Dual phase control dolomite scrap beach effective reservoir prediction method and device

Abstract

The invention relates to the technical field of oil and gas geophysical exploration, in particular to a dual-phase-control dolomite scrap-generating beach effective reservoir prediction method and a dual-phase-control dolomite scrap-generating beach effective reservoir prediction device. The method comprises the following steps: determining a target interval of a work area; determining the sedimentary microfacies according to the sedimentary ancient landform, the seismic attributes and the seismic facies diagram; determining karst microfacies according to the coherence attributes and the karst ancient landform; the shear wave velocity of the work area is predicted, and the boundary value of the dolomite debris beach reservoir is determined; performing pre-stack seismic waveform indication inversion on the seismic data of the work area, and extracting the reservoir thickness of the dolomite debris-generating beach in the target interval according to the threshold value; and according to the sedimentary microfacies, the karst microfacies and the thickness of the dolomite scrap beach reservoir, the effective thickness of the dolomite scrap beach reservoir of the target interval is determined. The invention aims to solve the problems that the result precision is low when spectral decomposition and wavelet decomposition reconstruction are adopted to predict the effective reservoir of the dolomite debris beach, the prediction accuracy is poor when an inversion method is adopted to predict, and the high-precision prediction of the effective reservoir of the dolomite debris beach cannot be realized in the prior art.

Description

Technical Field

[0001] The 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 beaches. 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 its effective reservoir thickness has always been an extremely difficult task.

[0003] At present, seismic attribute technology and geostatistical inversion technology are the main means of quantitative prediction of thin reservoirs. The most common seismic attribute technologies include spectral decomposition technology and wavelet decomposition and reconstruction technology. These technologies have improved the resolution of seismic prediction to a certain extent, but these technologies have not made full use of high-resolution logging data, and the prediction accuracy is low, and they cannot achieve fine prediction of thin dolomite bioclastic beach reservoirs. As the most common high-resolution inversion method, the geostatistical inversion method greatly improves the vertical resolution of reservoir inversion by using the idea of ​​random inversion simulation, but this method has the problem that it is difficult to fit a satisfactory function model with samples, resulting in a difference between the inversion results and the actual situation, and the prediction accuracy is poor, and it cannot achieve high-precision prediction of effective reservoirs of dolomite bioclastic beaches. At the same time, the formation of effective reservoirs of dolomite bioclastic beaches has specific geological conditions. Seismic attribute technology and geostatistical inversion technology are both geophysical prediction technologies, which have very large multi-solutions themselves, and the prediction results lack basic geological cause explanations, and the prediction accuracy is difficult to guarantee. Therefore, a technology for predicting effective reservoirs in thin dolomite bioclastic beaches is urgently needed to improve the geological rationality of the prediction results of effective reservoirs in dolomite bioclastic beaches and improve the accuracy of thin reservoir prediction. Summary of the invention

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

[0005] According to one aspect of the present invention, a method for predicting effective reservoirs of dual-phase-controlled dolomite bioclastic beaches is provided, comprising: Determine the dolomite bioclastic beach development strata in the work area as the target stratum; 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 the 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 beach is determined based on the prediction results; Perform pre-stack seismic waveform indication 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 dolomite bioclastic beach reservoir boundary value; 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 according to the constraint factors and the reservoir thickness of the dolomite bioclastic beach.

[0006] Preferably, the method of restoring the sedimentary paleogeomorphology of the work area, preferably the seismic attribute with the highest correlation with the reservoir, determining the seismic phase map of the work area, and determining the sedimentary microfacies according to the sedimentary paleogeomorphology, seismic attribute and seismic phase map includes: Determining a sedimentary paleo-geomorphology restoration layer section, and restoring the sedimentary paleo-geomorphology according to the sedimentary paleo-geomorphology 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 the plane 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; According to the core data, well logging curves, seismic data and the sedimentary paleo-geomorphology of the target layer, a rock, electrical and seismic identification chart is established; 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.

[0007] Preferably, the method of extracting seismic attributes of a work area and optimizing the seismic attributes comprises: Extract the amplitude statistics and frequency seismic attributes of the target layer section in the work area, which at least include: amplitude attribute, average instantaneous frequency attribute, average instantaneous phase attribute, arc length attribute, average reflection intensity, and peak skewness; The response relationship and correlation between each seismic attribute and the reservoir thickness and reservoir porosity of the target layer are analyzed to determine the seismic attribute with the highest correlation with the reservoir.

[0008] Preferably, 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 according to 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 relevant attributes of the work area and obtain a relevant attribute plane map; The karst microfacies are determined based on the coherent attribute plane map and the restored karst paleo-landform.

[0009] Preferably, the method for predicting the shear wave velocity of the work area using the rock physics modeling method comprises: 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 comprises: If the rock in the work area is homogeneous, select the Gassmann model; If the porosity of various types of pores in the work area has been obtained, then select Xu-payne and self-consistent models; If the work area has medium-low porosity strata that meet consolidation 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, select the Xu-White model, the single aspect ratio model, or the soft pore model.

