CN110727025A - Hidden fault recognition method - Google Patents
Hidden fault recognition method Download PDFInfo
- Publication number
- CN110727025A CN110727025A CN201910774894.2A CN201910774894A CN110727025A CN 110727025 A CN110727025 A CN 110727025A CN 201910774894 A CN201910774894 A CN 201910774894A CN 110727025 A CN110727025 A CN 110727025A
- Authority
- CN
- China
- Prior art keywords
- fault
- seismic data
- hidden
- seismic
- hidden fault
- 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
- 238000000034 method Methods 0.000 title claims abstract description 33
- 230000000739 chaotic effect Effects 0.000 claims abstract description 25
- 238000001914 filtration Methods 0.000 claims abstract description 19
- 238000012545 processing Methods 0.000 claims abstract description 12
- 230000004044 response Effects 0.000 claims description 19
- 238000009499 grossing Methods 0.000 claims description 10
- 230000008569 process Effects 0.000 claims description 9
- 239000011229 interlayer Substances 0.000 claims description 7
- 238000001514 detection method Methods 0.000 claims description 6
- 230000011218 segmentation Effects 0.000 claims description 3
- 230000001427 coherent effect Effects 0.000 abstract description 7
- 238000005553 drilling Methods 0.000 abstract description 3
- 238000011161 development Methods 0.000 description 6
- 230000018109 developmental process Effects 0.000 description 6
- 238000010586 diagram Methods 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 241000257303 Hymenoptera Species 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 230000001154 acute effect Effects 0.000 description 1
- 238000009412 basement excavation Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/282—Application of seismic models, synthetic seismograms
Landscapes
- Engineering & Computer Science (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Acoustics & Sound (AREA)
- Environmental & Geological Engineering (AREA)
- Geology (AREA)
- General Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Geophysics (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention relates to the technical field of seismic data processing, in particular to a hidden fault identification method. The chaotic attribute bodies are extracted from the seismic data after smooth filtering processing, and then the ant colony algorithm is used for detecting and picking up the hidden fault information, so that the influence of background noise is reduced, the hidden fault can be accurately identified even when the seismic event has no obvious fault, the identification of the hidden fault is relatively accurate, the problem of low coherent false appearance caused by using coherent bodies is avoided, and the well position drilling work is facilitated.
Description
Technical Field
The invention relates to the technical field of seismic data processing, in particular to a hidden fault identification method.
Background
In the existing exploration and development, obvious structural oil reservoirs are basically discovered, and the identification of hidden oil reservoirs is difficult to realize. In the development of hidden oil reservoirs, hidden fault identification is needed in the aspects of deployment of a development well pattern, solution of injection-production contradictions, oil-water contradictions, storage-production contradictions and the like. Because the hidden fault has a small fault distance and a short extension distance, the hidden fault is difficult to identify by naked eyes on a seismic section, and great difficulty is often caused in explaining the fault. Therefore, in the process of explaining the seismic data, various technical means need to be comprehensively applied to pertinently and finely identify the hidden fault.
A patent application document with Chinese patent application publication No. CN109001801A discloses a fault variable scale identification method based on a multiple iteration ant colony algorithm, which is used for preprocessing a seismic data body and processing actual seismic data by using an improved coherent body algorithm according to the preprocessed seismic data body; and determining threshold values and weight coefficients of various parameters of fault identification of different scales through multiple tests by using a multiple iteration ant colony algorithm, and finally realizing automatic fault identification. However, in the actual interpretation process, the influence of background noise and the influence of no obvious fault of the seismic event can cause low coherent artifact and difficult hidden fault identification of coherent bodies, which causes errors in hidden fault identification and may cause the loss of well position drilling deployed according to the result.
Disclosure of Invention
The invention aims to provide a hidden fault identification method which is used for solving the problem that the existing hidden fault identification is inaccurate.
In order to achieve the above object, the present invention provides a hidden fault identification method, including the steps of:
1) acquiring original three-dimensional seismic data, and performing smooth filtering processing on the original three-dimensional seismic data;
2) extracting a chaotic attribute body from the seismic data after the smoothing filtering processing;
3) and carrying out boundary detection and boundary pickup on the chaotic attribute body according to an iterative ant colony algorithm to obtain initial hidden fault information.
The method has the advantages that the chaotic attribute bodies are extracted from the seismic data after the smoothing filtering processing, and then the ant colony algorithm is used for detecting and picking up the hidden fault information, so that the influence of background noise is reduced, the hidden fault can be accurately identified even if the seismic event has no obvious fault, the identification of the hidden fault is relatively accurate, the problem of low coherent false image caused by using coherent bodies is avoided, and the well position drilling work is facilitated.
