WO2025227808A1 - 复杂岩相区优势碳酸盐岩储层识别方法、装置及电子设备 - Google Patents

复杂岩相区优势碳酸盐岩储层识别方法、装置及电子设备

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Publication number
WO2025227808A1
WO2025227808A1 PCT/CN2024/144136 CN2024144136W WO2025227808A1 WO 2025227808 A1 WO2025227808 A1 WO 2025227808A1 CN 2024144136 W CN2024144136 W CN 2024144136W WO 2025227808 A1 WO2025227808 A1 WO 2025227808A1
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inversion
data volume
dominant
parameter
low
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English (en)
French (fr)
Inventor
何文渊
范国章
王红平
张勇刚
王朝锋
朱晓辉
郭渊
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China National Oil And Gas Exploration And Development Co Ltd
China National Petroleum Corp
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China National Oil And Gas Exploration And Development Co Ltd
China National Petroleum Corp
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
    • G01V1/30—Analysis

Definitions

  • This application belongs to the field of petroleum exploration technology, specifically relating to a method, apparatus and electronic equipment for identifying dominant carbonate reservoirs in complex lithofacies zones.
  • Seismic lithology is the discipline that studies the extraction of information related to lithological and geomorphological facies from seismic data. It is an interdisciplinary product of seismic properties, seismic inversion, stochastic modeling techniques, and seismic sedimentation.
  • seismic exploration by observing the propagation and reflection of seismic waves in the subsurface medium, information related to rock properties, such as wave velocity, attenuation coefficient, and density, can be obtained. By analyzing this information, the lithological types and combinations of subsurface rocks can be inferred, providing an important basis for reservoir prediction.
  • lithofacies carbonate strata are primarily composed of limestone and igneous rocks.
  • Limestones mainly consist of algal limestone, spherulitic limestone, and shell limestone.
  • Igneous rocks are mainly intrusive and extrusive rocks.
  • Intrusive rocks refer to intrusive facies rock types, primarily diabase.
  • Extrusive rocks refer to extrusive facies rock types, primarily basalt.
  • the dominant carbonate reservoirs are found in the BVE Formation, where the lithology is mainly algal limestone and spherulitic limestone, and in the ITP Formation, primarily shell limestone.
  • Marl and micritic limestone due to their high mud content, poor physical properties, and high density, are generally not dominant reservoir sites.
  • Carbonate reservoir development is controlled by multiple factors, including tectonic setting, diagenesis, sedimentary environment, and subsequent dissolution and alteration, resulting in highly heterogeneous reservoir distribution.
  • Traditional seismic data inversion methods have the advantage of optimal vertical resolution, which can intuitively reflect reservoir lithology and thickness. However, they are limited by the loss of lateral resolution, which can easily obscure reservoir heterogeneity such as reservoir shape and structure.
  • the layered initial model used for inversion reduces the lateral resolution of the inversion and destroys the geomorphic 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.
  • Igneous rocks are a major type of special geological body developed in complex lithofacies carbonate rock regions. These regions typically also contain other special geological bodies such as turbidite channels, salt domes, and carbonate caves. Due to the presence of these special geological bodies, conventional inversion models, which do not consider stratigraphic attitude and lateral lithological variations, can produce significant errors in areas with dramatic lateral stratigraphic variations or large lithological changes, resulting in unrepresentative well curves.
  • the purpose of this application is to provide a method, apparatus, and electronic device for identifying dominant carbonate reservoirs in complex lithofacies areas, and also to provide a machine-readable storage medium to overcome the technical problem that traditional seismic data inversion cannot accurately identify dominant carbonate reservoirs in complex lithofacies areas.
  • a first aspect of this application provides a method for identifying dominant carbonate reservoirs in complex lithofacies zones, the method comprising:
  • an inversion low-frequency model under phase control constraints is constructed using an iterative modeling method
  • Multi-parameter pre-stack inversion of the study section was performed using the inversion low-frequency model to obtain the inversion parameter data volume of the study section;
  • a multi-parameter convergence method is used to predict the distribution of dominant reservoirs in the inversion parameter data volume.
  • the carving of the developmental pattern configuration of specific geological bodies in the rock type includes:
  • the developmental pattern of the special geological body is sculpted to obtain the developmental pattern configuration of the special geological body.
  • the step of constructing an inversion low-frequency model under phase control constraints based on the developmental pattern configuration includes:
  • inversion low-frequency model Based on the aforementioned developmental pattern configuration, special geological bodies are added to the inversion low-frequency model, and the inversion low-frequency model is updated. The updated inversion low-frequency model is then used to perform pre-stack multi-parameter inversion to obtain the inversion results.
  • the current low-frequency inversion model will be used as the final low-frequency inversion model.
  • the step of using the inversion low-frequency model to perform multi-parameter pre-stack inversion of the study section to obtain the inversion parameter data volume of the study section includes:
  • Synthetic seismic records for the study area were generated using the seismic wavelet decomposition and reconstruction method.
  • the synthetic seismic record is input into the low-frequency inversion model for multi-parameter pre-stack inversion to obtain the inversion parameter data volume of the study area.
  • the prediction of the dominant reservoir distribution in the inversion parameter data volume using a multi-parameter intersection method includes:
  • the lithological influence of special geological bodies in the inversion parameter data volume is eliminated by using a multi-parameter intersection method to obtain the remaining data volume, and the distribution of dominant reservoirs in the remaining data volume is identified.
  • the rock type includes limestone and igneous rock
  • the special geological body includes igneous rock
  • 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.
  • the step of using a multi-parameter intersection method to exclude the lithological influence of special geological bodies in the inversion parameter data volume, obtaining the remaining data volume, and identifying the dominant reservoir distribution in the remaining data volume includes:
  • Pre-stack migration data volume is generated by combining seismic data from the study area
  • the method further includes:
  • the thickness values of dominant reservoirs are extracted along the target layer for seismic interpretation in the study area, using the top and bottom of the layer as boundaries, to obtain a planar map of dominant reservoir thickness distribution.
  • a second aspect of this application provides a device for identifying dominant carbonate reservoirs in complex lithofacies zones, the device comprising:
  • the lithology determination module is used to determine the rock type and rock physical properties of the study area
  • the developmental pattern configuration module is used to sculpt the developmental pattern configuration of special geological bodies in rock types
  • the inversion low-frequency model construction module is used to construct an inversion low-frequency model under phase control constraints based on the developmental pattern configuration using an iterative modeling method.
  • the multi-parameter pre-stack inversion module is used to perform multi-parameter pre-stack inversion of the study section using the inversion low-frequency model, and obtain the inversion parameter data volume of the study section.
  • the reservoir identification module is used to predict the distribution of dominant reservoirs in the inversion parameter data volume using a multi-parameter intersection method.
  • the developmental pattern configuration module sculpts the developmental pattern configuration of a specific geological body within a rock type, including:
  • the developmental pattern of the special geological body is sculpted to obtain the developmental pattern configuration of the special geological body.
  • the low-frequency inversion model construction module based on the developmental pattern configuration, constructs a phase-controlled low-frequency inversion model using an iterative modeling method, including:
  • inversion low-frequency model Based on the aforementioned developmental pattern configuration, special geological bodies are added to the inversion low-frequency model, and the inversion low-frequency model is updated. The updated inversion low-frequency model is then used to perform pre-stack multi-parameter inversion to obtain the inversion results.
  • the current low-frequency inversion model will be used as the final low-frequency inversion model.
  • the multi-parameter pre-stack inversion module utilizes an inversion low-frequency model to perform multi-parameter pre-stack inversion of the study section, obtaining the inversion parameter data volume of the study section, including:
  • Synthetic seismic records for the study area were generated using the seismic wavelet decomposition and reconstruction method.
  • the synthetic seismic record is input into the low-frequency inversion model for multi-parameter pre-stack inversion to obtain the inversion parameter data volume of the study area.
  • the reservoir identification module uses a multi-parameter intersection method to predict the distribution of dominant reservoirs in the inversion parameter data volume, including:
  • the lithological influence of special geological bodies in the inversion parameter data volume is eliminated by using a multi-parameter intersection method to obtain the remaining data volume, and the distribution of dominant reservoirs in the remaining data volume is identified.
  • the rock type includes limestone and igneous rock
  • the special geological body includes igneous rock
  • 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.
  • the step of using a multi-parameter intersection method to exclude the lithological influence of special geological bodies in the inversion parameter data volume, obtaining the remaining data volume, and identifying the dominant reservoir distribution in the remaining data volume includes:
  • Pre-stack migration data volume is generated by combining seismic data from the study area
  • the device further includes a reservoir thickness mapping module, which is used to extract the thickness value of the dominant reservoir along the target segment for seismic interpretation in the study area based on the predicted results of the dominant reservoir distribution, using the top and bottom of the target segment as the boundary, to obtain a plane map of the dominant reservoir thickness distribution.
  • a reservoir thickness mapping module which is used to extract the thickness value of the dominant reservoir along the target segment for seismic interpretation in the study area based on the predicted results of the dominant reservoir distribution, using the top and bottom of the target segment as the boundary, to obtain a plane map of the dominant reservoir thickness distribution.
  • a third aspect of this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements a method for identifying dominant carbonate reservoirs in complex lithofacies zones as described in the first aspect of this application.
  • the fourth aspect of this application provides a machine-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for identifying dominant carbonate reservoirs in complex lithofacies zones as described in the first aspect of this application.
  • the above technical solution combines developmental pattern configuration, phase-controlled iterative modeling, multi-parameter pre-stack inversion, and multi-parameter convergence.