[0010] Preferably, if there are multiple preferred rock physics modeling methods, the multiple rock physics modeling methods are used to respectively model and perform shear wave prediction, and the obtained shear wave prediction results are cross-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.

[0011] Preferably, the method for determining the dolomite bioclastic beach reservoir boundary value according to the prediction results comprises: 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 according to the cross plot, the reservoir boundary value of the dolomite bioclastic beach is determined.

[0012] Preferably, the method for determining the effective reservoir thickness of dolomite bioclastic beach in the target layer according to the constraint factor and the reservoir thickness of dolomite bioclastic beach comprises: According to the constraint factor, the effective reservoir thickness of the dolomite bioclastic beach in the target layer is determined by using the calculation formula of the effective reservoir thickness; Wherein, the calculation formula of the effective reservoir thickness includes: H 有效储层 =H 储层 * 沉积微相 * 岩溶微相 ; Where: H储层 is the thickness of the dolomite bioclastic beach reservoir, ρ 沉积微相 is the sedimentary microfacies type constraint factor, ρ 岩溶微相 It is the constraint factor of karst microfacies type.

[0013] According to one aspect of the present invention, a dual-phase-controlled dolomite bioclastic beach effective reservoir prediction device is provided, comprising: The target layer determination unit is used to determine the dolomite bioclastic beach development strata in the work area as the target layer section; A sedimentary microfacies determination unit is used to restore the sedimentary paleo-geomorphology of the work area, select 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 paleo-geomorphology, 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 coherent attributes of the work area, and determine the karst microfacies according to the coherent 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 beach reservoir based on the prediction result; 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 extract 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 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.

[0014] The present invention has at least the following beneficial effects: 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 the dolomite bioclastic beach, by introducing sedimentary microfacies and karst microfacies, the present invention overcomes the problems of low prediction accuracy of carbonate thin 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

[0015] 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.

[0016] Figure 1 The core fracture and hole characteristic diagram of Heshen 4 well according to an embodiment of the present invention is shown; Figure 2A schematic diagram showing thickness selection for restoration of sedimentary paleo-geomorphology according to an embodiment of the present invention is shown; Figure 3 A plan view of the sedimentary paleo-geomorphology of the Maokou Formation Mao 2nd Member according to an embodiment of the present invention is shown; Figure 4 The cross-plot of reservoir thickness and amplitude and the cross-plot of reservoir porosity and amplitude of the Maokou Formation Mao2 Member according to an embodiment of the present invention are shown; Figure 5 The amplitude property plane diagram of the second section of Maokou Formation is shown according to an embodiment of the present invention; Figure 6 The sedimentary microfacies type litho-electric-seismic identification chart according to an embodiment of the present invention is shown; Figure 7 The multi-attribute fusion seismic phase plane diagram of the Maokou Formation Mao 2nd Member according to an embodiment of the present invention is shown; Figure 8 A plan view of the sedimentary microfacies of the Maokou Formation Mao2 according to an embodiment of the present invention is shown; Fig. 9 A characteristic diagram of seismic response of the karst valley on the top surface of the Maokou Formation according to an embodiment of the present invention is shown; Fig.10 A characteristic diagram of the karst valley on the top surface of the Maokou Formation is depicted according to coherent attributes of an embodiment of the present invention; Fig.11 A schematic diagram showing the thickness selection of the Maokou Formation karst paleo-geomorphology restoration according to an embodiment of the present invention; Fig.12 A thickness diagram of the Mao-3 section of the stratum according to an embodiment of the present invention is shown; Fig.13 The karst microfacies characteristic diagram of the Maokou Formation according to an embodiment of the present invention is shown; Fig.14 A comparison diagram of measured shear waves and predicted shear waves according to an embodiment of the present invention is shown; Fig.15 shows a cross-plot of predicted shear waves and measured shear waves according to an embodiment of the present invention; Fig.16 A plate showing the determination of the reservoir boundary of the Maokou Formation dolomite bioclastic beach according to an embodiment of the present invention is shown; Fig.17 A cross-sectional diagram of dolomite bioclastic beach reservoir prediction based on pre-stack waveform indication inversion of the Mao-2 segment according to an embodiment of the present invention is shown; Fig.18 A diagram showing the thickness of the bioclastic beach reservoir of the Mao 2nd Member dolomite according to an embodiment of the present invention is shown; Fig.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

[0017] 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 specified.

[0018] 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.

[0019] The term "and / or" herein is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C can represent including any one or more elements selected from the set consisting of A, B, and C.

[0020] In addition, in order to better illustrate the present invention, numerous specific details are provided in the following specific embodiments. It should be understood by those skilled in the art that the present invention can be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present invention.

