Method and apparatus for recognizing dominant carbonate rock reservoir in complex facies zone, and electronic device

Abstract

A method for recognizing a dominant carbonate rock reservoir in a complex facies zone, comprising: determining a lithologic type and lithologic physical properties of a study section (S100); carving out a development pattern configuration of a special geologic body in the lithologic type (S200); on the basis of the development pattern configuration, using an iterative modeling method to construct an inversion low-frequency model under a phase controlled constraint (S300); using the inversion low-frequency model to perform multi-parameter pre-stack inversion of the study section to obtain an inversion parameter data volume of the study section (S400); and using a multi-parameter intersection method to predict a dominant reservoir distribution in the inversion parameter data volume (S500). Also provided are an apparatus for recognizing a dominant carbonate rock reservoir in a complex facies zone, and an electronic device. By means of the phase controlled constraint, a configuration attribute representing geomorphic facies and lithologic inversion representing lithologic facies are comprehensively considered, and the attribute lateral resolution and the inversion longitudinal resolution are combined, thereby achieving high-precision recognition of a dominant carbonate rock reservoir in a complex facies zone.

Description

Method and device for identifying dominant carbonate reservoir in complex lithofacies area and electronic equipment

[0001] Cross-reference to Related Applications

[0002] This application claims the benefit of Chinese Patent Application No. 202410530509.0, filed on April 29, 2024, the contents of which are incorporated herein by reference. TECHNICAL FIELD

[0003] The present application belongs to the technical field of oil exploration, and specifically relates to a method and device for identifying a dominant carbonate reservoir in a complex lithofacies area and electronic equipment. BACKGROUND

[0004] Seismic lithology is a discipline that studies extracting information related to lithofacies and geomorphological facies from seismic data. It is a cross product of seismic attributes, seismic inversion, stochastic modeling technology, and seismic sedimentation. In seismic exploration, by observing the propagation and reflection of seismic waves in the underground medium, information related to rock properties such as wave velocity, attenuation coefficient, and density can be obtained. Through analysis of this information, the lithology type and combination of underground rocks can be inferred, providing an important basis for reservoir prediction.

[0005] In the complex lithofacies carbonate rock formation, limestone and igneous rock are mainly developed. Limestone mainly includes algal limestone, pelletal limestone, and shell limestone. Igneous rock mainly includes intrusive rock and eruption rock. Intrusive rock refers to intrusive facies rock types, mainly composed of diabase. Eruption rock refers to eruption facies rock types, mainly composed of basalt. The dominant carbonate reservoir development position in the BVE group formation is mainly algal limestone and pelletal limestone, and the dominant carbonate reservoir development position in the ITP group formation is mainly shell limestone. Marl and micritic limestone are not usually the dominant reservoir development position due to their high clay content, poor physical properties, and high density. The development of carbonate reservoirs is controlled by multiple factors such as tectonic background, diagenesis, sedimentary environment, and post-dissolution modification. The reservoir distribution is strongly heterogeneous. The traditional seismic data inversion method has the advantage of best longitudinal resolution, which can intuitively reflect the reservoir lithology and thickness. However, it loses the lateral resolution, which easily blurs the reservoir shape and structure, and other reservoir heterogeneities. The layered initial model used for inversion reduces the lateral resolution of the inversion and destroys the geomorphological facies characteristics of the reservoir, which is not conducive to studying reservoir heterogeneity. This method usually ignores the differences in lithofacies and cannot accurately describe the distribution characteristics of carbonate reservoirs.

[0006] The igneous rock is a main special geological body developed in a complex lithofacies carbonate rock region. In the complex lithofacies carbonate rock region, there are also special geological bodies such as turbidite channel, salt dome and carbonate cave. Based on the existence of these special geological bodies, if a conventional inversion model is used for parameter inversion, the formation occurrence, lithology lateral variation and the like are not considered, great error will be caused in the area where the formation lateral variation is relatively severe or the lithology variation is relatively large, and the obtained well curve is not representative.

[0007] In summary, in order to accurately identify the dominant reservoir space distribution of the complex lithofacies carbonate rock, it is urgent to improve the existing inversion model. SUMMARY

[0008] The embodiment of the application aims to provide a complex lithofacies dominant carbonate rock reservoir identification method, device and electronic equipment, and a machine readable storage medium, to overcome the technical problem that the conventional seismic data inversion cannot accurately identify the complex lithofacies dominant carbonate rock reservoir.

[0009] In order to achieve the above-mentioned purpose, the first aspect of the application provides a complex lithofacies dominant carbonate rock reservoir identification method, which comprises the following steps:

[0010] Determine the rock type and rock physical property of the study section;

[0011] Carve out the development mode configuration of the special geological body in the rock type;

[0012] On the basis of the development mode configuration, an inversion low-frequency model under phase control is constructed by using an iterative modeling method;

[0013] The multi-parameter prestack inversion of the study section is carried out by using the inversion low-frequency model, to obtain the inversion parameter data body of the study section;

[0014] The prediction of the dominant reservoir distribution in the inversion parameter data body is carried out by using a multi-parameter intersection method.

[0015] In an optional embodiment, the step of carving out the development mode configuration of the special geological body in the rock type comprises the following steps:

[0016] Construct a geological model of the special geological body, and carry out forward simulation to determine the development position feature of the special geological body;

[0017] Determine the development shape feature of the special geological body in combination with the rock physical property and actual seismic response;

[0018] According to the development position feature and the development shape feature, the development mode of the special geological body is carved out, to obtain the development mode configuration of the special geological body.

[0019] In an optional embodiment, the constructing the inversion low-frequency model under the phase control constraint based on the development pattern configuration comprises:

[0020] constructing the inversion low-frequency model of the initial state under the phase control constraint;

[0021] supplementing the special geological body to the inversion low-frequency model based on the development pattern configuration, updating the inversion low-frequency model, and performing the pre-stack multi-parameter inversion using the updated inversion low-frequency model to obtain the inversion result;

[0022] judging whether the inversion result is consistent with the real well filtration curve trend, if yes, executing the next step, otherwise, jumping to the previous step;

[0023] taking the current inversion low-frequency model as the final inversion low-frequency model.

[0024] In an optional embodiment, the performing the multi-parameter pre-stack inversion of the research section using the inversion low-frequency model to obtain the inversion parameter data volume of the research section comprises:

[0025] generating the synthetic seismic record of the research section through the seismic wavelet decomposition reconstruction method;

[0026] inputting the synthetic seismic record into the inversion low-frequency model to perform the multi-parameter pre-stack inversion and obtaining the inversion parameter data volume of the research section.

[0027] In an optional embodiment, the performing the prediction of the dominant reservoir distribution in the inversion parameter data volume using the multi-parameter intersection method comprises:

[0028] eliminating the lithology influence of the special geological body in the inversion parameter data volume using the multi-parameter intersection method to obtain the residual data volume and identify the dominant reservoir distribution in the residual data volume.

