CN112835096A - Gas layer identification method and device - Google Patents
Gas layer identification method and device Download PDFInfo
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- 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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- 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
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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
- G01V1/36—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
- G01V2210/60—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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- G01V2210/63—Seismic attributes, e.g. amplitude, polarity, instant phase
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Abstract
The invention provides a gas layer identification method and a device, and the method comprises the following steps: generating near offset stacking data and far offset stacking data according to the seismic data of the target work area and the offset range capable of reflecting the gas well characteristics; generating an instantaneous amplitude difference value between the near offset distance superposed data and the far offset distance superposed data of the gas layer of the target work area according to the near offset distance superposed data and the far offset distance superposed data; generating main frequency parameters of a water well and the gas well according to the far offset distance superposed data, and acquiring instantaneous frequency parameters of the far offset distance superposed 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 reservoir identification method provided by the invention fully utilizes the pre-stack AVO amplitude difference information and the seismic frequency information, effectively predicts the plane distribution of the gas reservoir, reduces the multi-solution property of gas reservoir identification and improves the precision of gas reservoir identification.
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 particularly relates to a gas layer identification method and a gas layer identification device.
Background
In recent years, the increasing exploration of natural gas has revealed that the reserves are increasing continuously, and how to improve the exploration and development benefits of natural gas and ensure the steady increase of the yield of natural gas is a very complicated problem. The geophysical gas layer detection is a key content for promoting efficient exploration and development of natural gas and is one of the most important difficulties at present. Typical gas beds have characteristics of low velocity, low density, low poisson's ratio, and the like.
The current common gas layer detection methods comprise an AVO technology, an absorption attenuation technology and a seismic inversion technology. After the AVO technology is proposed from Bortfeld in the 60 th century, the AVO technology is popularized and applied through research of numerous scholars, but the later period finds that the AVO technology mainly analyzes the characteristics (namely amplitude information) of the change of the amplitude along with the offset, and the gas layer has the characteristics of low speed, low density, low Poisson ratio and the like and has the multiple solution caused by small lithology difference or different fluids; the absorption attenuation technology is mainly used for detecting a gas layer based on frequency information, and a plurality of mature technologies are derived at present, but the prediction resolution is low, and strong multi-solution exists under the influence of lithology; through development of years, the seismic inversion technology, particularly the prestack seismic inversion technology, is mature and popularized and applied to gas reservoir detection, but the requirements on seismic data quality and logging data are high, and most blocks cannot meet the requirements on prestack inversion gas reservoir 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 seismic frequency information, effectively predicts the plane distribution of the gas layer, reduces the multi-solution property of the gas layer identification and improves the precision of the gas layer identification.
In order to solve the technical problems, the invention provides the following technical scheme:
in a first aspect, the present invention provides a gas layer identification method, including:
generating near offset stacking data and far offset stacking data according to the seismic data of the target work area and the offset range capable of reflecting the gas well characteristics;
generating an instantaneous amplitude difference value between the near offset distance superposed data and the far offset distance superposed data of the gas layer of the target work area according to the near offset distance superposed data and the far offset distance superposed data;
generating main frequency parameters of a water well and the gas well according to the far offset distance superposed data, and acquiring instantaneous frequency parameters of the far offset distance superposed 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 stacking data and the far offset stacking data are generated according to the target work area seismic data and an offset range capable of reflecting gas well characteristics;
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 using a prestack AVO forward modeling method;
and generating near offset stacking data and far offset stacking data according to the target work area seismic data and the offset range.
In an embodiment, the generating an amplitude difference value between the near offset stacking data and the far offset stacking data of the gas layer of the target work area according to the near offset stacking data and the far offset stacking data includes:
respectively extracting the instantaneous amplitudes of the offset superposed data and the far offset superposed data;
generating the amplitude difference 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 according to the amplitude difference, the main frequency parameter and the instantaneous frequency parameter includes:
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 factor.
