CN120539812A - A method for predicting grain bank reservoirs by combining seismic forward modeling and inversion - Google Patents
A method for predicting grain bank reservoirs by combining seismic forward modeling and inversionInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/30—Analysis
- G01V1/301—Analysis for determining seismic cross-sections or geostructures
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. for interpretation or for event detection
- G01V1/36—Effecting static or dynamic corrections on records, e.g. correcting spread; Correlating seismic signals; Eliminating effects of unwanted energy
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/61—Analysis by combining or comparing a seismic data set with other data
- G01V2210/616—Data from specific type of measurement
- G01V2210/6161—Seismic or acoustic, e.g. land or sea measurements
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/61—Analysis by combining or comparing a seismic data set with other data
- G01V2210/616—Data from specific type of measurement
- G01V2210/6169—Data from specific type of measurement using well-logging
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- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V2210/00—Details of seismic processing or analysis
- G01V2210/60—Analysis
- G01V2210/64—Geostructures, e.g. in 3D data cubes
- G01V2210/643—Horizon tracking
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Abstract
本发明公开了一种结合地震正演和反演共同预测颗粒滩储层的方法，涉及油气田勘探技术领域，并依据地震层位解释确定目标层位，分析目标层位储层的主要特征，结合测井资料和地震信息，明确储层之间的空间关系和发育模式；从地震资料和测井资料中获取地质参数；建立地质模型框架，在目标层位中嵌入不同储层发育模式的地质模型，并将统计的地质参数输入地质模型，构建正演体模型。本发明通过结合包括地震资料和测井资料的多属性数据，进行综合分析和预测，有效消除单一属性预测的不确定性，利用波形分类和波形指示模拟两种方法的叠合结果，能够更准确地识别颗粒滩储层的空间分布和有利区域，从而显著提高储层预测的精度，为油气勘探提供可靠依据。
The present invention discloses a method for predicting grain bank reservoirs by combining seismic forward modeling and inversion, which relates to the field of oil and gas field exploration technology. The method comprises determining a target horizon based on seismic horizon interpretation, analyzing the main characteristics of the reservoir in the target horizon, and clarifying the spatial relationship and development pattern between the reservoirs by combining well logging data and seismic information. Geological parameters are obtained from the seismic data and well logging data. A geological model framework is established, and geological models of different reservoir development patterns are embedded in the target horizon. The statistical geological parameters are input into the geological model to construct a forward model. The present invention combines multi-attribute data including seismic data and well logging data for comprehensive analysis and prediction, effectively eliminating the uncertainty of single-attribute prediction. By superimposing the results of waveform classification and waveform indication simulation, the spatial distribution and favorable areas of grain bank reservoirs can be more accurately identified, thereby significantly improving the accuracy of reservoir prediction and providing a reliable basis for oil and gas exploration.
Description
Technical Field
The invention relates to the technical field of oil and gas field exploration, in particular to a method for jointly predicting a particle beach reservoir by combining seismic forward modeling and inversion.
Background
With the continuous increase of global oil and gas resource demands and the continuous progress of exploration and development technologies, the exploration and development degree of oil and gas fields is increased gradually, the exploration difficulty is also increased remarkably, under the background, the exploration focus is gradually changed from the traditional structure oil and gas reservoirs to more complex and potential lithologic oil and gas reservoirs, wherein the particle beach reservoir is an important reservoir type of carbonate oil and gas reservoirs, becomes a hotspot and a difficulty in the oil and gas exploration field due to the unique deposition environment and the unique reservoir performance, has excellent porosity and permeability, is a good channel for oil and gas enrichment and flow, and has important significance for improving the recovery ratio and economic benefit of the oil and gas fields.
However, the carbonate beach has strong heterogeneity, the internal structure is complex and changeable, the difficulty of predicting the reservoir by the traditional seismic data is high, a complete theoretical method for predicting the particle beach is still lacking at present, in addition, the traditional inversion prediction method is limited by the quality of the well seismic data, has the defects of high uncertainty factors, low resolution, high randomness and the like, lacks sufficient theoretical basis support, and is difficult to meet the requirement of high-precision exploration, so that the method for jointly predicting the particle beach by combining the forward modeling and inversion of the earthquake is provided to solve the problems.
Disclosure of Invention
The invention aims to provide a method for jointly predicting a particle beach reservoir by combining forward modeling and inversion of an earthquake, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention adopts the following technical scheme:
A method of co-predicting a particulate beach reservoir in combination with seismic forward modeling and inversion, comprising the steps of:
S1, generating a synthetic seismic record by utilizing the acoustic time difference and the density curve, carrying out well seismic calibration, and carrying out horizon tracking according to the well seismic calibration result.
S2, analyzing main characteristics of the target layer reservoir, including development quantity, position, thickness and distribution rule of the reservoir, and determining spatial relationship and development mode between reservoirs by combining logging information and seismic information;
S3, obtaining geological parameters from the seismic data and the logging data, and determining a target horizon according to seismic horizon interpretation;
S4, building a geological model frame, embedding geological models of different reservoir development modes in the target horizon, inputting statistical geological parameters into the geological model, building a forward model, and simulating seismic reflection waveforms;
S5, classifying and grading the simulated seismic waveforms based on forward modeling results, identifying waveform characteristics corresponding to the reservoir, enabling the reservoir development mode to correspond to the seismic waveforms one by one, classifying and counting the seismic waveforms of the target layer, and determining the plane distribution range of the waveform corresponding to the high-quality particle beach reservoir;
s6, constructing a waveform indication simulation model based on the horizon tracking result and combining with the seismic data;
S7, comparing the separation degree of the rock peaks of the reservoir and the non-reservoir, optimizing a sensitivity curve, determining the effective sample number and the optimal cut-off frequency, and improving the simulation precision;
S8, inversion is carried out based on the optimal model and parameters, a waveform indication simulation inversion result is obtained, a plane attribute diagram is extracted, and boundaries of advantageous regions of the reservoir are delineated;
And S9, overlapping the waveform classification prediction result and the waveform indication simulation result, and preferably, repeatedly appearing areas on the plane by the two methods, namely, the area of the favorable reservoir with the highest probability.
