A dual-phase-controlled dolomite grain-shoal effective reservoir prediction method and device

Abstract

The present application relates to oil and gas geophysical exploration technology field, especially to a kind of double phase control dolomite bioclastic beach effective reservoir prediction method and device.Wherein the method includes: determining the target layer of work area;According to sedimentary paleogeomorphology, seismic attribute and seismic facies map to determine sedimentary microfacies;According to coherent attribute and karst paleogeomorphology to determine karst microfacies;The shear wave velocity of work area is predicted, and the limit value of dolomite bioclastic beach reservoir is determined;The prestack seismic waveform indicator inversion is carried out to the seismic data of work area, and the thickness of dolomite bioclastic beach reservoir in target layer is extracted according to the limit value;According to sedimentary microfacies, karst microfacies and dolomite bioclastic beach reservoir thickness, the effective reservoir thickness of dolomite bioclastic beach in target layer is determined.To solve the problem that the result precision of dolomite bioclastic beach effective reservoir prediction using spectral decomposition, wavelet decomposition reconstruction is low, the prediction accuracy using inversion method is poor, and high-precision prediction of dolomite bioclastic beach effective reservoir cannot be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas geophysical exploration, and in particular to a method and device for predicting effective reservoirs of dual-phase-controlled dolomite bioclastic banks. Background Art

[0002] In petroleum exploration and development, thin-layer dolomite bioclastic beach effective reservoirs are an important reservoir type with the characteristics of large distribution area and high production. However, the precise prediction of their effective reservoir thickness has always been an extremely difficult task.

[0003] Currently, seismic attribute technology and geostatistical inversion techniques are the primary means for quantitative prediction of thin reservoirs. The most common seismic attribute techniques include spectral decomposition and wavelet decomposition and reconstruction. While these techniques have improved the resolution of seismic prediction to a certain extent, they do not fully utilize high-resolution well logging data, resulting in low prediction accuracy and inability to accurately predict thin dolomite bioclastic bank reservoirs. Geostatistical inversion, as the most common high-resolution inversion method, utilizes the concept of stochastic inversion simulation to significantly improve the vertical resolution of reservoir inversion. However, this method suffers from the difficulty of fitting a satisfactory function model to the sample, resulting in discrepancies between the inversion results and the actual situation, poor prediction accuracy, and inability to accurately predict effective reservoirs in dolomite bioclastic banks. Furthermore, the formation of effective reservoirs in dolomite bioclastic banks is subject to specific geological conditions. Seismic attribute technology and geostatistical inversion techniques are both geophysical prediction techniques, inherently subject to significant ambiguity. The prediction results lack a fundamental geological explanation, making it difficult to guarantee accuracy. Therefore, a technology for predicting effective reservoirs in thin dolomite bioclastic banks is urgently needed to improve the geological rationality of the prediction results of effective reservoirs in dolomite bioclastic banks and improve the accuracy of thin reservoir prediction. Summary of the Invention

[0004] The present invention proposes a dual-phase-controlled dolomite bioclastic bank effective reservoir prediction method and device to solve the problem that the existing prediction results of dolomite bioclastic bank effective reservoir using spectral decomposition and wavelet decomposition reconstruction are low in accuracy, and the prediction accuracy using inversion method is poor, which cannot achieve high-precision prediction of dolomite bioclastic bank effective reservoir.

[0005] According to one aspect of the present invention, a method for predicting effective reservoirs in dual-phase-controlled dolomite bioclastic banks is provided, comprising:

[0006] The dolomite bioclastic beach development stratum in the work area was determined as the target interval;

[0007] Restore the sedimentary paleogeomorphology of the work area, select the seismic attributes with the highest correlation with the reservoir, determine the seismic facies map of the work area, and determine the sedimentary microfacies based on the sedimentary paleogeomorphology, seismic attributes and seismic facies map;

[0008] Restore the karst paleo-geomorphology of the work area, extract relevant attributes of the work area, and determine the karst microfacies based on the relevant attributes and the karst paleo-geomorphology;

[0009] The shear wave velocity of the work area is predicted using rock physics modeling methods, and the reservoir boundary value of the dolomite bioclastic bank is determined based on the prediction results;

[0010] Perform pre-stack seismic waveform inversion on the seismic data of the work area to obtain an inversion result, and extract the thickness of the dolomite bioclastic beach reservoir in the target layer from the inversion result according to the boundary value of the dolomite bioclastic beach reservoir;

[0011] The constraint factors of the sedimentary microfacies type and the karst microfacies type are defined respectively, and the effective reservoir thickness of the dolomite bioclastic beach in the target layer is determined based on the constraint factors and the dolomite bioclastic beach reservoir thickness.

[0012] Preferably, the method of restoring the sedimentary paleogeomorphology of the work area, preferably the seismic attributes with the highest correlation with the reservoir, determining the seismic facies map of the work area, and determining the sedimentary microfacies based on the sedimentary paleogeomorphology, seismic attributes and seismic facies map includes:

[0013] Determining a sedimentary paleogeomorphology restoration layer section, and restoring the sedimentary paleogeomorphology based on the sedimentary paleogeomorphology restoration layer section;

[0014] Extracting seismic attributes of the work area, optimizing the seismic attributes, determining the seismic attribute with the highest correlation with the reservoir, and extracting a planar map of the seismic attribute with the highest correlation from the three-dimensional seismic data of the work area;

[0015] Preferably, other sensitive seismic attributes of the reservoir other than the thickness feature are characterized, and the sensitive seismic attributes are fused with the seismic attributes with the highest correlation to obtain a seismic phase map;

[0016] Establish rock, electrical and seismic identification charts based on core data, well logging curves, seismic data and sedimentary paleo-geomorphology of the target layer;

[0017] The corresponding relationship between the sedimentary microfacies and the seismic facies map is determined according to the identification plate, and based on this, the seismic facies map is converted into a sedimentary microfacies map.

[0018] Preferably, the method of extracting seismic attributes of the work area and optimizing the seismic attributes includes:

[0019] Extract amplitude statistics and frequency seismic attributes of the target layer in the work area, including at least: amplitude attributes, average instantaneous frequency attributes, average instantaneous phase attributes, arc length attributes, average reflection intensity, and peak skewness;

[0020] The response relationship and correlation between each seismic attribute and the reservoir thickness and porosity of the target layer are analyzed to determine the seismic attribute with the highest correlation with the reservoir.

[0021] Preferably, the method of restoring the karst paleo-geomorphology of the work area, extracting coherent attributes of the work area, and determining karst microfacies based on the coherent attributes and the karst paleo-geomorphology includes:

[0022] determining a karst paleo-geomorphology restoration layer section, and restoring the karst paleo-geomorphology according to the karst paleo-geomorphology restoration layer section;

[0023] Extract the relevant attributes of the work area and obtain the relevant attribute plane map;

[0024] The karst microfacies is determined based on the coherent attribute plane map and the restored karst paleo-landform.

[0025] Preferably, the method for predicting the shear wave velocity of the work area using a rock physics modeling method includes:

[0026] Among all the rock physics modeling methods, the modeling method with the highest matching degree with the work area is preferred for modeling;

[0027] Wherein, the preferred method includes:

[0028] If the rock in the work area is homogeneous, the Gassmann model is selected;

[0029] If the porosity of various types of pores in the work area has been obtained, then select the Xu-payne and self-consistent models;

[0030] If the work area has medium-low porosity strata with consolidation that meets the requirements, the Pride model is selected;

[0031] If the formation porosity in the work area is greater than 15%, the formation pressure is greater than the predetermined value, and the formation is mainly dense and low-porosity, the Krief model is selected;

[0032] If the stratum in the work area is a low-porosity and dense stratum, the Xu-White model, single aspect ratio model or soft pore model should be selected.