Further, in order to finely pick up hidden fault information and filter false fault information in a chaotic body, the iterative ant colony algorithm is to firstly apply active ant tracking to carry out boundary detection, then use passive ant tracking to carry out boundary pick-up on the detected boundary information, and repeat the active ant tracking and the passive ant tracking until an iteration condition is met. The fracture information can be most effectively depicted through a combination mode of firstly tracing the active ants and then tracing the passive ants.
Further, in order to enable the reserved information to be useful fault information, performing attitude control on the initial concealed fault information to filter out interlayer information with an inclination angle smaller than a set angle, and obtaining final concealed fault information.
Furthermore, in order to achieve the purpose of finely explaining the hidden fault to obtain the fault distance and the spread characteristics of the hidden fault, the identification method also comprises the step of determining the final fault distance of the hidden fault information according to the seismic response characteristic analysis and the seismic section; the seismic response feature analysis comprises:
time segmentation is carried out on the original three-dimensional seismic data, and the main frequency of the seismic data in different time periods is obtained through analysis;
and according to the main frequency of the seismic data in different time periods, obtaining the seismic response characteristics of the fault with different fault distances and different main frequencies through forward modeling.
Furthermore, in the smooth filtering process, the parameters of the main survey line direction and the cross survey line direction in the selected filtering window are both smaller than the vertical parameters. Therefore, the effect of better filtering random noise can be achieved, the section is clearer and smoother, and fault information can be picked up conveniently.
Furthermore, in order to better identify the hidden fault and keep the clutter reflection information of the hidden fault as much as possible, the chaotic attribute body is obtained by calculating the dip angle similarity of the interlayer seismic reflection waves in the seismic data body according to the relative size of the local structure tensor eigenvalue and the combination parameter.
Drawings
FIG. 1 is a flow chart of a concealed fault identification method of the present invention;
FIG. 2 is a schematic representation of a comparison of the original three-dimensional seismic data of the present invention with smooth filtered three-dimensional seismic data;
FIG. 3 is a schematic diagram illustrating the results of the chaotic attribute body extraction for three-dimensional seismic data after smoothing filtering according to the present invention;
fig. 4 is a schematic diagram of ant body slices after active ant tracing according to the present invention;
fig. 5 is a schematic diagram of an ant body slice extracted after the combination of active ant tracing and passive ant tracing according to the present invention;
FIG. 6 is a schematic representation of a dominant frequency analysis of three-dimensional seismic data over different time periods in accordance with the present invention;
FIG. 7 is a schematic diagram of the results of seismic response characteristics of faults under different dominant frequencies and different fault distances obtained by forward simulation of the present invention;
FIG. 8 is a schematic illustration of the concealed fault identification results of the present invention;
FIG. 9 is a schematic illustration of the time slicing results of the present invention;
FIG. 10 is a schematic illustration of the seismic profiling results of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The invention provides a hidden fault identification method, as shown in figure 1, comprising the following steps:
1) and acquiring original three-dimensional seismic data, and performing smooth filtering processing on the original three-dimensional seismic data.
As shown in figure 2, the proper construction smoothing parameters are selected to carry out filtering smoothing processing on the original three-dimensional seismic data, the essence is to carry out median filtering on the original three-dimensional seismic data, the random noise interference is eliminated, and the real stratum reflection information is highlighted. As other embodiments, the smoothing filtering process may also be implemented by other prior art methods.
2) And extracting the chaotic attribute body from the seismic data after the smoothing filtering processing.
The chaotic attribute body is obtained by calculating the dip angle similarity of the inter-layer seismic reflection waves in the seismic data body according to the relative size of the local structure tensor eigenvalue and the determination of the combination parameters, and the chaotic reflection bands and the reflection-free bands among the orderly reflections are detected by describing the reflection characteristics of the seismic reflection waves in the stratum and the continuous change of the reflection structure, so that the seismic chaotic reflection phase is highlighted and is sensitive to micro-scale section information. As shown in fig. 3, the result of extracting the chaotic attribute body from the three-dimensional seismic data after the smoothing filtering process is shown. Because the identification of the large fault is relatively easy, when the chaotic attribute body is selected and applied to identify the hidden fault, the parameter selection is mainly used for identifying the hidden fault, so that the extracted parameter is as small as possible, and the chaotic reflection information of the hidden fault is kept as much as possible.
3) And carrying out boundary detection and boundary pickup on the chaotic attribute body according to an iterative ant colony algorithm to obtain initial hidden fault information.