[0029] In an optional embodiment, the rock types comprise limestone and igneous rock, the special geological body comprises igneous rock, and the inversion parameter data volume comprises the P-wave velocity data volume, the S-wave velocity data volume, the P-wave impedance data volume and the density data volume.

[0030] In an optional embodiment, the eliminating the lithology influence of the special geological body in the inversion parameter data volume using the multi-parameter intersection method to obtain the residual data volume and identify the dominant reservoir distribution in the residual data volume comprises:

[0031] generating the pre-stack migration data volume in combination with the seismic data of the research section;

[0032] distinguishing the limestone and the igneous rock through the intersection analysis of the P-wave velocity data volume and the S-wave velocity data volume, and eliminating the lithology influence of the igneous rock from the pre-stack migration data volume using the distinguishing result to obtain the limestone data volume.

[0033] The reservoirs and non-reservoirs are distinguished through the intersection analysis of the longitudinal wave impedance data body and the density data body, and the non-reservoirs are excluded from the limestone data body by using the distinguishing result, so as to obtain the data body of the distribution of the dominant reservoirs.

[0034] In an optional embodiment, the method further comprises:

[0035] According to the prediction result of the distribution of the dominant reservoirs, the thickness values of the dominant reservoirs are extracted along the interval with the top and bottom of the target interval in the study section as boundaries, so as to obtain the thickness distribution plan of the dominant reservoirs.

[0036] The second aspect of the present application provides a device for identifying the dominant carbonate reservoirs in a complex lithofacies area, and the device comprises:

[0037] A lithology determination module is configured to determine the rock types and rock physical properties of the study section;

[0038] A development pattern configuration module is configured to carve out the development pattern configuration of the special geological bodies in the rock types;

[0039] An inversion low-frequency model construction module is configured to construct the inversion low-frequency model under the phase control by using the iterative modeling method based on the development pattern configuration;

[0040] A multi-parameter pre-stack inversion module is configured to perform the multi-parameter pre-stack inversion of the study section by using the inversion low-frequency model, so as to obtain the inversion parameter data body of the study section;

[0041] A reservoir identification module is configured to predict the distribution of the dominant reservoirs in the inversion parameter data body by using the multi-parameter intersection method.

[0042] In an optional embodiment, in the development pattern configuration module, the development pattern configuration of the special geological bodies in the rock types comprises:

[0043] A geological model of the special geological bodies is constructed, and a forward simulation is performed to determine the development position characteristics of the special geological bodies;

[0044] The development shape characteristics of the special geological bodies are determined in combination with the rock physical properties and the actual seismic response;

[0045] According to the development position characteristics and the development shape characteristics, the development pattern of the special geological bodies is carved out to obtain the development pattern configuration of the special geological bodies.

[0046] In an optional embodiment, in the inversion low-frequency model construction module, the inversion low-frequency model under the phase control is constructed by using the iterative modeling method based on the development pattern configuration, which comprises:

[0047] constructing an inversion low-frequency model under the constraint of the phase control;

[0048] Based on the development pattern configuration, special geological bodies are supplemented into the inversion low-frequency model, and the inversion low-frequency model is updated, and the updated inversion low-frequency model is used for pre-stack multi-parameter inversion to obtain an inversion result;

[0049] It is judged whether the inversion result is consistent with the trend of the real well filter curve, if yes, the next step is executed, otherwise, it is jumped to the previous step;

[0050] The current inversion low-frequency model is taken as a final inversion low-frequency model.

[0051] In an optional embodiment, in the multi-parameter pre-stack inversion module, the inversion low-frequency model is used for multi-parameter pre-stack inversion of a study section to obtain an inversion parameter data volume of the study section, including:

[0052] The synthetic seismic record of the study section is generated by the seismic wavelet decomposition reconstruction method;

[0053] The synthetic seismic record is input into the inversion low-frequency model for multi-parameter pre-stack inversion to obtain the inversion parameter data volume of the study section.

[0054] In an optional embodiment, in the reservoir identification module, a multi-parameter intersection method is used for prediction of the distribution of the dominant reservoir in the inversion parameter data volume, including:

[0055] The lithology influence of the special geological body in the inversion parameter data volume is excluded by using the multi-parameter intersection method to obtain a residual data volume, and the distribution of the dominant reservoir in the residual data volume is identified.

[0056] In an optional embodiment, the rock types include limestone and igneous rock, the special geological body includes igneous rock, and the inversion parameter data volume includes a P-wave velocity data volume, a S-wave velocity data volume, a P-wave impedance data volume and a density data volume.

[0057] In an optional embodiment, the lithology influence of the special geological body in the inversion parameter data volume is excluded by using the multi-parameter intersection method to obtain a residual data volume, and the distribution of the dominant reservoir in the residual data volume is identified, including:

[0058] The pre-stack migration data volume is generated in combination with the seismic data of the study section;

[0059] The two types of lithology, limestone and igneous rock, are distinguished by intersection analysis of the P-wave velocity data volume and the S-wave velocity data volume, and the lithology influence of the igneous rock is excluded from the pre-stack migration data volume by using the distinguishing result to obtain a limestone data volume;

[0060] The reservoirs and non-reservoirs are distinguished through the crossplot analysis of the P-wave impedance data volume and the density data volume, and the non-reservoirs are excluded from the limestone data volume by using the distinguishing result, so as to obtain the data volume of the distribution of the dominant reservoirs.

[0061] In an optional embodiment, the device further comprises a reservoir thickness mapping module, which is configured to extract the thickness value of the dominant reservoir along the interval based on the prediction result of the distribution of the dominant reservoirs, and obtain the thickness distribution plan of the dominant reservoirs with the top and bottom of the target interval in the seismic interpretation as boundaries.

[0062] The third aspect of the present application provides an electronic device, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method for identifying the dominant carbonate reservoir in the complex facies area according to the first aspect of the present application when executing the program.

[0063] The fourth aspect of the present application provides a machine readable storage medium, which stores a computer program, and the computer program is executable on a processor to implement the method for identifying the dominant carbonate reservoir in the complex facies area according to the first aspect of the present application.

[0064] Through the above technical solution, the present application has the following main beneficial effects:

[0065] The above technical solution combines the development pattern configuration, the iterative method modeling under facies control, the multi-parameter pre-stack inversion, and the multi-parameter crossplot method. The iterative method modeling under facies control is based on the development pattern configuration when constructing the inversion low-frequency model, that is, the special geological body is supplemented into the model for iteration, the effective elimination of noise interference and the accurate extraction of useful information in the seismic data are realized, and then the accurate facies interpretation result is obtained, and the identification accuracy of the dominant carbonate reservoir in the complex facies area is improved.

