A method and system for seismic prediction of a trona deposit

Abstract

This invention discloses a seismic prediction method and system for natural alkali deposit exploration. The method includes: identifying the natural alkali development intervals and individual layer thicknesses through well logging lithology interpretation combined with well logging and core data, and determining natural alkali sensitivity logging curves; applying well-seismic joint interpretation technology to establish an isochronous stratigraphic framework, tracing the ore-bearing strata, and determining the structural characteristics of the top and bottom boundaries of the alkali-bearing strata and the seismic facies characteristics of the ore body; based on the structural characteristics of the top and bottom boundaries of the alkali-bearing strata, creating a stratigraphically constrained structural model, selecting sensitivity logging curves to reconstruct characteristic curves as control waveforms for seismic waveform simulation inversion, and predicting the distribution range and ore layer thickness of the natural alkali ore body; based on the seismic waveform simulation inversion results, characterizing the spatial morphology of the natural alkali ore body, and combining with a comprehensive evaluation of the deposit's geological conditions to determine the exploration engineering spacing and delineate the initial mining area. This invention applies seismic prediction technology to natural alkali deposit exploration for the first time, improving exploration efficiency and drilling success rate.

Description

Technical Field

[0001] This invention belongs to the field of earthquake technology, and specifically relates to an earthquake prediction method and system for natural alkali mineral exploration. Background Technology

[0002] Natural soda ash, with the chemical formula Na₂CO₃·NaHCO₃·2H₂O, also known as sesquialkali stone or sodium hydrocarbon stone, is an evaporative salt mineral. Its main product, soda ash, is widely used in glass, chemical, papermaking, and pharmaceutical industries. In my country, natural soda ash is a scarce resource. Domestic natural soda ash resources are mainly concentrated in the Anpeng and Wucheng soda ash mines in Henan Province, and the Chagan Nuoer, Ordos Alkali Lake, and Tamusu soda ash mines in Inner Mongolia. In 2022, my country's demand for soda ash reached 25.6 million tons, and this demand is likely to increase annually. Currently, chemical methods account for 90% of soda ash production in my country, while the natural soda ash production process is simple, low-cost, energy-efficient, low-emission, and safe, possessing unique environmental and price advantages. However, there are few precedents for exploiting natural soda ash associated with oil and gas in oil and gas basins in China. There is an urgent need for an effective seismic prediction technology to predict the distribution range of natural soda ash deposits, determine the scale of soda ash resources, and comprehensively promote the exploration and exploitation of natural soda ash.

[0003] Currently, seismic exploration methods for natural alkali deposits are relatively outdated, lacking effective technical means to accurately predict the geological structure of deep deposits. Traditional alkali deposit exploration methods mainly rely on well network deployment, lacking accurate methods for predicting the distribution range and thickness of ore bodies, resulting in a relatively low exploration success rate.

[0004] In 2012, *Zhongzhou Coal* published the application of well logging interpretation technology in alkali deposit exploration (Chai Maojia, Wang Chao. Application of well logging interpretation technology in alkali deposit exploration [J]. *Zhongzhou Coal*, 2012(05):33-36.), which used well logging interpretation technology in petroleum exploration to reinterpret the previously interpreted natural alkali deposits; in 2010, *Geological Exploration Forum* published the application of geophysical logging in the exploration of Anpeng natural alkali deposits (Chen Jianli. Application of geophysical logging in the exploration of Anpeng natural alkali deposits [J]. *Geological Exploration Forum*, 2010, 25(03):252-259.). Using various geophysical logging data, electrical boundaries for judging alkali layers and dividing alkali layer thickness, as well as electrical boundaries for removing interlayers, were established, thereby determining the location and thickness of alkali deposits. In 2021, Energy and Environmental Protection published the application of 3D seismic technology in alkali deposit exploration in the Biyang Depression (Chen Yingnan. Application of 3D seismic technology in alkali deposit exploration in the Biyang Depression [J]. Energy and Environmental Protection, 2021, 43(03): 52-58.). By interpreting 3D seismic data, the distribution range of approximately horizontal sedimentary strata in the Biyang Depression was delineated, and the distribution range of alkali-bearing rock layers was determined by using the lithology represented by the seismic reflection phase axis. Chinese Patent (Publication No. CN110118992B) discloses a method for exploring coal resources in fully concealed deep coalfields. Based on electrical resistivity tomography, seismic exploration, and integrated well logging, this invention aims to identify coal seams buried deep in depression basins. Through extensive experimental research and exploration practice, it solves the problem of how to determine the location and thickness of coal seams in fully concealed coalfields with burial depths of 1000–3000 m, and provides a method for exploring deep coal resources.

[0005] The above methods use geophysical well logging or seismic data to identify and describe alkali deposits, but they have the following limitations: ① The basic data they use are mostly drilling and well logging data, which only provide local features of the ore body in the horizontal or vertical direction, rather than its three-dimensional spatial morphology; ② They only use single techniques such as seismic time slices to describe alkali deposits that are distributed in an approximately horizontal manner, and the methods are not very applicable; ③ They require a large amount of exploration data or experimental research support, which results in high exploration costs and long prospecting cycles. Summary of the Invention

[0006] To address the above problems, this invention discloses a seismic prediction method for natural alkali deposit exploration, comprising the following steps:

[0007] By combining well logging lithology interpretation with well logging and core data, the natural alkali development intervals and single layer thicknesses are identified, and the natural alkali sensitivity logging curves are determined.

[0008] By applying well-seismic joint interpretation technology, an isochronous stratigraphic framework is established to trace ore-bearing strata, determine the structural features of the top and bottom boundaries of alkali-bearing strata and the seismic facies features of ore bodies;

[0009] Based on the structural characteristics of the top and bottom boundaries of the alkali-bearing strata, a stratigraphically constrained structural model is created. Sensitive well logging curves are selected to reconstruct characteristic curves as control waveforms. Seismic waveform simulation and inversion are then performed to predict the distribution range and ore layer thickness of the natural alkali ore body.

