CN112835096B - Gas layer identification method and device - Google Patents
Gas layer identification method and device Download PDFInfo
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- G—PHYSICS
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- 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
- G01V1/306—Analysis for determining physical properties of the subsurface, e.g. impedance, porosity or attenuation profiles
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- 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
- G01V1/307—Analysis for determining seismic attributes, e.g. amplitude, instantaneous phase or frequency, reflection strength or polarity
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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
- G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
- G01V1/362—Effecting static or dynamic corrections; Stacking
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- G01V2210/00—Details of seismic processing or analysis
- G01V2210/50—Corrections or adjustments related to wave propagation
- G01V2210/51—Migration
- G01V2210/512—Pre-stack
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- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/61—Analysis by combining or comparing a seismic data set with other data
- G01V2210/616—Data from specific type of measurement
- G01V2210/6169—Data from specific type of measurement using well-logging
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- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/63—Seismic attributes, e.g. amplitude, polarity, instant phase
- G01V2210/632—Amplitude variation versus offset or angle of incidence [AVA, AVO, AVI]
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Abstract
The invention provides a gas layer identification method and a device, wherein the method comprises the following steps: generating near offset superimposed data and far offset superimposed data according to the target work area seismic data and an offset range reflecting the characteristics of the gas well; generating an instantaneous amplitude difference value between the near offset superposition data and the far offset superposition data of the gas layer of the target work area according to the near offset superposition data and the far offset superposition data; generating a water well and a main frequency parameter of the gas well according to the long-offset superimposed data, and acquiring an instantaneous frequency parameter of the long-offset superimposed data; and identifying the gas layer of the target work area according to the amplitude difference, the main frequency parameter and the instantaneous frequency parameter. The gas layer identification method provided by the invention fully utilizes the pre-stack AVO amplitude difference information and the earthquake frequency information to effectively predict the plane distribution of the gas layer, reduces the multi-resolution of gas layer identification and improves the gas layer identification precision.
Description
Technical Field
The invention relates to the field of petroleum exploration, in particular to the technical field of hydrocarbon detection in the field of geophysical exploration, and in particular relates to a gas layer identification method and device.
Background
In recent years, the newly-increased natural gas exploration reserves are continuously increased, and how to improve the benefits of natural gas exploration and development and ensure the steady increase of the natural gas yield is a very complex problem. Geophysical gas layer detection is a key content for promoting efficient exploration and development of natural gas, and is one of the most main difficulties at present. Typical gas layers have low velocity, low density, low poisson's ratio, etc.
The currently common gas layer detection methods comprise an AVO technology, an absorption attenuation technology and an earthquake inversion technology. After being proposed from the Bortfeld in the 60 th century, the AVO technology is popularized and applied through researches of a plurality of scholars, but the AVO technology is found in the later period to mainly analyze the characteristic that the amplitude changes along with the offset distance (namely amplitude information), and the characteristics of low speed, low density, low Poisson ratio and the like of a gas layer exist, and the lithology difference is small or the multi-solution caused by different fluids exists; the absorption attenuation technology is mainly based on frequency information to detect the air layer, and a plurality of mature technologies are derived at present, but the prediction resolution is low, and stronger polynosicity exists under the influence of lithology; the seismic inversion technology, particularly the pre-stack seismic inversion technology, is gradually mature in gas layer detection and popularized and applied through the development of years, but has higher requirements on seismic data quality and logging data, and a plurality of blocks cannot meet the requirements on the pre-stack inversion gas layer detection.
Disclosure of Invention
Aiming at the problems in the prior art, the gas layer identification method provided by the invention fully utilizes the pre-stack AVO amplitude difference information and the earthquake frequency information to effectively predict the plane distribution of the gas layer, reduces the multi-resolution of gas layer identification and improves the gas layer identification precision.
In order to solve the technical problems, the invention provides the following technical scheme:
in a first aspect, the present invention provides a method of gas layer identification, comprising:
generating near offset superimposed data and far offset superimposed data according to the target work area seismic data and an offset range reflecting the characteristics of the gas well;
generating an instantaneous amplitude difference value between the near offset superposition data and the far offset superposition data of the gas layer of the target work area according to the near offset superposition data and the far offset superposition data;
generating a water well and a main frequency parameter of the gas well according to the long-offset superimposed data, and acquiring an instantaneous frequency parameter of the long-offset superimposed data;
and identifying the gas layer of the target work area according to the amplitude difference, the main frequency parameter and the instantaneous frequency parameter.
In one embodiment, the near offset superimposed data and the far offset superimposed data are generated from the target work area seismic data and an offset range that reflects the characteristics of the gas well;
Determining an offset range capable of reflecting the characteristics of a gas well on a seismic section according to the amplitude attribute of the position of the gas well on the seismic section by using a pre-stack AVO forward modeling method;
and generating near offset superposition data and far offset superposition data according to the target work area seismic data and the offset range.
