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 APPARATUSFOR RECOGNIZING DOMINANT CARBONATE ROCK RESERVOIR IN COMPLEXFACIES ZONE, AND ELECTRONIC DEVICE  Cross Reference to Related ApplicationsThe present disclosure claims the benefit of Chinese Patent Application No.202410530509.0 filed on April 29, 2024, the content of which is incorporated herein by reference. Fieldofthe InventionThe present disclosure belongs to the technical field of oil exploration, and particularly relates to a method and device for identifying a favorable carbonate reservoir in a complex lithofacies area, and an electronic apparatus. Backgroundofthe InventionAs a research discipline for extracting information related to lithologic facies and geomorphic facies from seismic data, seismic lithology is an interdisciplinary product of seismic attributes, seismic inversion, a stochastic modeling technique, and seismic sedimentation. During seismic exploration, information related to rock attributes, such as a wave velocity, an attenuation coefficient, and density, can be obtained by observing propagation and reflection of seismic waves in underground media. Lithologic types and lithologic combinations of underground rocks can be inferred by analyzing the information and provided as crucial bases for reservoir prediction.Limestone and igneous rocks are mainly developed on a complex-lithofacies carbonate stratum. The limestone mainly includes algae limestone, pellet limestone, shell limestone, etc. The igneous rocks mainly include intrusive rocks and eruptive rocks. The intrusive rocks refer to intrusive-facies rock types, mainly including diabase. The eruptive rocks refer to eruptive-facies rock types, mainly including basalt. Lithologic types of favorable-carbonate-reservoir development locations mainly include algae limestone and pellet limestone in a Barra Velha (BVE) formation, and mainly include shell limestone in an Itapema (ITP) formation. Due to more argillaceous material, poor physical properties, and high density, marlstone and micrite generally do not serve as favorable-reservoir development locations. Development of a carbonate reservoir is controlled by many factors such as structural setting, diagenesis, sedimentary environment, and later dissolution transformation. Moreover, heterogeneity of reservoir distribution is high. A traditional inversion method for seismic data has the best longitudinal resolution such that lithologic characters and a thickness of the reservoir can be directly reflected, but loses lateral resolution, such that shape, structure, and other heterogeneity of the reservoir is likely to be blurred. A layered initial model for inversion reduces lateral resolution of inversion and destroys geomorphic facies characteristics of the reservoir, which is not beneficial to research on reservoir heterogeneity. Such a model generally ignores lithofacies differences, and cannot accurately describe distribution characteristics of the carbonate reservoir.Igneous rock is a main special geologic body developed in a complex-lithofacies carbonate area. Generally, special geologic bodies such as turbidite channels, salt domes, and carbonate karst caves also exist in the complex-lithofacies carbonate area. Based on these special geologic bodies, if parameter inversion is performed by using a conventional inversion model, a stratum attitude, a lithologic lateral change, etc. are not considered. A great error will occur in an area where a stratum lateral change or a lithologic change is dramatic. Thus, an obtained well log is not representative.To sum up, to accurately identify spatial distribution of a favorable carbonate reservoir in a complex lithofacies area, it is urgent to improve the existing inversion model. Summaryofthe InventionAn objective of the embodiments of the present disclosure is to provide a method and device for identifying a favorable carbonate reservoir in a complex lithofacies area, and an electronic apparatus, and further provide a machine-readable storage medium, to solve the technical problem that a favorable carbonate reservoir in a complex lithofacies area cannot be accurately identified through traditional seismic data inversion.To achieve the above objective, a first aspect of the present disclosure provides a method for identifying a favorable carbonate reservoir in a complex lithofacies area. The method includes:determining a rock type and a petrophysical property of a study area;characterizing a development pattern configuration of a special geologic body of the rock type;constructing an inversion low-frequency model under facies-controlled constraint through an iterative modeling method based on the development pattern configuration;performing multi-parameter pre-stack inversion of the study area by using the inversion low-frequency model, and obtaining an inversion parameter data volume of the study area; andpredicting favorable-reservoir distribution in the inversion parameter data volume through a multi-parameter crossplot method.In an optional embodiment, the characterizing a development pattern configuration of a special geologic body of the rock type includes:constructing a geologic model of the special geologic body, performing forward modeling, and determining a development location characteristic of the special geologic body;determining a development shape characteristic of the special geologic body in combination with the petrophysical property and an actual seismic response; andcharacterizing a development pattern of the special geologic body according to the development location characteristic and the development shape characteristic, and obtaining the development pattern configuration of the special geologic body.In an optional embodiment, the constructing an inversion low-frequency model under facies-controlled constraint through an iterative modeling method based on the development pattern configuration includes:constructing an initial inversion low-frequency model under the facies-controlled constraint;adding the special geologic body to the inversion low-frequency model based on the development pattern configuration, updating the inversion low-frequency model, performing the pre-stack multi-parameter inversion by using the updated inversion low-frequency model, and obtaining an inversion result;determining whether a trend of the inversion result is consistent with a trend of a real filtered well log, if yes, performing a next step, otherwise, skipping to a previous step; andtaking a current inversion low-frequency model as a final inversion low-frequency model.In an optional embodiment, the performing multi-parameter pre-stack inversion of the study area by using the inversion low-frequency model, and obtaining an inversion parameter data volume of the study area include:generating a synthetic seismic record of the study area through a seismic wavelet decomposition and reconstruction method; andinputting the synthetic seismic record into the inversion low-frequency model, performing the multi-parameter pre-stack inversion, and obtaining the inversion parameter data volume of the study area.In an optional embodiment, the predicting favorable-reservoir distribution in the inversion parameter data volume through a multi-parameter crossplot method includes:eliminating a lithologic influence of the special geologic body from the inversion parameter data volume through the multi-parameter crossplot method, obtaining a remaining data volume, and identifying the favorable-reservoir distribution in the remaining data volume.In an optional embodiment, the rock type includes limestone and igneous rocks. The special geologic body includes the igneous rocks. The inversion parameter data volume includes a compressional-wave velocity data volume, a shear-wave velocity data volume, a compressional-wave impedance data volume, and a density data volume.In an optional embodiment, the eliminating a lithologic influence of the special geologic body from the inversion parameter data volume through the multi-parameter crossplot method, obtaining a remaining data volume, and identifying the favorable-reservoir distribution in the remaining data volume include:generating a pre-stack migration data volume in combination with seismic data of the study area;discriminating between the limestone and the igneous rocks through crossplot analysis of the compressional-wave velocity data volume and the shear-wave velocity data volume, eliminating a lithologic influence of the igneous rocks from the pre-stack migration data volume by using a discrimination result, and obtaining a limestone data volume; anddiscriminating between a reservoir and a non-reservoir through crossplot analysis of the compressional-wave impedance data volume and the density data volume, eliminating the non-reservoir from the limestone data volume by using a discrimination result, and obtaining a favorable-reservoir distribution data volume.In an optional embodiment, the method further includes:extracting, according to a prediction result of the favorable-reservoir distribution and by taking a top and a bottom of a target stratum for seismic interpretation of the study area as boundaries, a thickness of a favorable reservoir along the stratum, and obtaining a plan view of favorable-reservoir thickness distribution.A second aspect of the present disclosure provides a device for identifying a favorable carbonate reservoir in a complex lithofacies area. The device includes:a lithologic determination module configured to determine a rock type and a petrophysical property of a study area;a development pattern configuration module configured to characterize a development pattern configuration of a special geologic body of the rock type;an inversion low-frequency model construction module configured to construct an inversion low-frequency model under facies-controlled constraint through an iterative modeling method based on the development pattern configuration;a multi-parameter pre-stack inversion module configured to perform multi-parameter pre-stack inversion of the study area by using the inversion low-frequency model, and obtain an inversion parameter data volume of the study area; anda reservoir identification module configured to predict favorable-reservoir distribution in the inversion parameter data volume through