  • the phase-controlled iterative modeling is based on the developmental pattern configuration when constructing the inversion low-frequency model. That is, special geological bodies are added to the model for iteration, which effectively eliminates noise interference and accurately extracts useful information from seismic data, thereby obtaining accurate lithofacies interpretation results and improving the identification accuracy of dominant carbonate reservoirs in complex lithofacies areas.
  • Figure 1 is a schematic flowchart of a method for identifying dominant carbonate reservoirs in complex lithofacies zones provided in an embodiment of this application;
  • Figure 2 is another schematic flowchart of the method for identifying dominant carbonate reservoirs in complex lithofacies zones provided in the embodiments of this application;
  • Figure 3 is a schematic diagram of a developmental pattern configuration of igneous rocks
  • Figure 4 shows the inversion low-frequency model obtained after supplementing with special geological bodies.
  • Part A is a schematic diagram of the wave impedance inversion low-frequency model
  • Part B is a schematic diagram of the P-wave and S-wave velocity ratio inversion low-frequency model
  • Part C is a schematic diagram of the carved special geological bodies.
  • Figure 5 is a comparative schematic diagram of longitudinal wave impedance data volume and transverse wave impedance data volume, where part A is the longitudinal wave impedance data volume and part B is the transverse wave impedance data volume.
  • Figure 6 is a comparative diagram of density data volume and gamma data volume, where part A is density data volume and part B is gamma data volume;
  • Figure 7 is a schematic diagram of a multi-parameter intersection method
  • Figure 8 is a comparison of the prediction results of dominant reservoirs.
  • Part A is a cross-sectional schematic diagram of the prediction results of single-parameter dominant reservoirs
  • Part B is a cross-sectional schematic diagram of the prediction results of dominant reservoirs implemented in the embodiment of this application.
  • Figure 9 shows a plan view of the predicted thickness distribution of a dominant reservoir.
  • Study area The study area involved in this application is located in complex lithofacies subsalt carbonate rock strata.
  • Pre-stack inversion Pre-stack seismic data contains richer subsurface geological information than post-stack seismic data. At the same time, pre-stack inversion is superior to post-stack inversion in terms of inversion accuracy and reservoir prediction efficiency, and has now become one of the most important technical means in the fields of reservoir prediction and fluid identification.
  • Single-parameter heterogeneity Due to the heterogeneity of complex lithofacies carbonate reservoir regions, single-parameter reservoir non-reservoir heterogeneity is poor.
  • Cross-plot A tool for comprehensively analyzing multiple seismic attribute parameters. By visualizing the cross-relationships between different parameters, it provides a more comprehensive understanding of the spatial distribution and characteristics of reservoirs.
  • an embodiment of this application provides a method for identifying dominant carbonate reservoirs in complex lithofacies zones, which may include steps S100, S200, S300, S400, and S500.
  • the rock types include limestone and igneous rocks.
  • the existing drilling data in order to determine the rock type and rock physical properties of the study section, can be analyzed for complex lithofacies and rock physical properties and identified by well logging.
  • the first step is to distinguish the major lithological categories of the study area based on density differences.
  • the developed limestone is mainly algal limestone, spherulitic limestone, and shell limestone; the igneous rocks are mainly intrusive rocks and basalt.
  • the intrusive facies is dominated by diabase, while the extrusive facies is mainly dominated by basalt.
  • the rock density of intrusive rocks is generally greater than that of extrusive rocks.
  • the rock density of intrusive rocks is mostly greater than 2.80 g/ cm3 , with an average density of around 2.92 g/ cm3 .
  • the rock density of extrusive rocks is mostly greater than 2.70 g/ cm3 , with an average density of around 2.75 g/ cm3 .
  • the second step involves classifying and identifying different types of carbonate rocks.
  • High-quality carbonate reservoirs (the BVE group is dominated by algal limestone and spherulite, while the ITP group is dominated by shell limestone) are primarily composed of algal limestone, spherulite, and shell limestone, with a typical rock density of 2.3-2.6 g/ cm3 , and an average density of 2.46 g/ cm3 .
  • marl and micritic limestone due to their higher mud content, have poorer physical properties and higher densities, with an average density of 2.6 g/ cm3 .
  • the third step is igneous rock lithology identification, which can be based on ECS energy dispersive spectroscopy and GR energy dispersive spectroscopy well logging analysis.
  • Th, Fe, and K are used as indicator elements for igneous rock lithology identification.
  • Parameters that reflect the basic nature of igneous rocks are selected, and the igneous rock index (Lig) is introduced for quantitative identification.
  • L ⁇ 20 indicates diabase
  • L ⁇ 20 indicates basalt. This effectively distinguishes between extrusive rocks (mainly basalt) and intrusive rocks (mainly diabase) in igneous rocks.
  • Sample verification shows that the identification accuracy of diabase and basalt reaches 95%.
  • the Ca/Si ratio method can be used to distinguish the major lithological categories of the study area.
  • Ca and Si can be used as indicator elements for igneous rocks and carbonate rocks, respectively.
  • Ca/Si > 1.2 indicates carbonate rocks
  • Ca/Si ⁇ 1.2 indicates igneous rocks, thus achieving the major category identification of carbonate rocks and igneous rocks.
  • the Ca/Si ratio method proposed in this application was used to identify the lithology of 92 igneous rock samples. 88 igneous rock samples were successfully identified, with an identification accuracy rate of 95.65%.
  • a 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 mud content indicator curve is constructed using natural gamma curves and uranium-reduced gamma curves to identify algal limestone, argillaceous algal limestone, spherulitic limestone, argillaceous spherulitic limestone, and micritic limestone in the BE group.
  • Sample verification shows that this method achieves an 80% accuracy rate in identifying the lithology of the BVE group; 2) For the ITP group, four main lithology-sensitive curves are selected for artificial intelligence fuzzy clustering analysis to identify shell limestone, marl, and mudstone. The four main lithology-sensitive curves are the natural gamma curve, density curve, neutron curve, and porosity curve. Sample verification shows that the lithology identification accuracy rate of the ITP group based on artificial intelligence fuzzy clustering analysis reaches 100%.
  • development pattern configuration refers to the characterization of facies development patterns and the sculpting and configuration of igneous rocks that are the main special geological bodies of carbonate reservoirs.
  • the specific process of sculpting and morphing the main special geological bodies of igneous rocks in a carbonate reservoir is as follows:
  • the development pattern of the igneous rock is sculpted to obtain the development pattern configuration of the igneous rock.
  • extrusive rocks in igneous rocks Due to their inherent lithological characteristics and alteration changes, extrusive rocks in igneous rocks have significantly weaker seismic responses than intrusive rocks, exhibiting local blank reflections and chaotic reflections. Based on this pattern, the development pattern of extrusive rocks can be sculpted and shaped, obtaining the development pattern configuration of extrusive rocks.
  • the above scheme utilizes seismic multi-attribute spatial sculpting technology guided by underwater volcanic structure model, and combines seismic attribute analysis, multi-information fusion, velocity analysis, coherence analysis and three-dimensional volume slicing techniques. After predicting the spatial distribution of igneous rocks, its development pattern is sculpted and shaped to obtain the development pattern configuration of igneous rocks.
  • the seismic data is optimized and reprocessed before characterizing the seismic facies development model.
  • this improved embodiment proposes a grid tomography velocity-constrained seismic data reprocessing method. This method includes the following steps: grid tomography velocity updating, strengthening structural model and well information constraints, and performing structural-guided filtering.
  • the seismic facies development model under the influence of thick salt layer shielding is characterized by the following steps, specifically steps 1 to 3.
  • Step 1 Seismic data optimization and reprocessing.
  • Step 2 Based on the classification method of carbonate rocks and igneous rocks established in step S100 as described above, conduct forward modeling analysis of rock physics.
  • the impedance of the subsalt formation during actual drilling is ranked as follows: igneous rocks > limestone > mudstone > salt rocks.
  • the sedimentary lithology changes from limestone to argillaceous limestone from a structurally high position to a lower position, with wave crest reflection decreasing from strong to weak.
  • the sedimentary lithology changes from limestone to argillaceous limestone from a structurally high position to a lower position, with wave crest reflection increasing from weak to strong.
  • intrusive rocks are mainly diabase.
  • the lithological assemblage is characterized as "salt rock-anhydrite-diabase-limestone.”
  • the simulation confirms that the intrusive rock development location has the characteristics of "strong amplitude + low frequency + high impedance," indicating that the amplitude at the intrusive rock development location is relatively strong, and the amplitude increases with thickness, while the phase axis becomes wider.
  • the intrusive rocks are mostly vertically upwelling along faults and distributed in planar sheet-like patterns. This pattern is used to sculpt and morphologically define the development characteristics of the intrusive rocks.
  • Step 3 Characterize the seismic facies development model.
  • the sedimentary corresponding shallow lacustrine platform has a thin stratum thickness (less than 300m), a relatively small number of phase axes, and strong amplitude energy
  • the semi-deep lacustrine facies has a large stratum thickness (greater than 300m), a relatively large number of phase axes, and weak amplitude energy
  • the intraplatform subfacies has a thin stratum thickness, mainly exhibiting single-axis continuous reflection, and strong amplitude energy
  • the platform margin subfacies has a relatively large stratum thickness, exhibiting mound-like weak reflection characteristics
  • the platform slope subfacies has a stratum thickness between the intraplatform and platform margin, with low-frequency, medium-to-weak amplitude continuous reflection.
  • the specific process of constructing an inversion low-frequency model under phase control constraints based on the developmental pattern configuration using an iterative modeling method is as follows:
  • S330 Determine whether the inversion result is consistent with the trend of the actual well filter curve. If yes, proceed to S340; otherwise, jump to S320.
  • a threshold can be set.
  • the inversion result is considered to be consistent with the trend of the actual well filter curve.
