Earthquake prediction method and system for trona ore exploration

Abstract

The invention discloses an earthquake prediction method and system for trona ore exploration, and the method comprises the steps: recognizing a trona development layer section and the thickness of a single layer through logging lithology interpretation in combination with logging and core data, and determining a trona sensitivity logging curve; establishing an isochronous stratigraphic framework by applying a well-seismic combined interpretation technology, tracking the ore-bearing position, and determining top and bottom boundary structural characteristics of the alkali-bearing ore position and ore body seismic facies characteristics; based on the top and bottom boundary structural features of the alkali-containing ore layer, a layer constrained structural model is created, a sensitive logging curve is selected to reconstruct a feature curve, the feature curve serves as a control waveform to conduct seismic waveform simulation inversion, and the distribution range of the trona ore body and the thickness of the ore layer are predicted; and depicting the spatial form of the trona ore body based on a seismic waveform simulation inversion result, and determining an exploration project distance and enclosing a first mining area in combination with comprehensive evaluation of ore deposit geological conditions. The earthquake prediction technology is used in trona ore exploration for the first time, and the exploration efficiency and the drilling success rate are improved.

Description

Technical Field

[0001] The invention belongs to the field of earthquake technology, and in particular relates to an earthquake prediction method and system for natural alkali mine exploration. Background Art

[0002] Trona, with the chemical formula Na2CO3·NaHCO3·2H2O, also known as sesquialkali stone and sodium bicarbonate stone, is an evaporite mineral. Its main product, soda ash, can be widely used in glass, chemical, papermaking, medicine and other industrial fields. Trona is a scarce resource in my country. Domestic trona resources are mainly concentrated in Anpeng Soda Mine and Wucheng Soda Mine in Henan and Chagan Nur Soda Mine, Ordos Soda Lake and Tamusu Soda Mine in Inner Mongolia. In 2022, my country's demand for soda ash has reached 25.6 million tons, and it may increase year by year in the future. At present, 90% of my country's soda ash production process adopts chemical method, while the trona production process is simple, low cost, low energy consumption, low emission, high safety, and has unique environmental protection and price advantages. However, there are few precedents for the exploitation of oil and gas symbiotic trona in domestic oil and gas basins. There is an urgent need for a set of effective earthquake prediction technology to predict the distribution range of trona mines, implement the scale of alkali mine resources, and comprehensively promote the pace of trona exploration and exploitation.

[0003] At present, the seismic exploration method of natural alkali mines is relatively backward, and there is no effective technical means to accurately predict the geological structure of deep ore deposits. The traditional exploration method of alkali mines is mainly to deploy exploration according to the well network. There is no accurate prediction method for the distribution range and thickness of the ore body, and the exploration success rate is relatively low.

[0004] In 2012, Zhongzhou Coal published the application of comprehensive logging interpretation technology in alkali mine exploration (Chai Maojia, Wang Chao. Application of comprehensive logging interpretation technology in alkali mine exploration [J]. Zhongzhou Coal, 2012(05):33-36.), using the comprehensive logging interpretation technology in petroleum exploration to re-interpret the interpreted natural alkali ore layer; in 2010, Geological Prospecting Forum published the application of geophysical logging in the exploration of Anpeng natural alkali mine (Chen Jianli. Application of geophysical logging in the exploration of Anpeng natural alkali mine [J]. Geological Prospecting Forum, 2010, 25(03):252-259.) , using a variety of geophysical logging data, the electrical boundaries for judging the alkaline layer and dividing the thickness of the alkaline layer and the electrical boundaries for removing the interlayer were formulated, and then the location and thickness of the alkaline ore layer were determined; in 2021, "Energy and Environmental Protection" published the application of three-dimensional seismic technology in the exploration of alkaline mines in the Biyang Depression (Chen Yingnan. Application of three-dimensional seismic technology in the exploration of alkaline mines in the Biyang Depression [J]. Energy and Environmental Protection, 2021, 43(03): 52-58.), through the interpretation of three-dimensional seismic data, the distribution range of the approximately horizontal sedimentary strata in the Biyang Depression was delineated, and then the lithology represented by the seismic reflection phase axis was used to determine the distribution range of the alkaline rock layer. A Chinese patent (publication number CN110118992B) discloses a method for coal resource exploration in a fully concealed deep coalfield. The invention is based on electrical methods, seismic exploration and comprehensive logging, and aims to identify coal seams deeply buried in depression basins. Through a large number of experimental studies and exploration practices, it solves the problem of how to determine the location and thickness of coal seams in fully concealed coalfields with a burial depth of 1000 to 3000 meters, and provides a method for deep coal resource exploration.

[0005] The above methods use geophysical logging or seismic data to identify and describe alkali mines, but they have the following limitations: ① The basic data they use are mostly drilling and logging data, and only the local characteristics of the ore body in the horizontal or vertical direction are obtained, rather than the three-dimensional spatial form; ② They only use a single technology such as seismic time slicing to describe the alkali mines with approximately horizontal distribution, and the method is not very applicable; ③ They require a large amount of exploration data or experimental research support, and their exploration cost is high and the prospecting cycle is long. Summary of the invention

[0006] In view of the above problems, the present invention discloses an earthquake prediction method for natural alkali mine exploration, comprising the following steps:

[0007] Through the combination of logging lithology interpretation and logging and core data, the natural alkali development interval and single layer thickness are identified, and the natural alkali sensitivity logging curve is determined;

[0008] Apply well-seismic combined interpretation technology to establish an isochronous stratigraphic framework, trace the ore-bearing strata, and determine the structural characteristics of the top and bottom boundaries of the alkali-bearing strata and the seismic phase characteristics of the ore bodies;

[0009] Based on the structural characteristics of the top and bottom boundaries of the alkali-bearing strata, a stratum-constrained structural model is created, and the characteristic curve reconstructed by the sensitivity logging curve is selected as the control waveform to perform seismic waveform simulation inversion to predict the distribution range and ore thickness of the natural alkali ore body;

[0010] Based on the results of seismic waveform simulation inversion, the spatial morphology of the natural alkali ore body is depicted, and combined with a comprehensive evaluation of the geological conditions of the deposit, the spacing of exploration projects is determined and the first mining area is circled.