In order to finely pick up hidden fault information and filter false fault information in a chaotic attribute body, on the basis of the chaotic attribute body, active ant tracking is firstly used for detecting boundary information to obtain an active ant body, and the result is shown in figure 4. On the basis of the active ant body, the detected boundary information is picked up by using passive ant tracking to obtain a passive ant body, and as a result, as shown in fig. 5, the creeping characteristics of the ant are relatively conservative and a stronger signal is needed to process the trend of the next step. In this embodiment, the ant colony algorithm is an active-first passive-second passive manner, and as other embodiments, a passive-first active-second active manner may be adopted, or only active ant tracking is adopted, or only passive ant tracking is adopted.
The detection and extraction are carried out through a plurality of times of active-passive combination modes, and the situation that the fracture information can be most effectively described through the active-passive combination mode is found. In the process of extracting the chaotic attribute body, the active ant body and the passive ant body, in order to pick up more acute hidden fault information identified by the chaotic attribute body, the values of three parameters of the initial boundary range, the tracking step length and the ant tracking track deviation degree of the active ant body and the passive ant body are small, and the other three parameters are continuously adjusted according to the actual seismic data condition so as to achieve the best picking up effect.
4) And performing attitude control on the initial hidden fault information, and filtering out interlayer information with an inclination angle smaller than a set small angle to obtain final hidden fault information, wherein the set small angle is a smaller angle value.
The results of manual interpretation of seismic data show that the normal fault with a development dip angle of more than 45 degrees is mainly in the north-east direction, and the stratigraphic dip angle is less than 35 degrees in the research area. And applying the attitude control on the basis of the initial concealed fault information to filter out interlayer information with the inclination angle of less than 35 degrees, so that useful fault information is reserved.
5) And determining the fault distance of the final concealed fault information according to the seismic response characteristic analysis and the seismic section.
Wherein the seismic response characteristic analysis comprises:
time segmentation is carried out on the original three-dimensional seismic data, and the main frequency of the seismic data in different time periods is obtained through analysis;
and according to the main frequency of the seismic data in different time periods, obtaining the seismic response characteristics of the fault with different fault distances and different main frequencies through forward modeling.
Taking the three-dimensional seismic data of the GC area of the GC field as an example, the dominant frequencies of the three-dimensional seismic data in the three time periods of 100-500 ms, 500-1000 ms and 1000-1500ms within the time period of 100-1500ms are respectively analyzed, as shown in FIG. 6, the dominant frequencies in the three time periods are respectively 40Hz, 30Hz and 20 Hz. Meanwhile, faults with inclination angles larger than 45 degrees are mainly developed in the region, and the fault trend is mainly in the northeast direction.
The seismic response characteristics of the fault with the fault distance of 5 m-70 m are obtained by analyzing the main frequencies of 40Hz, 30Hz and 20Hz through forward modeling, as shown in FIG. 7, the fault with the fault distance of 10m or less is only slightly distorted on the seismic response and is difficult to identify by naked eyes, and the fault is difficult to explain. Because the response characteristics of the faults with the same fault distance under different main frequencies on the three-dimensional seismic data are different; therefore, the seismic response characteristics, which are the results of the forward modeling, can be used to determine the magnitude and reliability of the fault distance of the finally identified concealed fault.
Therefore, the finally identified hidden fault information is combined with the results of the seismic section and forward modeling to achieve the purpose of finely explaining the hidden fault, as shown in FIG. 8, FIG. 9 and FIG. 10, the hidden fault which is difficult to identify by the conventional method such as time slicing and the like is finely identified by obtaining 820ms chaotic ant slices through the application of the method of the present invention, taking northwest hidden faults such as ①, ② and ③ as an example, the time value falls within a 500ms-1000ms time period and the main frequency of seismic data is about 30Hz through combining the results of the forward modeling, the seismic section response characteristics of hidden faults such as No. ① and No. ③ identified by the method of the present invention are compared with the seismic response forward results of different fault distance under the condition of 30Hz, and the seismic response characteristics of seismic sections such as No. ①, ② and ③ identified by the method of the present invention are similar to the seismic response forward results of faults with the fault distance of 5m under the condition of 30Hz, so the fault distance should be about 5m, the fault distance should be about ② and ③ and the response characteristics of the fault be similar to the fault distance under the condition of 10 Hz.