In a second aspect, the present invention provides a gas layer identification apparatus, the apparatus comprising:
the stacking data generating unit is used for generating near offset stacking data and far offset stacking data according to the target work area seismic data and the offset range capable of reflecting the gas well characteristics;
the amplitude difference value generating unit is used for generating an instantaneous amplitude difference value between the near offset distance superposed data and the far offset distance superposed data of the gas layer of the target work area according to the near offset distance superposed data and the far offset distance superposed data;
the main frequency parameter generating unit is used for generating main frequency parameters of a water well and the gas well according to the far offset distance superposed data and acquiring instantaneous frequency parameters of the far offset distance superposed 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 overlay data generation unit includes:
the offset range determining module is used for 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 utilizing a prestack AVO forward modeling method;
and the stacking data generation module is used for generating near offset stacking data and far offset stacking data according to the target work area seismic data and the offset range.
In one embodiment, the amplitude difference value generation unit includes:
the instantaneous amplitude module is used for respectively extracting the instantaneous amplitudes of the offset superposed data and the far offset superposed data;
an amplitude difference generation module 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, the gas layer identification unit includes:
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 factor.
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, wherein the processor implements the steps of the gas layer identification method when executing the program.
In a fourth aspect, the invention provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method for gas layer identification.
As can be seen from the above description, the method and apparatus for identifying a gas layer provided in the embodiments of the present invention first generate near offset stacked data and far offset stacked data based on the sensitive amplitude variation range with offset of a typical gas well, and then construct an amplitude frequency difference attribute factor that can distinguish the gas layer and the water layer of a target work area according to the instantaneous amplitude difference between the near offset stacked data and the far offset stacked data of the gas layer of the target work area, the water well dominant frequency parameter, the gas well dominant frequency parameter, and the instantaneous frequency parameter of the far offset stacked data. The method fully utilizes the pre-stack AVO amplitude difference information and the seismic frequency information, effectively predicts the plane distribution of the gas layer, reduces the multi-solution of gas layer detection, and improves the precision of the gas layer detection.
Compared with the prior art, the method makes full use of the amplitude difference information of the AVO amplitude offset change before stacking and the frequency information with obvious gas layer absorption attenuation effect, and the amplitude frequency difference attribute factor is constructed by organically combining the amplitude difference and the frequency information, so that the multi-solution property of gas layer detection can be effectively reduced, and the seismic gas layer detection precision 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 used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
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 illustrating step 100 according to an embodiment of the present invention;
FIG. 3 is a flowchart of step 200 in an embodiment of the present invention;
FIG. 4 is a flowchart illustrating a step 400 according to an embodiment of the present invention;
FIG. 5 is a flowchart illustrating the operation of the gas layer identification method according to an embodiment of the present invention;
FIG. 6 is a schematic flow chart of a gas layer identification method in an embodiment of the present invention;
FIG. 7 is a seismic near-offset stack section in an example embodiment of the present invention;
FIG. 8 is a seismic far offset stack section in an example embodiment of the present invention;
FIG. 9 is a plan view of the amplitude difference between the near offset and the far offset in an embodiment of the present invention;
FIG. 10 is a graph of a typical gas well spectrum profile for an example embodiment of the present invention;
FIG. 11 is a spectrum distribution diagram of a typical water well in an embodiment of the present invention;
FIG. 12 is a spectrum diagram of a typical drywell in an embodiment of the present invention;
FIG. 13 is a cross-section of amplitude and frequency difference properties in an embodiment of the present invention;
FIG. 14 is a plan view of the amplitude frequency difference property in an embodiment of the present invention;
FIG. 15 is a schematic diagram of a gas layer identification apparatus according to an embodiment of the present invention;
fig. 16 is a block diagram showing the structure of an overlay data generation unit in the embodiment of the present invention;
fig. 17 is a block diagram of an amplitude difference value generation unit in the embodiment of the present invention;
FIG. 18 is a block diagram showing the structure of a gas layer identification unit in 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
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present 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 includes the following steps:
step 100: and generating near offset stacking data and far offset stacking data according to the seismic data of the target work area and the offset range capable of reflecting the gas well characteristics.