The technical scheme of the invention is further improved in that the S1 specifically comprises the following steps:
selecting a sound wave time difference (AC) and density (Den) log of a target horizon, and calculating a stratum reflection coefficient sequence according to a seismic wave propagation theory;
Reading Ricker wavelets by utilizing spectrum analysis, carrying out convolution operation on the Ricker wavelets and stratum reflection coefficients in a main frequency range of 20-60Hz, generating synthetic seismic records, comparing the synthetic seismic records with actual seismic data, and adjusting the time-depth relation of logging, so that layering on the logging can accurately indicate the double-journey travel time depth corresponding to the actual horizon, and the consistency of the well seismic data is ensured;
And combining the adjusted logging data and the seismic data, carrying out horizon tracking on the target horizon, identifying the reflection characteristics of the target horizon on the seismic profile, tracking the continuity of the target horizon in the seismic body according to the known well point data and the known seismic reflection characteristics, and determining the distribution range and the distribution form of the target horizon in the three-dimensional space through horizon tracking.
The technical scheme of the invention is further improved in that the S2 specifically comprises:
collecting logging information and seismic information of a target layer, primarily carding the development condition of the reservoir, determining the development quantity of the reservoir, namely the number of the reservoir identified in the target layer, simultaneously determining the position of the reservoir by comparing the seismic profile with a logging curve, precisely positioning the longitudinal and transverse positions of the reservoir in the target layer, counting the thickness of the reservoir, and analyzing the change rule of the thickness of the reservoir by using the thickness data in the logging interpretation result;
Observing continuity, fault distribution and contact relation with surrounding stratum of the reservoir in vertical and horizontal directions by utilizing the seismic section and three-dimensional seismic data, analyzing superposition relation and mutual influence among the reservoirs by combining lithology and physical property changes in logging data, and defining connectivity and closure characteristics of the reservoirs to construct a spatial distribution frame of the reservoir;
and (3) comprehensive logging and earthquake information, analyzing development rules of the reservoir in different areas, identifying main reservoir development types and control factors, and analyzing a causative mechanism of reservoir development by combining with regional geological background to form a complete reservoir development mode.
The technical scheme of the invention is further improved in that the S3 specifically comprises the following steps:
Collecting seismic data of a target area, including a two-dimensional seismic section and three-dimensional seismic data, identifying reflection characteristics and boundaries of the target layer through seismic horizon interpretation, determining the range and the position of the target layer by combining the geological background of the area and the existing geological research results, analyzing the amplitude, the frequency and the phase characteristics in the seismic data, and primarily judging the development area of a reservoir;
Collecting logging data in a target area, including logging curves of acoustic time difference, density, resistivity and natural gamma, extracting relevant geological parameters including stratum thickness, speed, density, porosity and permeability from the logging data according to the determined target horizon, counting the distribution rule of the geological parameters in the target horizon, and analyzing the relation between the geological parameters and reservoir development;
Integrating parameters obtained from the seismic data and the logging data, comparing the relation between the seismic reflection characteristics and the logging parameters, analyzing the change rule of the stratum thickness, speed and density parameters of the target horizon in space, identifying the existing reservoir development area, and judging the development characteristics and distribution rule of the reservoir by combining the seismic data and the logging data.
The technical scheme of the invention is further improved in that the S4 specifically comprises the following steps:
establishing an integral geological model frame according to the geological background of the target area and the existing research results, defining the range, boundary conditions and main stratum units and structural features of the model, determining the position and the range of the target layer in the geological model by combining seismic horizon interpretation and logging data, and analyzing main structural elements in the target area;
embedding geological models of different reservoir development modes in an established geological model frame, wherein a plurality of reservoir development modes are designed according to reservoir development rules and cause mechanisms of a target horizon, and specific positions and ranges of each reservoir development mode in the target horizon are determined by combining logging data and seismic information and are embedded into the geological model;
inputting the geological parameters obtained through statistics into a geological model embedded with a reservoir development mode, calibrating and adjusting parameters in the geological model according to logging and seismic data, ensuring rationality and accuracy of model parameters, constructing a forward model based on the input geological parameters, simulating seismic reflection waveforms, and further calculating and recording reflection conditions of seismic waves when encountering different geological interfaces through forward simulation, so as to generate a seismic reflection waveform pattern corresponding to the geological model.
The technical scheme of the invention is further improved in that the S5 specifically comprises the following steps:
classifying and grading the simulated seismic waveforms based on forward modeling results, dividing the seismic waveforms into different types and grades according to the amplitude, frequency and phase characteristics of the waveforms, identifying the types of the seismic waveforms corresponding to the reservoir development modes by comparing the seismic waveform characteristics under different reservoir development modes, and establishing the corresponding relation between the reservoir development modes and the seismic waveforms;
Classifying and counting actual seismic waveforms of a target horizon, comparing the seismic data of the target horizon with forward modeling results, identifying seismic waveform types corresponding to reservoir development modes in the target horizon, counting the distribution range and proportion of each waveform type in the target horizon, analyzing the plane distribution characteristics of each waveform type in different areas, and primarily determining the areas of high-quality particle beach reservoir development;
And combining the forward simulation result and the actual seismic waveform analysis of the target horizon, determining the plane distribution range of waveforms corresponding to the high-quality particle beach reservoir, identifying waveform types highly matched with the development modes of the high-quality reservoir by comparing the seismic waveform characteristics of different areas, comprehensively analyzing the distribution range of the identified waveform types on the plane, and finally determining the plane distribution range of the high-quality particle beach reservoir.
The technical scheme of the invention is further improved in that the S6 specifically comprises:
and based on the well earthquake calibration result and the horizon tracking data, completing the construction of the waveform indication simulation model.
The technical scheme of the invention is further improved in that the S7 specifically comprises:
Analyzing logging curve data through a statistical histogram, observing peak distribution differences of lithology of the reservoir and the non-reservoir on the logging curve, and comparing the separation degree of the lithology peaks of the reservoir and the non-reservoir;
Based on analysis results of lithology peak separation degree, optimizing a logging curve which is most sensitive to reservoir identification, wherein the peak separation degree of different logging curves is compared, and a curve with the best separation effect is selected as a sensitive curve;
And determining key parameters including effective sample numbers and optimal cut-off frequency in waveform indication simulation according to the sensitivity curve, wherein the effective sample numbers refer to the most similar well sample numbers with the current seismic waveform in the target layer, and determining the optimal cut-off frequency through experiments and comparative analysis so as to further improve simulation accuracy.
