CN111708085A - Hole detection enhancing method and device based on waveform separation - Google Patents
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- 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. analysis, for interpretation, for correction
- G01V1/30—Analysis
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- G—PHYSICS
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- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/62—Physical property of subsurface
- G01V2210/624—Reservoir parameters
Abstract
A method and a device for enhancing hole detection based on waveform separation are provided, wherein the method for enhancing hole detection based on waveform separation comprises the following steps: acquiring a seismic data volume to be processed; performing data dimension reduction on the seismic data to be processed, and separating a deposition background waveform component and a hole waveform component; and removing the deposition background waveform component to form a hole data body. Holes smaller than 10m can be identified by the enhancement treatment of the present invention; and the boundary and the communication relation between the adjacent holes can be more clearly distinguished. And better seismic data can be provided for exploration and deployment of the oil field.
Description
Technical Field
The invention relates to the field of geology, in particular to a hole detection enhancing method and device based on waveform separation.
Background
The hole type reservoir is a main type of carbonate reservoir, the reservoir generally shows larger hole development, the oil exploration searches the hole reservoir mainly through a three-dimensional acquired seismic data body, the seismic data body is a time domain imaging data body reflecting underground structure, deposition and abnormal geological features, the deposition strata formed in different geological periods of the underground show transverse continuous reflection axes on the seismic data, the holes can also form abnormal reflection, the stratum development conditions in different geological periods can be analyzed through observing the reflection axes on a longitudinal seismic section, abnormal geological phenomena can be identified, and the planar change of the structure and the deposition can also be reflected through horizontal slices and along-layer slices. Holes generally exhibit short axis strong reflections, beaded strong reflections, and glob-up strong reflections at seismic sections. The existing conventional processing seismic data generally has unclear performance on the holes with the sizes of less than 10m (referring to the longitudinal size), and has difficulty in identifying the boundary and connectivity of the combined holes.
Disclosure of Invention
Objects of the invention
The invention aims to provide a hole detection enhancement method and device based on waveform separation, which can accurately identify holes.
(II) technical scheme
In order to solve the above problem, a first aspect of the present invention provides a method for enhancing hole detection based on waveform separation, including: acquiring a seismic data volume to be processed; performing data dimension reduction on the seismic data to be processed, and separating a deposition background waveform component and a hole waveform component; and removing the deposition background waveform component to form a hole data body.
Optionally, the acquiring the seismic data volume to be processed includes: acquiring a seismic data volume of a target interval; performing seismic horizon interpretation on the seismic data volume of the target interval; and determining a processing time window by combining the horizon information to obtain a time domain seismic data volume within the processing time window.
Optionally, the seismic data volume to be processed is the time domain seismic data volume.
Optionally, the acquiring the seismic data volume to be processed further includes: and removing the regional structure influence of the time domain seismic data volume to obtain the seismic data volume to be processed.
Optionally, the removing the influence of the regional structure of the time domain seismic data volume to obtain the seismic data volume to be processed includes: converting the time domain seismic data volume into a Wheeler domain seismic data volume by using a Wheeler forward transform method to remove the influence of regional construction factors; and the Wheeler domain seismic data is the seismic data volume to be processed.
Optionally, the method for enhancing hole detection based on waveform separation further includes: and converting the Wheeler domain seismic data body into the time domain seismic data body by using an inverse Wheeler transformation method.
Optionally, the performing data dimension reduction on the seismic data volume to be processed to separate a deposition background waveform component and a hole waveform component includes: and performing data dimensionality reduction on the processed seismic data volume by utilizing a PCA (principal component analysis) technology, and separating and depositing a background waveform component and a hole waveform component.
Optionally, performing data dimensionality reduction on the processed seismic data volume using a PCA technique to separate a sediment background waveform component and a hole waveform component, including: and taking a seismic channel as a center, taking N preset adjacent channels to form a daughter, taking the daughter as a PCA unit, extracting a first main component as the deposition background waveform component, and separating, wherein the remaining main component is the hole waveform component.