In an embodiment, the generating the amplitude difference between the near offset superimposed data and the far offset superimposed data of the gas layer of the target work area according to the near offset superimposed data and the far offset superimposed data includes:
respectively extracting the instantaneous amplitude of the offset superposition data and the far offset superposition data;
the amplitude difference is generated from the instantaneous amplitude at the gas well, the instantaneous amplitude at the water well, and the instantaneous amplitude.
In one embodiment, the identifying the gas layer of the target work area based on the amplitude difference, the dominant frequency parameter, and the instantaneous frequency parameter comprises:
generating an amplitude frequency difference attribute factor of the target work area according to the amplitude difference, the main frequency parameter and the instantaneous frequency parameter;
and identifying the gas layer and the water layer of the target work area according to the amplitude frequency difference attribute factors.
In a second aspect, the present invention provides a gas layer identification device, the device comprising:
the superimposed data generation unit is used for generating near offset superimposed data and far offset superimposed data according to the seismic data of the target work area and the offset range capable of reflecting the characteristics of the gas well;
the amplitude difference generating unit is used for generating an instantaneous amplitude difference value between the near offset superposition data and the far offset superposition data of the gas layer of the target work area according to the near offset superposition data and the far offset superposition data;
the main frequency parameter generating unit is used for generating main frequency parameters of the water well and the gas well according to the long-offset superimposed data and acquiring instantaneous frequency parameters of the long-offset superimposed data;
and the gas layer identification unit is used for identifying the gas layer of the target work area according to the amplitude difference, the main frequency parameter and the instantaneous frequency parameter.
In one embodiment, the superimposed data generating unit includes:
the offset range determining module is used for determining an offset range capable of reflecting the characteristics of the gas well according to the amplitude attribute of the position of the gas well on the seismic section by utilizing a pre-stack AVO forward modeling method;
And the superposition data generation module is used for generating near offset superposition data and far offset superposition data according to the target work area seismic data and the offset range.
In one embodiment, the amplitude difference generating unit includes:
the instantaneous amplitude module is used for respectively extracting the instantaneous amplitudes of the offset superposition data and the far offset superposition data;
and the amplitude difference generating module is used for generating the amplitude difference according to the instantaneous amplitude at the gas well, the instantaneous amplitude at the water well and the instantaneous amplitude.
In an embodiment, the gas layer identification unit comprises:
the attribute factor generation module is used for generating an amplitude frequency difference attribute factor of the target work area according to the amplitude difference, the main frequency parameter and the instantaneous frequency parameter;
and the gas layer identification module is used for identifying the gas layer and the water layer of the target work area according to the amplitude frequency difference attribute factors.
In a third aspect, the present invention provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the gas layer identification method when executing the program.
In a fourth aspect, the present invention provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of a method for gas layer identification.
As can be seen from the foregoing description, the embodiment of the present invention provides a gas layer identification method and apparatus, which firstly generates near offset superimposed data and far offset superimposed data according to a range of variation of amplitude sensitive to a typical gas well along with offset, and then constructs an attribute factor of the amplitude frequency difference that can distinguish the gas layer and the water layer of a target work area by an instantaneous amplitude difference between the near offset superimposed data and the far offset superimposed data of the gas layer of the target work area, a main frequency parameter of the well, a main frequency parameter of the gas well, and an instantaneous frequency parameter of the far offset superimposed data. The method fully utilizes pre-stack AVO amplitude difference information and earthquake frequency information to effectively predict the plane distribution of the gas layer, reduces the multi-resolution of gas layer detection and improves the precision of gas layer detection.
Compared with the prior art, the method fully utilizes amplitude difference information of amplitude offset change of the pre-stack AVO and frequency information with obvious absorption and attenuation effects of the air layer, and builds an amplitude frequency difference attribute factor through organic combination of the amplitude difference and the frequency information, so that the multi-resolution of air layer detection can be effectively reduced, and the detection precision of the seismic air layer is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a gas layer identification method in an embodiment of the invention;
FIG. 2 is a flow chart of step 100 in an embodiment of the invention;
FIG. 3 is a flow chart of step 200 in an embodiment of the invention;
FIG. 4 is a flow chart of step 400 in an embodiment of the invention;
FIG. 5 is a flowchart of the gas layer identification method in an embodiment of the invention;
FIG. 6 is a schematic flow chart of a method for identifying an air layer in an embodiment of the invention;
FIG. 7 is a near offset stacking profile of an earthquake in an embodiment of the invention;
FIG. 8 is a cross section of a seismic long offset stack in an embodiment of the invention;
FIG. 9 is a plan view of near offset and far offset amplitude difference properties in an embodiment of the present invention;
FIG. 10 is a representative gas well spectrum profile for an example of an application of the present invention;
FIG. 11 is a graph showing a typical water well spectrum distribution in an embodiment of the present invention;
FIG. 12 is a representative drywell spectrum profile for an example of the practice of the invention;
FIG. 13 is a cross section of the amplitude frequency difference attribute in an embodiment of the present invention;
FIG. 14 is a plane view of the amplitude frequency difference attribute in an embodiment of the present invention;
FIG. 15 is a schematic diagram of a gas layer identification device according to an embodiment of the present invention;
fig. 16 is a block diagram of the structure of the superimposed data generating unit in the embodiment of the present invention;
fig. 17 is a block diagram showing the structure of an amplitude difference value generating unit in the embodiment of the present invention;
FIG. 18 is a block diagram illustrating a gas layer identification unit in accordance with an embodiment of the present invention;
fig. 19 is a schematic structural diagram of an electronic device in an embodiment of the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention provides a specific implementation manner of a gas layer identification method, and referring to fig. 1, the method specifically comprises the following steps:
step 100: and generating near offset superposition data and far offset superposition data according to the target work area seismic data and an offset range capable of reflecting the characteristics of the gas well.