The technical scheme of the invention is further improved in that the S8 specifically comprises the following steps:
Based on the waveform indication simulation model which is preferably determined and key parameters comprising a sensitive curve, an effective sample number and an optimal cut-off frequency, waveform indication simulation inversion is carried out, seismic data and logging data are combined, and an inversion technology is utilized to generate a high-resolution waveform indication simulation inversion result;
The method comprises the steps of extracting a plane attribute diagram reflecting reservoir characteristics according to research purposes and reservoir characteristics by analyzing amplitude, frequency and phase attributes in waveform indication simulation inversion results, and intuitively displaying distribution characteristics of the reservoir including thickness, continuity and porosity on a plane;
And (3) drawing the boundary of the favorable region of the reservoir by combining the extracted plane attribute map and using a geostatistical method, identifying the region with good development, larger thickness and better continuity of the reservoir by combining the geologic background and inversion results, further analyzing the attribute value change rules of different regions in the plane attribute map, determining the attribute value range corresponding to the favorable reservoir, marking the region meeting the attribute value range in the attribute map, and finally accurately drawing the boundary of the favorable region of the reservoir by comparing and correcting for multiple rounds.
The technical scheme of the invention is further improved in that the S9 specifically comprises:
respectively collecting related data of a waveform classification prediction result and a waveform indication simulation result, and carrying out standardization processing on the waveform classification prediction result and the waveform indication simulation result so as to carry out geographic registration on the two results;
Overlapping the standardized waveform classification prediction result and the waveform indication simulation result, and identifying the repeated areas of the two methods on the plane through a Geographic Information System (GIS), namely the potential favorable reservoir areas identified by the two methods together;
And further evaluating the repeated area identified in the superposition analysis by combining the geological background and the existing exploration data, analyzing the geological features and the reservoir development conditions of the repeated area, and finally optimizing the favorable reservoir area with the highest probability as a key target of the subsequent oil and gas exploration.
By adopting the technical scheme, compared with the prior art, the invention has the following technical progress:
1. the invention provides a method for jointly predicting a particle beach reservoir by combining forward modeling and inversion of an earthquake, which is used for carrying out comprehensive analysis and prediction by combining multi-attribute data comprising earthquake data and logging data, effectively eliminating uncertainty of single attribute prediction, and can more accurately identify spatial distribution and favorable areas of the particle beach reservoir by utilizing superposition results of two methods of waveform classification and waveform indication simulation, thereby obviously improving the accuracy of reservoir prediction and providing reliable basis for oil and gas exploration.
2. The invention provides a method for jointly predicting a particle beach reservoir by combining forward modeling and inversion of an earthquake, which is based on the result of forward modeling of the earthquake, provides a firm theoretical basis for identifying the reservoir by establishing earthquake waveform plates of different reservoirs for the earthquake waveform, enhances the scientificity of a prediction method, enables the prediction result to be more convincing, can adapt to different geological conditions and exploration requirements, and can predict particle beach reservoirs of different areas and different horizons by adjusting model parameters and an optimization algorithm, thereby showing stronger adaptability and flexibility.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a graph of a reservoir-less geologic model (a) and forward modeling results (b) of the present invention;
FIG. 3 is a graph of a geologic model (a) and forward modeling results (b) for a single wave case of the present invention;
FIG. 4 is a diagram of forward modeling (a) and forward results (b) for the complex wave case of the present invention;
FIG. 5 is a graph corresponding to the probability of classification of waveforms and the result of forward modeling according to the present invention;
FIG. 6 is a flat layout of a particle beach reservoir according to the present invention;
FIG. 7 is a drawing of a temple frame model in accordance with the present invention;
FIG. 8 is an analysis of the explanation conclusion of the temple group of the invention;
FIG. 9 is a graph of the quality control of the best fit sample-cut-off frequency of the present invention;
FIG. 10 is a seismic profile of an inversion body formation slice (layer, layer-added root mean square amplitude) of the present invention;
FIG. 11 is a comprehensive prediction graph of a reservoir according to the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment 1 as shown in fig. 1 to 11, the present invention provides a method for jointly predicting a particle beach reservoir in combination with seismic forward modeling and inversion, comprising the steps of:
S1, constructing a waveform indication simulation model by combining earthquake and logging data through well earthquake calibration (acoustic time difference and density curve) and horizon tracking. Selecting a sound wave time difference (AC) and density (Den) log curve of a target horizon, calculating a stratum reflection coefficient sequence according to a seismic wave propagation theory, reading Ricker wavelets by utilizing spectrum analysis, carrying out convolution operation on Ricker wavelets with stratum reflection coefficients in a main frequency range of 20-60Hz, generating a synthetic seismic record, comparing the synthetic seismic record with actual seismic data, adjusting the time-depth relation of logging, enabling layering on the logging to accurately indicate the two-way travel time depth corresponding to the actual horizon, ensuring the consistency of the well seismic data, carrying out horizon tracking on the target horizon by combining the adjusted logging data and the seismic data, identifying the reflection characteristics of the target horizon on a seismic profile, tracking the continuity of the target horizon in a seismic body according to known well point data and seismic reflection characteristics, determining the distribution range and the form of the target horizon in a three-dimensional space by horizon tracking, and determining the well seismic calibration result and horizon tracking data.