[0066] Other features and advantages of the embodiments of the present application will be described in detail in the following specific implementation part. BRIEF DESCRIPTION OF DRAWINGS

[0067] FIG. 1 is a schematic flowchart of the method for identifying the dominant carbonate reservoir in the complex facies area according to an embodiment of the present application;

[0068] FIG. 2 is another schematic flowchart of the method for identifying the dominant carbonate reservoir in the complex facies area according to an embodiment of the present application;

[0069] FIG. 3 is a schematic diagram of the development pattern configuration of the igneous rock;

[0070] Fig. 4 is an inversion low-frequency model obtained after supplementing special geological bodies, wherein, part A is a schematic view of a wave impedance inversion low-frequency model, part B is a schematic view of a P-S wave velocity ratio inversion low-frequency model, and part C is a schematic view of carved special geological bodies;

[0071] Fig. 5 is a schematic view of a comparison between P-wave impedance data volume and S-wave impedance data volume, wherein, part A is a P-wave impedance data volume, and part B is an S-wave impedance data volume;

[0072] Fig. 6 is a schematic view of a comparison between density data volume and gamma data volume, wherein, part A is a density data volume, and part B is a gamma data volume;

[0073] Fig. 7 is a schematic view of a multi-parameter intersection mode;

[0074] Fig. 8 is a comparison between prediction results of advantageous reservoirs, wherein, part A is a schematic view of a profile of a single-parameter prediction result of an advantageous reservoir, and part B is a schematic view of a profile of a prediction result of an advantageous reservoir achieved by the embodiment of the present application;

[0075] Fig. 9 is a plan view of a thickness distribution of a predicted advantageous reservoir. DETAILED DESCRIPTION

[0076] The specific implementation of the embodiment of the present application is described in detail below with reference to the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the embodiment of the present application, and is not used to limit the embodiment of the present application.

[0077] In order to facilitate understanding of the embodiment of the present application, the following describes the related technical features involved in the embodiment.

[0078] Research section: The research section involved in the present application is in a complex lithofacies subsalt carbonate rock formation.

[0079] Pre-stack inversion: Pre-stack seismic data contains more underground geological information than post-stack seismic data, at the same time, pre-stack inversion is superior to post-stack inversion in inversion accuracy and reservoir prediction efficiency, and has become one of the most important technical means in the field of reservoir prediction and fluid identification.

[0080] Single parameter differentiation: Influenced by the heterogeneity of the complex lithofacies carbonate rock reservoir region, the single parameter reservoir-non-reservoir differentiation is poor.

[0081] Intersection diagram: It is a tool for comprehensively analyzing multiple seismic attribute parameters, and can more comprehensively understand the spatial distribution and characteristics of the reservoir by visualizing the intersection relationship between different parameters.

[0082] Method embodiment

[0083] Referring to FIG. 1, the method for identifying the advantageous carbonate reservoir in a complex lithofacies area provided by the embodiment of the application can include the steps S100, S200, S300, S400 and S500.

[0084] S100. Determine the rock type and rock physical property of the study section.

[0085] In the embodiment, the rock type includes limestone and igneous rock.

[0086] In an optional embodiment of the application, the existing drilling data can be analyzed to determine the rock type and rock physical property of the study section.

[0087] As an example, the analysis of the existing drilling data shows that the two major lithologies of igneous rock and limestone are different in rock density value, and thus the first step can distinguish the major lithology of the study section according to the density difference. The developed limestone mainly includes algal limestone, pelletal limestone and shell limestone, and the igneous rock mainly includes intrusive rock and basalt, the intrusive rock type is mainly diabase, and the extrusive rock type is mainly basalt. The rock density value of the intrusive rock in the igneous rock is usually greater than that of the extrusive rock. The rock density value of the intrusive rock is mostly greater than 2.80 g / cm 3 , and the average density is usually about 2.92 g / cm 3 . The rock density value of the extrusive rock is mostly greater than 2.70 g / cm 3 , and the average density is about 2.75 g / cm 3 . The second step is the classification and identification of different types of carbonate rock. The developed position of the high-quality carbonate reservoir (the lithology of the BVE group is mainly algal limestone and pelletal limestone, and the lithology of the ITP group is mainly shell limestone) is mainly algal limestone, pelletal limestone and shell limestone, and the rock density value thereof is usually 2.3-2.6 g / cm 3 , and the average density is 2.46 g / cm 3 . The mudstone and micritic limestone have more mud and poor physical property, and the density is larger, and the average value thereof is 2.6 g / cm 3 . The third step is the identification of the igneous rock lithology. The Th, Fe and K can be used as the indicator elements for the identification of the igneous rock lithology based on the ECS and GR spectral logging analysis, the parameter reflecting the basicity of the igneous rock is preferably selected, and the igneous rock index (Lig) is introduced for quantitative identification. For example, L≥20 is diabase, and L<20 is basalt, which can better distinguish the extrusive rock (mainly basalt) and the intrusive rock (mainly diabase) in the igneous rock. The identification coincidence rate of the diabase and basalt reaches 95% after sample verification.

[0088] As another example, the Ca / Si element ratio method can be used to distinguish the main types of lithology in the study section. In the ECS logging, Ca and Si can be used as the indicator elements of igneous rocks and carbonate rocks, respectively. For example, in a specific application, Ca / Si>1.2 is carbonate rock, and Ca / Si<1.2 is igneous rock, achieving the identification of the main types of carbonate rock and igneous rock. To illustrate the effectiveness of the Ca / Si element ratio method in identifying the main types of lithology, 92 igneous rock samples were identified using the Ca / Si element ratio method proposed in this application. The identification of 88 igneous rock samples was successful, with an identification accuracy of 95.65%. In the classification and identification of different types of carbonate rocks, the carbonate rock lithology identification method based on curve reconstruction and artificial intelligence fuzzy clustering can be used. The identification method specifically refers to: 1) To identify the lithology of the BVE group, a shale content indicator curve is constructed using the natural gamma ray curve and the deuranium gamma ray curve to identify the lithology of the BVE group. The identification accuracy of the BVE group lithology is 80% after sample verification; 2) The main four lithology sensitive curves are selected for artificial intelligence fuzzy clustering analysis in the ITP group to identify the shell limestone, mudstone and mudstone. The main four lithology sensitive curves are the natural gamma ray curve, the density curve, the neutron curve and the porosity curve. The identification accuracy of the ITP group lithology based on artificial intelligence fuzzy clustering analysis is 100% after sample verification.

[0089] S200. Carving out the development pattern configuration of the special geological body in the rock type.

[0090] Specifically, in this application, the development pattern configuration refers to the facies development pattern carving, and the main special geological body of the carbonate rock reservoir, igneous rock, is carved and configured.