  • the consistency is relative consistency, which can be understood as the trend of the inversion result being close to that of the actual well filter curve.
  • the stacking velocity data during seismic processing is P-wave velocity data.
  • low-frequency and high-frequency information in seismic data are crucial for characterizing oil and gas reservoirs.
  • Low-frequency information is primarily used to define the geometric framework of subsurface oil reservoirs and is directly related to the absolute acoustic impedance of the subsurface medium. Due to the limited bandwidth of seismic data, pre-stack inversion exhibits ambiguity, meaning that multiple inversion models with different acoustic impedances typically match the actual seismic record.
  • existing technologies have proposed methods for constructing low-frequency inversion models under phase control constraints. For example, in some common embodiments, the construction of low-frequency inversion models under phase control constraints is based on the following principles.
  • seismic data typically lacks certain low-frequency components that cannot be obtained through inversion, yet these components constitute a portion of the absolute acoustic impedance.
  • Direct seismic inversion results do not include low-frequency components below 10Hz, requiring extraction and compensation from other data.
  • a basic low-frequency inversion initial model reflecting the geological characteristics of sedimentary bodies can be established using well data containing more low-frequency and high-frequency information. For example, establishing an initial wave impedance inversion low-frequency model or a well logging parameter inversion low-frequency model involves combining horizontally varying seismic interface information with high-resolution well logging information.
  • Interpolation or extrapolation uses spatial interpolation methods such as inverse distance weighted interpolation.
  • velocity data volume constraints can be used. It is known that the velocity data volume used must reflect the geological sedimentary change trend and have a good correlation with the wellbore velocity curve. Adding the very low frequency information of the velocity data volume retains the 2Hz component of the velocity spectrum and supplements the model with the 3-6Hz frequency components from the well logging data, thus producing a smooth, closed solid model, such as a wave impedance inversion low-frequency model or a well logging parameter inversion low-frequency model.
  • the most important constraint parameter during inversion is the continuous variation of the established model in the lateral direction. In essence, it means that the interpolation and extrapolation of the physical (sometimes electrical) parameters at each well point in the lateral direction are smooth. The purpose is to ensure that the inversion results conform to the known geological laws (assuming that the physical or electrical parameters at the well point are known).
  • the strata in the study area of this application embodiment contain special geological bodies such as igneous rocks.
  • the inversion results have large errors, and the obtained well curves are not representative. This is because the simple inter-well lateral linear interpolation and extrapolation methods do not consider the lateral changes in strata occurrence and lithology, which can lead to significant errors in areas with drastic lateral changes in strata or large lithological variations.
  • the above technical solution uses the development pattern configuration of the special geological bodies identified in S200 as a basis.
  • the special geological bodies are then added to the initial state of the inversion low-frequency model, and the inversion low-frequency model is updated using an iterative method until the inversion results output by the inversion low-frequency model are consistent with the trend of the actual well filter curve. This achieves the construction of a high-precision inversion low-frequency model and improves the accuracy of the predicted distribution of dominant carbonate reservoirs.
  • the geological constraints of seismic facies and sedimentary facies are established by using the inversion low-frequency model.
  • Multiple inversion parameter data volumes can be obtained simultaneously through waveform indication inversion, pre-stack elastic parameter inversion, and pre-stack Bayesian inversion.
  • the inversion parameter data volumes include elastic parameter data volumes and physical property parameter data volumes.
  • the elastic parameter data volumes include P-wave impedance data volumes, S-wave impedance data volumes, etc.
  • the physical property parameter data volumes include density data volumes and gamma data volumes, etc.
  • the seismic data input to the inversion low-frequency model is the synthetic seismic record of the study section generated by the seismic wavelet decomposition and reconstruction method.
  • the synthetic seismic record obtained by the seismic wavelet decomposition and reconstruction method has the best correlation with the actual seismic data.
  • the frequency of the seismic data at the bottom interface of the gypsum layer was significantly improved, and the energy of the high-frequency components in the seismic data was enhanced, thereby improving the accuracy of the inversion results.
  • a multi-parameter convergence method is used to predict the distribution of dominant reservoirs in the inversion parameter data volume.
  • the specific process of predicting the distribution of dominant reservoirs in the inversion parameter data volume using a multi-parameter intersection method is as follows:
  • the remaining data volume is obtained, and the distribution of dominant reservoirs in the remaining data volume is identified.
  • the rock physical characteristics of the study section are obtained through the analysis of existing drilling data in step S100. These rock physical characteristics indicate that the wave impedance of the eruptive rocks varies over a relatively large range and partially overlaps with that of the limestone. Therefore, it is relatively difficult to predict limestone reservoirs using only parameters such as wave impedance.
  • the reservoir parameters are not sensitive and the differentiation is not obvious, which leads to a large error in reservoir prediction based on a single parameter histogram. Therefore, the above technical solution overcomes the technical problem of the difficulty in lithology identification and reservoir distribution prediction by adopting a multi-parameter convergence method and proposing a multi-parameter convergence step-by-step method, thereby improving the accuracy of reservoir prediction. This allows for a more accurate quantitative description of the spatial distribution and spread characteristics of dominant reservoirs, such as generating a plane map of the thickness distribution of dominant reservoirs, providing reference maps for subsequent exploration deployment.
  • the inversion parameter data volume includes P-wave velocity data volume and S-wave velocity data volume, that is, the inversion low-frequency model constructed in S300 can perform P-wave velocity inversion and S-wave velocity inversion.
  • the limestone reservoirs in the study area reservoir and non-reservoir discrimination needs to be performed within the remaining data volume.
  • Limestone reservoirs which are dominant carbonate reservoirs, generally exhibit low impedance and low density petrophysical characteristics.
  • the inversion parameter data volume includes both P-wave impedance and density data volumes. That is, the low-frequency inversion model constructed in S300 can also perform P-wave impedance and density inversion.
  • a specific implementation process of S500 is as follows:
  • step S600 is further included after step S500.
  • the thickness values of dominant reservoirs are extracted along the target layer for seismic interpretation in the study area, taking the top and bottom of the layer as the boundary, to obtain a planar map of dominant reservoir thickness distribution.
  • extracting the thickness value of the dominant reservoir along the stratigraphic interval specifically refers to: quantitatively predicting the scale of the dominant reservoir in the subsalt carbonate rocks by using the parameter range of the dominant reservoir polygon delineated after multi-parameter intersection, and obtaining the thickness value of the dominant reservoir in each stratigraphic interval within the study area.
  • Figures 3 to 9 show the process and results of applying the above embodiments to the identification of subsalt carbonate reservoirs in a certain block.
  • igneous rocks as special geological bodies in the subsalt carbonate strata of complex lithofacies areas, have the following development patterns: intrusive rocks are mostly vertically upwelling along faults and distributed in planar bedding platy patterns; extrusive rocks have the distribution characteristics of "planar concave ring shape, vertically disordered stripes, and near-major faults".
  • the development pattern configuration of special geological bodies was added to both the constructed low-frequency impedance inversion model and the low-frequency P-wave velocity ratio inversion model.
  • the inversion results of the constructed low-frequency impedance inversion model were close to the trend of the actual well filter curve.
  • the inversion results of the constructed low-frequency P-wave velocity ratio inversion model were close to the trend of the actual well filter curve.
  • Part A is a cross-sectional view of the P-wave impedance data volume obtained by using the inverted low-frequency model
  • Part B is a cross-sectional view of the S-wave impedance data volume obtained by using the inverted low-frequency model.
  • the lateral variation of elastic parameters such as P-wave impedance and S-wave impedance conforms to the lateral variation trend of the subsalt carbonate rock strata in this block.
  • Part A is a cross-sectional view of the density data volume obtained using the inverted low-frequency model
  • Part B is a cross-sectional view of the gamma data volume obtained using the inverted low-frequency model.
  • the lateral variation of physical properties such as density and gamma is consistent with the lateral variation trend of the subsalt carbonate rock strata in this block.
  • the prediction error of the dominant limestone reservoir obtained by multi-parameter convergence differs by 88 meters at a certain stratum compared to single-parameter convergence. Specifically, the prediction error of the dominant limestone reservoir obtained by single-parameter convergence at the stratum shown in the figure reaches 124 meters, while the prediction error of the dominant limestone reservoir obtained by the multi-parameter convergence and step-by-step convergence method proposed in this embodiment is only 36 meters.
  • Figure 9 shows the contiguous distribution of reservoirs in the target seismic prediction zone within the block.
  • the reservoirs exhibit good contiguousness, with thicknesses ranging from 30 to 200 m, with the eastern reservoirs exhibiting the greatest thickness. This distribution is consistent with existing well data and sedimentary patterns.
  • the pre-drilling reservoir prediction accuracy was improved from 60% to 85% compared to traditional pre-stack inversion methods, demonstrating the effectiveness of this application.
  • This application provides a device for identifying dominant carbonate reservoirs in complex lithofacies zones, comprising a lithology determination module, a development pattern configuration module, a low-frequency inversion model construction module, a multi-parameter pre-stack inversion module, and a reservoir identification module connected in sequence, wherein:
  • the lithology determination module is used to determine the rock type and rock physical properties of the study area
  • the developmental pattern configuration module is used to sculpt the developmental pattern configuration of specific geological bodies within a rock type
  • the inversion low-frequency model construction module is used to construct an inversion low-frequency model under phase control constraints based on the developmental pattern configuration using an iterative modeling method.
  • the multi-parameter pre-stack inversion module is used to perform multi-parameter pre-stack inversion of the study section using the inversion low-frequency model, and obtain the inversion parameter data volume of the study section;
  • the reservoir identification module is used to predict the distribution of dominant reservoirs in the inversion parameter data volume using a multi-parameter intersection method.
