CN112731560B - High-precision deep dry-hot rock mass temperature field depicting method and system - Google Patents
High-precision deep dry-hot rock mass temperature field depicting method and system Download PDFInfo
- Publication number
- CN112731560B CN112731560B CN202011542465.1A CN202011542465A CN112731560B CN 112731560 B CN112731560 B CN 112731560B CN 202011542465 A CN202011542465 A CN 202011542465A CN 112731560 B CN112731560 B CN 112731560B
- Authority
- CN
- China
- Prior art keywords
- data
- attribute data
- target
- geophysical
- rock mass
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V11/00—Prospecting or detecting by methods combining techniques covered by two or more of main groups G01V1/00 - G01V9/00
Landscapes
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Geophysics (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention provides a high-precision depiction method and a high-precision depiction system for a deep dry-hot rock mass temperature field, which comprise the following steps of: performing gravity-magnetic-electric joint inversion operation based on earthquake on the earthquake data of the target area to obtain multi-element geophysical attribute data of deep dry-hot rock mass of the target area; carrying out temperature response sensitivity analysis on the multi-element geophysical attribute data based on the prior data information of the target area to obtain a simulated attribute data body; establishing a target association relation model for mutual conversion between the geophysical field and the dry-hot rock mass temperature field based on prior data information; and determining the temperature field distribution of the deep dry hot rock mass of the target region based on the quasi-attribute data volume and the target incidence relation model. The invention solves the technical problems of point outline, insufficient precision and strong human factor in the prior art.
Description
Technical Field
The invention relates to the technical field of deep high-temperature geothermal resource exploration and development, in particular to a deep dry-hot rock mass temperature field high-precision depicting method and system.
Background
In the exploration and evaluation research of deep hot and dry rock geothermal resources, accurate prediction and characterization of the spatial distribution of a hot and dry rock temperature field are crucial links. Generally, the temperature of the deep high-temperature dry-hot rock mass has obvious nonlinear change along with the depth, and the high-temperature dry-hot rock mass shows the singularity characteristic, so that the prediction and the drawing are very difficult. At present, temperature logging, resistivity parameter conversion, magnetic susceptibility parameter conversion, and the like are generally used. Although the methods can predict and depict the distribution of the dry-hot rock temperature field to a certain extent, within a certain range and under certain conditions, the methods have the defects of point outline, insufficient precision, strong artificial factors and the like. The main reasons for this are: firstly, in the exploration and development of the hot dry rock, the number of drilled wells is very limited, and a small amount of temperature logging information is difficult to control the whole exploration area or development field, so that the prediction precision of the hot dry rock temperature field is severely restricted; the electric, magnetic and electromagnetic exploration cannot be accurately fixed in depth due to the influence of the product effect, the inversion effect completely depends on the precision and rationality of the established initial inversion model, and in a hot dry rock exploration area or a development field seriously lacking prior knowledge information, the established initial inversion model completely depends on the experience of professionals and the prior geological information of the exploration area, but the traditional hot dry rock exploration has complex and various geothermal geological conditions, broken structure, fast transverse change and strong heterogeneity of hot dry rock heat storage parameters, difficult processing of inter-well interpolation and extrapolation, certain uncertainty and humanity in the established initial inversion model, and further difficult acquisition of an inversion result in accordance with reality; the ground observation electrical method, magnetic method and electromagnetic method exploration have the advantages that the space sampling interval is large, generally more than hundreds of meters or thousands of meters, the influence of landforms, observation environment interference and attachment effect is serious, the detailed characteristics of temperature field space distribution are difficult to accurately depict and represent, the structural performance of a prediction result is poor, and certain defects exist.
Disclosure of Invention
In view of the above, the present invention provides a method and a system for high-precision characterization of a deep dry-hot rock temperature field, so as to alleviate the technical problems of surface approximation, insufficient precision and strong human factors existing in the prior art.