[0091] For example, in a specific embodiment, the specific process of carving and configuring the main special geological body of the carbonate rock reservoir, igneous rock, is as follows:

[0092] S210. Constructing a geological model of the igneous rock and performing forward modeling to determine the development location characteristics of the igneous rock;

[0093] S220. Combining the petrophysical properties and the actual seismic response to determine the development shape characteristics of the igneous rock;

[0094] S230. Carving the development pattern of the igneous rock according to the development location characteristics and the development shape characteristics, and obtaining the development pattern configuration of the igneous rock.

[0095] The simulation proves that the development position of the intrusive rock in the igneous rock has the characteristics of "strong amplitude + low frequency + high impedance", the amplitude of the development position of the intrusive rock is strong, and the amplitude is enhanced and the event is widened with the increase of the thickness. Combined with the petrophysical properties and the actual seismic response, the development shape of the intrusive rock is mostly: upwelling along the fault in the vertical direction and sheet distribution in the plane. After determining the development position characteristics and the development shape characteristics of the intrusive rock, the development mode of the intrusive rock is carved and configured according to the above rules, so that the development mode configuration of the intrusive rock is obtained. Due to the lithological characteristics and alteration changes of the eruption rock in the igneous rock, the seismic response is obviously not as good as that of the intrusive rock, and local blank reflection and chaotic reflection are presented. According to the above rules, the development mode of the eruption rock can be carved and configured, and the development mode configuration of the eruption rock is obtained. The above scheme uses the seismic multi-attribute space carving technology guided by the underwater volcanic mechanism mode, and combines the seismic attribute analysis, multi-information fusion, velocity analysis, coherence analysis and three-dimensional body slicing and other technical means. After the spatial distribution of the igneous rock is predicted, the development mode of the igneous rock is carved and configured, and the development mode configuration of the igneous rock is obtained.

[0096] As an improved embodiment of the present application, before the seismic facies development mode is described, the seismic data is optimized and reprocessed. Due to the shielding of the thick salt layer, the uneven energy under the salt, the reliability of the structure form under the salt affected by the precision of the velocity model, and the low signal-to-noise ratio in the local area affecting the seismic imaging quality, the improved embodiment proposes a grid tomography velocity constrained seismic data reprocessing method, which includes the following steps: grid tomography velocity updating, strengthening the structure model and well information constraint, and conducting structure-oriented filtering.

[0097] Through verification, by the above reprocessing process of the seismic data, the amplitude energy under the salt in the seismic imaging is more balanced, the continuity of the events of the salt bottom and the strata under the salt is better, and the structure form is more reasonable. It can be seen that the above seismic data reprocessing method based on the grid tomography velocity constraint improves the seismic imaging quality of the structure under the salt, and provides reliable data guarantee for the subsequent fine description of the seismic facies distribution characteristics.

[0098] As an example, in a specific application, the seismic facies development mode under the influence of the thick salt layer shielding is described by the following steps 1 to 3.

[0099] Step 1, seismic data optimization and reprocessing.

[0100] Step 2, according to the carbonate rock and igneous rock classification method established in step S100 as described above, petrophysical forward analysis is carried out.

[0101] In this specific application, the actual drilled wave impedance of the subsalt formation is igneous rock > limestone > mudstone > salt rock, the lithology of the top of the subsalt formation (salt bottom interface) changes from limestone to mudstone at the high position to the low position, and the wave peak reflection changes from strong to weak. In this application, the lithology of the bottom of the target layer (K44 top) changes from limestone to mudstone at the high position to the low position, and the wave peak reflection changes from weak to strong. Relative to volcanic rocks, the main intrusive rock is diabase. According to the actual drilling data, the lithology combination is described, which refers to "salt rock-anhydrite-diabase-limestone". Using five kinds of geological models and forward simulation, it is proved that the development position of intrusive rock has the characteristics of "strong amplitude + low frequency + high impedance". It is considered that the development position of intrusive rock has strong amplitude, and the amplitude becomes stronger with the increase of thickness, and the event is wider. Combined with the petrophysical properties and the actual seismic response, it is predicted that the intrusive rock is mainly distributed along the fault upwelling in the vertical direction and in the form of sheet in the plane. The development characteristics of the intrusive rock are carved and configured according to this rule. Due to the lithological characteristics and alteration changes of the eruption rock, the seismic response is obviously not as good as that of the intrusive rock, and it presents blank reflection and chaotic reflection in some places. Under the guidance of the underwater volcanic mechanism mode, the seismic multi-attribute spatial carving technology is used, and the spatial distribution of the igneous rock is predicted and configured by using seismic attribute analysis, multi-information fusion, velocity analysis, coherence analysis and three-dimensional body slicing and other technical means. Based on the above forward research, the differences of seismic attributes of different facies and different lithofacies combinations are revealed, which provides a theoretical basis for subsequent seismic facies description.

[0102] Step 3, seismic facies development pattern description. In this specific application, in order to comprehensively utilize various seismic attributes to finely describe the seismic facies distribution characteristics, the thickness of the shallow lake platform corresponding to the sedimentary facies is thin (less than 300m), the number of events is relatively small, the amplitude energy is strong, the thickness of the semi-deep lake facies is large (more than 300m), the number of events is relatively large, the amplitude energy is weak, the thickness of the intra-platform subfacies of the sedimentary subfacies is thin, mainly single-axis continuous reflection, the amplitude energy is strong, the thickness of the platform margin subfacies is relatively large, the hill-shaped weak reflection characteristics, the thickness of the platform slope subfacies is between the intra-platform and the platform margin, the low-frequency medium-weak amplitude continuous reflection, based on the above understanding, the seismic facies is described, which is used as the facies constraint parameter for subsequent reservoir inversion.

[0103] In the prior art, affected by the coverage of thick salt layer and multi-stage igneous rock intrusion, the seismic response law of carbonate reservoir in complex lithofacies area is complex, the traditional prestack inversion method cannot accurately describe the distribution characteristics of carbonate reservoir, which brings challenges to the exploration evaluation and well deployment of the study section. Through steps S100 to S200, complex lithofacies petrophysical analysis, geophysical forward and special geological body development pattern configuration are implemented, which provides a basis for the construction of subsequent prestack inversion model.

[0104] S300. On the basis of the development pattern configuration, an iterative modeling method is used to construct an inversion low-frequency model under facies control.

[0105] For example, in one specific embodiment, the specific process of constructing the inversion low-frequency model under the phase control constraint on the basis of the development pattern configuration is as follows:

[0106] S310. Construct an inversion low-frequency model in an initial state under phase control constraint;

[0107] S320. Based on the development pattern configuration, supplement special geological bodies to the inversion low-frequency model, and update the inversion low-frequency model, use the updated inversion low-frequency model to perform pre-stack multi-parameter inversion to generate an inversion result;

[0108] S330. Determine whether the inversion result is consistent with the trend of the real well filter curve, if yes, execute S340, otherwise jump to S320;

[0109] S340. Take the current inversion low-frequency model as the final inversion low-frequency model.

