A method for predicting the partition of epikarst reservoirs based on geomorphic restoration

Abstract

The present invention discloses a method for predicting the partition of epigene karst reservoirs based on geomorphic restoration. The prediction method includes: determining the main frequency of seismic waves of the target formation; then determining the top and bottom interfaces of the target formation and the development positions of epigene karst reservoirs therein; selecting a geomorphic restoration method to restore the karst landform and divide the geomorphic units; establishing a forward reservoir development model based on the parameters of the target formation, marker formation and epigene karst reservoir in the geomorphic unit and performing excitation simulation through the main frequency of seismic waves to obtain the theoretical seismic response characteristics of the reservoir, extracting the characterization features that can characterize the development of the reservoir, and establishing the fitting formula of reservoir characterization features - reservoir thickness for each geomorphic unit. The present invention can comprehensively integrate geological, logging and seismic information, accurately qualitatively and quantitatively predict epigene karst reservoirs, and can be applied to the early stage of exploration and development of epigene karst reservoirs, with high working efficiency and low application cost.

Description

Technical Field

[0001] The present invention relates to the technical field of oil exploration, and particularly to the technical field of prediction methods for epigene karst reservoirs. Background Art

[0002] Among the proven reserves of oil and gas resources worldwide, the reserves of carbonate rock oil and gas reservoirs account for about 50%, and the production accounts for more than 60%. The formation of reservoirs in large and medium-sized marine carbonate rock oil and gas fields at home and abroad is almost related to karstification. Among them, epigene karst pore increase is one of the most important reservoir-forming processes in carbonate rocks. Therefore, the prediction of epigene karst reservoirs is very important in oil and gas exploration and development and energy strategic security.

[0003] Currently, the popular reservoir prediction methods at home and abroad mainly include geological, logging and seismic prediction methods. Among them, traditional geological and logging prediction methods mainly rely on data such as actual drilled wells, outcrops, cores, and logging to identify and predict reservoirs within the wellbore periphery range, and it is difficult to obtain the planar law of reservoirs between wellbores. For example, the current geological prediction model for epigene karst reservoirs identifies and divides karst geomorphic units, analyzes and obtains factors such as karst hydrodynamic conditions and dissolution ability within different geomorphic units to qualitatively describe the planar distribution characteristics and laws of reservoirs. It can only predict the reservoir differences between different geomorphic units, cannot well reflect the reservoir development differences within the same geomorphic unit, and the prediction accuracy is limited, unable to meet the increasing accuracy requirements.

[0004] Compared with geological and logging prediction methods, seismic prediction methods can better depict the vertical and horizontal laws of reservoirs and have obtained rapid development in recent years. Among seismic prediction methods, amplitude attribute analysis is one of the most efficient and widely used prediction means. However, due to the complexity of seismic reflection interfaces above and below the reservoir development intervals and the particularity of the deep-ultra-deep environment where epigene karst reservoirs are concentrated, the coincidence rate of this prediction means is insufficient when applied to epigene karst reservoirs. For some other seismic prediction methods, such as seismic inversion prediction methods, due to the long prediction working time, low efficiency, and high cost, it is difficult to be actually applied in the early stage of exploration and development.

[0005] In summary, there is still a lack of a method in the prior art that can well predict epigene karst reservoirs in the deep-ultra-deep environment and be applied efficiently and at low cost. Summary of the Invention

[0006] Aiming at the defects of the prior art, the object of the present invention is to propose a zoning prediction method for epigene karst reservoirs based on geomorphic restoration. This method can comprehensively integrate geological, logging and seismic information, accurately qualitatively and quantitatively predict epigene karst reservoirs, can be applied to the early stage of exploration and development of epigene karst reservoirs, and has high working efficiency and low application cost.