[0011] Furthermore, the identification of trona development intervals and single layer thicknesses through logging lithology interpretation combined with logging and core data, and determination of trona sensitivity logging curves include the following steps:

[0012] Processing of well logging data;

[0013] Plotting the processed logging data into a logging curve and preprocessing the logging curve;

[0014] The secondary interpretation of lithology is carried out based on the preprocessed logging curves.

[0015] Furthermore, the specific steps of processing the well logging data are as follows:

[0016] The logging data are processed and corrected using the drilling coring, logging and core analysis test data to eliminate abnormal values ​​in the logging data.

[0017] Furthermore, the specific steps of drawing the processed logging data into a logging curve and preprocessing the logging curve are as follows:

[0018] The processed logging data are plotted into logging curves, and each logging curve at each depth is classified as unified sampling point data;

[0019] Correct the inclined well curve into a vertical well curve;

[0020] Smoothing the logging curve;

[0021] Eliminate the influence of surrounding rock and mud intrusion within the detection range of logging instruments and obtain the true value of the formation;

[0022] The logging curves are numerically standardized to eliminate systematic errors.

[0023] Furthermore, the specific steps of performing secondary interpretation of lithology based on the pre-processed logging curve are as follows:

[0024] Using the core thin section identification results and the combined characteristics of the well logging curve, the corresponding relationship between the two is established;

[0025] Using core calibration logging, the logging curve values ​​of the same type of lithology are extracted and the intersection diagrams between different curve types are established;

[0026] Select the curves with concentrated distribution of the same type of lithology as the sensitivity logging curve for identifying lithology, and establish a lithology identification chart;

[0027] According to the lithology identification plate, lithology is assigned and secondary lithology interpretation is carried out to identify the natural alkali development layer and single layer thickness.

[0028] Furthermore, the application of well-seismic combined interpretation technology to establish an isochronous stratigraphic framework, track the ore-bearing strata, and determine the top and bottom structural characteristics of the alkali-bearing strata and the seismic phase characteristics of the ore body includes the following steps:

[0029] Loading logging curves;

[0030] Analyze the main frequency of seismic data;

[0031] Extract seismic wavelets from the seismic traces near the well, adjust the time-depth relationship synchronously, and obtain the wavelets;

[0032] In the process of synthetic record calibration, based on the marker layer, the matching relationship between synthetic seismic records and well-side seismic traces is continuously corrected by stretching and compressing, and the correlation between synthetic seismic records and seismic data is synchronously verified;

[0033] Analyze the contribution of sub-waves and find the contribution of seismic amplitude changes caused by different lithologies or fluids on logging to seismic reflection, laying the foundation for layer calibration and attribute time window selection;

[0034] After completing the well-seismic horizon calibration, analyze the seismic reflection structure and wave group characteristics, combine the attribute profile to interpret the horizon, and determine the top and bottom structural characteristics of the alkali-bearing strata;

[0035] The conventional profiles are combined with the attribute profiles to identify the profile faults, and the coherence volume slicing technology is used to interpret the fault plane to obtain the detailed seismic data interpretation results.

[0036] Furthermore, the stratigraphic interpretation is determined by section, horizontal slicing and along-layer slicing techniques.

[0037] Furthermore, the method of creating a stratigraphically constrained structural model based on the top and bottom structural features of the alkali-bearing strata, selecting a sensitivity logging curve to reconstruct a characteristic curve as a control waveform, performing seismic waveform simulation inversion, and predicting the distribution range and ore layer thickness of the natural alkali ore body includes the following steps:

[0038] Standardize the well logging curves;

[0039] Reconstruct characteristic curves;

[0040] Load seismic horizons and create a structural model;

[0041] Select the control waveform and determine the number of control waveforms;

[0042] The termination frequency condition is set to perform seismic waveform simulation inversion to predict the distribution range and thickness of the natural alkali ore body.

[0043] Furthermore, the specific steps of standardizing the well logging curve are as follows:

[0044] The logging curves are loaded and the acoustic time difference logging curves AC, density logging curves DEN, natural gamma logging curves GR, and resistivity logging curves RT of the wells completed in the study area are standardized.

[0045] Furthermore, the specific steps of reconstructing the characteristic curve are as follows:

[0046] Use various logging data and lithology to conduct intersection analysis to obtain sensitivity logging curves and sensitivity values;

[0047] The sensitivity logging curve is fitted to construct a new characteristic curve.

[0048] Furthermore, the sensitivity logging curves are natural gamma logging curves GR and resistivity logging curves RT.

[0049] Furthermore, the sensitivity value range of the natural gamma ray logging curve GR is 0 to 50 API.

[0050] Furthermore, the sensitive value range of the resistivity logging curve RT is 200 to 5000 Ω·m.

[0051] Furthermore, the formula of the characteristic curve is as follows:

[0052]

[0053] Among them, ReC is the characteristic curve.

[0054] Furthermore, the specific steps of setting the termination frequency condition, performing seismic waveform simulation inversion, and predicting the distribution range and thickness of the natural alkali ore body are as follows:

[0055] Analyze the waveform set distribution characteristics corresponding to different types of waveform structures, and establish the Bayesian inversion framework of different types of seismic phases;

[0056] Under different Bayesian inversion frameworks, the common parts of the waveform set are selected as the initial model for iterative inversion; the frequency parameters are adjusted, the termination frequency conditions are determined, and the model is continuously revised and optimized to obtain the simulation results with the highest correlation with the control waveform, which are the seismic waveform simulation inversion results.

[0057] Furthermore, the spatial morphology of the natural alkali ore body is described based on the seismic waveform simulation inversion results, and the exploration project spacing is determined in combination with the comprehensive evaluation of the geological conditions of the ore deposit, and the first mining area is circled, including the following steps:

[0058] Determine the type of ore body exploration in the study area;

[0059] Based on the results of seismic waveform simulation inversion, three-dimensional encrypted interpretation is carried out to depict the structural morphology of the ore body boundary, the top boundary of the ore body and the top boundary of each internal layer, and the top boundary structural map of each ore layer and the ore layer thickness map are compiled;

[0060] Determine the spacing of exploration projects according to the type of ore body exploration, geological characteristics and distribution range;

[0061] Based on the spacing between exploration projects, the thickness of the alkali ore layer, the burial depth of the alkali ore layer, and the industrial quality of the alkali ore layer, the first mining area was circled in an area with a flat structure and undeveloped faults.