When the step 100 is implemented, the method specifically comprises the following steps: according to the variation range of the amplitude sensitive to the typical gas well along with the offset, the seismic data are subjected to near offset and far offset data stacking on the basis of the variation range, and seismic near offset and far offset stacking data are obtained. 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 variation and the frequency information in which the gas layer absorption attenuation effect is significant.
Step 200: and generating an instantaneous amplitude difference value between the near offset distance superposed data and the far offset distance superposed data of the gas layer of the target work area according to the near offset distance superposed data and the far offset distance superposed data.
Firstly, extracting amplitude difference information between the near offset distance superposed data and the far offset distance superposed data: and extracting the instantaneous amplitude attributes of the seismic data with the near offset and the far offset respectively, fitting according to the amplitude values of the gas well and the water well, and searching for the optimal regulating 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 a water well and the gas well according to the far offset distance superposed data, and acquiring instantaneous frequency parameters of the far offset distance superposed data.
When the step 300 is implemented, the method specifically comprises the following steps: and respectively extracting the main frequency information of a typical gas well and a water well according to the far offset distance superposed 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 applies near offset and far offset amplitude difference information (AVO information whose amplitude changes with increasing offset), adds seismic frequency information sensitive to gas layer reaction, fully applies AVO characteristic difference information and seismic frequency information, and extracts amplitude frequency difference attribute factors, where the amplitude difference attribute factor is large, representing that the near and far offset amplitude difference is large, the seismic data dominant frequency is close to the gas layer dominant frequency, indicating that gas content is good, the amplitude difference attribute factor is small, representing that the near and far offset amplitude difference is small, the seismic data dominant frequency is close to the non-gas layer dominant frequency such as the water layer, and indicating that gas content is poor.
As can be seen from the above description, the method for identifying a gas layer provided in the embodiments of the present invention includes first generating near offset stacked data and far offset stacked data based on a variation range of a typical sensitive amplitude of a gas well with an offset, and then constructing an amplitude frequency difference attribute factor that can distinguish a gas layer and a water layer of a target work area according to an instantaneous amplitude difference between the near offset stacked data and the far offset stacked data of the gas layer of the target work area, a water well dominant frequency parameter, a gas well dominant frequency parameter, and an instantaneous frequency parameter of the far offset stacked data. The method fully utilizes the pre-stack AVO amplitude difference information and the seismic frequency information, effectively predicts the plane distribution of the gas layer, reduces the multi-solution of gas layer detection, and improves the precision of the 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 according to the amplitude attribute of the position of the gas well on the seismic section by using a prestack AVO forward modeling method.
The AVO (Amplitude variation with offset) technique in step 101 is used to study the variation characteristics of the seismic reflection Amplitude with the distance between the shot point and the receiver, i.e. the offset (or the incident angle), to study the variation of the reflection coefficient response with the offset (or the incident angle), and further determine the lithology characteristics and physical parameters of the overlying and underlying media of the reflection interface. And the step 101 of determining the offset range which can reflect the characteristics of the gas well according to the amplitude attribute of the position of the gas well on the seismic section means that the seismic data in the offset range all contain the characteristics of the typical gas well, and the characteristics of the typical gas well cannot be reflected or are not obvious when the offset range is out.
The prestack AVO forward modeling method is characterized in that AVO phenomena are simulated by forward modeling, AVO characteristics of oil, gas, water and special lithologic bodies under different geological conditions are analyzed by combining oil reservoir characteristics of a target work area, and corresponding AVO detection marks are established, so that gas layers and water layers are directly identified in actual seismic records.
Step 102: and generating near offset stacking data and far offset stacking data according to the target work area seismic data and the offset range.
And on the basis of the step 101, performing near offset and far offset data stacking on the seismic data to generate seismic near offset and far offset stacking data.
In one embodiment, referring to fig. 3, step 200 comprises:
step 201: and respectively extracting the instantaneous amplitude of the offset superposed data and the instantaneous amplitude of the far offset superposed data.