Step 100 is performed by: and according to the variation range of the sensitive amplitude of the typical gas well along with the offset, performing near offset and far offset data superposition on the seismic data based on the variation range, and obtaining the seismic near offset and far offset superposition data. In addition, the technical effect obtained in step 100 is to make full use of the amplitude difference information of the pre-stack AVO amplitude offset change and the frequency information with obvious air layer absorption attenuation effect.
Step 200: and generating an instantaneous amplitude difference value between the near offset superposition data and the far offset superposition data of the gas layer of the target work area according to the near offset superposition data and the far offset superposition data.
Firstly, extracting amplitude difference information between near offset superposition data and far offset superposition data: and respectively extracting the instantaneous amplitude attribute of the near offset and the far offset seismic data, fitting according to amplitude values of the gas well and the water well, and searching the optimal adjustment coefficient, so that the amplitude difference values of the gas well and the water well are more obvious and easier to distinguish.
Step 300: and generating main frequency parameters of the water well and the gas well according to the long-offset superimposed data, and acquiring instantaneous frequency parameters of the long-offset superimposed data.
Step 300 is performed by: and respectively extracting the main frequency information of the typical gas well and the water well according to the remote offset superposition data of the target work area.
Step 400: and identifying the gas layer of the target work area according to the amplitude difference, the main frequency parameter and the instantaneous frequency parameter.
Specifically, step 400 fully uses near offset and far offset amplitude difference information (AVO information with amplitude changing along with the increase of offset), and simultaneously adds seismic frequency information sensitive to gas layer reaction, fully uses AVO characteristic difference information and seismic frequency information, and extracts amplitude frequency difference attribute factors, wherein the amplitude difference attribute factors are large, represent that the near offset and far offset amplitude difference is large, the main frequency of the seismic data is close to the main frequency of the gas layer, the gas content is indicated to be good, the amplitude difference attribute factors are small, represent that the near offset and far offset amplitude difference is small, the main frequency of the seismic data is close to the main frequency of the non-gas layer such as the water layer, and the like, and the gas content is indicated to be poor.
As can be seen from the above description, the embodiment of the present invention provides a gas layer identification method, which firstly generates near offset superimposed data and far offset superimposed data according to the variation range of the amplitude sensitive to a typical gas well along with the offset, and then constructs an attribute factor capable of distinguishing the amplitude frequency difference between the gas layer and the water layer of the target work area by the instantaneous amplitude difference between the near offset superimposed data and the far offset superimposed data of the gas layer of the target work area, the main frequency parameter of the well, the main frequency parameter of the gas well, and the instantaneous frequency parameter of the far offset superimposed data. The method fully utilizes pre-stack AVO amplitude difference information and earthquake frequency information to effectively predict the plane distribution of the gas layer, reduces the multi-resolution of gas layer detection and improves the precision of gas layer detection.
In one embodiment, referring to fig. 2, step 100 comprises:
step 101: and determining an offset range capable of reflecting the characteristics of the gas well on the seismic section according to the amplitude attribute of the position of the gas well on the seismic section by using a pre-stack AVO forward modeling method.
The AVO (Amplitude variation with offset, amplitude versus offset) technique in step 101 is used to study the characteristic of the variation of the seismic reflection amplitude with the distance between the shot and the receiver, i.e., offset (or angle of incidence), to investigate the variation of the reflection coefficient response with offset (or angle of incidence), and to determine the lithology characteristics and physical parameters of the reflective interface overburden, underlying media. By "determining an offset range that can reflect the characteristics of the gas well based on the amplitude attribute of the gas well's location on the seismic profile" in step 101 is meant that the seismic data within the offset range all contain characteristics of a typical gas well, and outside of the offset range, the characteristics of a typical gas well cannot be reflected, or are not obvious.
The pre-stack AVO forward modeling method is to utilize model forward modeling to simulate AVO phenomenon, and analyze AVO characteristics of oil, gas, water and special lithology under different geological conditions by combining with oil reservoir characteristics of a target work area, and establish corresponding AVO detection marks, so that a gas layer and a water layer are directly identified in actual seismic records.