[0033] Preferably, if there are multiple preferred rock physics modeling methods, the multiple rock physics modeling methods are used to respectively perform modeling and shear wave prediction, and the obtained shear wave prediction results are subjected to intersection analysis with the measured shear waves;

[0034] The rock physics modeling method corresponding to the intersection result with the highest correlation coefficient is selected as the modeling method with the highest matching degree with the work area.

[0035] Preferably, the method for determining the dolomite bioclastic beach reservoir boundary value based on the prediction results includes:

[0036] According to the prediction results, a cross-plot of the ratio of P-wave velocity to S-wave velocity and P-wave impedance is established, and the boundary value of the dolomite bioclastic bank reservoir is determined according to the cross-plot.

[0037] Preferably, the method for determining the effective reservoir thickness of the dolomite bioclastic beach in the target layer according to the constraint factor and the reservoir thickness of the dolomite bioclastic beach comprises:

[0038] According to the constraint factors, the effective reservoir thickness of the dolomite bioclastic beach in the target layer is determined using the calculation formula of the effective reservoir thickness;

[0039] The calculation formula for the effective reservoir thickness includes:

[0040] H 有效储层 =H 储层 *ρ 沉积微相 *ρ 岩溶微相 ;

[0041] Where: H 储层 is the thickness of the dolomite bioclastic bank reservoir, ρ 沉积微相 is the sedimentary microfacies type constraint factor, ρ 岩溶微相 is the constraint factor of karst microfacies type.

[0042] According to one aspect of the present invention, a dual-phase-controlled dolomite bioclastic bank effective reservoir prediction device is provided, comprising:

[0043] Target layer determination unit, used to determine the dolomite bioclastic beach development stratum in the work area as the target layer section;

[0044] A sedimentary microfacies determination unit is used to restore the sedimentary paleogeomorphology of the work area, select the seismic attributes with the highest correlation with the reservoir, determine the seismic facies map of the work area, and determine the sedimentary microfacies based on the sedimentary paleogeomorphology, seismic attributes and seismic facies map;

[0045] The karst microfacies determination unit is used to restore the karst paleo-geomorphology of the work area, extract the relevant attributes of the work area, and determine the karst microfacies based on the relevant attributes and the karst paleo-geomorphology;

[0046] A limit value determination unit is used to predict the shear wave velocity of the work area using a rock physics modeling method and determine the limit value of the dolomite bioclastic bank reservoir based on the prediction results;

[0047] a reservoir thickness determination unit for performing pre-stack seismic waveform indication inversion on the seismic data of the work area to obtain an inversion result, and extracting the dolomite bioclastic beach reservoir thickness of the target layer from the inversion result according to the dolomite bioclastic beach reservoir boundary value;

[0048] The effective reservoir thickness determination unit is used to define the constraint factors of the sedimentary microfacies type and the karst microfacies type respectively, and determine the effective reservoir thickness of the dolomite bioclastic beach in the target layer according to the constraint factors and the dolomite bioclastic beach reservoir thickness.

[0049] The present invention has at least the following beneficial effects:

[0050] The present invention proposes a dual-phase-controlled dolomite bioclastic beach effective reservoir prediction method and device. Based on the analysis of the depositional mechanism of the effective reservoir of dolomite bioclastic beach, by introducing sedimentary microfacies and karst microfacies, the present invention overcomes the problems of low prediction accuracy of thin carbonate reservoirs, difficulty in fitting a satisfactory function model, and strong multi-solution of geophysical prediction technology in the background technology, reduces the uncertainty of the results of geophysical methods in predicting dolomite bioclastic beach reservoirs, and supports the efficient exploration and development process of gas fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings herein are incorporated into and constitute a part of the specification. These drawings illustrate embodiments consistent with the present invention and, together with the specification, are used to explain the technical solutions of the present invention.

[0052] Figure 1 A core fracture and hole characteristic diagram of Heshen 4 well according to an embodiment of the present invention is shown;

[0053] Figure 2 A schematic diagram showing thickness selection for sedimentary paleo-geomorphological restoration according to an embodiment of the present invention is shown;

[0054] Figure 3 A plan view of the sedimentary paleo-geomorphology of the Maokou Formation Mao 2 Member according to an embodiment of the present invention is shown;

[0055] Figure 4 The cross-plots of reservoir thickness and amplitude and reservoir porosity and amplitude of the Maokou Formation Mao2 Member according to an embodiment of the present invention are shown;

[0056] Figure 5 A plane diagram showing amplitude properties of the second section of the Maokou Formation according to an embodiment of the present invention is shown;

[0057] Figure 6 Showing a litho-electric-seismic identification plate of sedimentary microfacies types according to an embodiment of the present invention;

[0058] Figure 7 A multi-attribute fusion seismic phase plane diagram of the Maokou Formation Mao2 Member according to an embodiment of the present invention is shown;

[0059] Figure 8 A plan view of the sedimentary microfacies of the Maokou Formation Mao2 Member according to an embodiment of the present invention is shown;

[0060] Figure 9A diagram showing the seismic response characteristics of the karst valley on the top surface of the Maokou Formation according to an embodiment of the present invention is shown;

[0061] Figure 10 A characteristic diagram of the karst valley on the top surface of the Maokou Formation is depicted using coherent attributes according to an embodiment of the present invention;

[0062] Figure 11 A schematic diagram showing the thickness selection of the Maokou Formation karst paleo-landform restoration stratum according to an embodiment of the present invention is shown;

[0063] Figure 12 A diagram showing the thickness of the Mao 3rd section of the stratum according to an embodiment of the present invention is shown;

[0064] Figure 13 Showing the characteristic diagram of the karst microfacies of the Maokou Formation according to an embodiment of the present invention;

[0065] Figure 14 A comparison diagram of measured shear waves and predicted shear waves according to an embodiment of the present invention is shown;

[0066] Figure 15 shows a cross-plot of predicted shear waves and measured shear waves according to an embodiment of the present invention;

[0067] Figure 16 A diagram showing the determination of the reservoir boundary of the Maokou Formation dolomite bioclastic beach according to an embodiment of the present invention is shown;

[0068] Figure 17 A cross-sectional diagram showing the predicted dolomite bioclastic bank reservoir using pre-stack waveform indication inversion of the Mao-2 Member according to an embodiment of the present invention is shown;

[0069] Figure 18 A diagram showing the thickness of the bioclastic beach reservoir of the Mao 2 Member dolomite according to an embodiment of the present invention is shown;

[0070] Figure 19 A diagram showing the effective reservoir thickness of the Mao 2 dolomite bioclastic beach according to an embodiment of the present invention is shown. Implementation Method

[0071] Various exemplary embodiments, features, and aspects of the present invention will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

[0072] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0073] The term "and / or" herein simply describes an association relationship between associated objects, indicating that three relationships can exist. For example, "A and / or B" can represent the existence of three situations: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" herein refers to any combination of at least two of any one or more of a plurality of items. For example, "at least one of A, B, and C" can represent any one or more elements selected from the set consisting of A, B, and C.

[0074] In addition, numerous specific details are provided in the following detailed description to better illustrate the present invention. Those skilled in the art will appreciate that the present invention may be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of the present invention.