The method fully utilizes the advantage that the chaotic attribute bodies are sensitive to micro-scale fault identification, simultaneously further enhances the identification precision of the hidden fault by applying an active-passive combined ant tracking mode, filters non-fault information by applying occurrence control, and avoids the defects of the chaotic bodies in the process of identifying the hidden fault. And combining the final hidden fault identification result, the forward modeling result and the seismic section to finely identify the fault distance and the spread characteristic of the hidden fault. The method disclosed by the invention is applied to not only solve the injection-production contradiction among GC oil field development wells, provide a basis for reasonable deployment of a development well pattern, have good benefits and provide technical support for the excavation, potential and storage increasing work of old oil fields.
The present invention has been described in relation to particular embodiments thereof, but the invention is not limited to the described embodiments. In the thought given by the present invention, the technical means in the above embodiments are changed, replaced, modified in a manner that is easily imaginable to those skilled in the art, and the functions are basically the same as the corresponding technical means in the present invention, and the purpose of the invention is basically the same, so that the technical scheme formed by fine tuning the above embodiments still falls into the protection scope of the present invention.
Claims (6)
1. A hidden fault identification method is characterized by comprising the following steps:
1) acquiring original three-dimensional seismic data, and performing smooth filtering processing on the original three-dimensional seismic data;
2) extracting a chaotic attribute body from the seismic data after the smoothing filtering processing;
3) and carrying out boundary detection and boundary pickup on the chaotic attribute body according to an iterative ant colony algorithm to obtain initial hidden fault information.
2. The hidden fault identification method of claim 1, wherein the iterative ant colony algorithm is to perform boundary detection by using active ant tracking, perform boundary picking on detected boundary information by using passive ant tracking, and repeat the active ant tracking and the passive ant tracking until an iteration condition is satisfied.
3. The hidden fault identification method according to claim 1 or 2, wherein the initial hidden fault information is subjected to occurrence control to filter out interlayer information with an inclination angle smaller than a set angle, so as to obtain final hidden fault information.
4. The concealed fault identification method according to claim 3, characterized in that the identification method further comprises the step of determining the fault distance of the final concealed fault information according to the seismic response characteristic analysis and the seismic profile; the seismic response feature analysis comprises:
time segmentation is carried out on the original three-dimensional seismic data, and the main frequency of the seismic data in different time periods is obtained through analysis;
and according to the main frequency of the seismic data in different time periods, obtaining the seismic response characteristics of the fault with different fault distances and different main frequencies through forward modeling.
5. The concealed fault identification method of claim 1, wherein in the smoothing filtering process, the parameters of the inline direction and the crossline direction in the selected filtering window are both smaller than the vertical parameters.
6. The hidden fault identification method of claim 1, wherein the chaotic attribute body is obtained by calculating the dip similarity of interlayer seismic reflection waves in the seismic data volume according to the relative size of the local structure tensor eigenvalues and the combination parameters.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910774894.2A CN110727025A (en) | 2019-08-21 | 2019-08-21 | Hidden fault recognition method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910774894.2A CN110727025A (en) | 2019-08-21 | 2019-08-21 | Hidden fault recognition method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110727025A true CN110727025A (en) | 2020-01-24 |
Family
ID=69217119
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910774894.2A Pending CN110727025A (en) | 2019-08-21 | 2019-08-21 | Hidden fault recognition method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110727025A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113267815A (en) * | 2021-07-07 | 2021-08-17 | 中海油田服务股份有限公司 | Filtering method and device for repeated broken edge data |
| CN113640876A (en) * | 2021-07-09 | 2021-11-12 | 中国煤炭地质总局地球物理勘探研究院 | Method for finely identifying trapping column by using chaotic body attribute |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5724309A (en) * | 1996-03-06 | 1998-03-03 | Chevron U.S.A. Inc. | Method for geophysical processing and interpretation using instantaneous phase and its derivatives and their derivatives |
| EP0972263A1 (en) * | 1996-05-17 | 2000-01-19 | Shell Oil Company | Presentation and interpretation of seismic data |