[0007] The technical solution of the present invention is as follows:

[0008] A prediction method for partitioning epigene karst reservoirs based on geomorphic restoration, which includes:

[0009] S1 Collect the basic information of the geological area where the target formation, i.e., the formation to be predicted for epigene karst reservoirs, is located, i.e., the research area. The basic information includes geological information, logging information, and seismic information. Among them, the geological information includes geological survey data, single-well stratigraphic division data, outcrop data, well deviation data, and single-well production data of the research area; the logging information includes logging curve data and logging reservoir interpretation data; the seismic information includes post-stack migrated seismic data and seismic horizon interpretation data. Among them, the logging curve data includes acoustic time difference logging curves and density logging curves;

[0010] S2 Determine the seismic reflection time range of the target formation according to the seismic horizon interpretation data in the seismic information, and perform spectral analysis on the seismic signals of the target formation based on the post-stack migrated seismic data within this time range to determine the main frequency and effective frequency range of the seismic waves of the target formation;

[0011] S3 Perform well-seismic calibration on the upper and lower formations of the target formation and the epigene karst reservoirs within the target formation according to the logging information collected in S1 and the main frequency and effective frequency range of the seismic waves of the target formation determined in S2, and determine the top and bottom interfaces of the target formation, the upper and lower formation interfaces thereof, and the development positions of the epigene karst reservoirs within them;

[0012] S4 Determine the geomorphic restoration method of the research area according to the geological information collected in S1;

[0013] S5 Restore the karst landform in the research area according to the geomorphic restoration method obtained in S4 and divide the karst landform according to the restored landform conditions to obtain several geomorphic units;

[0014] S6 Analyze the stratigraphic sedimentary filling law of the target formation and its upper and lower formations in each geomorphic unit to obtain marker formations that can be used to characterize the karst landform characteristics of the target formation, and determine the thickness, acoustic time difference, and density values of different lithologic segments of the marker formation in the research area according to the well-seismic calibration records obtained in S3, and simultaneously determine the thickness, acoustic time difference, and density values of the target formation and the epigene karst reservoirs therein;

[0015] S7 Establish a theoretical seismic forward geological model of the formation according to the divided geomorphic units, the thickness, acoustic time difference, and density values of the obtained target formation, and the thickness, acoustic time difference, and density values of different lithologic segments of the marker formation in the research area, and add the thickness, acoustic time difference, and density values of the epigene karst reservoirs to the theoretical seismic forward geological model of the formation to obtain a reservoir development model;

[0016] S8 According to the main frequency of the seismic wave of the target formation obtained from S2, perform wavelet excitation simulation on the reservoir development model, and obtain the theoretical seismic response characteristics of the reservoir in different geomorphic units according to the simulation results;

[0017] S9 Compare the theoretical seismic response characteristics of the reservoir in the obtained different geomorphic units with the well-seismic calibration records of the epigene karst reservoir obtained from S3, and analyze whether the actual reservoir seismic response characteristics obtained from the well-seismic calibration records of the epigene karst reservoir are consistent with the theoretical seismic response characteristics;

[0018] S10 Extract a seismic attribute that can characterize the development of the reservoir from the theoretical seismic response characteristics consistent with the actual reservoir seismic response characteristics, that is, the reservoir characterization feature;

[0019] S11 According to the divided geomorphic units, perform correlation analysis on the reservoir characterization feature and the reservoir thickness for each unit, and obtain the fitting formula of the reservoir characterization feature-reservoir thickness for each geomorphic unit;

[0020] S12 Perform zonal prediction according to the fitting formula of the reservoir characterization feature-reservoir thickness by dividing into geomorphic units.

[0021] According to some preferred embodiments of the present invention, the S3 includes:

[0022] S31 Through the acoustic travel time log curve and the density log curve, combine with the main frequency of the seismic wave of the obtained target formation to obtain the single-well synthetic seismic record of the target formation;

[0023] S32 Perform well-seismic calibration on the target formation, its upper and lower formations and the epigene karst reservoir therein according to the obtained single-well synthetic seismic record of the target formation, and ensure that the correlation coefficient between the obtained single-well synthetic seismic record and the seismic trace beside the well is the highest as much as possible during calibration;

[0024] S33 After calibration, obtain the top and bottom interfaces of the target formation, the upper and lower formation interfaces and the main seismic wave reflection characteristics of the epigene karst reservoir therein, and determine the development positions of the top and bottom interfaces of the target formation, its upper and lower formation interfaces and the epigene karst reservoir therein according to the main seismic wave reflection characteristics.