  • the developmental pattern configuration of a specific geological body within a rock type is sculpted in the developmental pattern configuration module, including:
  • the development pattern of special geological bodies is sculpted to obtain the development pattern configuration of special geological bodies.
  • an iterative modeling method is used to construct a phase-controlled constrained low-frequency inversion model, including:
  • the current low-frequency inversion model will be used as the final low-frequency inversion model.
  • a multi-parameter pre-stack inversion of the study section is performed using an inversion low-frequency model to obtain the inversion parameter data volume of the study section, including:
  • Synthetic seismic records for the study area were generated using the seismic wavelet decomposition and reconstruction method.
  • the synthetic seismic records are input into the low-frequency inversion model for multi-parameter pre-stack inversion to obtain the inversion parameter data volume of the study area.
  • the reservoir identification module uses a multi-parameter intersection method to predict the distribution of dominant reservoirs in the inversion parameter data volume, including:
  • the lithological influence of special geological bodies in the inversion parameter data volume is eliminated by using a multi-parameter intersection method to obtain the remaining data volume, and the distribution of dominant reservoirs in the remaining data volume is identified.
  • the rock type includes limestone and igneous rock
  • the special geological body includes igneous rock
  • 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.
  • a multi-parameter intersection method is used to exclude the lithological influence of special geological bodies in the inversion parameter data volume, obtaining the remaining data volume, and identifying the dominant reservoir distribution in the remaining data volume, including:
  • Pre-stack migration data volume is generated by combining seismic data from the study area
  • the device for identifying dominant carbonate reservoirs in complex lithofacies zones proposed in this application further includes a reservoir thickness mapping module.
  • the reservoir thickness mapping module is used to extract the thickness value of the dominant reservoir along the layer segment based on the predicted distribution of the dominant reservoir, with the top and bottom of the target layer segment for seismic interpretation in the study area as the boundary, to obtain a planar map of the dominant reservoir thickness distribution.
  • Each module of the device for identifying dominant carbonate reservoirs in complex lithofacies zones can be applied to a computing device containing a memory and a processor.
  • embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor.
  • the processor executes the computer program, it implements a method for identifying dominant carbonate reservoirs in complex lithofacies zones as described in the above method embodiments.
  • embodiments of this application also provide a machine-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a method for identifying dominant carbonate reservoirs in complex lithofacies zones as described in the above method embodiments.
  • the device embodiments described above are merely illustrative.
  • the units described as separate components may or may not be physically separate.
  • the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.
  • each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware.
  • This computer software product can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

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Abstract

一种复杂岩相区优势碳酸盐岩储层识别方法，包括：确定研究区段的岩石类型和岩石物理性质（S100）；雕刻出岩石类型中特殊地质体的发育模式构型（S200）；在发育模式构型基础上，采用迭代建模方法构建相控约束下的反演低频模型（S300）；利用反演低频模型进行研究区段的多参数叠前反演，得到研究区段的反演参数数据体（S400）；采用多参数交汇方式进行反演参数数据体中优势储层分布的预测（S500）。还提供一种复杂岩相区优势碳酸盐岩储层识别装置和电子设备。通过相控约束将代表地貌相的构型属性和代表岩性相的岩性反演综合考虑，属性横向分辨率和反演纵向分辨率相结合，实现复杂岩相区优势碳酸盐岩储层的高精度识别。

Description

复杂岩相区优势碳酸盐岩储层识别方法、装置及电子设备
相关申请的交叉引用
本申请要求2024年04月29日提交的中国专利申请202410530509.0的权益，该申请的内容通过引用被合并于本文。
技术领域
本申请属于石油勘探技术领域，具体地涉及一种复杂岩相区优势碳酸盐岩储层识别方法、装置及电子设备。
背景技术
地震岩性学是研究从地震资料中提取有关岩性相和地貌相相关信息的学科，它是地震属性、地震反演、随机建模技术和地震沉积多学科的交叉产物。在地震勘探中，通过观测地震波在地下介质中的传播和反射情况，可以获得与岩石属性相关的信息，如波速、衰减系数、密度等，通过对这些信息的分析，可以推断地下岩石的岩性类型和组合，为储层预测提供重要依据。