a multi-parameter crossplot method.In an optional embodiment, the development pattern configuration module is configured to characterize the development pattern configuration of the special geologic body of the rock type as follows:constructing a geologic model of the special geologic body, performing forward modeling, and determining a development location characteristic of the special geologic body;determining a development shape characteristic of the special geologic body in combination with the petrophysical property and an actual seismic response; andcharacterizing a development pattern of the special geologic body according to the development location characteristic and the development shape characteristic, and obtaining the development pattern configuration of the special geologic body.In an optional embodiment, the inversion low-frequency model construction module is configured to construct the inversion low-frequency model under the facies-controlled constraint through the iterative modeling method based on the development pattern configuration as follows:constructing an initial inversion low-frequency model under the facies-controlled constraint;adding the special geologic body to the inversion low-frequency model based on the development pattern configuration, updating the inversion low-frequency model, performing the pre-stack multi-parameter inversion by using the updated inversion low-frequency model, and obtaining an inversion result;determining whether a trend of the inversion result is consistent with a trend of a real filtered well log, if yes, performing a next step, otherwise, skipping to a previous step; andtaking a current inversion low-frequency model as a final inversion low-frequency model.In an optional embodiment, the multi-parameter pre-stack inversion module is configured to perform the multi-parameter pre-stack inversion of the study area by using the inversion low-frequency model, and obtain the inversion parameter data volume of the study area as follows:generating a synthetic seismic record of the study area through a seismic wavelet decomposition and reconstruction method; andinputting the synthetic seismic record into the inversion low-frequency model, performing the multi-parameter pre-stack inversion, and obtaining the inversion parameter data volume of the study area.In an optional embodiment, the reservoir identification module is configured to predict the favorable-reservoir distribution in the inversion parameter data volume through the multi-parameter crossplot method as follows:eliminating a lithologic influence of the special geologic body from the inversion parameter data volume through the multi-parameter crossplot method, obtaining a remaining data volume, and identifying the favorable-reservoir distribution in the remaining data volume.In an optional embodiment, the rock type includes limestone and igneous rocks. The special geologic body includes the igneous rocks. The inversion parameter data volume includes a compressional-wave velocity data volume, a shear-wave velocity data volume, a compressional-wave impedance data volume, and a density data volume.In an optional embodiment, the eliminating a lithologic influence of the special geologic body from the inversion parameter data volume through the multi-parameter crossplot method, obtaining a remaining data volume, and identifying the favorable-reservoir distribution in the remaining data volume include:generating a pre-stack migration data volume in combination with seismic data of the study area;discriminating between the limestone and the igneous rocks through crossplot analysis of the compressional-wave velocity data volume and the shear-wave velocity data volume, eliminating a lithologic influence of the igneous rocks from the pre-stack migration data volume by using a discrimination result, and obtaining a limestone data volume; anddiscriminating between a reservoir and a non-reservoir through crossplot analysis of the compressional-wave impedance data volume and the density data volume, eliminating the non-reservoir from the limestone data volume by using a discrimination result, and obtaining a favorable-reservoir distribution data volume.In an optional embodiment, the device further includes a reservoir thickness view generation module. The reservoir thickness view generation module is configured to extract, according to a prediction result of the favorable-reservoir distribution and by taking a top and a bottom of a target stratum for seismic interpretation of the study area as boundaries, a thickness of a favorable reservoir along the stratum, and obtain a plan view of favorable-reservoir thickness distribution.A third aspect of the present disclosure provides an electronic apparatus. The electronic apparatus includes a memory, a processor, and a computer program stored in the memory and runnable by the processor. The processor, when executing the program, implements the method for identifying a favorable carbonate reservoir in a complex lithofacies area mentioned in the first aspect of the present disclosure.A fourth aspect of the present disclosure provides a machine-readable storage medium. The machine-readable storage medium stores a computer program. The computer program, when executed by a processor, implements the method for identifying a favorable carbonate reservoir in a complex lithofacies area mentioned in the first aspect of the present disclosure.Through the above technical solution, the present disclosure has main beneficial effects as follows:according to the above technical solution, a development pattern configuration, iterative modeling under facies-controlled constraint, multi-parameter pre-stack inversion, and a multi-parameter crossplot method are combined. Based on the development pattern configuration, an inversion low-frequency model is constructed through the iterative modeling under facies-controlled constraint. That is, a special geologic body is added to a model to perform iteration. Thus, noise interference is effectively eliminated, and useful information in seismic data is accurately extracted. Further, an accurate lithofacies interpretation result is obtained, and identification precision of a favorable carbonate reservoir in a complex lithofacies area is improved.Other features and advantages of the embodiments of the present disclosure are described in detail in detailed description of embodiments. Brief Description of DrawingsFIG. 1 is a schematic flowchart of a method for identifying a favorable carbonate reservoir in a complex lithofacies area according to an embodiment of the present disclosure;FIG. 2 is another schematic flowchart of a method for identifying a favorable carbonate reservoir in a complex lithofacies area according to an embodiment of the present disclosure;FIG. 3 is a schematic diagram of a development pattern configuration of igneous rocks;FIG. 4 shows an inversion low-frequency model obtained after a special geologic body is added, where part A is a schematic diagram of an inversion low-frequency model of wave impedance, part B is a schematic diagram of an inversion low-frequency model of a compressional-to-shear-wave velocity ratio, and part C is a schematic diagram of a characterized special geologic body;FIG. 5 is a schematic diagram showing a comparison between a compressional-wave impedance data volume and a shear-wave impedance data volume, where part A is the compressional-wave impedance data volume, and part B is the shear-wave impedance data volume;FIG. 6 is a schematic diagram showing a comparison between a density data volume and a gamma data volume, where part A is the density data volume, and part B is the gamma data volume;FIG. 7 is a schematic diagram of a multi-parameter crossplot method;FIG. 8 is a diagram showing a comparison between favorable reservoir prediction results, where part A is a sectional view of a favorable reservoir prediction result achieved through a single parameter method, and part B is a sectional view of a favorable reservoir prediction result achieved according to an embodiment of the present disclosure; andFIG. 9 is a plan view of predicted favorable-reservoir thickness distribution. Detailed Description of the EmbodimentsThe particular implementations of the embodiments of the present disclosure are described in detail below in combination with the accompanying drawings. It should be understood that the particular implementations described herein are merely illustrative of the embodiments of the present disclosure and are not intended to limit the embodiments of the present disclosure.To facilitate understanding of the embodiments of the present disclosure, the related technical features involved in the embodiments are described below.Study area: a study area involved in the present disclosure is located on a complex-lithofacies carbonate stratum.Pre-stack inversion: pre-stack seismic data include richer underground geologic information than post-stack seismic data. Moreover, pre-stack inversion is superior to post-stack inversion in inversion accuracy and reservoir prediction efficiency, and becomes one of the most significant technical means in the field of reservoir prediction and fluid identification.Single-parameter heterogeneity: influenced by heterogeneity of a complex-lithofacies carbonate reservoir area, single-parameter heterogeneity of a reservoir and a non-reservoir is poor.Crossplot: a crossplot is a tool for comprehensively analyzing a plurality of seismic attribute parameters. By visualizing cross relationship between different parameters, spatial distribution and characteristics of a reservoir can be more comprehensively understood.Method embodimentsWith reference to FIG. 1, a method for identifying a favorable carbonate reservoir in a complex lithofacies area provided in the embodiments of the present disclosure may include step S100, step S200, step S300, step S400, and step S500.S100: a rock type and a petrophysical property of a study area are determined.In the embodiment, the rock type includes limestone and igneous rocks.In an optional embodiment of the present disclosure, to determine a rock type and a petrophysical property of a study area, petrophysical analysis and logging identification of can be performed by using existing drilling data, and then a complex