The formation reflection coefficient is calculated as follows:
Wherein R j is the stratum reflection coefficient of the jth sampling point, ρ j is the density in the log data of the jth sampling point, and v j is the speed in the log data of the jth sampling point;
the calculation formula for the synthetic seismic record is as follows:
S(t)=W(t)*R(t);
wherein S (t) is a synthetic seismic record, W (t) is a seismic wavelet, represents a convolution operator, R (t) is a stratum reflection coefficient sequence;
S2, analyzing main characteristics of a target layer reservoir, including development quantity, position, thickness and distribution rules of the reservoir, combining logging information and earthquake information, defining a spatial relationship and development mode between the reservoirs, collecting logging information and earthquake information of the target layer, primarily carding the development condition of the reservoir, defining the development quantity of the reservoir, namely, the number of the reservoir identified in the target layer, simultaneously, determining the longitudinal and transverse positions of the reservoir in the target layer through comparison of an earthquake section and a logging curve, counting the thickness of the reservoir, analyzing the thickness change rule of the reservoir by utilizing thickness data in logging interpretation results, observing continuity, fault distribution and contact relationship with surrounding strata in the vertical and horizontal directions of the reservoir, combining lithologic and physical change in the logging information, analyzing the stacking relationship and mutual influence of the reservoir, defining connectivity and sealing characteristics of the reservoir, comprehensively analyzing the well logging and earthquake information, analyzing the spatial distribution frames of the reservoir in different areas, identifying the main areas, controlling the development type and the development rule of the reservoir, and forming a complete development mechanism by combining the geological development rule;
S3, acquiring geological parameters from seismic data and well logging data, determining a target horizon according to seismic horizon interpretation, collecting seismic data of a target area, including a two-dimensional seismic section and three-dimensional seismic data, identifying reflection characteristics and boundaries of the target horizon through seismic horizon interpretation, combining regional geological background and existing geological research results, determining the range and position of the target horizon, analyzing amplitude, frequency and phase characteristics in the seismic data, primarily judging the development area of a reservoir, collecting well logging data in the target area, including acoustic time difference, density, resistivity and natural gamma well logging curves, extracting relevant geological parameters including formation thickness, speed, density, porosity and permeability from the well logging data according to the determined target horizon, counting the distribution law of the geological parameters in the target horizon, analyzing the relation between the geological parameters and the development of the reservoir, integrating the parameters obtained from the seismic data and the well logging data, comparing the relation between the seismic reflection characteristics and the well logging parameters, analyzing the change of the thickness, the speed and the density parameters of the target horizon in space, identifying the development area of the reservoir, and judging the development law of the well logging, and combining the seismic distribution law and the well logging characteristics;
S4, establishing a geologic model frame, embedding geologic models of different reservoir development modes in a target horizon, inputting statistical geologic parameters into the geologic model, constructing a forward model, simulating seismic reflection waveforms, establishing an integral geologic model frame according to the geologic background of the target horizon and the existing research results, defining the range and boundary conditions of the model, and main stratum units and structural features, combining seismic horizon interpretation and logging data, determining the position and the range of the target horizon in the geologic model, analyzing main structural elements including faults, folds and the like in the target horizon, ensuring that the model can reflect the integral features of the regional geologic structure, embedding geologic models of different reservoir development modes in the established geologic model frame, designing various reservoir development modes according to the reservoir development rules and the formation mechanisms of the target horizon, combining logging data and seismic information, determining the specific position and the range of each reservoir development mode in the target horizon, inputting the geologic parameters obtained by statistics into the geologic model embedded with the reservoir development modes, and accurately calculating the geologic model according to the logging and the seismic wave, and correcting the geologic model based on the error of the geologic model, and the forward model, and the geologic model is further ensuring that the geologic model is accurately calculated by the waveform is not correspondingly calibrated to the geologic model;
S5, classifying and grading the simulated seismic waveforms based on forward modeling results, identifying the waveform characteristics corresponding to the reservoir, classifying and counting the seismic waveforms of the target layer, determining the plane distribution range of the waveform corresponding to the high-quality particle beach reservoir, dividing the seismic waveforms into different types and levels according to the amplitude, frequency and phase characteristics of the waveforms, identifying the seismic waveform types corresponding to the reservoir development modes by comparing the seismic waveform characteristics of the different reservoir development modes, establishing the correspondence between the reservoir development modes and the seismic waveforms, classifying and counting the actual seismic waveforms of the target layer, comparing the seismic data of the target layer with the forward modeling results, identifying the seismic waveform types corresponding to the reservoir development modes in the target layer, counting the distribution range and proportion of each waveform type in the target layer, primarily determining the area of the high-quality particle beach reservoir development, determining the plane distribution range corresponding to the high-quality particle beach reservoir by combining the forward modeling results with the actual seismic waveform analysis of the target, comparing the plane distribution range corresponding to the high-quality particle beach reservoir development modes with the plane distribution range, identifying the waveform types of the high-quality particle beach layer, and comprehensively determining the high-quality particle beach layer by comparing the plane distribution range with the waveform types of the surface distribution range of the high-quality particle beach layer;
S6, based on the well earthquake calibration result and the horizon tracking data, constructing a waveform indication simulation model is completed;
S7, comparing the separation degree of the rock peaks of the reservoir and the non-reservoir, optimizing a sensitive curve, determining the effective sample number and the optimal cut-off frequency, improving the simulation precision, analyzing the data of the well-logging curve through a statistical histogram, observing the peak distribution difference of the rock peaks of the reservoir and the non-reservoir on the well-logging curve, comparing the separation degree of the rock peaks of the reservoir and the non-reservoir, wherein the more obvious the peak separation is, the higher the separation degree of the reservoir and the non-reservoir is, optimizing the well-logging curve which is most sensitive to the reservoir based on the analysis result of the separation degree of the rock peaks, wherein the curve with the best separation effect is selected as the sensitive curve for comparing the peak separation degree of different well-logging curves, the sensitive curve can more accurately reflect the difference between the reservoir and the non-reservoir, determining the key parameters comprising the effective sample number and the optimal cut-off frequency in the waveform indication simulation according to the sensitive curve, wherein the effective sample number is the most similar to the current seismic waveform in the target layer, selecting the larger effective sample number to improve the prediction precision in a stable deposition area, and selecting the smaller effective sample number to be more accurate in a complex deposition area, and further improving the simulation precision through the analysis of the sample number and the optimal cut-off frequency;
S8, inverting based on a preferred model and parameters to obtain a waveform indication simulation inversion result, extracting a plane attribute graph, drawing a plane attribute graph reflecting characteristics of a reservoir according to a research purpose and characteristics of the reservoir, visually displaying the boundary of the favorable region of the reservoir by combining the extracted plane attribute graph, combining seismic data with logging data, generating a high-resolution waveform indication simulation inversion result by using an inversion technology, ensuring that model parameters are matched with actual geological features in the inversion process so as to improve the reliability and precision of the inversion result, analyzing amplitude, frequency and phase attributes in the waveform indication simulation inversion result, extracting a plane attribute graph reflecting characteristics of the reservoir according to the research purpose and characteristics of the reservoir, visually displaying the distribution characteristics including thickness, continuity and porosity of the reservoir, utilizing a geological statistics method to draw the boundary of the favorable region of the reservoir, combining geological background and inversion result, identifying regions with good development, large thickness and good continuity of the reservoir, further analyzing attribute value changes of different regions in the plane attribute graph, determining the corresponding value ranges of the favorable region and accurately comparing the attribute values with the boundary of the map, and drawing a more accurate value range of the favorable region by comparing the attribute values;
S9, overlapping the result of waveform classification prediction and the result of waveform indication simulation, preferably overlapping the two methods on a plane, namely, the region with the largest probability (most likely) of the region of the favorable reservoir, respectively collecting related data of the waveform classification prediction result and the waveform indication simulation result, carrying out standardization processing on the waveform classification prediction result and the waveform indication simulation result, ensuring that the two results are consistent in spatial resolution and range, carrying out geographic registration on the two results, enabling the two results to be subjected to comparison analysis under the same geographic coordinate system, overlapping the standardized waveform classification prediction result and the standardized waveform indication simulation result, identifying the region with the largest probability of the region with the two methods on the plane, namely, the region with the largest probability of the potential favorable reservoir, which is identified by the two methods together, has higher prediction reliability, reducing uncertainty brought by the single method, carrying out further evaluation on the geological background and the existing exploration data, analyzing the geological features and the reservoir development conditions of the repeated region, and finally optimizing the region with the largest probability of the favorable reservoir as a subsequent target of overlapping oil and gas exploration.