复杂岩相碳酸盐岩地层内主要发育灰岩和火成岩。灰岩主要为藻灰岩、球粒灰岩和介壳灰岩等。火成岩主要为侵入岩和喷发岩。侵入岩是指侵入相岩石类型，主要以辉绿岩为主。喷发岩是指喷出相岩石类型，主要以玄武岩为主。碳酸盐岩优势储层发育部位在BVE组地层的岩性主要以藻灰岩和球粒灰岩为主，在ITP组地层的岩性主要以介壳灰岩为主。泥灰岩和泥晶灰岩由于含泥质较多，物性较差，密度较大，因此通常不为优势储层发育部位。碳酸盐岩储层发育受控于构造背景、成岩作用、沉积环境和后期溶蚀改造等多种因素，储层展布非均质较强。传统的地震资料反演方法优势在于具有最佳纵向分辨率，能直观反映储层岩性和厚度，局限在于损失了横向分辨率，易模糊储层形状和结构等储层非均质性，用于反演的层状初始模型降低了反演的横向分辨率，破坏了储层的地貌相特征，对研究储层非均质性不利，这种通常忽略了岩相的差异性，无法准确描述碳酸盐岩储层的分布特征。
火成岩为复杂岩相碳酸盐岩区域所发育的一种主要特殊地质体，通常在复杂岩相碳酸盐岩区域还存在例如浊积水道、盐丘和碳酸盐岩溶洞等特殊地质体。基于这些特殊地质体的存在，若利用常规反演模型进行参数反演，其未考虑地层产状、岩性横向变化等，在地层横向变化比较剧烈或者岩性变化比较大的地区会产生很大的误差，所获得的井曲线不具有代表性。
综上所述，为了准确地识别复杂岩相区碳酸盐岩的优势储层空间展布，亟需对现有的反演模型进行改进。
发明内容
本申请实施例的目的是提供一种复杂岩相区优势碳酸盐岩储层识别方法、装置及电子设备，还提供一种机器可读存储介质，用以克服传统地震资料反演无法准确识别复杂岩相区优势碳酸盐岩储层的技术问题。
为了实现上述目的，本申请的第一方面提供一种复杂岩相区优势碳酸盐岩储层识别方法，所述方法包括：
确定研究区段的岩石类型和岩石物理性质；
雕刻出岩石类型中特殊地质体的发育模式构型；
在所述发育模式构型基础上，采用迭代建模方法构建相控约束下的反演低频模型；
利用反演低频模型进行研究区段的多参数叠前反演，得到研究区段的反演参数数据体；
采用多参数交汇方式进行反演参数数据体中优势储层分布的预测。
在一个可选实施例中，所述雕刻出岩石类型中特殊地质体的发育模式构型，包括：
构建特殊地质体的地质模型，并进行正演模拟，确定出特殊地质体的发育位置特征；
结合岩石物理性质和实际地震响应，确定出特殊地质体的发育形状特征；
根据所述发育位置特征和发育形状特征，对特殊地质体的发育模式进行雕刻，得到特殊地质体的发育模式构型。
在一个可选实施例中，所述在所述发育模式构型基础上，采用迭代建模方法构建相控约束下的反演低频模型，包括：
在相控约束下构建初始状态的反演低频模型；
以所述发育模式构型为基础，向反演低频模型中补充特殊地质体，并更新反演低频模型，利用更新后的反演低频模型进行叠前多参数反演得到反演结果；
判断反演结果是否与真实井过滤曲线趋势一致，若是，则执行下一步，否则跳转至上一步；
将当前反演低频模型作为最终的反演低频模型。
在一个可选实施例中，所述利用反演低频模型进行研究区段的多参数叠前反演，得到研究区段的反演参数数据体，包括：
通过地震子波分解重构方法生成研究区段的合成地震记录；
将所述合成地震记录输入反演低频模型中进行多参数叠前反演，得到研究区段的反演参数数据体。
在一个可选实施例中，所述采用多参数交汇方式进行反演参数数据体中优势储层分布的预测，包括：
采用多参数交汇方式排除反演参数数据体中特殊地质体的岩性影响，得到剩余数据体，并识别出剩余数据体中的优势储层分布。
在一个可选实施例中，所述岩石类型包括灰岩和火成岩，所述特殊地质体包括火成岩，所述反演参数数据体包括纵波速度数据体、横波速度数据体、纵波阻抗数据体和密度数据体。
在一个可选实施例中，所述采用多参数交汇方式排除反演参数数据体中特殊地质体的岩性影响，得到剩余数据体，并识别出剩余数据体中的优势储层分布，包括：
结合研究区段的地震数据生成叠前偏移数据体；
通过纵波速度数据体和横波速度数据体交汇分析，区分出灰岩和火成岩两类岩性，并利用该区分结果从叠前偏移数据体中排除火成岩岩性影响，得到灰岩数据体；
通过纵波阻抗数据体和密度数据体交汇分析，区分出储层和非储层，并利用该区分结果从灰岩数据体中排除非储层，得到优势储层分布数据体。
在一个可选实施例中，所述方法还包括：
根据优势储层分布的预测结果，以研究区段中进行地震解释的目的层段顶底为界，沿层段提取优势储层的厚度值，得到优势储层厚度分布平面图。
本申请的第二方面提供一种复杂岩相区优势碳酸盐岩储层识别装置，所述装置包括：
岩性确定模块，用于确定研究区段的岩石类型和岩石物理性质；
发育模式构型模块，用于雕刻出岩石类型中特殊地质体的发育模式构型；
反演低频模型构建模块，用于在所述发育模式构型基础上，采用迭代建模方法构建相控约束下的反演低频模型；
多参数叠前反演模块，用于利用反演低频模型进行研究区段的多参数叠前反演，得到研究区段的反演参数数据体；
储层识别模块，用于采用多参数交汇方式进行反演参数数据体中优势储层分布的预测。
在一个可选实施例中，所述发育模式构型模块中，雕刻出岩石类型中特殊地质体的发育模式构型，包括：
构建特殊地质体的地质模型，并进行正演模拟，确定出特殊地质体的发育位置特征；
结合岩石物理性质和实际地震响应，确定出特殊地质体的发育形状特征；
根据所述发育位置特征和发育形状特征，对特殊地质体的发育模式进行雕刻，得到特殊地质体的发育模式构型。
在一个可选实施例中，所述反演低频模型构建模块中，在所述发育模式构型基础上，采用迭代建模方法构建相控约束下的反演低频模型，包括：
在相控约束下构建初始状态的反演低频模型；
以所述发育模式构型为基础，向反演低频模型中补充特殊地质体，并更新反演低频模型，利用更新后的反演低频模型进行叠前多参数反演得到反演结果；
判断反演结果是否与真实井过滤曲线趋势一致，若是，则执行下一步，否则跳转至上一步；
将当前反演低频模型作为最终的反演低频模型。
在一个可选实施例中，所述多参数叠前反演模块中，利用反演低频模型进行研究区段的多参数叠前反演，得到研究区段的反演参数数据体，包括：
通过地震子波分解重构方法生成研究区段的合成地震记录；
将所述合成地震记录输入反演低频模型中进行多参数叠前反演，得到研究区段的反演参数数据体。
在一个可选实施例中，所述储层识别模块中，采用多参数交汇方式进行反演参数数据体中优势储层分布的预测，包括：
采用多参数交汇方式排除反演参数数据体中特殊地质体的岩性影响，得到剩余数据体，并识别出剩余数据体中的优势储层分布。
在一个可选实施例中，所述岩石类型包括灰岩和火成岩，所述特殊地质体包括火成岩，所述反演参数数据体包括纵波速度数据体、横波速度数据体、纵波阻抗数据体和密度数据体。
在一个可选实施例中，所述采用多参数交汇方式排除反演参数数据体中特殊地质体的岩性影响，得到剩余数据体，并识别出剩余数据体中的优势储层分布，包括：
结合研究区段的地震数据生成叠前偏移数据体；
通过纵波速度数据体和横波速度数据体交汇分析，区分出灰岩和火成岩两类岩性，并利用该区分结果从叠前偏移数据体中排除火成岩岩性影响，得到灰岩数据体；
通过纵波阻抗数据体和密度数据体交汇分析，区分出储层和非储层，并利用该区分结果从灰岩数据体中排除非储层，得到优势储层分布数据体。
在一个可选实施例中，所述装置还包括储层厚度构图模块，所述储层厚度构图模块用于根据优势储层分布的预测结果，以研究区段中进行地震解释的目的层段顶底为界，沿层段提取优势储层的厚度值，得到优势储层厚度分布平面图。
本申请的第三方面提供一种电子设备，包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述处理器执行所述程序时实现如本申请的第一方面所述的一种复杂岩相区优势碳酸盐岩储层识别方法。
本申请的第四方面提供一种机器可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现如本申请的第一方面所述的一种复杂岩相区优势碳酸盐岩储层识别方法。
通过上述技术方案，本申请具有的主要有益效果为：
上述技术方案结合了发育模式构型、相控约束下迭代法建模、多参数叠前反演和多参数交汇方式，其中，相控约束下迭代法建模构建反演低频模型时以发育模式构型为基础，即补入了特殊地质体到模型中进行迭代，实现了噪声干扰的有效消除以及地震数据中有用信息的精确提取，进而得到精确的岩相解释结果，提高了复杂岩相区优势碳酸盐岩储层的识别精度。
本申请实施例的其它特征和优点将在随后的具体实施方式部分予以详细说明。
附图说明
图1为本申请实施例提供的复杂岩相区优势碳酸盐岩储层识别方法的一种示意性流程图；
图2为本申请实施例提供的复杂岩相区优势碳酸盐岩储层识别方法的另一种示意性流程图；
图3为火成岩发育模式构型的一种示意图；
图4为特殊地质体补充后得到的反演低频模型，其中，A部分为波阻抗反演低频模型示意图，B部分为纵横波速度比反演低频模型示意图，C部分为雕刻的特殊地质体示意图；
图5为纵波阻抗数据体和横波阻抗数据体的一种对比示意图，其中，A部分为纵波阻抗数据体，B部分为横波阻抗数据体；
图6为密度数据体和伽马数据体的一种对比示意图，其中，A部分为密度数据体，B部分为伽马数据体；
图7为多参数交汇方式的一种示意图；
图8为优势储层预测结果对比图，其中，A部分为单参数优势储层预测结果的一种剖面示意图，B为本申请实施例实现的优势储层预测结果的一种剖面示意图；
图9为预测得到的一种优势储层厚度分布平面图。
具体实施方式
以下结合附图对本申请实施例的具体实施方式进行详细说明。应当理解的是，此处所描述的具体实施方式仅用于说明和解释本申请实施例，并不用于限制本申请实施例。
为便于理解本申请实施例，以下内容就实施例涉及的相关技术特征进行说明。
研究区段：本申请所涉及的研究区段处于复杂岩相盐下碳酸盐岩地层。
叠前反演：叠前地震资料比叠后地震资料所包含的地下地质信息更丰富，与此同时，叠前反演在反演精度和储层预测效率上要优于叠后反演，现已成为储层预测和流体识别领域最主要的技术手段之一。
