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

Abstract

The invention provides a complex lithofacies region dominant carbonate reservoir identification method and device and electronic equipment, and relates to the technical field of oil exploration, and the method comprises the steps: determining the rock type and rock physical properties of a research section; engraving a development mode configuration of a special geologic body in the rock type; on the basis of the development mode configuration, constructing an inversion low-frequency model under phase control constraint by adopting an iterative modeling method; performing multi-parameter pre-stack inversion of the research section by using the inversion low-frequency model to obtain an inversion parameter data volume of the research section; and predicting dominant reservoir distribution in the inversion parameter data volume by adopting a multi-parameter intersection mode. According to the method, the configuration attribute representing the geomorphic facies and the lithologic inversion representing the lithologic facies are comprehensively considered through phase control, the attribute transverse resolution and the inversion longitudinal resolution make the best of each other, and the high-precision identification of the dominant carbonate reservoir in the complex lithofacies region is realized by combining a multi-parameter intersection step-by-step method.

Description

Technical Field

[0001] This invention 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. Background Technology

[0002] 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. In 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.

[0003] Complex 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.

[0004] 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.

[0005] In conclusion, in order to accurately identify the spatial distribution of dominant reservoirs in carbonate rocks in complex lithofacies zones, it is urgent to improve the existing inversion models. Summary of the Invention

[0006] The purpose of this invention 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.

[0007] To achieve the above objectives, a first aspect of the present invention provides a method for identifying dominant carbonate reservoirs in complex lithofacies zones, the method comprising:

[0008] Determine the rock type and physical properties of the study area;

[0009] Carve out the developmental pattern and configuration of special geological bodies in rock types;

[0010] Based on the aforementioned developmental pattern configuration, an inversion low-frequency model under phase control constraints is constructed using an iterative modeling method;

[0011] 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;

[0012] A multi-parameter convergence method is used to predict the distribution of dominant reservoirs in the inversion parameter data volume.

[0013] In an optional embodiment, the carving of the developmental pattern configuration of specific geological bodies in the rock type includes:

[0014] Construct geological models of special geological bodies and perform forward modeling to determine the development location characteristics of these special geological bodies;

[0015] By combining rock physical properties and actual seismic response, the developmental shape characteristics of special geological bodies were determined;

[0016] Based on the developmental location and shape characteristics, the developmental pattern of the special geological body is sculpted to obtain the developmental pattern configuration of the special geological body.

[0017] In an optional embodiment, the step of constructing an inversion low-frequency model under phase control constraints based on the developmental pattern configuration includes:

[0018] Construct an inversion low-frequency model of the initial state under phase control constraints;

[0019] 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.

[0020] Determine whether the inversion result is consistent with the trend of the actual well filter curve. If so, proceed to the next step; otherwise, jump to the previous step.

[0021] The current low-frequency inversion model will be used as the final low-frequency inversion model.

[0022] In an optional embodiment, 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:

[0023] Synthetic seismic records for the study area were generated using the seismic wavelet decomposition and reconstruction method.

[0024] 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.

[0025] In an optional embodiment, the prediction of the dominant reservoir distribution in the inversion parameter data volume using a multi-parameter intersection method includes:

[0026] 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.

[0027] In one alternative embodiment, the rock type includes limestone and igneous rock, the special geological body includes igneous rock, and the inversion parameter data volume includes P-wave velocity data volume, S-wave velocity data volume, P-wave impedance data volume, and density data volume.

[0028] In an optional embodiment, 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:

[0029] Pre-stack migration data volume is generated by combining seismic data from the study area;

[0030] By analyzing the intersection of P-wave velocity data volumes and S-wave velocity data volumes, two types of lithology, limestone and igneous rocks, were distinguished. The influence of igneous rock lithology was then eliminated from the pre-stack migration data volume using this distinction, and the limestone data volume was obtained.

[0031] By cross-analysis of P-wave impedance data volume and density data volume, reservoirs and non-reservoirs are distinguished, and the non-reservoirs are excluded from the limestone data volume using the distinction results, thus obtaining the dominant reservoir distribution data volume.

[0032] In an optional embodiment, the method further includes:

[0033] Based on the predicted distribution of dominant reservoirs, 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.