Step 202: generating the amplitude difference from the instantaneous amplitude at the gas well, the instantaneous amplitude at the water well, and the instantaneous amplitude.
In steps 201 and 202, near offset and far offset amplitude difference information is extracted: and extracting the instantaneous amplitude attributes of the seismic data with the near offset and the far offset respectively, fitting according to the amplitude values of the gas well and the water well, and searching for the optimal regulating 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 includes:
step 401: and 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.
Step 402: and identifying the gas layer and the water layer of the target work area according to the amplitude frequency difference attribute factor.
It can be understood that the amplitude frequency difference attribute factor is large, which represents that the amplitude difference of the near offset and the far offset is large, the seismic data main frequency is close to the air layer main frequency, indicating that the air content is good, the amplitude difference attribute factor is small, which represents that the amplitude difference of the near offset and the far offset is small, and the seismic data main frequency is close to the water layer and other non-air layer main frequencies, indicating that the air content is poor.
As can be seen from the above description, the method for identifying a gas layer provided in the embodiments of the present invention includes first generating near offset stacked data and far offset stacked data based on a variation range of a typical sensitive amplitude of a gas well with an offset, and then constructing an amplitude frequency difference attribute factor that can distinguish a gas layer and a water layer of a target work area according to an instantaneous amplitude difference between the near offset stacked data and the far offset stacked data of the gas layer of the target work area, a water well dominant frequency parameter, a gas well dominant frequency parameter, and an instantaneous frequency parameter of the far offset stacked data. The method fully utilizes the pre-stack AVO amplitude difference information and the seismic frequency information, effectively predicts the plane distribution of the gas layer, reduces the multi-solution of gas layer detection, and improves the precision of the gas layer detection.
Compared with the prior art, the method makes full use of the amplitude difference information of the AVO amplitude offset change before stacking and the frequency information with obvious gas layer absorption attenuation effect, and the amplitude frequency difference attribute factor is constructed by organically combining the amplitude difference and the frequency information, so that the multi-solution property of gas layer detection can be effectively reduced, and the seismic gas layer detection precision is improved.
To further illustrate the present invention, the present invention provides a specific application example of the gas layer identification method by taking a certain oil field H block as an example, and the specific application example specifically includes the following contents, see fig. 5 and fig. 6.
S0: an offset range is determined that is effective to identify the sub-offset stack data for the air layer.
Specifically, by means of pre-stack AVO forward modeling, AVO characteristics of gas wells and water wells of the H block are analyzed, the variation range of the sensitive amplitude of a typical gas well along with the offset distance is counted, and the offset distance range of the seismic sub-offset stacking data capable of effectively identifying the gas layer is determined.
S1: and constructing the superposition data of the near offset and the far offset.
Based on the preferred effective offset range, the large offset data with low signal-to-noise ratio is cut off to construct the near offset and far offset seismic data in step S0. Fig. 7 is a near offset seismic stack section obtained by applying the preferred gas bed sensitive near offset range stacking, and fig. 8 is a far offset seismic stack section obtained by applying the preferred gas bed sensitive near offset range stacking, the two sets of data being the basic pre-stack seismic data for the subsequent development of analysis.
S2: and generating the seismic instantaneous frequency information of the far offset data and the main frequency information of the typical gas well and water well.
And calculating the amplitude difference between the near offset and the far offset of the target interval according to the seismic data, and acquiring 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 the near offset and the far offset: extracting the instantaneous amplitude attributes of the seismic data with the near offset and the far offset respectively, fitting according to the amplitude values at the gas well and the water well, and searching for the optimal regulating coefficients a and b to enable the amplitude difference values M of the gas well and the water well to be distinguished more easily;
M＝a*Amp(Far)-b*Amp(Near)
fig. 9 is a graph of the near offset and far offset amplitude difference attribute obtained by analyzing and fitting the optimal adjustment coefficient a to 2, b to 1, and calculating, wherein red yellow represents that the amplitude difference between the near offset and the far offset data is large, and light blue represents that the amplitude difference between the near offset and the far offset data is small.