In a first aspect, an embodiment of the present invention provides a high-precision characterization method for a deep dry-hot rock temperature field, including: performing gravity-magnetic-electric joint inversion operation based on earthquake on the earthquake data of a target area to obtain multi-element geophysical attribute data of deep dry-hot rock mass of the target area; based on the prior data information of the target area, carrying out temperature response sensitivity analysis on the multi-element geophysical attribute data to obtain a quasi-attribute data body; the prior data information includes: temperature logging data of the target region and petrophysical experimental data of the target region; the quasi-attribute data volume is a fusion data volume of data containing temperature response sensitive information in the multi-element geophysical attribute data; establishing a target association relation model for mutual conversion between the geophysical field and the dry-hot rock mass temperature field based on the prior data information; and determining the temperature field distribution of the deep dry hot rock mass of the target area based on the quasi-attribute data volume and the target incidence relation model.
Further, performing seismic-based heavy magnetic electric joint inversion operation on the seismic data of the target area, including: carrying out construction and interpretation on the seismic data of the target area to obtain a construction and interpretation result; establishing a stratum lattice model of joint inversion based on the construction interpretation result; performing attribute inversion on the seismic data of the target area to obtain seismic inversion attribute data; establishing an initial inversion model based on the stratigraphic framework model and the seismic inversion attribute data; and performing heavy magnetic electricity and electricity joint inversion operation based on earthquake on the earthquake data of the target area based on the initial inversion model.
Further, based on the stratigraphic grid model and the seismic inversion attribute data, an initial inversion model is established, comprising: carrying out consistent gridding processing on the stratum lattice model and the seismic inversion attribute data to obtain a common lattice unit; based on the prior data information, establishing an incidence relation model for solving initial values of common grid cells of other geophysical methods by using the seismic inversion attribute data; the other geophysical methods include: gravity, magnetic, electrical; determining initial values of the other geophysical methods based on the correlation model and the seismic inversion attribute data; and filling the initial values of the other geophysical methods into the co-grid unit to obtain an initial inversion model.
Further, determining the temperature field distribution of the deep dry hot rock mass of the target region based on the quasi-attribute data volume and the target association relation model, including: substituting the quasi-attribute data volume into the target incidence relation model to obtain a temperature data volume of the deep dry-hot rock mass of the target area; and carrying out grid interpolation smoothing treatment on the temperature data volume to obtain the temperature field distribution of the deep dry-hot rock mass of the target area.
Further, the grid interpolation smoothing processing is performed on the temperature data volume, and the processing comprises: carrying out grid interpolation smoothing processing on the temperature data volume by using a target interpolation method; the target interpolation method includes any one of: distance reciprocal multiplication method, kriging method, minimum curvature method, multiple regression.
Further, the method further comprises: and (3) depicting the temperature field distribution of the deep dry-hot rock mass of the target region by using a visual display method.
In a second aspect, an embodiment of the present invention further provides a deep dry-hot rock mass temperature field high-precision characterization system, including: the system comprises a joint inversion module, a sensitivity analysis module, an establishment module and a determination module, wherein the joint inversion module is used for performing seismic-based gravity, magnetic and electric joint inversion operation on seismic data of a target area to obtain multi-element geophysical attribute data of deep dry hot rock mass of the target area; the sensitivity analysis module is used for carrying out temperature response sensitivity analysis on the multi-element geophysical attribute data based on the prior data information of the target area to obtain a quasi-attribute data body; the prior data information includes: temperature logging data of the target region and petrophysical experimental data of the target region; the quasi-attribute data volume is a fusion data volume of data containing temperature response sensitive information in the multi-element geophysical attribute data; the establishing module is used for establishing a target association relation model for mutual conversion between the geophysical field and the dry-hot rock mass temperature field based on the prior data information; the determining module is used for determining the temperature field distribution of the deep dry hot rock mass of the target area based on the quasi-attribute data volume and the target incidence relation model.
Further, the system further comprises: and the display module is used for depicting the temperature field distribution of the deep dry-hot rock mass of the target area by using a visual display method.
In a third aspect, an embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method according to the first aspect when executing the computer program.
In a fourth aspect, the present invention further provides a computer-readable medium having non-volatile program code executable by a processor, where the program code causes the processor to execute the method according to the first aspect.