[0025] According to some preferred embodiments of the present invention, the S4 includes: According to the geological information, determine the isochronous interfaces closest to the target formation in the upper and lower formations of the target formation, and select the karst geomorphic restoration method of the research area according to the relationship between the formation where the isochronous interface is located and the target formation.

[0026] According to some preferred embodiments of the present invention, the geomorphic restoration method includes the residual thickness method and / or the imprint method.

[0027] According to some preferred embodiments of the present invention, the geomorphic conditions in S5 include one or more of the form, height, and slope of the geomorphology.

[0028] According to some preferred embodiments of the present invention, the marker formation in S6 is the formation that is closest to the target formation and has large differences in acoustic travel time and density.

[0029] According to some preferred embodiments of the present invention, the wavelet in S8 is a Ricker wavelet.

[0030] According to some preferred embodiments of the present invention, the seismic attribute is selected from any one of amplitude, frequency, and phase.

[0031] According to some preferred embodiments of the present invention, the zonal prediction according to the reservoir characterization feature-reservoir thickness fitting formula by dividing into geomorphic units includes:

[0032] Calculating the reservoir thickness of different geomorphic units according to the reservoir characterization feature-reservoir thickness fitting formula to obtain the reservoir plane thickness distribution map of each geomorphic unit, and fusing the obtained reservoir plane thickness maps to obtain the final reservoir plane distribution map.

[0033] According to some preferred embodiments of the present invention, the zonal prediction according to the reservoir characterization feature-reservoir thickness fitting formula by dividing into geomorphic units further includes: correcting and / or removing the outliers in the reservoir plane thickness distribution map of each geomorphic unit obtained according to the actually measured single-well reservoir thickness, and then fusing to obtain the final reservoir plane distribution map.

[0034] The present invention has the following beneficial effects:

[0035] (1) The present invention can be realized under the existing seismic exploration technology conditions, saving the time cycle from the development to the application of reservoir prediction attributes or algorithms.

[0036] (2) The post-stack migration data is adopted for the basic seismic data of the present invention. Compared with pre-stack reservoir prediction and inversion reservoir prediction, the calculation amount is smaller, the calculation speed is faster, the work efficiency is higher, and it has extremely strong practicability for the early stage of oil and gas field exploration and development.

[0037] (3) In view of the particularity of the epigene karst reservoir, the present invention adopts the method of karst geomorphic zoning and attribute analysis for quantitative prediction, organically divides the same research area through different karst geomorphic conditions, and conducts reservoir prediction, greatly solving the problem of multi-solution of large-area attribute analysis in the same area, effectively simplifying complex problems, improving the efficiency and accuracy of reservoir prediction, and promoting the qualitative prediction of the existing technology to qualitative and quantitative prediction. Description of the Drawings

[0038] Figure 1 It is a flowchart of the prediction method for epigene karst reservoir zoning in the specific implementation manner.

[0039] Figure 2 It is a spectrogram of the target layer obtained from the post-stack migration seismic data in Example 1.

[0040] Figure 3 It is a karst geomorphological map of the target layer in Example 1.

[0041] Figure 4 It is a geological model diagram of the strata filling and reservoir development above the exposed surface of the target layer in Example 1.

[0042] Figure 5 It is a seismic forward geological model and forward seismic trace gather diagram in Example 1.

[0043] Figure 6 It is an analysis diagram of the prediction effectiveness before and after zoning under the constraint of geomorphology in Example 1.