A second aspect of the present invention provides a hole detection enhancement apparatus based on waveform separation, comprising: the acquisition module is used for acquiring a seismic data volume to be processed; the dimension reduction separation module is used for carrying out data dimension reduction on the seismic data to be processed and separating a deposition background waveform component and a hole waveform component; and the dimension reduction separation module is used for removing the deposition background waveform component to form a hole data body.
Optionally, the obtaining module includes: the collecting unit is used for acquiring a seismic data volume of a target interval; the interpretation unit is used for performing seismic horizon interpretation on the seismic data volume of the target interval; and the time domain seismic data volume unit is used for determining a processing time window by combining the horizon information to obtain a time domain seismic data volume within the processing time window range.
(III) advantageous effects
The technical scheme of the invention has the following beneficial technical effects:
holes smaller than 10m can be identified by the enhancement treatment of the present invention; and the boundary and the communication relation between the adjacent holes can be more clearly distinguished. And better seismic data can be provided for exploration and deployment of the oil field.
Drawings
FIG. 1 is a forward modeling seismic model of horizontal formation sections with different sized voids;
FIG. 2 illustrates a forward modeling seismic model of different size holes in an inclined formation;
FIG. 3 is a flowchart of a method for enhancing hole detection based on waveform separation according to embodiment 1 of the present invention;
FIG. 4 is a flow chart of a method for acquiring a seismic data volume to be processed according to embodiment 2 of the present invention;
FIG. 5 is a flow chart of a method for acquiring a seismic data volume to be processed according to embodiment 3 of the present invention;
FIG. 6 is a graphical comparison of before and after the influence of the removed area configuration of the present invention;
FIG. 7 is a graph showing a sedimentary background waveform model representing the development of sedimentary formations during non-cavern development;
FIG. 8 is a diagram of an aggregate model of depositional background waveforms and pore waveforms for a normal seismic data imaging section;
FIG. 9 is a graph of a waveform model of the remaining pores after separation of the deposited background waveform;
FIG. 10 is a cross-sectional view comparing the effect of seismic sections before and after the hole detection enhancement method provided in example 3 of the present invention;
FIG. 11 is a plan view of the root mean square amplitude of the in-situ seismics of a target zone in a test zone;
FIG. 12 is a plan view of RMS amplitude of the seismic events in a target interval of a test area after treatment using the hole detection enhancement method provided in example 3 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
FIG. 1 is a forward modeling seismic model of horizontal formation sections with different sized voids; FIG. 2 illustrates a forward seismic model of different size holes in an inclined formation.
As shown in fig. 1 and 2, the image obtained by conventional processing of seismic data has unclear appearance of holes with the size of 10m (which means the longitudinal size), and when the holes are large, the energy reflected by the seismic is similar to the energy reflected by the background stratum, and the images are difficult to distinguish on a plane by using the amplitude energy attribute. In addition, when the stratum is inclined, the imaging effect is worse due to the fact that the imaging effect is larger for small holes of 5-10 m.
Example 1
Fig. 3 is a flowchart of a hole detection enhancement method based on waveform separation according to embodiment 1 of the present invention.
As shown in fig. 3, the present embodiment provides a method for enhancing hole detection based on waveform separation, including: acquiring a seismic data volume to be processed; performing data dimension reduction on the seismic data to be processed, and separating a deposition background waveform component and a hole waveform component; and removing the deposition background waveform component to form a hole data body. Holes below 10m can be identified by the enhancement treatment of the invention; and the boundary and the communication relation between the adjacent holes can be more clearly distinguished. And better seismic data can be provided for exploration and deployment of the oil field.
Wherein, the deposition background waveform shows a transversely continuous long-cycle waveform, and the hole waveform shows a short-axis strong reflection, namely a short-cycle waveform.