The embodiment of the invention provides a high-precision method and a system for depicting a deep dry-hot rock mass temperature field, which fully play the advantages of a high-density sampling and accurate depth-fixing technology of seismic exploration, acquire high-quality multi-element geophysical attribute data by relying on a heavy-magnetoelectric combined inversion technology based on an earthquake, and then establish an incidence relation model between a geophysical field and the temperature field by combining prior data information on the basis of rock physical experiments, well control processing and geophysical attribute analysis, so that comprehensive prediction and representation of spatial distribution of the dry-hot rock mass temperature field by using the multi-element geophysical information are realized, the prediction precision of temperature parameters is greatly improved, and the technical problems of point probability, insufficient precision and strong human factor existing in the prior art are solved.
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 described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a high-precision method for depicting a deep dry-hot rock temperature field according to an embodiment of the invention;
FIG. 2 is a flow chart of a seismic-based gravity, magnetic and electric joint inversion operation on seismic data of a target area according to an embodiment of the present invention;
FIG. 3 is a flowchart of establishing an initial inversion model according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a deep dry-hot rock temperature field high-precision characterization system according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a joint inversion module according to an embodiment of the present invention;
fig. 6 is a schematic diagram of another deep dry-hot rock temperature field high-precision characterization system according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood 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 first embodiment is as follows:
FIG. 1 is a flow chart of a high-precision method for depicting a deep dry-hot rock temperature field according to an embodiment of the invention. As shown in fig. 1, the method specifically includes the following steps:
step S102, carrying out gravity-magnetic-electric joint inversion operation based on earthquake on the earthquake data of the target area to obtain multi-element geophysical attribute data of the deep dry-hot rock mass of the target area. Optionally, the multivariate geophysical attribute data comprises: seismic, gravity, magnetic and electrical methods.
Step S104, performing temperature response sensitivity analysis on the multi-element geophysical attribute data based on the prior data information of the target area to obtain a simulated attribute data volume; the prior data information includes: temperature logging data of the target area and petrophysical experimental data of the target area; the quasi-attribute data volume is a fused data volume of data containing temperature response sensitive information in the multi-element geophysical attribute data.
And S106, establishing a target association relation model for mutual conversion between the geophysical field and the dry-hot rock mass temperature field based on the prior data information.
And S108, determining the temperature field distribution of the deep dry hot rock mass of the target area based on the quasi-attribute data volume and the target incidence relation model.
The embodiment of the invention provides a high-precision method for depicting a deep dry-hot rock mass temperature field, which fully exerts the advantages of a high-density sampling and accurate depth-fixing technology of seismic exploration, acquires high-quality multi-element geophysical attribute data by relying on a heavy-magnetoelectric combined inversion technology based on an earthquake, then establishes an incidence relation model between a geophysical field and the temperature field by combining prior data information on the basis of rock physical experiments, well control processing and geophysical attribute analysis, realizes comprehensive prediction and representation of spatial distribution of the dry-hot rock mass temperature field by using the multi-element geophysical information, further greatly improves the prediction precision of temperature parameters, and relieves the technical problems of point probability, insufficient precision and strong human factor existing in the prior art.
Optionally, fig. 2 is a flowchart of performing a seismic-based gravity-magnetic-electric joint inversion operation on seismic data of a target area according to an embodiment of the present invention. As shown in fig. 2, the method specifically includes the following steps:
step S201, the seismic data of the target area are constructed and explained to obtain a construction and explanation result.
And S202, establishing a jointly inverted stratum trellis model based on the construction interpretation result.
And S203, performing attribute inversion on the seismic data of the target area to obtain seismic inversion attribute data.
And S204, establishing an initial inversion model based on the stratum lattice model and the seismic inversion attribute data.
And S205, performing seismic-based gravity-magnetic-electric joint inversion operation on the seismic data of the target area based on the initial inversion model.