[0044] Figure 7 It is a predicted plane result diagram after zoning under the constraint of geomorphology in Example 1. Specific implementation manner

[0045] The present invention will be described in detail below in conjunction with the embodiments and the drawings. However, it should be understood that the embodiments and the drawings are only used for exemplary description of the present invention, and do not constitute any limitation to the protection scope of the present invention. All reasonable transformations and combinations within the scope of the inventive concept of the present invention fall within the protection scope of the present invention.

[0046] Referring to the appended Figure 1 , according to the technical solution of the present invention, in some specific implementation manners, the prediction method for epigene karst reservoir zoning based on geomorphic restoration includes the following steps:

[0047] S1 Collect the basic information of the area to be predicted for epigene karst reservoir, that is, the research area, including the geological information, logging information and seismic information of the area. Among them, the geological information includes the geological survey data of the research area, that is, the regional survey data, single-well stratigraphic division data, outcrop data, well deviation data and single-well production data; the logging information includes logging curve data and logging reservoir interpretation data; the seismic information includes post-stack migration seismic data and seismic horizon interpretation data; among them, the logging curve data includes acoustic time difference logging curves and density logging curves.

[0048] S2 According to the seismic horizon interpretation data in the basic information, determine the seismic reflection time range of the target layer, and perform spectral analysis on the post-stack migration seismic data within this time range to determine the main frequency and effective width of the post-stack migration seismic data of the target layer.

[0049] S3 determines the main frequency of the seismic wave of the target layer based on the well logging information and that determined in S2, and performs well-seismic calibration on the study area, including making a synthetic seismic record of a single well of the target layer by using the acoustic travel time logging curve, the density logging curve and the main frequency of the seismic wave of the target layer, and calibrating the top and bottom interfaces of the target layer, the upper and lower formation interfaces and the reservoir development position according to the synthetic seismic record of the single well.

[0050] S3 may further include the following steps:

[0051] S31 Obtain a synthetic seismic record of a single well of the target layer through the acoustic travel time logging curve, the density logging curve combined with the main frequency of the seismic wave of the target layer;

[0052] S32 Perform well-seismic calibration on the target layer, its upper and lower formations and the epigene karst reservoir therein according to the obtained synthetic seismic record of the single well. When calibrating, ensure that the correlation coefficient between the synthetic seismic record and the seismic trace beside the well is the highest as much as possible, and a correlation coefficient greater than 0.8 is optimal;

[0053] After the calibration in S33, obtain the main seismic wave reflection characteristics of the top and bottom interfaces of the target layer, its upper and lower formation interfaces and the epigene karst reservoir therein, so as to determine the top and bottom interfaces of the target layer, the upper and lower formation interfaces and the reservoir development position.

[0054] S4 Optimize the geomorphic restoration method according to the geological information.

[0055] Specifically, it may include: determine the isochronous interface closest to the target layer according to the geological information such as well logging data (single well formation stratification data, well deviation data and single well productivity data), outcrop data and regional geological survey data in the geological information, and select the karst geomorphic restoration method for the study area according to the relationship between the formation where the isochronous interface is located and the target layer, such as the residual thickness method, the impression method, etc.

[0056] S5 Perform karst geomorphic restoration and geomorphic unit division.

[0057] It may further include:

[0058] S51 Restore the karst geomorphology in the study area according to the optimized karst geomorphic restoration method;

[0059] S52 Divide the study area into different geomorphic units according to the situation of the restored karst geomorphology, such as the shape, height, slope, etc. of the geomorphology.

[0060] S6 analyzes the stratigraphic sedimentary filling laws of the target layer and its upper and lower strata in each geomorphic unit, obtains the marker strata that can be used to characterize the karst geomorphic features of the target layer, and determines the thickness, acoustic travel time, and density values of different lithologic segments of the marker strata within the study area based on the well-seismic calibration records obtained in S3. At the same time, the thickness, acoustic travel time, and density values of the target layer are determined.

[0061] Among them, the marker strata are preferably the strata that are closest to the target layer and have significant differences in acoustic travel time and density in lithologic characteristics.

[0062] S7 establishes a theoretical seismic forward geological model and a reservoir development model for the strata.