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

[0011] Furthermore, the process of identifying natural alkali-developed intervals and individual layer thicknesses by combining well logging lithology interpretation with well logging and core data, and determining natural alkali sensitivity logging curves, includes the following steps:

[0012] Process the well logging data;

[0013] The processed logging data is plotted into logging curves, and the logging curves are preprocessed.

[0014] Lithology secondary interpretation is performed based on the pre-processed well logging curves.

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

[0016] The logging data is processed and corrected using drilling core, logging, and core analysis test data to remove outliers.

[0017] Furthermore, the specific steps for plotting the processed logging data into logging curves and preprocessing the logging curves are as follows:

[0018] The processed logging data is plotted into logging curves, and each logging curve at each depth is grouped into a unified sampling point data.

[0019] Correct the deviated well curve to a vertical well curve;

[0020] Smooth the logging curves;

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

[0022] Numerical standardization of the logging curves is performed to eliminate systematic errors.

[0023] Furthermore, the specific steps for secondary lithological interpretation based on the preprocessed well logging curves are as follows:

[0024] By combining the identification results of core thin sections with the characteristics of well logging curves, a correspondence between the two can be established.

[0025] Using core calibration logging, logging curve values ​​of geologically similar lithologies are extracted, and cross-plots between different curve types are established;

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

[0027] Based on the lithological identification chart, lithology is repositioned and secondary interpreted to identify the natural alkali development zones and individual layer thicknesses.

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

[0029] Load well logging curves;

[0030] Analyze the dominant frequency of earthquake data;

[0031] Seismic wavelets are extracted from the seismic traces near the well, and the time-depth relationship is adjusted synchronously to obtain the wavelets;

[0032] During the synthetic record calibration process, based on the marker layer, stretching and compression are used to continuously correct the matching relationship between the synthetic seismic record and the well-side seismic trace, and the correlation between the synthetic seismic record and the seismic data is verified simultaneously.

[0033] Analyzing wavelet contributions helps identify the contribution of seismic amplitude variations caused by different lithologies or fluids in well logging to seismic reflections, laying the foundation for stratigraphic calibration and attribute window selection.

[0034] After completing the well-seismic stratigraphic calibration, the seismic reflection structure and wave group characteristics were analyzed, and the stratigraphic interpretation was carried out in combination with the attribute profile to determine the structural characteristics of the top and bottom boundaries of the alkali-bearing strata.

[0035] By combining conventional and attribute profiles, faults in the seismic data are identified, and coherence slicing techniques are used to interpret the fault plane, resulting in detailed seismic data interpretation.

[0036] Furthermore, the stratigraphic interpretation is determined through cross-sectioning, horizontal slicing, and layer-by-layer slicing techniques.

[0037] Furthermore, the step of creating a stratigraphically constrained structural model based on the top and bottom structural characteristics of the alkali-bearing strata, selecting sensitive well logging curves to reconstruct characteristic curves as control waveforms, and performing seismic waveform simulation inversion to predict the distribution range and ore layer thickness of natural alkali ore bodies includes the following steps:

[0038] Standardize the logging curves;

[0039] Reconstruct the characteristic curve;

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

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

[0042] By setting termination frequency conditions, seismic waveform simulation and inversion are performed to predict the distribution range and ore layer thickness of natural alkali ore bodies.

[0043] Furthermore, the specific steps for standardizing the well logging curves are as follows:

[0044] Load the logging curves and standardize the acoustic transit time logging curves (AC), density logging curves (DEN), natural gamma logging curves (GR), and resistivity logging curves (RT) of the completed wells in the study area.

[0045] Furthermore, the specific steps for reconstructing the feature curve are as follows:

[0046] By performing cross-analysis of various well logging data and lithology, sensitivity logging curves and sensitivity values ​​are obtained;

[0047] A new characteristic curve is constructed by fitting the sensitivity logging curve.

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

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

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

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

[0052]

[0053] ReC is the characteristic curve.

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

[0055] Analyze the distribution characteristics of waveform sets corresponding to different types of waveform structures, and establish Bayesian inversion frameworks for different types of seismic phases respectively;

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

[0057] Furthermore, the process of characterizing the spatial morphology of the natural alkali deposit based on seismic waveform simulation and inversion results, and determining the exploration engineering spacing and delineating the initial mining area in conjunction with a comprehensive evaluation of the deposit's geological conditions, includes the following steps:

[0058] Determine the type of ore body to be explored in the study area;

[0059] Based on the seismic waveform simulation and inversion results, a three-dimensional in-depth 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 to compile the structural map of the top boundary of each ore layer and the thickness map of the ore layer.

[0060] The spacing between exploration projects is determined based on the type of ore body exploration, geological characteristics, and distribution range.

[0061] Based on the spacing of 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 is delineated in areas with gentle structure and undeveloped faults.

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

[0063] This invention also discloses an earthquake prediction system for natural alkali mineral exploration, comprising:

[0064] The sensitivity logging curve determination unit is used to identify the natural alkali development intervals and single-layer thicknesses by combining logging lithology interpretation with logging and core data, and to determine the natural alkali sensitivity logging curves.

[0065] Create a unit to apply well-seismic joint interpretation technology, establish an isochronous stratigraphic framework, trace ore-bearing strata, and determine the structural features of the top and bottom boundaries of alkali-bearing strata and the seismic facies features of the ore body;

[0066] The simulation inversion unit is used to create a stratigraphically constrained structural model based on the structural characteristics of the top and bottom boundaries of the alkali-bearing strata, select sensitive well logging curves to reconstruct characteristic curves as control waveforms, perform seismic waveform simulation inversion, and predict the distribution range and ore layer thickness of natural alkali ore bodies.

