguished, the thin interlayer recognition precision based on the logging curve is improved, and the evaluation of reservoir effectiveness is improved.
The above embodiments are only for illustrating the present invention, not for limiting the present invention, and various changes and modifications may be made by one of ordinary skill in the relevant art without departing from the spirit and scope of the present invention, and therefore, all equivalent technical solutions are also within the scope of the present invention, and the scope of the present invention is defined by the claims.
Claims (8)
1. A shale oil reservoir thin interlayer logging identification method based on an envelope curve algorithm is characterized by comprising the following steps of: the method comprises the following steps:
s1: the method comprises the steps of (1) drilling and coring a target horizon, namely, deeply homing the core according to physical analysis data in order to be close to the real situation of underground geology and enable the depth of the core to be consistent with that of logging data; reading a file in a standard logging format, preprocessing and normalizing logging data, wherein the logging data comprises: natural gamma GR, deep lateral RD, acoustic time difference AC, compensation density DEN;
s2: according to the logging data, a difference quotient algorithm and a Savitzky-Golay filtering algorithm are utilized to obtain an envelope curve of the deep lateral resistivity logging;
s3: moving the envelope curve to enable the envelope curve to intersect with the deep lateral resistivity curve to obtain the peak height and the peak width of the intersection section, and obtaining the height-width ratio by utilizing the peak height and the peak width;
s4: comparing the interlayer sections of the coring sections, continuously adjusting the left shift value of the envelope line to ensure that the peak width is consistent with the thickness of the coring sections, recording the aspect ratio at the moment, and establishing a thin interlayer identification standard by taking the area corresponding to the envelope line as a thin interlayer;
s5: based on thin interlayer identification standard, using density logging curve value to distinguish calcareous and sandyLithology, realizing quantitative identification of thin interlayer lithology of shale oil reservoir
2. The method for identifying the shale oil reservoir thin interlayer logging based on the envelope curve algorithm as claimed in claim 1, wherein the method comprises the following steps: when the logging data is preprocessed in the step S1, only the deep lateral RD is preprocessed, and the preprocessing formula is as follows:
（1）
in the method, in the process of the invention,RD i for a deep lateral resistivity value at depth i,RD new to take the value of the logarithmic calculated deep lateral resistivity
3. The method for identifying the shale oil reservoir thin interlayer logging based on the envelope curve algorithm as claimed in claim 2, wherein the method comprises the following steps: normalizing the preprocessed logging data to ensure that the logging data are in the same order of magnitude, wherein a normalization formula is as follows:
（2）
in the method, in the process of the invention,Xfor each of the preprocessed well log data,X max at the point of maximum value of the energy,X min at the level of the minimum value of the values,X new is calculated according to the formula (2).
4. The method for identifying the shale oil reservoir thin interlayer logging based on the envelope curve algorithm as claimed in claim 1, wherein the method comprises the following steps: the specific implementation steps of the step S2 are as follows:
s21: and calculating the maximum value of the deep lateral resistivity in the depth direction by using a difference quotient algorithm, wherein the specific formula is as follows:
（3）
in the method, in the process of the invention,is deep lateral resistivity at depth +>Rate of change at; />And->Respectively at depth->And->Deep lateral resistivity values at;
s22: taking the maximum value of the deep lateral resistivity in the depth direction as a control point, and interpolating by using a Savitzky-Golay filtering algorithm to obtain the envelope curve of the deep lateral resistivity logging
5. The method for identifying the shale oil reservoir thin interlayer logging based on the envelope curve algorithm as claimed in claim 1, wherein the method comprises the following steps: the calculation formula of the aspect ratio in the step S3 is as follows:
（4）
in the method, in the process of the invention,HWRrepresenting the aspect ratio of the height to the width,represents the distance from the peak top of a single intersection to the envelope,/->Representing the width of a single intersection in the envelope>
6. The method for identifying the shale oil reservoir thin interlayer logging based on the envelope curve algorithm as claimed in claim 1, wherein the method comprises the following steps: in step S5, the density logging curve value is
7. A memory device, characterized by: the storage device stores instructions and data for realizing the shale oil reservoir thin interlayer logging identification method based on the envelope algorithm according to any one of claims 1-6
8. Shale oil reservoir thin interlayer logging identifier equipment based on envelope curve algorithm, its characterized in that: comprising the following steps: a processor and a storage device; the processor loads and executes instructions and data in the storage device for implementing the shale oil reservoir thin interlayer logging identification method based on the envelope algorithm as claimed in any one of claims 1 to 6
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410089548.1A CN117609741B (en) | 2024-01-23 | 2024-01-23 | Shale oil reservoir thin interlayer logging identification method based on envelope curve algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410089548.1A CN117609741B (en) | 2024-01-23 | 2024-01-23 | Shale oil reservoir thin interlayer logging identification method based on envelope curve algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117609741A CN117609741A (en) | 2024-02-27 |