[0021] The present invention discloses 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 paleo-geomorphology of the work area, selecting the seismic attribute with the highest correlation with the reservoir, determining the seismic phase diagram of the work area, and determining the sedimentary microfacies according to the sedimentary paleo-geomorphology, seismic attribute and seismic phase diagram; step S03: restoring the karst paleo-geomorphology of the work area, extracting the coherent attributes of the work area, and determining the karst microfacies according to the coherent attributes and the karst paleo-geomorphology; step S04: using rock objects to predict the karst microfacies; step S05: restoring the karst paleo-geomorphology of the work area, extracting the coherent attributes of the work area, and determining the karst microfacies according to the coherent attributes and the karst paleo-geomorphology; step S06: determining the karst microfacies according to the rock objects; step S07: determining the karst microfacies according to the rock objects; step S08: determining the karst microfacies according to the rock objects; step S09: determining the karst microfacies according to the rock objects; step S100: determining the karst microfacies according to the rock objects; step S110: determining the karst microfacies according to the rock objects; step S120: determining the karst microfacies according to the rock objects; step S130: determining the karst microfacies according to the rock objects; step S140: determining the karst microfacies according to the rock objects; step S150: determining the karst microfacies according to the rock objects; step S160: determining the karst microfacies according to the rock objects; step S171: determining the karst micro The shear wave velocity of the work area is predicted by a theoretical modeling method, and the boundary value of the dolomite bioclastic beach reservoir is determined according to 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 according to 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 according to the constraint factors and the dolomite bioclastic beach reservoir thickness.

[0022] A dual-phase-controlled dolomite bioclastic beach effective reservoir prediction method provided by an embodiment of the present invention specifically comprises the following steps: Step S01: Determine the dolomite bioclastic beach development stratum in the work area as the target stratum.

[0023] In the embodiment of the present invention, the area with relatively high ancient sedimentary landform is a favorable area for the development of bioclastic beaches. The bioclastic beaches formed in this area generally have more biological species, relatively high volume fraction of particles, relatively low mud content, thick layers of bioclastic limestone and dolomite, and the developed bioclastic beach type belongs to high-energy bioclastic beach, showing a high-energy hydrodynamic environment. In the area with relatively low ancient sedimentary landform, 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-low energy bioclastic beaches, showing a low-energy hydrodynamic environment.

[0024] Therefore, the determination methods of dolomite bioclastic beaches include: intra-platform dolomite bioclastic beaches are mainly developed in areas with relatively high local landforms and controlled by wave action; the lithology is mainly sparry granular limestone, bioclastic limestone and fine-medium crystalline dolomite. It contains a lot of bioclastics, and the particles are cemented by sparry calcite. In terms of well logging characteristics, the natural gamma curve is low, often showing a low gamma box type.

[0025] The work area of ​​the embodiment of the present invention takes the Maokou Formation in the Sichuan Basin as an example, wherein the Maokou Formation includes the first, second and third Maokou Members. The drilling results of the work area show that the dolomite bioclastic beach reservoir of the second Mao Member of Heshen 4 Well is well developed, which is a fracture-hole type reservoir with good gas content. Figure 1 As shown, Figure 1 This is the core fracture and hole characteristic map of Heshen 4 Well. Figure 1 Figure a is the core image of the 4339.7m well of Heshen 4, which is brown-gray gray dolomite, and figure b is the imaging logging image of the 40th layer, which shows that the pores and fractures in the formation are relatively developed. Figure c is the core image of the 4339.7m section of the Mao II member, which is medium-fine crystalline dolomite. Figure d is the detail image of the core of the 4339.25m section of the Mao II member under a microscope, which is medium-crystalline dolomite, intercrystalline solution pores and intracrystalline solution pores. Figure e is the core image of the 4339.35m section of the Mao II member, which is medium-fine crystalline dolomite, solution caves and intercrystalline residual micropores, and figure f is the detail image of the core of the 4339.25m section of the Mao II member under a microscope, which is medium-crystalline dolomite, solution pores and solution fractures.

[0026] The core description of Heshen 4 well revealed that interlayer karstification occurred in the Mao 2nd Member, and a large fracture-cave system was developed. Small dissolution holes and cracks were observed in the core. Intercrystalline pores and intercrystalline dissolution pores were mainly developed under the thin sections. Imaging logging showed that cracks and dissolution holes were developed, and high-angle fractures were visible. Porphyritic dissolution was observed in the dolomite development section, which was horizontally distributed and resembled karst structure from top to bottom, with the characteristics of transition from vertical seepage zone to horizontal undercurrent zone. Therefore, it was judged that the favorable sedimentary facies type of the Mao 2nd Member was high-energy bioclastic beach.