[0075] The present invention provides a dual-phase-controlled dolomite bioclastic beach effective reservoir prediction method, comprising: step S01: determining the dolomite bioclastic beach development stratum in the work area as the target layer; step S02: restoring the sedimentary paleogeomorphology of the work area, selecting the seismic attribute with the highest correlation with the reservoir, determining the seismic facies map of the work area, and determining the sedimentary microfacies based on the sedimentary paleogeomorphology, seismic attributes and seismic facies map; step S03: restoring the karst paleogeomorphology of the work area, extracting the coherent attributes of the work area, and determining the karst microfacies based on the coherent attributes and the karst paleogeomorphology; step S04: using rock material to predict the effective reservoir of dolomite bioclastic beach in the work area; step S05: restoring the sedimentary paleogeomorphology of the work area, extracting the coherent attributes of the work area, and determining the karst microfacies based on the coherent attributes and the karst paleogeomorphology; step S06: using rock material to predict the effective reservoir of dolomite bioclastic beach in the work area; step S07: determining the effective reservoir of dolomite bioclastic beach in the work area; step S08: determining the effective reservoir of dolomite bioclastic beach in the work area; step S09: determining the effective reservoir of dolomite bioclastic beach in the work area; step S10: determining the effective reservoir of dolomite bioclastic beach in the work area; step S11: determining the effective reservoir of dolomite bioclastic beach in the work area; step S12: determining the effective reservoir of dolomite bioclastic beach in the work area; step S13: determining the effective reservoir of dolomite bioclastic beach in the work area; step S14: determining the effective reservoir of dolomite bioclastic beach in the work area; step S15: determining the effective reservoir of dolomite bioclastic beach The shear wave velocity of the work area is predicted using a theoretical modeling method, and the boundary value of the dolomite bioclastic beach reservoir is determined based on the prediction result; Step S05: Pre-stack seismic waveform indication inversion is performed on the seismic data of the work area to obtain an inversion result, and the dolomite bioclastic beach reservoir thickness of the target layer is extracted from the inversion result based on the boundary value of the dolomite bioclastic beach reservoir; Step S06: Constraint factors of the sedimentary microfacies type and the karst microfacies type are defined respectively, and the effective dolomite bioclastic beach reservoir thickness of the target layer is determined based on the constraint factors and the dolomite bioclastic beach reservoir thickness.

[0076] An embodiment of the present invention provides a method for predicting effective reservoirs in dolomite bioclastic banks controlled by dual phases, which specifically includes the following steps:

[0077] Step S01: Determine the target layer in the dolomite bioclastic beach development stratum in the work area.

[0078] In the embodiments of the present invention, areas with relatively high paleo-sedimentary landforms are favorable regions for the development of bioclastic beaches. The bioclastic beaches formed in these areas generally have a large number of biological species, a relatively high volume fraction of particles, a relatively low mud content, and thick layers of bioclastic limestone and dolomite. The developed bioclastic beaches are high-energy bioclastic beaches, indicating a high-energy hydrodynamic environment. In areas with relatively low paleo-sedimentary landforms, bioclastic beaches are also widely developed, but the particle volume fraction of the formed bioclastic beaches is relatively low, the developed bioclastic limestone and dolomite become thinner, and the formed bioclastic beaches are medium-to-low-energy bioclastic beaches, indicating a low-energy hydrodynamic environment.

[0079] Therefore, the identification method for dolomite bioclastic banks includes the following: intra-platform dolomite bioclastic banks primarily develop in areas with relatively high local landforms and controlled by wave action; the lithology is primarily sparry grainstone, bioclastic limestone, and fine- to medium-crystalline dolomite. They contain a high concentration of bioclastics, with grains cemented by sparry calcite. Well logging characteristics indicate a low natural gamma ray curve, often exhibiting a low-gamma box pattern.

[0080] The working area of ​​the present invention is taken as an example from the Maokou Formation in the Sichuan Basin, where the Maokou Formation includes the first, second and third members of the Maokou Formation. The drilling results of the working area show that the dolomite bioclastic beach reservoir of the Mao second member of the Heshen 4 well is well developed, which is a fracture-pore type reservoir with good gas content. Figure 1 As shown, Figure 1 This is the characteristic map of fractures and holes in the core of Heshen 4 Well. Figure 1 Figure a is a core image of the 4339.7m deep well in Heshen 4, which is brown-gray gray dolomite. Figure b is an imaging logging image of the 40th layer, which shows that the pores and fractures in the formation are relatively developed. Figure c is a core image of the 4339.7m deep well in Mao II Member, which is medium-fine crystalline dolomite. Figure d is a detailed image of the core of the 4339.25m deep well in Mao II Member under a microscope, which is medium-crystalline dolomite with intercrystalline and intracrystalline solution pores. Figure e is a core image of the 4339.35m deep well in Mao II Member, which is medium-fine crystalline dolomite with caves and intercrystalline residual micropores. Figure f is a detailed image of the core of the 4339.25m deep well in Mao II Member under a microscope, which is medium-crystalline dolomite with solution pores and solution fractures.

[0081] Core analysis from Well Heshen 4 reveals interlayer karstification in the Mao-2 Member, resulting in the development of a large fracture-vug system. Small dissolution pores and fractures are visible in the core. Thin sections reveal predominantly intercrystalline pores and intercrystalline dissolution pores. Imaging logging reveals the development of fractures and dissolution pores, including high-angle fractures. Porphyritic dissolution is observed in dolomite-rich sections, with a horizontal distribution and a karst-like structure from top to bottom, characterized by a transition from a vertical vadose zone to a horizontal undercurrent zone. Therefore, the favorable sedimentary facies in the Mao-2 Member is determined to be a high-energy bioclastic beach.

[0082] Dolomite bioclastic bank genetic mechanism is: the sedimentary facies belt of carbonate reservoir development is common bioclastic bank, reef, oolitic / sand bank and the like. Bioclastic bank rock structure component is mainly developed with biological debris, and most of the intergranular grains are bright crystal cementation, which belongs to the environment with strong hydrodynamic force. Reef: is carbonate rock formed by reef-building organisms. The reservoir of the reef is an important reservoir in carbonate rock, and the development scale of the reef controls the reservoir. The reef structure is unique on the earthquake, and is easy to identify. Oolitic / sand bank: is often formed in the high-energy environment of the platform below the tide, and is mainly controlled by the tide. The rock component is mainly oolitic / sand limestone, and the oolitic / sand content is greater than 50%

[0083] The above analysis shows that the genetic mechanism of the effective reservoir of the dolomite bioclastic bank is: the high-energy bioclastic bank is the basis, and the karstification is the key. Therefore, the description of the effective reservoir of the dolomite bioclastic bank should start from three aspects: first, the distribution range of the high-energy bioclastic bank is settled by carrying out the sedimentary microfacies research; second, the strong karstification area is settled by carrying out the karst microfacies research; third, under the control of the sedimentary microfacies and the karst microfacies, the work of the rock physics modeling of the dolomite bioclastic bank reservoir, the establishment of the effective reservoir identification chart and the quantitative prediction of the effective reservoir by the prestack waveform indicator inversion is carried out from the geophysical method, so as to finely describe the effective reservoir of the dolomite bioclastic bank.

[0084] Step S02: restoring the sedimentary paleogeomorphology of the work area, preferably selecting the seismic attribute with the highest reservoir correlation, determining the seismic facies map of the work area, and determining the sedimentary microfacies according to the sedimentary paleogeomorphology, the seismic attribute and the seismic facies map.