[0034] A second aspect of the present invention provides a device for identifying dominant carbonate reservoirs in complex lithofacies zones, the device comprising:

[0035] The lithology determination module is used to determine the rock type and rock physical properties of the study area;

[0036] The developmental pattern configuration module is used to sculpt the developmental pattern configuration of special geological bodies in rock types;

[0037] 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.

[0038] 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.

[0039] 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.

[0040] In an optional embodiment, the developmental pattern configuration module sculpts the developmental pattern configuration of a specific geological body within a rock type, including:

[0041] Construct geological models of special geological bodies and perform forward modeling to determine the development location characteristics of these special geological bodies;

[0042] By combining rock physical properties and actual seismic response, the developmental shape characteristics of special geological bodies were determined;

[0043] Based on the developmental location and shape characteristics, the developmental pattern of the special geological body is sculpted to obtain the developmental pattern configuration of the special geological body.

[0044] In an optional embodiment, 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:

[0045] Construct an inversion low-frequency model of the initial state under phase control constraints;

[0046] 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.

[0047] Determine whether the inversion result is consistent with the trend of the actual well filter curve. If so, proceed to the next step; otherwise, jump to the previous step.

[0048] The current low-frequency inversion model will be used as the final low-frequency inversion model.

[0049] In an optional embodiment, 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:

[0050] Synthetic seismic records for the study area were generated using the seismic wavelet decomposition and reconstruction method.

[0051] 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.

[0052] In an optional embodiment, the reservoir identification module uses a multi-parameter intersection method to predict the distribution of dominant reservoirs in the inversion parameter data volume, including:

[0053] 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.

[0054] In one alternative embodiment, the rock type includes limestone and igneous rock, the special geological body includes igneous rock, and the inversion parameter data volume includes P-wave velocity data volume, S-wave velocity data volume, P-wave impedance data volume, and density data volume.

[0055] In an optional embodiment, 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:

[0056] Pre-stack migration data volume is generated by combining seismic data from the study area;

[0057] By analyzing the intersection of P-wave velocity data volumes and S-wave velocity data volumes, two types of lithology, limestone and igneous rocks, were distinguished. The influence of igneous rock lithology was then eliminated from the pre-stack migration data volume using this distinction, and the limestone data volume was obtained.

[0058] By cross-analysis of P-wave impedance data volume and density data volume, reservoirs and non-reservoirs are distinguished, and the non-reservoirs are excluded from the limestone data volume using the distinction results, thus obtaining the dominant reservoir distribution data volume.

[0059] In an optional embodiment, 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.

[0060] A third aspect of the present invention 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 the present invention.

[0061] A fourth aspect of the present invention 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 the present invention.

[0062] The main beneficial effects of the present invention through the above technical solution are as follows:

[0063] The above technical solution combines developmental pattern configuration, phase-controlled iterative modeling, multi-parameter pre-stack inversion, and multi-parameter convergence. Among them, 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.

[0064] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0065] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0066] Figure 1 A schematic flowchart illustrating a method for identifying dominant carbonate reservoirs in complex lithofacies zones provided in an embodiment of the present invention;

[0067] Figure 2 Another schematic flowchart for the method of identifying dominant carbonate reservoirs in complex lithofacies zones provided in this embodiment of the invention;

[0068] Figure 3 A schematic diagram of the development mode configuration of igneous rocks;

[0069] Figure 4 The inversion low-frequency model obtained after supplementing the special geological body is shown in part A, which 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, and part C is a schematic diagram of the sculpted special geological body.

[0070] Figure 5This 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.

[0071] Figure 6 This is a comparative diagram of density data volume and gamma data volume, where part A is the density data volume and part B is the gamma data volume;

[0072] Figure 7 This is a schematic diagram of a multi-parameter intersection method;

[0073] Figure 8 The diagram shows a comparison of the prediction results of dominant reservoirs. Part A is a cross-sectional schematic diagram of the prediction results of a single-parameter dominant reservoir, and Part B is a cross-sectional schematic diagram of the prediction results of dominant reservoirs implemented in an embodiment of the present invention.

[0074] Figure 9 This is a plan view of the thickness distribution of a predicted advantageous reservoir. Detailed Implementation

[0075] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0076] To facilitate understanding of the embodiments of the present invention, the following describes the relevant technical features involved in the embodiments.