In actual production life, the obtained data set is often high in dimensionality in characteristics, time consumed for processing the high-dimensionality data is large, and excessive characteristic variables can also hinder establishment of a search rule. The data is subjected to dimensionality reduction, and the method has the following advantages: (1) making the data set easier to use; (2) the calculation overhead of a plurality of algorithms is reduced; (3) removing noise; (4) making the results clearly understandable. The method for reducing the dimension of the data used in the embodiment includes, but is not limited to: principal Component Analysis (PCA: Principal Component Analysis); singular Value Decomposition (SVD), Factor Analysis (FA) Linear Discriminant Analysis (LDA), and the like. The PCA dimension reduction technology is preferably selected, and has a better identification effect on the carbonate complex hole types.
Example 2
Fig. 4 is a flowchart of a method for acquiring a seismic data volume to be processed according to embodiment 2 of the present invention.
As shown in fig. 4, this embodiment further includes, on the basis of embodiment 1:
the acquiring of the seismic data volume to be processed comprises the following steps: acquiring a seismic data volume of a target interval; performing seismic horizon interpretation on the seismic data volume of the target interval; and determining a processing time window by combining the horizon information to obtain a time domain seismic data volume within the processing time window. And the seismic data volume to be processed is the time domain seismic data volume.
Example 3
FIG. 5 is a flow chart of a method for acquiring a seismic data volume to be processed according to embodiment 3 of the present invention;
as shown in fig. 5, this embodiment further includes, on the basis of embodiment 1:
the acquiring of the seismic data volume to be processed comprises the following steps: the acquiring of the seismic data volume to be processed comprises the following steps: acquiring a seismic data volume of a target interval; performing seismic horizon interpretation on the seismic data volume of the target interval; and determining a processing time window by combining the horizon information to obtain a time domain seismic data volume within the processing time window. And removing the regional structure influence of the seismic data volume within the processing time window range to obtain the seismic data volume to be processed.
Specifically, the removing the influence of the regional structure of the time domain seismic data volume to obtain the seismic data volume to be processed includes: converting the time domain seismic data volume into a Wheeler domain seismic data volume by using a Wheeler forward transform method to remove the influence of regional construction factors; and the Wheeler domain seismic data is the seismic data volume to be processed. Referring to fig. 6, the upper half is a graph displayed by a time domain seismic data volume, and the lower half is a graph displayed by a Wheeler domain seismic data volume, wherein the Wheeler domain seismic data volume can be displayed by the Wheeler graph, the Wheeler graph is called a chronostratigraphic, and for data of a geological time-space domain, a horizon interpretation-based Wheeler data generation method proposed by Nordlund is generally adopted, by marking stratum contact relations, sorting according to geological times, and converting time-space domain data into geological time-space domain data based on horizon control, the influence of unclear identification of small holes caused by stratum inclination can be eliminated, the transverse resolution is improved, subsequent dimensionality reduction processing is facilitated, and the efficiency of hole identification is improved.
FIG. 7 is a graph showing a sedimentary background waveform model representing the development of sedimentary formations during non-cavern development; FIG. 8 is a diagram of an aggregate model of depositional background waveforms and pore waveforms for a normal seismic data imaging section; FIG. 9 is a graph of a model of the pore waveform remaining after background waveform separation.
As shown in fig. 7 to 9, the performing data dimension reduction on the seismic data volume to be processed to separate a deposition background waveform component and a hole waveform component includes: and performing data dimensionality reduction on the processed seismic data volume by utilizing a PCA (principal component analysis) technology, and separating and depositing a background waveform component and a hole waveform component.
The processing of the data using PCA techniques of the present application is explained in detail below.