Step 102: and generating near offset superposition data and far offset superposition data according to the target work area seismic data and the offset range.
And (3) based on the step (101), performing near offset and far offset data superposition on the seismic data, thereby generating seismic near offset and far offset superposition data.
In one embodiment, referring to fig. 3, step 200 comprises:
step 201: and respectively extracting the instantaneous amplitude of the offset superposition data and the far offset superposition data.
Step 202: the amplitude difference is generated from the instantaneous amplitude at the gas well, the instantaneous amplitude at the water well, and the instantaneous amplitude.
In step 201 and step 202, near offset and far offset amplitude difference information is extracted: and respectively extracting the instantaneous amplitude attribute of the near offset and the far offset seismic data, fitting according to amplitude values of the gas well and the water well, and searching the optimal adjustment coefficient, so that the amplitude difference values of the gas well and the water well are more obvious and easier to distinguish.
In one embodiment, referring to fig. 4, step 400 comprises:
step 401: and generating an amplitude frequency difference attribute factor of the target work area according to the amplitude difference, the dominant frequency parameter and the instantaneous frequency parameter.
Step 402: and identifying the gas layer and the water layer of the target work area according to the amplitude frequency difference attribute factors.
It can be understood that the amplitude frequency difference attribute factor is large, which represents that the amplitude difference of the near and far offset distances is large, the main frequency of the seismic data is close to the main frequency of the air layer, the indication gas content is good, the amplitude difference attribute factor is small, which represents that the amplitude difference of the near and far offset distances is small, the main frequency of the seismic data is close to the main frequency of the non-air layer such as the water layer, and the indication gas content is poor.
As can be seen from the above description, the embodiment of the present invention provides a gas layer identification method, which firstly generates near offset superimposed data and far offset superimposed data according to the variation range of the amplitude sensitive to a typical gas well along with the offset, and then constructs an attribute factor capable of distinguishing the amplitude frequency difference between the gas layer and the water layer of the target work area by the instantaneous amplitude difference between the near offset superimposed data and the far offset superimposed data of the gas layer of the target work area, the main frequency parameter of the well, the main frequency parameter of the gas well, and the instantaneous frequency parameter of the far offset superimposed data. The method fully utilizes pre-stack AVO amplitude difference information and earthquake frequency information to effectively predict the plane distribution of the gas layer, reduces the multi-resolution of gas layer detection and improves the precision of gas layer detection.
Compared with the prior art, the method fully utilizes amplitude difference information of amplitude offset change of the pre-stack AVO and frequency information with obvious absorption and attenuation effects of the air layer, and builds an amplitude frequency difference attribute factor through organic combination of the amplitude difference and the frequency information, so that the multi-resolution of air layer detection can be effectively reduced, and the detection precision of the seismic air layer is improved.
To further illustrate the present solution, the present invention provides a specific application example of a gas layer identification method, which specifically includes the following matters, referring to fig. 5 and fig. 6.
S0: an offset range that can effectively identify the partial offset overlay data of the gas layer is determined.
Specifically, through forward AVO modeling, gas well and water well AVO characteristics of an H block are analyzed, the range of variation of amplitude sensitive to a typical gas well along with offset is counted, and the offset range of seismic sub-offset superposition data capable of effectively identifying a gas layer is determined.
S1: and constructing near offset and far offset superposition data.
Based on the step S0, the optimized effective offset range is used as a basis, and the large offset data with partial data with low signal to noise ratio is cut off to construct near offset and far offset seismic data. Fig. 7 is a near offset seismic stack section obtained by applying a preferred close offset range stack for the gas layer sensitivity, and fig. 8 is a far offset seismic stack section obtained by applying a preferred close offset range stack for the gas layer sensitivity, the two sets of data being the base pre-stack seismic data for subsequent analysis.
S2: remote data seismic instantaneous frequency information and typical gas and water well primary frequency information are generated.
And calculating the amplitude difference of the near offset and the far offset of the target interval according to the seismic data, and obtaining the seismic instantaneous frequency information of the far offset data and the main frequency information of a typical gas well and a water well.
S3: an instantaneous amplitude difference between the near offset superimposed data and the far offset superimposed data is generated.
Extracting amplitude difference information of a near offset and a far offset: respectively extracting the instantaneous amplitude attribute of the near offset and the far offset seismic data, fitting according to amplitude values of the gas well and the water well, and searching the optimal adjusting coefficients a and b so that the amplitude difference value M of the gas well and the water well is easier to distinguish;
M＝a*Amp(Far)-b*Amp(Near)
fig. 9 is a plan view of near offset and far offset amplitude difference attributes obtained by analyzing and fitting to obtain optimal adjustment coefficients a to obtain 2 and b to obtain 1, wherein red and yellow represent large difference of near offset and far offset data amplitude, light blue represent small difference of near offset and far offset data amplitude, and as can be seen from the figure, a gas well is generally located in a large range of red and yellow amplitude difference, and a water well and a dry well are located in a small range of light blue amplitude difference.