[0027] The genesis mechanism of dolomite bioclastic beaches is as follows: bioclastic beaches, bioherms, and oolite / psaphyranthes beaches are common in sedimentary facies zones where carbonate reservoirs are developed. Bioclastics are the main components of bioclastic rocks, and most of the particles are cemented with bright crystals, which belongs to an environment with strong hydrodynamics. Bioherms: carbonate rocks formed by reef-building organisms. Bioherms are an important reservoir in carbonate rocks, and their reservoir distribution is not mainly controlled by the scale of reef development. Bioherms often have unique reef structures in earthquakes and are easy to identify. Oolite / psaphyranthes beaches: often formed in high-energy subtidal sedimentary environments on platforms, and are mainly controlled by tidal action. The rock components are mainly oolite / psaphyranthes limestone, and the oolite / psaphyranthes component is greater than 50%. The above analysis shows that the genetic mechanism of the effective reservoir of dolomite bioclastic beach is: high-energy bioclastic beach is the basis, and karstification is the key. Therefore, the characterization of the effective reservoir of dolomite bioclastic beach should start from three aspects: first, to carry out sedimentary microfacies research to determine the distribution range of high-energy bioclastic beach; second, to carry out karst microfacies research to determine the strong karstification area; third, under the dual control of sedimentary microfacies and karst microfacies, starting from geophysical methods, to carry out three aspects of work, including rock physical modeling of dolomite bioclastic beach reservoir, establishment of effective reservoir identification plate, and quantitative prediction of effective reservoir by prestack waveform indication inversion, so as to finely characterize the effective reservoir of dolomite bioclastic beach.

[0028] Step S02: Restore the sedimentary paleo-geomorphology of the work area, select the seismic attributes with the highest correlation with the reservoir, determine the seismic phase map of the work area, and determine the sedimentary microfacies based on the sedimentary paleo-geomorphology, seismic attributes and seismic phase map.

[0029] In the present invention, the method of restoring the sedimentary paleogeomorphology of the work area, optimizing the seismic attributes with the highest correlation with the reservoir, determining the seismic phase map of the work area, and determining the sedimentary microfacies according to the sedimentary paleogeomorphology, seismic attributes and seismic phase map includes: determining the sedimentary paleogeomorphology restoration layer section, and restoring the sedimentary paleogeomorphology according to the sedimentary paleogeomorphology restoration layer section; extracting the seismic attributes of the work area, optimizing the seismic attributes, determining the seismic attributes with the highest correlation with the reservoir, and extracting the plane map of the seismic attributes with the highest correlation from the three-dimensional seismic data of the work area; preferably characterizing other sensitive seismic attributes of the reservoir except the thickness characteristics, fusing the sensitive seismic attributes with the seismic attributes with the highest correlation to obtain a seismic phase map; establishing a rock, electricity and seismic identification plate according to the core data, logging curves, seismic data and the sedimentary paleogeomorphology of the target layer; determining the correspondence between the sedimentary microfacies and the seismic phase map according to the identification plate, and converting the seismic phase map into a sedimentary microfacies map based on this.

[0030] In an embodiment of the present invention, the method for determining a sedimentary paleo-geomorphology restoration layer section includes: selecting a layer section from an initially deposited layer to a target layer that is in a sedimentary stable period as a sedimentary paleo-geomorphology restoration layer section.

[0031] Figure 2 Schematic diagram of thickness selection for restoration of sedimentary paleogeomorphology, where the upper figure is a comparison profile of the well-linked strata, and the lower figure is a seismic reflection characteristic profile. From the regional geological knowledge, it can be known that large-scale karstification occurred on the top surface of the Maokou Formation in the work area, and the Mao 3rd, Mao 2nd and Mao 1st sections developed in the study area. According to step S01, it is determined that the dolomite bioclastic beach reservoir is mainly developed in the Mao 2nd section. The Mao 1st section is the largest marine flooding surface, with a stable interface. The karstification in the late deposition of the Maokou Formation has no effect on it. The residual thickness of the Mao 2nd section represents the sedimentary paleogeomorphology of the Maokou Formation. Therefore, the thickness of the Mao 1st section + Mao 2nd section (i.e., the thickness from the bottom of Mao 3 to the bottom of Mao 1) is selected for the restoration of sedimentary paleogeomorphology. Among them, the method for restoring sedimentary paleogeomorphology is: the residual thickness method is used to restore the sedimentary paleogeomorphology of the Mao 2nd section of the Maokou Formation.

[0032] Figure 3 This is the restored plan view of the sedimentary paleogeomorphology of the Mao 2nd Member of the Maokou Formation. The sedimentary paleogeomorphology of the Mao 2nd Member of the Maokou Formation is generally high in the west and low in the east, and the paleogeomorphological high-uplift belt is distributed in a northwest-trending strip.

[0033] 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 lithology of each seismic attribute and reservoir thickness and reservoir porosity of the target layer section, and determining the seismic attribute with the highest correlation with the reservoir.

[0034] In an embodiment of the present invention, a variety of seismic attributes of amplitude statistics and frequency are extracted for the Mao II dolomite bioclastic beach reservoir, wherein the response relationship and correlation analysis method are as follows: each seismic attribute is respectively cross-analyzed with the reservoir thickness and porosity to obtain cross-plots with the reservoir thickness and porosity, and among the cross-plots of all seismic attributes, the seismic attribute corresponding to the cross-plot with the largest correlation coefficient is selected as the preferred seismic attribute.

