A method for deploying exploration well positions for polyhalite

Abstract

The present invention provides a method for deploying exploration well positions for polyhalite, which mainly includes the following steps: 1) Using physical cores to clarify the logging response characteristics of the main lithologies; 2) Based on the obvious response characteristics, using the radioactive difference to measure the potassium content, determining the correlation between the potassium content and the total radioactivity, and constructing a mathematical model; 3) Using the preferably selected logging curves sensitive to polyhalite to construct a polyhalite characterization curve, taking the curve as the inversion target curve, using the frequency division attribute inversion method, and simultaneously adding the wave impedance inversion result as a constraint to carry out polyhalite prediction to obtain the distribution range of polyhalite; 4) Selecting polyhalite drilling well positions within the distribution range of polyhalite in step 3) above. The present invention can effectively improve the well position selection efficiency, improve the drilling success rate, achieve high-economic exploration, and at the same time can effectively eliminate the interference of other potassium-containing minerals such as mudstone, greatly improving the accuracy of inversion prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of geological exploration, and particularly relates to a method for deploying exploration well positions for polyhalite. Background Art

[0002] Polyhalite is a double salt of soluble potassium salt and sulfate, with the chemical formula K2Ca2Mg(SO4)4·2H2O. Under an electron microscope, polyhalite mainly has a micro-fine granular structure, is colorless and transparent under single polarized light, is often semi-automorphic - xenomorphic granular, tabular, or long strip-shaped, has a positive low relief, and has an interference color up to second-order blue under cross-polarized light, and common polysynthetic twins are often seen.

[0003] Rock salt-type polyhalite potassium salt is formed during the rock salt deposition stage. When the seawater further evaporates and concentrates and has not yet fully reached the potassium salt deposition stage, seawater rich in Ca 2+ repeatedly intrudes, mixes with ancient seawater rich in K + and Mg 2+ to precipitate polyhalite, which is of primary sedimentary origin. Later, due to tectonic fractures and folding, the polyhalite breaks and is scattered and embedded in the rock salt in patches and dot-like folds. After being transformed by underground fluids, it shows the characteristics of "salt-wrapped potassium" with irregular boundaries and granular shapes. Scanning electron microscopy shows that rock salt-type polyhalite potassium salt coexists with rock salt and has clear boundaries. The genetic model of rock salt-type polyhalite potassium salt deposit is summarized as "primary sedimentation, tectonic adjustment, and fracture dissolution and filtration".

[0004] Rock salt-type polyhalite consists of polyhalite intraclast particles of different sizes scattered in the rock salt matrix, with strong anisotropy in the longitudinal and transverse directions, lacking a corresponding logging interpretation sensitive parameter model, and the quantitative calculation method for potassium content logging is still blank.

[0005] Rock salt-type polyhalite in the Sichuan Basin develops in the gypsum-salt rock of the Jia 4-5 member. Affected by tectonic adjustment and transformation, the gypsum-salt rock undergoes plastic deformation, with large lateral thickness variations, large lateral differences in the thickness of the gypsum-salt formation and seismic wave groups, and it is difficult to conduct fine structural interpretation. Conventional seismic inversion based on a layered model is not applicable. In the past, logging sensitive parameters for polyhalite included multiple logging parameters in different value ranges, but from the perspective of seismic prediction, high-precision inversion results for all parameters could not be given. Therefore, it is necessary to optimize the most sensitive logging parameters.

[0006] Currently, the well position deployment mainly relies on the cross-section comparison method of existing drilling data and the magnetotelluric sounding (MT) and audio magnetotelluric sounding (AMT) methods. The cross-section comparison method of existing drilling data requires multiple known drillings and cannot be realized in mining areas with low geological work levels. The magnetotelluric sounding (MT) and audio magnetotelluric sounding (AMT) methods are greatly affected by structures, have low detection accuracy in places where the surface structure is relatively complex and there are developed fault zones, and almost fail after reaching a depth of 2000 meters and cannot effectively guide well position deployment.