In embodiment 2, as shown in fig. 1 to 11, on the basis of embodiment 1, the invention provides a technical example, and the invention takes the plane prediction of the particle beach reservoir of the north slope temple group in Sichuan basin, wherein the target layer temple group is divided into a first dragon section and a second dragon section, the top and bottom interfaces of the temple group are clear, the temple group is in integrated contact with the lower-lying sea-wave pavement group, the upper-lying high-level group stratum is deleted to different degrees in a local area, and the carbonate particle beach reservoir of the north slope temple group in Sichuan basin is very developed and is influenced by the thickness of the stratum, the non-uniformity of the reservoir and the development mode, so that the difficulty of reservoir prediction is high by utilizing the existing seismic data. In the work, a plurality of groups of seismic models are established, the seismic response of the reservoir is restrained by a seismic forward modeling method, the beneficial areas of the reservoir are predicted by using waveform classification, and then the particle beach reservoir is comprehensively predicted by combining a seismic waveform indication modeling technology.
1. Reservoir development law analysis and statistics
The development of the first-stage particle beach reservoir is divided into a low-speed reservoir and a high-speed reservoir, and the development of the second-stage particle beach reservoir affects the waveform characteristics of the first-stage particle beach reservoir, so that the waveform characteristics of the first-stage particle beach reservoir need to be discussed in a comprehensive way, and the first-stage particle beach reservoir can be divided into 6 types of particle beach reservoirs, (1) the first-stage particle beach reservoir, the second-stage particle beach reservoir, (2) the first-stage particle beach reservoir, the second-stage particle beach reservoir, (3) the first-stage particle beach reservoir, the second-stage particle beach reservoir, (4) the first-stage particle beach reservoir, the second-stage particle beach reservoir, and (5) the first-stage particle beach reservoir;
2. Forward parameter statistics
And (3) counting three-dimensional seismic data of the northern slope of the Chuan-in-ancient bump and 6-well data to obtain forward simulated geological parameters and geophysical parameters (table 1 and table 2). According to the thickness, speed and density of the temple group and the surrounding stratum (the plateau group and the cang wave paving group), the geological model parameters are summarized (table 1), and the particle beach reservoir thickness, speed and density of the temple group are summarized to obtain the particle beach reservoir geological model parameters under different reservoir physical properties (table 2). The geophysical parameters used for forward modeling are Ricker wavelet 32Hz, sampling interval of 2ms, number of shots of 20, shot interval of 50m, number of detectors of 96 and detector interval of 10m.
TABLE 1 geologic model parameters
| Stratum layer | Model thickness (m) | Formation velocity (m/s) | Formation Density (g/cm 3) |
| High table group | 200 | 6120 | 2.743 |
| Temple group of Dragon king | 60-130 | 6600 | 2.784 |
| Canglang (sea wave) laying set | 200 | 5470 | 2.662 |
Table 2 parameters of particle beach reservoir geologic model
| Reservoir type | Reservoir thickness (m) | Reservoir velocity (m/s) | Reservoir Density (g/cm 3) |
| High-speed reservoirs | 0-25m | 6355 | 2.768 |
| Low-speed reservoir | 0-25m | 6050 | 2.713 |
3. Forward modeling
And 2, integrating the reservoir development rule of the ancient swelling north slope in Chuan, establishing 2 geologic models of the non-development reservoir and the development reservoir of the temple group of Longwang, and carrying out seismic waveform forward modeling by using Tesseral D software by using the geologic parameters and the geophysical parameters counted in the step 2 (table 1 and table 2) to obtain the seismic waveform characteristics under different conditions.
3.1 Forward modeling of non-developing reservoirs
When the temple group does not develop a reservoir, two waveforms are co-developed in the temple group, one is a single-wave peak (the thickness is about 80m when developing in a thin layer of the temple group), and the other is a double-wave peak (the thickness is more than 100m when developing in a thick layer of the temple group).
When the temple group is a thin layer, a single wave peak is developed inside, the top of the temple group corresponds to the middle upper part of the wave peak, and the bottom corresponds to the middle upper part of the wave trough. When the temple group is thick, the interior develops double wave peaks, the top of the temple corresponds to the middle upper part of the first wave peak, and the bottom corresponds to the middle upper part of the wave trough (figure 2).
3.2 Forward modeling when developing reservoirs
The seismic waveforms corresponding to the development of the reservoir under the conditions of forward Long Wangmiao groups of thin layers (about 80 meters, corresponding to single waves) and thick layers (about 100 meters, corresponding to complex waves) are discussed.
(1) Forward modeling under thin layer condition of Longwang temple group
① Class 1 reservoirs, when the first-stage dragon develops a low-speed reservoir and the second-stage dragon does not develop a reservoir, the top of the temple appears as peak upward movement, the amplitude is enhanced, and the frequency is increased.
② And 2 types of reservoirs, when the first dragon stage develops a high-speed reservoir and the second dragon stage does not develop a reservoir, the peak at the top of the temple of the dragon king moves upwards, the amplitude is enhanced, the frequency is increased, and the phenomenon is similar to that of the 1 types of reservoirs.
③ And 3 types of reservoirs, when the first dragon section and the second dragon section develop a low-speed reservoir at the same time, the top of the temple appears as wave crest downward movement, the amplitude is weakened, and the frequency is reduced.