S4: a long offset instantaneous frequency message is generated.
Extracting the instantaneous frequency information of the distant channel earthquake: performing seismic spectrum analysis on the long-offset data to obtain long-offset instantaneous frequency information; and extracting the main frequency information of the typical air layer, the water layer and the dry layer of the H block.
Fig. 10 is a typical gas layer spectrum analysis chart from which a typical gas layer dominant frequency of 28Hz is seen, fig. 11 is a typical water layer spectrum analysis chart from which a water layer dominant frequency of 33Hz is seen, and fig. 12 is a typical dry layer spectrum analysis chart from which a dry layer dominant frequency of 38Hz is seen. The characteristic that the main frequency of the air layer is smaller than that of the water layer is smaller than that of the dry layer is found through spectral analysis and comparison.
S5: an amplitude frequency difference attribute factor is generated.
And constructing an amplitude frequency difference attribute factor by using the amplitude difference of the near offset and the far offset data and the main frequency and seismic spectrum analysis data of the gas well and the water well. It can be understood that the amplitude frequency difference attribute factor fully applies near offset and far offset amplitude difference information (AVO information that amplitude changes with increasing offset), and simultaneously adds seismic frequency information sensitive to gas layer reaction, fully applies AVO feature difference information and seismic frequency information, and extracts amplitude frequency difference attribute factor MF, wherein the amplitude difference attribute factor is large, and represents that the near and far offset amplitude difference is large, the main frequency of the seismic data is close to the main frequency of the gas layer, and indicates that the gas content is good, the amplitude difference attribute factor is small, represents that the near and far offset amplitude difference is small, the main frequency of the seismic data is close to the main frequency of the non-gas layer such as the water layer, and indicates that the gas content is poor:
a. b is an amplitude fitting coefficient, and fitting analysis is carried out according to amplitude values of gas wells and water wells to obtain the amplitude fitting coefficient;
c is a frequency coefficient, and is obtained by analyzing the main frequency information of a typical gas well;
e is an adjustment coefficient, and is a constant between 0 and 1.
FIG. 13 is a graph showing the contrast profile of the amplitude frequency difference attribute factor of the target layer with the near offset and far offset seismic data, wherein a is 2, b is 1, c is 28, and e is 0.05 during the calculation. As can be seen from the comparison section, the far offset amplitude of the target layer (the range of the section circle) at the two gas wells is larger than the near offset, the far offset data has low apparent frequency (the reflection phase axis is wide), the amplitude frequency difference attribute factor value is larger, the near offset and the far offset amplitude of the target layer of the dry well have small difference, the far offset data has higher apparent frequency (the reflection phase axis is narrower), and the amplitude frequency difference attribute factor value is smaller.
S6: and identifying the air layer and the water layer according to the amplitude frequency difference attribute factors.
And carrying out gas-water layer identification according to the attribute factors: the amplitude difference attribute factor values of the drilled gas well, the drilled water well and the drilled dry well are mainly combined for analysis, reasonable plane distribution ranges of the gas layer and the non-gas layer are selected, and the gas layer detection precision is effectively improved.
Fig. 14 is a plan view of the amplitude frequency difference attribute calculated in this embodiment. Where the MF value is greater than a, it indicates that the gas content is good, and where the MF value is less than a, it indicates that the amplitude difference is small, it indicates that the gas content is general. After the method proposed by the specific application example is applied in the figure, 14 wells are drilled successively, wherein 13 wells accord with the predicted result. By means of example analysis, the method can effectively improve the earthquake gas-bearing prediction accuracy
As can be seen from the above description, the embodiment of the present invention provides a gas layer identification method, which firstly generates near offset superimposed data and far offset superimposed data according to the variation range of the amplitude sensitive to a typical gas well along with the offset, and then constructs an attribute factor capable of distinguishing the amplitude frequency difference between the gas layer and the water layer of the target work area by the instantaneous amplitude difference between the near offset superimposed data and the far offset superimposed data of the gas layer of the target work area, the main frequency parameter of the well, the main frequency parameter of the gas well, and the instantaneous frequency parameter of the far offset superimposed data. The method fully utilizes pre-stack AVO amplitude difference information and earthquake frequency information to effectively predict the plane distribution of the gas layer, reduces the multi-resolution of gas layer detection and improves the precision of gas layer detection.
Compared with the prior art, the method fully utilizes amplitude difference information of amplitude offset change of the pre-stack AVO and frequency information with obvious absorption and attenuation effects of the air layer, and builds an amplitude frequency difference attribute factor through organic combination of the amplitude difference and the frequency information, so that the multi-resolution of air layer detection can be effectively reduced, and the detection precision of the seismic air layer is improved.
