CN114488294A - Composite igneous rock classification fine identification method and identification device and electronic equipment - Google Patents
Composite igneous rock classification fine identification method and identification device and electronic equipment Download PDFInfo
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- CN114488294A CN114488294A CN202011150097.6A CN202011150097A CN114488294A CN 114488294 A CN114488294 A CN 114488294A CN 202011150097 A CN202011150097 A CN 202011150097A CN 114488294 A CN114488294 A CN 114488294A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
- G01V1/30—Analysis
- G01V1/306—Analysis for determining physical properties of the subsurface, e.g. impedance, porosity or attenuation profiles
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/62—Physical property of subsurface
- G01V2210/624—Reservoir parameters
Abstract
The invention provides a composite igneous rock classification fine identification method, a composite igneous rock classification fine identification device and electronic equipment. The method for finely identifying the classification of the composite igneous rock comprises the following steps: carrying out forward modeling on the igneous rock and the underburden, and analyzing the influence of different lithological igneous rocks on the underburden; aiming at actual logging data, carrying out multi-parameter intersection analysis, and establishing a relation between the lithology of igneous rocks and logging parameters; and the fine description of different types of igneous rocks on the plane and the space is realized by utilizing the analysis data. The invention establishes the technical process of the fine description of different lithologies of the inner curtain of the composite igneous rock for the first time, and utilizes the method to carry out the classification fine description of the igneous rock of actual data. The method realizes the fine description of different lithologies of the composite igneous rock, improves the accuracy of the igneous rock depicting, lays a good foundation for subsequent speed modeling and imaging work, and reduces exploration risks.
Description
Technical Field
The invention belongs to the field of seismic data interpretation, and particularly relates to a technical process for classifying and identifying composite igneous rocks.
Background
Lithology prediction of seismic data is one of the contents of reservoir prediction. Different sedimentary facies zones are contained in a seismic sequence, and due to the different sedimentary environments, the lithology parameters can show great differences, including rock composition, particle size and shape, cementation degree, porosity, fluid composition and saturation in the pores, temperature, pressure, sedimentary thickness and the like. The change in lithology causes a change in an elastic parameter including modulus of elasticity, density, velocity, poisson's ratio, absorption characteristics, and the like. The change in the elastic parameters will in turn cause a change in the reflection characteristics of the seismic section, including amplitude, waveform, frequency content, wave interference, coherence, etc., and thus appear as different seismic attributes, i.e., different seismic phase modes, on the seismic section. And extracting and analyzing the seismic facies attributes in the seismic sequence, and identifying the sections which have the same seismic facies attributes and belong to the same seismic facies mode, so as to achieve the purpose of dividing the seismic facies.
Since the exploration and production of oil and gas on a large scale, research on reservoirs is not stopped, sedimentary rock oil and gas reservoirs are more important, but the igneous rock developing in sedimentary basins is not considered. Nowadays, the consumption of energy is getting larger and larger, and igneous rock oil and gas reservoirs gradually come into the visual field of people. However, due to a series of reasons such as deep buried depth of igneous rock, no outcrop on the ground, lack of logging information, various lithologic type reservoir space types and the like, effective analysis cannot be performed. Therefore, the seismic exploration of igneous rocks becomes a worldwide problem and has very important theoretical research value.
Igneous rock research has been of great interest in oil field exploration since the discovery of igneous rock reservoirs. The seismic method can find the igneous rock and determine the igneous rock depth with high precision, even define the igneous rock range, but is difficult to determine the lithologic facies of the igneous rock.
In an actual stratum, igneous rocks are erupted in stages, so that most of igneous rocks are various lithological combinations, the longitudinal and transverse changes are very complex, and the seismic imaging precision of an underlying stratum is usually greatly influenced due to the abnormal speed of the igneous rocks. Most of the research developed at present is to analyze various complex igneous rock combinations as a whole, and although a certain effect is achieved, the degree of fine carving is obviously insufficient. The conventional igneous rock identification process has limited basis and cannot carry out vertical and horizontal fine portrayal, so that research and application of a new method need to be carried out.
Disclosure of Invention
Aiming at the problems in the current actual production, the invention explores and establishes a set of identification flows based on different igneous rock self-response characteristics and different influences on the underburden. And finally, realizing fine identification of the igneous rock combination.