First, mathematical principle analysis: assuming that there are n samples, each sample has p variables, and a geographic data matrix of order n × p is formed, when p is large, it is troublesome to examine the problem in a p-dimensional space. In order to overcome this difficulty, it is necessary to perform dimension reduction processing, i.e. to replace the original more variable indexes with fewer comprehensive indexes, and to make these fewer comprehensive indexes reflect the information reflected by the original more variable indexes as much as possible, and at the same time, they are independent from each other. And (3) seismic data analysis: assuming that the three-dimensional seismic data has 100 × 100 channels, each channel has data variables with characteristics of reaction strata, faults, holes, river channels and the like, and only 2 main variables of the strata and the holes are considered by combining the characteristics of a hole development area, namely the idea of reducing the dimension.
Using PCA technique to perform data dimension reduction on the processed seismic data volume, and separating and depositing a background waveform component and a hole waveform component, comprising: and taking a seismic channel as a center, taking N preset adjacent channels to form a daughter, taking the daughter as a PCA unit, extracting a first main component as the deposition background waveform component, and separating, wherein the remaining main component is the hole waveform component.
Specifically, 1, sample set data input: taking fig. 5 as an example: and (c) carrying out artificial horizon interpretation (h) on the bottom surface of the stratum 2, determining a time window section (h-100ms to h +300ms) comprising the stratum 1-the stratum 5 as a target layer (the cave is developed between the stratum 3 and the stratum 4) by using horizon control, selecting 5 sampling point channels (the waveform of the actual seismic data cave-free seismic channel has a small difference, and the waveform in the model is the same waveform) as long waveforms, wherein the seismic point channels 1, 2, 5, 8 and 9 have no cave and the seismic point channels have no cave.
……
……
each seismic trace sampling point comprises: and x and y coordinates, wherein the longitudinal direction is generally 4ms, the sampling points are selected as input data within a time window range of a target layer, and the number of the channels serving as the sampling points can be determined according to actual data.
Mean calculation of 2.5 spline traces:
Zs-h-100＝(z1-h-100+z2-h-100+……z9-h-100)/5
Zs-h-96＝(z1-h-96+z2-h-96+……z9-h-96)/5
……
the objective is to form a normalized sample of the mean waveform-mathematical sense of the sedimentary background waveform (long-convoluted waveform).
3. Calculating standard deviation and standard deviation mean (sample characteristic value of mathematical significance):
and calculating the difference between each longitudinal sampling point of each channel and the mean value waveform, namely the difference between the long-wave waveform and the mean long-wave waveform of the actual channel, and calculating the mean value of the difference values to be used as the characteristic value of each longitudinal sampling point.
4. And solving the eigenvalue and the eigenvector of the covariance matrix of the sample seismic traces.
5. Inputting seismic data:
and aiming at the target interval of the three-dimensional seismic data, inputting the seismic data in a time window range of h-100ms to h +300ms controlled by the interpretation horizon. And extracting data sets of all the channels of the seismic data, wherein the data format is the same as 1.
6. Calculating the characteristic value and the characteristic vector of each data point of each track of the destination layer:
calculating the difference value of the mean value waveform of each channel and the sample channel to form the characteristic value and the characteristic vector of each channel, and comparing the difference between the characteristic value and the characteristic vector of the sample.
7. And (3) seismic data output:
the first method comprises the following steps: separating the data to form 2 data
Data 1: the characteristic value of each data point is less than the characteristic value of the sampling point of the corresponding sample channel, the value is assigned to 0, the value is greater than the characteristic value of the sampling point, the original value is assigned, the output data is hole waveform (short cycle waveform) seismic data, and the development of the cave can be reflected by data imaging; data 2: and the characteristic value of each sampling point of each channel is greater than the characteristic value of the longitudinal sampling point corresponding to the sample channel, the sampling point value corresponding to the mean value waveform of the deposition background waveform (long-circle waveform) is assigned and is less than the characteristic value of the sampling point, an original value is assigned, the output data is deposition background waveform seismic data, and only the reflection of the deposition stratum is reflected.
And the second method comprises the following steps: the hole enhancement data, still one (the investigator is used to observe on 1 data), suppresses the deposited background waveform (long-convoluted waveform) imaging energy, emphasizes the energy of the hole waveform.