[0085] In the present application, the method for restoring the sedimentary paleogeomorphology of the work area, preferably selecting the seismic attribute with the highest reservoir correlation, determining the seismic facies map of the work area, and determining the sedimentary microfacies according to the sedimentary paleogeomorphology, the seismic attribute and the seismic facies map comprises: determining the sedimentary paleogeomorphology restoration layer section, restoring the sedimentary paleogeomorphology according to the sedimentary paleogeomorphology restoration layer section; extracting the seismic attribute of the work area, selecting the seismic attribute with the highest reservoir correlation, and extracting the planar map of the seismic attribute with the highest correlation in the three-dimensional seismic data of the work area; selecting the other sensitive seismic attribute of the reservoir except the thickness characteristic, and fusing the sensitive seismic attribute and the seismic attribute with the highest correlation to obtain the seismic facies map; establishing the rock, electricity and seismic identification chart according to the core data, the well logging curve, the seismic data and the sedimentary paleogeomorphology of the target layer section; determining the corresponding relationship between the sedimentary microfacies and the seismic facies map according to the identification chart, and converting the seismic facies map into the sedimentary microfacies map based on the corresponding relationship.

[0086] In the embodiment of the present application, the method for determining the sedimentary paleogeomorphology restoration layer section comprises: selecting the layer section from the initial deposition layer to the target layer as the sedimentary paleogeomorphology restoration layer section, which is in the stable deposition period.

[0087] Figure 2 Schematic diagram of thickness selection for sedimentary paleogeomorphology restoration. The top figure is a well-connected stratigraphic comparison profile, and the bottom figure is a seismic reflection profile. Regional geological knowledge indicates that large-scale karstification occurred at the top of the Maokou Formation in the study area. Members Mao-3, Mao-2, and Mao-1 are developed in the study area. According to step S01, the dolomite bioclastic beach reservoir is primarily located in Member Mao-2. Member Mao-1 represents the maximum flooding surface and a stable interface. Karstification during the late stages of the Maokou Formation deposition had no impact on it. The residual thickness of Member Mao-2 represents the sedimentary paleogeomorphology of the Maokou Formation. Therefore, the stratigraphic thickness of Member Mao-1 + Member Mao-2 (i.e., the thickness from the bottom of Member Mao-3 to the bottom of Member Mao-1) is selected for sedimentary paleogeomorphology restoration. The sedimentary paleogeomorphology restoration method uses the residual thickness method to restore the sedimentary paleogeomorphology of Member Mao-2 of the Maokou Formation.

[0088] Figure 3 This is the restored plan view of the sedimentary paleogeomorphology of the Mao-2 Member of the Maokou Formation. The sedimentary paleogeomorphology of the Mao-2 Member of the Maokou Formation is generally characterized by high in the west and low in the east, and the paleogeomorphological high-uplift belt is distributed as a northwest-trending strip.

[0089] In the present invention, the method for extracting seismic attributes of the work area and optimizing the seismic attributes includes: extracting amplitude statistics and frequency seismic attributes of the target layer section of the work area, which at least include: amplitude attributes, average instantaneous frequency attributes, average instantaneous phase attributes, arc length attributes, average reflection intensity, and peak skewness; performing response relationship and correlation analysis between each seismic attribute and the reservoir thickness and reservoir porosity of the target layer section, and determining the seismic attribute with the highest correlation with the reservoir.

[0090] In an embodiment of the present invention, multiple seismic attributes of amplitude statistics and frequency are extracted for the Mao II dolomite bioclastic beach reservoir. The response relationship and correlation analysis method are as follows: each seismic attribute is intersected and analyzed with the reservoir thickness and porosity, and intersection plots with the reservoir thickness and porosity are obtained respectively. Among the intersection plots of all seismic attributes, the seismic attribute corresponding to the intersection plot with the largest correlation coefficient is selected as the preferred seismic attribute.

[0091] The optimization results show that the amplitude attribute has the highest correlation with the reservoir. The intersection of reservoir thickness and reservoir porosity interpreted by well logging with amplitude, and the intersection of reservoir porosity with amplitude show that: reservoir thickness, porosity and amplitude are positively correlated. The greater the reservoir thickness and porosity in the lower Mao II Member, the stronger the amplitude; as the reservoir thickness and porosity decrease, the amplitude becomes weaker. Figure 4 The diagrams show the cross-plots of reservoir thickness and amplitude and reservoir porosity and amplitude in the Maokou Formation Mao2 Member. Figure 4 Figure a is the cross-plot of reservoir thickness and amplitude in the Mao2 Member of the Maokou Formation, and Figure b is the cross-plot of reservoir porosity and amplitude.

[0092] The amplitude of multi-well correlation analysis can basically reflect the changes in reservoir thickness, reservoir porosity and maximum amplitude in the study area to a certain extent. The amplitude attribute plane map is extracted using 3D seismic data, such as Figure 5 The figure shows the amplitude attribute plane of the Mao2 Member of the Maokou Formation, which shows the sedimentary microfacies variation characteristics of the dolomite bioclastic beach reservoir.

[0093] Among sedimentary microfacies, higher-energy banks are primarily composed of powder crystal and medium-fine-crystalline dolomite, resulting in strong seismic amplitudes. Low-energy banks are composed of micritic bioclastic limestone, resulting in weak seismic peaks. Medium-slope slopes are composed of micritic limestone, resulting in complex seismic troughs. However, due to factors such as the strong amplitude of the surrounding rock, the amplitude attributes extracted above cannot fully reflect reservoir characteristics and require analysis in conjunction with seismic attributes associated with different microfacies types.

[0094] Sensitive seismic attributes can be selected by selecting the seismic attributes corresponding to the two highest correlation coefficients (excluding the amplitude attribute) from the intersection results of the seismic attributes obtained above with reservoir thickness and porosity. Among the seismic attributes extracted above, the two sensitive seismic attributes with relatively high correlation coefficients are the high-frequency average instantaneous frequency attribute and the average instantaneous phase attribute.

[0095] Based on the selected sensitive seismic attributes of each layer, the multi-attribute fusion imaging technology is used to fuse the selected amplitude attributes of the same layer to obtain the seismic phase map.

[0096] Through multi-attribute fusion for seismic attribute clustering, we can overcome the defect that a single attribute cannot fully characterize the dolomite bioclastic beach reservoir in this area, conduct qualitative reservoir prediction, improve the reservoir prediction coincidence rate, obtain seismic facies distribution, and lay the foundation for further sedimentary microfacies research. Figure 7 This is the multi-attribute fusion seismic phase plane map of the Mao 2 section.

[0097] In the embodiment of the present invention, based on the core of the Mao2 Member, i.e. thin section data, well logging curves, seismic response characteristics (seismic data) and restored sedimentary paleo-geomorphology, a rock, electrical and seismic identification plate of the sedimentary microfacies of the Maokou Formation was established, and the rock-electrical-seismic characteristics of different microfacies types are clear, such as Figure 6 The figure shows the litho-electro-seismic identification plate for sedimentary microfacies types. Based on the established litho-electro-seismic identification plate for sedimentary microfacies, the microfacies type of the dolomite bioclastic bank reservoir of the Maokou Formation is identified as high-frequency, strong-amplitude reflection.