[0062] Furthermore, the distance between the survey projects ranges from 1.5 to 2.5 km.

[0063] The present invention also discloses an earthquake prediction system for natural alkali mine exploration, comprising:

[0064] The sensitivity logging curve determination unit is used to identify the natural alkali development layer and single layer thickness and determine the natural alkali sensitivity logging curve through the well logging lithology interpretation combined with the logging and core data;

[0065] Create units for applying well-seismic combined interpretation technology, establish isochronous stratigraphic framework, track mineralized horizons, determine top and bottom structural features of alkaline mineralized horizons and seismic facies characteristics of ore bodies;

[0066] The simulation inversion unit is used to create a stratigraphically constrained structural model based on the structural features of the top and bottom boundaries of the alkali-bearing strata, select the sensitivity logging curve to reconstruct the characteristic curve as the control waveform, perform seismic waveform simulation inversion, and predict the distribution range and ore thickness of the natural alkali ore body;

[0067] The first mining area determination unit is used to characterize the spatial morphology of the natural alkali ore body based on the seismic waveform simulation inversion results, and to determine the spacing of exploration projects and circle out the first mining area in combination with the comprehensive evaluation of the geological conditions of the ore deposit.

[0068] Furthermore, the sensitivity logging curve determination unit is specifically used to:

[0069] Processing of well logging data;

[0070] Plotting the processed logging data into a logging curve and preprocessing the logging curve;

[0071] The secondary interpretation of lithology is carried out based on the preprocessed logging curves.

[0072] Furthermore, the creation unit is specifically used to:

[0073] Loading logging curves;

[0074] Analyze the main frequency of seismic data;

[0075] Extract seismic wavelets from the seismic traces near the well, adjust the time-depth relationship synchronously, and obtain the wavelets;

[0076] In the process of synthetic record calibration, based on the marker layer, the matching relationship between synthetic seismic records and well-side seismic traces is continuously corrected by stretching and compressing, and the correlation between synthetic seismic records and seismic data is synchronously verified;

[0077] Analyze the contribution of sub-waves and find the contribution of seismic amplitude changes caused by different lithologies or fluids on logging to seismic reflection, laying the foundation for layer calibration and attribute time window selection;

[0078] After completing the well-seismic horizon calibration, analyze the seismic reflection structure and wave group characteristics, combine the attribute profile to interpret the horizon, and determine the top and bottom structural characteristics of the alkali-bearing strata;

[0079] The conventional profiles are combined with the attribute profiles to identify the profile faults, and the coherence volume slicing technology is used to interpret the fault plane to obtain the detailed seismic data interpretation results.

[0080] Compared with the prior art, the present invention has the following beneficial effects:

[0081] 1) The present invention combines the characteristics of natural alkali ore mineralization and applies the seismic prediction technology of oil and gas exploration to the exploration of natural alkali ore for the first time, thereby improving the exploration efficiency and drilling success rate and reducing the exploration cost. In the Naiman area, this method was applied to deploy and implement 3 exploration wells, all of which encountered alkali ore, with a success rate of up to 100%, which is 50% higher than the previous success rate of 50%;

[0082] 2) In terms of seismic inversion prediction, the present invention uses the key technology of combining characteristic curve reconstruction with seismic waveform simulation inversion to perform iterative inversion in view of the characteristics of the thin single layer thickness of the natural alkali ore layer and the interlayer development with mudstone. The inversion result uses the change of the seismic waveform in the horizontal direction to distinguish the range of lithology changes; it is consistent with the high-frequency information of well logging in the vertical direction, has a high vertical resolution, can meet the requirements of describing the spatial morphology of the ore body, and greatly improves the accuracy of the inversion. In addition, the present invention is not affected by the number of wells and the distribution of well locations, which makes up for the lack of well data in the early stage of exploration and expands the scope of application. With the improvement of the well control level in the later stage, the prediction results will be more precise and accurate, and can also provide a basis for the deployment of well networks in the detailed investigation stage.

[0083] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0085] Figure 1 A flow chart showing an earthquake prediction method for natural alkali mine exploration according to an embodiment of the present invention is shown;

[0086] Figure 2 A schematic diagram of the reconstruction of the characteristic curve of the N10 well according to an embodiment of the present invention is shown;

[0087] Figure 3 An inversion cross-section through line 1520 of the N10 well according to an embodiment of the present invention is shown;

[0088] Figure 4 A seismic waveform simulation inversion prediction plane diagram according to an embodiment of the present invention is shown;

[0089] Figure 5 A thickness map of alkali-bearing strata according to an embodiment of the present invention is shown;

[0090] Figure 6 An inversion profile through N10-N35 according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0091] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0092] In the field of oil and gas exploration, seismic technology has been widely used as a method of reservoir and fluid detection with high accuracy. The present invention creatively applies the theoretical understanding and accumulated data formed in the oil and gas exploration process to the exploration of alkali mines, starting from the distribution characteristics of the strata hosting the ore body, and using three-dimensional seismic prediction technology to depict the spatial morphology of the natural alkali ore body, thereby realizing the prediction of the distribution range of the natural alkali ore body.

[0093] The purpose of the present invention is to provide a technical method for predicting the spatial distribution characteristics of natural alkali ore bodies based on geophysical technology. The present invention uses seismic data, drilling and logging data accumulated in oil and gas exploration, and uses seismic inversion prediction methods to accurately describe the distribution of alkali ore bodies, so as to change the limitations of traditional methods that rely on well network extrapolation for exploration, or only describe alkali ore from a certain aspect, thereby achieving the purpose of improving exploration accuracy, shortening exploration cycle, and reducing exploration costs.

[0094] Figure 1 FIG. 2 is a flow chart showing a method for earthquake prediction in natural alkali mine exploration according to an embodiment of the present invention. Figure 1 As shown, the earthquake prediction method for natural alkali mine exploration proposed by the present invention comprises the following steps:

[0095] Step S1: interpret the lithology of the completed well by combining logging lithology interpretation with cuttings logging and core data, identify the natural alkali development layer and single layer thickness, establish the corresponding relationship between lithology and curve combination characteristics, and determine the natural alkali sensitivity logging curve;

[0096] 1. Process the logging data;

[0097] The logging data are processed and corrected using the drilling coring, logging and core analysis test data to eliminate abnormal values ​​in the logging data caused by engineering reasons.