Based on the same inventive concept, the embodiments of the present application also provide a gas layer recognition device, which can be used to implement the method described in the above embodiments, such as the following embodiments. Because the principle of solving the problem of the gas layer recognition device is similar to that of the gas layer recognition method, the implementation of the gas layer recognition device can be realized by referring to the gas layer recognition method, and the repetition is not repeated. As used below, the term "unit" or "module" may be a combination of software and/or hardware that implements the intended function. While the system described in the following embodiments is preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
An embodiment of the present application provides a specific implementation manner of a gas layer recognition device capable of implementing a gas layer recognition method, referring to fig. 15, the gas layer recognition device specifically includes:
And the superposition data generating unit 10 is used for generating near offset superposition data and far offset superposition data according to the target work area seismic data and an offset range capable of reflecting the characteristics of the gas well.
And the amplitude difference generating unit 20 is used for generating an instantaneous amplitude difference between the near offset superposition data and the far offset superposition data of the gas layer of the target work area according to the near offset superposition data and the far offset superposition data.
And the main frequency parameter generating unit 30 is used for generating main frequency parameters of the water well and the gas well according to the remote offset superposition data and acquiring instantaneous frequency parameters of the remote offset superposition data.
And a gas layer identification unit 40 for identifying the gas layer of the target work area according to the amplitude difference, the main frequency parameter and the instantaneous frequency parameter.
In one embodiment, referring to fig. 16, the superimposed data generating unit 10 includes:
the offset range determining module 101 is configured to determine, on a seismic section, an offset range that can reflect characteristics of a gas well according to an amplitude attribute of a position of the gas well on the seismic section by using a prestack AVO forward modeling method.
And the superposition data generating module 102 is used for generating near offset superposition data and far offset superposition data according to the target work area seismic data and the offset range.
In one embodiment, referring to fig. 17, the amplitude difference generating unit 20 includes:
an instantaneous amplitude module 201, configured to extract instantaneous amplitudes of the offset superimposed data and the far offset superimposed data, respectively.
An amplitude difference generation module 202 is configured to generate the amplitude difference based on the instantaneous amplitude at the gas well, the instantaneous amplitude at the water well, and the instantaneous amplitude.
In one embodiment, referring to fig. 18, the gas layer recognition unit 40 includes:
and the attribute factor generating module 401 is configured to generate an attribute factor of the amplitude frequency difference of the target work area according to the amplitude difference, the dominant frequency parameter and the instantaneous frequency parameter.
And a gas layer identification module 402, configured to identify a gas layer and a water layer of the target work area according to the amplitude frequency difference attribute factor.
As can be seen from the above description, the embodiment of the present invention provides a gas layer identification device, which firstly generates near offset superimposed data and far offset superimposed data according to the variation range of the amplitude sensitive to a typical gas well along with the offset, and then constructs an attribute factor capable of distinguishing the amplitude frequency difference between the gas layer and the water layer of the target work area by the instantaneous amplitude difference between the near offset superimposed data and the far offset superimposed data of the gas layer of the target work area, the main frequency parameter of the well, the main frequency parameter of the gas well, and the instantaneous frequency parameter of the far offset superimposed data. The method fully utilizes pre-stack AVO amplitude difference information and earthquake frequency information to effectively predict the plane distribution of the gas layer, reduces the multi-resolution of gas layer detection and improves the precision of gas layer detection.
Compared with the prior art, the method fully utilizes amplitude difference information of amplitude offset change of the pre-stack AVO and frequency information with obvious absorption and attenuation effects of the air layer, and builds an amplitude frequency difference attribute factor through organic combination of the amplitude difference and the frequency information, so that the multi-resolution of air layer detection can be effectively reduced, and the detection precision of the seismic air layer is improved.
The embodiment of the present application further provides a specific implementation manner of an electronic device capable of implementing all the steps in the air layer identification method in the foregoing embodiment, and referring to fig. 19, the electronic device specifically includes the following contents:
a processor 1201, a memory 1202, a communication interface (Communications Interface) 1203, and a bus 1204;
wherein the processor 1201, the memory 1202 and the communication interface 1203 perform communication with each other through the bus 1204; the communication interface 1203 is configured to implement information transmission between related devices such as a server device, an acquisition device, and a user device.
The processor 1201 is configured to invoke a computer program in the memory 1202, and when the processor executes the computer program, the processor implements all the steps in the gas layer identification method in the above embodiment, for example, when the processor executes the computer program, the processor implements the following steps:
Step 100: and generating near offset superposition data and far offset superposition data according to the target work area seismic data and an offset range capable of reflecting the characteristics of the gas well.
Step 200: and generating an instantaneous amplitude difference value between the near offset superposition data and the far offset superposition data of the gas layer of the target work area according to the near offset superposition data and the far offset superposition data.
Step 300: and generating main frequency parameters of the water well and the gas well according to the long-offset superimposed data, and acquiring instantaneous frequency parameters of the long-offset superimposed data.
Step 400: and identifying the gas layer of the target work area according to the amplitude difference, the main frequency parameter and the instantaneous frequency parameter.