[0077] Study area: The study area involved in this invention is located in complex lithofacies subsalt carbonate rock strata.

[0078] 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.

[0079] Single-parameter heterogeneity: Due to the heterogeneity of complex lithofacies carbonate reservoir regions, single-parameter reservoir non-reservoir heterogeneity is poor.

[0080] 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.

[0081] Method Implementation Examples

[0082] See Figure 1 This invention provides a method for identifying dominant carbonate reservoirs in complex lithofacies zones, comprising the following implementation steps:

[0083] S100. Determine the rock type and physical properties of the study area.

[0084] In this embodiment, the rock types include limestone and igneous rocks. It is known that the rock type and physical properties of the study area can be determined by analyzing existing drilling data. Analysis of existing drilling data shows that igneous rocks and limestone differ in rock density values; therefore, the lithological categories of the study area are first distinguished by density differences. The rock density value of intrusive rocks within igneous rocks is generally greater than that of extrusive rocks. The rock density value of most intrusive rocks is greater than 2.80 g / cm³. 3 The average density is typically 2.92 g / cm³. 3 Approximately. The rock density of eruptive rocks is mostly greater than 2.70 g / cm³. 3 The average density is 2.75 g / cm³. 3 High-quality carbonate reservoirs are mainly found in algal limestone, spherulite limestone, and shell limestone, with rock densities typically ranging from 2.3 to 2.6 g / cm³. 3 The average density is 2.46 g / cm³. 3 Furthermore, based on ECS and GR energy dispersive spectroscopy logging analysis, Th, Fe, and K can be used as indicator elements for igneous rock lithology identification, and the igneous rock index can be introduced for quantitative analysis to distinguish between extrusive and intrusive rocks in igneous rocks.

[0085] S200. Carve out the developmental pattern and configuration of special geological bodies in rock types.

[0086] In this embodiment, the igneous rocks, which are the main special geological bodies of the carbonate reservoir, are sculpted and shaped.

[0087] For example, in one specific embodiment, the specific process of sculpting and morphing the main special geological bodies of igneous rocks in a carbonate reservoir is as follows:

[0088] S210. Construct a geological model of igneous rocks and perform forward modeling to determine the developmental location characteristics of igneous rocks;

[0089] S220. Based on the physical properties of rocks and actual seismic responses, determine the developmental shape characteristics of igneous rocks;

[0090] S230. Based on the development location characteristics and development shape characteristics, the development pattern of the igneous rock is sculpted to obtain the development pattern configuration of the igneous rock.

[0091] Through the construction of geological models and forward modeling, simulations confirmed that the development location of intrusive rocks in igneous rocks exhibits characteristics of "strong amplitude + low frequency + high impedance." The amplitude of intrusive rock development locations is relatively strong, and it increases with thickness, while the phase axis widens. Combined with rock physical properties and actual seismic responses, the development shape of intrusive rocks is mostly: longitudinal upwelling along faults and planar sheet-like distribution. After determining the development location and shape characteristics of intrusive rocks, their development patterns are sculpted and shaped according to these patterns, thus obtaining the development pattern configuration of intrusive 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.

[0092] In existing technologies, due to the influence of thick salt layers and multiple igneous intrusions, the seismic response of carbonate reservoirs in complex lithofacies areas is complex. Traditional pre-stack inversion methods cannot accurately describe the distribution characteristics of carbonate reservoirs, posing challenges to exploration evaluation and well placement in the study area. Through steps S100 to S200, complex lithofacies petrophysical analysis, geophysical forward modeling, and development model configuration of special geological bodies were implemented, providing a foundation for the construction of subsequent pre-stack inversion models.

[0093] S300. Based on the developmental pattern configuration, an inversion low-frequency model under phase control constraints is constructed using an iterative modeling method.

[0094] For example, in a specific embodiment, 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:

[0095] S310. Construct an inversion low-frequency model of the initial state under phase control constraints;

[0096] S320. Based on the 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 generate inversion results.

[0097] 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.

[0098] S340. Use the current inverted low-frequency model as the final inverted low-frequency model.