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

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

[0069] Process the well logging data;

[0070] The processed logging data is plotted into logging curves, and the logging curves are preprocessed.

[0071] Lithology secondary interpretation is performed based on the pre-processed well logging curves.

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

[0073] Load well logging curves;

[0074] Analyze the dominant frequency of earthquake data;

[0075] Seismic wavelets are extracted from the seismic traces near the well, and the time-depth relationship is adjusted synchronously to obtain the wavelets;

[0076] During the synthetic record calibration process, based on the marker layer, stretching and compression are used to continuously correct the matching relationship between the synthetic seismic record and the well-side seismic trace, and the correlation between the synthetic seismic record and the seismic data is verified simultaneously.

[0077] Analyzing wavelet contributions helps identify the contribution of seismic amplitude variations caused by different lithologies or fluids in well logging to seismic reflections, laying the foundation for stratigraphic calibration and attribute window selection.

[0078] After completing the well-seismic stratigraphic calibration, the seismic reflection structure and wave group characteristics were analyzed, and the stratigraphic interpretation was carried out in combination with the attribute profile to determine the structural characteristics of the top and bottom boundaries of the alkali-bearing strata.

[0079] By combining conventional and attribute profiles, faults in the seismic data are identified, and coherence slicing techniques are used to interpret the fault plane, resulting in detailed seismic data interpretation.

[0080] Compared with the prior art, the beneficial effects of the present invention are:

[0081] 1) This invention combines the characteristics of natural alkali deposit formation and applies seismic prediction technology from oil and gas exploration to the exploration of natural alkali deposits for the first time, improving exploration efficiency and drilling success rate while reducing exploration costs. In the Naiman area, this method was applied to deploy and implement three exploration wells, all of which encountered alkali deposits, with a success rate of 100%, which is 50% higher than the previous 50% success rate.

[0082] 2) In terms of seismic inversion prediction, this invention addresses the characteristics of thin single-layer natural alkali deposits and their interbedded formation with argillaceous rocks. It employs a key technology combining characteristic curve reconstruction and seismic waveform simulation inversion for iterative inversion. Horizontally, the inversion results utilize changes in seismic waveforms to distinguish the range of lithological variations. Vertically, they match high-frequency well logging information, exhibiting high vertical resolution and meeting the requirements for describing the spatial morphology of ore bodies, thus significantly improving inversion accuracy. Furthermore, this invention is unaffected by the number and location of wells, compensating for the lack of well data in the early stages of exploration and expanding its applicability. As well control levels increase in later stages, the prediction results will become more refined and accurate, providing a basis for well network deployment during the detailed exploration phase.

[0083] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0084] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0085] Figure 1 A flowchart of a seismic prediction method for natural alkali deposit 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 well N10 according to an embodiment of the present invention is shown;

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

[0088] Figure 4 A seismic waveform simulation inversion prediction planar 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 is shown according to an embodiment of the present invention. Detailed Implementation

[0091] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0092] In the field of oil and gas exploration, seismic technology, as a highly accurate method for reservoir and fluid detection, has been widely used. This invention creatively applies the theoretical understanding and accumulated data from the oil and gas exploration process to the exploration of alkali deposits. Starting from the distribution characteristics of the strata hosting the ore body, it uses three-dimensional seismic prediction technology to characterize the spatial morphology of natural alkali deposits, thereby predicting the distribution range of natural alkali deposits.

[0093] The purpose of this invention is to provide a technical method for predicting the spatial distribution characteristics of natural alkali deposits based on geophysical technology. This invention utilizes seismic, drilling, and logging data accumulated in oil and gas exploration, and employs seismic inversion prediction to provide a detailed description of the distribution of alkali deposits. This overcomes the limitations of traditional methods that rely on well network extrapolation for exploration or describe alkali deposits from only one aspect, thereby improving exploration accuracy, shortening the exploration cycle, and reducing exploration costs.

[0094] Figure 1 A flowchart illustrating a seismic prediction method for natural alkali deposit exploration according to an embodiment of the present invention is shown. Figure 1 As shown, the present invention proposes a seismic prediction method for natural alkali deposit exploration, comprising the following steps:

[0095] Step S1: Interpret the lithology of the completed well by combining well logging lithology interpretation with data such as cuttings logging and core samples, identify the natural alkali development intervals and single layer thicknesses, establish the correspondence between lithology and curve combination characteristics, and determine the natural alkali sensitivity logging curves.

[0096] 1. Process the well logging data;

[0097] The logging data is processed and corrected using drilling core, logging, and core analysis test data to remove outliers caused by engineering reasons.

[0098] The specific steps for processing and correcting well logging data are as follows:

[0099] During field logging operations, the influence of non-formation environment and measurement factors can make it difficult to achieve consistent depths among logging curves, and the values ​​of each logging curve can be affected. Therefore, logging data preprocessing and correction are necessary, mainly including digitization of analog curves, logging curve depth correction, and environmental correction. Specifically, the analog logging curves are digitized, converted into corresponding logging data, and processed using a computer. After digitization, the curves are replayed using a plotting program and compared with the original curves before digitization to check and verify the quality of the digitization. Depth correction generally uses two methods: one is to use a natural gamma curve as a depth control curve, measuring a natural gamma curve for each logging operation and using this curve as a reference to align the depths of each measurement; the other is to use a correlation comparison method, where curves from the same well exhibit correlation. A curve with high resolution, clear characteristic features, and good quality is selected as the reference curve, and depth movement is performed manually or using computer software to achieve the purpose of depth correction.