④ And 4 kinds of reservoirs, when the dragon first-stage development is low-speed, and the dragon second-stage development is high-speed, the top of the temple appears as wave crest upward movement, the amplitude is reduced, and the frequency is increased.
⑤ And 5 types of reservoirs, when the dragon first-stage development high-speed reservoir and the dragon second-stage development low-speed reservoir are developed, the peak at the top of the dragon king temple moves downwards, the amplitude is enhanced and the frequency is increased at the position of the top of the dragon king temple corresponding to the zero phase of the waveform.
⑥ Class 6 reservoirs, when the dragon first and second sections develop high-speed reservoirs at the same time, the top of the dragon temple appears as a weak peak up-shift, the amplitude is weak enhanced, and the frequency is unchanged (figure 3).
(2) Forward modeling under thick layer condition of Longwang temple group
① And 7 types of reservoirs, when the first dragon section develops a low-speed reservoir and the second dragon section does not develop a reservoir, the top of the temple appears as peak up-shift amplitude enhancement, frequency enhancement, peak down-shift of the second set, amplitude weakening and frequency enhancement.
② And 8 types of reservoirs, when the first dragon section develops a high-speed reservoir and the second dragon section does not develop a reservoir, the peak at the top of the temple of the dragon king moves upwards, the amplitude is enhanced, the frequency is unchanged, the peak of the second set moves downwards, the amplitude is weakened, and the frequency is reduced.
③ And when the first dragon section and the second dragon section develop the low-speed reservoir, only a single peak is developed in the temple of the dragon king, the top of the temple of the dragon king shows that the peak moves downwards, the amplitude is enhanced, and the frequency is unchanged.
④ And when the dragon grows in a first-stage low-speed reservoir and the dragon grows in a second-stage high-speed reservoir, the top of the temple appears as a weak upward movement of the wave crest, the amplitude is enhanced, the frequency is increased, the wave crest of the second set is downward moved, the amplitude is weakened, and the frequency is reduced.
⑤ 11 Kinds of reservoirs, when the dragon first-stage development high-speed reservoir and the dragon second-stage development low-speed reservoir are developed, the interior of the dragon temple group presents a single peak, the peak at the top of the dragon temple moves downwards, the amplitude is enhanced and the frequency is reduced at the position of the top of the dragon temple corresponding to the zero phase of the waveform,
⑥ And when the first dragon section and the second dragon section develop high-speed reservoirs simultaneously, the inside of the dragon temple group presents a single peak, the top of the dragon temple appears as a weak downward movement of the peak, the amplitude is slightly reduced, and the frequency is reduced (figure 4).
4. Waveform classification prediction reservoir
Based on forward results, 7 seismic reflection waveform features are established, wherein 4 types of waveforms can correspond to features of reservoir development:
Class II waveforms are characterized in that the top boundary of the temple corresponds to the wave crest, the bottom boundary corresponds to the low-frequency wave trough, and double wave crests are presented between layers, wherein the waveforms represent when the thickness of the temple group layer is 100m, and the first dragon section and the second dragon section develop a high-speed reservoir simultaneously.
The V-shaped waveform is that the top of Long Wangmiao corresponds to the waveform, the bottom boundary corresponds to the high-frequency trough, the characteristic of single wave crest is presented between layers, compared with the situation that the top wave crest of the temple moves upwards when no reservoir exists, the waveform represents that when the layer thickness of the temple group layer is 80m, the class 1 reservoir dragon first-stage development low-speed reservoir is represented, the class 2 reservoir dragon first-stage development high-speed reservoir is represented, the class 4 reservoir dragon first-stage development low-speed reservoir is represented, and the class 4 reservoir dragon second-stage development high-speed reservoir is represented.
Class VI waveforms, when the waveforms represent Long Wangmiao groups of layers with thickness of 80m, long Wangmiao tops correspond to the upper half parts of the wave crests, the temple bottoms correspond to the high-frequency wave troughs, and the waveforms represent that the temple groups develop 6 types of reservoirs, namely, a first-stage reservoir and a second-stage reservoir, and develop high-speed reservoirs simultaneously. When such waveforms represent Long Wangmiao sets of layers of 100m thick, long Wangmiao tops correspond to the lower half of the valleys, the bottom of the temple corresponds to the high frequency valleys, such waveforms represent the dragon first-development high-speed reservoir and the dragon second-development low-speed reservoir of the 11-class reservoir developed by the temple group of the Dragon king.
And a class VII waveform, wherein when the waveform represents Long Wangmiao groups of layers with thickness of 80m, the top of the Loongwang temple corresponds to the upper half part of the weak amplitude wave crest and the bottom of the Loongwang temple corresponds to the lower half part of the weak amplitude wave trough, the waveform represents Loongwang temple group to develop a class 3 reservoir and a class 5 reservoir, namely a Loongone section and a Loongtwo section simultaneously developed low-speed reservoir and a Loongone section developed high-speed reservoir, respectively. When such waveforms represent Long Wangmiao sets of layers 100m thick, the top of the temple corresponds to the lower part of the weak amplitude trough, and such waveforms represent 9 types of waveforms developed by the temple sets, namely, a low-speed reservoir developed by the first dragon segment and the second dragon segment (figure 5).
The waveforms in the temple group are classified by GeoEast software, 4 types of waveforms representing reservoirs in the temple group are extracted, and analogy is performed on the plate recognition of fig. 5. The II type waveform displayed by the waveform classification result is considered to correspond to 12 types of reservoirs, the V type waveform corresponds to 1,2 and 4 types of reservoirs, the VI type waveform corresponds to 6 and 11 types of reservoirs, the VII type waveform corresponds to 3, 5 and 9 types of reservoirs, the II, V, VI, VII type waveform classified by the waveform is respectively filled with colors and displayed on a plan view, and the potential particle beach reservoir of the Dragon section is found to be mainly distributed in the north part of a research area, and a small part is distributed in the south part of the research area (figure 6).
5. Simulation model construction
And (3) carrying out well vibration calibration on the wells A1, A2, A3, A4, A5 and A6 in the research areas, completing the closed tracking work of the top layer of the temple, the bottom, the top and the top layer of the temple, and establishing a target layer frame model by taking the bottom box and the top layer Wang Miaoding layer of the temple as a frame to provide space constraint for subsequent waveform indication simulation (figure 7).