[0099] It should be noted that in S330, when determining whether the inversion result is consistent with the trend of the actual well filter curve, a threshold can be set. When the difference between the trend of the inversion parameter curve corresponding to the inversion result and the trend of the actual well filter curve corresponding to the inversion parameter is less than the threshold, the inversion result is considered to be consistent with the trend of the actual well filter curve. It can be seen that 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.

[0100] Furthermore, in S310, a specific implementation process for constructing the inversion low-frequency model of the initial state under phase control constraints is as follows:

[0101] S3101. Based on the seismic stratigraphic interpretation results of the study area, establish a stratigraphic framework structure;

[0102] S3102. Using spatial interpolation methods, well logging data and / or stacking velocity data generated during seismic processing are interpolated within each stratum within the stratigraphic framework to obtain an inversion low-frequency model of the initial state.

[0103] For example, to construct a low-frequency model for wave impedance inversion, well logging data is wave impedance data; to construct a low-frequency model for P-wave velocity inversion, the stacking velocity data during seismic processing is P-wave velocity data.

[0104] In geophysical exploration, both 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. To minimize ambiguity in reservoir prediction, 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.

[0105] Due to limitations in seismic acquisition systems, 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. Starting with seismic data and building upon well logging and drilling 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. This involves interpolating or extrapolating the stacked velocity data generated during well logging or seismic processing under the constraints of the formation framework. Interpolation or extrapolation uses spatial interpolation methods such as inverse distance weighted interpolation. When extrapolating well logging data, 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. Therefore, reasonably establishing the formation framework and defining an appropriate spatial interpolation method are the two most critical parts in constructing an inversion low-frequency model under phase control constraints. 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).

[0106] However, the strata in the study area of ​​this invention contain special geological bodies such as igneous rocks. When performing pre-stack inversion using the low-frequency inversion model under phase control constraints in the conventional embodiments, the inversion results have significant 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 variations in strata occurrence and lithology, which can lead to large errors in areas with significant lateral variations in strata or lithology. Therefore, in this invention, for complex lithofacies carbonate strata containing special geological bodies, to fully consider factors such as the occurrence and lateral variations in lithology of such strata, 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 iteratively until the inversion results output by the inversion low-frequency model are consistent with the trend of the actual well filter curves. This achieves the construction of a high-precision inversion low-frequency model and improves the accuracy of the predicted distribution of dominant carbonate reservoirs.

[0107] S400. Utilize the low-frequency inversion model to perform multi-parameter pre-stack inversion of the study section, obtaining the inversion parameter data volume of the study section. It is important to understand that multi-parameter pre-stack inversion refers to having multiple target inversion parameters, meaning the obtained inversion parameter data volume includes data volumes of multiple different inversion parameters.

[0108] For example, in one specific embodiment:

[0109] S400. Use 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;

[0110] The seismic data input to the low-frequency inversion model are synthetic seismic records of the study area generated by the seismic wavelet decomposition and reconstruction method.

[0111] By comparing the seismic data before and after wavelet decomposition and reconstruction, it can be seen that the synthetic seismic record obtained by the seismic wavelet decomposition and reconstruction method has the best correlation with the actual seismic data. In particular, it was found that 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.

[0112] Accordingly, one specific implementation process of S400 is as follows:

[0113] S410. Generate synthetic seismic records for the study section using the seismic wavelet decomposition and reconstruction method;

[0114] S420. Input the synthetic seismic record into the inversion low-frequency model for multi-parameter pre-stack inversion to obtain the inversion parameter data volume of the study area.

[0115] S500. A multi-parameter convergence method is used to predict the distribution of dominant reservoirs in the inversion parameter data volume.

[0116] For example, in one specific embodiment, 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:

[0117] After excluding the lithological influence of special geological bodies in the inversion parameter data volume by using a multi-parameter intersection method, the remaining data volume is obtained, and the distribution of dominant reservoirs in the remaining data volume is identified.

[0118] As can be seen, 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.