[0035] 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 section, the stronger the amplitude; as the reservoir thickness and porosity decrease, the amplitude becomes weaker. Figure 4 The following are the cross plots of reservoir thickness and amplitude and reservoir porosity and amplitude in the Maokou Formation Mao2. Figure 4 In the figure, Figure a is the cross-plot of reservoir thickness and amplitude in the Maokou Formation Mao 2 Member, and Figure b is the cross-plot of reservoir porosity and amplitude.

[0036] 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 diagram of the Mao-2 Member of the Maokou Formation, which shows the sedimentary microfacies variation characteristics of the dolomite bioclastic beach reservoir.

[0037] Among the sedimentary microfacies types, the lithology of the higher energy beach is mainly powder crystal and medium-fine crystal dolomite, and the earthquake is strong amplitude; the lithology of the low energy beach is micritic bioclastic limestone, and the earthquake is weak wave peak; the lithology of the medium gentle slope is micritic limestone, and the earthquake is complex wave trough. However, due to factors such as the strong amplitude of the surrounding rock, the amplitude attributes extracted above cannot fully reflect the reservoir characteristics, and need to be analyzed in combination with the seismic attributes related to different microfacies types.

[0038] The selection of sensitive seismic attributes can be selected from the intersection results of the above-obtained seismic attributes with the reservoir thickness and porosity, and the seismic attributes corresponding to the two highest correlation coefficients except the amplitude attribute are selected as sensitive seismic attributes. 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.

[0039] 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.

[0040] Through the fusion of multiple attributes 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 consistency, obtain the 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 2nd Member.

[0041] In the embodiment of the present invention, based on the core of the second member of the Maokou Formation, i.e. thin section data, well logging curves, seismic response characteristics (seismic data) and restored sedimentary paleo-geomorphology, a rock, electricity and seismic identification plate of the sedimentary microfacies of the Maokou Formation is established, and the rock-electricity-seismic characteristics of different microfacies types are clear, such as Figure 6 The figure shows the sedimentary microfacies type litho-electric-seismic identification plate. According to the established sedimentary microfacies litho-electric-seismic identification plate, the microfacies type of the dolomite bioclastic beach reservoir of the Maokou Formation is identified as high-frequency, strong-amplitude reflection.

[0042] The target layer sedimentary microfacies can be determined based on the rock-electric-seismic identification plate, 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.

[0043] From the sedimentary microfacies plan, it can be seen that the sedimentary microfacies types of the Mao II Member are divided into 4 categories, including: higher energy bioclastic beach, low energy beach, inter-beach and medium gentle slope. Among them, low energy beach is widely developed, higher energy beach is in the shape of a lump, distributed in a near north-south strip, and high-yield wells (Heshen 4, Tongshen 3, Tongshen 4, Tongshen 11) are mainly located in higher energy beach microfacies; higher energy beach is a favorable microfacies.

[0044] Step S03: extracting relevant attributes of the work area, restoring the karst paleo-geomorphology of the work area, and determining the karst microfacies according to the relevant attributes and the karst paleo-geomorphology.

[0045] In the present invention, 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 according to the coherent attributes and the karst paleo-geomorphology includes: determining the karst paleo-geomorphology restoration layer section, and restoring the karst paleo-geomorphology according to the karst paleo-geomorphology restoration layer section; extracting the coherent attributes of the work area to obtain a coherent attribute plane map; determining the karst microfacies according to the coherent attribute plane map and the restored karst paleo-geomorphology.

[0046] 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.

[0047] 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 thickness of the Mao3 Member is selected as the karst paleo-geomorphology restoration layer of the Maokou Formation, that is, the top surface of the Maokou Formation to the top surface of the Mao2 Member; Fig.11 The following is a schematic diagram of the thickness selection of the Maokou Formation karst paleo-geomorphological restoration. Fig.12 The figure shows the thickness map of the Mao-3 section. The thickness of the Mao-3 section 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.

[0048] The thickness of the Maosan section of the work area was extracted. The greater the thickness, the stronger the karst effect. The karst paleo-landform karst microfacies was divided into three categories according to the thickness of the formation: 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.

[0049] 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 the regional geological knowledge, the top surface of the Maokou Formation is an erosion exposure surface, and the seismic plane and profile characteristics of the karst valley are obvious, such as Fig. 9 The figure shows the seismic response characteristics of the karst valley on the top of the Maokou Formation. Fig. 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.

[0050] Extract the regional coherent attributes and obtain the coherent attribute plane map, such as Fig.10 The figure shows the characteristics of the karst valleys on the top of the Maokou Formation using coherent attributes. Fig.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.

[0051] according to Fig.12 Re-superposition of identified karst microfacies Fig.10 The karst valleys identified showed that the karst microfacies of the Maokou Formation in the study area can be divided into four types, including karst residual hills, karst highlands, karst depressions and karst valleys. Fig.13 Shown is the characteristic diagram of karst microfacies of Maokou Formation.