[0063] It may further include:

[0064] S71 establishes a theoretical seismic forward geological model for the strata based on the divided geomorphic units, the obtained thickness, acoustic travel time, and density values of the target layer, and the thickness, acoustic travel time, and density values of different lithologic segments of the marker strata within the study area;

[0065] S72 adds the thickness, acoustic travel time, density, and development location of the epigene karst reservoir obtained into the theoretical seismic forward geological model of the strata to obtain a reservoir development model.

[0066] S8 conducts forward simulation on the reservoir development model.

[0067] It may further include:

[0068] S81 selects a theoretical Ricker wavelet consistent with the main frequency of the seismic wave in S2 or extracts a wavelet to conduct excitation simulation on the reservoir development model;

[0069] S82 obtains the theoretical seismic response characteristics of the reservoir through analyzing the seismic characteristics of the target layer and the reservoir section in the seismic profile obtained from the forward simulation, where the seismic attributes used for analysis include amplitude, frequency, phase, etc.

[0070] S9 conducts comparative analysis of the reservoir seismic response characteristics.

[0071] After the forward simulation, by analyzing the reservoir seismic response characteristics of the actual seismic data of a single well, under the condition of ensuring the authenticity of the seismic data, its response characteristics should be consistent with the characteristics of the seismic profile obtained from the theoretical forward simulation.

[0072] S10 conducts optimization of reservoir characterization attributes.

[0073] It may further include: based on the comparative analysis of the reservoir seismic response characteristics in S9, select the attributes that are most sensitive to reservoir development as the characterization features, establish the relationship between the characterization features and the reservoir development parameters, conduct plane analysis of the plane attributes, and predict the plane distribution of the reservoir.

[0074] S11 performs planar evaluation of reservoir prediction before and after geomorphic unit division.

[0075] It may further include:

[0076] S111 Based on the geomorphic unit division results in S6, establish the relationship of sensitive attributes in S10 by region;

[0077] S112 Compare the relationship between the sensitive attributes established without region division and the reservoir with the relationship established after region division, and distinguish the correlation and prediction accuracy after region division;

[0078] S113 If the correlation and prediction accuracy are greatly improved after region division, then use the region division prediction method for prediction. Otherwise, return to S4 to re-select geomorphic restoration, geomorphic unit division and subsequent reservoir characterization features, etc.

[0079] After reservoir prediction and establishment of the attribute relationship, taking the development of the reservoir in a single well as unconstrained, eliminate the prediction anomalies caused by systematic errors such as data reasons, and eliminate the relevant areas. According to the geomorphic division unit, finally merge into a map, and the reservoir prediction is completed.

[0080] Embodiment 1

[0081] According to the process of the above specific implementation manners, perform partition prediction of epigene karst reservoirs. The research area is the central part of a certain basin. Among them, the target stratum, that is, the target layer, is the Dengying Formation.

[0082] In this embodiment, through the analysis of the collected seismic data and seismic horizon data, the seismic reflection time range of the Dengying Formation is determined, that is, its specific development location is determined. Further, through the spectral analysis technology, the main frequency of the seismic wave of the target layer is determined to be 32 Hz, the effective frequency bandwidth is 16 - 48 Hz, and the absolute frequency bandwidth is 36 Hz, as shown in the appendix Figure 2 as shown.

[0083] Through the obtained synthetic seismic record of a single well, it is determined that the top boundary of the Dengying Formation is a peak reflection, and there is an epigene karst reservoir inside. The reservoir develops in the area below the peak. At the same time, the upper and lower strata of the Dengying Formation are synchronously calibrated to determine the seismic tracking characteristics of each geological layer.