S4: far offset instantaneous frequency information is generated.
Extracting far-path seismic instantaneous frequency information: performing seismic spectrum analysis on the far offset data to obtain far offset instantaneous frequency information; and extracting the main frequency information of typical gas layer, water layer and dry layer of the H block.
Fig. 10 is a diagram of a typical gas layer spectrum analysis showing a typical gas layer dominant frequency of 28Hz, fig. 11 is a diagram of a typical water layer spectrum analysis showing a water layer dominant frequency of 33Hz, fig. 12 is a diagram of a typical dry layer spectrum analysis showing a dry layer dominant frequency of 38 Hz. And finding the characteristic that the dominant frequency of the gas layer is smaller than that of the water layer and smaller than that of the dry layer on the whole through frequency spectrum analysis and comparison.
S5: an amplitude frequency difference attribute factor is generated.
And constructing an amplitude frequency difference attribute factor by using the near offset and far offset data amplitude difference and the main frequency of the gas well and the water well and seismic frequency spectrum analysis data. It can be understood that the amplitude and frequency difference attribute factor fully applies near offset and far offset amplitude difference information (AVO information whose amplitude changes with the increase of offset), and adds seismic frequency information sensitive to gas layer reaction, and fully applies AVO characteristic difference information and seismic frequency information to extract the amplitude and frequency difference attribute factor MF, wherein the amplitude difference attribute factor is large, which indicates that the near and far offset amplitude difference is large, the seismic data dominant frequency is close to the gas layer dominant frequency, which indicates that the gas content is good, the amplitude difference attribute factor is small, which indicates that the near and far offset amplitude difference is small, the seismic data dominant frequency is close to the non-gas layer dominant frequency such as the water layer, which indicates that the gas content is poor:
a. b is an amplitude fitting coefficient, and fitting analysis is carried out according to the amplitude values of gas wells and water well points to obtain the amplitude fitting coefficient;
c is a frequency coefficient and is obtained by analyzing typical gas well dominant frequency information;
e is an adjusting coefficient and is a constant between 0 and 1.
FIG. 13 is a comparison section of calculated target interval amplitude frequency difference attribute factors and near offset and far offset seismic data, wherein in the calculation process, a is 2, b is 1, c is 28, and e is 0.05. From the comparison section, the far offset amplitude of the target layer (section circle range) at two gas wells is larger than the near offset, the video frequency of the far offset data is low (reflection in-phase axis width), the amplitude frequency difference attribute factor value is larger, the near offset and far offset amplitude difference of the target layer of the dry well is small, the video frequency of the far offset data is higher (reflection in-phase axis is narrower), and the amplitude frequency difference attribute factor value is smaller.
S6: and identifying the gas layer and the water layer according to the amplitude frequency difference attribute factor.
And identifying a gas-water layer according to the attribute factors: the method mainly combines the amplitude difference attribute factor values of drilled gas wells, water wells and dry wells for analysis, selects reasonable gas layer and non-gas layer plane distribution ranges, and effectively improves the gas layer detection precision.
Fig. 14 is a plan view of the amplitude frequency difference property calculated in this embodiment. The place where the MF value is greater than A represents that the amplitude difference value is large, and the indication gas content is good, and the place where the MF value is less than A represents that the amplitude difference value is small, and the indication gas content is general. After the method proposed by the present embodiment is applied, 14 wells are drilled, and 13 wells are matched with the predicted result. Example analysis shows that the method can effectively improve the prediction precision of the gas content of the earthquake
As can be seen from the above description, the method for identifying a gas layer provided in the embodiments of the present invention includes first generating near offset stacked data and far offset stacked data based on a variation range of a typical sensitive amplitude of a gas well with an offset, and then constructing an amplitude frequency difference attribute factor that can distinguish a gas layer and a water layer of a target work area according to an instantaneous amplitude difference between the near offset stacked data and the far offset stacked data of the gas layer of the target work area, a water well dominant frequency parameter, a gas well dominant frequency parameter, and an instantaneous frequency parameter of the far offset stacked data. The method fully utilizes the pre-stack AVO amplitude difference information and the seismic frequency information, effectively predicts the plane distribution of the gas layer, reduces the multi-solution of gas layer detection, and improves the precision of the gas layer detection.