lithofacies is determined.As an example, it can be seen through analysis of the existing drilling data that the igneous rocks and the limestone are different in rock density. Thus, in the first step, a lithologic type of the study area can be discriminated according to a density difference. Specifically, the developed limestone mainly includes algae limestone, pellet limestone, and shell limestone. The igneous rocks mainly include intrusive rocks and basalt. The intrusive-facies rock type mainly includes diabase. The eruptive-facies rock type mainly includes basalt. The density of the intrusive rocks of the igneous rocks is generally greater than that of the eruptive rocks. The rock density of the intrusive rocks is mostly greater than 2.80 g / cm3, and average density is generally about 2.92 g / cm3. The density of the eruptive rocks is mostly greater than 2.70 g / cm3, and average density is 2.75 g / cm3. The second step is classification and identification of different types of carbonate rocks. The high-quality carbonate reservoir development location (mainly including algae limestone and pellet limestone at a Barra Velha (BVE) formation, and mainly including shell limestone at an Itapema (ITP) formation) mainly include algae limestone, pellet limestone, and shell limestone. Rock density is generally 2.3 g / cm3 to 2.6 g / cm3, and average density is 2.46 g / cm3. Due to more argillaceous material, poor physical properties, and high density, marlstone and micrite have average density of 2.6 g / cm3. The third step is identification of lithology of the igneous rocks. Based on elemental capture spectroscopy (ECS) logging analysis and gamma-ray (GR) spectroscopy logging analysis, Th, Fe and K are taken as indicator elements for identification of lithology of the igneous rocks. Parameters reflecting basicity of the igneous rocks are optimized. Igneous rock index (Lig) is introduced for quantitative identification. For example, L ≥ 20 represents diabase, and L < 20 represents basalt. Eruptive rocks (mainly including basalt) and intrusive rocks (mainly including diabase) of igneous rocks can be well discriminated. After sample verification, an identification coincidence rate of the diabase and the basalt reaches 95%.As another example, a lithologic type of the study area can be discriminated through a Ca / Si ratio method. Specifically, Ca and Si during ECS logging can be taken as indicator elements of the igneous rocks and the carbonate rocks respectively. For example, in particular application, Ca / Si > 1.2 represents the carbonate rocks, and Ca / Si < 1.2 represents the igneous rocks. Thus, type identification of the carbonate rocks and the igneous rocks is achieved. To describe effectiveness of the Ca / Si ratio method for lithologic type identification, lithologic identification is performed on 92 igneous rock samples through the Ca / Si ratio method provided in the present disclosure. As a result, 88 igneous rock samples are successfully identified, and an identification coincidence rate reaches 95.65%. When classifying and identifying different types of carbonate rocks, a lithologic identification method for carbonate rocks based on curve reconstruction and artificial intelligence fuzzy clustering can be used. The identification method specifically includes: 1) to identify a BVE formation, a shale volume indication curve is constructed by applying a natural gamma curve and a uranium-removing gamma curve, to identify algae limestone, argillaceous algae limestone, pellet limestone, argillaceous pellet limestone, and micrite in the BVE formation. After sample verification, an identification coincidence rate of the BVE formation through the method reaches 80%. 2) In a case of the ITP formation, four main lithologic sensitivity curves are selected to perform artificial intelligence fuzzy clustering analysis, to identify shell limestone, marlstone, and mudstone. The four main lithologic sensitivity curves are a natural gamma curve, a density curve, a neutron curve, and a porosity curve respectively. After sample verification, an identification coincidence rate of the ITP formation based on the artificial intelligence fuzzy clustering analysis reaches 100%.S200: a development pattern configuration of a special geologic body of the rock type is characterized.Specifically, in the present disclosure, the development pattern configuration refers to characterization of a facies development pattern, and characterization and configuration of igneous rocks of a main special geologic body of a carbonate reservoir.For example, in a particular embodiment, a specific process for characterization and configuration of igneous rocks of a main special geologic body of a carbonate reservoir is as follows:S210: a geologic model of the igneous rocks is constructed, forward modeling is performed, and a development location characteristic of the igneous rocks is determined.S220: a development shape characteristic of the igneous rocks is determined in combination with the petrophysical property and an actual seismic response.S230: a development pattern of the igneous rocks is characterized according to the development location characteristic and the development shape characteristic, and the development pattern configuration of the igneous rocks is obtained.Through geologic modeling and forward modeling, it is proved that a development location of intrusive rocks of igneous rocks has characteristics of "great amplitude + low frequency + high impedance". The development location of the intrusive rocks has a great amplitude. With the increase of thickness, the amplitude increases, and an event widens. In combination with the petrophysical property and an actual seismic response, a development shape of the intrusive rocks is mostly as follows: vertical upwelling along a fault and planar flaky distribution. After a development location characteristic and a development shape characteristic of the intrusive rocks are determined, a development pattern of the intrusive rocks is characterized and configured according to the rule, such that the development pattern configuration of the intrusive rocks is obtained. Due to lithologic characteristics and alteration changes, a seismic response of the eruptive rocks of the igneous rocks is obviously inferior to that of the intrusive rocks, and blank reflection and random reflection are shown in some parts. According to the rule, a development pattern of the eruptive rocks can be characterized and configured, such that the development pattern configuration of the eruptive rocks is obtained. Through the above solution, a seismic multi-attribute spatial characterizing technique under the guidance of an underwater volcanic mechanism model is used. Technical means such as seismic attribute analysis, multi-information fusion, velocity analysis, coherence analysis, and three-dimensional body slicing are combined. After spatial distribution of the igneous rocks is predicted, the development pattern of the igneous rocks is characterized and configured, such that the development pattern configuration of the igneous rocks is obtained.As an improved embodiment of the present disclosure, seismic data are optimized and reprocessed before characterization of a seismic facies development pattern. Since subsalt lateral energy is uneven because original seismic data are blocked by a thick salt layer, accuracy of a velocity model affects reliability of a subsalt structural configuration, and a small local signal-to-noise ratio affects seismic imaging quality, etc., the improved embodiment provides a method for reprocessing seismic data based on a grid tomography velocity constraint. The method includes steps as follows: a grid tomography velocity is updated, a structural model and a well information constraint are strengthened, and structure-oriented filtering is performed.After verification, by reprocessing the seismic data, subsalt amplitude energy during seismic imaging is more balanced, event continuity of a salt-bottom stratum and a subsalt stratum is better, and a structural configuration is more reasonable. Thus, through the above method for reprocessing seismic data based on a grid tomography velocity constraint, seismic imaging quality of a subsalt structure is improved, and a reliable data guarantee is provided for subsequent fine characterization of seismic facies distribution characteristics.As an example, in a particular application, characterization of a seismic facies development pattern under the influence of thick salt blockage is performed through the following method. The method specifically includes step 1 to step 3.Step 1: seismic data are optimized and reprocessed.Step 2: petrophysical forward analysis is performed according to a classification method for carbonate and igneous rocks constructed in step S100 mentioned above.In the particular application, in terms of wave impedance of a drilled subsalt stratum, igneous rocks > limestone > mudstone > salt rocks. From a higher location to a lower location of the subsalt stratum top (salt-bottom interface) structure, a sedimentary lithologic type changes from limestone to marlstone, and wave crest reflection changes from strong to weak. In the application, from a higher location to a lower location of a target stratum bottom (K44 top), a sedimentary lithologic type changes from limestone to marlstone, and wave crest reflection changes from weak to strong. Compared with volcanic rocks, the intrusive rocks mainly include diabase. A lithologic association is characterized according to actual drilling data. The lithologic association refers to "salt rocks - anhydrite - diabase - limestone". By using five geologic models and through forward modeling, it is confirmed through simulation that the development location of the intrusive rocks has the characteristics of "great amplitude + low frequency + high impedance". It is deemed that the amplitude of the development location of the intrusive rocks is stronger. With the increase of thickness, the amplitude is greater, and an event is wider. In combination with the petrophysical property and an actual seismic response, it is predicted that the intrusive rocks most have a shape as follows: vertical upwelling along a fault and planar flaky distribution. The development characteristics of