[0084] Through the study of regional geological survey data, single-well stratigraphic division data, outcrop data, logging curve data, post-stack migrated seismic data, and seismic horizon interpretation data in the study area, it is found that the isochronous interface closest to the Dengying Formation and recognizable and traceable regionally is the top boundary of the Qiongzhusi Formation. Moreover, the Qiongzhusi Formation is the first set of strata that filled and compensated after the exposure of the Dengying Formation. The top boundary of the Dengying Formation is the bottom boundary of the Qiongzhusi Formation. Therefore, the thickness of the Qiongzhusi Formation can represent the karst geomorphic characteristics after the exposure of the Dengying Formation. In the study area, the karst geomorphology of the Dengying Formation can be restored using the impression method, which is achieved through the distribution of the thickness of the Qiongzhusi Formation.

[0085] Using the collected seismic horizon data, a planar distribution map of the formation thickness of the Qiongzhusi Formation is obtained. If the collected seismic horizon data does not include the seismic horizon data required for use, such as the top and bottom boundary data of the Qiongzhusi Formation, then through well-seismic calibration, this interface can be manually traced to obtain the required data, and then a complete planar distribution map of the formation thickness of the Qiongzhusi Formation can be obtained.

[0086] In this embodiment, according to the distribution law of the formation thickness in the planar distribution map of the formation thickness of the Qiongzhusi Formation, the karst geomorphology within the Dengying Formation is divided into three geomorphic units: karst highlands, karst slopes, and karst lowlands, as shown in the appendix Figure 3 shown.

[0087] Based on the analysis of the mudstone sections with high gamma ray (GR) and high acoustic time difference (AC) at the bottom of the overlying Qiongzhusi Formation according to the divided geomorphic units, it is found that the thickness of this set of strata is different in different geomorphic units, and as the karst geomorphology gradually rises, this set of strata gradually thins. On the basis of clarifying the distribution law and thickness of this set of strata, the thickness, acoustic time difference value, and density value of this set of strata and the overlying strata are obtained through logging data.

[0088] Furthermore, through logging curve analysis, the thickness, acoustic time difference value, and density value of the Dengying Formation are obtained; through reservoir logging interpretation data and logging curve analysis, the thickness of the reservoir, its distance from the top boundary of the Dengying Formation, acoustic time difference value, and density value are obtained. According to these parameters, combined with the thickness, acoustic time difference value, and density value of the obtained overlying strata, a theoretical forward seismic geological model of the formation is designed. This geological model is divided into three formation units, namely the Dengying Formation, the overlying Qiongzhusi high-gamma formation, and the overlying remaining Qiongzhusi formation. The karst geomorphology of the model gradually increases from left to right, and the Qiongzhusi Formation and its lower high-gamma section gradually thin.

[0089] Furthermore, reservoir parameters are superimposed on the theoretical forward seismic geological model of the formation to obtain a reservoir development model, as shown in the appendix Figure 4 shown.

[0090] In the theoretical seismic forward geological model and reservoir development model of the strata, the parameters set for each stratum or reservoir unit include thickness (H), velocity (V), and density (ρ), where the velocity V is obtained by converting the acoustic time difference value of the unit.

[0091] Furthermore, according to the obtained main frequency wavelet of the seismic wave of the target layer, the reservoir development model is excited and simulated. In this embodiment, it is the Ricker wavelet with a main frequency of 32 hz. After excitation, the response characteristics of the obtained theoretical profile are analyzed. As shown in the appendix Figure 5 As shown, the figure shows that when the reservoir is not developed, the top boundary of the Dengying Formation is a peak reflection. As the thickness of the overlying high gamma formation gradually thins, the amplitude of the top boundary of the Dengying Formation gradually weakens. In the area where the reservoir is developed, this peak will have a weakening characteristic.

[0092] According to the calibration situation of the reservoir in the obtained synthetic seismic record of a single well and the forward simulation situation of the reservoir development model, analyze whether the actual seismic response characteristics of the reservoir are consistent with the theoretical seismic response characteristics obtained by forward simulation. In this embodiment, the actual seismic response characteristics of the reservoir are consistent with the theoretical seismic response characteristics, that is, the amplitude of the top boundary of the Dengying Formation in the low karst landform area is stronger than that in the karst slope and stronger than that in the karst highland. When the epigene karst reservoir is developed, the peak amplitude attribute of the top boundary will have a weakening characteristic.