The method is based on forward modeling, and analyzes the influence of igneous rocks with different speeds, thicknesses and distribution ranges on the imaging precision of the underburden when the igneous rocks are not subjected to fine identification and speed modeling; and then, identifying various igneous rocks on the logging curve by utilizing well data intersection analysis, and determining the seismic response characteristics of the different igneous rocks by combining the seismic profile. And establishing an identification mode of various igneous rocks in actual data by combining with plane amplitude attributes and the like, and finally performing classification identification on the composite igneous rocks through the conclusion, thereby obtaining a good effect in the application of the actual data, improving the precision of classification identification on the multiple igneous rocks and confirming the practicability of the method.
According to one aspect of the invention, a composite igneous rock classification fine identification method is provided, and comprises the following steps:
carrying out forward modeling on igneous rocks and an underburden, and analyzing the influence of different lithologic igneous rocks on the underburden;
aiming at actual logging data, carrying out multi-parameter intersection analysis, and establishing a relation between the lithology of igneous rocks and logging parameters;
and the fine description of different types of igneous rocks on the plane and the space is realized by utilizing the analysis data.
Further, forward modeling analysis is carried out by establishing forward models of different single variables, and the influence degree of different parameters of igneous rocks on accurate imaging of the underburden is analyzed.
Furthermore, the stratum is pulled upwards to correspond to high-speed igneous rocks, the stratum sinks to correspond to low-speed igneous rocks, and the stratum shakes slightly to correspond to small-area igneous rocks.
And further, carrying out intersection analysis by using the logging data, counting sensitive parameters, carrying out lithology classification on the well data, comparing well seismic profiles, and determining the relation between different lithologies and seismic response characteristics.
Furthermore, the imaging form of the underburden and the seismic response characteristics of various igneous rocks are combined to carry out detailed identification on the igneous rocks in the longitudinal direction, and the distribution of various igneous rocks in the space is identified by combining the plane attribute.
According to another aspect of the present invention, there is provided a composite igneous rock classification fine recognition device, comprising:
the forward modeling unit is used for performing forward modeling on the igneous rocks and the underburden and analyzing the influence of the igneous rocks with different lithologies on the underburden;
the analysis unit is used for carrying out multi-parameter intersection analysis aiming at actual logging data and establishing a relation between the lithology of the igneous rock and the logging parameters;
and the identification unit is used for realizing the fine description of different types of igneous rocks on the plane and the space by utilizing the analysis data.
Furthermore, the forward modeling unit carries out forward modeling analysis by establishing forward models of different single variables, and analyzes the influence degree of different parameters of igneous rocks on accurate imaging of the underburden.
Furthermore, the analysis unit performs intersection analysis by using the logging data, counts sensitive parameters, performs lithology classification on the well data, compares well seismic profiles, and determines the relationship between different lithologies and seismic response characteristics.
Furthermore, the identification unit is used for finely identifying the igneous rocks in the longitudinal direction by combining the imaging form of the underlying stratum and the seismic response characteristics of various igneous rocks, and identifying the distribution of various igneous rocks in the space by combining the plane attribute.
According to another aspect of the present invention, there is provided an electronic apparatus including:
a memory storing executable instructions;
a processor executing the executable instructions in the memory to implement the composite igneous rock classification fine identification method.
Compared with the prior art, the invention has the following advantages:
the method establishes the relation between various parameter changes of igneous rocks and the imaging accuracy of the underburden, and lays a good foundation for subsequent research.
Based on actual logging data and seismic data, the classification and identification of the composite igneous rocks with different lithologies are realized, and the accuracy of igneous rock carving is improved.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent by describing in greater detail exemplary embodiments thereof with reference to the attached drawings, in which like reference numerals generally represent like parts throughout.
Fig. 1 is a flow chart of the composite igneous rock classification fine identification method of the present invention.
Fig. 2 is a flowchart of an identification method according to an embodiment of the present invention.
Fig. 3a is a diagram of a igneous rock model with different distribution ranges according to an embodiment of the present invention.
Fig. 3b is a diagram of a igneous rock model with different thicknesses according to an embodiment of the present invention.
Fig. 3c is a diagram of a model of igneous rocks at different velocities according to an embodiment of the present invention.
Fig. 4 a-4 c are forward analysis results according to an embodiment of the present invention.
FIG. 5 is a well data intersection plot according to an embodiment of the present invention.
FIG. 6 is a igneous rock classification and seismic response signature graph according to an embodiment of the invention.
Fig. 7 is a classification recognition diagram on a igneous rock section according to an embodiment of the present invention.
Fig. 8 a-8 d are classification recognition diagrams on igneous rock planes according to an embodiment of the present invention.