Compared with the prior art, the method makes full use of the amplitude difference information of the AVO amplitude offset change before stacking and the frequency information with obvious gas layer absorption attenuation effect, and the amplitude frequency difference attribute factor is constructed by organically combining the amplitude difference and the frequency information, so that the multi-solution property of gas layer detection can be effectively reduced, and the seismic gas layer detection precision is improved.
Based on the same inventive concept, the embodiments of the present application further provide a gas layer identification apparatus, which can be used to implement the methods described in the above embodiments, such as the following embodiments. Because the principle of the gas layer identification device for solving the problems is similar to that of the gas layer identification method, the implementation of the gas layer identification device can be realized by the gas layer identification method, and repeated parts are not described again. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. While the system described in the embodiments below is preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.
An embodiment of the present invention provides a specific implementation of a gas layer identification apparatus capable of implementing a gas layer identification method, and referring to fig. 15, the gas layer identification apparatus specifically includes the following contents:
and the stacking data generating unit 10 is used for generating near offset stacking data and far offset stacking data according to the target work area seismic data and the offset range capable of reflecting the gas well characteristics.
And the amplitude difference value generating unit 20 is configured to generate an instantaneous amplitude difference value between the near offset distance stacked data and the far offset distance stacked data of the gas layer of the target work area according to the near offset distance stacked data and the far offset distance stacked data.
And the main frequency parameter generating unit 30 is configured to generate main frequency parameters of the water well and the gas well according to the far offset distance superposition data, and acquire an instantaneous frequency parameter of the far offset distance superposition data.
And the gas layer identification unit 40 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, referring to fig. 16, the superposition data generating unit 10 includes:
the offset range determining module 101 is configured to determine, on a seismic section, an offset range that may reflect a characteristic 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 stacking data generation module 102 is configured to generate near offset stacking data and far offset stacking data according to the target work area seismic data and the offset range.
In one embodiment, referring to fig. 17, the amplitude difference value generating unit 20 includes:
an instantaneous amplitude module 201, configured to extract instantaneous amplitudes of the offset stacking data and the far offset stacking data, respectively.
An amplitude difference generation module 202 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 identification unit 40 includes:
an attribute factor generating module 401, configured to generate 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.
And the gas layer identification module 402 is 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 gas layer identification apparatus provided in the embodiments of the present invention first generates near offset stacked data and far offset stacked data according to the sensitive amplitude variation range with offset of a typical gas well, and then constructs the amplitude frequency difference attribute factor that can distinguish the gas layer and the water layer of the target work area according to the instantaneous amplitude difference between the near offset stacked data and the far offset stacked data of the gas layer of the target work area, the water well dominant frequency parameter, the gas well dominant frequency parameter, and the instantaneous frequency parameter of the far offset stacked data. The method fully utilizes the pre-stack AVO amplitude difference information and the seismic frequency information, effectively predicts the plane distribution of the gas layer, reduces the multi-solution of gas layer detection, and improves the precision of the gas layer detection.
Compared with the prior art, the method makes full use of the amplitude difference information of the AVO amplitude offset change before stacking and the frequency information with obvious gas layer absorption attenuation effect, and the amplitude frequency difference attribute factor is constructed by organically combining the amplitude difference and the frequency information, so that the multi-solution property of gas layer detection can be effectively reduced, and the seismic gas layer detection precision is improved.
An embodiment of the present application further provides a specific implementation manner of an electronic device capable of implementing all steps in the gas layer identification method in the foregoing embodiment, and referring to fig. 19, the electronic device specifically includes the following contents:
a processor (processor)1201, a memory (memory)1202, a communication Interface 1203, and a bus 1204;
the processor 1201, the memory 1202 and the communication interface 1203 complete 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-side device, an acquisition device, and a client device.