[0100] Downhole logging instruments are typically required to be centered in the wellbore and close to the well wall during measurement. Irregular wellbores and the deployment of logging instruments can cause the instrument measurement position to be incorrect, resulting in abnormal measurement values ​​during logging operations. 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 operations can also cause abnormal logging data.

[0101] 2. The processed logging data is plotted into logging curves, and the logging curves are preprocessed.

[0102] 2.1 Depth Alignment: The processed logging data is plotted as logging curves, so that each logging curve at each depth is grouped into a unified sampling point data;

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

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

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

[0106] 2.5. Numerical standardization of logging curves is performed to eliminate systematic errors. Specifically, numerical standardization addresses the systematic errors inherent in various logging curves due to factors such as wellbore conditions, logging series, instrument calibration, measurement time, and differences in operators. Therefore, standardization is necessary for lithological interpretation. Common methods include histogram methods and trend surface methods.

[0107] 3. Perform secondary interpretation of lithology based on the pre-processed well logging curves.

[0108] 3.1 By utilizing the identification results of core thin sections of alkali-bearing strata and the surrounding rocks at the top and bottom without mineralization, and the combination characteristics of different series of well logging curves, a correspondence between the two is established;

[0109] 3.2 Using core calibration logging, well logging curve values ​​of geologically similar lithologies are extracted, and cross-plots between different curve types are established;

[0110] 3.3 Select curves with concentrated distribution of the same type of lithology and obvious differentiation from other types of lithology as sensitive logging curves for lithology identification, and establish a lithology identification chart;

[0111] 3.4 Based on the lithological identification chart, lithology was repositioned and secondary interpretation was performed to identify the natural alkali development intervals and the thickness of individual layers.

[0112] Step S2: Apply well-seismic joint 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 facies characteristics of the ore body;

[0113] The formation of natural alkali deposits is closely related to the regional tectonic development, requiring detailed structural interpretation to determine the top and bottom structural characteristics of the strata hosting the alkali deposits. Furthermore, the inversion method involved in this invention requires using the three-dimensional seismic horizon of the study area as a model framework to create a structural model; therefore, detailed structural interpretation is one of the fundamental tasks of this invention. Detailed structural interpretation includes the following steps:

[0114] 1. Loading of logging curves (AC, DEN);

[0115] 2. Seismic data dominant frequency analysis;

[0116] 3. Extract seismic wavelets from the seismic traces near the well, and simultaneously adjust the time-depth relationship to obtain the optimal morphological wavelet;

[0117] 4. Wave group matching: During the calibration of the synthetic record, characteristic waveforms are fully considered. Based on the marker layer, appropriate stretching and compression are applied (without changing the curve in the original depth domain) to continuously correct the matching relationship between the synthetic seismic record and the well-side seismic trace.

[0118] 5. Verify the correlation between the synthetic seismic record and the seismic data; where geological conditions are met, the correlation coefficient between the synthetic seismic record and the seismic data should be at least greater than 60%. This verification process is carried out simultaneously with the synthetic record calibration and can be performed by most seismic interpretation software.

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

[0120] 7. After completing the well-seismic stratigraphic calibration, analyze the seismic reflection structure and wave group characteristics, and use methods such as profiles, horizontal slices, and along-layer slices to interpret the stratigraphic position and determine the structural characteristics of the top and bottom boundaries of the ore-bearing strata.

[0121] 8. By combining conventional profiles with attribute profiles, faults in the profiles can be identified. Fault plane interpretation can be performed using methods such as coherence slices, thereby obtaining detailed seismic data interpretation results.

[0122] Step S3: Based on the structural characteristics of the top and bottom boundaries of the alkali-bearing strata, create a stratigraphically constrained structural model, select sensitive logging curves to reconstruct characteristic curves as control waveforms for constraint, and use the seismic waveform differences caused by lithological changes 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 technique in oil exploration, but its application in the exploration of natural alkali deposits is unprecedented. Before conducting inversion predictions for a study area, it is necessary to analyze the area's geological characteristics, lithological characteristics, and well data to select the most suitable inversion technique.

[0124] The main lithology of natural alkali deposits belongs to the evaporite group. Core analysis reveals that its mineral composition is mainly sodium carbonate, while the non-ore layers between the ore layers are mainly dolomitic mudstone (mineral composition includes feldspar, sodium carbonate calcium carbonate, sodium carbonate magnesium carbonate, and clay minerals). The alkali ore layers and non-ore layers in the mineralization zone are interbedded vertically, and the single-layer thickness of the ore body is relatively thin. According to drilling data, the single-layer thickness of natural alkali is between 1 and 12 meters, requiring high inversion resolution. Through model comparison, the seismic waveform simulation inversion method can overcome the limitations of seismic inversion resolution and has a strong ability to identify thin layers. This invention utilizes an optimized seismic waveform simulation inversion method to predict and finely describe alkali ore bodies.

[0125] The specific process includes the following steps:

[0126] 1. Well Logging Curve Standardization: Well logging curves (AC, DEN, GR, RT, etc.) from completed wells in the study area are standardized to eliminate systematic and random errors caused by manual operation and instruments, ensuring they conform to geological principles. The specific standardization method involves setting up standard wells and standard layers. Referring to the logging curve values ​​of the standard layers, the value ranges of curves from other wells are calculated and unified to the range of the standard wells.