[0098] The target layer sedimentary microfacies can be determined based on the rock-electric-seismic identification chart, and the seismic phase map can be matched with the seismic phase map. Based on the corresponding relationship, the seismic phase map can be converted into a sedimentary microfacies map. Figure 8 Shown is a plan view of the sedimentary microfacies of the Mao 2 Member of the Kou Formation.

[0099] The sedimentary microfacies plan view shows that the sedimentary microfacies of the Mao-2 Member are divided into four categories: higher-energy bioclastic beaches, low-energy beaches, inter-shoal areas, and moderately gentle slopes. Low-energy beaches are widely developed, while higher-energy beaches are mound-like and distributed in a nearly north-south strip. High-yield wells (Heshen 4, Tongshen 3, Tongshen 4, and Tongshen 11) are primarily located in higher-energy beach microfacies; higher-energy beaches are favorable microfacies.

[0100] Step S03: extracting relevant attributes of the work area, restoring the karst paleo-geomorphology of the work area, and determining the karst microfacies based on the relevant attributes and the karst paleo-geomorphology.

[0101] In the present invention, the method for restoring the karst paleo-geomorphology of the work area, extracting the coherent attributes of the work area, and determining the karst microfacies based on the coherent attributes and the karst paleo-geomorphology includes: determining the karst paleo-geomorphology restoration layer section, and restoring the karst paleo-geomorphology based on the karst paleo-geomorphology restoration layer section; extracting the coherent attributes of the work area to obtain a coherent attribute plane map; and determining the karst microfacies based on the coherent attribute plane map and the restored karst paleo-geomorphology.

[0102] In the embodiment of the present invention, the method for determining the karst paleo-geomorphology restoration layer section is as follows: the layer section from the exposed erosion surface to the top surface of the target layer section is the karst paleo-geomorphology restoration layer section.

[0103] The top surface of the Mao2 Member is a stable trough, the top surface of the Maokou Formation is an exposed erosion surface, and the change in the thickness of the Mao3 Member represents the change in the karst paleo-geomorphology of the Maokou Formation. Therefore, the karst paleo-geomorphology restoration interval of the Maokou Formation is the thickness of the Mao3 Member, that is, from the top surface of the Maokou Formation to the top surface of the Mao2 Member; Figure 11 The following is a schematic diagram of the thickness selection of the karst paleo-geomorphological restoration of the Maokou Formation. Figure 12 The figure shows the thickness map of the Mao-3 Member. The thickness of the Mao-3 Member is used to characterize the karst paleo-geomorphology of the Maokou Formation. The residual thickness method is used to restore the karst paleo-geomorphological trend of the Maokou Formation.

[0104] The thickness of the Maosan section of the extraction work area is obtained. The thicker the formation, the stronger the karst effect. The karst paleo-landform and karst microfacies are divided into three categories according to the formation thickness: if the formation thickness value is greater than 36, it is a karst residual hill; if the formation thickness value is 28-36, it is a karst highland; if the formation thickness value is less than 28, it is a karst depression.

[0105] In the embodiment of the present invention, the location of the karst valley in the karst microfacies can be determined based on the coherent attributes. According to regional geological knowledge, the top surface of the Maokou Formation is an erosion exposure surface, and the seismic plane and cross-section characteristics of the karst valley are obvious, such as Figure 9 The figure shows the seismic response characteristics of the karst valley on the top of the Maokou Formation. Figure 9 In the middle, the plane has the distribution characteristics of a river channel, and the karst valley is obviously cut down in the section, showing high frequency and strong amplitude characteristics.

[0106] Extract regional coherent attributes and obtain a coherent attribute plane graph, such as Figure 10 The following is a map of the karst valley characteristics of the Maokou Formation top surface using coherent attributes. Figure 10 It can be seen from the figure that large karst valleys are developed on the top surface of the Maokou Formation. The karst valleys converge from the high part of the ancient structure in the east to the west. The karst valleys in the west are relatively developed and the karstification is strong. The strong karst area in the west is prone to dissolution, forming a horizontal runoff belt with higher physical properties and better reservoirs, while the karst degree of the low-energy bioclastic beach in the east is low.

[0107] according to Figure 12 Re-superposition of identified karst microfacies Figure 10 The karst valleys identified in the study area are divided into four types of karst microfacies in the Maokou Formation, including karst residual hills, karst highlands, karst depressions and karst valleys. Figure 13 Shown is the karst microfacies characteristic map of the Maokou Formation.

[0108] Step S04: using rock physics modeling methods to predict the shear wave velocity of the work area, and determining the dolomite bioclastic bank reservoir boundary value based on the prediction results.

[0109] In the present invention, the method for predicting the shear wave velocity of the work area using the rock physics modeling method includes: among all the rock physics modeling methods, the modeling method with the highest matching degree with the work area is preferably used for modeling; wherein the preferred method includes: if the rock in the work area is homogeneous, the Gassmann model is selected; if the porosity of various types of pores in the work area has been obtained, the Xu-payne and self-consistent models are selected; if the work area has medium- and low-porosity formations that meet consolidation requirements, the Pride model is selected; if the porosity of the formation in the work area is greater than 15%, the formation pressure is greater than a predetermined value, and the formation is mainly dense and low-porosity, the Krief model is selected; if the formation in the work area is a low-porosity and dense formation, the Xu-White model, the single aspect ratio model, or the soft pore model is selected.

[0110] In the present invention, if there are multiple preferred rock physics modeling methods, multiple rock physics modeling methods are used to separately model and perform shear wave prediction, and the obtained shear wave prediction results are intersected and analyzed with the measured shear waves; the rock physics modeling method corresponding to the intersection result with the highest correlation coefficient is selected as the modeling method with the highest matching degree with the work area.

[0111] In the embodiments of the present invention, there are three main types of shear wave prediction methods: estimation methods based on empirical values, prediction methods based on rock physics models, and calculation methods based on machine learning. The estimation methods based on empirical values ​​are based on the empirical relationships between P and S waves proposed by Greenberg, Gastagna, and others. These methods are simple, easy to use, and computationally efficient. However, they have an averaging effect and cannot accurately express the complex relationship between actual P and S waves, nor can they accurately estimate S-wave velocities. Some wells in the work area lack S-wave curves, and some wells have quality issues with S-waves. The calculation methods based on machine learning require a large amount of data and are highly complex. Therefore, the rock physics modeling method was chosen to predict S-waves.

[0112] Rock physics modeling methods include the Xu-White model, the Gassmann model, the Pride model, and the Krief model. These methods can accurately calculate shear wave velocity through rigorous experimental research and mathematical derivation.

[0113] The Maokou Formation is a carbonate reservoir with diverse pore types, so a method that can reflect the pore geometry must be used. Since the porosity of various pore types is not available, the Xu-Payne and self-consistent models cannot be applied. Therefore, the Xu-White model, single aspect ratio model, and soft pore model are preferred for modeling.

[0114] If you want to select the model with the highest prediction accuracy for shear wave prediction, you need to use the Xu-White model, the single aspect ratio model and the soft pore model to build models, perform shear wave predictions, and compare the shear waves predicted by different rock physics models with the measured shear waves. Figure 14 The figure shows the comparison between the measured shear wave and the predicted shear wave. Figure 14 It can be seen that the shear wave curve predicted by the single aspect ratio model is most similar to the measured shear wave.