[0007] In summary, it is necessary to further innovate the existing technology. Summary of the Invention

[0008] In view of the technical problems existing in the above-mentioned background technology, the present invention proposes a method for deploying exploration well positions for polyhalite, which has a reasonable concept and a simple process, can effectively improve the efficiency of well position selection, increase the success rate of drilling, achieve high-economic exploration, and at the same time can effectively exclude the interference of other potassium-containing minerals such as mudstone, greatly improve the accuracy of inversion prediction, and achieve the precise positioning of polyhalite ore bodies in complex structures, deep burial, huge thickness, and easily soluble gypsum rock layers, thereby guiding the deployment of well positions.

[0009] To solve the above technical problems, a method for deploying exploration well positions for polyhalite provided by the present invention is characterized by mainly including the following steps:

[0010] 1) Using physical cores to clarify the logging response characteristics of main lithologies;

[0011] 2) Based on the obvious response characteristics, measuring the potassium content using radioactive differences, determining the correlation between the potassium content and the total radioactivity, and constructing a calculation model;

[0012] 3) Using the preferably selected logging curves sensitive to polyhalite to construct a polyhalite characterization curve, taking the curve as the inversion target curve, using the frequency division attribute inversion method, and adding the wave impedance inversion result as a constraint at the same time to carry out polyhalite prediction and obtain the distribution range of polyhalite;

[0013] 4) In the distribution range of polyhalite in step 3) above, combining the structural position, fault distribution, surface construction conditions, etc. to select the polyhalite drilling well positions.

[0014] For the method for deploying exploration well positions for polyhalite, the specific process of step 1) is as follows: Selecting core samples from actual drilled wells to carry out rock physical parameter measurements, analyzing the rock physical parameters and seismic elastic sensitive parameters; Collecting core data of polyhalite-bearing intervals in the measured wells to prepare rock samples; Through rock sample analysis, testing that the rock samples are mainly gypsum rock, rock salt, and halite-type polyhalite, and according to the needs of physical analysis, carrying out laboratory tests on the porosity, velocity, and density parameters of the rock samples for the rock samples, and analyzing the velocity, density, and elastic parameter characteristics of potassium-rich salt ores with different contents and different formation structures; Based on clarifying the logging response characteristics of different lithologies, establishing the logging identification mode of main lithologies in the Jialingjiang Formation in the study area.

[0015] The method for deploying exploration well positions for polyhalite, wherein the specific process of step 2) is as follows: Based on the obvious response characteristics, calculate the formation shale content using the thorium curve, inversely calculate the potassium-rich mineral content using the classical shale content model, and finally correct and eliminate the influence of potassium content in shale minerals through gamma energy spectrum difference correction to form a quantitative calculation model for potassium content based on radioactive difference analysis.

[0016] The method for deploying exploration well positions for polyhalite, wherein the specific process of calculating the formation shale content using the thorium curve is as follows:

[0017] Calculate the formation shale content using the change in thorium element value, as shown in the following formulas (3-3) and (3-4):

[0018]

[0019] In the above formulas (3-3) and (3-4), SH TH is the shale content index, TH max is the thorium value of the pure shale layer, TH min is the thorium value of the pure sandstone formation, V TH is the total shale content, GCUR is the formation constant, and 2 is taken for old formations.