单参数分异性：受复杂岩相碳酸盐岩储层区域的非均质性影响，单参数的储层非储层分异性较差。
交汇图：为一种综合分析多个地震属性参数的工具，通过可视化不同参数之间的交汇关系，能够更加全面地了解储层的空间分布和特征。
方法实施例
参阅图1，本申请实施例提供的一种复杂岩相区优势碳酸盐岩储层识别方法可包括步骤S100、步骤S200、步骤S300、步骤S400和步骤S500。
S100.确定研究区段的岩石类型和岩石物理性质。
本实施例中，岩石类型包括灰岩和火成岩。
在本申请的一个可选实施例中，为确定研究区段的岩石类型和岩石物理性质，可以对已有钻井资料进行复杂岩相岩石物理分析和测井识别后确定。
作为一种示例，分析已有钻井资料可知，火成岩和灰岩两大类岩性在岩石密度值上存在差异，因此第一步可通过密度差异区分出研究区段的岩性大类。其中，发育灰岩主要为藻灰岩、球粒灰岩和介壳灰岩，火成岩主要为侵入岩和玄武岩，侵入相岩石类型以辉绿岩为主，喷出相岩石类型主要以玄武岩为主。火成岩中侵入岩的岩石密度值通常大于喷发岩。侵入岩的岩石密度值大多大于2.80g/cm3，密度平均值通常在2.92g/cm3左右。喷发岩的岩石密度值大多大于2.70g/cm3，密度平均值在2.75g/cm3。第二步为不同类型碳酸盐岩的分类识别，碳酸盐岩优质储层发育部位(BVE组岩性以藻灰岩、球粒灰岩为主，ITP组岩性以介壳灰岩为主)主要是藻灰岩、球粒灰岩和介壳灰岩，其岩石密度值通常为2.3-2.6g/cm3，密度平均值为2.46g/cm3。而泥灰岩、泥晶灰岩由于含泥质较多，物性较差，密度较大，其均值为2.6g/cm3。第三步为火成岩岩性识别，可基于ECS能谱和GR能谱测井分析，将Th、Fe、K作为火成岩岩性识别的指示元素，优选反映火成岩基性的参数，并引入火成岩指数(Lig)进行定量识别，例如，L≥20为辉绿岩，L＜20为玄武岩，较好地判别出火成岩中的喷发岩(以玄武岩为主)及侵入岩(以辉绿岩为主)，经样品验证，辉绿岩和玄武岩的识别符合率达95％。
作为另一种示例，可采用Ca/Si元素比值法区分研究区段的岩性大类，ECS测井中Ca、Si可以分别作为火成岩与碳酸盐岩指示元素。例如，在一个具体应用中，Ca/Si>1.2为碳酸盐岩，Ca/Si<1.2为火成岩，实现碳酸盐岩与火成岩的大类识别。为说明Ca/Si元素比值法对岩性大类识别的有效性，利用本申请提出的Ca/Si元素比值法对92个火成岩样品进行岩性识别，火成岩样品识别成功88个，识别符合率95.65％。在进行不同类型碳酸盐岩的分类识别时，可采用基于曲线重构和人工智能模糊聚类的碳酸盐岩岩性识别方法。该识别方法具体是指：1)为识别BVE组岩性，应用自然伽马曲线、去铀伽马曲线构建一条含泥量指示曲线，识别BE组中的藻灰岩、泥质藻灰岩、球粒灰岩、泥质球粒灰岩、泥晶灰岩，经样品验证，该方法对BVE组岩性的识别符合率达80％；2)ITP组选用主要的四条岩性敏感曲线进行人工智能模糊聚类分析，识别出介壳灰岩、泥灰岩和泥岩，其中，主要的四条岩性敏感曲线分别为自然伽马曲线、密度曲线、中子曲线和孔隙度曲线，经样品验证，基于人工智能模糊聚类分析的ITP组岩性识别符合率达100％。
S200.雕刻出岩石类型中特殊地质体的发育模式构型。
具体而言，本申请中，发育模式构型是指相发育模式刻画，并且针对碳酸盐岩储层的主要特殊地质体火成岩进行雕刻和构型。
例如，在一个具体实施例中，针对碳酸盐岩储层的主要特殊地质体火成岩进行雕刻和构型的具体过程如下：
S210.构建火成岩的地质模型，并进行正演模拟，确定出火成岩的发育位置特征；
S220.结合岩石物理性质和实际地震响应，确定出火成岩的发育形状特征；
S230.根据所述发育位置特征和发育形状特征，对火成岩的发育模式进行雕刻，得到火成岩的发育模式构型。
通过地质模型的构建和正演模拟，模拟证实火成岩中的侵入岩的发育位置具有“强振幅+低频率+高阻抗”特征，侵入岩发育位置振幅较强，并且随着厚度增加，振幅增强，同相轴增宽。并结合岩石物理性质和实际地震响应，侵入岩的发育形状多为：纵向上沿断层上涌、平面片状分布。在确定出侵入岩的发育位置特征和发育形状特征后，据此规律对其发育模式进行雕刻和构型，从而得到侵入岩的发育模式构型。火成岩中的喷发岩由于本身岩性特征和蚀变变化等，地震响应明显不如侵入岩，局部呈现空白反射以及杂乱反射，据此规律可对喷发岩的发育模式进行雕刻和构型，得到喷发岩的发育模式构型。上述方案运用了水下火山机构模式指导下的地震多属性空间雕刻技术，并结合了地震属性分析、多信息融合、速度分析、相干分析和三维体切片等技术手段，在对火成岩的空间分布进行预测后再进行其发育模式的雕刻和构型，得到火成岩的发育模式构型。
作为本申请的一种改进实施例，在进行地震相发育模式刻画前，对地震资料进行优化重处理。因原始地震资料受厚盐层屏蔽盐下横向能量不均、速度模型精度影响盐下构造形态可靠性、局部信噪比低影响地震成像品质等，本改进实施例提出了一种网格层析速度约束地震资料重处理方法，该方法包括以下步骤：网格层析速度更新，强化结构模型和井信息约束，并进行构造导向滤波。
经验证，通过对地震资料进行上述重处理过程，使得地震成像中的盐下振幅能量更加均衡，盐底和盐下地层同相轴连续性更好，构造形态更加合理，可见，上述基于网格层析速度约束的地震资料重处理方法，提升了盐下构造地震成像品质，为后续的精细刻画地震相展布特征提供了可靠的数据保障。
作为一种示例，在一个具体应用中，通过以下方式进行厚盐层屏蔽影响下的地震相发育模式刻画，具体包括步骤1至步骤3。
步骤1，地震资料优化重处理。
步骤2，根据如前所述步骤S100中建立的碳酸盐岩和火成岩分类方法，开展岩石物理正演分析。
在该具体应用中，实钻盐下地层波阻抗大小为火成岩>灰岩>泥岩>盐岩，盐下地层顶部(盐底界面)构造高部位向低部位沉积岩性由灰岩变为含泥灰岩，波峰反射由强变到弱，在该应用中，目的层底部(K44顶)构造高向低部位沉积灰岩到含泥岩，波峰反射由弱到强。而相对于火山岩，侵入岩以辉绿岩为主，根据实际的钻井资料进行岩性组合的刻画，岩性组合是指“盐岩-硬石膏-辉绿岩-灰岩”，利用五种地质模型及正演模拟，模拟证实侵入岩发育位置具有“强振幅+低频率+高阻抗”特征，认为侵入岩发育位置振幅较强，并且随着厚度增加，振幅越强，同相轴越宽。结合岩石物理性质和实际地震响应，预测多为纵向上沿断层上涌、平面片状分布，以此规律对其侵入岩发育特征进行雕刻和构型。喷发岩由于本身岩性特征和蚀变变化等，地震响应明显不如侵入岩，局部呈现空白反射、杂乱反射，以水下火山机构模式指导下地震多属性空间雕刻技术，利用地震属性分析，多信息融合、速度分析、相干分析及三维体切片等技术手段，预测火成岩空间分布进行构型。基于以上正演研究，揭示出地层不同相带和不同岩相组合的地震属性差异性，为后续地震相刻划提供理论基础。
步骤3，进行地震相发育模式刻画。在该具体应用中，为综合利用多种地震属性精细刻画出地震相展布特征，沉积相对应的浅湖台地的地层厚度薄(小于300m)，同相轴数量相对较少，振幅能量强，半深湖相的地层厚度大(大于300m)，同相轴数量相对较多，振幅能量弱，沉积亚相的台内亚相地层厚度薄，主要为单轴连续反射，振幅能量强，台缘亚相的地层厚度相对较大，丘状弱反射特征，台坡亚相的地层厚度介于台内和台缘，低频中弱振幅连续反射，基于以上认识进行地震相刻画，作为后续储层反演的相约束参数。
在现有技术中，受巨厚盐层覆盖和多期火成岩侵入影响，复杂岩相区碳酸盐岩储层的地震响应规律复杂，传统叠前反演方法无法准确描述碳酸盐岩储层的分布特征，给研究区段的勘探评价和井位部署带来挑战，通过步骤S100至S200，实施了复杂岩相岩石物理分析、地球物理正演和特殊地质体的发育模式构型，为后续叠前反演模型的构建提供基础。
S300.在发育模式构型基础上，采用迭代建模方法构建相控约束下的反演低频模型。
例如，在一个具体实施例中，在发育模式构型基础上，采用迭代建模方法构建相控约束下的反演低频模型的具体过程如下：
S310.在相控约束下构建初始状态的反演低频模型；
S320.以发育模式构型为基础，向反演低频模型中补充特殊地质体，并更新反演低频模型，利用更新后的反演低频模型进行叠前多参数反演，生成反演结果；
S330.判断反演结果是否与真实井过滤曲线趋势一致，若是，则执行S340，否则跳转至S320；
S340.将当前反演低频模型作为最终的反演低频模型。
需要说明的是，S330中，判断反演结果是否与真实井过滤曲线趋势一致时，可以通过设定一个阈值，当反演结果对应的反演参数曲线趋势与该反演参数对应的真实井过滤曲线趋势之间的差异度小于该阈值时，认为反演结果与真实井过滤曲线趋势一致，可见，所述一致为相对一致，可以理解为反演结果与真实井过滤曲线的趋势接近。
此外，S310中，在相控约束下构建初始状态的反演低频模型的一种具体实施过程如下：
S3101.根据研究区段的地震层位解释结果，建立地层框架结构；
S3102.通过空间插值方法，在地层框架结构内将测井数据和/或地震处理时生成的叠加速度数据在每个地层内进行插值，得到初始状态的反演低频模型。
例如：为构建波阻抗反演低频模型，测井数据为波阻抗数据；为构建纵波速度反演低频模型，地震处理时的叠加速度数据为纵波速度数据。
在地球物理勘探中，地震数据中低频和高频信息对于刻画油气储层均非常重要，低频信息主要用于定义地下油藏的几何格架，低频信息与地下介质的绝对波阻抗有着直接的联系。由于地震数据频带宽度有限，叠前反演具有多解性特征，即通常有多个不同波阻抗反演模型与实际地震记录匹配。为最大限度地降低储层预测过程中的多解性，现有技术中提出了相控约束下低频反演模型的构建方法。例如，在一些普通实施例中，相控约束下低频反演模型的构建基于如下原理进行。