The characteristic value of each data point of each channel is smaller than the characteristic value of the corresponding longitudinal sampling point of the sample channel (according with the deposition background waveform characteristic), and the output value is 80 percent of the original value (after imaging, the energy is weakened to 80 percent); the characteristic value of the data point is greater than that of the sampling point (it is not strange that the characteristic value does not conform to the long-wave shape), the output value is 120 percent of the original value (after imaging, the energy is reduced and enhanced to 120 percent), and the output data is hole enhanced seismic data (as shown in an attached figure 10).
The method for enhancing hole detection based on waveform separation further includes:
and converting the Wheeler domain seismic data body into the time domain long cycle data body and the time domain short cycle data body by using an inverse Wheeler transformation method. The Wheeler inverse transformation is to convert the Wheeler domain back to the time domain to reflect the actual spatial position of the hole. Wheeler domain data do not reflect the spatial position of the slot and are only convenient and accurate to calculate.
And for the short-cycle data volume in the time domain, displaying the carbonate rock hole identification processing effect by adopting frequency division display and seismic attributes.
FIG. 10 is a cross-sectional view comparing the effect of seismic sections before and after the hole detection enhancement method provided in example 3 of the present invention.
As shown in FIG. 10, the section (lower) is the seismic section after the hole enhancement treatment, and drilling in the left box reveals a developed 5m hole, the display capability of the original seismic section is weak, the existence of the hole cannot be identified, and the reflected energy is obviously enhanced after the dimension reduction treatment. The holes in the right square frame are combined holes, the boundaries and the combination relation of the holes cannot be identified on the original seismic section, the transverse resolution is greatly improved after enhancement processing, and the boundaries of the combined holes can be clearly identified.
FIG. 11 is a plan view of the root mean square amplitude of the in-situ seismics of a target zone in a test zone; FIG. 12 is a plan view of RMS amplitude of the seismic events in a target interval of a test area after treatment using the hole detection enhancement method provided in example 3 of the present invention.
As can be seen from fig. 11 and 12, the seismic rms amplitude plane graph without being processed by the hole detection enhancement method of the present embodiment has weak amplitude attribute energy of part of the holes identified in the graph, and some small holes cannot be identified as compared with fig. 12, it can be seen that the seismic rms amplitude plane graph processed by the hole detection enhancement method of the present embodiment has enhanced energy of all the holes identified in the graph, and the number of the added hole identifications and the boundary relationship of the combined holes are clearer.
Example 4
The embodiment provides a hole detection enhancing device based on waveform separation, which is used for implementing the hole detection enhancing method based on waveform separation of the embodiment 1, and the method includes: the acquisition module is used for acquiring a seismic data volume to be processed; the dimension reduction separation module is used for carrying out data dimension reduction on the seismic data to be processed and separating a deposition background waveform component and a hole waveform component; and the dimension reduction separation module is used for removing the deposition background waveform component to form a hole data body. Holes below 10m can be identified by the enhancement treatment of the invention; and the boundary and the communication relation between the adjacent holes can be more clearly distinguished. And better seismic data can be provided for exploration and deployment of the oil field.
Wherein, the deposition background waveform shows a transversely continuous long-cycle waveform, and the hole waveform shows a short-axis strong reflection, namely a short-cycle waveform.
In actual production life, the obtained data set is often high in dimensionality in characteristics, time consumed for processing the high-dimensionality data is large, and excessive characteristic variables can also hinder establishment of a search rule. The data is subjected to dimensionality reduction, and the method has the following advantages: (1) making the data set easier to use; (2) the calculation overhead of a plurality of algorithms is reduced; (3) removing noise; (4) making the results clearly understandable. The method for reducing the dimension of the data used in the embodiment includes, but is not limited to: principal Component Analysis (PCA: Principal Component Analysis); singular Value Decomposition (SVD), Factor Analysis (FA) Linear Discriminant Analysis (LDA), and the like. The PCA dimension reduction technology is preferably selected, and has a better identification effect on the carbonate complex hole types.