[0110] It should be noted that in S330, when determining whether the inversion result is consistent with the trend of the real well filter curve, a threshold can be set, and when the difference between the trend of the inversion parameter curve corresponding to the inversion result and the trend of the real well filter curve corresponding to the inversion parameter is less than the threshold, it is considered that the inversion result is consistent with the trend of the real well filter curve. It can be seen that the consistency is relative consistency, which can be understood as that the inversion result is close to the trend of the real well filter curve.

[0111] In addition, in S310, one specific implementation process of constructing an inversion low-frequency model in an initial state under phase control constraint is as follows:

[0112] S3101. Establish a stratigraphic framework structure according to the seismic horizon interpretation result of the study section;

[0113] S3102. Through a spatial interpolation method, interpolate the logging data and / or the stacking velocity data generated during seismic processing in each stratum in the stratigraphic framework structure to obtain an inversion low-frequency model in an initial state.

[0114] For example, to construct a wave impedance inversion low-frequency model, the logging data is wave impedance data; to construct a P-wave velocity inversion low-frequency model, the stacking velocity data during seismic processing is P-wave velocity data.

[0115] In geophysical exploration, low frequency and high frequency information in seismic data is very important for depicting oil and gas reservoirs, low frequency information is mainly used to define the geometric framework of the underground reservoir, and low frequency information is directly related to the absolute wave impedance of the underground medium. Due to the limited frequency band width of seismic data, pre-stack inversion has the characteristics of multiple solutions, that is, there are usually multiple different wave impedance inversion models that match the actual seismic record. In order to minimize the multiple solutions in the reservoir prediction process, the construction method of low frequency inversion model under phase control constraint is proposed in the prior art. For example, in some ordinary embodiments, the construction of low frequency inversion model under phase control constraint is based on the following principles.

[0116] Due to the limitation of seismic acquisition system, seismic data usually does not contain some low frequency components, which cannot be obtained by inversion, but are part of the absolute wave impedance. The low frequency components below 10 Hz are not included in the direct inversion result of the earthquake, and need to be extracted from other data to compensate. Starting from seismic data, based on well logging data and drilling data, the well data containing more low frequency information and high frequency information can be used to establish a low frequency inversion initial model which basically reflects the geological characteristics of the sedimentary body. For example, establishing an initial wave impedance inversion low frequency model or a well logging curve parameter inversion low frequency model is a process of combining the continuously changing seismic interface information in the horizontal direction with the high resolution well logging information, that is, interpolating or extrapolating the well logging data or the stacking velocity data generated during seismic processing under the constraint of the stratigraphic framework structure. The interpolation or extrapolation adopts spatial interpolation methods such as inverse distance weighted interpolation, and the velocity data body can be selected for constraint control when the well logging data is extrapolated. It is known that the velocity data body should be able to reflect the geological sedimentary change trend, and the velocity body should have good correlation with the velocity curve on the well. Adding the very low frequency information of the velocity data body, that is, retaining the 2 Hz component of the velocity spectrum, and supplementing the 3-6 Hz frequency component in the well logging data to the model, thereby generating a smooth and closed entity model, such as a wave impedance inversion low frequency model or a well logging curve parameter inversion low frequency model. Therefore, reasonably establishing the stratigraphic framework structure and defining the appropriate spatial interpolation method are the two most critical parts of constructing the inversion low frequency model under phase control constraint. The continuous change of the established model in the horizontal direction is the most important constraint parameter in inversion, which means that the interpolation and extrapolation of the physical property (sometimes electrical property) parameters at each well point in the horizontal direction is smooth, and the purpose is to ensure that the inversion result conforms to the known geological law (assuming that the physical property parameters or electrical property parameters at the well point are known).

[0117] However, the strata in the research section in the embodiments of the present application contain special geological bodies such as igneous rocks, and when the prestack inversion is performed by using the low-frequency inversion model in the ordinary embodiments under the phase control constraint, the inversion result has a large error, and the well curve obtained is not representative, because the simple interwell lateral linear interpolation and extrapolation method does not consider the occurrence, lateral variation of lithology and the like of the strata, and a large error will be generated in areas where the strata have a relatively sharp lateral variation or the lithology changes greatly. Therefore, in the embodiments of the present application, for the complex lithofacies carbonate rock strata containing special geological bodies, in order to fully consider the occurrence, lateral variation of lithology and the like of the strata, the development pattern configuration of the special geological bodies identified in S200 is taken as the basis in the above technical solution, the special geological bodies are supplemented in the initial state inversion low-frequency model constructed, and the inversion low-frequency model is updated by using the iteration method until the inversion result output by the inversion low-frequency model is consistent with the trend of the real well filtration curve, so that the construction of the high-precision inversion low-frequency model is realized, and the accuracy of the prediction result of the advantageous carbonate rock reservoir distribution is improved.

[0118] S400. Perform multi-parameter prestack inversion of the research section by using the inversion low-frequency model to obtain an inversion parameter data volume of the research section. It should be understood that the multi-parameter prestack inversion means that the target inversion parameter is multiple, that is, the inversion parameter data volume obtained includes data volumes of multiple different inversion parameters.

[0119] Exemplarily, in a specific embodiment, by using the inversion low-frequency model, the geological rule constraint of the seismic facies and the sedimentary facies is established, and multiple inversion parameter data volumes can be obtained simultaneously by using waveform indication inversion, prestack elastic parameter inversion and prestack Bayesian inversion and the like, the inversion parameter data volume includes an elastic parameter data volume and a physical property parameter data volume, the elastic parameter data volume includes a P-wave impedance data volume, a S-wave impedance data volume and the like, and the physical property parameter data volume includes a density data volume and a gamma data volume and the like.

[0120] Exemplarily, in a specific embodiment:

[0121] When the multi-parameter prestack inversion of the research section is performed by using the inversion low-frequency model, the seismic data input into the inversion low-frequency model is the synthetic seismic record of the research section generated by the seismic wavelet decomposition reconstruction method.

[0122] By comparing the seismic data before and after the wavelet decomposition reconstruction, it can be known that the synthetic seismic record obtained by the seismic wavelet decomposition reconstruction method has the best correlation with the actual seismic data, and it is found that the frequency of the seismic data of the bottom interface of the gypsiferous rock layer is obviously improved, and the energy of the high-frequency component in the seismic data is enhanced, so that the accuracy of the inversion result is improved.

[0123] Correspondingly, a specific implementation process of S400 is:

[0124] S410. Generating a synthetic seismic record of the study section by a seismic wavelet decomposition reconstruction method;

[0125] S420. Inputting the synthetic seismic record into an inversion low-frequency model to perform multi-parameter pre-stack inversion to obtain an inversion parameter data volume of the study section.