[0098] Among them, the processing and correction of logging data are specifically as follows:

[0099] During the field logging operation, the influence of non-stratum environment and measurement factors will make it difficult to achieve the consistency of depth between the logging curves, and the values ​​of each logging curve will be affected. Therefore, it is necessary to carry out logging data preprocessing and correction, mainly including analog curve digitization, logging curve depth correction and environmental correction. The specific method is to digitize the analog logging curve, convert it into the corresponding logging data and use the computer for data processing. After the curve is digitized, the drawing program is run to replay the curve and compare it with the original curve before digitization to check and proofread the quality of the digitized curve; and there are generally two methods for depth correction. One is to use the natural gamma curve as the depth control curve. Each time the well is logged, a natural gamma curve is measured. With this curve as the benchmark, the depth of each measured curve is aligned. The other method is to use the correlation comparison method. There is a correlation between the curves of the same well. The curve with high resolution, obvious feature marks and good quality is selected as the benchmark curve. The depth movement is implemented manually or by computer software to achieve the purpose of depth correction.

[0100] During the measurement process, downhole logging instruments are usually required to be located in the center of the wellbore and close to the wellbore wall for measurement. Irregular wellbores and the placement of logging instruments will cause the instrument measurement position to not meet the requirements, resulting in abnormal measurement values ​​during logging construction. In addition, the logging speeds of various logging methods are not the same, and the maximum logging speed of the slowest measurement method needs to be considered. Poor control of the logging speed during logging construction will also cause abnormal logging data.

[0101] 2. The processed logging data is drawn into a logging curve, and the logging curve is preprocessed;

[0102] 2.1 Depth alignment: Draw the processed logging data into logging curves so that each logging curve at each depth is classified as a unified sampling point data;

[0103] 2.2 Correct the inclined well curve to a vertical well curve;

[0104] 2.3 Smoothing of logging curves: Smoothing outliers or minor changes not worth considering that are not caused by formation reasons;

[0105] 2.4 Environmental correction: Eliminate the influence of surrounding rock and mud intrusion within the detection range of the logging instrument to obtain the true value of the formation;

[0106] 2.5. Perform numerical standardization on the logging curve to eliminate systematic errors. Among them, numerical standardization is specifically affected by factors such as wellbore conditions, logging series, instrument scale, measurement time and different operating personnel, which make each logging curve have systematic errors. Therefore, it is necessary to standardize the logging curve when conducting lithological interpretation. Common methods include histogram method and trend surface method.

[0107] 3. Perform secondary interpretation of lithology based on the preprocessed logging curves.

[0108] 3.1 Using the core thin section identification results of the alkali-bearing strata and the surrounding rocks without ore at the top and bottom and the combined characteristics of different series of well logging curves, the corresponding relationship between the two is established;

[0109] 3.2 Using core calibration logging, extract the logging curve values ​​of the same type of lithology in geology and establish the intersection diagram between different curve types;

[0110] 3.3 Select the curves with concentrated distribution of the same type of lithology and obvious distinction from other types of lithology as the sensitivity logging curve for identifying lithology and establish the lithology identification chart;

[0111] 3.4 Perform lithology assignment and secondary lithology interpretation based on the lithology identification plate to identify the natural alkali development layer and single layer thickness.

[0112] Step S2: Apply well-seismic combined interpretation technology to establish an isochronous stratigraphic framework, track the ore-bearing strata, and determine the structural characteristics of the top and bottom boundaries of the alkali-bearing strata and the seismic phase characteristics of the ore body;

[0113] The formation of natural alkali mines is closely related to the structural development of the region, and it is necessary to implement the structural characteristics of the top and bottom boundaries of the natural alkali ore body through detailed structural interpretation. At the same time, the inversion method involved in the present invention needs to use the three-dimensional seismic horizon of the study area as the model framework to create a structural model, so detailed structural interpretation is one of the basic works of the present invention. Detailed structural interpretation includes the following steps:

[0114] 1. Logging curve loading (AC, DEN);

[0115] 2. Analysis of main frequency of seismic data;

[0116] 3. Extract seismic wavelets from the seismic traces near the well, adjust the time-depth relationship synchronously, and obtain the wavelets with the optimal shape;

[0117] 4. Wave group matching: In the process of synthetic record calibration, the characteristic waveform is fully considered, based on the marker layer, the appropriate range of stretching and compression (without changing the original depth domain curve) is used to continuously correct the matching relationship between the synthetic seismic record and the wellside seismic trace;

[0118] 5. Verify the correlation between synthetic seismic records and seismic data; in which, in accordance with geological laws, the correlation coefficient between synthetic seismic records and seismic data must be at least greater than 60%. This verification process is carried out simultaneously with the calibration of synthetic records, and can be achieved by general seismic interpretation software.

[0119] 6. Analyze the contribution of sub-waves and find the contribution of seismic amplitude changes caused by different lithologies or fluids on logging to seismic reflection, laying the foundation for layer calibration and attribute time window selection;

[0120] 7. After completing the well-seismic horizon calibration, analyze the seismic reflection structure and wave group characteristics, use profiles, horizontal slices, and layer slices to interpret the horizon and determine the top and bottom structural characteristics of the ore-bearing horizon;

[0121] 8. Use conventional sections and attribute sections to identify section faults, and use coherence volume slicing and other methods to interpret the fault plane, so as to obtain detailed seismic data interpretation results.

[0122] Step S3: Based on the structural features of the top and bottom boundaries of the alkali-bearing strata, a stratum-constrained structural model is created, and the characteristic curve reconstructed by the sensitivity logging curve is selected as the control waveform for constraint. The seismic waveform difference caused by the lithology change is used to perform seismic waveform simulation inversion to predict the distribution range and ore layer thickness of the natural alkali ore body;

[0123] Seismic inversion is a relatively mature reservoir or fluid prediction technology in oil exploration, but it is the first time that it has been applied in the exploration of natural soda ore. Before conducting inversion prediction in a study area, it is necessary to analyze the geological characteristics, lithological characteristics and well data of the area and select the most suitable inversion technology.