| CN103412331A (en) * | 2013-08-30 | 2013-11-27 | 电子科技大学 | Automatic extraction method for three-dimensional earthquake fault |
| CN106154327A (en) * | 2016-08-17 | 2016-11-23 | 中国石油化工股份有限公司 | A kind of method improving hidden fault recognizing precision |
| CN106896405A (en) * | 2017-02-28 | 2017-06-27 | 中国石油化工股份有限公司 | A kind of sand-conglomerate body spatial Forecasting Methodology and device based on chaos attribute |
| CN106990436A (en) * | 2017-04-14 | 2017-07-28 | 中国矿业大学(北京) | The recognition methods of karst collapse col umn and device |
| CN109581485A (en) * | 2018-12-04 | 2019-04-05 | 成都捷科思石油天然气技术发展有限公司 | A method of carrying out automatic slit detection directly on pre-stack depth migration seismic data |
-
2019
- 2019-08-21 CN CN201910774894.2A patent/CN110727025A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5724309A (en) * | 1996-03-06 | 1998-03-03 | Chevron U.S.A. Inc. | Method for geophysical processing and interpretation using instantaneous phase and its derivatives and their derivatives |
| EP0972263A1 (en) * | 1996-05-17 | 2000-01-19 | Shell Oil Company | Presentation and interpretation of seismic data |
| CN103412331A (en) * | 2013-08-30 | 2013-11-27 | 电子科技大学 | Automatic extraction method for three-dimensional earthquake fault |
| CN106154327A (en) * | 2016-08-17 | 2016-11-23 | 中国石油化工股份有限公司 | A kind of method improving hidden fault recognizing precision |
| CN106896405A (en) * | 2017-02-28 | 2017-06-27 | 中国石油化工股份有限公司 | A kind of sand-conglomerate body spatial Forecasting Methodology and device based on chaos attribute |
| CN106990436A (en) * | 2017-04-14 | 2017-07-28 | 中国矿业大学(北京) | The recognition methods of karst collapse col umn and device |
| CN109581485A (en) * | 2018-12-04 | 2019-04-05 | 成都捷科思石油天然气技术发展有限公司 | A method of carrying out automatic slit detection directly on pre-stack depth migration seismic data |
Non-Patent Citations (2)
| Title |
|---|
| 李楠等: "利用高清蚂蚁体精细解释复杂断裂带", 《石油地球物理勘探》 * |
| 杨卫琪: "古城-毕店地区构造类隐蔽油藏识别技术", 《内江科技》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113267815A (en) * | 2021-07-07 | 2021-08-17 | 中海油田服务股份有限公司 | Filtering method and device for repeated broken edge data |
| CN113267815B (en) * | 2021-07-07 | 2022-05-10 | 中海油田服务股份有限公司 | Method and device for filtering repeated broken edge data |
| CN113640876A (en) * | 2021-07-09 | 2021-11-12 | 中国煤炭地质总局地球物理勘探研究院 | Method for finely identifying trapping column by using chaotic body attribute |
| CN113640876B (en) * | 2021-07-09 | 2023-05-30 | 中国煤炭地质总局地球物理勘探研究院 | Method for carrying out fine recognition on collapse column by utilizing chaos attribute |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | A method for low-frequency noise suppression based on mathematical morphology in microseismic monitoring | |
| CN105259572B (en) | The seismic facies computational methods classified automatically based on seismic multi-attribute parametrical nonlinearity | |
| CN108415077A (en) | New edge detection low order fault recognition methods | |
| CN109001801B (en) | Fault variable-scale identification method based on multiple iteration ant colony algorithm | |
| CN111665559B (en) | Method and system for describing sliding fracture zone | |
| CN110749924A (en) | Fracture zone identification method | |
| Chehrazi et al. | Seismic data conditioning and neural network-based attribute selection for enhanced fault detection | |
| WO2009081210A1 (en) | Method of and apparatus for exploring a region below a surface of the earth | |
| CN104360382A (en) | Method for detecting oil and gas by aid of stacked seismic data | |
| EP1504291A2 (en) | Method for morphologic analysis of seismic objects | |
| Delf et al. | A comparison of automated approaches to extracting englacial-layer geometry from radar data across ice sheets | |
| CN109425891B (en) | Fracture imaging quality detection method based on geological model | |
| CN103869358A (en) | Fault identification method and equipment based on histogram equalization | |
| CN115220096B (en) | A fracture prediction method and system | |
| CN110727025A (en) | Hidden fault recognition method | |
| CN114994758A (en) | Wave impedance extraction and structure characterization method and system for carbonate fracture control reservoir | |
| CN110261905A (en) | Complex value based on pitch angle control is concerned with microfault recognition methods | |
| CN117452486A (en) | Multi-attribute fusion low-order fault intelligent identification method | |
| CN114152985B (en) | Method for determining boundary of underground ancient river channel and thickness of thin sand body in boundary | |
| CN117492081A (en) | Multi-attribute fault classification and identification method and device for earthquake | |
| CN114185091B (en) | Ant body crack tracking method and device based on frequency spectrum decomposition and electronic equipment | |
| Kneller et al. | Integration of broadband seismic data into reservoir characterization workflows: A case study from the Campos Basin, Brazil | |
| CN116933473A (en) | Gradient structure tensor coherent computing method based on Gaussian differential operator | |
| CN116027417B (en) | Reservoir prediction method based on differential resonance | |
| Zhang et al. | Study on the method of identifying fractures by ant tracking technique |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200124 |