[0020] The method for deploying exploration well positions for polyhalite, wherein the specific process of inversely calculating the potassium-rich mineral content using the classical shale content model is as follows:

[0021] After calculating the shale content using the thorium curve, calculate the apparent shale content of the formation, that is, the sum of the shale content and potassium-rich minerals, using the potassium curve, and introduce the GCUR empirical constant to optimize the calculation model, as shown in the following formulas (3-5) and (3-6):

[0022]

[0023] In the above formulas (3-5) and (3-6), SH K* is the shale content index, V K * is the apparent shale content calculated for potassium, K max is the potassium value of the pure shale layer, K min is the potassium value of the pure sandstone formation, GCUR is the formation constant, and 2 is taken for old formations;

[0024] After calculating the apparent shale content V K *, calibrate the thorium curve and potassium curve with anhydrite formation as the reference lithology, and calculate the potassium-rich mineral content V K using the "gamma energy spectrum difference analysis method", as shown in the following formula (3-7):

[0025]

[0026] In the above formula (3-7), VTH is the total shale content, V K* is the content of potassium-rich minerals, and a is an empirical parameter, which is calibrated using core data. In the absence of core data, a is defaulted to 1.

[0027] The method for deploying the polyhalite exploration well positions, wherein the specific process of step 3) is as follows: constructing a polyhalite characterization curve using the preferably selected polyhalite-sensitive logging curves, and adopting the BP neural network machine learning method to construct the polyhalite curve by establishing a non-linear relationship; then, using the constructed polyhalite curve as the inversion target curve, adopting the frequency division attribute inversion method, through the amplitude-frequency variation response relationship between the amplitude and frequency under different formation thicknesses, and simultaneously adding the constraint condition of the wave impedance inversion result, predicting the spatial distribution of polyhalite; then obtaining the development time thickness of polyhalite from the polyhalite seismic inversion result, combining with the amplitude value of the polyhalite seismic inversion, obtaining the polyhalite distribution thickness result, and then comparing with the polyhalite thickness divided by the well logging of the drilled wells to predict the average absolute thickness error.

[0028] The method for deploying the polyhalite exploration well positions, wherein the specific process of step 4) is as follows: using the seismic velocity field to perform time-depth conversion on the strata related to ore-bearing in the study area: TT3t, TT2l3, TT2l1, and TT1j 4_5 A total of 4 seismic reflection layers are converted to form 4 structure maps in total. It is required that the structural morphology and detailed changes are clearly visible, and combined with the distribution range of polyhalite obtained in step 3), the exploration well positions are deployed.

[0029] The method for deploying the polyhalite exploration well positions, wherein: the time-depth conversion is to continuously iterate and reciprocate the seismic data processing, velocity analysis, and well logging data to optimize the conversion result.

[0030] Adopting the above technical solution, the present invention has the following beneficial effects:

[0031] The method for deploying the polyhalite exploration well positions of the present invention has a reasonable concept and a simple process. It fully utilizes the combination of physical core data and well logging data, reveals the sensitive parameters for logging identification of halite-type polyhalite, forms a quantitative calculation method for potassium content based on gamma energy spectrum difference analysis, and further combines with the seismic inversion technology to optimize the well position selection method, which can effectively improve the well position selection efficiency, improve the drilling success rate, and achieve high-economic exploration. Based on the obvious response characteristics, the present invention utilizes the radioactive difference to measure the potassium content and constructs a mathematical model, effectively excluding the interference of other potassium-containing minerals such as mudstone, greatly improving the accuracy of inversion prediction, and realizing the precise positioning of polyhalite ore bodies in complex structures, deep burial, thick, and easily soluble gypsum rock layers, thereby guiding the deployment of well positions. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0033] Figure 1 It is a flow chart of the method for deploying exploration well positions for polyhalite in the present invention;

[0034] Figure 2 It is a comparison chart of potassium content obtained from core analysis and well logging calculation involved in the method for deploying exploration well positions for polyhalite in the present invention;

[0035] Figure 3 It is a curve graph of the fitting relationship between the potassium (K) content obtained from core analysis and the potassium (K) content obtained from gamma energy spectrum involved in the method for deploying exploration well positions for polyhalite in the present invention;

[0036] Figure 4 It is a fitting relationship graph between the potassium (K) content obtained from core analysis and the potassium (K) content obtained from gamma energy spectrum involved in the method for deploying exploration well positions for polyhalite in the present invention;