[0127] 2. Characteristic Curve Reconstruction: Sensitive parameters for alkali deposits require identifying the logging curves that best reflect the characteristics of the deposit from all logging data. This primarily involves cross-plotting various logging data with lithological data to find sensitive logging curves and their sensitivity values. Through cross-plotting multiple sets of curves, it was found that GR and RT curves are most sensitive to alkali deposits, which exhibit high RT (200–5000 Ω·m) and low GR (0–50 API) characteristics on logging curves. However, other rock types in the area, such as oil-bearing sandstone and conglomerate, exhibit high RT (50–6000 Ω·m), and basalt exhibits low GR (25–60 API). The logging value ranges of these rock types largely overlap with those of the alkali deposit. If only one curve is used for inversion, the results will be ambiguous and cannot accurately characterize the alkali deposit. Therefore, GR and RT are refitted to construct new characteristic curves (ReC) for simulation.

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

[0129]

[0130] 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 agreement with the alkali mineral layer, indicating that this characteristic curve is sensitive to the alkali mineral layer. Therefore, this characteristic curve was chosen to be used for simulation inversion.

[0132] 3. Loading seismic horizons and creating a structural model: The key to building a structural model and model interpolation is accurate horizon interpretation and precise time-depth relationships. Based on seismic reflection characteristics and sedimentary patterns, stratigraphic contact relationships are set, and the structural model is calculated.

[0133] 4. Optimization of control waveforms and quantity: Dynamic clustering analysis of seismic trace waveforms near control points is achieved through matrix orthogonal decomposition. The correspondence between seismic waveform structure and well logging curve structure is established, generating a set of well logging curve waveforms representing different types of seismic phases. The control waveforms are then optimized through comparison and evaluation.

[0134] 4.1 The correlation coefficients of the seismic traces near the control points were determined by extracting and comparing the seismic waveforms from the well-side seismic traces. It should be noted that similar lithological combinations generally have similar seismic waveform characteristics, which is the basis for waveform simulation and inversion.

[0135] 4.2 Similarity analysis of control well characteristic curves: Well logging curves have very high frequencies. By reducing the frequency and filtering the curves, the similarity between the characteristic curves and the seismic traces near the well can be improved. When the similarity is close to the correlation coefficient of the seismic traces near the well, a connection between the seismic waveform and the high-frequency information of the well logging is established.

[0136] 4.3 Compare and evaluate the seismic waveforms at the predicted points with those at the control points, rank the waveforms based on their similarity, and select the control waveforms. Then, fit all characteristic curves, pick the values ​​corresponding to their inflection points (not greater than the number of wells in the waveform set), determine the values ​​of the control waveforms, and then appropriately increase or decrease them based on the geological conditions of the study area.

[0137] 5. Set termination frequency conditions, perform seismic waveform simulation and inversion, and predict the distribution range and ore layer thickness of natural alkali ore bodies.

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

[0139] 5.2 Under different Bayesian inversion frameworks, the common components of the waveform set are selected as the initial model for iterative inversion. Frequency parameters are adjusted, and the optimal frequency is used as the termination condition to continuously correct and optimize the model, obtaining the simulation result with the highest correlation to the control waveform, which is the final seismic waveform simulation inversion result. The initial model is obtained by comparing and optimizing the seismic waveform at the point to be predicted with the waveform set, finding the value corresponding to the seismic waveform with the highest similarity. This value is the simulation result for the point to be predicted, and the set of simulation results for all points to be predicted constitutes the initial model. Specifically, it refers to the original model that has not undergone multiple iterations, smoothing, or other optimization processes.

[0140] The optimal frequency is determined jointly by the frequency range of the most similar logging curves obtained from the analysis in section 4.2 and the frequency range that meets the target layer resolution requirements. Its value is generally not less than the frequency of the characteristic curve after frequency reduction processing. Furthermore, when the number of control wells in the study area is small, the weight of seismic data can be appropriately increased. The weight setting can be adjusted by referring to the degree of agreement between the orebody size identified using seismic attributes and the simulation results.

[0141] In terms of seismic inversion prediction, this invention addresses the characteristics of thin single-layer natural alkali deposits and their interbedded formations with argillaceous rocks. It employs a key technology combining characteristic curve reconstruction and seismic waveform simulation inversion for iterative inversion. The inversion results follow the changes in seismic waveforms horizontally, distinguishing the range of lithological variations. Vertically, they match high-frequency well logging information, exhibiting high vertical resolution and meeting the requirements for describing the spatial morphology of ore bodies, significantly improving inversion accuracy. Furthermore, this invention is unaffected by the number and location of wells, compensating for the lack of well data in the early stages of exploration and expanding its applicability. As well control levels increase in later stages, the prediction results will become more refined and accurate, providing a basis for well network deployment during detailed exploration.

[0142] Step S4: Based on the seismic waveform simulation and inversion results, characterize the spatial morphology of the natural alkali ore body (ore layer), and in conjunction with the comprehensive evaluation of the geological conditions of the deposit, determine the spacing of the exploration projects, delineate the first mining area, and provide a basis for effective and rational development.

[0143] The purpose of geological exploration is to thoroughly investigate the basic characteristics of the ore-bearing strata in the study area (occurrence, scale, and distribution of the alkali ore layers, and ore assemblage types of the ore-bearing rocks), ore characteristics (mineral composition and chemical composition), and the lithology of the surrounding rocks and interbedded rocks, to gain a deeper understanding of the geological characteristics of the deposit. For associated and coexisting minerals with industrial value, the types, content, and occurrence characteristics should be preliminarily identified, and the possibility of comprehensive utilization should be studied by analogy.

[0144] The specific steps are as follows:

[0145] 1. Determine the orebody exploration type in the study area. By comparing with similar deposits, determine the orebody type based on the size of the orebody extension, the complexity of the structure, the stability of the orebody distribution, and the development of salt dissolution in the study area.

[0146] 2. Describe the spatial distribution characteristics of the ore body. Perform three-dimensional in-depth interpretation of the above inversion results, finely depicting the ore body boundaries, top boundary, and structural morphology of each internal layer's top boundary, and compile structural maps of each ore layer's top boundary and thickness (e.g., ...). Figure 3 (as shown in Figure 4).