Example 5
The present embodiment provides a hole detection enhancement apparatus based on waveform separation, which is used to implement the hole detection enhancement method based on waveform separation of embodiment 2. The acquisition module includes: the collecting unit is used for acquiring a seismic data volume of a target interval; the interpretation unit is used for performing seismic horizon interpretation on the seismic data volume of the target interval; and the time domain seismic data volume unit is used for determining a processing time window by combining the horizon information to obtain a time domain seismic data volume within the processing time window range. And the seismic data volume to be processed is the time domain seismic data volume.
Example 6
The present embodiment provides a waveform separation-based hole detection enhancement apparatus, which is used to implement the waveform separation-based hole detection enhancement method of embodiment 3. The acquisition module includes: the collecting unit is used for acquiring a seismic data volume of a target interval; the interpretation unit is used for performing seismic horizon interpretation on the seismic data volume of the target interval; and the time domain seismic data volume unit is used for determining a processing time window by combining the horizon information to obtain a time domain seismic data volume within the processing time window range. And the area structure influence removing unit is used for removing the area structure influence of the seismic data body within the processing time window range to obtain the seismic data body to be processed. The rest of the parts are the same as those in embodiment 3, and are not described in detail in this embodiment.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.
Claims (10)
1. A hole detection enhancement method based on waveform separation is characterized by comprising the following steps:
acquiring a seismic data volume to be processed;
performing data dimension reduction on the seismic data to be processed, and separating a deposition background waveform component and a hole waveform component;
and removing the deposition background waveform component to form a hole data body.
2. The hole detection enhancement method of claim 1, wherein said obtaining a seismic data volume to be processed comprises:
acquiring a seismic data volume of a target interval;
performing seismic horizon interpretation on the seismic data volume of the target interval;
and determining a processing time window by combining the horizon information to obtain a time domain seismic data volume within the processing time window.
3. The hole detection enhancement method of claim 2, wherein the seismic data volume to be processed is the time domain seismic data volume.
4. The hole detection enhancement method of claim 2, wherein said obtaining a seismic data volume to be processed further comprises:
and removing the regional structure influence of the time domain seismic data volume to obtain the seismic data volume to be processed.
5. The hole detection enhancement method of claim 4, wherein the removing of the area structure influence of the time domain seismic data volume to obtain the seismic data volume to be processed comprises: converting the time domain seismic data volume into a Wheeler domain seismic data volume by using a Wheeler forward transform method to remove the influence of regional construction factors;
and the Wheeler domain seismic data is the seismic data volume to be processed.
6. The hole detection enhancement method of claim 5, further comprising:
and converting the Wheeler domain seismic data body into the time domain seismic data body by using an inverse Wheeler transformation method.
7. The method of enhancing hole detection according to claim 1, wherein said performing data dimensionality reduction on said seismic data volume to be processed to separate a sediment background waveform component and a hole waveform component comprises:
and performing data dimensionality reduction on the processed seismic data volume by utilizing a PCA (principal component analysis) technology, and separating and depositing a background waveform component and a hole waveform component.
8. The hole detection enhancement method of claim 7 wherein performing a data dimension reduction on the processed seismic data volume using a PCA technique to separate a sediment background waveform component from a hole waveform component comprises:
and taking a seismic channel as a center, taking preset N channels to form a daughter, taking the daughter as a PCA unit, extracting a first main component as the deposition background waveform component, and separating, wherein the remaining main component is the hole waveform component.
9. A hole detection enhancement device based on waveform separation, comprising:
the acquisition module is used for acquiring a seismic data volume to be processed;
the dimension reduction separation module is used for carrying out data dimension reduction on the seismic data to be processed and separating a deposition background waveform component and a hole waveform component;
and the dimension reduction separation module is used for removing the deposition background waveform component to form a hole data body.