In step S204, a process of establishing an initial inversion model is shown in fig. 3, and specifically includes the following steps:
step S2041, carrying out consistent gridding processing on the stratum lattice model and the seismic inversion attribute data to obtain a common lattice unit;
step S2042, establishing an incidence relation model for solving initial values of the common grid cells of other geophysical methods by utilizing seismic inversion attribute data based on prior data information; other geophysical methods include: gravity, magnetic, electrical;
step S2043, determining initial values of other geophysical methods based on the incidence relation model and the seismic inversion attribute data;
and step S2044, filling the initial values of other geophysical methods into the common grid unit to obtain an initial inversion model.
In the embodiment of the invention, for the deep dry-hot rock mass temperature field characterization process of the target area, gravity, magnetism and electricity joint inversion based on earthquake is firstly carried out. Specifically, preparation and model construction of relevant data of gravity-magnetic-electric joint inversion based on earthquake are carried out based on an earthquake interpretation and inversion software platform, attribute inversion of earthquake data is carried out on the basis of earthquake structure interpretation, earthquake attribute analysis is carried out on the obtained earthquake inversion attribute data, an incidence relation model for estimating initial values of other geophysical methods of a common grid unit by using the earthquake inversion attribute data is established or a relevant empirical formula is quoted, and a corresponding unknown coefficient is determined; and then establishing an initial prior inversion model required by joint inversion by using the seismic structure interpretation result, carrying out grid subdivision common-grid unit processing, estimating an initial value of filling of the heavy magnetic electric joint inversion common-grid unit by using seismic attribute data, and finally carrying out heavy magnetic electric seismic joint inversion to obtain multi-element geophysical attribute data for solving the dry hot rock temperature parameter.
And then carrying out fusion processing on the multi-element geophysical attribute data. Specifically, based on multi-element geophysical attribute data obtained by joint inversion, the data of the heavy magnetic and electric attributes (density, resistivity, magnetic susceptibility and the like) with the same sampling interval, channel spacing and starting and stopping depth as the seismic inversion attribute data are obtained, so that the heavy magnetic and electric seismic attribute data can be directly compared, processed and interpreted in space. On the basis of the multi-element geophysical attribute data standardization processing, temperature response sensitivity analysis of geophysical attribute data is carried out by combining prior data information such as temperature logging, rock physics experiments and the like, sensitive geophysical attribute data are optimized, multi-element geophysical attribute data fusion processing (fusion between two or more types of attribute data) is carried out by selecting a preset data fusion method, and then a quasi-attribute data body (or a geophysical attribute data body) which is required by fine description of deep dry-hot rock mass temperature field spatial distribution and contains sensitive information is obtained. The preset data fusion method may be: algebra, statistical regression, clustering, neural network, and the like.
Specifically, in step S106, regarding the establishment of the target association relationship model, firstly, the temperature logging data of the target area and the petrophysical experimental data of the target area need to be processed.
Specifically, the processing process of the petrophysical experimental data comprises the following steps: the method comprises the steps of carrying out precision testing and model construction on the petrophysical parameters, then carrying out geological and geophysical model construction, carrying out large-scale forward modeling, and finally carrying out geophysical corresponding characteristic analysis along with the change of a temperature field to obtain the change rule of the revealing geophysical attribute parameters along with the temperature field.
The processing of temperature log data (i.e., log-confined geophysical data) includes: the well logging data are subjected to standardized processing, correlation analysis of the temperature measurement data and other well logging data is carried out, then the well logging data are subjected to target processing, the corresponding relationship analysis of the well logging data and the well-side geophysical data is carried out, and finally the correlation relationship between the revealed temperature measurement data and other well logging data and well-side geophysical attribute data is obtained.
And finally, establishing a target association relation model for mutual conversion between the geophysical field and the dry hot rock mass temperature field by combining and revealing the change rule of the geophysical attribute parameters along with the temperature field, and revealing the correlation between the temperature measurement data and other logging data and the near-well geophysical attribute data.
Optionally, step S108 further includes the steps of:
and step S1081, substituting the quasi-attribute data volume into the target incidence relation model to obtain a temperature data volume of the deep dry-hot rock body of the target area.
And step S1082, performing grid interpolation smoothing processing on the temperature data volume to obtain the temperature field distribution of the deep dry-hot rock mass in the target area.