[0124] The main lithology of natural alkali ore belongs to evaporite. Core analysis test revealed that its mineral composition is mainly natrona, and the non-ore layer between the ore layers is mainly dolomitic mudstone (the mineral composition is feldspar, natrona, natrona, clay minerals). The alkali ore layer and non-ore layer of the mineralization belt are interlayered vertically, and the single layer thickness of the ore body is relatively thin. According to the drilling exposure, the single layer thickness of natural alkali is between 1 and 12m, and the inversion resolution requirement is high. Through model comparison, the seismic waveform simulation inversion method can break through the limitation of seismic inversion resolution, and its ability to identify thin layers is strong. The present invention uses the optimized seismic waveform simulation inversion method to predict and finely describe the alkali ore body.

[0125] The specific process includes the following steps:

[0126] 1. Standardization of logging curves: Load the logging curves, and standardize the acoustic time difference logging curves AC, density logging curves DEN, natural gamma logging curves GR, resistivity logging curves RT and other logging curves of the wells in the study area to eliminate the systematic errors and random errors caused by manual operation and instruments, so as to make them conform to geological laws. The specific method of standardization is to set standard wells and standard layers, refer to the logging curve values ​​of the standard layers, and unify the curve value ranges of other wells into the range of the standard well value through calculation.

[0127] 2. Reconstruction of characteristic curves: The sensitive parameters of the alkali ore layer need to find the logging curve that best reflects the characteristics of the ore layer from all the logging curves. It is mainly to use various logging data and lithology for intersection analysis to find sensitive logging curves and their sensitive values. Through the intersection of multiple groups of curves, it is found that the GR and RT curves are most sensitive to the alkali ore layer section. The alkali ore layer section has the characteristics of high RT (200-5000Ω·m) and low GR (0-50API) on the logging curve. However, other rock types developed in the area, such as oil-bearing sandstone and conglomerate, have high RT (50-6000Ω·m) characteristics, and basalt has low GR (25-60API) characteristics. The logging value ranges of these types of rocks overlap greatly with the value range of the alkali ore layer section. If only one curve is used for inversion, the result has multiple solutions and cannot accurately characterize the alkali ore layer section. Therefore, GR and RT are refitted to construct a new characteristic curve (ReC) for simulation.

[0128] The formula for the characteristic curve is as follows:

[0129]

[0130] Among them, ReC is the characteristic curve.

[0131] from Figure 2 It can be seen that the high-value segment of the reconstructed characteristic curve has a high degree of consistency with the alkali ore, indicating that this characteristic curve is sensitive to the alkali ore layer segment, so this characteristic curve is selected for simulation inversion.

[0132] 3. Load seismic layers and create a structural model: The key to building a structural model and model interpolation is accurate layer interpretation and detailed time-depth relationship. Set the stratum contact relationship based on seismic reflection characteristics and sedimentary laws and calculate the structural model.

[0133] 4. Optimize the control waveform and quantity: Through matrix orthogonal decomposition, realize dynamic clustering analysis of seismic waveforms near the control point well, establish the correspondence between the seismic waveform structure and the logging curve structure, generate a set of logging curve waveforms representing different types of seismic phases, and optimize the control waveform through comparison and evaluation.

[0134] 4.1 Determine the correlation coefficient of the seismic traces near the well by extracting and comparing the seismic waveforms of the wellbore seismic traces. It should be noted that similar lithology combinations generally have similar seismic waveform characteristics, which is the basis of waveform simulation inversion.

[0135] 4.2 Similarity analysis of control well characteristic curve: The logging curve has a very high frequency. Through frequency reduction and filtering, the similarity between the characteristic curve and the seismic trace near the well is improved. When the similarity is close to the correlation coefficient of the seismic trace near the well, the connection between the seismic waveform and the logging high-frequency information is established.

[0136] 4.3 Compare and evaluate the seismic waveform of the prediction point and the waveform of the control point, sort the similarity of the two waveforms, and select the control waveform; then fit all characteristic curves, pick up the value corresponding to the inflection point position (not greater than the number of wells in the waveform set), determine the value of the control waveform, and then increase or decrease it appropriately based on the geological conditions of the study area.

[0137] 5. Set the end frequency condition, perform seismic waveform simulation inversion, and predict the distribution range and thickness of the natural alkali ore body

[0138] 5.1 Analyze the distribution characteristics of waveform sets corresponding to different types of waveform structures, and establish the Bayesian inversion framework of different types of seismic phases.

[0139] 5.2 Under different Bayesian inversion frameworks, the common parts of the waveform set are selected as the initial model for iterative inversion; the frequency parameters are adjusted, and the optimal frequency is used as the termination condition to continuously revise and optimize the model to obtain the simulation result with the highest correlation with the control waveform, which is the final seismic waveform simulation inversion result. Among them, the initial model is to compare and optimize the seismic waveform of the point to be predicted with the waveform set, and find the value corresponding to the seismic waveform with the highest similarity. This value is the simulation result of the point to be predicted, and the set of simulation results of all points to be predicted is the initial model. Specifically, it refers to the original model that has not undergone multiple iterations, smoothing and other optimization processes.

[0140] The optimal frequency is determined by the frequency range of the well logging curve with the greatest similarity obtained through the analysis in 4.2 and the frequency range that can meet the resolution requirements of the target layer. Its value is generally not less than the frequency of the characteristic curve after frequency reduction. In addition, when the number of control wells in the study area is small, the weight of the seismic data can be appropriately increased. The weight setting can be adjusted by referring to the degree of conformity between the scale of the ore body identified by seismic attributes and the simulation results.

[0141] In terms of seismic inversion prediction, the present invention uses the key technology of combining characteristic curve reconstruction with seismic waveform simulation inversion to perform iterative inversion in view of the characteristics of the thin single-layer thickness of the natural alkali ore layer and the interlayer development with mudstone. The inversion result follows the change of the seismic waveform in the horizontal direction and can distinguish the range of lithology changes. It is consistent with the high-frequency information of well logging in the vertical direction, has a high vertical resolution, can meet the requirements of describing the spatial morphology of the ore body, and greatly improves the accuracy of inversion. In addition, the present invention is not affected by the number of wells and the distribution of well locations, makes up for the lack of well data in the early stage of exploration, and expands the scope of application. With the improvement of the well control degree in the later stage, the prediction result will be more precise and accurate, and can also provide a basis for the deployment of the well network in the detailed investigation stage.