The processor 1201 is configured to call the computer program in the memory 1202, and the processor executes the computer program to implement all the steps of the gas layer identification method in the above embodiments, for example, the processor executes the computer program to implement the following steps:
step 100: and generating near offset stacking data and far offset stacking data according to the seismic data of the target work area and the offset range capable of reflecting the gas well characteristics.
Step 200: and generating an instantaneous amplitude difference value between the near offset distance superposed data and the far offset distance superposed data of the gas layer of the target work area according to the near offset distance superposed data and the far offset distance superposed data.
Step 300: and generating main frequency parameters of a water well and the gas well according to the far offset distance superposed data, and acquiring instantaneous frequency parameters of the far offset distance superposed 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, in the electronic device in the embodiment of the present application, first, according to the variation range of the amplitude sensitive to the typical gas well with the offset, the near offset stacked data and the far offset stacked data are generated based on the variation range, and then, the amplitude frequency difference attribute factor that can distinguish the gas layer and the water layer of the target work area is constructed by the instantaneous amplitude difference between the near offset stacked data and the far offset stacked data of the gas layer of the target work area, the water well dominant frequency parameter, the gas well dominant frequency parameter, and the instantaneous frequency parameter of the far offset stacked data. The method fully utilizes the pre-stack AVO amplitude difference information and the seismic frequency information, effectively predicts the plane distribution of the gas layer, reduces the multi-solution of gas layer detection, and improves the precision of the gas layer detection.
Compared with the prior art, the method makes full use of the amplitude difference information of the AVO amplitude offset change before stacking and the frequency information with obvious gas layer absorption attenuation effect, and the amplitude frequency difference attribute factor is constructed by organically combining the amplitude difference and the frequency information, so that the multi-solution property of gas layer detection can be effectively reduced, and the seismic gas layer detection precision is improved.
Embodiments of the present application further provide a computer-readable storage medium capable of implementing all steps in the gas layer identification method in the foregoing embodiments, where the computer-readable storage medium stores thereon a computer program, and the computer program implements all steps of the gas layer identification method in the foregoing embodiments when executed by a processor, for example, the processor implements the following steps when executing the computer program:
step 100: and generating near offset stacking data and far offset stacking data according to the seismic data of the target work area and the offset range capable of reflecting the gas well characteristics.
Step 200: and generating an instantaneous amplitude difference value between the near offset distance superposed data and the far offset distance superposed data of the gas layer of the target work area according to the near offset distance superposed data and the far offset distance superposed data.
Step 300: and generating main frequency parameters of a water well and the gas well according to the far offset distance superposed data, and acquiring instantaneous frequency parameters of the far offset distance superposed 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 first generates the near offset stacked data and the far offset stacked data based on the variation range of the amplitude sensitive to the typical gas well with the offset, and then constructs the amplitude frequency difference property factor that can distinguish the gas layer and the water layer of the target work area according to the instantaneous amplitude difference between the near offset stacked data and the far offset stacked data of the gas layer of the target work area, the water well dominant frequency parameter, the gas well dominant frequency parameter, and the instantaneous frequency parameter of the far offset stacked data. The method fully utilizes the pre-stack AVO amplitude difference information and the seismic frequency information, effectively predicts the plane distribution of the gas layer, reduces the multi-solution of gas layer detection, and improves the precision of the gas layer detection.
Compared with the prior art, the method makes full use of the amplitude difference information of the AVO amplitude offset change before stacking and the frequency information with obvious gas layer absorption attenuation effect, and the amplitude frequency difference attribute factor is constructed by organically combining the amplitude difference and the frequency information, so that the multi-solution property of gas layer detection can be effectively reduced, and the seismic gas layer detection precision is improved.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the hardware + program class embodiment, since it is substantially similar to the method embodiment, the description is simple, and the relevant points can be referred to the partial description of the method embodiment.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may 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 may also be possible or may be advantageous.