Detailed Description
Preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The invention establishes a set of method flow for classifying and finely identifying the composite igneous rock, firstly, forward modeling analysis is carried out by establishing forward models with different single variables, and the influence degree of different parameters of the igneous rock on accurate imaging of an underlying stratum is analyzed; and then, performing intersection analysis by using the logging data, counting sensitive parameters, performing lithology classification on the well data, comparing well-passing seismic profiles, determining the relation between different lithologies and seismic response characteristics, finally performing longitudinal igneous rock fine identification by combining the imaging form of the underlying stratum and the seismic response characteristics of various igneous rocks, and finally identifying the spatial distribution of various igneous rocks by combining the plane attribute. The method has good effect when being applied in an actual work area, improves the classification and drawing precision of the complex igneous rocks, and proves the practicability of the method.
As shown in fig. 1, the invention provides a composite igneous rock classification fine identification method, which comprises the following steps:
carrying out forward modeling on the igneous rock and the underburden, and analyzing the influence of different lithological igneous rocks on the underburden;
aiming at actual logging data, carrying out multi-parameter intersection analysis, and establishing a relation between the lithology of igneous rocks and logging parameters;
and the fine description of different types of igneous rocks on the plane and the space is realized by utilizing the analysis data.
The method mainly comprises three parts of forward modeling analysis, well data intersection analysis and igneous rock classification and fine identification.
By establishing a composite igneous rock model with different lithological parameters, thicknesses and distribution areas, a forward record is obtained, and through statistical analysis, the influence of different types of igneous rocks on the imaging effect of the underburden is determined, and the important significance of various abnormal structures of the continuously deposited gentle stratum on the identification of the overburden igneous rocks is determined. If the stratum is pulled upwards to correspond to the high-speed igneous rock, the stratum sinks to correspond to the low-speed igneous rock, and the stratum shakes slightly to correspond to the small-area igneous rock distribution.
Aiming at the logging data of a plurality of uniformly distributed wells, intersection analysis is carried out on a plurality of marking logging parameters, and sensitive parameters for distinguishing various igneous rocks are determined. And establishing a relation between the logging parameters and the igneous rock categories, and determining the seismic response characteristics corresponding to various igneous rocks according to the well seismic imaging section, thereby laying a foundation for the subsequent igneous rock classification and fine identification.
On the basis of the research, igneous rock fine identification is carried out by combining amplitude attributes, and various igneous rock planes and spatial distribution ranges are obtained.
The method disclosed by the invention reversely deduces the type of the overlying igneous rock by designing the igneous rock and the underlying stratum forward models with different densities, thicknesses and distribution ranges and analyzing the influence of different types of igneous rocks on the imaging precision of the underlying stratum. And then the seismic response characteristics of different types of igneous rocks are researched through logging data, igneous rock classification and identification are carried out on the section, the distribution of various types of igneous rocks on the space is finally determined by combining the planar attributes such as amplitude, and a good effect is obtained in the application of actual data.
The innovation point of the method is that the technical process of fine description of different lithologies of the inner curtain of the composite igneous rock is established for the first time, and classification fine description of actual data of the igneous rock is developed by using the method. The method has the advantages that the relation between various parameter changes of the igneous rock and imaging accuracy of the underburden is analyzed by using the forward modeling, meanwhile, the actual logging data is analyzed and researched, the relation between the igneous rock types and the seismic response characteristics is established, the fine description of different lithologies of the composite igneous rock is finally realized, the accuracy of the igneous rock carving drawing is improved, a good foundation is laid for subsequent speed modeling and imaging work, and the exploration risk is reduced.
To facilitate understanding of the solution of the embodiments of the present invention and the effects thereof, a specific application example is given below. It will be understood by those skilled in the art that this example is merely for the purpose of facilitating an understanding of the present invention and that any specific details thereof are not intended to limit the invention in any way.
Example 1
As shown in fig. 2, the specific implementation steps of the present invention are as follows:
and performing forward modeling on the igneous rocks and the underburden, and analyzing the influence of the igneous rocks with different lithologies on the underburden. Specifically, a forward model of the influence of different variables of igneous rocks on the underburden is designed, forward records are obtained through forward simulation, and therefore the relationship between each parameter of the igneous rocks and imaging abnormity of the underburden is combined with the imaging result of an actual target layer to reversely deduce the igneous rocks.
Meanwhile, multi-parameter intersection analysis is carried out on actual logging data to obtain sensitive parameter intervals of various igneous rocks, so that the corresponding relation between various igneous rocks and earthquake response characteristics is established.
And finally, selecting sensitive attributes for analysis on the basis of seismic response characteristics, and combining the attribute analysis result plane slices to finely identify various igneous rocks in the longitudinal direction and the transverse direction. Finally, the detailed description of different types of igneous rocks on the plane and the space is realized.