the intrusive rocks are characterized and configured according to the rule. Due to lithologic characteristics and alteration changes, a seismic response of the eruptive rocks is obviously inferior to that of the intrusive rocks, and blank reflection and random reflection are shown in some parts. Through a seismic multi-attribute spatial characterizing technique under the guidance of an underwater volcanic mechanism model, and by using technical means such as seismic attribute analysis, multi-information fusion, velocity analysis, coherence analysis, and three-dimensional body slicing, spatial distribution of the igneous rocks is predicted, and configuration is performed. Based on the above forward modeling, seismic attribute differences of different facies belts and different lithofacies combinations of a stratum are revealed. A theoretical basis is provided for subsequent seismic facies characterization.Step 3: a seismic facies development pattern is characterized. In the particular application, to comprehensively finely characterize the seismic facies distribution characteristics by using a plurality of seismic attributes, a shallow lake platform corresponding to a sedimentary facies has a small stratum thickness (less than 300 m), a relatively small number of events, and strong amplitude energy. A semi-deep lake platform has a great stratum thickness (greater than 300 m), a relatively large number of events, and weak amplitude energy. An intra-platform subfacies of a sedimentary subfacies has a less stratum thickness, mainly uniaxial continuous reflection, and strong amplitude energy. A platform margin subfacies has a relatively great stratum thickness and a hummocky low-amplitude reflection characteristic. A platform slope subfacies has a stratum thickness between that of the intra-platform subfacies and that of the platform margin subfacies, and low-frequency, moderate-to-low amplitude, and continuous reflection. Seismic facies characterization is performed based on the above understanding, and a result is taken as a facies constraint parameter of subsequent reservoir inversion.In the prior art, under the influence of extremely thick salt layer coverage and multi-stage igneous rock intrusion, a seismic response rule of a carbonate reservoir in a complex lithofacies area is complex. A distribution characteristic of the carbonate reservoir cannot be accurately described through a traditional pre-stack inversion method. Challenges are brought to exploration evaluation and well location deployment of a study area. Through step S100 and step S200, complex lithofacies petrophysical analysis, geophysical forward modeling, and development pattern configuration of a special geologic body are implemented, such that a basis is provided for construction of a subsequent pre-stack inversion model.S300: an inversion low-frequency model under facies-controlled constraint is constructed through an iterative modeling method based on the development pattern configuration.For example, in a particular embodiment, a specific process of the step that an inversion low-frequency model under facies-controlled constraint is constructed through an iterative modeling method based on the development pattern configuration is as follows:S310: an initial inversion low-frequency model is constructed under the facies-controlled constraint.S320: the special geologic body is added to the inversion low-frequency model based on the development pattern configuration, the inversion low-frequency model is updated, the pre-stack multi-parameter inversion is performed by using the updated inversion low-frequency model, and an inversion result is generated.S330: whether a trend of the inversion result is consistent with a trend of a real filtered well log is determined, if yes, S340 is performed, otherwise, S320 is performed.S340: a current inversion low-frequency model is taken as a final inversion low-frequency model.It should be noted that in S330, when whether an inversion result is consistent with a trend of a real filtered well log is determined, a threshold can be set. In a case that a difference between a trend of an inversion parameter curve corresponding to the inversion result and a trend of a real filtered well log corresponding to the inversion parameter is less than the threshold, it is deemed that the inversion result is consistent with the trend of the real filtered well log. It can be seen that consistency is relative consistency. It can be understood that the inversion result is close to the trend of the real filtered well log.In addition, a specific implementation process of S310 that an initial inversion low-frequency model is constructed under the facies-controlled constraint is as follows:S3101: a stratum framework structure is constructed based on a seismic horizon interpretation result of the study area.S3102: an initial inversion low-frequency model is obtained through a spatial interpolation method by interpolating logging data and / or stack velocity data generated during seismic processing in each stratum in the stratum framework structure.For example, to construct a wave impedance inversion low-frequency model, logging data are wave impedance data. To construct a compressional-wave velocity inversion low-frequency model, stack velocity data during seismic processing are compressional-wave velocity data.In geophysical exploration, both low-frequency information and high-frequency information in the seismic data are extremely significant for characterizing an oil and gas reservoir. The low-frequency information is mainly used for defining a geometric framework of an underground oil reservoir. The low-frequency information has a direct relationship with absolute wave impedance of an underground medium. Due to a limited bandwidth of the seismic data, pre-stack inversion has a multiple-solution characteristic. That is, generally a plurality of different wave impedance inversion models match actual seismic records. To reduce the multiple-solution characteristic in a reservoir prediction process to a greatest extent, a method for constructing a low-frequency inversion model under facies-controlled constraint is provided in the prior art. For example, in some general embodiments, a low-frequency inversion model under facies-controlled constraint is constructed based on the following principles.Due to the limitation of a seismic acquisition system, seismic data generally do not include some low-frequency components, which cannot be obtained through inversion, but are part of absolute wave impedance. A seismic direct inversion result includes no low-frequency component below 10 Hz, which are required to be extracted from other data for compensation. By using the seismic data and based on logging data and drilling data, an initial low-frequency inversion model basically reflecting a geologic characteristic of a sedimentary body can be constructed through well data including more low-frequency information and high-frequency information. For example, construction of an initial wave impedance inversion low-frequency model or well log parameter inversion low-frequency model is a process of combining seismic interface information continuously changing in a lateral direction with logging information having high resolution, that is, interpolating or extrapolating logging data or stack velocity data generated during seismic processing under the constraint of a stratum framework structure. The interpolation or extrapolation is performed through a spatial interpolation method such as inverse distance weight interpolation. Control can be constrained by using a velocity data volume during extrapolation of the logging data. It can be known that a velocity data volume required to be used can reflect a geologic sedimentation change trend, and the velocity volume has an excellent correlation with a well velocity curve. Extremely low-frequency information of the velocity data volume is added. That is, a 2 Hz component of a velocity spectrum is retained, and a 3 Hz to 6 Hz frequency component in the logging data is added to the model, such that a smooth and closed entity model is generated. For example, a wave impedance inversion low-frequency model or a well log parameter inversion low-frequency model is obtained. Thus, reasonable construction of a stratum framework structure, and an appropriately-defined spatial interpolation method are two most critical parts in construction of an inversion low-frequency model under facies-controlled constraint. A continuous change of a constructed model in a lateral direction is the most significant constraint parameter during inversion. The parameter essentially means that interpolation and extrapolation of a physical (sometimes electrical) parameter at each well point in the lateral direction are smooth. The purpose is to ensure that an inversion result conforms to a known geologic rule (assuming that the physical parameter or electrical parameter at the well point are known).However, the stratum of the study area in the embodiments of the present disclosure includes special geologic bodies such as igneous rocks. When pre-stack inversion is performed by using a low-frequency inversion model under facies-controlled constraint in a general embodiment, an inversion result has a great error, and an obtained well log is not representative. Since a stratum attitude, a lithologic lateral change, etc. are not considered in a simple cross-well lateral linear interpolation and extrapolation method, a great error will occur in an area where a stratum lateral change or a lithologic change is relatively dramatic. Thus, in the embodiments of the present disclosure, in a case of a complex-lithofacies carbonate stratum including a special geologic body, to fully consider factors such as a stratum attitude and a lithologic lateral change, in the above technical solution, based on a development pattern configuration of the special geologic body identified in S200, the special geologic body is added to a constructed initial inversion low-frequency model. Moreover, the inversion low-frequency model is updated through an iterative method until an inversion result outputted by the inversion low-frequency model is consistent with a trend of a real filtered well log. Thus, the high-precision inversion low-frequency