10. The hole detection enhancement apparatus of claim 9, wherein the acquisition module comprises:
the collecting unit is used for acquiring a seismic data volume of a target interval;
the interpretation unit is used for performing seismic horizon interpretation on the seismic data volume of the target interval;
and the time domain seismic data volume unit is used for determining a processing time window by combining the horizon information to obtain a time domain seismic data volume within the processing time window range.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112698382A (en) * | 2020-12-04 | 2021-04-23 | 中国石油天然气股份有限公司 | Small-scale fault control karst reservoir earthquake prediction method and device |
| CN115877460A (en) * | 2023-02-28 | 2023-03-31 | 福瑞升(成都)科技有限公司 | Method for enhancing karst fracture-cave type reservoir of carbonate rock |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102200588A (en) * | 2011-03-22 | 2011-09-28 | 成都理工大学 | Method for analyzing waveform similarity body curvature of seismic data |
| CN102253412A (en) * | 2011-06-02 | 2011-11-23 | 中国石油天然气股份有限公司 | Method and system for identifying favorable reservoir through trend anomaly |
| CN102650702A (en) * | 2012-05-03 | 2012-08-29 | 中国石油天然气股份有限公司 | Seismic waveform analysis and reservoir prediction method and device |
| RU2563323C1 (en) * | 2014-04-02 | 2015-09-20 | Открытое акционерное общество "Нефтяная компания "Роснефть" | Method of reconstruction of fine structure of geological object and forecast of its fluid saturation |
| CN107976713A (en) * | 2017-11-08 | 2018-05-01 | 中国石油天然气股份有限公司 | A kind of lower method and device for removing sedimentation setting of higher-dimension seismic data input |
-
2020
- 2020-06-30 CN CN202010621008.5A patent/CN111708085A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102200588A (en) * | 2011-03-22 | 2011-09-28 | 成都理工大学 | Method for analyzing waveform similarity body curvature of seismic data |
| CN102253412A (en) * | 2011-06-02 | 2011-11-23 | 中国石油天然气股份有限公司 | Method and system for identifying favorable reservoir through trend anomaly |
| CN102650702A (en) * | 2012-05-03 | 2012-08-29 | 中国石油天然气股份有限公司 | Seismic waveform analysis and reservoir prediction method and device |
| RU2563323C1 (en) * | 2014-04-02 | 2015-09-20 | Открытое акционерное общество "Нефтяная компания "Роснефть" | Method of reconstruction of fine structure of geological object and forecast of its fluid saturation |
| CN107976713A (en) * | 2017-11-08 | 2018-05-01 | 中国石油天然气股份有限公司 | A kind of lower method and device for removing sedimentation setting of higher-dimension seismic data input |
Non-Patent Citations (4)
| Title |
|---|
| 宋承云: "多维地震信号反射模式分析方法研究" * |
| 赵惊涛;李明;张研;: "基于绕射波的储层预测方法及其应用" * |
| 逯宇佳;曹俊兴;刘哲哿;田仁飞;肖学: "波形分类技术在缝洞型储层流体识别中的应用" * |
| 马灵伟;杨勤勇;李宗杰;林正良;刘军;魏华动;: "利用波形分解技术识别塔中北坡强反射界面之下的储层响应" * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112698382A (en) * | 2020-12-04 | 2021-04-23 | 中国石油天然气股份有限公司 | Small-scale fault control karst reservoir earthquake prediction method and device |
| CN112698382B (en) * | 2020-12-04 | 2023-09-26 | 中国石油天然气股份有限公司 | Small-scale breaking control karst reservoir earthquake prediction method and device |
| CN115877460A (en) * | 2023-02-28 | 2023-03-31 | 福瑞升(成都)科技有限公司 | Method for enhancing karst fracture-cave type reservoir of carbonate rock |
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Application publication date: 20200925 |