[0052] Step S04: Use rock physics modeling methods to predict the shear wave velocity of the work area, and determine the dolomite bioclastic beach reservoir boundary value based on the prediction results.

[0053] 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 formation porosity 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-pore, the Krief model is selected; if the formation in the work area is a low-porosity and dense formation, the Xu-White model or the single aspect ratio model or the soft pore model is selected.

[0054] In the present invention, if there are multiple preferred rock physics modeling methods, multiple rock physics modeling methods are used to respectively build models and perform shear wave predictions, 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.

[0055] In the embodiment 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 relationship between longitudinal and transverse waves of Greenberg, Gastagna, etc. The estimation methods based on empirical values ​​are simple and easy to use, and have high calculation efficiency. However, this method has an averaging effect and cannot accurately express the complex relationship between actual longitudinal and transverse waves, and cannot estimate the accurate shear wave velocity. For individual wells in the work area, there is a lack of shear wave curves, and there are quality problems with the shear waves of some wells. The calculation method based on machine learning requires a large amount of data and is highly complex. Therefore, the rock physics modeling method is selected to predict shear waves.

[0056] Methods based on rock physics modeling include the Xu-White model, the Gassmann model, the Pride model, the Krief model, etc. This type of method can accurately calculate the shear wave velocity through rigorous experimental research and mathematical derivation.

[0057] The Maokou Formation belongs to a carbonate reservoir with various pore types. It is necessary to use a method that can reflect the pore geometry. Because the porosity of various pore types is not obtained, the Xu-payne and self-consistent models cannot be applied. The Xu-White model, single aspect ratio model and soft pore model are preferred for modeling.

[0058] 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 hole model to build models, perform shear wave predictions, and compare the shear waves predicted by different rock physics models with the measured shear waves. Fig.14 The figure shows the comparison between the measured shear wave and the predicted shear wave. Fig.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.

[0059] In order to further test the reliability of the three methods, the shear waves predicted by the three methods were intersected with the measured shear waves, with the data points all clustered on the 45° line, the slope close to 1, and the highest correlation coefficient. Fig.15 The figure shows the intersection diagram of shear waves predicted by different modeling methods and measured shear waves. Fig.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, proving 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.

[0060] 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.

[0061] In the present invention, the method for determining the boundary value of dolomite bioclastic beach reservoir according to the prediction results comprises: establishing a cross plot of P-wave velocity ratio and P-wave impedance according to the prediction results, and determining the boundary value of dolomite bioclastic beach reservoir according to the cross plot.

[0062] In an embodiment of the present invention, the ratio of 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.

[0063] 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 the abnormal values ​​on the intersection diagram, such as Fig.16 The figure shows the boundary determination of the dolomite bioclastic beach 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. Fig.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.

[0064] Step S05: Perform pre-stack seismic waveform indication inversion on the seismic data of the work area to obtain an inversion result, and extract the dolomite bioclastic beach reservoir thickness of the target layer from the inversion result according to the dolomite bioclastic beach reservoir boundary value.

[0065] In the embodiment of the present invention, there are two main types of phase control inversion methods at present, one is conventional geostatistical phase control inversion; the other is phase control based on "seismic waveform indication inversion". The phase control of conventional geostatistical inversion includes multiple phase control solutions such as 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 constraint inversion results. This idea itself is highly subjective and uncertain. Disadvantages of phase control of conventional geostatistical inversion: 1. It requires a large number of wells and the distribution should be as uniform as possible; 2. It is not suitable for predicting sand bodies with rapid lateral changes (the average width of the sand body is not less than the well spacing); 3. The phase boundary is controlled by the interpolation algorithm and the variogram, and the geological concept is unclear; 4. Modeling and inversion are disconnected; 5. The variogram fitting effect is not ideal, and the range is roughly characterized by the spatial structure change; 6. The lateral resolution is low, the plane regularity is poor, and it is a non-phase-controlled inversion method; 7. The inversion results are highly random and the calculation efficiency is low.

[0066] "Seismic waveform indication inversion" does not require phase constraints or phase as a prerequisite, but uses the waveform's own characteristics to achieve the purpose of phase control. Phase laws are used to verify the inversion results, not the prerequisite for inversion, so as to achieve true "phase-controlled" inversion. Main features: 1. High seismic lateral resolution; 2. No special requirements for well distribution; 3. Integrated inversion modeling process.

[0067] The basic idea of ​​seismic waveform feature indication inversion is to refer to the two factors of waveform similarity and spatial distance when screening statistical samples, and to sort the samples according to the distribution distance on the basis of ensuring the consistency of sample structural characteristics, so that the inversion results reflect the constraints of sedimentary facies in space and are more in line with sedimentary laws and characteristics in plane. The core technology adopts the most advanced "Seismic Waveform Indication Markov Chain Monte Carlo Stochastic Simulation (SMCMC)" algorithm in the industry. Compared with the traditional random inversion based on variogram, it better reflects the idea of ​​"phase control", has the characteristics of high precision and low randomness of inversion results, so that the inversion results go from completely random to gradually determined, and are suitable for uneven well distribution, which can provide a more reliable quantitative prediction model for thin reservoirs in the evaluation-development stage.