[0093] Furthermore, extract the seismic attributes that can better characterize the reservoir development situation from the theoretical seismic response characteristics consistent with the actual seismic response characteristics of the reservoir, that is, the reservoir characterization characteristics. In this embodiment, this characteristic is selected as the amplitude. The weaker the amplitude, the more developed the reservoir.

[0094] According to the divided geomorphic units and the extracted reservoir characterization characteristics, perform a correlation analysis on the reservoir development situation and the characterization characteristics to obtain the reservoir characterization characteristic-reservoir thickness fitting formula for each geomorphic unit. This includes extracting the amplitude value and reservoir thickness at the well points, performing an intersection analysis on the amplitude value and reservoir thickness value without dividing the geomorphic units, analyzing the correlation coefficient, fitting the calculation formula, and then separately performing an intersection analysis on the single wells in different zones, analyzing the correlation coefficient, and fitting the reservoir thickness calculation formula. In this embodiment, before zoning, the correlation coefficient between the amplitude value and the reservoir thickness is only 0.48, which means that there are great uncertainties and errors in using this fitting formula for prediction. After being constrained by the karst geomorphic unit, the correlation coefficient is increased to 0.78, 0.93, and 0.92, which means that the accuracy of zoned prediction has been greatly improved. As shown in the appendix Figure 6 As shown, it shows that under the division of geomorphic units, the credibility of the predicted reservoir thickness calculated by the amplitude value is enhanced.

[0095] According to the reservoir characterization characteristics obtained in different karst landforms, the reservoir thickness is calculated by partitioning and fitting formula, and the reservoir plane thickness distribution map is obtained. Then, the single well reservoir thickness obtained by combining the logging reservoir interpretation data is used to correct the abnormal interval, and the abnormal values ​​caused by data abnormality are eliminated. Finally, the reservoir plane thickness maps of different karst landforms are fused to obtain the final reservoir plane distribution map, as shown in the attached figure. Figure 7 shown.

[0096] The above embodiments are only preferred implementations of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A method for predicting the partition of epikarst reservoirs based on geomorphic restoration, characterized in that, It includes: S1 collects the basic information of the geological area where the formation to be predicted for epigene karst reservoirs, i.e., the target formation, is located, i.e., the research area. The basic information includes geological information, logging information, and seismic information. Among them, the geological information includes geological survey data of the research area, single-well formation stratification data, outcrop data, well deviation data, and single-well productivity data; the logging information includes logging curve data and logging reservoir interpretation data; the seismic information includes post-stack migrated seismic data and seismic horizon interpretation data. Among them, the logging curve data includes acoustic time difference logging curves and density logging curves; S2 determines the seismic reflection time range of the target formation according to the seismic horizon interpretation data in the seismic information, and performs spectral analysis on the seismic signals of the target formation based on the post-stack migrated seismic data within this time range to determine the main frequency and effective frequency range of the seismic waves of the target formation; S3 performs well-seismic calibration on the overlying and underlying formations of the target formation and the epigene karst reservoirs within the target formation according to the logging information collected in S1 and the main frequency and effective frequency range of the seismic waves of the target formation determined in S2, and determines the top and bottom interfaces of the target formation, the interfaces of its overlying and underlying formations, and the development positions of the epigene karst reservoirs within it; S4 determines the geomorphic restoration method of the research area according to the geological information collected in S1; S5 restores the karst landforms in the research area according to the geomorphic restoration method obtained in S4 and divides the karst landforms according to the restored geomorphic conditions to obtain several geomorphic units; S6 analyzes the stratigraphic sedimentary filling laws of the target formation and its overlying and underlying formations in each geomorphic unit to obtain marker formations that can be used to characterize the karst geomorphic features of the target formation, and determines the thickness, acoustic time difference, and density values of different lithologic segments of the marker formation in the research area according to the well-seismic calibration records obtained in S3, and at the same time determines the thickness, acoustic time difference, and density values of the target formation and the epigene karst reservoirs within it; S7 establishes a theoretical seismic forward geological model of the formation according to the divided geomorphic units, the thickness, acoustic time difference, and density values of the obtained target formation, and the thickness, acoustic time difference, and density values of different lithologic segments of the marker formation in the research area, and adds the thickness, acoustic time difference, and density values of the epigene karst reservoirs to the theoretical seismic forward geological model of the formation to obtain a reservoir development model; S8 performs wavelet excitation simulation on the reservoir development model according to the main frequency of the seismic waves of the target formation obtained in S2, and obtains the theoretical seismic response characteristics of the reservoirs in different geomorphic units according to the simulation results; S9 compares the theoretical seismic response characteristics of the reservoirs in different geomorphic units obtained with the well-seismic calibration records of the epigene karst reservoirs obtained in S3 to analyze whether the actual reservoir seismic response characteristics obtained from the well-seismic calibration records of the epigene karst reservoirs are consistent with the theoretical seismic response characteristics; S10 extracts a seismic attribute that can characterize the reservoir development situation, i.e., the reservoir characterization feature, from the theoretical seismic response characteristics that are consistent with the actual reservoir seismic response characteristics; S11 According to the divided geomorphic units, perform a correlation analysis on the reservoir characterization features and reservoir thickness for each unit to obtain the reservoir characterization feature-reservoir thickness fitting formula for each geomorphic unit; S12 Perform zonal prediction according to the reservoir characterization feature-reservoir thickness fitting formula by geomorphic unit division.