[0115] In order to further test the reliability of the three methods, the shear waves predicted by the three methods were intersected and analyzed with the measured shear waves. The highest correlation coefficient was obtained when all the data points were clustered on the 45° line, the slope was close to 1, and the correlation coefficient was high. Figure 15 The figure shows the intersection of shear waves predicted by different modeling methods and measured shear waves. Figure 15 Figure a is the intersection diagram of the shear waves predicted by the single aspect ratio model and the measured shear waves, Figure b is the intersection diagram of the shear waves predicted by the soft hole model and the measured shear waves, and Figure c is the intersection diagram of the shear waves predicted by the Xu White model and the measured shear waves. It can be seen from the three intersection diagrams that the correlation coefficient between the shear waves predicted by the single aspect ratio model and the measured shear waves is as high as 0.92, which proves that the shear wave prediction effect of the single aspect ratio model is better. Therefore, the single aspect ratio model modeling method is selected as the modeling method with the highest matching degree with the work area.

[0116] The shear wave velocity in the target layer, namely the Mao-2 Member, is predicted using the single aspect ratio model method to obtain the shear wave velocity in the Mao-2 Member.

[0117] In the present invention, the method for determining the boundary value of dolomite bioclastic beach reservoir based on the prediction results includes: establishing a cross-plot of P-wave velocity ratio and P-wave impedance based on the prediction results, and determining the boundary value of dolomite bioclastic beach reservoir based on the cross-plot.

[0118] In an embodiment of the present invention, the ratio of the longitudinal and shear wave velocities is determined based on the shear wave velocity predicted by the aspect ratio modeling method and the longitudinal wave velocity obtained by logging, and then combined with the longitudinal wave impedance extracted from the logging data to establish a cross-plot of the longitudinal and shear wave velocity ratio and the longitudinal wave impedance.

[0119] The shear wave prediction based on the single aspect ratio model solves the problem of poor overall matching between the longitudinal and shear waves in the Mao-2 section and eliminates abnormal values ​​on the intersection diagram, such as Figure 16 The figure shows the boundary determination of the dolomite bioclastic bank reservoir of the Maokou Formation, i.e. the cross-plot of the ratio of P-wave velocity to S-wave velocity and P-wave impedance. Figure 16 It can be seen that the tight limestone and dolomite bioclastic beach reservoirs are well distinguished, and the P-wave and S-wave velocity ratio is less than 1.85, which is the dolomite bioclastic beach reservoir. That is, the boundary value of the dolomite bioclastic beach reservoir is determined to be 1.85.

[0120] Step S05: performing pre-stack seismic waveform indication inversion on the seismic data of the work area to obtain an inversion result, and extracting the thickness of the dolomite bioclastic beach reservoir in the target layer from the inversion result according to the dolomite bioclastic beach reservoir boundary value.

[0121] In the embodiments of the present invention, current phase-controlled inversion methods generally fall into two categories: conventional geostatistical phase-controlled inversion; and phase-controlled inversion based on seismic waveform indication. Conventional geostatistical inversion phase-controlled inversion includes multiple phase-controlled solutions, including one-dimensional, two-dimensional, and three-dimensional lithofacies probability constraints, seismic phases, and deterministic inversion constraints. All solutions are based on first providing a "phase" and then using the phase-constrained inversion results. This approach inherently carries a high degree of subjectivity and uncertainty. The disadvantages of conventional geostatistical inversion phase-controlled inversion include: 1. It requires a large number of wells with a minimally uniform distribution; 2. It is not suitable for predicting sand bodies with rapid lateral variations (the average width of the sand body should be no less than the well spacing); 3. Phase boundaries are controlled by interpolation algorithms and variograms, resulting in unclear geological concepts; 4. Modeling and inversion are disconnected; 5. The variogram fitting effect is unsatisfactory, and the range of variation provides a rough representation of spatial structural changes; 6. It has low lateral resolution, poor planar regularity, and is a non-phase-controlled inversion method; 7. The inversion results are highly random, resulting in low computational efficiency.

[0122] Seismic waveform-indicated inversion does not require phase constraints or preconditions. Instead, it leverages the waveform's inherent characteristics to achieve phase control. Phase patterns are used to validate inversion results, not as prerequisites. This is the only way to achieve true "phase-controlled" inversion. Key features: 1. High seismic lateral resolution; 2. No special requirements for well distribution; 3. Integrated inversion and modeling process.

[0123] The basic concept of seismic waveform feature inversion is to consider waveform similarity and spatial distance when selecting statistical samples. While ensuring consistency in sample structural characteristics, samples are sorted by distribution distance. This ensures that the inversion results reflect the constraints of sedimentary facies spatially and better conform to sedimentary patterns and characteristics in planar terms. The core technology utilizes the industry's most advanced "Seismic Waveform Markov Chain Monte Carlo Stochastic Simulation (SMCMC)" algorithm. Compared to traditional variogram-based stochastic inversion, this algorithm better embodies the concept of "phase control," offering high accuracy and minimal randomness in inversion results. This allows inversion results to move from completely random to gradually deterministic, and is suitable for uneven well distributions. It can provide a more reliable quantitative prediction model for thin reservoirs during the evaluation and development phases.

[0124] According to the inversion of pre-stack seismic waveform indications, on the basis of partial stacking data and elastic parameter curve data, by establishing a sample model of the gather and elastic parameter curve, the common structure (intercept, gradient, amplitude, phase, etc.) of the area to be predicted and the existing samples is found. Under the constraint of the gather combination with similar common structures, the inversion prediction work is carried out to obtain high-resolution inversion results.

[0125] The pre-stack waveform indication inversion has higher vertical resolution, and can more clearly depict thin reservoir structures and lateral sand body boundaries. The inversion results are highly consistent with seismic data, with natural lateral changes and strong certainty. It is highly consistent with actual drilling data and has high predictability.

[0126] like Figure 17 The figure shows the predicted section of dolomite bioclastic bank reservoir from the pre-stack waveform inversion of Mao2 Member. Figure 17 The reservoir inversion profile shows a high consistency with the logging interpretation results of known wells, high vertical resolution, reasonable structure, natural transition of effective reservoir changes between wells, clear reservoir boundaries, consistent occurrence of effective reservoirs with that of seismic phase axes, and reasonable widening of the effective frequency band. The resolution of the effective reservoir inversion profile is significantly higher than that of conventional seismic profiles. The four verification wells that did not participate in the inversion predicted dolomite bioclastic beach reservoirs with an accuracy of 10-25 meters, achieving high-precision prediction of dolomite bioclastic beach thin reservoirs. At the same time, (Heshen 401, Heshen 402, Tongshen 401, Tongshen 402) the average absolute error is 1.2m, and the average relative error is 7.9%, as shown in Table 1 below, that is, the consistency rate is 92.1%.

[0127] According to the dolomite bioclastic beach reservoir boundary value determined in step S04, the thickness H of the dolomite bioclastic beach reservoir of the Maokou Formation Mao 2 Member in the portion where the P-wave velocity ratio is less than 1.85 is extracted using the prestack waveform indication inversion result. 储层 , the extraction results are as follows Figure 18 The following is a map of the thickness of the dolomite bioclastic reservoir in the Mao 2 Member. Figure 18 It can be seen that the dolomite bioclastic beach reservoir development area is mainly distributed in the central part of the work area, and is distributed in a northwest strip-like manner. The maximum thickness of the effective reservoir is 29m, and the general thickness is 8-20m. The effective reservoir has poor connectivity, strong heterogeneity, and a more obvious "lump-like" distribution feature.

[0128] Step S06: defining constraint factors for the sedimentary microfacies type and the karst microfacies type respectively, and determining the effective reservoir thickness of the dolomite bioclastic beach in the target layer according to the constraint factors and the dolomite bioclastic beach reservoir thickness.