[0142] Step S4: Based on the results of seismic waveform simulation inversion, the spatial morphology of the natural alkali ore body (ore layer) is characterized, and combined with the comprehensive evaluation of the geological conditions of the ore deposit, the exploration project spacing is determined, and the first mining area is circled to provide a basis for effective and reasonable development.

[0143] The purpose of geological exploration is to find out in detail the basic characteristics of the ore-bearing layers of the alkali deposits in the study area (occurrence, scale and distribution of the alkali ore layers, ore combination types of ore-bearing rocks), ore characteristics (mineral composition, chemical composition), surrounding rock and intercalated rock lithology, and to gain an in-depth understanding of the geological characteristics of the deposits. For the paragenetic and associated minerals with industrial utilization value, their types, contents, and occurrence characteristics are preliminarily identified, and the possibility of comprehensive utilization is studied by analogy.

[0144] The specific steps are as follows:

[0145] 1. Determine the type of ore body exploration in the study area. By analogy with similar ore deposits, determine the type of ore body based on the extension scale, structural complexity, stability of ore body distribution, and development of salt dissolution in the study area.

[0146] 2. Describe the spatial distribution characteristics of the ore body. Perform a three-dimensional encrypted interpretation of the above inversion results, carefully depict the ore body boundary, the top boundary of the ore body, and the top boundary structure of each layer, and compile the top boundary structure map of each ore layer and the ore layer thickness map (such as Figure 3 , as shown in 4).

[0147] 3. Design the spacing between exploration projects. Subsequent exploration work should be considered when arranging exploration projects. Design the basic exploration project spacing based on the exploration type, geological characteristics and distribution range of the ore body; the project spacing in different parts of the same ore body can be different, the project spacing can be appropriately enlarged along the direction where the ore body structure is relatively gentle, and a denser project spacing can be used along the direction where the ore body changes greatly. Among them, the range of exploration project spacing is 1.5 to 2.5 km. Preferably, the basic exploration project spacing is 2.0 km.

[0148] 4. Delineate the first mining area: The selection of the first mining area for exploration should comprehensively consider the distance between exploration projects, the distribution characteristics of the alkali ore layer and the development of faults. Through comprehensive evaluation of the thickness, burial depth, industrial quality, etc. of the alkali ore layer, delineate the first mining area in areas with gentle structures and undeveloped faults, to provide a basis for later efficient mining.

[0149] It should be noted that the exploration of ore bodies in oil and gas basins must be carried out without destroying the existing oil and gas reservoirs. The use of the present invention can meet this condition and can carry out mineral exploration work more efficiently and reasonably.

[0150] The present invention combines the characteristics of natural alkali ore mineralization and applies the seismic prediction technology of oil and gas exploration to the exploration of natural alkali ore for the first time, thereby improving the exploration efficiency and reducing the exploration cost. In the Naiman area, this method was applied to deploy and implement 3 exploration wells, all of which encountered alkali ore, with a success rate of up to 100%, which is 50% higher than the previous success rate of 50%.

[0151] The seismic prediction method for natural alkali mine exploration of the present invention is applied to the exploration of a mineral deposit in Naiman Sag. In this area, oil and gas and natural alkali are in a symbiotic state, the exploration level is low, and few wells are drilled. The success rate of the traditional method of deployment based on the well network is low. The application of the present invention improves the drilling success rate.

[0152] Step 1: Through the lithological interpretation of the completed well logging and the core analysis test data, the vertical development layer and single layer thickness of the natural alkali are identified, and the sensitivity logging curves of the mineralized layer are determined as GR and RT;

[0153] Step 2: Through the application of well-seismic combined technology, identify the alkali-bearing strata, establish an isochronous stratigraphic framework, complete the structural interpretation of 3D seismic data, and determine the top and bottom structural morphologies of the alkali-bearing strata.

[0154] Since the number of wells drilled in the mineralization belt in the study area is relatively small, the seismic data in the study area and the surrounding well data are fully utilized to interpret the stratigraphic and fault positions.

[0155] Step 3: Input the seismic horizon into the inversion software to establish a structural model; use the logging curve standardization module of the inversion software to calibrate the AC, DEN, GR, RT and other logging curves of the completed well; then use the AC and DEN curves to perform fine synthetic record calibration; then select the logging curves GR and RT that are sensitive to natural alkali to reconstruct the characteristic curves, perform seismic waveform simulation inversion, and predict the distribution range and thickness of the ore body.

[0156] Key parameter settings for this inversion:

[0157] (1) According to the geological conditions and existing data of the study area, the control waveform value is selected as 5;

[0158] (2) Through frequency reduction filtering, when the frequency range of the logging curve is between 100 and 200 Hz, its similarity is closest to that of the seismic waveform;

[0159] (3) Taking into account the frequency range of the above logging curve and the single layer thickness of the target layer, the end frequency is set to 220 Hz.

[0160] Step 4: Use the inversion results to characterize the three-dimensional spatial morphology of the natural alkali ore body and confirm the mineralization area of ​​32km 2, with a maximum thickness of 200m; the ore body in the study area has a large extension scale, stable distribution, relatively simple structure, and undeveloped salt dissolution. It belongs to Class I ore body, and the basic exploration project spacing is determined to be 2km. Due to the steep occurrence of the ore layer in the northern part of the study area, the exploration project spacing is shortened to 1.5km. A comprehensive evaluation is conducted based on the ore body exploration type and the structural characteristics of the ore-bearing layer, and the first mining area of ​​3.9km is delineated in the middle of the ore body. 2 (like Figure 5 shown).

[0161] This example successfully applied a natural alkali seismic exploration method to guide the exploration of a mineral deposit in the Naiman area. Three exploration wells, such as N35, were deployed in the area. Figure 6 As shown in the figure, all the explorations were successful, with a success rate of 100%, which reduced the exploration costs and saved a lot of money.