[0126] S500. Predicting the distribution of the dominant reservoir in the inversion parameter data volume by using a multi-parameter intersection method.

[0127] Exemplarily, in one specific embodiment, the specific process of predicting the distribution of the dominant reservoir in the inversion parameter data volume by using a multi-parameter intersection method is as follows:

[0128] After excluding the lithology influence of the special geological body in the inversion parameter data volume by using the multi-parameter intersection method, a residual data volume is obtained, and the distribution of the dominant reservoir in the residual data volume is identified.

[0129] It is known that, the petrophysical characteristics of the study section are obtained by analyzing the existing drilling data in step S100, which indicates that the wave impedance variation range of the eruption rock is relatively large, and partially overlaps with the limestone, so it is relatively difficult to predict the limestone reservoir by only using one parameter such as wave impedance, the reservoir parameter sensitivity is poor, the differentiation is not obvious, and thus there is a large error in predicting the reservoir by only using a single parameter histogram, therefore, in the above technical solution, the multi-parameter intersection method and the multi-parameter intersection step-by-step method are used to overcome the technical problems that the lithology discrimination and the reservoir distribution prediction are relatively difficult, and the accuracy of the reservoir prediction is improved, so as to more accurately quantitatively describe the spatial distribution and the spread characteristics of the dominant reservoir, for example, to generate a dominant reservoir thickness distribution plan, and to provide a reference map for subsequent exploration deployment.

[0130] In combination with the analysis of the existing drilling data in step S100, because the special geologic body in the study section is igneous rock, to exclude the lithologic influence of igneous rock from the inversion parameter data body, the P-S wave velocity crossplot can be used, therefore, the inversion parameter data body includes the P-wave velocity data body and the S-wave velocity data body, that is, the inversion low-frequency model constructed in S300 can be used for P-wave velocity inversion and S-wave velocity inversion. To identify the distribution of the dominant reservoir in the residual data body, that is, to identify the limestone reservoir in the stratum of the study section, the discrimination of reservoir and non-reservoir in the residual data body is needed, the limestone reservoir as the dominant reservoir of carbonate rock generally has the rock physical characteristics of low impedance and low density, therefore, the crossplot analysis of P-wave impedance and density can be used to select out the relatively low P-wave impedance and density, that is, the scale of the dominant reservoir of carbonate rock can be predicted, therefore, the inversion parameter data body includes the P-wave impedance data body and the density data body, that is, the inversion low-frequency model constructed in S300 can also be used for P-wave impedance inversion and density inversion. Correspondingly, one specific implementation process of S500 is as follows:

[0131] S510. Generating the pre-stack migration data body in combination with the seismic data of the study section;

[0132] S520. Distinguishing the limestone and igneous rock through the crossplot analysis of the P-wave velocity data body and the S-wave velocity data body, and using the distinguishing result to exclude the influence of igneous rock lithology from the pre-stack migration data body, and then obtaining the limestone data body;

[0133] For example: the P-wave velocity data body and the S-wave velocity data body are crossplotted, if the P-S wave velocity ratio is greater than 1.7, it is determined that the part of data corresponds to igneous rock, if the P-S wave velocity ratio is less than 1.7, it is determined that the part of data corresponds to limestone;

[0134] S530. Distinguishing the reservoir and non-reservoir through the crossplot analysis of the P-wave impedance data body and the density data body, and using the distinguishing result to exclude the non-reservoir from the limestone data body, and obtaining the dominant reservoir distribution data body;

[0135] For example: the P-wave impedance data body and the density data body are crossplotted, if the P-wave impedance is relatively low and the density is also relatively low, it is determined that the part of data corresponds to the dominant limestone reservoir, otherwise, it is the non-reservoir.

[0136] Referring to FIG. 2, as a modification of the above embodiment, after step S500, step S600 is further included.

[0137] S600. According to the prediction result of the dominant reservoir distribution, taking the top and bottom of the target section for the purpose of seismic interpretation in the study section as the boundary, the thickness value of the dominant reservoir is extracted along the section, and the dominant reservoir thickness distribution plan is obtained.

[0138] As an example, the thickness value of the advantageous reservoir extracted along the layer section specifically refers to: the quantitative prediction of the scale of the subsalt carbonate rock advantageous reservoir in the parameter range of the polygon of the advantageous reservoir circled after the multi-parameter intersection, and the thickness value of the advantageous reservoir in each layer section in the study section is obtained.

[0139] Figures 3 to 9 give the process diagram and result diagram of applying the above-mentioned embodiment to the identification of the subsalt carbonate rock reservoir of a certain block.

[0140] As shown in Figure 3, the igneous rock is a special geological body in the subsalt carbonate rock formation in the complex lithofacies area, and its development mode is mainly: the intrusive rock is mainly upwelling along the fault in the vertical direction and is mainly distributed in the form of sheet along the layer in the plane; the eruption rock has the distribution characteristics of "concave ring in the plane, longitudinal chaotic strip and near large fault".

[0141] As shown in Figure 4, in the constructed wave impedance inversion low-frequency model and the P-S wave velocity ratio inversion low-frequency model, the development mode configuration of the special geological body is supplemented, through the introduction of the development mode configuration, the prestack inversion is carried out by using the constructed wave impedance inversion low-frequency model, the inversion result is close to the trend of the real well filter curve, and the prestack inversion is carried out by using the constructed P-S wave velocity ratio inversion low-frequency model, the inversion result is close to the trend of the real well filter curve.

[0142] As shown in Figure 5, part A is a profile display diagram of the P-wave impedance data body obtained by using the inversion low-frequency model, and part B is a profile display diagram of the S-wave impedance data body obtained by using the inversion low-frequency model, and as can be seen from the figure, the lateral variation of the P-wave impedance and the S-wave impedance and other elastic parameters conforms to the lateral variation trend of the subsalt carbonate rock formation in the block.

[0143] As shown in Figure 6, part A is a profile display diagram of the density data body obtained by using the inversion low-frequency model, and part B is a profile display diagram of the gamma data body obtained by using the inversion low-frequency model, and as can be seen from the figure, the lateral variation of the density and the gamma and other physical parameters conforms to the lateral variation trend of the subsalt carbonate rock formation in the block.

[0144] In combination with Figures 7 to 8, the prediction result of the advantageous limestone reservoir obtained by the multi-parameter intersection is compared with that of the single parameter, and the reservoir thickness prediction error of a certain layer is 88 meters, wherein the reservoir thickness prediction error of the advantageous limestone reservoir obtained by the single parameter in the illustrated layer is 124 meters, and the reservoir thickness prediction error of the advantageous limestone reservoir obtained by the multi-parameter intersection and the step-by-step intersection method proposed in the embodiment in the illustrated layer is only 36 meters.