[0129] In the present invention, the method for determining the effective reservoir thickness of the dolomite bioclastic beach in the target layer according to the constraint factor and the dolomite bioclastic beach reservoir thickness comprises: determining the effective reservoir thickness of the dolomite bioclastic beach in the target layer according to the constraint factor using the effective reservoir thickness calculation formula;

[0130] The calculation formula for effective reservoir thickness includes:

[0131] H 有效储层 =H 储层 *ρ 沉积微相 *ρ 岩溶微相 (1);

[0132] Where: H 储层 is the thickness of the dolomite bioclastic bank reservoir, ρ 沉积微相 is the sedimentary microfacies type constraint factor, ρ 岩溶微相 is the constraint factor of karst microfacies type.

[0133] In the embodiment of the present invention, the constraint factor is defined by assigning values ​​from high to low according to the importance of each type in the formation of the effective reservoir of the dolomite bioclastic beach; when defining the constraint factor, the assigned value range is between 0 and 1, and the difference between each type is 0.15 to 0.3.

[0134] The results of sedimentary microfacies research show that the sedimentary microfacies types of the Mao-2 Member can be divided into four categories: higher-energy bioclastic beach, low-energy beach, inter-beach and medium-gentle slope. The possibility of these four sedimentary microfacies types forming effective reservoirs of dolomite bioclastic beach decreases in sequence. In order to quantitatively characterize the control of sedimentary microfacies on the effective reservoir of dolomite bioclastic beach, the sedimentary microfacies type constraint factor ρ is defined. 沉积微相 The values ​​are: higher energy debris beach = 1, low energy beach = 0.75, inter-beach = 0.5, medium gentle slope = 0.25.

[0135] Karst microfacies research results show that the karst microfacies types of the Maokou Formation are divided into four categories: karst residual hillocks, karst highlands, karst depressions and karst valleys. The effects of these four karst microfacies on the formation of effective reservoirs in dolomite bioclastic banks decrease in descending order. In order to quantitatively characterize the control of karst microfacies on the effective reservoirs in dolomite bioclastic banks, the karst microfacies constraint factor ρ is defined. 岩溶微相 The values ​​are: karst residual hill = 1, karst highland = 0.75, karst depression = 0.5, and karst valley = 0.25.

[0136] Comprehensive consideration of dolomite bioclastic bank reservoir thickness and sedimentary microfacies constraint factor ρ 沉积微相 and karst microfacies constraint factor ρ 岩溶微相 , determine the effective reservoir thickness H of the dolomite bioclastic beach 有效储层 The specific calculation formula is formula (1). According to formula (1), the effective reservoir thickness of the dolomite bioclastic beach at each location of the Mao 2nd Member in the work area is calculated. The results are as follows: Figure 19 The figure shows the effective reservoir thickness of the dolomite bioclastic beach in the Mao 2 Member. This method has enabled the prediction of effective reservoirs in the dolomite bioclastic beach under the dual control of sedimentary and karst microfacies.

[0137] It can be understood that the above-mentioned various method embodiments mentioned in the present invention can be combined with each other to form combined embodiments without violating the principle logic. Due to space limitations, the present invention will not elaborate on them.

[0138] The dual-phase-controlled dolomite bioclastic bank effective reservoir prediction method may be executed by a dual-phase-controlled dolomite bioclastic bank effective reservoir prediction device. For example, the dual-phase-controlled dolomite bioclastic bank effective reservoir prediction method may be executed by a terminal device, a server, or other processing device, wherein the terminal device may be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, an in-vehicle device, a wearable device, etc. In some possible implementations, the dual-phase-controlled dolomite bioclastic bank effective reservoir prediction method may be implemented by a processor invoking computer-readable instructions stored in a memory.

[0139] Those skilled in the art will understand that in the above-mentioned method of the specific implementation method, the writing order of each step does not mean a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0140] The present invention also provides a dual-phase-controlled dolomite bioclastic beach effective reservoir prediction device, comprising: a target layer determination unit, used to determine the dolomite bioclastic beach development stratum in the work area as the target layer; a sedimentary microfacies determination unit, used to restore the sedimentary paleogeomorphology of the work area, preferably the seismic attribute with the highest correlation with the reservoir, determine the seismic facies map of the work area, and determine the sedimentary microfacies based on the sedimentary paleogeomorphology, seismic attributes and seismic facies map; a karst microfacies determination unit, used to restore the karst paleogeomorphology of the work area, extract the coherent attributes of the work area, and determine the karst microfacies based on the coherent attributes and the karst paleogeomorphology; a limit value determination unit, used to The shear wave velocity of the work area is predicted using a rock physics modeling method, and the boundary value of the dolomite bioclastic beach reservoir is determined based on the prediction results; a reservoir thickness determination unit is used to perform pre-stack seismic waveform indication inversion on the seismic data of the work area to obtain an inversion result, and based on the boundary value of the dolomite bioclastic beach reservoir, the dolomite bioclastic beach reservoir thickness of the target layer is extracted from the inversion result; an effective reservoir thickness determination unit is used to define constraint factors for the sedimentary microfacies type and the karst microfacies type, respectively, and determine the effective reservoir thickness of the dolomite bioclastic beach in the target layer based on the constraint factors and the dolomite bioclastic beach reservoir thickness.

[0141] In some embodiments, the functions or modules and units included in the device provided by the embodiment of the present invention can be used to execute the method described in the above method embodiment. Its specific implementation can refer to the description of the above method embodiment. For the sake of brevity, it will not be repeated here.

[0142] The study of facies-controlled carbonate reservoirs is crucial for characterizing sedimentary facies zones. Currently, most research approaches rely on core data observation, sequence framework construction, and geological theory to identify and classify sedimentary microfacies. This invention not only considers sedimentary facies zones but also constrains dolomite distribution by incorporating karst facies zones. Simultaneously, three key areas of research are involved: petrophysical modeling of dolomite bioclastic reservoirs, development of effective reservoir identification charts, and quantitative prediction of effective reservoirs using prestack waveform inversion. This approach allows for a detailed characterization of effective reservoirs within dolomite bioclastic banks.

[0143] The present invention achieves two main beneficial effects. First, based on the analysis of the depositional mechanism of effective reservoirs in dolomite bioclastic banks, the present invention defines sedimentary microfacies constraint factors and karst microfacies constraint factors, thereby achieving quantitative prediction of effective reservoirs in dolomite bioclastic banks based on sedimentary and karst microfacies. This improves the geological interpretability of the prediction results of effective reservoirs in dolomite bioclastic banks and reduces the uncertainty of geophysical prediction results of dolomite bioclastic banks. Second, the accuracy of dolomite bioclastic bank reservoir predictions achieved through prestack waveform indication inversion reaches 10-25 meters, achieving high-precision prediction of thin dolomite bioclastic bank reservoirs and providing high-precision reservoir prediction results for subsequent exploration and development.