由于地震采集系统的限制，地震数据通常都不包含一些低频成分，这些是不能通过反演得到的，但确是绝对波阻抗的一部分，地震直接反演结果中不包含10Hz以下的低频成分，需从其他资料提取予以补偿。从地震资料出发，以测井资料和钻井数据为基础，通过包含更多低频信息和高频信息的井数据可建立基本反映沉积体地质特征的低频反演初始模型。例如，建立初始波阻抗反演低频模型或测井曲线参数反演低频模型，就是把横向上连续变化的地震界面信息与高分辨率的测井信息相结合的过程，即将测井数据或地震处理时生成的叠加速度数据在地层框架结构的约束下进行内插或外推，内插或外推采用诸如反距离权重插值等空间插值方法，测井数据外推时可以选择使用速度数据体约束控制，可知的，要求使用的速度数据体能够反映地质沉积变化趋势，并且速度体与井上速度曲线具有很好的相关性，把速度数据体的甚低频信息加进去，即保留了速度谱的2Hz成分，又将测井数据中的3～6Hz频率成分补充到了模型中，从而产生一个平滑、闭合的实体模型，例如得到波阻抗反演低频模型或测井曲线参数反演低频模型等。因此，合理地建立地层框架结构和定义恰当的空间插值方式是相控约束下反演低频模型构建时最为关键的两个部分。建立起的模型在横向上连续变化是反演时最重要的约束参数，实质是指各井点处的物性(有时为电性)参数在横向上的内插和外推是光滑的，目的是确保反演结果符合已知的(假定井点处的物性参数或电性参数为已知的)地质规律。
然而，本申请实施例中的研究区段地层含有火成岩等特殊地质体，通过普通实施例中的相控约束下低频反演模型进行叠前反演时，反演结果存在较大误差，所获得的井曲线不具有代表性，因为简单的井间横向线性内插和外推方式未考虑地层产状、岩性横向变化等，在地层横向变化比较剧烈或者岩性变化较大的地区会产生很大的误差。因此，本申请实施例中，针对含有特殊地质体的复杂岩相碳酸盐岩地层，为充分考虑该类地层的产状、岩性横向变化等因素，在如上技术方案中，以S200中识别得到的特殊地质体的发育模式构型为基础，在构建的初始状态的反演低频模型中补入特殊地质体，并通过迭代法更新反演低频模型，直至反演低频模型输出的反演结果与真实井过滤曲线趋势一致，实现了高精度地反演低频模型的构建，提升了优势碳酸盐岩储层分布预测结果的准确性。
S400.利用反演低频模型进行研究区段的多参数叠前反演，得到研究区段的反演参数数据体。需要理解的是，多参数叠前反演是指目标反演参数为多个，即得到的反演参数数据体包括多个不同反演参数的数据体。
示例性地，在一个具体实施例中，利用反演低频模型，建立了地震相和沉积相地质规律约束，可通过波形指示反演、叠前弹性参数反演和叠前贝叶斯反演等，同时获得多个反演参数数据体，反演参数数据体包括弹性参数数据体和物性参数数据体，弹性参数数据体包括纵波阻抗数据体、横波阻抗数据体等，物性参数数据体包括密度数据体和伽马数据体等。
示例性的，在一个具体实施例中：
利用反演低频模型进行研究区段的多参数叠前反演时，输入反演低频模型的地震资料为通过地震子波分解重构方法生成的研究区段的合成地震记录。
通过对比子波分解重构前后的地震资料可知，通过地震子波分解重构方法得到的合成地震记录与实际地震资料的相关性最好，其中，发现膏岩层底界面的地震资料频率有了较为明显地提高，地震资料中的高频成分的能量有所增强，从而提高了反演结果的准确性。
相应地，S400的一种具体实施过程为：
S410.通过地震子波分解重构方法生成研究区段的合成地震记录；
S420.将合成地震记录输入反演低频模型中进行多参数叠前反演，得到研究区段的反演参数数据体。
S500.采用多参数交汇方式进行反演参数数据体中优势储层分布的预测。
示例性地，在一个具体实施例中，采用多参数交汇方式进行反演参数数据体中优势储层分布的预测的具体过程如下：
采用多参数交汇方式排除反演参数数据体中特殊地质体的岩性影响后得到剩余数据体，并识别出剩余数据体中的优势储层分布。
可知的，经步骤S100对已有钻井资料分析获悉到研究区段的岩石物理特征，这些岩石物理特征表明喷发岩的波阻抗变化范围相对较大，和灰岩部分重叠，因此仅仅通过例如波阻抗这个参数进行灰岩储层的预测相对比较困难，储层参数敏感性较差，分异性不明显，从而导致仅仅通过某一个单参数直方图来进行储层预测存在较大的误差，因此上述技术方案中，通过采用多参数交汇方式以及提出了多参数交汇分步法，克服了岩性判别和储层分布预测比较困难的技术问题，提升了储层预测的精度，以便于对优势储层空间分布和展布特征进行更为精确的定量化描述，例如生成优势储层厚度分布平面图，为后续勘探部署提供参考图件。
结合步骤S100中针对已有钻井资料的分析，因为研究区段的特殊地质体为火成岩，为从反演参数数据体中排除火成岩的岩性影响，可采用纵横波速度交汇实现，因此，相应地，反演参数数据体中包括纵波速度数据体和横波速度数据体，即S300中构建的反演低频模型可进行纵波速度反演和横波速度反演。为识别出剩余数据体中的优势储层分布，即识别出研究区段地层中的灰岩储层，需在剩余数据体中进行储层和非储层的判别，作为碳酸盐岩优势储层的灰岩储层一般具有低阻抗和低密度岩石物理特征，因此可以通过采用纵波阻抗和密度进行交汇分析，将相对较低的纵波阻抗和密度优选出来，即可预测碳酸盐岩优势储层的规模，因此，相应地，反演参数数据体中包括纵波阻抗数据体和密度数据体，即S300中构建的反演低频模型还可进行纵波阻抗反演和密度反演。对应地，S500的一种具体实施过程如下：
S510.结合研究区段的地震数据生成叠前偏移数据体；
S520.通过纵波速度数据体和横波速度数据体交汇分析，区分出灰岩和火成岩两类岩性，并利用该区分结果从叠前偏移数据体中排除火成岩岩性的影响，而后得到灰岩数据体；
例如：将纵波速度数据体和横波速度数据体进行交汇，若纵横波速度比大于1.7，则确定该部分数据对应火成岩，若纵横波速度比小于1.7，则确定该部分数据对应灰岩；
S530.通过纵波阻抗数据体和密度数据体交汇分析，区分出储层和非储层，并利用该区分结果从灰岩数据体中排除非储层，得到优势储层分布数据体；
例如：将纵波阻抗数据体和密度数据体进行交汇分析，若纵波阻抗相对较低的同时密度也相对较低，那么确定该部分数据对应优势灰岩储层，反之，为非储层。
参阅图2，作为上述实施例的一种改进，在步骤S500之后还包括步骤S600。
S600.根据优势储层分布的预测结果，以研究区段中进行地震解释的目的层段顶底为界，沿层段提取优势储层的厚度值，得到优势储层厚度分布平面图。
作为一种示例，沿层段提取优势储层的厚度值具体是指：以多参数交汇方式交汇后圈定的优势储层多边形参数范围定量预测盐下碳酸盐岩优势储层规模，获得研究区段内各个层段优势储层的厚度值。
图3至图9给出了将上述实施例应用于某区块盐下碳酸盐岩储层识别的过程图和结果图。
如图3所示，火成岩作为复杂岩相区盐下碳酸盐岩地层中的特殊地质体，其发育模式主要为：侵入岩多为纵向上沿断层上涌、平面顺层片状分布为主；喷发岩具有“平面凹环状、纵向杂乱条带、近大断裂”的分布特征。
如图4所示，在构建的波阻抗反演低频模型和纵横波速度比反演低频模型中，均补入了特殊地质体的发育模式构型，通过发育模式构型的引入，利用构建的波阻抗反演低频模型进行叠前反演，反演结果与真实井过滤曲线趋势接近，利用构建的纵横波速度比反演低频模型进行叠前反演，反演结果与真实井过滤曲线趋势接近。
如图5所示，A部分为利用反演低频模型获得的纵波阻抗数据体的剖面展示图，B部分为利用反演低频模型获得的横波阻抗数据体的剖面展示图，由图可知，纵波阻抗和横波阻抗等弹性参数的横向变化情况符合该区块盐下碳酸盐岩地层的横向变化趋势。
如图6所示，A部分为利用反演低频模型获得的密度数据体的剖面展示图，B部分为利用反演低频模型获得的伽马数据体的剖面展示图，由图可知，密度和伽马等物性参数的横向变化情况符合该区块盐下碳酸盐岩地层的横向变化趋势。
结合图7至图8，通过多参数交汇得到的优势灰岩储层预测结果相比于单参数而言，在某个层位的储层厚度预测误差相差88米，其中，单参数方式得到的优势灰岩储层在图示层位的储层厚度预测误差达到了124米，而通过本实施例提出的多参数交汇和分步交汇方法得到的优势灰岩储层在图示层位的储层厚度预测误差仅有36米。
图9呈现了该区块内地震预测目的层段的储层连片分布情况，由图可知，储层连片性较好，厚度范围在30～200m，其中东部储层厚度最大，该分布情况与已钻井数据和沉积规律符合性较好。通过使用本申请实施例提出的复杂岩相区优势碳酸盐岩储层识别方法进行储层预测，钻前储层预测精度从传统叠前反演方法的60％提升到了85％，印证了本申请的有效性。
装置实施例
本申请实施例一种复杂岩相区优势碳酸盐岩储层识别装置，包括依次连接的岩性确定模块、发育模式构型模块、反演低频模型构建模块、多参数叠前反演模块和储层识别模块，其中：
岩性确定模块用于确定研究区段的岩石类型和岩石物理性质；
发育模式构型模块用于雕刻出岩石类型中特殊地质体的发育模式构型；
反演低频模型构建模块用于在发育模式构型基础上，采用迭代建模方法构建相控约束下的反演低频模型；
多参数叠前反演模块用于利用反演低频模型进行研究区段的多参数叠前反演，得到研究区段的反演参数数据体；
储层识别模块用于采用多参数交汇方式进行反演参数数据体中优势储层分布的预测。
在一个可选实施例中，在发育模式构型模块中，雕刻出岩石类型中特殊地质体的发育模式构型，包括：
构建特殊地质体的地质模型，并进行正演模拟，确定出特殊地质体的发育位置特征；
结合岩石物理性质和实际地震响应，确定出特殊地质体的发育形状特征；
根据发育位置特征和发育形状特征，对特殊地质体的发育模式进行雕刻，得到特殊地质体的发育模式构型。
在一个可选实施例中，在反演低频模型构建模块中，在发育模式构型基础上，采用迭代建模方法构建相控约束下的反演低频模型，包括：
在相控约束下构建初始状态的反演低频模型；
以发育模式构型为基础，向反演低频模型中补充特殊地质体，并更新反演低频模型，利用更新后的反演低频模型进行叠前多参数反演得到反演结果；
判断反演结果是否与真实井过滤曲线趋势一致，若是，则执行下一步，否则跳转至上一步；
将当前反演低频模型作为最终的反演低频模型。
在一个可选实施例中，在多参数叠前反演模块中，利用反演低频模型进行研究区段的多参数叠前反演，得到研究区段的反演参数数据体，包括：
通过地震子波分解重构方法生成研究区段的合成地震记录；
将合成地震记录输入反演低频模型中进行多参数叠前反演，得到研究区段的反演参数数据体。
在一个可选实施例中，在储层识别模块中，采用多参数交汇方式进行反演参数数据体中优势储层分布的预测，包括：
采用多参数交汇方式排除反演参数数据体中特殊地质体的岩性影响，得到剩余数据体，并识别出剩余数据体中的优势储层分布。