Example 2
In a certain exploration area in the west, the binary covers a large amount of igneous rocks, the igneous rocks are erupted and deposited in multiple batches, lithologies in the transverse direction and the longitudinal direction change very fast, and the existence of the igneous rocks forms great interference on fine imaging of an underlying reservoir stratum as a speed abnormal body in stratum deposition.
Fig. 3a is a diagram of models of igneous rocks with different distribution ranges according to an embodiment of the present invention, fig. 3b is a diagram of models of igneous rocks with different thicknesses according to an embodiment of the present invention, and fig. 3c is a diagram of models of igneous rocks with different velocities according to an embodiment of the present invention.
The model of the relationship between the parameter change of the igneous rock and the underburden layer is made, and the relationship between each parameter and the travel time difference of the imaging result of the underburden layer is shown when the igneous rock is not finely identified and the imaging speed is not accurate in actual work. As shown in fig. 3 a-3 c, the three models are all single variable models. In the figure 3a, the speed of the igneous rock is fixed to 5500m/s, the thickness is fixed to 350m, the imaging speed is 5000m/s which is lower than the real speed of the igneous rock, and the imaging relation between the distribution range of the igneous rock and the underlying stratum is established by changing the width of the igneous rock; in FIG. 3b, the igneous rock velocity is fixed to 5500m/s, the width is fixed to 800m, the imaging speed is 5000m/s, and forward analysis is performed by changing the thickness of the igneous rock; in the figure 3c, the width of the igneous rock is fixed to be 800m, the thickness of the igneous rock is fixed to be 350m, nine igneous rock abnormal bodies with different speeds from 4700m/s to 6300m/s are arranged, the imaging speed is 5500m/s uniformly, and the igneous rock abnormal bodies are used for researching the influence of different imaging speed errors of the igneous rock on the imaging result of the underlying target layer.
4 a-4 c are forward analysis results according to an embodiment of the present invention, and it can be seen from FIG. 4a that the distribution range of igneous rocks has an influence on the imaging of the underlying stratum but is relatively disordered and not significant in lifting when the distribution range of igneous rocks is less than 160m, and the distribution range of igneous rocks is above 320m, and the imaging of the stratum at four kilometers is significantly influenced, the error is about 28 meters, and the degree of error is substantially unchanged with the increase of the thickness; from fig. 4b it can be seen that the travel time difference is greater as the igneous rock thickness increases; figure 4c shows that the identification of igneous rock is not accurate and the greater the velocity error, the greater the imaging error of the underburden.
FIG. 5 is a well data intersection plot according to an embodiment of the present invention.
FIG. 6 is a igneous rock classification and seismic response signature according to an embodiment of the invention.
In order to further establish the relationship between the igneous rocks of different categories and the seismic response characteristics, intersection analysis is carried out on the existing logging data, and the GR curve can be found to better distinguish the three types of igneous rocks of the experimental work area (see figure 5). And performing lithology division on the whole working area logging curve by taking the lithology division as a basis, and establishing a relation between lithogenesis and seismic response characteristics by contrasting a well-passing section (see figure 6).
Fig. 7 is a classification recognition diagram on a igneous rock section according to an embodiment of the present invention.
By combining the analysis results and comparing the actual section of the working area, various igneous rocks are finely identified in the longitudinal direction by combining the imaging form of the underlying target layer (see figure 7).
Fig. 8 a-8 d are classification recognition diagrams on igneous rock planes according to an embodiment of the present invention.
Finally, the sensitive attributes are selected on the basis of seismic response characteristics for analysis, and various igneous rocks can be finely identified in the longitudinal direction and the transverse direction by combining with attribute analysis result plane slices (see figures 8 a-8 d).
Example 3
This embodiment provides a categorised meticulous recognition device of compound igneous rock, includes:
the forward modeling unit is used for performing forward modeling on the igneous rocks and the underburden and analyzing the influence of the igneous rocks with different lithologies on the underburden;
the analysis unit is used for carrying out multi-parameter intersection analysis aiming at actual logging data and establishing a relation between the lithology of the igneous rock and the logging parameters;
and the identification unit is used for realizing the fine description of different types of igneous rocks on the plane and the space by utilizing the analysis data.
The forward modeling unit and the analysis unit are respectively in communication connection with the identification unit and used for respectively sending forward modeling results and intersection analysis to the identification unit. And the identification unit is used for realizing the fine description of different types of igneous rocks on the plane and the space by utilizing the analysis data.