[0144] While various embodiments of the present invention have been described above, the foregoing description is intended to be illustrative, non-exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or technological improvements in the marketplace, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A dual-phase-controlled dolomite bioclastic bank effective reservoir prediction method, characterized by: include: The dolomite bioclastic beach development stratum in the work area was determined as the target interval; Restore the sedimentary paleogeomorphology of the work area, select the seismic attributes with the highest correlation with the reservoir, determine the seismic facies map of the work area, and determine the sedimentary microfacies based on the sedimentary paleogeomorphology, seismic attributes and seismic facies map; Restore the karst paleo-geomorphology of the work area, extract relevant attributes of the work area, and determine the karst microfacies based on the relevant attributes and the karst paleo-geomorphology; The shear wave velocity of the work area is predicted using rock physics modeling methods, and the reservoir boundary value of the dolomite bioclastic bank is determined based on the prediction results; Perform pre-stack seismic waveform inversion on the seismic data of the work area to obtain an inversion result, and extract the thickness of the dolomite bioclastic beach reservoir in the target layer from the inversion result according to the boundary value of the dolomite bioclastic beach reservoir; The constraint factors of the sedimentary microfacies type and the karst microfacies type are respectively defined, and the effective reservoir thickness of the dolomite bioclastic beach in the target layer is determined based on the constraint factors and the dolomite bioclastic beach reservoir thickness. The method comprises: determining the effective reservoir thickness of the dolomite bioclastic beach in the target layer using an effective reservoir thickness calculation formula based on the constraint factors; wherein the effective reservoir thickness calculation formula includes: H 有效储层 =H 储层 *r 沉积微相 *r 岩溶微相 ; Where: H 储层 is the thickness of the dolomite bioclastic bank reservoir, ρ 沉积微相 is the sedimentary microfacies type constraint factor, ρ 岩溶微相 is the constraint factor of karst microfacies type.

2. The dual-phase-controlled dolomite bioclastic bank effective reservoir prediction method according to claim 1, characterized in that: The method of restoring the sedimentary paleogeomorphology of the work area, selecting the seismic attributes with the highest correlation with the reservoir, determining the seismic facies map of the work area, and determining the sedimentary microfacies based on the sedimentary paleogeomorphology, seismic attributes and seismic facies map includes: Determining a sedimentary paleogeomorphology restoration layer section, and restoring the sedimentary paleogeomorphology based on the sedimentary paleogeomorphology restoration layer section; Extracting seismic attributes of the work area, optimizing the seismic attributes, determining the seismic attribute with the highest correlation with the reservoir, and extracting a planar map of the seismic attribute with the highest correlation from the three-dimensional seismic data of the work area; Preferably, other sensitive seismic attributes of the reservoir other than the thickness feature are characterized, and the sensitive seismic attributes are fused with the seismic attributes with the highest correlation to obtain a seismic phase map; Establish rock, electrical and seismic identification charts based on core data, well logging curves, seismic data and sedimentary paleo-geomorphology of the target layer; The corresponding relationship between the sedimentary microfacies and the seismic facies map is determined according to the identification plate, and based on this, the seismic facies map is converted into a sedimentary microfacies map.

3. The dual-phase-controlled dolomite bioclastic beach effective reservoir prediction method according to claim 2, characterized in that: The method of extracting seismic attributes of a work area and optimizing the seismic attributes includes: Extract amplitude statistics and frequency seismic attributes of the target layer in the work area, which include at least: amplitude attributes, average instantaneous frequency attributes, average instantaneous phase attributes, arc length attributes, average reflection intensity, and peak skewness; The response relationship and correlation between each seismic attribute and the reservoir thickness and porosity of the target layer are analyzed to determine the seismic attribute with the highest correlation with the reservoir.

4. The dual-phase-controlled dolomite bioclastic beach effective reservoir prediction method according to claim 1, characterized in that: The method of restoring the karst paleo-geomorphology of the work area, extracting the coherent attributes of the work area, and determining the karst microfacies based on the coherent attributes and the karst paleo-geomorphology includes: determining a karst paleo-geomorphology restoration layer section, and restoring the karst paleo-geomorphology according to the karst paleo-geomorphology restoration layer section; Extract the relevant attributes of the work area and obtain the relevant attribute plane map; The karst microfacies is determined based on the coherent attribute plane map and the restored karst paleo-landform.

5. The dual-phase-controlled dolomite bioclastic beach effective reservoir prediction method according to claim 1, characterized in that: The method for predicting the shear wave velocity of the work area using the rock physics modeling method includes: Among all the rock physics modeling methods, the modeling method with the highest matching degree with the work area is preferred for modeling; Wherein, the preferred method includes: If the rock in the work area is homogeneous, the Gassmann model is selected; If the porosity of various types of pores in the work area has been obtained, then the Xu-payne and self-consistent models are selected; If the work area has medium-low porosity strata with consolidation that meets the requirements, the Pride model is selected; If the formation porosity in the work area is greater than 15%, the formation pressure is greater than the predetermined value, and the formation is mainly dense and low-porosity, the Krief model is selected; If the stratum in the work area is a low-porosity and dense stratum, the Xu-White model, single aspect ratio model or soft pore model should be selected.

6. The dual-phase-controlled dolomite bioclastic bank effective reservoir prediction method according to claim 5, characterized in that: If there are multiple preferred rock physics modeling methods, use the multiple rock physics modeling methods to separately model and perform shear wave prediction, and perform intersection analysis on the obtained shear wave prediction results and the measured shear waves; The rock physics modeling method corresponding to the intersection result with the highest correlation coefficient is selected as the modeling method with the highest matching degree with the work area.

7. The dual-phase-controlled dolomite bioclastic bank effective reservoir prediction method according to claim 1, characterized in that: The method for determining the dolomite bioclastic beach reservoir boundary value based on the prediction results includes: According to the prediction results, a cross-plot of the ratio of P-wave velocity to S-wave velocity and P-wave impedance is established, and the boundary value of the dolomite bioclastic bank reservoir is determined according to the cross-plot.

8. A dual-phase-controlled dolomite bioclastic beach effective reservoir prediction device, characterized by: include: Target layer determination unit, used to determine the dolomite bioclastic beach development stratum in the work area as the target layer section; A sedimentary microfacies determination unit is used to restore the sedimentary paleogeomorphology of the work area, select the seismic attributes with the highest correlation with the reservoir, determine the seismic facies map of the work area, and determine the sedimentary microfacies based on the sedimentary paleogeomorphology, seismic attributes and seismic facies map; The karst microfacies determination unit is used to restore the karst paleo-geomorphology of the work area, extract the relevant attributes of the work area, and determine the karst microfacies based on the relevant attributes and the karst paleo-geomorphology; A limit value determination unit is used to predict the shear wave velocity of the work area using a rock physics modeling method and determine the limit value of the dolomite bioclastic bank reservoir based on the prediction results; a reservoir thickness determination unit for performing pre-stack seismic waveform indication inversion on the seismic data of the work area to obtain an inversion result, and extracting the dolomite bioclastic beach reservoir thickness of the target layer from the inversion result according to the dolomite bioclastic beach reservoir boundary value; The effective reservoir thickness determination unit is used to define constraint factors for the sedimentary microfacies type and the karst microfacies type, respectively, and determine the effective reservoir thickness of the dolomite bioclastic beach in the target layer according to the constraint factors and the dolomite bioclastic beach reservoir thickness. The method includes: determining the effective reservoir thickness of the dolomite bioclastic beach in the target layer according to the constraint factors using an effective reservoir thickness calculation formula; wherein the effective reservoir thickness calculation formula includes: H 有效储层 =H 储层 *r 沉积微相 *r 岩溶微相 ; Where: H 储层 is the thickness of the dolomite bioclastic bank reservoir, ρ 沉积微相 is the sedimentary microfacies type constraint factor, ρ 岩溶微相 is the constraint factor of karst microfacies type.

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