6. Simulation curve and parameters are preferred
6.1 Optimal Curve preference
Abnormal value rejection is performed on density, natural gamma, deep lateral resistivity, acoustic wave time difference, resistivity, well diameter, compensated neutron, photoelectric absorption frequency and flushing band resistivity sensitive to waveform indication simulation. After pretreatment, the curves are analyzed in the interpretation conclusion of the temple group, the correlation between logging reservoirs and logging curves of the temple group is fitted, and the curves which can effectively distinguish the reservoirs from the non-reservoirs in the north slope area of the temple group are preferably obtained, wherein the natural gamma curve can effectively distinguish the reservoirs from the non-reservoirs, so the example selects the natural gamma curve for simulation (figure 8).
6.2 Optimal sample number and optimal cut-off frequency
And (3) carrying out fitting sample-cut-off frequency analysis on the natural gamma curve, establishing a logging curve sample set corresponding to the seismic waveform structure, and obtaining the reduction of the natural gamma correlation index transformation amplitude when the sample number is 4 through analysis. In addition, the optimal high-pass frequency of natural gamma approaches the inflection point when the phase index is at 250Hz, and the optimal high-cut frequency is 400Hz, which means that the correlation index transformation approaches to be stable, so that the optimal high-pass cut-off frequency is selected to be 250Hz, and the optimal high-cut frequency is 400Hz (figure 9).
7 Waveform indication simulation
Based on 4 optimal samples in the early stage, the optimal high-pass cutoff frequency is 250Hz, the optimal high-pass cutoff frequency is 400Hz, waveform indication simulation is carried out, A1, A3, A4, A5 and A6 are selected to participate in inversion, and the A2 well is used as a post verification well to not participate in inversion.
By observing the A2 well in the simulated section, the effect that the waveform indication simulation reflects the reservoir in the section of the well to be finer can be obtained, and the coincidence degree between the reservoir part indicated by the simulation body and the reservoir part in the logging is higher (figure 10 b).
In order to analyze the distribution of the Loongone section of favorable reservoir, attribute extraction is performed on the Loongone section of waveform indication simulator, and interlayer root mean square attribute is selected to perform attribute prediction, wherein the specific distribution is shown in the figure, and a low GR value area in the figure is a reservoir enrichment area (dark area in the figure) (figure 10 a).
8. Facilitating beach body analysis
The distribution of the favorable areas of the waveform classification prediction reservoir and the favorable areas of the waveform indication simulation prediction reservoir are overlapped, and the repeated prediction areas of the waveform classification prediction reservoir and the waveform indication simulation prediction reservoir are the favorable areas of the final sand body, so that the exploration and development work of the North slope Longwang temple reservoir can be guided (figure 11).
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for co-predicting a particulate beach reservoir in combination with seismic forward modeling and inversion, comprising the steps of:
s1, generating a synthetic seismic record by utilizing a sound wave time difference and a density curve, carrying out well seismic calibration, and carrying out horizon tracking according to the well seismic calibration result;
S2, analyzing main characteristics of the target layer reservoir, and determining spatial relation and development mode between reservoirs by combining logging data and seismic information;
S3, obtaining geological parameters from the seismic data and the logging data, and determining a target horizon according to seismic horizon interpretation;
S4, building a geological model frame, embedding geological models of different reservoir development modes in a target horizon, inputting statistical geological parameters into the geological model, and building a forward model;
s5, classifying and grading the simulated seismic waveforms based on forward modeling results, identifying waveform characteristics corresponding to the reservoir, classifying and counting the seismic waveforms of the target horizon, and determining the plane distribution range of the waveform corresponding to the high-quality particle beach reservoir;
s6, constructing a waveform indication simulation model based on the horizon tracking result and combining with the seismic data;
S7, comparing the separation degree of the rock peaks of the reservoir and the non-reservoir, optimizing a sensitivity curve, and determining the effective sample number and the optimal cut-off frequency;
S8, inversion is carried out based on the optimal model and parameters, a waveform indication simulation inversion result is obtained, a plane attribute diagram is extracted, and boundaries of advantageous regions of the reservoir are delineated;
And S9, overlapping the waveform classification prediction result and the waveform indication simulation result, and preferably, repeatedly appearing areas on the plane by the two methods, namely, the area of the favorable reservoir with the highest probability.
2. The method for jointly predicting a granular beach reservoir in combination with forward and inversion of an earthquake of claim 1, wherein S1 comprises:
selecting an acoustic time difference and density logging curve of a target horizon, and calculating a stratum reflection coefficient sequence according to a seismic wave propagation theory;
Reading Ricker wavelets by utilizing spectrum analysis, carrying out convolution operation on the Ricker wavelets and stratum reflection coefficients to generate synthetic seismic records, comparing the synthetic seismic records with actual seismic data, and adjusting the time-depth relation of logging to enable layering on the logging to be indicated to the double-journey travel time depth corresponding to the real horizon;
And combining the adjusted logging data and the seismic data, carrying out horizon tracking on the target horizon, identifying the reflection characteristics of the target horizon on the seismic profile, tracking the continuity of the target horizon in the seismic body according to the known well point data and the known seismic reflection characteristics, and determining the distribution range and the distribution form of the target horizon in the three-dimensional space through horizon tracking.
3. The method for jointly predicting a granular beach reservoir in combination with seismic forward modeling and inversion of claim 2, wherein S2 comprises:
Collecting logging information and seismic information of a target layer, primarily carding the development condition of the reservoir, determining the development quantity of the reservoir, namely the number of the reservoir identified in the target layer, simultaneously, comparing the seismic profile with a logging curve, determining the position of the reservoir, positioning the longitudinal and transverse positions of the reservoir in the target layer, counting the thickness of the reservoir, and analyzing the change rule of the thickness of the reservoir by using thickness data in logging interpretation results;
Observing continuity, fault distribution and contact relation with surrounding stratum of the reservoir in vertical and horizontal directions by utilizing the seismic section and three-dimensional seismic data, analyzing superposition relation and mutual influence among the reservoirs by combining lithology and physical property changes in logging data, and defining connectivity and closure characteristics of the reservoirs to construct a spatial distribution frame of the reservoir;
and (3) comprehensive logging and earthquake information, analyzing development rules of the reservoir in different areas, identifying main reservoir development types and control factors, and analyzing a causative mechanism of reservoir development by combining with regional geological background to form a complete reservoir development mode.