As can be seen from the above description, the electronic device in the embodiment of the present application firstly generates near offset superimposed data and far offset superimposed data according to the variation range of the amplitude sensitive to the typical gas well along with the offset, and then constructs the attribute factors of the amplitude frequency difference that can distinguish the gas layer and the water layer of the target work area by the instantaneous amplitude difference between the near offset superimposed data and the far offset superimposed data of the gas layer of the target work area, the main frequency parameter of the well, the main frequency parameter of the gas well, and the instantaneous frequency parameter of the far offset superimposed data. The method fully utilizes pre-stack AVO amplitude difference information and earthquake frequency information to effectively predict the plane distribution of the gas layer, reduces the multi-resolution of gas layer detection and improves the precision of gas layer detection.
Compared with the prior art, the method fully utilizes amplitude difference information of amplitude offset change of the pre-stack AVO and frequency information with obvious absorption and attenuation effects of the air layer, and builds an amplitude frequency difference attribute factor through organic combination of the amplitude difference and the frequency information, so that the multi-resolution of air layer detection can be effectively reduced, and the detection precision of the seismic air layer is improved.
The embodiment of the present application also provides a computer-readable storage medium capable of implementing all the steps of the gas layer identification method in the above embodiment, and a computer program stored on the computer-readable storage medium, the computer program implementing all the steps of the gas layer identification method in the above embodiment when executed by a processor, for example, the processor implementing the following steps when executing the computer program:
step 100: and generating near offset superposition data and far offset superposition data according to the target work area seismic data and an offset range capable of reflecting the characteristics of the gas well.
Step 200: and generating an instantaneous amplitude difference value between the near offset superposition data and the far offset superposition data of the gas layer of the target work area according to the near offset superposition data and the far offset superposition data.
Step 300: and generating main frequency parameters of the water well and the gas well according to the long-offset superimposed data, and acquiring instantaneous frequency parameters of the long-offset superimposed data.
Step 400: and identifying the gas layer of the target work area according to the amplitude difference, the main frequency parameter and the instantaneous frequency parameter.
As can be seen from the above description, the computer readable storage medium in the embodiment of the present application firstly generates near offset superimposed data and far offset superimposed data according to the variation range of the amplitude sensitive to the typical gas well along with the offset, and then constructs an amplitude frequency difference attribute factor capable of distinguishing the gas layer and the water layer of the target work area by the instantaneous amplitude difference between the near offset superimposed data and the far offset superimposed data of the gas layer of the target work area, the main frequency parameter of the well, the main frequency parameter of the gas well, and the instantaneous frequency parameter of the far offset superimposed data. The method fully utilizes pre-stack AVO amplitude difference information and earthquake frequency information to effectively predict the plane distribution of the gas layer, reduces the multi-resolution of gas layer detection and improves the precision of gas layer detection.
Compared with the prior art, the method fully utilizes amplitude difference information of amplitude offset change of the pre-stack AVO and frequency information with obvious absorption and attenuation effects of the air layer, and builds an amplitude frequency difference attribute factor through organic combination of the amplitude difference and the frequency information, so that the multi-resolution of air layer detection can be effectively reduced, and the detection precision of the seismic air layer is improved.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for a hardware+program class embodiment, the description is relatively simple, as it is substantially similar to the method embodiment, as relevant see the partial description of the method embodiment.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
Although the application provides method operational steps as described in the examples or flowcharts, more or fewer operational steps may be included based on conventional or non-inventive labor. The order of steps recited in the embodiments is merely one way of performing the order of steps and does not represent a unique order of execution. When implemented by an actual device or client product, the instructions may be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment) as shown in the embodiments or figures.
Although the present description provides method operational steps as described in the examples or flowcharts, more or fewer operational steps may be included based on conventional or non-inventive means. The order of steps recited in the embodiments is merely one way of performing the order of steps and does not represent a unique order of execution. When implemented in an actual device or end product, the instructions may be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment, or even in a distributed data processing environment) as illustrated by the embodiments or by the figures. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, it is not excluded that additional identical or equivalent elements may be present in a process, method, article, or apparatus that comprises a described element.
For convenience of description, the above devices are described as being functionally divided into various modules, respectively. Of course, when implementing the embodiments of the present disclosure, the functions of each module may be implemented in the same or multiple pieces of software and/or hardware, or a module that implements the same function may be implemented by multiple sub-modules or a combination of sub-units, or the like. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
Those skilled in the art will also appreciate that, in addition to implementing the controller in a pure computer readable program code, it is well possible to implement the same functionality by logically programming the method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Such a controller can be regarded as a hardware component, and means for implementing various functions included therein can also be regarded as a structure within the hardware component. Or even means for achieving the various functions may be regarded as either software modules implementing the methods or structures within hardware components.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It will be appreciated by those skilled in the art that embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the present specification embodiments may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present description embodiments may take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present embodiments may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The embodiments of the specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, as relevant to see a section of the description of method embodiments. In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the embodiments of the present specification. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
The foregoing is merely an example of an embodiment of the present disclosure and is not intended to limit the embodiment of the present disclosure. Various modifications and variations of the illustrative embodiments will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, or the like, which is within the spirit and principles of the embodiments of the present specification, should be included in the scope of the claims of the embodiments of the present specification.