| CN117609741B true CN117609741B (en) | 2024-04-02 |
Family
ID=89958275
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410089548.1A Active CN117609741B (en) | 2024-01-23 | 2024-01-23 | Shale oil reservoir thin interlayer logging identification method based on envelope curve algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117609741B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113311502A (en) * | 2021-04-16 | 2021-08-27 | 中国石油天然气股份有限公司 | Method and device for identifying conventional oil layer and shale oil layer in shale layer system |
| CN113779812A (en) * | 2021-09-28 | 2021-12-10 | 中海油田服务股份有限公司 | Thin interlayer identification method using logging while drilling data in highly deviated well environment |
| CN114638255A (en) * | 2022-02-18 | 2022-06-17 | 中国地质大学（武汉） | Tight sandstone fracture logging comprehensive identification method combined with deep learning |
| CN115700321A (en) * | 2021-07-27 | 2023-02-07 | 中国石油天然气集团有限公司 | Complex lithology identification method based on logging composite parameters |
| CN115964667A (en) * | 2023-02-15 | 2023-04-14 | 成都理工大学 | River-lake lithofacies well logging identification method based on deep learning and resampling |
| CN116027416A (en) * | 2022-09-21 | 2023-04-28 | 中国石油化工股份有限公司 | Method for identifying lithology trap of gravel rock mass |
| CN116168224A (en) * | 2021-11-23 | 2023-05-26 | 中国石油化工股份有限公司 | Machine learning lithology automatic identification method based on imaging gravel content |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3256885B1 (en) * | 2015-02-13 | 2020-12-30 | Services Petroliers Schlumberger | Diagenetic and depositional rock analysis |
| US11385214B2 (en) * | 2018-08-13 | 2022-07-12 | Saudi Arabian Oil Company | Stratigraphic boundaries identification from chemostratigraphic intervals in well logs based on first and second derivatives |
-
2024
- 2024-01-23 CN CN202410089548.1A patent/CN117609741B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113311502A (en) * | 2021-04-16 | 2021-08-27 | 中国石油天然气股份有限公司 | Method and device for identifying conventional oil layer and shale oil layer in shale layer system |
| CN115700321A (en) * | 2021-07-27 | 2023-02-07 | 中国石油天然气集团有限公司 | Complex lithology identification method based on logging composite parameters |
| CN113779812A (en) * | 2021-09-28 | 2021-12-10 | 中海油田服务股份有限公司 | Thin interlayer identification method using logging while drilling data in highly deviated well environment |
| CN116168224A (en) * | 2021-11-23 | 2023-05-26 | 中国石油化工股份有限公司 | Machine learning lithology automatic identification method based on imaging gravel content |
| CN114638255A (en) * | 2022-02-18 | 2022-06-17 | 中国地质大学（武汉） | Tight sandstone fracture logging comprehensive identification method combined with deep learning |
| CN116027416A (en) * | 2022-09-21 | 2023-04-28 | 中国石油化工股份有限公司 | Method for identifying lithology trap of gravel rock mass |
| CN115964667A (en) * | 2023-02-15 | 2023-04-14 | 成都理工大学 | River-lake lithofacies well logging identification method based on deep learning and resampling |
Non-Patent Citations (3)
| Title |
|---|
| Data-driven diagenetic facies classification and well-logging identification based on machine learning methods:A case study on Xujiahe tight sandstone in Sichuan Basin;Chengjin Zhao et al.;《Journal of Petroleum Science and Engineering》;20220630;1-16 * |
| Tiantian Wu et al..Quantitative and Comprehensive Prediction of Shale Oil Sweet Spots in Qingshankou Formation,Songliao Basin.《Journal of Geoscience and Environment Protection》.2023,1-26. * |
| 基于Kohomen神经网络的页岩油岩相测井识别方法—以吉木萨尔凹陷二叠系芦草沟组为例;李红斌等;《沉积学报》;20220630;626-640 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117609741A (en) | 2024-02-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108303752B (en) | Conventional well logging quantitative identification method for effective glutenite reservoir | |
| CN111257926B (en) | Method for predicting ancient valley uranium reservoir by using old seismic data | |
| CN103529475A (en) | Method for identifying and interpreting carbonate rock ancient karst reservoir layer three-dimensional structure | |
| CN104948176B (en) | A kind of method based on infiltration Magnification identification carbonate reservoir crack | |
| CN112526107A (en) | Method for recognizing and quantitatively characterizing desserts in fractured compact sandstone reservoir | |
| CN105488248A (en) | Judgment method for deep ore stratum and rock stratum | |
| CN107831540A (en) | The direct new method for extracting of reservoir physical parameter | |
| CN109283577B (en) | Seismic horizon calibration method | |
| CN108957540B (en) | Method for efficiently extracting attenuation quality factors in complex reservoir | |
| CN112946782B (en) | Earthquake fine depicting method for dense oil-gas storage seepage body | |
| CN117609741B (en) | Shale oil reservoir thin interlayer logging identification method based on envelope curve algorithm | |
| CN114876454B (en) | Method and system for identifying complex lithology flooding layer of highly-deviated well | |
| CN111781663B (en) | Method and model for rapidly judging fluid properties of sandstone reservoir | |
| CN115700321A (en) | Complex lithology identification method based on logging composite parameters | |
| CN113219531A (en) | Method and device for identifying gas-water distribution of tight sandstone | |
| CN112014881B (en) | Water flooding speed prediction method based on time lapse earthquake | |
| CN109917489B (en) | Novel method for determining underground pressure-bearing water level | |
| CN114482995A (en) | Fine determination method for argillaceous content of fine-grain sediment | |
| CN112630839B (en) | Logging curve standardization method and system | |
| CN110687599B (en) | Well control self-coding lithology identification method for igneous rock development area | |
| CN112987091A (en) | Reservoir detection method and device, electronic equipment and storage medium | |
| CN113740928B (en) | Fluid identification method for tight sandstone reservoir | |
| CN113267808B (en) | Amplitude compensation method and device | |
| CN114135269B (en) | Dense sandstone oil layer identification method and device | |
| CN113495293B (en) | Reservoir fluid prediction method and device |
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 |