2. The prediction method according to claim 1, wherein The S3 includes: S31 Through the acoustic travel time log curve and the density log curve, combined with the main frequency of the seismic wave of the obtained target formation, obtain the single-well synthetic seismic record of the target formation; S32 Perform well-seismic calibration on the target formation, its upper and lower formations, and the epigene karst reservoir therein according to the obtained single-well synthetic seismic record of the target formation. When calibrating, try to ensure the highest correlation coefficient between the obtained single-well synthetic seismic record and the seismic trace beside the well; After calibration, obtain the top and bottom interfaces of the target formation, the upper and lower formation interfaces, and the main seismic wave reflection characteristics of the epigene karst reservoir therein. According to the main seismic wave reflection characteristics, determine the development positions of the top and bottom interfaces of the target formation, its upper and lower formation interfaces, and the epigene karst reservoir therein.

3. The prediction method according to claim 1, characterized in that The S4 includes: According to the geological information, determine the isochronous interfaces closest to the target formation in the upper and lower formations of the target formation, and select the karst geomorphic restoration method for the study area according to the relationship between the formation where the isochronous interface is located and the target formation.

4. The prediction method according to claim 3, wherein The geomorphic restoration method includes the residual thickness method and / or the imprint method.

5. The prediction method according to claim 1, wherein The geomorphic conditions in S5 include one or more of the morphology, height, and slope of the geomorphology.

6. The prediction method according to claim 1, characterized in that The marker formation in S6 is the formation closest to the target formation with large differences in acoustic travel time and density.

7. The prediction method according to claim 1, wherein The wavelet in S8 is the Ricker wavelet.

8. The prediction method according to claim 1, wherein The seismic attribute is selected from any one of amplitude, frequency, and phase.

9. The prediction method according to claim 1, characterized in that, The performing zonal prediction according to the reservoir characterization feature-reservoir thickness fitting formula by geomorphic unit division includes: Calculate the reservoir thickness of different geomorphic units according to the reservoir characterization feature-reservoir thickness fitting formula, obtain the reservoir plane thickness distribution map of each geomorphic unit, and fuse the obtained reservoir plane thickness maps to obtain the final reservoir plane distribution map.

10. The prediction method according to claim 9, wherein It also includes: Correct and / or eliminate the abnormal values in the reservoir plane thickness distribution maps of each geomorphic unit obtained according to the actually measured single-well reservoir thickness, and then perform fusion to obtain the final reservoir plane distribution map.

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