Claims (6)
1. A method of identifying a gas layer, comprising:
generating near offset superimposed data and far offset superimposed data according to the target work area seismic data and an offset range reflecting the characteristics of the gas well;
generating instantaneous amplitude differences between the near offset superimposed data and the far offset superimposed data of the gas layer of the target work area according to the near offset superimposed data and the far offset superimposed data;
generating a water well and a main frequency parameter of the gas well according to the long-offset superimposed data, and acquiring an instantaneous frequency parameter of the long-offset superimposed data;
identifying a gas layer of the target work area according to the instantaneous amplitude difference, the dominant frequency parameter and the instantaneous frequency parameter;
the generating the instantaneous amplitude difference between the near offset superimposed data and the far offset superimposed data of the gas layer of the target work area according to the near offset superimposed data and the far offset superimposed data comprises the following steps:
Respectively extracting the instantaneous amplitudes of the near offset superposition data and the far offset superposition data, fitting according to the instantaneous amplitudes at the gas well and the water well, searching the optimal amplitude fitting coefficients a and b, and generating the instantaneous amplitude difference:
；
wherein M is the instantaneous amplitude difference;
the identifying the gas layer of the target work area according to the instantaneous amplitude difference, the dominant frequency parameter and the instantaneous frequency parameter comprises the following steps:
generating an amplitude frequency difference attribute factor MF of the target work area according to the instantaneous amplitude difference, the main frequency parameter and the instantaneous frequency parameter:
；
a and b are amplitude fitting coefficients, and fitting analysis is carried out according to amplitude values of gas wells and water wells to obtain the amplitude fitting coefficients; c is a frequency coefficient, and is obtained by analyzing the main frequency information of a typical gas well; e is an adjustment coefficient, and is a constant between 0 and 1;
and identifying the gas layer and the water layer of the target work area according to the amplitude frequency difference attribute factors.
2. The method of claim 1, wherein generating near offset overlay data and far offset overlay data from the target work area seismic data and an offset range that reflects a characteristic of a gas well comprises:
Determining an offset range capable of reflecting the characteristics of a gas well on a seismic section according to the amplitude attribute of the position of the gas well on the seismic section by using a pre-stack AVO forward modeling method;
and generating near offset superposition data and far offset superposition data according to the target work area seismic data and the offset range.
3. An air layer identification device, comprising:
the superimposed data generation unit is used for generating near offset superimposed data and far offset superimposed data according to the seismic data of the target work area and the offset range capable of reflecting the characteristics of the gas well;
the amplitude difference generating unit is used for generating instantaneous amplitude differences between the near offset superposition data and the far offset superposition data of the gas layer of the target work area according to the near offset superposition data and the far offset superposition data;
the main frequency parameter generating unit is used for generating main frequency parameters of the water well and the gas well according to the long-offset superimposed data and acquiring instantaneous frequency parameters of the long-offset superimposed data;
the gas layer identification unit is used for identifying the gas layer of the target work area according to the instantaneous amplitude difference, the main frequency parameter and the instantaneous frequency parameter;
The amplitude difference generating unit includes:
the instantaneous amplitude module is used for respectively extracting the instantaneous amplitudes of the near offset superposition data and the far offset superposition data;
the amplitude difference generating module is used for carrying out fitting according to the instantaneous amplitude at the gas well and the instantaneous amplitude at the water well, searching the optimal amplitude fitting coefficients a and b and generating the instantaneous amplitude difference:
；
wherein M is the instantaneous amplitude difference;
the gas layer identification unit includes:
the attribute factor generation module is configured to generate an amplitude frequency difference attribute factor MF of the target work area according to the instantaneous amplitude difference, the dominant frequency parameter, and the instantaneous frequency parameter:
；
a and b are amplitude fitting coefficients, and fitting analysis is carried out according to amplitude values of gas wells and water wells to obtain the amplitude fitting coefficients; c is a frequency coefficient, and is obtained by analyzing the main frequency information of a typical gas well; e is an adjustment coefficient, and is a constant between 0 and 1;
and the gas layer identification module is used for identifying the gas layer and the water layer of the target work area according to the amplitude frequency difference attribute factors.
4. A gas layer identification device according to claim 3, wherein the superimposed data generation unit comprises:
the offset range determining module is used for determining an offset range capable of reflecting the characteristics of the gas well according to the amplitude attribute of the position of the gas well on the seismic section by utilizing a pre-stack AVO forward modeling method;
And the superposition data generation module is used for generating near offset superposition data and far offset superposition data according to the target work area seismic data and the offset range.
5. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the gas layer identification method of any one of claims 1 to 2 when the program is executed by the processor.
6. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program, when being executed by a processor, realizes the steps of the gas layer identification method according to any of claims 1 to 2.
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