[0119] Based on the analysis of existing drilling data in step S100, since the special geological body in the study area is igneous rock, the lithological influence of igneous rock can be eliminated from the inversion parameter data volume by using P-wave and S-wave velocity convergence. Therefore, 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. To identify the dominant reservoir distribution in the remaining data volume, specifically 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. Therefore, by performing cross-analysis of P-wave impedance and density, the relatively lower P-wave impedance and density can be optimized, thus predicting the scale of dominant carbonate reservoirs. Consequently, 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. Correspondingly, a specific implementation process of S500 is as follows:

[0120] S510. Generate a pre-stack migration data volume by combining seismic data from the study area;

[0121] S520. By analyzing the intersection of P-wave velocity data and S-wave velocity data, two types of lithology, limestone and igneous rocks, are distinguished. The influence of igneous rock lithology is then eliminated from the pre-stack migration data using this distinction, and the limestone data is obtained.

[0122] For example: intersect the P-wave velocity data volume and the S-wave velocity data volume. If the P-wave velocity ratio is greater than 1.7, then the data part is determined to correspond to igneous rock. If the P-wave velocity ratio is less than 1.7, then the data part is determined to correspond to limestone.

[0123] S530. By analyzing the intersection of P-wave impedance data volume and density data volume, reservoirs and non-reservoirs are distinguished, and the results of this distinction are used to exclude non-reservoirs from the limestone data volume to obtain the dominant reservoir distribution data volume.

[0124] For example, by performing cross-analysis of P-wave impedance data and density data, if the P-wave impedance is relatively low and the density is also relatively low, then this part of the data corresponds to a dominant limestone reservoir; otherwise, it is a non-reservoir.

[0125] See Figure 2 As an improvement to the above embodiment, the method further includes the following after step S500:

[0126] S600. Based on the predicted distribution of dominant reservoirs, 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.

[0127] Figures 3 to 9 The process and results of applying the above embodiments to the identification of subsalt carbonate reservoirs in a certain block are shown in the diagram.

[0128] like Figure 3 As shown, 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".

[0129] like Figure 4 As shown, the developmental 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. By introducing the developmental configuration, 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.

[0130] like Figure 5 As shown in the figure, Part A is a cross-sectional view of the P-wave impedance data volume obtained by using the inverted low-frequency model, and Part B is a cross-sectional view of the S-wave impedance data volume obtained by using the inverted low-frequency model. As can be seen from the figure, the lateral variation of elastic parameters such as P-wave impedance and S-wave impedance is consistent with the lateral variation trend of the subsalt carbonate rock strata in this block.

[0131] like Figure 6 As shown in the figure, Part A is a cross-sectional view of the density data volume obtained by using the inverted low-frequency model, and Part B is a cross-sectional view of the gamma data volume obtained by using the inverted low-frequency model. As can be seen from the figure, 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.

[0132] Combination Figures 7 to 8 Compared with single-parameter methods, the prediction error of the dominant limestone reservoir obtained by multi-parameter convergence at a certain layer is 88 meters. Specifically, the prediction error of the dominant limestone reservoir obtained by single-parameter method at the layer shown in the figure reaches 124 meters, while the prediction error of the dominant limestone reservoir obtained by multi-parameter convergence and step-by-step convergence methods proposed in this embodiment is only 36 meters.

[0133] Figure 9 The figure presents the reservoir contiguous distribution of the target seismic prediction zone within the block. As shown, the reservoir contiguousness is good, with thicknesses ranging from 30 to 200 m, and the eastern reservoirs having the greatest thickness. This distribution is consistent with drilled well data and sedimentary patterns. By using the dominant carbonate reservoir identification method for complex lithofacies zones proposed in this invention, the pre-drilling reservoir prediction accuracy was improved from 60% to 85% compared to traditional pre-stack inversion methods, demonstrating the effectiveness of this invention.

[0134] Device Examples

[0135] This invention provides a device for identifying dominant carbonate reservoirs in complex lithofacies zones, comprising a lithology determination module, a development pattern and 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:

[0136] The lithology determination module is used to determine the rock type and rock physical properties of the study area;

[0137] The developmental pattern configuration module is used to sculpt the developmental pattern configuration of specific geological bodies within a rock type;

[0138] 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.

[0139] 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;

[0140] 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.

[0141] In an optional embodiment, the developmental pattern configuration of a specific geological body within a rock type is sculpted in the developmental pattern configuration module, including:

[0142] Construct geological models of special geological bodies and perform forward modeling to determine the development location characteristics of these special geological bodies;

[0143] By combining rock physical properties and actual seismic response, the developmental shape characteristics of special geological bodies were determined;

[0144] Based on the characteristics of development location and development shape, the development pattern of special geological bodies is sculpted to obtain the development pattern configuration of special geological bodies.