在一个可选实施例中，岩石类型包括灰岩和火成岩，特殊地质体包括火成岩，反演参数数据体包括纵波速度数据体、横波速度数据体、纵波阻抗数据体和密度数据体。
在一个可选实施例中，采用多参数交汇方式排除反演参数数据体中特殊地质体的岩性影响，得到剩余数据体，并识别出剩余数据体中的优势储层分布，包括：
结合研究区段的地震数据生成叠前偏移数据体；
通过纵波速度数据体和横波速度数据体交汇分析，区分出灰岩和火成岩两类岩性，并利用该区分结果从叠前偏移数据体中排除火成岩岩性影响，得到灰岩数据体；
通过纵波阻抗数据体和密度数据体交汇分析，区分出储层和非储层，并利用该区分结果从灰岩数据体中排除非储层，得到优势储层分布数据体。
在一个可选实施例中，本申请实施例提出的复杂岩相区优势碳酸盐岩储层识别装置还包括储层厚度构图模块，储层厚度构图模块用于根据优势储层分布的预测结果，以研究区段中进行地震解释的目的层段顶底为界，沿层段提取优势储层的厚度值，得到优势储层厚度分布平面图。
本申请实施例提供的一种复杂岩相区优势碳酸盐岩储层识别装置的各个模块均可以应用于包含存储器和处理器的计算设备上。
另一方面，本申请实施例还提供一种电子设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，处理器执行该计算机程序时实现如上述方法实施例所述的一种复杂岩相区优势碳酸盐岩储层识别方法。
又一方面，本申请实施例还提供一种机器可读存储介质，其上存储有计算机程序，计算机程序被处理器执行时实现如上述方法实施例所述的一种复杂岩相区优势碳酸盐岩储层识别方法。
以上所描述的装置实施例仅仅是示意性的，其中所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。本领域普通技术人员在不付出创造性的劳动的情况下，即可以理解并实施。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到各实施方式可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件。基于这样的理解，上述技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品可以存储在计算机可读存储介质中，如ROM/RAM、磁碟、光盘等，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行各个实施例或者实施例的某些部分所述的方法。
最后应说明的是：以上实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的精神和范围。

Claims (16)

  1. 一种复杂岩相区优势碳酸盐岩储层识别方法，其特征在于，所述方法包括：
    确定研究区段的岩石类型和岩石物理性质；
    雕刻出岩石类型中特殊地质体的发育模式构型；
    在所述发育模式构型基础上，采用迭代建模方法构建相控约束下的反演低频模型；
    利用反演低频模型进行研究区段的多参数叠前反演，得到研究区段的反演参数数据体；
    采用多参数交汇方式进行反演参数数据体中优势储层分布的预测。
  2. 根据权利要求1所述的复杂岩相区优势碳酸盐岩储层识别方法，其特征在于，所述雕刻出岩石类型中特殊地质体的发育模式构型，包括：
    构建特殊地质体的地质模型，并进行正演模拟，确定出特殊地质体的发育位置特征；
    结合岩石物理性质和实际地震响应，确定出特殊地质体的发育形状特征；
    根据所述发育位置特征和发育形状特征，对特殊地质体的发育模式进行雕刻，得到特殊地质体的发育模式构型。
  3. 根据权利要求1所述的复杂岩相区优势碳酸盐岩储层识别方法，其特征在于，所述在所述发育模式构型基础上，采用迭代建模方法构建相控约束下的反演低频模型，包括：
    在相控约束下构建初始状态的反演低频模型；
    以所述发育模式构型为基础，向反演低频模型中补充特殊地质体，并更新反演低频模型，利用更新后的反演低频模型进行叠前多参数反演得到反演结果；
    判断反演结果是否与真实井过滤曲线趋势一致，若是，则执行下一步，否则跳转至上一步；
    将当前反演低频模型作为最终的反演低频模型。
  4. 根据权利要求1所述的复杂岩相区优势碳酸盐岩储层识别方法，其特征在于，所述利用反演低频模型进行研究区段的多参数叠前反演，得到研究区段的反演参数数据体，包括：
    通过地震子波分解重构方法生成研究区段的合成地震记录；
    将所述合成地震记录输入反演低频模型中进行多参数叠前反演，得到研究区段的反演参数数据体。
  5. 根据权利要求1所述的复杂岩相区优势碳酸盐岩储层识别方法，其特征在于，所述采用多参数交汇方式进行反演参数数据体中优势储层分布的预测，包括：
    采用多参数交汇方式排除反演参数数据体中特殊地质体的岩性影响，得到剩余数据体，并识别出剩余数据体中的优势储层分布。
  6. 根据权利要求5所述的复杂岩相区优势碳酸盐岩储层识别方法，其特征在于，所述岩石类型包括灰岩和火成岩，所述特殊地质体包括火成岩，所述反演参数数据体包括纵波速度数据体、横波速度数据体、纵波阻抗数据体和密度数据体。
  7. 根据权利要求6所述的复杂岩相区优势碳酸盐岩储层识别方法，其特征在于，所述采用多参数交汇方式排除反演参数数据体中特殊地质体的岩性影响，得到剩余数据体，并识别出剩余数据体中的优势储层分布，包括：
    结合研究区段的地震数据生成叠前偏移数据体；
    通过纵波速度数据体和横波速度数据体交汇分析，区分出灰岩和火成岩两类岩性，并利用该区分结果从叠前偏移数据体中排除火成岩岩性影响，得到灰岩数据体；
    通过纵波阻抗数据体和密度数据体交汇分析，区分出储层和非储层，并利用该区分结果从灰岩数据体中排除非储层，得到优势储层分布数据体。
  8. 根据权利要求1所述的复杂岩相区优势碳酸盐岩储层识别方法，其特征在于，所述方法还包括：
    根据优势储层分布的预测结果，以研究区段中进行地震解释的目的层段顶底为界，沿层段提取优势储层的厚度值，得到优势储层厚度分布平面图。
  9. 根据权利要求1所述的复杂岩相区优势碳酸盐岩储层识别方法，其特征在于，所述岩石类型包括灰岩和火成岩，确定研究区段的岩石类型和岩石物理性质，包括：
    根据获取的研究区段的岩石物理分析结果判断岩石中的钙硅元素比是否大于第一阈值，若是，则确定岩石类型为灰岩，否则为火成岩；
    若岩石类型为灰岩，则根据获取的研究区段的测井曲线构建含泥量指示曲线，识别出灰岩中的BVE组，以及对所述测井曲线中的岩性敏感曲线进行模糊聚类分析，识别出灰岩中的ITP组；
    若岩石类型为火成岩，则根据研究区段的ECS能谱和GR能谱测井结果，识别出火成岩岩性。
  10. 根据权利要求1所述的复杂岩相区优势碳酸盐岩储层识别方法，其特征在于，所述岩石类型包括灰岩和火成岩，确定研究区段的岩石类型和岩石物理性质，包括：
    根据获取的研究区段钻井资料确定岩石密度差异，根据岩石密度差异区分灰岩和火成岩以及灰岩岩性；
    若岩石类型为火成岩，则根据研究区段的ECS能谱和GR能谱测井结果，识别出火成岩岩性。
  11. 根据权利要求2所述的复杂岩相区优势碳酸盐岩储层识别方法，其特征在于，所述雕刻出岩石类型中特殊地质体的发育模式构型，还包括：
    对获取的地震资料进行以下重处理：网格层析速度更新、增强结构模型和井信息约束、进行构造导向滤波，以触发执行构建特殊地质体的地质模型，并进行正演模拟，确定出特殊地质体的发育位置特征。
  12. 一种复杂岩相区优势碳酸盐岩储层识别装置，其特征在于，所述装置包括：
    岩性确定模块，用于确定研究区段的岩石类型和岩石物理性质；
    发育模式构型模块，用于雕刻出岩石类型中特殊地质体的发育模式构型；
    反演低频模型构建模块，用于在所述发育模式构型基础上，采用迭代建模方法构建相控约束下的反演低频模型；
    多参数叠前反演模块，用于利用反演低频模型进行研究区段的多参数叠前反演，得到研究区段的反演参数数据体；
    储层识别模块，用于采用多参数交汇方式进行反演参数数据体中优势储层分布的预测。
  13. 根据权利要求12所述的复杂岩相区优势碳酸盐岩储层识别装置，其特征在于，所述雕刻出岩石类型中特殊地质体的发育模式构型，包括：
    构建特殊地质体的地质模型，并进行正演模拟，确定出特殊地质体的发育位置特征；
    结合岩石物理性质和实际地震响应，确定出特殊地质体的发育形状特征；
    根据所述发育位置特征和发育形状特征，对特殊地质体的发育模式进行雕刻，得到特殊地质体的发育模式构型。
  14. 根据权利要求12所述的复杂岩相区优势碳酸盐岩储层识别装置，其特征在于，所述在所述发育模式构型基础上，采用迭代建模方法构建相控约束下的反演低频模型，包括：
    在相控约束下构建初始状态的反演低频模型；
    以所述发育模式构型为基础，向反演低频模型中补充特殊地质体，并更新反演低频模型，利用更新后的反演低频模型进行叠前多参数反演得到反演结果；
    判断反演结果是否与真实井过滤曲线趋势一致，若是，则执行下一步，否则跳转至上一步；
    将当前反演低频模型作为最终的反演低频模型。
  15. 一种电子设备，包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，其特征在于，所述处理器执行所述程序时实现权利要求1至11中任一项所述的复杂岩相区优势碳酸盐岩储层识别方法。
  16. 一种机器可读存储介质，其上存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现权利要求1至11中任一项所述的复杂岩相区优势碳酸盐岩储层识别方法。
PCT/CN2024/144136 2024-04-29 2024-12-31 复杂岩相区优势碳酸盐岩储层识别方法、装置及电子设备 Pending WO2025227808A1 (zh)

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