Although the present application provides method steps as described in an embodiment or flowchart, additional or fewer steps may be included based on conventional or non-inventive efforts. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or client product executes, it may execute sequentially or in parallel (e.g., in the context of parallel processors or multi-threaded processing) according to the embodiments or methods shown in the figures.
Although embodiments of the present description provide method steps as described in embodiments or flowcharts, more or fewer steps may be included based on conventional or non-inventive means. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or end product executes, it may execute sequentially or in parallel (e.g., parallel processors or multi-threaded environments, or even distributed data processing environments) according to the method shown in the embodiment or 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, the presence of additional identical or equivalent elements in a process, method, article, or apparatus that comprises the recited elements is not excluded.
For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, in implementing the embodiments of the present description, the functions of each module may be implemented in one or more software and/or hardware, or a module implementing the same function may be implemented by a combination of multiple sub-modules or sub-units, and the like. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may therefore be considered as a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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 a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
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 computer storage media 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, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The embodiments of this specification 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 described embodiments 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.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean 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 an embodiment of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer 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, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
The above description is only an example of the embodiments of the present disclosure, and is not intended to limit the embodiments of the present disclosure. Various modifications and variations to the embodiments described herein will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present specification should be included in the scope of the claims of the embodiments of the present specification.
Claims (10)
1. A gas layer identification method, comprising:
generating near offset stacking data and far offset stacking data according to the seismic data of the target work area and the offset range capable of reflecting the gas well characteristics;
generating an instantaneous amplitude difference value between the near offset distance superposed data and the far offset distance superposed data of the gas layer of the target work area according to the near offset distance superposed data and the far offset distance superposed data;
generating main frequency parameters of a water well and the gas well according to the far offset distance superposed data, and acquiring instantaneous frequency parameters of the far offset distance superposed 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.
2. The gas layer identification method as claimed in claim 1, wherein the generating of the near offset stack data and the far offset stack data is based on 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 the gas well according to the amplitude attribute of the position of the gas well on the seismic section by using a prestack AVO forward modeling method;
and generating near offset stacking data and far offset stacking data according to the target work area seismic data and the offset range.
3. The gas layer identification method according to claim 1, wherein the generating an amplitude difference value between the near offset stacking data and the far offset stacking data of the gas layer of the target work area according to the near offset stacking data and the far offset stacking data comprises:
respectively extracting the instantaneous amplitudes of the offset superposed data and the far offset superposed data;
generating the amplitude difference from the instantaneous amplitude at the gas well, the instantaneous amplitude at the water well, and the instantaneous amplitude.
4. The method of claim 1, wherein identifying the gas layer of the target work zone based on the amplitude difference, the primary 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 factor.
5. A gas layer identification device, comprising:
the stacking data generating unit is used for generating near offset stacking data and far offset stacking data according to the target work area seismic data and the offset range capable of reflecting the gas well characteristics;
the amplitude difference value generating unit is used for generating an instantaneous amplitude difference value between the near offset distance superposed data and the far offset distance superposed data of the gas layer of the target work area according to the near offset distance superposed data and the far offset distance superposed data;
the main frequency parameter generating unit is used for generating main frequency parameters of a water well and the gas well according to the far offset distance superposed data and acquiring instantaneous frequency parameters of the far offset distance superposed 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.
6. The gas layer identification apparatus according to claim 5, wherein the superimposed data generation unit includes:
the offset range determining module is used for 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 utilizing a prestack AVO forward modeling method;
and the stacking data generation module is used for generating near offset stacking data and far offset stacking data according to the target work area seismic data and the offset range.
7. The gas layer identification apparatus according to claim 5, wherein the amplitude difference value generation unit includes:
the instantaneous amplitude module is used for respectively extracting the instantaneous amplitudes of the offset superposed data and the far offset superposed data;
an amplitude difference generation module 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.
8. The gas layer identification apparatus according to claim 5, wherein 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 factor.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the method of gas layer identification according to any of claims 1 to 4.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of gas layer identification according to any one of claims 1 to 4.
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