[0068] 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 the 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 of similar common structures, the inversion prediction work is carried out to obtain high-resolution inversion results.

[0069] 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, and the lateral changes are natural and certain. They are highly consistent with actual drilling data and have high predictability.

[0070] like Fig.17 The figure shows the predicted section of dolomite bioclastic beach reservoir from the prestack waveform indication inversion of the Mao II Member. Fig.17 From the reservoir inversion profile, the agreement rate is high with the logging interpretation results of known wells, the vertical resolution is high, the structure is reasonable, the transition of effective reservoir changes between wells is natural, the reservoir boundary is clearly portrayed, the occurrence of effective reservoirs is consistent with the occurrence of seismic phase axis, the effective frequency band is reasonably widened, and the resolution of effective reservoir inversion profile is significantly higher than that of conventional seismic profile. The four verification wells that did not participate in the inversion predicted the dolomite bioclastic beach reservoir with an accuracy of 10-25 meters, realizing 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 agreement rate is 92.1%. 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 part with a P-S wave velocity ratio less than 1.85 is extracted using the prestack waveform indication inversion result. 储层 , the extraction results are as follows Fig.18 The figure shows the thickness of the dolomite bioclastic beach reservoir in the second member of Maoshan Formation. Fig.18 It can be seen that the dolomite bioclastic beach reservoir development area is mainly distributed in the middle of the work area, extending in a northwest strip-like distribution. 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.

[0071] Step S06: respectively defining the constraint factors of the sedimentary microfacies type and the karst microfacies type, 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.

[0072] 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 and using the effective reservoir thickness calculation formula; Among them, the calculation formula of effective reservoir thickness includes: H 有效储层 =H 储层 * 沉积微相 * 岩溶微相 (1); Where: H 储层 is the thickness of the dolomite bioclastic beach reservoir, ρ 沉积微相 is the sedimentary microfacies type constraint factor, ρ 岩溶微相 It is the constraint factor of karst microfacies type.

[0073] 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 effective reservoirs of dolomite bioclastic beaches; when defining the constraint factor, the value range of the assignment is between 0 and 1, and the difference between each type is 0.15 to 0.3.

[0074] 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 turn. 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 bioclastic beach = 1, low energy beach = 0.75, between beaches = 0.5, and medium gentle slope = 0.25.

[0075] The research results of karst microfacies show that the karst microfacies types of Maokou Formation can be divided into four categories: karst residual hillock, karst highland, karst depression and karst valley. The effects of these four karst microfacies on the formation of effective reservoirs of dolomite bioclastic beaches decrease in turn. In order to quantitatively characterize the control effect of karst microfacies on the effective reservoirs of dolomite bioclastic beaches, the karst microfacies constraint factor ρ is defined. 岩溶微相 They are: karst residual hill = 1, karst highland = 0.75, karst depression = 0.5, karst valley = 0.25.

[0076] Comprehensive consideration of dolomite bioclastic beach 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 dolomite bioclastic beach at various locations in the Mao 2nd Member of the work area is calculated. The results are as follows: Fig.19 The figure shows the effective reservoir thickness of the dolomite bioclastic beach in the second section of Maoshan Formation. The prediction of the effective reservoir of the dolomite bioclastic beach under the dual control of sedimentary microfacies and karst microfacies is realized.

[0077] It can be understood that the above-mentioned various method embodiments mentioned in the present invention can be combined with each other to form a combined embodiment without violating the principle logic. Due to space limitations, the present invention will not go into details.

[0078] The execution subject of the dual phase-controlled dolomite bioclastic beach effective reservoir prediction method may be a dual phase-controlled dolomite bioclastic beach effective reservoir prediction device, for example, the dual phase-controlled dolomite bioclastic beach effective reservoir prediction method may be executed by a terminal device or 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, a vehicle-mounted device, a wearable device, etc. In some possible implementations, the dual phase-controlled dolomite bioclastic beach effective reservoir prediction method may be implemented by a processor calling a computer-readable instruction stored in a memory.

[0079] Those skilled in the art will appreciate that, in the above method of specific implementation, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

[0080] 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 paleo-geomorphology of the work area, preferably the seismic attribute with the highest correlation with the reservoir, determine the seismic phase map of the work area, and determine the sedimentary microfacies according to the sedimentary paleo-geomorphology, seismic attribute and seismic phase map; a karst microfacies determination unit, used to restore the karst paleo-geomorphology of the work area, extract the coherent attributes of the work area, and determine the karst microfacies according to the coherent attributes and the karst paleo-geomorphology; a limit value determination unit, used to The shear wave velocity of the work area is predicted by using a rock physics modeling method, and the boundary value of the dolomite bioclastic beach reservoir is determined according to the prediction result; a reservoir thickness determination unit is used to perform prestack seismic waveform indication inversion 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 according to the boundary value of the dolomite bioclastic beach reservoir; an effective reservoir thickness determination unit is used to define the constraint factors of the sedimentary microfacies type and the karst microfacies type respectively, and the effective reservoir thickness of the dolomite bioclastic beach in the target layer is determined according to the constraint factors and the dolomite bioclastic beach reservoir thickness.