Example 4
The present embodiment provides an electronic device including: a memory storing executable instructions; and the processor runs the executable instructions in the memory to realize the composite igneous rock classification fine identification method.
An electronic device according to an embodiment of the present disclosure includes a memory and a processor.
The memory is to store non-transitory computer readable instructions. In particular, the memory may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc.
The processor may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of the disclosure, the processor is configured to execute the computer readable instructions stored in the memory.
Those skilled in the art should understand that, in order to solve the technical problem of how to obtain a good user experience, the present embodiment may also include well-known structures such as a communication bus, an interface, and the like, and these well-known structures should also be included in the protection scope of the present disclosure.
For the detailed description of the present embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, which are not repeated herein.
Example 5
The present embodiment provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the composite igneous rock classification fine identification method.
A computer-readable storage medium according to an embodiment of the present disclosure has non-transitory computer-readable instructions stored thereon. The non-transitory computer readable instructions, when executed by a processor, perform all or a portion of the steps of the methods of the embodiments of the disclosure previously described.
The computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROMs and DVDs), magneto-optical storage media (e.g., MOs), magnetic storage media (e.g., magnetic tapes or removable disks), media with built-in rewritable non-volatile memory (e.g., memory cards), and media with built-in ROMs (e.g., ROM cartridges).
It will be appreciated by persons skilled in the art that the above description of embodiments of the invention is intended only to illustrate the benefits of embodiments of the invention and is not intended to limit embodiments of the invention to any examples given.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.
Claims (10)
1. A composite igneous rock classification fine identification method is characterized by comprising the following steps:
carrying out forward modeling on the igneous rock and the underburden, and analyzing the influence of different lithological igneous rocks on the underburden;
aiming at actual logging data, carrying out multi-parameter intersection analysis, and establishing a relation between the lithology of igneous rocks and logging parameters;
and the fine description of different types of igneous rocks on the plane and the space is realized by utilizing the analysis data.
2. The method for classifying and finely recognizing composite igneous rock as claimed in claim 1, wherein forward modeling analysis is performed by establishing forward models of different single variables to analyze the degree of influence of different parameters of igneous rock on the accurate imaging of the underlying stratum.
3. The method for classifying and finely identifying composite igneous rocks as claimed in claim 2, wherein the stratum is pulled up corresponding to high-speed igneous rocks, the stratum is sunk corresponding to low-speed igneous rocks, and the stratum is slightly shaken corresponding to small-area igneous rocks.
4. The method of claim 1, wherein the well log data is used for intersection analysis, sensitive parameters are counted, lithology classification is performed on the well data, well seismic profiles are compared, and the relationship between different lithologies and seismic response characteristics is determined.
5. The method for classifying and finely identifying composite igneous rocks as claimed in claim 1, wherein the detailed identification of igneous rocks in the longitudinal direction is performed in combination with the imaging morphology of the underburden and the seismic response characteristics of various types of igneous rocks, and the spatial distribution of various types of igneous rocks is identified in combination with the planar attributes.
6. The utility model provides a categorised meticulous recognition device of compound igneous rock which characterized in that includes:
the forward modeling unit is used for performing forward modeling on the igneous rocks and the underburden and analyzing the influence of the igneous rocks with different lithologies on the underburden;
the analysis unit is used for carrying out multi-parameter intersection analysis aiming at actual logging data and establishing a relation between the lithology of the igneous rock and the logging parameters;
and the identification unit is used for realizing the fine description of different types of igneous rocks on the plane and the space by utilizing the analysis data.
7. The device for finely identifying the classification of composite igneous rocks according to claim 6, wherein the forward modeling unit performs forward modeling analysis by establishing forward models of different single variables, and analyzes the degree of influence of different parameters of igneous rocks on the accurate imaging of the underlying stratum.
8. The device for finely identifying the classification of the composite igneous rock as claimed in claim 6, wherein the analysis unit performs intersection analysis by using the well log data, counts sensitive parameters, performs lithology classification on the well data, compares well seismic profiles, and determines the relationship between different lithologies and seismic response characteristics.
9. The composite igneous rock classification fine identification device as claimed in claim 6, wherein the identification unit is used for carrying out fine identification on the igneous rocks in the longitudinal direction by combining the imaging form of the underlying stratum and the seismic response characteristics of various igneous rocks, and identifying the spatial distribution of various igneous rocks by combining the plane attributes.
10. An electronic device, characterized in that the electronic device comprises:
a memory storing executable instructions;
a processor executing the executable instructions in the memory to implement the composite igneous rock classification fine identification method of any one of claims 1-5.
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