Preferably, a target interpolation method is used for carrying out grid interpolation smoothing processing on the temperature data volume; the target interpolation method includes any one of: distance reciprocal multiplication method, kriging method, minimum curvature method, multiple regression.
According to the high-precision characterization method for the deep dry-hot rock temperature field, provided by the embodiment of the invention, on the basis of dry-hot rock structure interpretation, the dry-hot rock distribution range and the boundary of a research area are defined, the geophysical attribute data in the dry-hot rock range are extracted, and the simulated attribute data are directly converted into the dry-hot rock stratum temperature data by using the target association relation model established in the step S106, so that the high-precision prediction of the deep dry-hot rock temperature field distribution is realized.
Optionally, the method provided in the embodiment of the present invention further includes: and (3) depicting the temperature field distribution of the deep dry-hot rock mass of the target region by using a visual display method.
Specifically, in the embodiment of the invention, the interpolation methods such as reciprocal distance multiplication, kriging, minimum curvature, multiple regression and the like are utilized to perform grid interpolation smoothing processing on the deep dry hot rock formation temperature data volume obtained in the step S108, and visual display methods such as variable density display, color display, slice display, arbitrary tangent display and the like are adopted to finely describe the temperature field distribution of the deep dry hot rock, reveal the distribution rule thereof, analyze the formation reason thereof, and perform heat source mechanism inference to provide data support for accurately predicting and estimating the amount of the dry hot rock geothermal resources, thereby achieving the technical effect of reducing the risk of dry hot rock geothermal resource exploration and development.
Example two:
fig. 4 is a schematic diagram of a high-precision characterization system for the deep dry-hot rock temperature field according to an embodiment of the invention. As shown in fig. 4, the system includes: joint inversion module 10, sensitivity analysis module 20, building module 30 and determination module 40.
Specifically, the joint inversion module 10 is configured to perform a gravity magnetic electric joint inversion operation based on the earthquake on the seismic data of the target area, so as to obtain multivariate geophysical attribute data of deep hot dry rock of the target area. Optionally, the multivariate geophysical attribute data comprises: seismic, gravity, magnetic and electrical methods.
The sensitivity analysis module 20 is used for performing temperature response sensitivity analysis on the multi-element geophysical attribute data based on the prior data information of the target area to obtain a quasi-attribute data body; the prior data information includes: temperature logging data of the target area and petrophysical experimental data of the target area; the quasi-attribute data volume is a fused data volume of data containing temperature response sensitive information in the multi-element geophysical attribute data.
The establishing module 30 is configured to establish a target association relationship model for mutual conversion between the geophysical field and the dry-hot rock mass temperature field based on the prior data information.
And the determining module 40 is used for determining the temperature field distribution of the deep dry hot rock mass of the target region based on the quasi-attribute data volume and the target incidence relation model.
The embodiment of the invention provides a high-precision depiction system for a deep dry-hot rock mass temperature field, which fully exerts the advantages of a high-density sampling and accurate depth-fixing technology of seismic exploration, acquires high-quality multi-element geophysical attribute data by relying on a heavy-magnetoelectric combined inversion technology based on an earthquake, then establishes an incidence relation model between a geophysical field and the temperature field by combining prior data information on the basis of rock physical experiments, well control processing and geophysical attribute analysis, realizes comprehensive prediction and representation of spatial distribution of the dry-hot rock mass temperature field by using the multi-element geophysical information, further greatly improves the prediction precision of temperature parameters, and relieves the technical problems of point probability, insufficient precision and strong human factor existing in the prior art.
Optionally, fig. 5 is a schematic diagram of a joint inversion module according to an embodiment of the present invention, and as shown in fig. 5, the joint inversion module 10 further includes: an interpretation unit 11, a first building unit 12, an inversion unit 13, a second building unit 14 and a joint inversion unit 15.
Specifically, the interpretation unit 11 is configured to perform structural interpretation on the seismic data of the target area to obtain a structural interpretation result.
The first establishing unit 12 is used for establishing a jointly inverted stratigraphic framework model based on the construction interpretation result.
And the inversion unit 13 is configured to perform attribute inversion on the seismic data of the target area to obtain seismic inversion attribute data.