[0145] In an optional embodiment, in the low-frequency inversion model construction module, based on the developmental pattern configuration, an iterative modeling method is used to construct a phase-controlled low-frequency inversion model, including:

[0146] Construct an inversion low-frequency model of the initial state under phase control constraints;

[0147] Based on the 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.

[0148] Determine whether the inversion result is consistent with the trend of the actual well filter curve. If so, proceed to the next step; otherwise, jump to the previous step.

[0149] The current low-frequency inversion model will be used as the final low-frequency inversion model.

[0150] In an optional embodiment, in the multi-parameter pre-stack inversion module, 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:

[0151] Synthetic seismic records for the study area were generated using the seismic wavelet decomposition and reconstruction method.

[0152] 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.

[0153] In an optional embodiment, the reservoir identification module uses a multi-parameter intersection method to predict the distribution of dominant reservoirs in the inversion parameter data volume, including:

[0154] 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.

[0155] In one alternative embodiment, the rock type includes limestone and igneous rock, the special geological body includes igneous rock, and the inversion parameter data volume includes P-wave velocity data volume, S-wave velocity data volume, P-wave impedance data volume, and density data volume.

[0156] In an optional embodiment, 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:

[0157] Pre-stack migration data volume is generated by combining seismic data from the study area;

[0158] By analyzing the intersection of P-wave velocity data volumes and S-wave velocity data volumes, two types of lithology, limestone and igneous rocks, were distinguished. The influence of igneous rock lithology was then eliminated from the pre-stack migration data volume using this distinction, and the limestone data volume was obtained.

[0159] By cross-analysis of P-wave impedance data volume and density data volume, reservoirs and non-reservoirs are distinguished, and the non-reservoirs are excluded from the limestone data volume using the distinction results, thus obtaining the dominant reservoir distribution data volume.

[0160] In an optional embodiment, the device for identifying dominant carbonate reservoirs in complex lithofacies zones proposed in this invention 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, with the top and bottom of the target layer segment for seismic interpretation in the study area as the boundary, based on the predicted results of the dominant reservoir distribution, to obtain a planar map of the dominant reservoir thickness distribution.

[0161] On the other hand, embodiments of the present invention also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When 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.

[0162] In another aspect, embodiments of the present invention 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.

[0163] 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.

[0164] Through the above description of the embodiments, those skilled in the art can clearly understand that 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. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. 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.

[0165] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and 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 the embodiments of the present invention.

Claims

1. A method for identifying dominant carbonate reservoirs in complex lithofacies zones, characterized in that, The method includes: Determine the rock type and physical properties of the study area; Carve out the developmental pattern and configuration of special geological bodies in rock types; Based on the aforementioned developmental pattern configuration, 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.

2. The method for identifying dominant carbonate reservoirs in complex lithofacies zones according to claim 1, characterized in that, The carved developmental patterns of special geological bodies within the rock type include: Construct geological models of special geological bodies and perform forward modeling to determine the development location characteristics of these special geological bodies; By combining rock physical properties and actual seismic response, the developmental shape characteristics of special geological bodies were determined; Based on the developmental location and shape characteristics, the developmental pattern of the special geological body is sculpted to obtain the developmental pattern configuration of the special geological body.

3. The method for identifying dominant carbonate reservoirs in complex lithofacies zones according to claim 1, characterized in that, Based on the aforementioned developmental pattern configuration, an iterative modeling method is used to construct an inversion low-frequency model under phase control constraints, including: Construct an inversion low-frequency model of the initial state under phase control constraints; 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. Determine whether the inversion result is consistent with the trend of the actual well filter curve. If so, proceed to the next step; otherwise, jump to the previous step. The current low-frequency inversion model will be used as the final low-frequency inversion model.

4. The method for identifying dominant carbonate reservoirs in complex lithofacies zones according to claim 1, characterized in that, The method of using the low-frequency inversion model to perform multi-parameter pre-stack inversion of the study section yields 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.

5. The method for identifying dominant carbonate reservoirs in complex lithofacies zones according to claim 1, characterized in that, The prediction of 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.

6. The method for identifying dominant carbonate reservoirs in complex lithofacies zones according to claim 5, characterized in that, The rock types include limestone and igneous rocks, the special geological bodies include igneous rocks, and the inversion parameter data volume includes P-wave velocity data volume, S-wave velocity data volume, P-wave impedance data volume, and density data volume.

7. The method for identifying dominant carbonate reservoirs in complex lithofacies zones according to claim 6, characterized in that, The process of using a multi-parameter intersection method to eliminate 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; By analyzing the intersection of P-wave velocity data volumes and S-wave velocity data volumes, two types of lithology, limestone and igneous rocks, were distinguished. The influence of igneous rock lithology was then eliminated from the pre-stack migration data volume using this distinction, and the limestone data volume was obtained. By cross-analysis of P-wave impedance data volume and density data volume, reservoirs and non-reservoirs are distinguished, and the non-reservoirs are excluded from the limestone data volume using the distinction results, thus obtaining the dominant reservoir distribution data volume.

8. The method for identifying dominant carbonate reservoirs in complex lithofacies zones according to claim 1, characterized in that, The method further includes: Based on the predicted distribution of dominant reservoirs, 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.

9. A device for identifying dominant carbonate reservoirs in complex lithofacies zones, characterized in that, The device includes: 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.

10. The device for identifying dominant carbonate reservoirs in complex lithofacies zones according to claim 9, characterized in that, The carved developmental patterns of special geological bodies within the rock type include: Construct geological models of special geological bodies and perform forward modeling to determine the development location characteristics of these special geological bodies; By combining rock physical properties and actual seismic response, the developmental shape characteristics of special geological bodies were determined; Based on the developmental location and shape characteristics, the developmental pattern of the special geological body is sculpted to obtain the developmental pattern configuration of the special geological body.

11. The device for identifying dominant carbonate reservoirs in complex lithofacies zones according to claim 9, characterized in that, Based on the aforementioned developmental pattern configuration, an iterative modeling method is used to construct an inversion low-frequency model under phase control constraints, including: Construct an inversion low-frequency model of the initial state under phase control constraints; 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. Determine whether the inversion result is consistent with the trend of the actual well filter curve. If so, proceed to the next step; otherwise, jump to the previous step. The current low-frequency inversion model will be used as the final low-frequency inversion model.

12. The device for identifying dominant carbonate reservoirs in complex lithofacies zones according to claim 9, characterized in that, The method of using the low-frequency inversion model to perform multi-parameter pre-stack inversion of the study section yields 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.

13. The device for identifying dominant carbonate reservoirs in complex lithofacies zones according to claim 9, characterized in that, The reservoir identification module employs 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.

14. The device for identifying dominant carbonate reservoirs in complex lithofacies zones according to claim 13, characterized in that, The rock types include limestone and igneous rocks, the special geological bodies include igneous rocks, and the inversion parameter data volume includes P-wave velocity data volume, S-wave velocity data volume, P-wave impedance data volume, and density data volume.

15. The device for identifying dominant carbonate reservoirs in complex lithofacies zones according to claim 14, characterized in that, The process of using a multi-parameter intersection method to eliminate 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; By analyzing the intersection of P-wave velocity data volumes and S-wave velocity data volumes, two types of lithology, limestone and igneous rocks, were distinguished. The influence of igneous rock lithology was then eliminated from the pre-stack migration data volume using this distinction, and the limestone data volume was obtained. By cross-analysis of P-wave impedance data volume and density data volume, reservoirs and non-reservoirs are distinguished, and the non-reservoirs are excluded from the limestone data volume using the distinction results, thus obtaining the dominant reservoir distribution data volume.

16. The device for identifying dominant carbonate reservoirs in complex lithofacies zones according to claim 9, characterized in that, The device also includes a reservoir thickness mapping module; The reservoir thickness mapping module is used to extract the thickness values ​​of the dominant reservoirs along the target segment for seismic interpretation in the study area based on the predicted distribution of the dominant reservoirs, and to obtain a planar map of the dominant reservoir thickness distribution.

17. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for identifying dominant carbonate reservoirs in complex lithofacies zones as described in any one of claims 1 to 8.

18. A machine-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for identifying dominant carbonate reservoirs in complex lithofacies zones as described in any one of claims 1 to 8.

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