4. The method for jointly predicting a granular beach reservoir in combination with seismic forward modeling and inversion of claim 3, wherein S3 comprises:
Collecting seismic data of a target area, including a two-dimensional seismic section and three-dimensional seismic data, identifying reflection characteristics and boundaries of the target layer through seismic horizon interpretation, determining the range and the position of the target layer by combining the geological background of the area and the existing geological research results, analyzing the amplitude, the frequency and the phase characteristics in the seismic data, and primarily judging the development area of a reservoir;
Collecting logging data in a target area, including logging curves of acoustic time difference, density, resistivity and natural gamma, extracting relevant geological parameters including stratum thickness, speed, density, porosity and permeability from the logging data according to the determined target horizon, counting the distribution rule of the geological parameters in the target horizon, and analyzing the relation between the geological parameters and reservoir development;
Integrating parameters obtained from the seismic data and the logging data, comparing the relation between the seismic reflection characteristics and the logging parameters, analyzing the change rule of the stratum thickness, speed and density parameters of the target horizon in space, identifying the existing reservoir development area, and judging the development characteristics and distribution rule of the reservoir by combining the seismic data and the logging data.
5. The method for jointly predicting a granular beach reservoir in combination with seismic forward modeling and inversion of claim 4, wherein S4 comprises:
establishing an integral geological model frame according to the geological background of the target area and the existing research results, defining the range, boundary conditions and main stratum units and structural features of the model, determining the position and the range of the target layer in the geological model by combining seismic horizon interpretation and logging data, and analyzing main structural elements in the target area;
embedding geological models of different reservoir development modes in an established geological model frame, wherein a plurality of reservoir development modes are designed according to reservoir development rules and cause mechanisms of a target horizon, and specific positions and ranges of each reservoir development mode in the target horizon are determined by combining logging data and seismic information and are embedded into the geological model;
inputting the geological parameters obtained through statistics into a geological model embedded with a reservoir development mode, calibrating and adjusting the parameters in the geological model according to logging and seismic data, constructing a forward model based on the input geological parameters, simulating seismic reflection waveforms, and further calculating and recording reflection conditions of seismic waves when encountering different geological interfaces through forward simulation to generate a seismic reflection waveform chart corresponding to the geological model.
6. The method for jointly predicting a granular beach reservoir in combination with seismic forward modeling and inversion of claim 5, wherein S5 comprises:
classifying and grading the simulated seismic waveforms based on forward modeling results, dividing the seismic waveforms into different types and grades according to the amplitude, frequency and phase characteristics of the waveforms, identifying the types of the seismic waveforms corresponding to the reservoir development modes by comparing the seismic waveform characteristics under different reservoir development modes, and establishing the corresponding relation between the reservoir development modes and the seismic waveforms;
Classifying and counting actual seismic waveforms of a target horizon, comparing the seismic data of the target horizon with forward modeling results, identifying seismic waveform types corresponding to reservoir development modes in the target horizon, counting the distribution range and proportion of each waveform type in the target horizon, analyzing the plane distribution characteristics of each waveform type in different areas, and primarily determining the areas of high-quality particle beach reservoir development;
And combining the forward simulation result and the actual seismic waveform analysis of the target horizon, determining the plane distribution range of waveforms corresponding to the high-quality particle beach reservoir, identifying waveform types highly matched with the development modes of the high-quality reservoir by comparing the seismic waveform characteristics of different areas, comprehensively analyzing the distribution range of the identified waveform types on the plane, and finally determining the plane distribution range of the high-quality particle beach reservoir.
7. The method for jointly predicting a granular beach reservoir in combination with seismic forward modeling and inversion of claim 6, wherein S6 comprises:
and based on the well earthquake calibration result and the horizon tracking data, completing the construction of the waveform indication simulation model.
8. The method for jointly predicting a granular beach reservoir in combination with seismic forward modeling and inversion of claim 7, wherein S7 comprises:
Analyzing logging curve data through a statistical histogram, observing peak distribution differences of lithology of the reservoir and the non-reservoir on the logging curve, and comparing the separation degree of the lithology peaks of the reservoir and the non-reservoir;
Based on analysis results of lithology peak separation degree, optimizing a logging curve which is most sensitive to reservoir identification, wherein the peak separation degree of different logging curves is compared, and a curve with the best separation effect is selected as a sensitive curve;
And determining key parameters including effective sample numbers and optimal cut-off frequency in the waveform indication simulation according to the sensitivity curve, wherein the effective sample numbers refer to the most similar well sample numbers with the current seismic waveform in the target layer, and determining the optimal cut-off frequency through experiments and comparative analysis.
9. The method for jointly predicting a granular beach reservoir in combination with seismic forward modeling and inversion of claim 8, wherein S8 comprises:
Based on the waveform indication simulation model which is preferably determined and key parameters comprising a sensitive curve, an effective sample number and an optimal cut-off frequency, waveform indication simulation inversion is carried out, seismic data and logging data are combined, and an inversion technology is utilized to generate a high-resolution waveform indication simulation inversion result;
The method comprises the steps of extracting a plane attribute diagram reflecting reservoir characteristics according to research purposes and reservoir characteristics by analyzing amplitude, frequency and phase attributes in waveform indication simulation inversion results, and intuitively displaying distribution characteristics of the reservoir including thickness, continuity and porosity on a plane;
And (3) drawing the boundary of the favorable region of the reservoir by combining the extracted plane attribute map and using a geostatistical method, further analyzing the change rule of the attribute values of different regions in the plane attribute map, determining the attribute value range corresponding to the favorable reservoir, marking the region meeting the attribute value range in the attribute map, and finally drawing the boundary of the favorable region of the reservoir accurately by comparing and correcting for multiple rounds.
10. The method for jointly predicting a granular beach reservoir in combination with seismic forward modeling and inversion of claim 9, wherein S9 comprises:
respectively collecting related data of a waveform classification prediction result and a waveform indication simulation result, and carrying out standardization processing on the waveform classification prediction result and the waveform indication simulation result so as to carry out geographic registration on the two results;
Overlapping the standardized waveform classification prediction result and the waveform indication simulation result, and identifying the repeated areas of the two methods on the plane through a geographic information system, namely, the potential favorable reservoir areas identified by the two methods together;
And further evaluating the repeated area identified in the superposition analysis by combining the geological background and the existing exploration data, analyzing the geological features and the reservoir development conditions of the repeated area, and finally optimizing the favorable reservoir area with the highest probability.
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