And the second establishing unit 14 is used for establishing an initial inversion model based on the stratigraphic framework model and the seismic inversion attribute data.
And the joint inversion unit 15 is used for performing seismic-based gravity magnetic-electric joint inversion operation on the seismic data of the target area based on the initial inversion model.
Optionally, the second establishing unit 14 is further configured to: carrying out consistent gridding processing on the stratum lattice model and the seismic inversion attribute data to obtain a common lattice unit; based on prior data information, establishing an incidence relation model for solving initial values of common grid cells of other geophysical methods by utilizing seismic inversion attribute data; other geophysical methods include: gravity, magnetic, electrical; determining initial values of other geophysical methods based on the incidence relation model and the seismic inversion attribute data; and filling the initial values of other geophysical methods into the common grid unit to obtain an initial inversion model.
Optionally, the determining module 40 is further configured to: substituting the quasi-attribute data volume into the target incidence relation model to obtain a temperature data volume of the deep dry-hot rock body in the target area; and carrying out grid interpolation smoothing treatment on the temperature data volume to obtain the temperature field distribution of the deep dry-hot rock mass in the target area.
Preferably, a target interpolation method is used for carrying out grid interpolation smoothing processing on the temperature data volume; the target interpolation method includes any one of: distance reciprocal multiplication method, kriging method, minimum curvature method, multiple regression.
Optionally, fig. 6 is a schematic diagram of another deep dry-hot rock mass temperature field high-precision characterization system provided according to an embodiment of the invention. As shown in fig. 6, the system further includes: and the display module 50 is used for depicting the temperature field distribution of the deep dry-hot rock body of the target area by using a visual display method.
The embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and when the processor executes the computer program, the steps of the method in the first embodiment are implemented.
The embodiment of the invention also provides a computer readable medium with a non-volatile program code executable by a processor, wherein the program code causes the processor to execute the method in the first embodiment.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A high-precision deep dry-hot rock temperature field depicting method is characterized by comprising the following steps:
performing gravity-magnetic-electric joint inversion operation based on earthquake on the earthquake data of a target area to obtain multi-element geophysical attribute data of deep dry-hot rock mass of the target area;
based on the prior data information of the target area, carrying out temperature response sensitivity analysis on the multi-element geophysical attribute data to obtain a quasi-attribute data body; the prior data information includes: temperature logging data of the target region and petrophysical experimental data of the target region; the quasi-attribute data volume is a fusion data volume of data containing temperature response sensitive information in the multi-element geophysical attribute data;
establishing a target association relation model for mutual conversion between the geophysical field and the dry-hot rock mass temperature field based on the prior data information;
and determining the temperature field distribution of the deep dry hot rock mass of the target area based on the quasi-attribute data volume and the target incidence relation model.
2. The method of claim 1, wherein performing a seismic-based heavy-magnetic-electric joint inversion operation on the seismic data of the target zone comprises:
carrying out construction and interpretation on the seismic data of the target area to obtain a construction and interpretation result;
establishing a stratum lattice model of joint inversion based on the construction interpretation result;
performing attribute inversion on the seismic data of the target area to obtain seismic inversion attribute data;
establishing an initial inversion model based on the stratigraphic framework model and the seismic inversion attribute data;
and performing heavy magnetic electricity and electricity joint inversion operation based on earthquake on the earthquake data of the target area based on the initial inversion model.
3. The method of claim 2, wherein building an initial inversion model based on the stratigraphic grid model and the seismic inversion attribute data comprises:
carrying out consistent gridding processing on the stratum lattice model and the seismic inversion attribute data to obtain a common lattice unit;
based on the prior data information, establishing an incidence relation model for solving initial values of common grid cells of other geophysical methods by using the seismic inversion attribute data; the other geophysical methods include: gravity, magnetic, electrical;
determining initial values of the other geophysical methods based on the correlation model and the seismic inversion attribute data;
and filling the initial values of the other geophysical methods into the co-grid unit to obtain an initial inversion model.