model is constructed, and accuracy of a prediction result of favorable carbonate reservoir distribution is improved.S400: multi-parameter pre-stack inversion of the study area is performed by using the inversion low-frequency model, and an inversion parameter data volume of the study area is obtained. It should be understood that multi-parameter pre-stack inversion refers to a plurality of target inversion parameters. That is, an obtained inversion parameter data volume includes a data volume having a plurality of different inversion parameters.Illustratively, in a particular embodiment, seismic facies and sedimentary facies geologic rule constraints are constructed by using an inversion low-frequency model. A plurality of inversion parameter data volumes can be simultaneously obtained through waveform indication inversion, pre-stack elastic parameter inversion, and pre-stack Bayesian inversion. An inversion parameter data volume includes an elastic parameter data volume and a physical parameter data volume. The elastic parameter data volume includes a compressional-wave impedance data volume, a shear-wave impedance data volume, etc. The physical parameter data volume includes a density data volume and a gamma data volume.Illustratively, in a particular embodiment,when multi-parameter pre-stack inversion of the study area is performed by using the inversion low-frequency model, seismic data inputted to the inversion low-frequency model are synthetic seismic records of the study area generated through a seismic wavelet decomposition and reconstruction method.By comparing seismic data before and after wavelet decomposition and reconstruction, it can be known that synthetic seismic records obtained through the seismic wavelet decomposition and reconstruction method have the best correlation with actual seismic data. Specifically, it is found that seismic data frequency of a gypsolyte bottom interface is obviously improved. Energy of a high-frequency component in the seismic data is enhanced. Thus, accuracy of an inversion result is improved.Accordingly, a specific implementation process of S400 is as follows:S410: a synthetic seismic record of the study area is generated through a seismic wavelet decomposition and reconstruction method.S420: the synthetic seismic record is inputted into the inversion low-frequency model, the multi-parameter pre-stack inversion is performed, and the inversion parameter data volume of the study area is obtained.S500: favorable-reservoir distribution in the inversion parameter data volume is predicted through a multi-parameter crossplot methodIllustratively, in a particular embodiment, a specific process of the step that favorable-reservoir distribution in the inversion parameter data volume is predicted through a multi-parameter crossplot method is as follows:a lithologic influence of the special geologic body is eliminated from the inversion parameter data volume through the multi-parameter crossplot method, a remaining data volume is obtained, and the favorable-reservoir distribution in the remaining data volume is identified.It can be known that the petrophysical characteristics of the study area are obtained by analyzing existing drilling data in step S100. These petrophysical characteristics indicate that the wave impedance change range of the eruptive rocks is relatively large and partially overlaps that of the limestone. Thus, it is relatively difficult to predict a limestone reservoir by using only a parameter such as wave impedance. Sensitivity of a reservoir parameter is poor, and differentiation is not obvious, such that a great error exists if a reservoir is predicted by using only a single parameter histogram. Thus, in the above technical solution, by using a multi-parameter crossplot method and putting forward a multi-parameter cross stepwise method, technical problems of difficult lithologic determination and reservoir distribution prediction are overcome, and accuracy of reservoir prediction is improved. Further, spatial distribution and distribution characteristics of a favorable reservoir are more accurately quantitatively described. For example, a plan view of favorable-reservoir thickness distribution is generated, such that a reference diagram is provided for subsequent exploration and deployment.In combination with analysis of existing drilling data in step S100, since the special geologic body in the study area is igneous rocks, to eliminate a lithologic influence of igneous rocks from the inversion parameter data volume, cross of a compressional-wave velocity and a shear-wave velocity can be used. Thus, the inversion parameter data volume includes a compressional-wave velocity data volume and a shear-wave velocity data volume. That is, compressional-wave velocity inversion and shear-wave velocity inversion can be performed by using the inversion low-frequency model constructed in S300. To identify favorable-reservoir distribution in the remaining data volume, that is, identify a limestone reservoir in the stratum of the study area, it is required to discriminate between a reservoir and a non-reservoir in the remaining data volume. As a favorable carbonate reservoir, a limestone reservoir generally has petrophysical characteristics of low impedance and low density. Thus, relatively low compressional-wave impedance and density are selected by performing crossplot analysis by using compressional-wave impedance and density, such that a scale of the favorable carbonate reservoir can be predicted. Correspondingly, the inversion parameter data volume includes a compressional-wave impedance data volume and a density data volume. That is, compressional-wave impedance inversion and density inversion can be performed by using the inversion low-frequency model constructed in S300. Accordingly, a specific implementation process of S500 is as follows:S510: a pre-stack migration data volume is generated in combination with seismic data of the study area.S520: the limestone and the igneous rocks are discriminated through crossplot analysis of the compressional-wave velocity data volume and the shear-wave velocity data volume, a lithologic influence of the igneous rocks is eliminated from the pre-stack migration data volume by using a discrimination result, and a limestone data volume is obtained.For example, cross is performed on the compressional-wave velocity data volume and the shear-wave velocity data volume. If a compressional-to-shear-wave velocity ratio is greater than 1.7, it is determined that the data corresponds to the igneous rocks. If the compressional-to-shear-wave velocity ratio is less than 1.7, it is determined that the data corresponds to the limestone.S530: a reservoir and a non-reservoir are discriminated through crossplot analysis of the compressional-wave impedance data volume and the density data volume, the non-reservoir is eliminated from the limestone data volume by using a discrimination result, and a favorable-reservoir distribution data volume is obtained.For example, crossplot analysis is performed on the compressional-wave impedance data volume and density data volume. If the compressional-wave impedance is relatively low and the density is relatively low, it is determined that the data corresponds to the favorable limestone reservoir, otherwise, it is determined that the data corresponds to the non-reservoir.With reference to FIG. 2, as an improvement of the above embodiment, step S600 is further included after step S500.S600: according to a prediction result of the favorable-reservoir distribution and by taking a top and a bottom of a target stratum for seismic interpretation of the study area as boundaries, a thickness of a favorable reservoir is extracted along the stratum, and a plan view of favorable-reservoir thickness distribution is obtained.As an example, extraction of a thickness of a favorable reservoir along a stratum specifically includes: a scale of a subsalt carbonate favorable reservoir is quantitatively predicted by using a polygon parameter range of a favorable reservoir delineated after cross is performed through a multi-parameter crossplot method, and a thickness of a favorable reservoir of each stratum of the study area is obtained.FIG. 3 to FIG. 9 show a process diagram and a result diagram of applying the above embodiment to identification of a subsalt carbonate reservoir in a block.As shown in FIG. 3, igneous rock is a special geologic body in a subsalt carbonate stratum in a complex lithofacies area. A development pattern of the igneous rocks is mainly as follows: intrusive rocks mostly have a shape of vertical upwelling along a fault and planar flaky distribution. Eruptive rocks have distribution characteristics of "planar concave ring, vertical chaotic belt, and location near a large fault".As shown in FIG. 4, a development pattern configuration of a special geologic body is added to a constructed wave impedance inversion low-frequency model and a constructed compressional-to-shear-wave velocity ratio inversion low-frequency model. By introducing the development pattern configuration, pre-stack inversion is performed by using the constructed wave impedance inversion low-frequency model. An inversion result is close to a trend of a real filtered well log. Pre-stack inversion is performed by using a constructed compressional-to-shear-wave velocity ratio inversion low-frequency model. An inversion result is close to a trend of a real filtered well log.As shown in FIG. 5, part A is a sectional view of a compressional-wave impedance data volume obtained by using an inversion low-frequency model. Part B is a sectional view of a shear-wave impedance data volume obtained by using an inversion low-frequency model. It can be seen from the figure that lateral changes of elastic parameters such as compressional-wave impedance and shear-wave impedance conform to lateral change trends of a subsalt carbonate stratum in the block.As shown in FIG. 6, part A is a sectional view of a density data volume obtained by using an inversion low-frequency model. Part B is a sectional view of a gamma data volume obtained by using an inversion low-frequency model. It can be seen from the figure that physical parameters such as lateral changes of density and gamma conform to lateral change trends of a subsalt carbonate stratum in the block.In combination with FIG. 7 and FIG. 8, a reservoir thickness prediction error of a prediction result of a favorable limestone reservoir at a stratum obtained through a multi-parameter crossplot method is different by 88 meters from a prediction error of a prediction result obtained through a single parameter method. The reservoir thickness prediction error of the favorable limestone reservoir at the illustrated stratum obtained through the single parameter method reaches 124 meters, while the reservoir thickness prediction error of the favorable limestone reservoir at the illustrated stratum obtained through the multi-parameter crossplot method and the stepwise crossplot method provided in the embodiment is merely 36 meters.FIG. 9 shows reservoir continuity distribution of a seismic prediction target stratum in the block. It can be seen from the figure that reservoir continuity is excellent. A thickness ranges from 30 m to 200 m. An eastern reservoir has a maximum thickness. The distribution relatively well conforms to drilling data and sedimentation rule. Reservoir prediction is performed through a method for identifying a favorable carbonate reservoir in a complex lithofacies area provided in the embodiments of the present disclosure. Pre-drilling reservoir prediction accuracy is improved from 60% to 85% of that of a traditional pre-stack inversion method, which proves effectiveness of the present disclosure.Device embodimentsThe embodiments of the present disclosure provide a device for identifying a favorable carbonate reservoir in a complex lithofacies area. The device includes a lithologic 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 which are sequentially connected.The lithologic determination module is configured to determine a rock type and a petrophysical property of a study area.The development pattern configuration module is configured to characterize a development pattern configuration of a special geologic body of the rock type.The inversion low-frequency model construction module is configured to construct an inversion low-frequency model under facies-controlled constraint through an iterative modeling method based on the development pattern configuration.The multi-parameter pre-stack inversion module is configured to perform multi-parameter pre-stack inversion of the study area by using the inversion low-frequency model, and obtain an inversion parameter data volume of the study area.The reservoir identification module is configured to predict favorable-reservoir distribution in the inversion parameter data volume through a multi-parameter crossplot method.In an optional embodiment, the development pattern configuration module is configured to characterize the development pattern configuration of the special geologic body of the rock type as follows:a geologic model of the special geologic body is constructed, forward modeling is performed, and a development location characteristic of the special geologic body is determined;a development shape characteristic of the special geologic body is determined in combination with the petrophysical property and an actual seismic response; anda development pattern of the special geologic body is characterized according to the development location characteristic and the development shape characteristic, and the development pattern configuration of the special geologic body is obtained.In an optional embodiment, the inversion low-frequency model construction module is configured to construct the inversion low-frequency model under the facies-controlled constraint through the iterative modeling method based on the development pattern configuration as follows:an initial inversion low-frequency model is constructed under the facies-controlled constraint;the special geologic body is added to the inversion low-frequency model based on the development pattern configuration, the inversion low-frequency model is updated, the pre-stack multi-parameter inversion is performed by using the updated inversion low-frequency model, and an inversion result is obtained;whether a trend of the inversion result is consistent with a trend of a real filtered well log is determined, if yes, a next step is performed, otherwise, a previous step is performed; anda current inversion low-frequency model is taken as a final inversion low-frequency model.In an optional embodiment, the multi-parameter pre-stack inversion module is configured to perform multi-parameter pre-stack inversion of the study area by using the inversion low-frequency model, and obtain an inversion parameter data volume of the study area as follows:a synthetic seismic record of the study area is generated through a seismic wavelet decomposition and reconstruction method; andthe synthetic seismic record is inputted into the inversion low-frequency model, the multi-parameter pre-stack inversion is performed, and the inversion parameter data volume of the study area is obtained.In an optional embodiment, the reservoir identification module is configured to predict the favorable-reservoir distribution in the inversion parameter data volume through the multi-parameter crossplot method as follows:a lithologic influence of the special geologic body is eliminated from the inversion parameter data volume through the multi-parameter crossplot method, a remaining data volume is obtained, and the favorable-reservoir distribution in the remaining data volume is identified.In an optional embodiment, the rock type includes limestone and igneous rocks. The special geologic body includes the igneous rocks. The inversion parameter data volume includes a compressional-wave velocity data volume, a shear-wave velocity data volume, a compressional-wave impedance data volume, and a density data volume.In an optional embodiment, the step that a lithologic influence of the special geologic body in the inversion parameter data volume is eliminated through the multi-parameter crossplot method, a remaining data volume is obtained, and the favorable-reservoir distribution in the remaining data volume is identified includes:a pre-stack migration data volume is generated in combination with seismic data of the study area;the limestone and the igneous rocks are discriminated through crossplot analysis of the compressional-wave velocity data volume and the shear-wave velocity data volume, a lithologic influence of the igneous rocks is eliminated from the pre-stack migration data volume by using a discrimination result, and a limestone data volume is obtained; anda reservoir and a non-reservoir are discriminated through crossplot analysis of the compressional-wave impedance data volume and the density data volume, the non-reservoir is eliminated from the limestone data volume by using a discrimination result, and a favorable-reservoir distribution data volume is obtained.In an optional embodiment, the device for identifying a favorable carbonate reservoir in a complex lithofacies area provided in the embodiments of the present disclosure further includes a reservoir thickness view generation module. The reservoir thickness view generation module is configured to extract, according to a prediction result of the favorable-reservoir distribution and by taking a top and a bottom of a target stratum for seismic interpretation of the study area as boundaries, a thickness of a favorable reservoir along the stratum, and obtain a plan view of favorable-reservoir thickness distribution.Each module of the device for identifying a favorable carbonate reservoir in a complex lithofacies area provided in the embodiments of the present disclosure can be applied to a computing apparatus including a memory and a processor.In another aspect, the embodiments of the present disclosure further provide an electronic apparatus. The electronic apparatus includes a memory, a processor, and a computer program stored in the memory and runnable by the processor. The processor, when executing the computer program, implements the method for identifying a favorable carbonate reservoir in a complex lithofacies area mentioned in the above method embodiments.In yet another aspect, the embodiments of the present disclosure further provide a machine-readable storage medium. The machine-readable storage medium stores a computer program. The computer program, when executed by a processor, implements the method for identifying a favorable carbonate reservoir in a complex lithofacies area mentioned in the above method embodiments.The device embodiments described above are merely illustrative. The units described as separate components may be physically separated or not, and the components displayed as units may be physical units or not, that is, may be located in one location, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to an actual requirement to achieve the objective of the solution of the embodiment. Those of ordinary skill in the art can understand and implement the present disclosure without making creative efforts.From the description of the above implementations, those skilled in the art can clearly understand that the implementations may be achieved by software plus a necessary general-purpose hardware platform, and certainly may alternatively be achieved by hardware. Based on the understanding, the above technical solution may be embodied in a form of a software product in essence or a part contributing to the prior art. The computer software product can be stored in a computer-readable storage medium, such as a read-only memory (ROM) / random access memory (RAM), a magnetic disk, and an optical disk, and includes several instructions to cause a computer apparatus (which may be a personal computer, a server, a network device, etc.) to perform the method mentioned in the embodiments or in some parts of the embodiments.Finally, it should be noted that the above embodiments are merely used for describing the technical solutions of the present disclosure, rather than limiting same. Although the present disclosure is described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they still can modify the technical solutions recited in the foregoing embodiments, or equivalently replace some of the technical features. Moreover, these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of all embodiments of the present disclosure. 

Claims

1. 1. A method for identifying a favorable carbonate reservoir in a complex lithofacies area, comprising:determining a rock type and a petrophysical property of a study area;characterizing a development pattern configuration of a special geologic body of the rock type;constructing an inversion low-frequency model under facies-controlled constraint through an iterative modeling method based on the development pattern configuration;performing multi-parameter pre-stack inversion of the study area by using the inversion low-frequency model, and obtaining an inversion parameter data volume of the study area; andpredicting favorable-reservoir distribution in the inversion parameter data volume through a multi-parameter crossplot method. 

2. <p>2. The method for identifying a favorable carbonate reservoir in a complex lithofacies area according to claim 1, wherein the characterizing a development pattern configuration of a special geologic body of the rock type comprises:constructing a geologic model of the special geologic body, performing forward modeling, and determining a development location characteristic of the special geologic body;determining a development shape characteristic of the special geologic body in combination with the petrophysical property and an actual seismic response; andcharacterizing a development pattern of the special geologic body according to the development location characteristic and the development shape characteristic, and obtaining the development pattern configuration of the special geologic body. 

3. <p>3. The method for identifying a favorable carbonate reservoir in a complex lithofacies area according to claim 1, wherein the constructing an inversion low-frequency model under facies-controlled constraint through an iterative modeling method based on the development pattern configuration comprises:constructing an initial inversion low-frequency model under the facies-controlled constraint;adding the special geologic body to the inversion low-frequency model based on the development pattern configuration, updating the inversion low-frequency model, performing the pre-stack multi-parameter inversion by using the updated inversion low-frequency model, and obtaining an inversion result;determining whether a trend of the inversion result is consistent with a trend of a real filtered well log, if yes, performing a next step, otherwise, skipping to a previous step; andtaking a current inversion low-frequency model as a final inversion low-frequency model. 

4. <p>4. The method for identifying a favorable carbonate reservoir in a complex lithofacies area according to claim 1, wherein the performing multi-parameter pre-stack inversion of the study area by using the inversion low-frequency model, and obtaining an inversion parameter data volume of the study area comprise:generating a synthetic seismic record of the study area through a seismic wavelet decomposition and reconstruction method; andinputting the synthetic seismic record into the inversion low-frequency model, performing the multi-parameter pre-stack inversion, and obtaining the inversion parameter data volume of the study area. 

5. 5. The method for identifying a favorable carbonate reservoir in a complex lithofacies area according to claim 1, wherein the predicting favorable-reservoir distribution in the inversion parameter data volume through a multi-parameter crossplot method comprises:eliminating a lithologic influence of the special geologic body from the inversion parameter data volume through the multi-parameter crossplot method, obtaining a remaining data volume, and identifying the favorable-reservoir distribution in the remaining data volume. 

6. 6. The method for identifying a favorable carbonate reservoir in a complex lithofacies area according to claim 5, wherein the rock type comprises limestone and igneous rocks, the special geologic body comprises the igneous rocks, and the inversion parameter data volume comprises a compressional-wave velocity data volume, a shear-wave velocity data volume, a compressional-wave impedance data volume, and a density data volume. 

7. <p>7. The method for identifying a favorable carbonate reservoir in a complex lithofacies area according to claim 6, wherein the eliminating a lithologic influence of the special geologic body from the inversion parameter data volume through the multi-parameter crossplot method, obtaining a remaining data volume, and identifying the favorable-reservoir distribution in the remaining data volume comprise:generating a pre-stack migration data volume in combination with seismic data of the study area;discriminating between the limestone and the igneous rocks through crossplot analysis of the compressional-wave velocity data volume and the shear-wave velocity data volume, eliminating a lithologic influence of the igneous rocks from the pre-stack migration data volume by using a discrimination result, and obtaining a limestone data volume; anddiscriminating between a reservoir and a non-reservoir through crossplot analysis of the compressional-wave impedance data volume and the density data volume, eliminating the non-reservoir from the limestone data volume by using a discrimination result, and obtaining a favorable-reservoir distribution data volume. 

8. 8. The method for identifying a favorable carbonate reservoir in a complex lithofacies area according to claim 1, further comprising:extracting, according to a prediction result of the favorable-reservoir distribution and by taking a top and a bottom of a target stratum for seismic interpretation of the study area as boundaries, a thickness of a favorable reservoir along the stratum, and obtaining a plan view of favorable-reservoir thickness distribution. 

9. <p>9. The method for identifying a favorable carbonate reservoir in a complex lithofacies area according to claim 1, wherein the rock type comprises limestone and igneous rocks; and the determining a rock type and a petrophysical property of a study area comprises:determining whether a calcium-to-silicon ratio of rocks is greater than a first threshold according to an acquired petrophysical analysis result of the study area, if yes, determining that the rock type is the limestone, otherwise, determining that the rock type is the igneous rocks;constructing, in a case that the rock type is the limestone, a shale volume indication curve according to an acquired well log of the study area, identifying a Barra Velha (BVE) formation in the limestone, performing fuzzy clustering analysis on a lithologic sensitivity curve of the well log, and identifying an Itapema (ITP) formation in the limestone; andidentifying, in a case that the rock type is the igneous rocks, lithology of the igneous rocks according to an elemental capture spectroscopy (ECS) logging result and a gamma-ray (GR) spectroscopy logging result of the study area. 

10. <p>10. The method for identifying a favorable carbonate reservoir in a complex lithofacies area according to claim 1, wherein the rock type comprises limestone and igneous rocks; and the determining a rock type and a petrophysical property of a study area comprises:determining rock density differences according to acquired drilling data of the study area, discriminating, according to the rock density differences, between the limestone and the igneous rocks, and identifying lithology of the limestone; andidentifying, in a case that the rock type is the igneous rocks, lithology of the igneous rocks according to an ECS logging result and a GR spectroscopy logging result of the study area. 

11. <p>11. The method for identifying a favorable carbonate reservoir in a complex lithofacies area according to claim 2, wherein the characterizing a development pattern configuration of a special geologic body of the rock type further comprises:reprocessing acquired seismic data as follows: updating a grid tomography velocity, enhancing a structural model and well information constraint, and performing structure-oriented filtering; and triggering and performing the constructing a geologic model of the special geologic body, performing forward modeling, and determining a development location characteristic of the special geologic body. 

12. <p>12. A device for identifying a favorable carbonate reservoir in a complex lithofacies area, comprising:a lithologic determination module configured to determine a rock type and a petrophysical property of a study area;a development pattern configuration module configured to characterize a development pattern configuration of a special geologic body of the rock type;an inversion low-frequency model construction module configured to construct an inversion low-frequency model under facies-controlled constraint through an iterative modeling method based on the development pattern configuration;a multi-parameter pre-stack inversion module configured to perform multi-parameter pre-stack inversion of the study area by using the inversion low-frequency model, and obtain an inversion parameter data volume of the study area; anda reservoir identification module configured to predict favorable-reservoir distribution in the inversion parameter data volume through a multi-parameter crossplot method. 

13. <p>13. The device for identifying a favorable carbonate reservoir in a complex lithofacies area according to claim 12, wherein the development pattern configuration of the special geologic body of the rock type is characterized as follows:constructing a geologic model of the special geologic body, performing forward modeling, and determining a development location characteristic of the special geologic body;determining a development shape characteristic of the special geologic body in combination with the petrophysical property and an actual seismic response; andcharacterizing a development pattern of the special geologic body according to the development location characteristic and the development shape characteristic, and obtaining the development pattern configuration of the special geologic body. 

14. <p>14. The device for identifying a favorable carbonate reservoir in a complex lithofacies area according to claim 12, wherein the inversion low-frequency model construction module is configured to construct the inversion low-frequency model under the facies-controlled constraint through the iterative modeling method based on the development pattern configuration as follows:constructing an initial inversion low-frequency model under the facies-controlled constraint;adding the special geologic body to the inversion low-frequency model based on the development pattern configuration, updating the inversion low-frequency model, performing the pre-stack multi-parameter inversion by using the updated inversion low-frequency model, and obtaining an inversion result;determining whether a trend of the inversion result is consistent with a trend of a real filtered well log, if yes, performing a next step, otherwise, skipping to a previous step; andtaking a current inversion low-frequency model as a final inversion low-frequency model. 

15. 15. An electronic apparatus, comprising a memory, a processor, and a computer program stored in the memory and runnable by the processor, wherein the processor, when executing the program, implements the method for identifying a favorable carbonate reservoir in a complex lithofacies area according to any one of claims 1 to 11. 

16. 16. A machine-readable storage medium, storing a computer program, wherein the computer program, when executed by a processor, implements the method for identifying a favorable carbonate reservoir in a complex lithofacies area according to any one of claims 1 to 11. 

Images