[0147] 3. Design the spacing of exploration works. When arranging exploration works, subsequent exploration work must be considered. The basic spacing of exploration works should be designed based on the type of ore body exploration, geological characteristics, and distribution range. The spacing can vary between different parts of the same ore body; the spacing can be appropriately increased along directions with relatively gentle ore body structures, and a denser spacing should be used along directions with significant ore body variations. The range of exploration work spacing is 1.5–2.5 km. Preferably, the basic exploration work spacing is 2.0 km.

[0148] 4. Delineate the initial mining area: The selection of the initial mining area should comprehensively consider the spacing of exploration projects, the distribution characteristics of alkali ore layers, and the development of faults. By comprehensively evaluating the thickness, burial depth, and industrial quality of alkali ore layers, the initial mining area should be delineated in areas with gentle structures and undeveloped faults, providing a basis for efficient mining in the later stages.

[0149] It should be noted that the exploration of ore bodies in oil and gas basins must be carried out without damaging the existing oil and gas reservoirs. This invention can meet this condition and enable more efficient and rational mineral exploration.

[0150] This invention combines the characteristics of natural alkali deposit formation and applies seismic prediction technology from oil and gas exploration to the exploration of natural alkali deposits for the first time, improving exploration efficiency and reducing exploration costs. In the Naiman area, this method was applied to deploy and implement three exploration wells, all of which encountered alkali deposits, achieving a success rate of 100%, which is 50% higher than the previous success rate of 50%.

[0151] This invention relates to a seismic prediction method for natural alkali deposit exploration, applied to the exploration of a deposit in the Naiman Depression. In this area, oil and gas coexist with natural alkali, but exploration is limited, with few completed wells. Traditional well-network-based deployment methods have low success rates. This invention improves the drilling success rate.

[0152] Step 1: By combining well logging lithology interpretation with core analysis and testing data, identify the vertically developed natural alkali layers and their individual thicknesses, and determine the ore-bearing layer's sensitivity logging curves as GR and RT;

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

[0154] Since the number of wells in the metallogenic belt of the study area is relatively small, the seismic data of the study area and the well data of the surrounding area are fully utilized to interpret the stratigraphy and faults.

[0155] Step 3: Input the seismic horizon into the inversion software and establish a structural model; use the well logging curve standardization module of the inversion software to correct the AC, DEN, GR, RT and other well logging curves of the completed wells; then use the AC and DEN curves to perform fine synthesis and record calibration; then select the GR and RT well logging curves 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) Based on the geological conditions and existing data of the study area, the control waveform value was selected as 5;

[0158] (2) Through frequency reduction filtering, the similarity of the logging curve to the seismic waveform is closest when the frequency range is 100-200Hz.

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

[0160] Step 4: Use the inversion results to characterize the three-dimensional spatial morphology of the natural alkali ore body and determine the area of ​​the mineralized zone as 32 km². 2The maximum thickness reaches 200m. The ore body in the study area is large in scale, stable in distribution, relatively simple in structure, and poorly soluble in salt, classifying it as a Class I ore body. The basic exploration spacing is determined to be 2km. Due to the steeper dip of the ore layer in the northern part of the study area, the exploration spacing is shortened to 1.5km. Based on a comprehensive evaluation considering the ore body exploration type and the structural characteristics of the ore-bearing strata, a 3.9km² initial mining area is delineated in the central part of the ore body. 2 (like Figure 5 (As shown).

[0161] This embodiment successfully applied a seismic exploration method for natural alkali to guide the exploration of a mineral deposit in the Naiman area, deploying three exploration wells, including N35, in the area (e.g., Figure 6 As shown in the figure, all were successful, with a success rate of 100%, which reduced exploration costs and saved a lot of money.

[0162] Based on the above-mentioned seismic prediction method for natural alkali deposit exploration, this invention also provides a seismic prediction system for natural alkali deposit exploration, comprising:

[0163] The sensitivity logging curve determination unit is used to identify the natural alkali development intervals and single-layer thicknesses by combining logging lithology interpretation with logging and core data, and to determine the natural alkali sensitivity logging curves.

[0164] Create a unit to apply well-seismic joint interpretation technology, establish an isochronous stratigraphic framework, trace ore-bearing strata, and determine the structural features of the top and bottom boundaries of alkali-bearing strata and the seismic facies features of the ore body;

[0165] The simulation inversion unit is used to create a stratigraphically constrained structural model based on the structural characteristics of the top and bottom boundaries of the alkali-bearing strata, select sensitive well logging curves to reconstruct characteristic curves as control waveforms, perform seismic waveform simulation inversion, and predict the distribution range and ore layer thickness of natural alkali ore bodies.

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

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

[0168] Process the well logging data;

[0169] The processed logging data is plotted into logging curves, and the logging curves are preprocessed.

[0170] Lithology secondary interpretation is performed based on the pre-processed well logging curves.

[0171] In some embodiments, the creation unit is specifically used for:

[0172] Load well logging curves;

[0173] Analyze the dominant frequency of earthquake data;

[0174] Seismic wavelets are extracted from the seismic traces near the well, and the time-depth relationship is adjusted synchronously to obtain the wavelets;

[0175] During the synthetic record calibration process, based on the marker layer, stretching and compression are used to continuously correct the matching relationship between the synthetic seismic record and the well-side seismic trace, and the correlation between the synthetic seismic record and the seismic data is verified simultaneously.

[0176] Analyzing wavelet contributions helps identify the contribution of seismic amplitude variations caused by different lithologies or fluids in well logging to seismic reflections, laying the foundation for stratigraphic calibration and attribute window selection.

[0177] After completing the well-seismic stratigraphic calibration, the seismic reflection structure and wave group characteristics were analyzed, and the stratigraphic interpretation was carried out in combination with the attribute profile to determine the structural characteristics of the top and bottom boundaries of the alkali-bearing strata.

[0178] By combining conventional and attribute profiles, faults in the seismic data are identified, and coherence slicing techniques are used to interpret the fault plane, resulting in detailed seismic data interpretation.

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

Claims

1. A seismic prediction method for natural alkali deposit exploration, characterized in that, Includes the following steps: By combining well logging lithology interpretation with well logging and core data, the natural alkali development intervals and single layer thicknesses are identified, and the natural alkali sensitivity logging curves are determined. By applying well-seismic joint interpretation technology, an isochronous stratigraphic framework is established to trace ore-bearing strata, determine the structural features of the top and bottom boundaries of alkali-bearing strata and the seismic facies features of ore bodies; Based on the structural characteristics of the top and bottom boundaries of the alkali-bearing strata, a stratigraphically constrained structural model is created. Sensitive well logging curves are selected to reconstruct characteristic curves as control waveforms. Seismic waveform simulation and inversion are then performed to predict the distribution range and ore layer thickness of the natural alkali ore body. Based on the seismic waveform simulation and inversion results, the spatial morphology of the natural alkali ore body is characterized, and combined with the comprehensive evaluation of the geological conditions of the deposit, the spacing of the exploration project is determined and the first mining area is delineated. The process of creating a stratigraphically constrained structural model based on the top and bottom structural characteristics of the alkali-bearing strata, selecting sensitive well logging curves to reconstruct characteristic curves as control waveforms, and performing seismic waveform simulation and inversion to predict the distribution range and ore layer thickness of natural alkali ore bodies includes the following steps: Standardize the logging curves; Reconstruct the characteristic curve; Load seismic horizons and create a structural model; Select the control waveform and determine the number of control waveforms; Set termination frequency conditions, perform seismic waveform simulation and inversion, and predict the distribution range and ore layer thickness of natural alkali ore bodies; The specific steps for reconstructing the feature curve are as follows: By performing cross-analysis of various well logging data and lithology, sensitivity logging curves and sensitivity values ​​are obtained; By fitting the sensitivity logging curves, a new characteristic curve is constructed; The sensitivity logging curves are the natural gamma logging curve GR and the resistivity logging curve RT; The formula for the characteristic curve is as follows: ReC is the characteristic curve.

2. The seismic prediction method for natural alkali deposit exploration according to claim 1, characterized in that, The process of identifying natural alkali-developed intervals and individual layer thicknesses, and determining natural alkali sensitivity logging curves through well logging lithology interpretation combined with well logging and core data, includes the following steps: Process the well logging data; The processed logging data is plotted into logging curves, and the logging curves are preprocessed. Lithology secondary interpretation is performed based on the pre-processed well logging curves.

3. The seismic prediction method for natural alkali deposit exploration according to claim 2, characterized in that, The specific steps for processing the well logging data are as follows: The logging data is processed and corrected using drilling core, logging, and core analysis test data to remove outliers.

4. The seismic prediction method for natural alkali deposit exploration according to claim 2, characterized in that, The specific steps for plotting the processed logging data into logging curves and preprocessing the logging curves are as follows: The processed logging data is plotted into logging curves, and each logging curve at each depth is grouped into a unified sampling point data. Correct the deviated well curve to a vertical well curve; Smooth the logging curves; Eliminate the influence of surrounding rock and mud intrusion within the detection range of logging instruments to obtain true formation values; Numerical standardization of the logging curves is performed to eliminate systematic errors.

5. The seismic prediction method for natural alkali deposit exploration according to claim 2, characterized in that, The specific steps for secondary lithological interpretation based on the preprocessed well logging curves are as follows: By combining the identification results of core thin sections with the characteristics of well logging curves, a correspondence between the two can be established. Using core calibration logging, logging curve values ​​of geologically similar lithologies are extracted, and cross-plots between different curve types are established; Select curves with concentrated distribution of the same type of lithology as sensitive logging curves for lithology identification and establish a lithology identification chart; Based on the lithological identification chart, lithology is repositioned and secondary interpreted to identify the natural alkali development zones and individual layer thicknesses.

6. The seismic prediction method for natural alkali deposit exploration according to claim 1, characterized in that, The application of well-seismic joint interpretation technology to establish an isochronous stratigraphic framework, trace ore-bearing strata, and determine the structural features of the top and bottom boundaries of alkali-bearing strata and the seismic facies characteristics of ore bodies includes the following steps: Load well logging curves; Analyze the dominant frequency of earthquake data; Seismic wavelets are extracted from the seismic traces near the well, and the time-depth relationship is adjusted synchronously to obtain the wavelets; During the synthetic record calibration process, based on the marker layer, stretching and compression are used to continuously correct the matching relationship between the synthetic seismic record and the well-side seismic trace, and the correlation between the synthetic seismic record and the seismic data is verified simultaneously. Analyzing wavelet contributions helps identify the contribution of seismic amplitude variations caused by different lithologies or fluids in well logging to seismic reflections, laying the foundation for stratigraphic calibration and attribute window selection. After completing the well-seismic stratigraphic calibration, the seismic reflection structure and wave group characteristics were analyzed, and the stratigraphic interpretation was carried out in combination with the attribute profile to determine the structural characteristics of the top and bottom boundaries of the alkali-bearing strata. By combining conventional and attribute profiles, faults in the seismic data are identified, and coherence slicing techniques are used to interpret the fault plane, resulting in detailed seismic data interpretation.

7. The seismic prediction method for natural alkali deposit exploration according to claim 6, characterized in that, The stratigraphic interpretation was determined using cross-section, horizontal slicing, and strata-side slicing techniques.

8. The seismic prediction method for natural alkali deposit exploration according to claim 1, characterized in that, The specific steps for standardizing the well logging curves are as follows: Load the logging curves and standardize the acoustic transit time logging curves (AC), density logging curves (DEN), natural gamma logging curves (GR), and resistivity logging curves (RT) of the completed wells in the study area.

9. The seismic prediction method for natural alkali deposit exploration according to claim 1, characterized in that, The sensitivity range of the natural gamma logging curve GR is 0–50 API.

10. The seismic prediction method for natural alkali deposit exploration according to claim 1, characterized in that, The sensitivity range of the resistivity logging curve RT is 200–5000 Ω•m.

11. The seismic prediction method for natural alkali deposit exploration according to claim 1, characterized in that, The specific steps for setting termination frequency conditions, performing seismic waveform simulation and inversion, and predicting the distribution range and ore layer thickness of natural alkali ore bodies are as follows: Analyze the distribution characteristics of waveform sets corresponding to different types of waveform structures, and establish Bayesian inversion frameworks for different types of seismic phases respectively; Under different Bayesian inversion frameworks, the common part of the waveform set is selected as the initial model for iterative inversion; Adjusting the frequency parameters and determining the termination frequency condition, the model is continuously revised and optimized to obtain the simulation result with the highest correlation to the control waveform, which is the seismic waveform simulation inversion result.

12. The seismic prediction method for natural alkali deposit exploration according to claim 1, characterized in that, The process of characterizing the spatial morphology of natural alkali deposits based on seismic waveform simulation and inversion results, and determining the spacing of exploration projects and delineating the initial mining area in conjunction with a comprehensive evaluation of the deposit's geological conditions, includes the following steps: Determine the type of ore body to be explored in the study area; Based on the seismic waveform simulation and inversion results, a three-dimensional in-depth 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 to compile the structural map of the top boundary of each ore layer and the thickness map of the ore layer. The spacing between exploration projects is determined based on the type of ore body exploration, geological characteristics, and distribution range. Based on the spacing of 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 is delineated in areas with gentle structure and undeveloped faults.

13. The seismic prediction method for natural alkali deposit exploration according to claim 12, characterized in that, The distance between the exploration projects is 1.5 to 2.5 km.

14. A seismic prediction system for natural alkali deposit exploration, characterized in that, include: The sensitivity logging curve determination unit is used to identify the natural alkali development intervals and single-layer thicknesses by combining logging lithology interpretation with logging and core data, and to determine the natural alkali sensitivity logging curves. Create a unit to apply well-seismic joint interpretation technology, establish an isochronous stratigraphic framework, trace ore-bearing strata, and determine the structural features of the top and bottom boundaries of alkali-bearing strata and the seismic facies features of the ore body; The simulation inversion unit is used to create a stratigraphically constrained structural model based on the structural characteristics of the top and bottom boundaries of the alkali-bearing strata, select sensitive well logging curves to reconstruct characteristic curves as control waveforms, perform seismic waveform simulation inversion, and predict the distribution range and ore layer thickness of natural alkali ore bodies. The first mining area is defined as a unit used to characterize the spatial morphology of the natural alkali ore body based on the seismic waveform simulation and inversion results, and to determine the spacing of exploration projects and delineate the first mining area in conjunction with a comprehensive evaluation of the geological conditions of the deposit. The process of creating a stratigraphically constrained structural model based on the top and bottom structural characteristics of the alkali-bearing strata, selecting sensitive well logging curves to reconstruct characteristic curves as control waveforms, and performing seismic waveform simulation and inversion to predict the distribution range and ore layer thickness of natural alkali ore bodies includes the following steps: Standardize the logging curves; Reconstruct the characteristic curve; Load seismic horizons and create a structural model; Select the control waveform and determine the number of control waveforms; Set termination frequency conditions, perform seismic waveform simulation and inversion, and predict the distribution range and ore layer thickness of natural alkali ore bodies; The specific steps for reconstructing the feature curve are as follows: By performing cross-analysis of various well logging data and lithology, sensitivity logging curves and sensitivity values ​​are obtained; By fitting the sensitivity logging curves, a new characteristic curve is constructed; The sensitivity logging curves are the natural gamma logging curve GR and the resistivity logging curve RT; The formula for the characteristic curve is as follows: ReC is the characteristic curve.

15. The seismic prediction system for natural alkali deposit exploration according to claim 14, characterized in that, The sensitivity logging curve determination unit is specifically used for: Process the well logging data; The processed logging data is plotted into logging curves, and the logging curves are preprocessed. Lithology secondary interpretation is performed based on the pre-processed well logging curves.

16. The seismic prediction system for natural alkali deposit exploration according to claim 14, characterized in that, The creation unit is specifically used for: Load well logging curves; Analyze the dominant frequency of earthquake data; Seismic wavelets are extracted from the seismic traces near the well, and the time-depth relationship is adjusted synchronously to obtain the wavelets; During the synthetic record calibration process, based on the marker layer, stretching and compression are used to continuously correct the matching relationship between the synthetic seismic record and the well-side seismic trace, and the correlation between the synthetic seismic record and the seismic data is verified simultaneously. Analyzing wavelet contributions helps identify the contribution of seismic amplitude variations caused by different lithologies or fluids in well logging to seismic reflections, laying the foundation for stratigraphic calibration and attribute window selection. After completing the well-seismic stratigraphic calibration, the seismic reflection structure and wave group characteristics were analyzed, and the stratigraphic interpretation was carried out in combination with the attribute profile to determine the structural characteristics of the top and bottom boundaries of the alkali-bearing strata. By combining conventional and attribute profiles, faults in the seismic data are identified, and coherence slicing techniques are used to interpret the fault plane, resulting in detailed seismic data interpretation.

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