[0145] Figure 9 presents the reservoir distribution of the predicted target layer in the block, and it can be seen from the figure that the reservoir continuity is good, and the thickness ranges from 30 to 200 m, and the thickness of the eastern reservoir is the largest. The distribution is in good agreement with the drilled well data and the sedimentary rule. The reservoir prediction accuracy is improved from 60% of the traditional pre-stack inversion method to 85% by using the complex lithofacies area advantage carbonate reservoir identification method proposed in the embodiments of the present application, which verifies the effectiveness of the present application.

[0146] Device embodiments

[0147] The complex lithofacies area advantage carbonate reservoir identification device according to an embodiment of the present application comprises, in sequence, a lithology determination module, a development pattern configuration module, an inversion low-frequency model construction module, a multi-parameter pre-stack inversion module, and a reservoir identification module, wherein:

[0148] The lithology determination module is configured to determine the rock types and rock physical properties of the research section;

[0149] The development pattern configuration module is configured to carve out the development pattern configuration of the special geological body in the rock type;

[0150] The inversion low-frequency model construction module is configured to construct the inversion low-frequency model under the phase control constraint by using the iterative modeling method based on the development pattern configuration;

[0151] The multi-parameter pre-stack inversion module is configured to perform multi-parameter pre-stack inversion on the research section by using the inversion low-frequency model to obtain the inversion parameter data volume of the research section;

[0152] The reservoir identification module is configured to predict the distribution of the advantage reservoir in the inversion parameter data volume by using the multi-parameter intersection method.

[0153] In an optional embodiment, in the development pattern configuration module, the development pattern configuration of the special geological body in the rock type is carved out, comprising:

[0154] A geological model of the special geological body is constructed, and forward modeling is performed to determine the development position characteristics of the special geological body;

[0155] The development shape characteristics of the special geological body are determined in combination with the rock physical properties and the actual seismic response;

[0156] The development pattern of the special geological body is carved out according to the development position characteristics and the development shape characteristics to obtain the development pattern configuration of the special geological body.

[0157] In an optional embodiment, in the inversion low-frequency model construction module, the inversion low-frequency model under the phase control constraint is constructed by using the iterative modeling method based on the development pattern configuration, comprising:

[0158] Inversion low-frequency model of initial state is constructed under phase control constraint;

[0159] Based on development pattern configuration, special geological body is supplemented into the inversion low-frequency model, and the inversion low-frequency model is updated, and the updated inversion low-frequency model is used for pre-stack multi-parameter inversion to obtain inversion results;

[0160] It is judged whether the inversion results are consistent with the trend of the real well filter curve, if yes, the next step is executed, otherwise, it is jumped to the previous step;

[0161] The current inversion low-frequency model is taken as the final inversion low-frequency model.

[0162] In an optional embodiment, in the multi-parameter pre-stack inversion module, the inversion low-frequency model is used for multi-parameter pre-stack inversion of the research section to obtain inversion parameter data volume of the research section, including:

[0163] The synthetic seismic record of the research section is generated by the seismic wavelet decomposition reconstruction method;

[0164] The synthetic seismic record is input into the inversion low-frequency model for multi-parameter pre-stack inversion to obtain the inversion parameter data volume of the research section.

[0165] In an optional embodiment, in the reservoir identification module, the multi-parameter intersection method is used for prediction of the distribution of the dominant reservoir in the inversion parameter data volume, including:

[0166] The lithology influence of the special geological body in the inversion parameter data volume is excluded by using the multi-parameter intersection method to obtain a residual data volume, and the distribution of the dominant reservoir in the residual data volume is identified.

[0167] In an optional embodiment, the rock types include limestone and igneous rock, the special geological body includes igneous rock, and the inversion parameter data volume includes P-wave velocity data volume, S-wave velocity data volume, P-wave impedance data volume and density data volume.

[0168] In an optional embodiment, the lithology influence of the special geological body in the inversion parameter data volume is excluded by using the multi-parameter intersection method to obtain a residual data volume, and the distribution of the dominant reservoir in the residual data volume is identified, including:

[0169] The pre-stack migration data volume is generated in combination with the seismic data of the research section;

[0170] Through intersection analysis of the P-wave velocity data volume and the S-wave velocity data volume, two types of lithology, limestone and igneous rock, are distinguished, and the influence of the igneous rock lithology is excluded from the pre-stack migration data volume to obtain a limestone data volume;

[0171] The reservoirs and non-reservoirs are distinguished through the crossplot analysis of the longitudinal wave impedance data body and the density data body, and the non-reservoirs are excluded from the limestone data body by using the distinguishing result, so as to obtain the data body of the distribution of the dominant reservoirs.

[0172] In an optional embodiment, the device for identifying the dominant carbonate reservoir in a complex lithofacies area further comprises a reservoir thickness mapping module, which is configured to extract the thickness value of the dominant reservoir along the section based on the prediction result of the distribution of the dominant reservoir, and obtain the planar map of the thickness distribution of the dominant reservoir.

[0173] The device for identifying the dominant carbonate reservoir in a complex lithofacies area provided by the embodiments of the present application can be applied to a computing device comprising a memory and a processor.

[0174] In another aspect, the embodiments of the present application further provide an electronic device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the method for identifying the dominant carbonate reservoir in a complex lithofacies area when executing the computer program.

[0175] In another aspect, the embodiments of the present application further provide a machine readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method for identifying the dominant carbonate reservoir in a complex lithofacies area.

[0176] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, i.e., they can be located in one place, or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiments according to actual needs. Those skilled in the art can understand and implement it without creative labor.

[0177] Through the description of the above embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software and the necessary general hardware platform, and of course, they can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in the embodiments or some parts of the embodiments.

[0178] Finally, it should be noted that the above examples are only used to illustrate the technical solutions of the present application, and are not intended to limit the same; although the present application has been described in detail with reference to the foregoing examples, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for identifying a dominant carbonate reservoir in a complex lithofacies area, characterized in that, The method comprises: determining rock types and rock physical properties of a study section; carving out a development pattern configuration of a special geological body in the rock types; constructing an inversion low-frequency model under phase control using an iterative modeling method based on the development pattern configuration; performing multi-parameter pre-stack inversion of the study section using the inversion low-frequency model to obtain an inversion parameter data volume of the study section; predicting the distribution of dominant reservoirs in the inversion parameter data volume using a multi-parameter intersection method.

2. The method for identifying a complex facies zone dominant carbonate rock reservoir according to claim 1, characterized in that, The carving out of the development pattern configuration of the special geological body in the rock types comprises: constructing a geological model of the special geological body and performing forward modeling to determine the development location characteristics of the special geological body; combining rock physical properties and actual seismic responses to determine the development shape characteristics of the special geological body; carving out the development pattern of the special geological body according to the development location characteristics and the development shape characteristics to obtain the development pattern configuration of the special geological body.