[0037] Figure 5 It is a graph of the correlation analysis (n = 20) between the potassium content obtained from core chemical analysis and the fitting potassium content of Well A in northeastern Sichuan involved in the method for deploying exploration well positions for polyhalite in the present invention;

[0038] Figure 6 It is a predicted spatial distribution map of polyhalite involved in the method for deploying exploration well positions for polyhalite in the present invention;

[0039] Figure 7 It is a predicted profile diagram of polyhalite in the Jiasansiwu Formation passing through Well Chuanxuan 2 involved in the method for deploying exploration well positions for polyhalite in the present invention;

[0040] Figure 8 It is a predicted thickness distribution map of polyhalite in the Jiasansiwu Formation involved in the method for deploying exploration well positions for polyhalite in the present invention. Specific embodiments

[0041] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the drawings. Obviously, the described embodiments are some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0042] The following will further explain and illustrate the present invention in conjunction with specific embodiments.

[0043] Such as Figure 1As shown in the figure, a method for deploying exploration well positions for polyhalite mainly includes the following steps:

[0044] 1) Using physical cores, clarify the logging response characteristics of main lithologies such as traditional polyhalite, halite-type polyhalite, anhydrite, and halite rock.

[0045] The rock physical parameter characteristics of the halite-type polyhalite layer determine its logging parameter characteristics and seismic reflection characteristics. Through the analysis of the rock physical characteristics of the polyhalite-bearing strata, it helps to clarify the sensitive attribute parameters of the polyhalite-bearing strata contained in logging and seismic information, providing a basis for the description and prediction of the polyhalite-bearing strata.

[0046] In order to accurately analyze the rock physical characteristics of the polyhalite-bearing layers, physical drilling cores were sampled in northeastern Sichuan to carry out rock physical parameter measurements, and the rock physical parameters and seismic elastic sensitive parameters were analyzed. More than 30 polyhalite-bearing core data were collected in the polyhalite-bearing well sections of the measured wells for rock sample preparation. Through rock sample analysis, the tested rock samples are mainly gypsum rock, rock salt, and halite-type polyhalite. According to the needs of physical analysis, laboratory tests of parameters such as porosity, velocity, and density of these rock samples were carried out to analyze the velocity, density, and elastic parameter characteristics of potassium-rich salt mines with different contents and different stratigraphic structures.

[0047] On the basis of clarifying the logging response characteristics of different lithologies, establish the logging identification mode of the main lithologies in the Jialingjiang Formation strata in the study area.

[0048] Table 1 Logging classification and identification mode of the main lithologies in the Jialingjiang Formation strata

[0049]

[0050] 2) On the basis of obvious response characteristics, use the radioactive difference to measure the potassium content, determine the correlation between the potassium content and the total radioactivity, and construct a calculation model.

[0051] In this example, using the core sample test analysis data of Well A in northeastern Sichuan and combining with logging data, a mathematical conversion relationship between chemical analysis data and logging data is established, providing a basis for calculating the potassium (K) content of the ore-bearing layers in other oil and gas wells without coring but with complete gamma-ray spectrometry logging data. The potassium (K) content of the core is fitted with its corresponding logging potassium content data (as shown in Table 2 and Figure 3 )), and the calculation model of the linear relationship (as shown in Figure 4 ) is as follows:

[0052] K 测试 = 1.175×K 测井 - 0.021 (R = 0.955);

[0053] Figure 3 and Figure 4It shows that the data points of Well A in northeastern Sichuan have a good correlation with the existing fitted curve, reflecting good consistency. Therefore, this linear relationship can be applied to perform fitting correction equivalent to the potassium content of the measured samples for the gamma energy spectrum potassium (K) logging data of other single-coreless wells.

[0054] Table 2: Core potassium (K) content test and gamma energy spectrum logging potassium data table of Well A in northeastern Sichuan

[0055]

[0056]

[0057]

[0058] To verify the reliability of the mathematical conversion model, the chemical analysis data and the fitted data of the cored Well B in northeastern Sichuan were compared (as Figure 5 ), and the correlation between the two was good (R = 0.86), indicating that the mathematical conversion model can be used for the quantitative calculation of potassium content in oil and gas well logging.

[0059] The polyhalite-bearing formation has the characteristics of "high potassium, low thorium, and low density". Based on the obvious response characteristics, the thorium (TH) curve is used to calculate the shale content of the formation, and the potassium content in it is inversely calculated using the classical shale content model. Finally, the influence of potassium content in shale minerals is eliminated through gamma energy spectrum difference correction, forming a "three-step" quantitative calculation method for potassium content based on radioactive difference analysis.

[0060] 2.1) Shale content calculation

[0061] The natural radioactivity of the formation mainly comes from uranium (U), thorium (TH), and potassium (K) elements in the formation. For evaporite formations, the U element mainly comes from clay, kerogen, formation water, carbonate rocks, etc., which are widely present in the formation and cannot be used as an indicator element for shale; the TH element mainly comes from shale, which is the main factor controlling its distribution in sedimentary rocks. It is the adsorption of TH by shale and the existence of thorium (TH) in stable minerals; K mainly comes from shale and polyhalite. Since the K content in the formation is small, it can be considered that K is mainly contributed by potassium-rich minerals.

[0062] Since the existence of TH in the formation is relatively stable and has a strong correlation with the sedimentary environment and shale content, the change of TH value can be used to calculate the shale content of the formation, as shown in the following formulas (3-3) and (3-4):

[0063]

[0064] In the above formulas (3-3) and (3-4), SH TH , shale content index; TH max, thorium value of pure mudstone layer, unit PPM; TH min , thorium value of pure sandstone formation, unit PPM; V TH , total shale content, %; GCUR, formation constant, generally taken as 2 for old formations.

[0065] 2.2) Calculation of potassium-rich mineral content

[0066] When there are no potassium-rich minerals in the formation, the shale content calculated using the TH curve and the K curve is basically the same. When there is a difference between the two, especially when the shale content calculated by the K curve is higher than the result calculated by the TH curve, this difference is mainly affected by potassium-rich minerals. Therefore, after calculating the shale content using the TH curve, the apparent shale content of the formation (the sum of shale content and potassium-rich minerals) is calculated using the K curve, and the GCUR empirical constant is introduced to optimize the calculation model as shown in the following formulas (3-5) and (3-6):

[0067]

[0068] In the above formulas (3-5) and (3-6), SH K* , shale content index; V K *, apparent shale content calculated by potassium; K max , potassium value of pure mudstone layer, %; K min , potassium value of pure sandstone formation, unit %; GCUR, formation constant, generally taken as 2 for old formations.

[0069] After calculating the apparent shale content V K *, calibrate the TH curve and the K curve based on the anhydrite formation as the reference lithology, and calculate the potassium-rich mineral content V using the "gamma energy spectrum difference analysis method" K :

[0070]

[0071] In the above formula (3-7), V TH , total shale content, %; V K* , potassium-rich mineral content, %; a, empirical parameter, calibrated using core data. In the absence of core data, a is defaulted to 1.

[0072] Calculate the potassium content of a certain well using the above model (such as Figure 2 ). In the modeling study, potassium-rich minerals are regarded as one of the mineral components for logging calculation, and a targeted quantitative calculation model is established, which can effectively solve the problem of large errors between the calculated mineral content and the actual situation. In this study, the shale content is calculated using the thorium curve, and the lithology profile calculated with the potassium-rich mineral content as one of the main mineral components is more in line with the actual situation, effectively improving the logging evaluation accuracy of the Jialingjiang Formation.

[0073] 3) Construct the carnallite characterization curve using the preferably selected sensitive logging curves of carnallite. Take the curve as the inversion target curve, and use the frequency division attribute inversion method. Meanwhile, add the wave impedance inversion result as a constraint to conduct carnallite prediction and obtain the distribution range of carnallite.

[0074] In order to further reduce the cumulative error caused by multiple inversions, attempt to construct the carnallite characterization curve using the preferably selected sensitive logging curves of carnallite. Since there is no obvious linear relationship between each individual sensitive curve and the carnallite reservoir, the BP neural network machine learning method is adopted to establish a non-linear relationship to construct the carnallite curve.

[0075] Finally, take the constructed carnallite curve as the inversion target curve, adopt the frequency division attribute inversion method, and through the AVF response relationship between amplitude and frequency under different formation thicknesses, and at the same time add the constraint condition of the wave impedance inversion result to predict the spatial distribution of carnallite (such as Figure 6 ).

[0076] Figure 7 Figure 12 is the prediction profile of carnallite in the Jiasisiwu Formation across Well Guochuan 2. On the left side of the well trajectory in the figure is the potassium of the gamma energy spectrum curve, and on the right side is the logging interpretation conclusion. By comparing the logging interpretation and seismic prediction results, it is considered that the developed intervals of carnallite given by the two have a good corresponding relationship.

[0077] Obtain the development time thickness of carnallite through the seismic inversion result of carnallite, and combine it with the inversion amplitude value to obtain the distribution thickness result of carnallite in the Jiasisiwu Formation ( Figure 8 ). Compare it with the logging-divided carnallite thickness of 5 drilled wells, and the predicted average absolute thickness error is 8.78 m (Table 3).

[0078] Table 3: Comparative analysis of the predicted thickness and actual drilled thickness of rock carnallite in the Jiasisiwu Formation

[0079]

[0080] 4) Within the distribution range of carnallite in the above step 3), combine the structural position, fault distribution, surface construction conditions, etc., and select the drilling well positions of carnallite.

[0081] Use the seismic velocity field to conduct time-depth conversion on the strata related to ore-bearing in the study area: TT3t, TT2l3, TT2l1, and TT1j 4_5 A total of 4 seismic reflection layers, and a total of 4 structural maps are formed. It is required that the structural form and detailed changes are clearly visible, and combined with the distribution range of carnallite obtained in step 3), deploy exploration well positions.

[0082] The inventive concept is reasonable and the process is simple, which can effectively improve the well location selection efficiency, increase the drilling success rate, achieve high-economic exploration, effectively exclude the interference of other potassium-containing minerals such as mudstone, greatly improve the accuracy of inversion prediction, and realize the precise positioning of polyhalite ore bodies in complex structures, deep burial, extremely thick and easily soluble gypsum rock layers, thereby guiding the well location deployment.

[0083] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for deploying exploration well positions of polyhalite, characterized in that, It mainly includes the following steps: 1) Using physical cores to clarify the logging response characteristics of main lithologies; The specific process is as follows: Select cores sampled from actual drilled wells to conduct rock physical parameter measurements, and analyze the rock physical parameters and seismic elastic sensitive parameters; Collect core data of polyhalite-bearing rocks in the polyhalite-bearing well sections of the actual measured wells for rock sample preparation; Through rock sample analysis, it is tested that the rock samples are mainly composed of gypsum, rock salt, and sylvinite polyhalite. According to the needs of petrophysical analysis, conduct laboratory tests on the porosity, velocity, and density parameters of the rock samples, and analyze the velocity, density, and elastic parameter characteristics of potassium-rich salt deposits with different contents and different stratigraphic structures; On the basis of clarifying the logging response characteristics of different lithologies, establish the logging identification model of the main lithologies in the Jialingjiang Formation in the study area; 2) On the basis of obvious response characteristics, use the radioactivity difference to measure the potassium content, determine the correlation between the potassium content and the total radioactivity, and construct a calculation model; The specific process is as follows: On the basis of obvious response characteristics, use the thorium curve to calculate the shale content of the formation, use the classical model of shale content to inversely calculate the potassium-rich mineral content, and finally correct and eliminate the influence of potassium content in shale minerals through gamma energy spectrum difference correction to form a quantitative calculation model of potassium content based on radioactivity difference analysis; 3) Use the polyhalite-sensitive logging curve to construct a polyhalite characterization curve, use the curve as the inversion target curve, and use the frequency division attribute inversion method. At the same time, add the wave impedance inversion result as a constraint to carry out polyhalite prediction to obtain the distribution range of polyhalite; The specific process is as follows: Use the polyhalite-sensitive logging curve to construct a polyhalite characterization curve, adopt the BP neural network machine learning method, and establish a non-linear relationship to carry out the construction of the polyhalite curve; Then, use the constructed polyhalite curve as the inversion target curve, adopt the frequency division attribute inversion method, and through the response relationship of the amplitude changing with frequency between the amplitude and frequency under different formation thicknesses, and at the same time add the constraint condition of the wave impedance inversion result to predict the spatial distribution of polyhalite; Then, obtain the development time thickness of polyhalite through the seismic inversion result of polyhalite, combine the amplitude value of the seismic inversion of polyhalite to obtain the distribution thickness result of polyhalite, and then compare it with the polyhalite thickness divided by the logging of the drilled well to predict the mean absolute thickness error; 4) Select polyhalite drilling well positions within the distribution range of polyhalite in step 3) above.

2. The kainite exploration well location deployment method according to claim 1, characterized in that , The specific process of using the thorium curve to calculate the shale content of the formation is as follows: Calculate the shale content of the formation using the change of thorium element value, as shown in the following formulas (3-3) and (3-4): In the above equations (3-3) and (3-4), SH TH is the shale content index, TH max is the thorium value of the pure shale layer, TH min is the thorium value of the pure sandstone formation, V TH is the total shale content, GCUR is the formation constant, and 2 is taken for old formations.

3. The kainite exploration well location deployment method according to claim 1, characterized in that , The specific process of inversely calculating the potassium-rich mineral content using the classical model of shale content is as follows: After calculating the shale content using the thorium curve, then use the potassium curve to calculate the apparent shale content of the formation, that is, the sum of shale content and potassium-rich minerals, and introduce the GCUR empirical constant to optimize the calculation model, as shown in the following formulas (3-5) and (3-6): In the above equations (3-5) and (3-6), SH K* is the shale content index, V K * is the apparent shale content calculated by potassium, K max is the potassium value of the pure shale layer, K min is the potassium value of the pure sandstone formation, and GCUR is the formation constant, taking 2 for old formations; Calculate the apparent shale content V K After that, calibrate the thorium curve and potassium curve based on the anhydrite formation as the reference lithology, and calculate the potassium-rich mineral content V using the "gamma energy spectrum difference analysis method" K , as shown in the following formula (3-7): In the above formula (3-7), V TH is the total shale content, V K* is the content of potassium-rich minerals, a is an empirical parameter, calibrated using core data, and in the absence of core data, a is defaulted to 1.

4. The method for deploying potash feldspar exploration well positions as described in claim 1, wherein , The specific process of step 4) is as follows: Using the seismic velocity field, perform time-depth conversion on the strata related to ore-bearing in the study area: TT3t, TT2l3, TT2l1, and TT1j 4_5 A total of 4 seismic reflection layers are converted to form 4 structural maps. It is required that the structural morphology and detailed changes are clearly visible. Combining with the distribution range of polyhalite obtained in step 3), exploration well positions are deployed.

5. The method for deploying exploration well positions of polyhalite as claimed in claim 4, characterized in that: The time-depth conversion is to continuously iterate and repeat the seismic data processing, velocity analysis, and logging data to optimize the conversion result.

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