[0162] Based on the above-mentioned earthquake prediction method for natural alkali mine exploration, the present invention also provides an earthquake prediction system for natural alkali mine exploration, comprising:

[0163] The sensitivity logging curve determination unit is used to identify the natural alkali development layer and single layer thickness and determine the natural alkali sensitivity logging curve through the well logging lithology interpretation combined with the logging and core data;

[0164] Create units for applying well-seismic combined interpretation technology, establish isochronous stratigraphic framework, track mineralized horizons, determine top and bottom structural features of alkaline mineralized horizons and seismic facies characteristics of ore bodies;

[0165] The simulation inversion unit is used to create a stratigraphically constrained structural model based on the structural features of the top and bottom boundaries of the alkali-bearing strata, select the sensitivity logging curve to reconstruct the characteristic curve as the control waveform, perform seismic waveform simulation inversion, and predict the distribution range and ore thickness of the natural alkali ore body;

[0166] The first mining area determination unit is used to characterize the spatial morphology of the natural alkali ore body based on the seismic waveform simulation inversion results, and to determine the spacing of exploration projects and circle out the first mining area in combination with the comprehensive evaluation of the geological conditions of the ore deposit.

[0167] In some embodiments, the sensitivity logging curve determination unit is specifically used to:

[0168] Processing of well logging data;

[0169] Plotting the processed logging data into a logging curve and preprocessing the logging curve;

[0170] The secondary interpretation of lithology is carried out based on the preprocessed logging curves.

[0171] In some embodiments, the creating unit is specifically used to:

[0172] Loading logging curves;

[0173] Analyze the main frequency of seismic data;

[0174] Extract seismic wavelets from the seismic traces near the well, adjust the time-depth relationship synchronously, and obtain the wavelets;

[0175] In the process of synthetic record calibration, based on the marker layer, the matching relationship between synthetic seismic records and well-side seismic traces is continuously corrected by stretching and compressing, and the correlation between synthetic seismic records and seismic data is synchronously verified;

[0176] Analyze the contribution of sub-waves and find the contribution of seismic amplitude changes caused by different lithologies or fluids on logging to seismic reflection, laying the foundation for layer calibration and attribute time window selection;

[0177] After completing the well-seismic horizon calibration, analyze the seismic reflection structure and wave group characteristics, combine the attribute profile to interpret the horizon, and determine the top and bottom structural characteristics of the alkali-bearing strata;

[0178] The conventional profiles are combined with the attribute profiles to identify the profile faults, and the coherence volume slicing technology is used to interpret the fault plane to obtain the detailed seismic data interpretation results.

[0179] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for earthquake prediction in natural alkali mine exploration, characterized in that: The following steps are involved: Through the combination of logging lithology interpretation and logging and core data, the natural alkali development interval and single layer thickness are identified, and the natural alkali sensitivity logging curve is determined; Apply well-seismic combined interpretation technology to establish an isochronous stratigraphic framework, trace the ore-bearing strata, and determine the structural characteristics of the top and bottom boundaries of the alkali-bearing strata and the seismic phase characteristics of the ore bodies; Based on the structural characteristics of the top and bottom boundaries of the alkali-bearing strata, a stratum-constrained structural model is created, and the characteristic curve reconstructed by the sensitivity logging curve is selected as the control waveform to perform seismic waveform simulation inversion to predict the distribution range and ore thickness of the natural alkali ore body; Based on the results of seismic waveform simulation inversion, the spatial morphology of the natural alkali ore body is depicted, and combined with a comprehensive evaluation of the geological conditions of the deposit, the spacing of exploration projects is determined and the first mining area is circled.

2. The earthquake prediction method for natural alkali mine exploration according to claim 1, characterized in that: The method of identifying the natural alkali development interval and single layer thickness by combining logging lithology interpretation with logging and core data, and determining the natural alkali sensitivity logging curve comprises the following steps: Processing of well logging data; Plotting the processed logging data into a logging curve and preprocessing the logging curve; The secondary interpretation of lithology is carried out based on the preprocessed logging curves.

3. The earthquake prediction method for natural alkali mine exploration according to claim 2, characterized in that: The specific steps of processing the logging data are as follows: The logging data are processed and corrected using the drilling coring, logging and core analysis test data to eliminate abnormal values ​​in the logging data.

4. The earthquake prediction method for natural alkali mine exploration according to claim 2, characterized in that: The specific steps of drawing the processed logging data into a logging curve and preprocessing the logging curve are as follows: The processed logging data are plotted into logging curves, and each logging curve at each depth is classified as unified sampling point data; Correct the inclined well curve into a vertical well curve; Smoothing the logging curve; Eliminate the influence of surrounding rock and mud intrusion within the detection range of logging instruments and obtain the true value of the formation; The logging curves are numerically standardized to eliminate systematic errors.

5. The earthquake prediction method for natural alkali mine exploration according to claim 2, characterized in that: The specific steps of performing secondary lithology interpretation based on the pre-processed logging curve are as follows: Using the core thin section identification results and the combined characteristics of the well logging curve, the corresponding relationship between the two is established; Using core calibration logging, the logging curve values ​​of the same type of lithology are extracted and the intersection diagrams between different curve types are established; Select the curves with concentrated distribution of the same type of lithology as the sensitivity logging curve for identifying lithology, and establish a lithology identification chart; According to the lithology identification plate, lithology is assigned and secondary lithology interpretation is carried out to identify the natural alkali development layer and single layer thickness.

6. The earthquake prediction method for natural alkali mine exploration according to claim 1, characterized in that: The application of well-seismic combined interpretation technology to establish an isochronous stratigraphic framework, track the ore-bearing strata, and determine the top and bottom structural features of the alkali-bearing strata and the seismic phase characteristics of the ore body includes the following steps: Loading logging curves; Analyze the main frequency of seismic data; Extract seismic wavelets from the seismic traces near the well, and synchronously adjust the time-depth relationship to obtain the wavelets; In the process of synthetic record calibration, based on the marker layer, the matching relationship between synthetic seismic records and well-side seismic traces is continuously corrected by stretching and compressing, and the correlation between synthetic seismic records and seismic data is synchronously verified; Analyze the contribution of sub-waves and find the contribution of seismic amplitude changes caused by different lithologies or fluids on logging to seismic reflection, laying the foundation for layer calibration and attribute time window selection; After completing the well-seismic horizon calibration, analyze the seismic reflection structure and wave group characteristics, combine the attribute profile to interpret the horizon, and determine the top and bottom structural characteristics of the alkali-bearing strata; The conventional profiles are combined with the attribute profiles to identify the profile faults, and the coherence volume slicing technology is used to interpret the fault plane to obtain the detailed seismic data interpretation results.

7. The earthquake prediction method for natural alkali mine exploration according to claim 6, characterized in that: The stratigraphic interpretation is determined by section, horizontal slicing and along-layer slicing techniques.

8. The earthquake prediction method for natural alkali mine exploration according to claim 1, characterized in that: The method of creating a structure model constrained by the horizon based on the top and bottom structural features of the alkali-bearing horizon, selecting a sensitivity logging curve to reconstruct a characteristic curve as a control waveform, performing seismic waveform simulation inversion, and predicting the distribution range and thickness of the natural alkali ore body comprises the following steps: Standardize the well logging curves; Reconstruct characteristic curves; Load seismic horizons and create a structural model; Select the control waveform and determine the number of control waveforms; The termination frequency condition is set to perform seismic waveform simulation inversion to predict the distribution range and thickness of the natural alkali ore body.

9. The earthquake prediction method for natural alkali mine exploration according to claim 8, characterized in that: The specific steps of standardizing the well logging curve are as follows: The logging curves are loaded and the acoustic time difference logging curves AC, density logging curves DEN, natural gamma logging curves GR, and resistivity logging curves RT of the wells completed in the study area are standardized.

10. The earthquake prediction method for natural alkali mine exploration according to claim 8, characterized in that: The specific steps of reconstructing the characteristic curve are as follows: Use various logging data and lithology to conduct intersection analysis to obtain sensitivity logging curves and sensitivity values; The sensitivity logging curve is fitted to construct a new characteristic curve.

11. The earthquake prediction method for natural alkali mine exploration according to claim 10, characterized in that: The sensitivity logging curves are the natural gamma logging curve GR and the resistivity logging curve RT.

12. The earthquake prediction method for natural alkali mine exploration according to claim 11, characterized in that: The sensitivity value range of the natural gamma ray logging curve GR is 0 to 50 API.

13. The earthquake prediction method for natural alkali mine exploration according to claim 11, characterized in that: The sensitive value range of the resistivity logging curve RT is 200 to 5000 Ω·m.

14. The earthquake prediction method for natural alkali mine exploration according to claim 10 or 11, characterized in that: The formula of the characteristic curve is as follows: Among them, ReC is the characteristic curve.

15. The earthquake prediction method for natural alkali mine exploration according to claim 8, characterized in that: The specific steps of setting the termination frequency condition, performing seismic waveform simulation inversion, and predicting the distribution range and thickness of the natural alkali ore body are as follows: Analyze the waveform set distribution characteristics corresponding to different types of waveform structures, and establish the Bayesian inversion framework of different types of seismic phases; Under different Bayesian inversion frameworks, the common parts of the waveform set are selected as the initial model for iterative inversion; Adjust the frequency parameters, determine the termination frequency conditions, continuously revise and optimize the model, and obtain the simulation result with the highest correlation with the control waveform, which is the seismic waveform simulation inversion result.

16. The earthquake prediction method for natural alkali mine exploration according to claim 1, characterized in that: The method of describing the spatial morphology of the natural alkali ore body based on the seismic waveform simulation inversion results, and determining the exploration project spacing and delineating the first mining area in combination with the comprehensive evaluation of the geological conditions of the ore deposit includes the following steps: Determine the type of ore body exploration in the study area; Based on the results of seismic waveform simulation inversion, three-dimensional encrypted interpretation is carried out to depict the structural morphology of the ore body boundary, the top boundary of the ore body and the top boundary of each internal layer, and the top boundary structural map of each ore layer and the ore layer thickness map are compiled; Determine the spacing of exploration projects according to the type of ore body exploration, geological characteristics and distribution range; Based on the spacing between exploration projects, the thickness of the alkali ore layer, the burial depth of the alkali ore layer, and the industrial quality of the alkali ore layer, the first mining area was circled in an area with a flat structure and undeveloped faults.

17. The earthquake prediction method for natural alkali mine exploration according to claim 16, characterized in that: The distance between the survey projects ranges from 1.5 to 2.5 km.

18. An earthquake prediction system for natural alkali mine exploration, characterized in that: include: The sensitivity logging curve determination unit is used to identify the natural alkali development layer and single layer thickness and determine the natural alkali sensitivity logging curve through the well logging lithology interpretation combined with the logging and core data; Create units for applying well-seismic combined interpretation technology, establish isochronous stratigraphic framework, track mineralized horizons, determine top and bottom structural features of alkaline mineralized horizons and seismic facies characteristics of ore bodies; The simulation inversion unit is used to create a stratigraphically constrained structural model based on the structural features of the top and bottom boundaries of the alkali-bearing strata, select the sensitivity logging curve to reconstruct the characteristic curve as the control waveform, perform seismic waveform simulation inversion, and predict the distribution range and ore thickness of the natural alkali ore body; The first mining area determination unit is used to characterize the spatial morphology of the natural alkali ore body based on the seismic waveform simulation inversion results, and to determine the spacing of exploration projects and circle out the first mining area in combination with the comprehensive evaluation of the geological conditions of the ore deposit.

19. The earthquake prediction system for natural alkali mine exploration according to claim 18, characterized in that: The sensitivity logging curve determination unit is specifically used for: Processing of well logging data; Plotting the processed logging data into a logging curve and preprocessing the logging curve; The secondary interpretation of lithology is carried out based on the preprocessed logging curves.

20. The earthquake prediction system for natural alkali mine exploration according to claim 18, characterized in that: The creation unit is specifically used for: Loading logging curves; Analyze the main frequency of seismic data; Extract seismic wavelets from the seismic traces near the well, and synchronously adjust the time-depth relationship to obtain the wavelets; In the process of synthetic record calibration, based on the marker layer, the matching relationship between synthetic seismic records and well-side seismic traces is continuously corrected by stretching and compressing, and the correlation between synthetic seismic records and seismic data is synchronously verified; Analyze the contribution of sub-waves and find the contribution of seismic amplitude changes caused by different lithologies or fluids on logging to seismic reflection, laying the foundation for layer calibration and attribute time window selection; After completing the well-seismic horizon calibration, analyze the seismic reflection structure and wave group characteristics, combine the attribute profile to interpret the horizon, and determine the top and bottom structural characteristics of the alkali-bearing strata; The conventional profiles are combined with the attribute profiles to identify the profile faults, and the coherence volume slicing technology is used to interpret the fault plane to obtain the detailed seismic data interpretation results.

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