3. The method for identifying a complex facies zone- dominant carbonate reservoir according to claim 1, characterized in that, The construction of the inversion low-frequency model under phase control using the iterative modeling method based on the development pattern configuration comprises: constructing an initial state inversion low-frequency model under phase control; supplementing the special geological body to the inversion low-frequency model based on the development pattern configuration and updating the inversion low-frequency model, and performing pre-stack multi-parameter inversion using the updated inversion low-frequency model to obtain an inversion result; determining whether the inversion result is consistent with the true well filtration curve trend, if yes, performing the next step, otherwise, jumping to the previous step; taking the current inversion low-frequency model as the final inversion low-frequency model.

4. The method for identifying complex facies zone- dominant carbonate reservoirs according to claim 1, characterized in that, The multi-parameter pre-stack inversion of the study section using the inversion low-frequency model to obtain the inversion parameter data volume of the study section comprises: generating a synthetic seismic record of the study section through a seismic wavelet decomposition reconstruction method; inputting the synthetic seismic record into the inversion low-frequency model to perform multi-parameter pre-stack inversion to obtain the inversion parameter data volume of the study section.

5. The method for identifying complex facies zone- dominant carbonate reservoirs according to claim 1, characterized in that, The prediction of the distribution of dominant reservoirs in the inversion parameter data volume using the multi-parameter intersection method comprises: excluding the lithology influence of the special geological body in the inversion parameter data volume using the multi-parameter intersection method to obtain a residual data volume, and identifying the distribution of dominant reservoirs in the residual data volume.

6. The method for identifying a complex facies zone- dominant carbonate reservoir according to claim 5, characterized in that, The rock types comprise limestone and igneous rock, the special geological body comprises igneous rock, and the inversion parameter data volume comprises a P-wave velocity data volume, a S-wave velocity data volume, a P-wave impedance data volume, and a density data volume.

7. The method for identifying a complex facies zone- dominant carbonate reservoir according to claim 6, characterized in that, The exclusion of the lithology influence of the special geological body in the inversion parameter data volume using the multi-parameter intersection method to obtain the residual data volume, and the identification of the distribution of dominant reservoirs in the residual data volume, comprise: generating a pre-stack migration data volume combining seismic data of the study section; distinguishing limestone and igneous rock through intersection analysis of the P-wave velocity data volume and the S-wave velocity data volume, and excluding the lithology influence of igneous rock from the pre-stack migration data volume using the distinguishing result to obtain a limestone data volume; distinguishing reservoirs and non-reservoirs through intersection analysis of the P-wave impedance data volume and the density data volume, and excluding non-reservoirs from the limestone data volume using the distinguishing result to obtain a dominant reservoir distribution data volume.

8. The method for identifying complex facies zone- preferential carbonate reservoirs according to claim 1, characterized in that, The method further comprises: According to the prediction result of the distribution of the advantageous reservoir, the thickness value of the advantageous reservoir is extracted along the interval of the target interval for seismic interpretation in the research section, and a thickness distribution plan of the advantageous reservoir is obtained.

9. The method for identifying complex facies zone- preferential carbonate reservoirs according to claim 1, characterized in that, The rock types include limestone and igneous rock, and the rock types and rock physical properties of the research section are determined, including: According to the rock physical analysis result of the research section, it is judged whether the calcium-silicon element ratio in the rock is greater than a first threshold value, if yes, the rock type is determined as limestone, otherwise as igneous rock; If the rock type is limestone, a shale content indication curve is constructed according to the obtained logging curve of the research section, the BVE group in the limestone is identified, and a fuzzy clustering analysis is performed on the lithology sensitive curve in the logging curve to identify the ITP group in the limestone; If the rock type is igneous rock, the igneous rock lithology is identified according to the ECS energy spectrum and GR energy spectrum logging result of the research section.

10. The method for identifying complex facies zone- preferential carbonate reservoirs according to claim 1, characterized in that, The rock types include limestone and igneous rock, and the rock types and rock physical properties of the research section are determined, including: According to the rock density difference determined according to the drilling data of the research section, the limestone and igneous rock and the limestone lithology are distinguished; If the rock type is igneous rock, the igneous rock lithology is identified according to the ECS energy spectrum and GR energy spectrum logging result of the research section.

11. The method for identifying complex facies zone- preferential carbonate reservoirs according to claim 2, characterized in that, The development mode configuration of the special geological body in the rock type is further included: The obtained seismic data is reprocessed as follows: grid tomography velocity update, enhanced structure model and well information constraint, structure-oriented filtering is performed to trigger the construction of the geological model of the special geological body, and forward modeling is performed to determine the development location characteristics of the special geological body.

12. A device for identifying dominant carbonate reservoirs in complex lithofacies zones, characterized in that, The device includes: A lithology determination module for determining the rock types and rock physical properties of the research section; A development mode configuration module for carving out the development mode configuration of the special geological body in the rock type; An inversion low-frequency model construction module for constructing an inversion low-frequency model under phase control by using an iterative modeling method based on the development mode configuration; A multi-parameter pre-stack inversion module for performing multi-parameter pre-stack inversion of the research section by using the inversion low-frequency model to obtain an inversion parameter data volume of the research section; A reservoir identification module for predicting the distribution of the advantageous reservoir in the inversion parameter data volume by using a multi-parameter intersection method.

13. The apparatus for complex facies zone preferential carbonate reservoir identification of claim 12, wherein, The development mode configuration of the special geological body in the rock type includes: A geological model of the special geological body is constructed, and forward modeling is performed to determine the development location characteristics of the special geological body; The development shape characteristics of the special geological body are determined in combination with the rock physical properties and actual seismic response; The development mode of the special geological body is carved according to the development location characteristics and the development shape characteristics to obtain the development mode configuration of the special geological body.

14. The apparatus for complex facies zone preferential carbonate reservoir identification of claim 12, wherein, The inversion low-frequency model under phase control is constructed by using an iterative modeling method based on the development mode configuration, including: An inversion low-frequency model in an initial state is constructed under phase control; Based on the development pattern configuration, special geological bodies are supplemented to the inversion low-frequency model, and the inversion low-frequency model is updated, and the updated inversion low-frequency model is used for pre-stack multi-parameter inversion to obtain an inversion result; It is judged whether the inversion result is consistent with the trend of the real well filter curve, if yes, the next step is executed, otherwise, it is jumped to the previous step; The current inversion low-frequency model is taken as a final inversion low-frequency model.

15. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the complex lithofacies zone dominant carbonate reservoir identification method in any one of claims 1 to 11 when executing the program.

16. A machine-readable storage medium having stored thereon a computer program, characterized in that, The computer program implements the complex lithofacies zone dominant carbonate reservoir identification method in any one of claims 1 to 11 when executed by the processor.

Images