4. The method of claim 1, wherein determining the temperature field distribution of the deep hot dry rock mass of the target region based on the pseudo-attribute data volume and the target correlation model comprises:
substituting the quasi-attribute data volume into the target incidence relation model to obtain a temperature data volume of the deep dry-hot rock mass of the target area;
and carrying out grid interpolation smoothing treatment on the temperature data volume to obtain the temperature field distribution of the deep dry-hot rock mass of the target area.
5. The method of claim 4, wherein performing a mesh interpolation smoothing process on the temperature data volume comprises:
carrying out grid interpolation smoothing processing on the temperature data volume by using a target interpolation method; the target interpolation method includes any one of: distance reciprocal multiplication method, kriging method, minimum curvature method, multiple regression.
6. The method of claim 1, further comprising:
and (3) depicting the temperature field distribution of the deep dry-hot rock mass of the target region by using a visual display method.
7. The utility model provides a deep dry hot rock mass temperature field high accuracy depiction system which characterized in that includes: a joint inversion module, a sensitivity analysis module, a building module and a determination module, wherein,
the joint inversion module is used for performing seismic-based gravity, magnetic and electric joint inversion operation on the seismic data of the target area to obtain multi-element geophysical attribute data of deep dry hot rock mass of the target area;
the sensitivity analysis module is used for carrying out temperature response sensitivity analysis on the multi-element geophysical attribute data based on the prior data information of the target area to obtain a quasi-attribute data body; the prior data information includes: temperature logging data of the target region and petrophysical experimental data of the target region; the quasi-attribute data volume is a fusion data volume of data containing temperature response sensitive information in the multi-element geophysical attribute data;
the establishing module is used for establishing a target association relation model for mutual conversion between the geophysical field and the dry-hot rock mass temperature field based on the prior data information;
the determining module is used for determining the temperature field distribution of the deep dry hot rock mass of the target area based on the quasi-attribute data volume and the target incidence relation model.
8. The system of claim 7, further comprising: and the display module is used for depicting the temperature field distribution of the deep dry-hot rock mass of the target area by using a visual display method.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method of any of the preceding claims 1 to 6 are implemented when the computer program is executed by the processor.
10. A computer-readable medium having non-volatile program code executable by a processor, wherein the program code causes the processor to perform the method of any of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011542465.1A CN112731560B (en) | 2020-12-23 | 2020-12-23 | High-precision deep dry-hot rock mass temperature field depicting method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011542465.1A CN112731560B (en) | 2020-12-23 | 2020-12-23 | High-precision deep dry-hot rock mass temperature field depicting method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112731560A CN112731560A (en) | 2021-04-30 |
| CN112731560B true CN112731560B (en) | 2021-12-07 |
Family
ID=75604954
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011542465.1A Active CN112731560B (en) | 2020-12-23 | 2020-12-23 | High-precision deep dry-hot rock mass temperature field depicting method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112731560B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008029420A1 (en) * | 2006-09-04 | 2008-03-13 | Geosystem S.R.L. | Method for building velocity models for pre-stack depth migration via the simultaneous joint inversion of seismic, gravity and magnetotelluric data |
| CN108318937A (en) * | 2017-12-29 | 2018-07-24 | 中国石油天然气集团公司 | Geologic interpretation method and apparatus |
| CN109061750A (en) * | 2018-04-20 | 2018-12-21 | 湖南继善高科技有限公司 | A kind of method and device of detection hot dry rock probing target area |
| CN111538075A (en) * | 2020-05-11 | 2020-08-14 | 中国地质调查局水文地质环境地质调查中心 | Hot dry rock exploration method and device, electronic equipment and storage medium |