[0081] 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.

[0082] The study of phase-controlled carbonate reservoirs is key to the characterization of sedimentary facies. Currently, most research methods are based on core data observation, sequence framework construction and geological theory to identify and divide sedimentary microfacies. The present invention not only takes into account the sedimentary facies, but also constrains the distribution of dolomite in combination with the karst facies. At the same time, it carries out three aspects of work, including rock physical modeling of dolomite bioclastic beach reservoirs, establishment of effective reservoir identification plates, and quantitative prediction of effective reservoirs by prestack waveform indication inversion, to finely characterize the effective reservoirs of dolomite bioclastic beaches.

[0083] The present invention mainly achieves two beneficial effects. First, based on the analysis of the depositional mechanism of effective reservoirs of dolomite bioclastic beaches, the present invention defines sedimentary microfacies constraint factors and karst microfacies constraint factors, realizes the prediction of effective reservoirs of dolomite bioclastic beaches with quantitative sedimentary microfacies and karst microfacies, improves the geological cause interpretability of the prediction results of effective reservoirs of dolomite bioclastic beaches, and reduces the uncertainty of the results of geophysical methods for predicting dolomite bioclastic beaches; second, the accuracy of dolomite bioclastic beaches reservoirs predicted by prestack waveform indication inversion reaches 10-25 meters, realizing high-precision prediction of thin reservoirs of dolomite bioclastic beaches, and providing high-precision reservoir prediction results for subsequent exploration and development.

[0084] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A dual phase-controlled dolomite bioclastic beach effective reservoir prediction method, characterized in that: include: Determine the dolomite bioclastic beach development strata in the work area as the target stratum; 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 the 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 beach is determined based on the prediction results; Perform pre-stack seismic waveform indication 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 dolomite bioclastic beach reservoir boundary value; 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 according to the constraint factors and the dolomite bioclastic beach reservoir thickness.

2. The dual phase-controlled dolomite bioclastic beach 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 attribute with the highest correlation with the reservoir, determining the seismic phase map of the work area, and determining the sedimentary microfacies according to the sedimentary paleogeomorphology, seismic attributes and seismic phase map includes: Determining a sedimentary paleo-geomorphology restoration layer section, and restoring the sedimentary paleo-geomorphology according to the sedimentary paleo-geomorphology 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 the plane 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; According to the core data, well logging curves, seismic data and the sedimentary paleo-geomorphology of the target layer, a rock, electrical and seismic identification chart is established; 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 comprises: Extract the amplitude statistics and frequency seismic attributes of the target layer section in the work area, which at least include: amplitude attribute, average instantaneous frequency attribute, average instantaneous phase attribute, arc length attribute, average reflection intensity, and peak skewness; The response relationship and correlation between each seismic attribute and the reservoir thickness and reservoir 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 according to 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 relevant attributes of the work area and obtain a relevant attribute plane map; The karst microfacies are 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 comprises: If the rock in the work area is homogeneous, select the Gassmann model; If the porosity of various types of pores in the work area has been obtained, then select Xu-payne and self-consistent models; If the work area has medium-low porosity strata that meet consolidation 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, select the Xu-White model, the single aspect ratio model, or the soft pore model.

6. The dual phase-controlled dolomite bioclastic beach 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 respectively build models 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 beach effective reservoir prediction method according to claim 1, characterized in that: The method for determining the dolomite bioclastic beach reservoir boundary value according to the prediction results comprises: 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 according to the cross plot, the reservoir boundary value of the dolomite bioclastic beach is determined.

8. The dual phase-controlled dolomite bioclastic beach effective reservoir prediction method according to any one of claims 1 to 7, characterized in that: The method for determining the effective reservoir thickness of dolomite bioclastic beach in the target layer according to the constraint factor and the reservoir thickness of dolomite bioclastic beach comprises: According to the constraint factor, the effective reservoir thickness of the dolomite bioclastic beach in the target layer is determined by using the calculation formula of the effective reservoir thickness; Wherein, the calculation formula of the effective reservoir thickness 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 beach reservoir, ρ 沉积微相 is the sedimentary microfacies type constraint factor, ρ 岩溶微相 It is the constraint factor of karst microfacies type.

9. A dual phase-controlled dolomite bioclastic beach effective reservoir prediction device, characterized in that: include: The target layer determination unit is used to determine the dolomite bioclastic beach development strata in the work area as the target layer section; A sedimentary microfacies determination unit is used to restore the sedimentary paleo-geomorphology of the work area, select 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 paleo-geomorphology, 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 coherent attributes of the work area, and determine the karst microfacies according to the coherent 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 beach reservoir based on the prediction result; 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 extract 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 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.

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