| CN112069644A (en) * | 2019-06-11 | 2020-12-11 | 中国石油化工股份有限公司 | Method and system for constructing dry-hot rock mass heat storage parameter model |
-
2020
- 2020-12-23 CN CN202011542465.1A patent/CN112731560B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2008029420A1 (en) * | 2006-09-04 | 2008-03-13 | Geosystem S.R.L. | Method for building velocity models for pre-stack depth migration via the simultaneous joint inversion of seismic, gravity and magnetotelluric data |
| CN108318937A (en) * | 2017-12-29 | 2018-07-24 | 中国石油天然气集团公司 | Geologic interpretation method and apparatus |
| CN109061750A (en) * | 2018-04-20 | 2018-12-21 | 湖南继善高科技有限公司 | A kind of method and device of detection hot dry rock probing target area |
| CN112069644A (en) * | 2019-06-11 | 2020-12-11 | 中国石油化工股份有限公司 | Method and system for constructing dry-hot rock mass heat storage parameter model |
| CN111538075A (en) * | 2020-05-11 | 2020-08-14 | 中国地质调查局水文地质环境地质调查中心 | Hot dry rock exploration method and device, electronic equipment and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| 基于随机分布共网格模型的重磁电震联合反演技术及应用;李华东 等;《石油地球物理勘探》;20150831;第50卷(第4期);742-748 * |
| 热冲击作用下花岗岩温度场分布规律数值模拟研究;熊贵明 等;《太原理工大学学报》;20181130;第49卷(第6期);820-826 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112731560A (en) | 2021-04-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20170337302A1 (en) | Iterative and repeatable workflow for comprehensive data and processes integration for petroleum exploration and production assessments | |
| RU2596627C1 (en) | Simulation of temperature, limited by geophysical data and kinematic reduction | |
| Olea | Fundamentals of semivariogram estimation, modeling, and usage | |
| US10620339B2 (en) | Static earth model calibration methods and systems using tortuosity evaluations | |
| Li et al. | Estimation of fracture trace length distributions using probability weighted moments and L-moments | |
| Yang et al. | Multiobjective sensitivity analysis and optimization of distributed hydrologic model MOBIDIC | |
| CN103282908A (en) | System and method for characterizing reservoir formation evaluation uncertainty | |
| Lepillier et al. | From outcrop scanlines to discrete fracture networks, an integrative workflow | |
| WO2018222054A1 (en) | Method of calculating temperature and porosity of geological structure | |
| Linde et al. | Tomogram-based comparison of geostatistical models: Application to the Macrodispersion Experiment (MADE) site | |
| Zhang et al. | Investigating the effect of geological heterogeneity of strata on the bearing capacity of shallow foundations using Markov random field | |
| Dekkers et al. | Resource assessment: estimating the potential of a geothermal reservoir | |
| CN112731560B (en) | High-precision deep dry-hot rock mass temperature field depicting method and system | |
| CN108227033A (en) | Method and device for predicting porosity of sandstone reservoir and computer storage medium | |
| Likanapaisal et al. | Dynamic data integration and quantification of prediction uncertainty using statistical-moment equations | |
| WO2022170061A1 (en) | Method and system for ranking reactivities for kinetics assignment in basin modeling | |
| Moreno et al. | Real-time estimation of hydraulic fracture characteristics from production data | |
| CN112415584B (en) | Initial value filling method and device for gravity, magnetic and seismic joint inversion common grid unit | |
| Howley et al. | Developing an integrated structural modelling workflow | |
| CN116819647B (en) | Hydrologic geophysical data fusion method based on cross gradient structure constraint | |
| Hou | The Influence of Roughness on Electrical Properties of Single Rock Fractures | |
| US20220246247A1 (en) | Method and system for evaluating reactivity in source rock evaluation | |
| Wang et al. | Karst Phase Control Modeling Method for Carbonate Fracture-Cavity Reservoir–––A Case from No. 4 Block of Tahe Oilfield | |
| Shi et al. | Machine Learning of Sparse Site Investigation Data for Landslide Risk Assessment | |
| Mou et al. | Identification of volcanic lithofacies using box-counting method calculating fractal dimension of logging data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information |
Address after: 300000 5-1-101, Warner Jinghu garden, Dongli District, Tianjin Applicant after: Hydrogeological and Environmental Geological Survey Center of China Geological Survey Address before: Floor 16, building 3, No.21, Jiuxianqiao Road, Chaoyang District, Beijing Applicant before: Hydrogeological and Environmental Geological Survey Center of China Geological Survey |
|
| CB02 | Change of applicant information | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |