A prospecting method for halite-type carnallite

Abstract

This invention provides a prospecting method for halite-type carnallite, comprising the following steps: 1) Locating the center of potassium salt accumulation through lithofacies paleogeography, mineralogy, and petrology analysis; 2) Analyzing the characteristics of carnallite on well logging curves through geophysical logging and comparison with actual rock cores, summarizing the well logging response characteristics that differ from other lithologies, and establishing a well logging identification model; 3) Utilizing 3D seismic data, obtaining the tectonic deformation and potassium-bearing salt layer distribution characteristics through well-seismic calibration and forward and inverse tectonic evolution analysis; then, based on rock physics testing, seismic attribute analysis, and forward modeling methods; and finally, establishing a seismic inversion framework model under complex tectonic deformation conditions to determine the distribution characteristics of carnallite-bearing salt layers. This invention is rationally conceived and can determine the distribution range of ore bodies through multi-hole logging, 3D seismic analysis, and multiple methods including mineralogy, sedimentology, and geochemistry.

Description

Technical Field

[0001] This invention relates to the field of geological exploration technology, specifically to a method for prospecting halite-type halide. Background Technology

[0002] Polyhalite is a soluble double salt of potassium and sulfate, with the chemical formula K₂Ca₂Mg(SO₄)₄·2H₂O. Under an electron microscope, polyhalite is mainly observed to have a micro-fine granular structure. It is colorless and transparent under single-polarized light, and is often found in subhedral to anhedral granular, tabular, or elongated shapes with low positive relief. Under crossed polarized light, its interference color reaches up to second-order blue, and it commonly exhibits polysynthetic twinning.

[0003] Among them, the numerous detrital particles of carnallite that are associated with halite and distributed in the halite matrix are named "halite-type carnallite potash deposits" because they are significantly different from carnallite interlayered with dolomite and anhydrite. This is a new type of marine soluble solid potash deposit, the most significant feature of which is its solubility in water. It can be mined on a large scale using environmentally friendly freshwater dissolution, making it a usable "live deposit".

[0004] In halite aggregates and clastic particles of varying sizes are scattered within a halite matrix. Upon injection of fresh water, the halite matrix, acting as a cement, rapidly dissolves, causing the halite particles to lose their support and enter the brine solution. There, they move randomly and are further dissolved in the water, becoming soluble clastic halite. These salt crystal-cemented clastic halite particles are comparable to soluble potassium salt deposits such as halite and carnallite, facilitating water-soluble extraction. Water injection extraction can be carried out using docking wells, significantly reducing production costs and greatly improving production efficiency. Potassium-rich brine containing dissolved clastic halite can be directly used to produce high-quality potassium sulfate-based potassium fertilizer or compound potassium-magnesium fertilizer. Therefore, this type of "salt crystal clastic halite rock" is referred to as "a halite-type halite potassium salt deposit."

[0005] Halite-type halide potassium salts are formed during the halite deposition stage, when seawater further evaporates and concentrates, but before the potassium halite deposition stage is fully achieved. These halide salts are rich in Ca. 2+ Repeated seawater intrusion, combined with potassium-rich water... + Mg 2+ The mixture of ancient seawater and the precipitation of carnallite indicates a primary sedimentary origin. Subsequently, tectonic fracturing and folding caused the carnallite to fracture and disperse in patches and dots embedded in the salt rock. After being altered by underground fluids, it exhibits the characteristic of "salt-encased potassium" with irregularly shaped granular boundaries. Scanning electron microscopy shows that halite-type carnallite potassium salts are characterized by coexistence with halite and clear boundaries. The genetic model of halite-type carnallite potassium salt deposits is summarized as "primary sedimentation, tectonic adjustment and fracturing dissolution".

[0006] The halite-type halide in the Sichuan Basin is developed in the gypsum-salt rocks of the Jia 4-5 section. Due to tectonic adjustment and modification, the gypsum-salt rocks have undergone plastic deformation with large lateral thickness variations, making conventional prospecting methods unsuitable.

[0007] Currently, the main methods for deep potash exploration can be categorized as follows:

[0008] (1) Regional geological analysis and comparison: Based on the analysis of the geological structure and sedimentary environment of the study area, the potential location of potash deposits is predicted by comparing with the geological characteristics of known potash deposits.

[0009] (2) Oil and Potassium Exploration: This method is mainly dependent on oil exploration. Since many large potash deposits are discovered during oil exploration, potash deposit exploration can be carried out simultaneously during oil exploration.

[0010] (3) Deep potash deposits were discovered during the exploration of rock salt mines: Potassium anomalies were discovered through geochemical exploration during the exploration of rock salt mines, thus finding potash deposits.

[0011] The above-mentioned deep potash exploration methods have relatively low accuracy and efficiency, especially in the Puguang area of ​​northeastern Sichuan, where the geological structure is extremely complex and the success rate of exploration is very low.

[0012] Therefore, determining the distribution range of ore bodies through multi-hole logging, 3D seismic analysis, and analysis using mineralogy, sedimentology, geochemistry, and other methods is one of the key scientific problems and difficulties that this invention aims to solve. It is also a prerequisite for further determining the distribution area of ​​carnallite and delineating high-grade mineralization areas. Summary of the Invention

[0013] To address the technical problems existing in the background art mentioned above, this invention proposes a prospecting method for halite-type carnallite. Its concept is reasonable, combining geological prospecting methods with geophysical methods such as well logging identification and seismic prediction, and establishing a three-in-one prospecting model of "geology + well logging interpretation + seismic prediction" for deep halite-type carnallite. It also proposes scientific target area selection and well location deployment methods, which greatly improves the success rate of prospecting for deep halite-type carnallite.

[0014] To solve the above-mentioned technical problems, the present invention provides a method for prospecting halite-type carnallite, which includes the following steps:

[0015] 1) Locate the center of potassium salt accumulation through lithofacies paleogeography, mineralogy, and petrology analysis;

[0016] 2) By comparing geophysical logging with physical core samples, the characteristics of carnallite on the logging curves were analyzed, and logging response characteristics that are different from those of other lithologies were summarized, and a logging identification model was established.

[0017] 3) Using 3D seismic data, well-seismic calibration is carried out, and forward and inverse structural evolution analysis is conducted to obtain the characteristics of tectonic deformation and potassium-bearing salt layer distribution. Then, based on rock physics testing, seismic attribute analysis and forward modeling, a seismic inversion framework model under complex tectonic deformation conditions is established to determine the distribution characteristics of heterohalite salt layers.

[0018] The prospecting method for halite-type halide includes: step 1) collecting data on mineralization, potassium-forming index chlorine isotopes, and potassium and lithium concentrations, and then compiling paleogeographic maps using the residual thickness method to find paleogeographic backgrounds of large-scale saline-potassium accumulation basins from the maps.

[0019] The prospecting method for halite-type halide includes: step 2) clarifying the logging response characteristics of the main lithologies through well logging and core calibration logging, and establishing a logging identification model for the main lithologies of the strata in the study area based on clarifying the logging response characteristics of different lithologies.

[0020] The prospecting method for halite-type carnallite, wherein the specific process of establishing a well logging identification model for the main lithology of the stratigraphy in the study area is as follows:

[0021] 2.1) High-precision identification of halite-type carnallite

[0022] First, by combining the potassium and thorium curves, the sensitivity to polyhalite is expanded, and the potassium to thorium ratio is used as the sensitive curve for identifying polyhalite. Based on the first clear identification of the three sensitive parameters of halite-type polyhalite, namely "low density, high potassium, and low thorium", a potassium index KI characteristic identification curve is constructed, as shown in the following equation (3-2):

[0023]

[0024] In equations (3-1) and (3-2) above, K_TH is the potassium-thorium ratio, POTA is the potassium content in the formation as shown by natural gamma ray spectroscopy logging, THOR is the thorium content in the formation as shown by natural gamma ray spectroscopy logging, KI is the potassium index, and K_TH min The minimum potassium-thorium ratio, K_TH max The maximum potassium-thorium ratio is given, and DEN is the density data from the well logging data. min DEN is the minimum density data in well logging data. max This refers to the maximum density data in the well logging data;

[0025] Then, by calibrating core data, the KI threshold values ​​for different lithologies and halite-type carnallites were determined. KI values ​​between 0.1 and 0.2 were defined as Class III carnallite layers; KI values ​​between 0.2 and 0.4 were defined as Class II carnallite layers; and KI values ​​greater than 0.4 were defined as Class I carnallite layers. The KI values ​​were then used to perform high-precision logging identification of carnallite quality.

[0026] 2.2) Identification of carnallite logging

[0027] The formation containing carnallite exhibits characteristics of "high potassium, low thorium, and low density." Based on this, the formation clay content is calculated using the thorium curve in the natural gamma ray spectroscopy logging curve. The potassium content is then calculated using the classical clay content model. Finally, the influence of potassium content in clay minerals is eliminated through gamma ray spectroscopy difference correction.

[0028] The prospecting method for halite-type carnallite, wherein step 3) first involves analyzing the deformation and displacement characteristics of potassium-rich gypsum-salt rock strata under complex tectonic alteration, clarifying the deformation, displacement, and accumulation characteristics of potassium-bearing gypsum-salt rocks as "early wing accumulation, later superimposed alteration," and showing a linear positive correlation between salt layer thickness and the development scale of halite-type carnallite; based on this, a detailed seismic sequence characterization based on a global isochronous stratigraphic framework is carried out, and a global isochronous framework model is established; combined with the analysis of potassium-rich strata petrography and sensitive parameters, by carrying out the extraction and application of potassium-rich strata sensitive parameters mainly based on seismic inversion, and on the basis of Bayesian wave impedance random inversion, a convolutional neural network is used to directly predict the potassium content curve, thereby realizing the description and prediction of the spatial distribution characteristics of potassium-rich strata.

[0029] The prospecting method for halite-type carnallite, wherein: the specific process of obtaining the structural deformation and potassium-bearing salt layer distribution characteristics in step 3) is as follows: by establishing a potassium-bearing rock layer description and inversion method based on seismic, geological, and well logging data, guided by potassium-rich rock sedimentary facies analysis and fine inversion constraint model, and with facies-controlled high-precision inversion as the core, a comprehensive analysis and distribution characteristic description of potassium-bearing rock strata can be achieved.

[0030] The prospecting method for halite-type carnallite, wherein the specific process of rock physics testing in step 3) is as follows: core samples are taken from actual drilled wells to measure rock physics parameters, and the rock physics parameters and seismic elastic sensitivity parameters are analyzed. Core data containing carnallite are collected in the carnallite-bearing section of the actual drilled well, and rock samples are prepared. Through rock sample analysis, the rock samples are mainly composed of gypsum, rock salt, and halite-type carnallite. According to the needs of rock physics analysis, laboratory tests are conducted on the rock sample parameters to analyze the velocity, density, and elastic parameter characteristics of potassium-rich salt minerals with different contents and different stratigraphic structures. Then, X-ray diffraction and thin-section microscopic tests are performed on some rock samples to obtain the rock mineral composition and structural form of the rock samples. Then, the samples are divided into two types of structural minerals, halite and gypsum, and the longitudinal wave velocity analysis of the salt rock framework minerals and gypsum rock framework minerals is carried out.

[0031] The prospecting method for halite-type carnallite, wherein: step 3) of establishing a seismic inversion framework model under complex tectonic deformation conditions mainly involves establishing a rock physics model of potassium-rich strata. The specific process is as follows: first, based on the core analysis of potassium-rich strata, when the rock strata have low porosity, random and sparse distribution of inclusions, and the inclusions have bearing capacity, it can be considered as its geometric characteristics, forming a rock physics parameter calculation model; the measured P and S wave velocities of the core are compared and analyzed with the calculated values, and the error does not exceed 5%, proving that the model is relatively reliable, and the relative error of the predicted P and S wave velocities of salt rock and gypsum rock is within 5%.

[0032] The prospecting method for halite-type carnallite, wherein the seismic attribute analysis and forward modeling process in step 3) is as follows:

[0033] 3.1) Inversion of Sensitive Factors in Potassium-Rich Rock Strata Using Active Limitation Learning Machine

[0034] The receptive field is divided by using a convolution operator to mine useful information in the local area of ​​the potassium-rich layer, solve the uncertainty of point-to-point calculation, and obtain potassium salt content profile map and seismic inversion wave impedance results.

[0035] 3.2) Analysis of Sensitive Factors of Regional Carnallite and Potassium-Rich Brine Strata

[0036] Based on the interpretation of complex and folded stratigraphic structures of the target strata in the region, and using the inversion results of potassium-rich salt layer sensitive factors, a study was conducted to predict the distribution characteristics of complex halides and potassium-rich brine layers in the area.

[0037] By adopting the above technical solution, the present invention has the following beneficial effects:

[0038] The prospecting method for halite-type carnallite in this invention is rationally conceived. It combines geological prospecting methods with geophysical methods such as well logging identification and seismic prediction, and establishes a three-in-one prospecting model for deep halite-type carnallite, which integrates geology, well logging interpretation and seismic prediction. It also proposes scientific methods for target area selection and well location deployment, which greatly improves the success rate of prospecting for deep halite-type carnallite.

[0039] This invention clarifies the three sensitive parameters of halite-type polyhalite: low density, high potassium, and low thorium. It constructs a potassium index characteristic identification curve, achieving a lithological identification accuracy of 97%, significantly superior to current domestic and international methods that identify lithology and reservoirs using a single parameter for polyhalite, which have low accuracy. It also develops a quantitative calculation method for potassium content based on radioactivity difference analysis, achieving a prediction accuracy of 90%, superior to current domestic and international methods that utilize fitting relationships for quantitative calculation of potassium content in polyhalite, which have low well-well accuracy. Furthermore, it establishes key technologies for geophysical prediction of halite-type polyhalite with spatial constraints of potassium-bearing salt layers, achieving a thickness prediction accuracy of over 85%. Currently, domestic and international methods for predicting potassium-bearing strata largely ignore the differences between polyhalite and the geological conditions of oil and gas resource occurrence.

[0040] This invention can determine the distribution range of ore bodies through multi-hole logging, 3D seismic analysis, and analysis using mineralogy, sedimentology, geochemistry, and other methods, making it suitable for promotion and application. Attached Figure Description

[0041] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific 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 from these drawings without creative effort.

[0042] Figure 1 This is a flowchart of the prospecting method for halite-type carnallite according to the present invention;

[0043] Figure 2 This is a well logging response diagram of a traditional type of carnallite rock involved in the prospecting method for halite-type carnallite of the present invention;

[0044] Figure 3 This is a well logging response diagram of salt crystal grains in haloallic rocks involved in the prospecting method of halite-type haloallic rocks of the present invention;

[0045] Figure 4 This is a conventional well logging response characteristic diagram of anhydrite rock involved in the prospecting method of halite-type carnallite of the present invention;

[0046] Figure 5 This is a conventional well logging response characteristic diagram of halite rock involved in the prospecting method of halite-type carnallite of the present invention;

[0047] Figure 6 This is a well logging identification diagram of halide in well HB107, which is involved in the prospecting method for halite-type halide of the present invention.

[0048] Figure 7 This is a well logging identification diagram of halide in well YB16, which is involved in the prospecting method for halite-type halide of the present invention.

[0049] Figure 8 These are microscopic images of salt rock and gypsum rock samples involved in the prospecting method for halite-type carnallite of the present invention.

[0050] Figure 9 This is a profile of the formation sensitivity factor prediction of halite-bearing strata in wells Puguang 8-Puguang 10, which is involved in the prospecting method for halite-type carnallite in this invention.

[0051] Figure 10 This is a predicted cross-section of the halide 102-Dawan 3-Puguang 11 halide involved in the prospecting method for halite-type halide of the present invention. Detailed Implementation

[0052] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0053] The present invention will be further explained below with reference to specific embodiments.

[0054] like Figure 1 As shown, the prospecting method for halite-type carnallite provided in this embodiment includes the following steps:

[0055] 1) Analyze the petrographic paleogeography, mineralogy, petrology, etc., to find the center of potassium salt accumulation.

[0056] Potash development is closely related to evaporite deposition (RD Matthews, 1970). Salt-forming periods often correspond to specific salinization cycles. Theoretically, the evaporite depositional sequence increases with water concentration, successively depositing carbonate rocks (limestone, dolomite) – sulfate rocks (gypsum) – chlorides (halite, potash). During the six tectonic cycles of the Sichuan Basin, in the Caledonian cycle, influenced by the Xingkai Orogeny, the Middle Cambrian was hot and arid, with intense evaporation lasting for a long time, leading to further salinization of the seawater. The Sichuan Central Uplift and the Guizhou Central Uplift isolated the basin, resulting in the development of evaporative lagoons in southeastern Sichuan, along with evaporative gypsum-salt rocks. During the Early Triassic Jialingjiang Orogeny of the Indosinian cycle, due to the presence of high-altitude terrain surrounding the basin, such as the Daba Mountains, the Longmenshan Island Chain, the Qinling Mountains, and the Jiangnan Ancient Landmass, the Sichuan Basin became a semi-enclosed inland sea basin. Seawater intruded from east to west, and with alternating transgressions and regressions, the basin was deposited with abundant marine carbonate rocks (limestone, dolomite) and gypsum-salt rocks. During the Middle Triassic, controlled by paleogeographic environments, a series of restricted platforms-evaporation platforms, dominated by carbonate rocks and evaporites, developed in the Lei 3 Member in central Sichuan and the Lei 4 Member in southwestern and western Sichuan. The lithology is primarily limestone, dolomite, gypsum, and salt rocks. By collecting data on mineralization, potassium-forming indices (chlorine isotopes), and potassium and lithium concentrations, the potassium-forming indices (δ¹⁴ ppm of chlorine isotopes) of the Jia 4-5 Member in the Triassic were determined. 37 Cl-0.83‰, mineralization 150-327g / L, K + Concentration 3200-7183 mg / L, Li + 33-124 mg / L. The overall brine salinity and potassium-forming element indicators are good, indicating that the Jiaxing Section 4-5 salt basin has a large area, is relatively isolated and closed, and has strong evaporation, which is conducive to the high salinization and concentration of brine and the large-scale supersaturated deposition of potassium salts.

[0057] By interpreting the top and bottom of the Triassic Jia 4-5 Member through a two-dimensional grid seismic profile in the Sichuan Basin, and then compiling paleogeomorphic maps using the residual thickness method, it can be concluded that Yuanba, Tongjiang, Puguang, and Fuling in eastern to northeastern Sichuan are generally located in lower paleogeomorphic areas. To the west and southwest, they were uplifted under the control of the Luzhou Paleo-Uplift, thus forming a paleogeomorphic background of large-scale saline-potassium basins. Statistical results of gypsum-salt rocks show that the thickest gypsum-salt rock deposits reach over 400 meters, and four large salt lakes were discovered in Yuanba, Tongjiang, Puguang, and Fuling.

[0058] Based on well and seismic characteristics, the gypsum-salt rock strata of the Jialingjiang Formation in northeastern Sichuan are stable, with good correlation, continuity, and considerable thickness. East-west seismic profiles show that the Yuanba and Tongjiang salt basins were relatively separated in the early stages of salt basin formation, with a local high point in the middle. Later, they merged into a unified salt basin, with upward uplift and erosion on both the east and west sides, suggesting a close relationship with the overall Indosinian tectonic movement. Similarly, east-west well comparisons show that in the western Longtan 1 well, where the gypsum-salt rock thickness decreases, the gypsum-salt thickness is 181 meters, and the salt rock thickness is 35 meters, with no potash salt development overall. In the Yuanba 16 well, the gypsum-salt thickness is 226 meters, and the salt rock thickness reaches 165 meters, with 90 meters of salt rock-type halide logging interpretation. Near the Tongjiang Heba 102 well, halide is nearly 40 meters thick. Further east to the Heichiliang area, the overall composition is dominated by anhydrite, with no halide development. Through systematic research, four large-scale saline-potassium-accumulating basins—Yuanba, Tongjiang, Puguang, and Fuling—were finally identified, filling the gap in the early understanding of the absence of potassium-accumulating structures in northeastern Sichuan.

[0059] 2) By using geophysical logging and comparing it with physical core samples, we can analyze the characteristics of carnallite on the logging curves, summarize the logging response characteristics (absolute values ​​of each logging item, curve shape, etc.) that are different from other lithologies, and establish a logging identification model.

[0060] The depositional environment of the Jialingjiang Formation in northeastern Sichuan changed rapidly and the lithology was complex during its depositional period. By calibrating well logging data from well logging and core samples, the logging response characteristics of major lithologies such as traditional carnallite, halite-type carnallite, anhydrite, and halite were clarified.

[0061] ① Traditional type of carnallite

[0062] Traditional carnallite is a dense, hard, massive mineral that is sparingly soluble in water. On conventional well logging curves, it exhibits high natural gamma values, generally between 171 and 180 API, with an average of 176 API; and relatively low density, ranging from 2.57 to 2.71 g / cm³. 3 Between, the average is 2.65 g / cm³. 3The acoustic wave concentration was relatively high, ranging from 55 to 64 μs / ft, with an average of 60 μs / ft; the compensating neutron concentration was relatively high, ranging from 16.1% to 22.5%, with an average of 18.9%; the resistivity was generally high, mainly between 10924 and 13963 Ω·m, with an average of 12424 Ω·m; the uranium concentration was relatively low, ranging from 0.204 to 1.142 ppm, with an average of 0.735 ppm; the thorium concentration was relatively low, ranging from 0.78 to 1.16 ppm, with an average of 0.98 ppm; and the potassium concentration was relatively high, ranging from 9.94% to 11.01%, with an average of 10.46% (e.g., ...). Figure 2 (As shown).

[0063] ②Halalite-type carnallite

[0064] Halite-type carnallite is a type of potassium salt mineral with crystalline carnallite particles. It is distributed in star-shaped, irregular clumps, or band-like patterns in the halite matrix. Carnallite clumps, aggregates, and other granular particles of varying sizes are scattered in the halite matrix. After fresh water is injected, the halite matrix, which acts as a cement, dissolves rapidly. The carnallite particles lose their support and enter the brine solution, where they are in a state of random motion and are further dissolved in the water, becoming soluble granular carnallite particles.

[0065] Because this type of carnallite is associated with halite, it is often accompanied by enlargement, which affects the logging curve and makes it difficult to reflect its true logging framework values. Statistical analysis shows that carnallite with salt crystal grains exhibits the following characteristics on conventional logging curves: high natural gamma ray values, generally between 69 and 121 API, with an average of 100 API; and low density values, ranging from 2.22 to 2.64 g / cm³. 3 The average value is 2.39 g / cm³. 3 The acoustic wave concentration was relatively high, ranging from 57 to 71 μs / ft, with an average of 61 μs / ft; the compensated neutron concentration was relatively high, ranging from 6.4% to 12%, with an average of 8.7%; the resistivity was generally high, ranging from 10,000 to 15,000 Ω·m, with an average of 12,500 Ω·m; the uranium concentration was relatively low, ranging from 0.13 to 2.56 ppm, with an average of 1.02 ppm; the thorium concentration was relatively low, ranging from 0.82 to 2.43 ppm, with an average of 1.38 ppm; and the potassium concentration was relatively high, ranging from 3.82% to 7.86%, with an average of 5.84% (e.g., ...). Figure 3 (As shown).

[0066] ③ Anhydrite rock

[0067] Conventional logging curves show the following: low natural gamma values, generally between 11 and 19 API, with an average of 14 API; and relatively high density values, ranging from 2.89 to 3.03 g / cm³. 3 Between, the average was 2.96 g / cm³.3 The relative median value for sound waves is between 47 and 54 μs / ft, with an average of 50 μs / ft; the relative low value for compensated neutrons is between -1.5% and -0.2%, with an average of -0.9%; the overall resistivity is relatively high, mainly between 62,000 and 99,990 Ω·m, with an average of 81,000 Ω·m; the relative low value for uranium is between 1.29 and 2.34 ppm, with an average of 1.73 ppm; the relative low value for thorium is between 1.04 and 2.32 ppm, with an average of 1.43 ppm; the relative low value for potassium is between 0.29% and 0.49%, with an average of 0.38% (e.g., ...). Figure 4 (As shown).

[0068] ④Halal rock

[0069] On conventional well logging curves, the following characteristics are observed: natural gamma ray is low, generally between 12 and 22 API, with an average of 17 API; density is relatively low, ranging from 1.55 to 2.48 g / cm³. 3 Between, the average was 1.94 g / cm³. 3 The acoustic wave density was relatively high, ranging from 63 to 88 μs / ft, with an average of 70 μs / ft; the compensating neutron density was relatively low, ranging from 13% to 22%, with an average of 18.0%; the resistivity was generally high, mainly between 17,000 and 99,990 Ω·m, with an average of 92,500 Ω·m; the uranium density was relatively low, ranging from 0.53 to 1.82 ppm, with an average of 1.23 ppm; the thorium density was relatively low, ranging from 1.08 to 4.17 ppm, with an average of 1.76 ppm; and the potassium density was relatively low, ranging from 0.27% to 0.75%, with an average of 0.44% (e.g., ...). Figure 5 (As shown).

[0070] Based on the clear understanding of the logging response characteristics of different lithologies, a logging identification model for the main lithologies of the Jialingjiang Formation in the study area was established (Table 3-1).

[0071] Table 3-1 Classification and Identification Patterns of Major Lithological Formations in the Jialingjiang Formation via Well Logging

[0072]

[0073]

[0074] Traditional polyhalite is a sparingly soluble potassium salt, occurring in banded, layered, and massive forms within anhydrite. Its logging curves exhibit a "three highs and two lows" characteristic: high natural gamma ray, high potassium, high resistivity, low thorium, and low uranium. Crystalline polyhalite differs significantly from traditional polyhalite, consisting of intraclastic particles of varying sizes scattered within the halite matrix in spots, nodules, and aggregates. Its logging curves show a "three highs, three lows, and one expansion" characteristic: high natural gamma ray, high potassium, high resistivity, low thorium, low uranium, and low density. Because halite is an easily soluble salt, it often exhibits diameter expansion; "low density and diameter expansion" are the most typical identifying features of crystalline polyhalite.

[0075] The specific process for establishing the well logging identification model of the main lithology of the stratigraphy in step 2) above is as follows:

[0076] 2.1) High-precision identification of halite-type carnallite

[0077] Thorium levels in formations are primarily influenced by variations in clay content and grain size. Thorium values ​​increase as the rock grain size decreases, and also increase with increasing clay content. In carbonate formations, low thorium values ​​indicate low clay content, thus eliminating the influence of clay content on polyhalite identification. Potassium levels are mainly affected by potassium-rich minerals in the formation. The potassium curve is sensitive to polyhalite; potassium values ​​increase significantly when polyhalite is present. Furthermore, the density curve serves as a key differentiator between conventional and halite-type polyhalite. Therefore, by combining potassium and thorium curves, the sensitivity to polyhalite can be broadened, and the potassium-to-thorium ratio can be used as a sensitive curve for polyhalite identification.

[0078] Based on the initial identification of the three sensitive parameters of halite-type carnallite—"low density, high potassium, and low thorium"—a potassium index (KI) characteristic identification curve was constructed, as shown in equation (3-2) below:

[0079]

[0080] In equations (3-1) and (3-2) above, K_TH is the potassium-thorium ratio, POTA is the potassium content in the formation as shown by natural gamma ray spectroscopy logging, THOR is the thorium content in the formation as shown by natural gamma ray spectroscopy logging, KI is the potassium index, and K_TH min The minimum potassium-thorium ratio, K_TH max The maximum potassium-thorium ratio is given, and DEN is the density data from the well logging data. min DEN is the minimum density data in well logging data. max This refers to the maximum density data in the well logging data;

[0081] The KI threshold values ​​for different lithologies and halite-type carnallites in the Jiaxing No. 45 rock formation were determined by core data calibration. KI values ​​between 0.1 and 0.2 were defined as Class III carnallite layers; between 0.2 and 0.4 as Class II carnallite layers; and greater than 0.4 as Class I carnallite layers. High-precision well logging identification of carnallite quality was performed using KI values ​​(Table 3-2).

[0082] Figure 6 The image shows the logging results for the carnallite layer in well HB107. A total of 5 carnallite layers, totaling 44.0 m in length, were interpreted, with individual layer thicknesses ranging from 2.7 to 16.0 m and an average thickness of 8.8 m. The logging response characteristics are as follows: the overall natural gamma ray in the carnallite-bearing section is medium to high, between 13.3 and 183.4 API; the gamma ray without uranium is between 12.5 and 146.0 API; the resistivity is high, between 1258.5 and 100,000 Ω·m; and the density is between 1.83 and 2.98 g / cm³. 3 between.

[0083] Table 3-2 Classification and Evaluation Standards for Well Logging in Carnallite Layers

[0084] Serial Number Evaluation criteria Classification 1 KI ≥ 0.4 A type of halophyllite layer 2 0.2 ≤ KI < 0.4 Type II carnallite layer 3 0.1 ≤ KI < 0.2 Type III carnallite layers 4 KI < 0.1 Non-halite layer

[0085] Figure 7 This is the logging results for the carnallite layer in well YB16. A total of 10 carnallite-bearing layers, totaling 99.8 m in length, were interpreted. The thickness of each layer ranged from 1.5 to 30.0 m, with an average thickness of 10.0 m. The logging response characteristics are as follows: the overall natural gamma ray in the carnallite-bearing section is medium to high, ranging from 12.8 to 163.9 API; the gamma ray without uranium is between 7.2 and 157.2 API; the resistivity is high, ranging from 33 to 99990 Ω·m; and the core density is between 2.22 and 3.04 g / cm³. 3 between.

[0086] Using the potassium index (KI) characteristic identification curve to identify heterohalite significantly improves accuracy compared to conventional methods.

[0087] 2.2) Identification of carnallite logging

[0088] The formation containing carnallite exhibits characteristics of "high potassium, low thorium, and low density." Based on the obvious response characteristics, the formation clay content is calculated using the thorium curve in the natural gamma ray spectroscopy logging curve. The potassium content is then calculated by using the classical clay content model. Finally, the influence of potassium content in clay minerals is eliminated by gamma ray spectroscopy difference correction, forming a "three-step" method for quantitatively calculating potassium content based on radioactivity difference analysis.

[0089] 3) Using 3D seismic data, well-seismic calibration is carried out, and forward and inverse structural evolution analysis is conducted to obtain the characteristics of tectonic deformation and potassium-bearing salt layer distribution. Then, based on rock physics testing, seismic attribute analysis, forward modeling and other methods, a seismic inversion framework model under complex tectonic deformation conditions is established to determine the distribution characteristics of mixed halide salt layers.

[0090] The halite-type halide in the Sichuan Basin is developed in the gypsum-salt rocks of the Jia 4-5 section. Due to tectonic adjustment and modification, the gypsum-salt rocks have undergone plastic deformation with large lateral thickness variations. The thickness of the gypsum-salt strata and the lateral differences in seismic wave groups are large, making it difficult to interpret the structure in detail. Conventional seismic inversion methods based on layered modeling are not applicable.

[0091] To address the aforementioned issues, we first analyzed the deformation and displacement characteristics of potassium-rich gypsum-salt strata under complex tectonic alteration, clarifying the deformation, displacement, and accumulation characteristics of potassium-rich gypsum-salt rocks as "early accumulation in the limbs and later superimposed alteration." The thickness of the salt layer showed a linear positive correlation with the development scale of halite-type carnallite. Based on this, we conducted research on seismic sequence stratigraphy based on a global isochronous stratigraphic framework, establishing a global isochronous framework model. Combining the analysis of petrography and sensitive parameters of potassium-rich strata, we extracted and applied sensitive parameters of potassium-rich strata primarily through seismic inversion. Based on Bayesian wave impedance stochastic inversion, we used convolutional neural networks to directly predict potassium content curves, achieving a description and prediction of the spatial distribution characteristics of potassium-rich strata, providing technical support for finding favorable potassium-rich strata.

[0092] In step 3) above, the specific process of obtaining the structural deformation and potassium-bearing salt layer distribution characteristics by using 3D seismic data, conducting well-seismic calibration, and performing forward and inverse structural evolution analysis is as follows: According to the research needs, a potassium-bearing rock layer description and inversion technology scheme is established, based on seismic, geological, and well logging data, guided by potassium-bearing rock sedimentary facies analysis and fine inversion constraint models, and centered on facies-controlled high-precision inversion, to achieve comprehensive analysis and distribution characteristic description of potassium-bearing rock strata.

[0093] The specific process of the above-mentioned potash-rich strata description and inversion technology scheme, which is based on phase-controlled high-precision inversion, is as follows: By studying the geological characteristics of potash-rich strata, favorable sedimentary facies are summarized, namely, the deformation, displacement, and accumulation characteristics of potash-bearing gypsum-salt rocks under the superimposed tectonic activity of multiple phases and boundaries, characterized by "early wing accumulation and later superimposed alteration." A positive correlation is found between salt layer thickness and the development scale of halite-type carnallite. Then, existing well logging data in favorable areas of potash-rich strata are analyzed, and fine calibration of halite-type carnallite well logging is carried out to establish a fine well logging identification model. Finally, combined with the petrological and sensitive parameters of the potash-rich strata... Analysis revealed that by extracting and applying sensitive parameters of potassium-rich strata primarily through seismic inversion, and based on Bayesian impedance stochastic inversion, convolutional neural networks were used to directly predict potassium content curves, enabling the description and prediction of the spatial distribution characteristics of potassium-rich strata and providing technical support for finding favorable potassium-rich strata. Synthetic seismic record calibration was performed on well logging data, following a method of "starting from wells, from points to lines, and from lines to areas." First, the main seismic profiles and connecting lines of individual wells were interpreted. The interpretation of 3D seismic data was then carried out from sparse to dense, and from coarse to fine, gradually refining the interpretation of each seismic reflection layer in the entire area. To further improve the accuracy of geophysical inversion, research was conducted on seismic sequence stratigraphy based on a global isochronous stratigraphic framework to finely characterize the target geological bodies.

[0094] The specific process of the rock physics test in step 3) above is as follows:

[0095] The petrophysical parameters of halite-type halide layers determine their well logging parameters and seismic reflection characteristics. Analyzing the petrophysical characteristics of halide-bearing strata helps to clarify the sensitive attribute parameters of halide-bearing strata contained in well logging and seismic information, providing a basis for the description and prediction of halide-bearing strata.

[0096] To accurately analyze the petrophysical characteristics of halide-bearing strata, core samples were taken from drilled wells in northeastern Sichuan to measure petrophysical parameters and analyze their relationship with seismic elastic sensitivity parameters. More than 30 halide-bearing core samples were collected from the halide-bearing sections of the drilled wells for sample preparation. Analysis of these samples revealed that they primarily consisted of gypsum, rock salt, and halite-type halide. Based on the requirements of petrophysical analysis, laboratory tests were conducted on parameters such as porosity, velocity, and density of these samples. The velocity, density, and elasticity characteristics of potassium-rich salt deposits with different contents and stratigraphic structures were analyzed.

[0097] To investigate the mineral composition and structure of the tested rock samples, X-ray diffraction and thin-section microscopic analysis were performed on some samples. The analysis revealed significant differences in rock structure and anisotropic properties between gypsum rock and rock salt due to their different structures. Among them, the rock salt exhibited a purer mineral composition (e.g.,...). Figure 8The rock salt contains fractures, but these fractures are randomly distributed and relatively few in number, exhibiting weak anisotropy. The inclusions in the rock salt are carnallite or argillaceous material, distributed as inclusions. The anisotropy of the gypsum salt is closely related to the directional arrangement of fractures; random fractures are highly developed, leading to pressure-sensitive changes in sound velocity and weak anisotropy.

[0098] The experiments tested the sonic velocities of rock salt and gypsum salt. The P-wave velocities of rock salt ranged from 4400 m / s to 4600 m / s; those of gypsum salt ranged from 5400 m / s to 5800 m / s; and those of carnallite-bearing rock ranged from 4800 to 5300 m / s. Rock salt exhibited significantly lower P-wave velocities and densities compared to gypsum salt, while carnallite fell in between, with velocities and densities greater than halite but less than gypsum.

[0099] For salt and gypsum rocks, the velocity of sound waves is primarily controlled by framework minerals. Outside of framework minerals, harder inclusions (magnesite) result in higher sound velocities, while softer inclusions (carnallite) result in lower sound velocities. The velocity of sound waves in gypsum rocks is mainly determined by the velocity of the framework minerals, with the content and type of inclusions causing the velocity to fluctuate around the framework velocity. The framework minerals of salt rocks (halite) have relatively low elastic stiffness compared to other mineral components, and the main inclusion is carnallite.

[0100] Based on the two types of mineral textures—halite and gypsum—the P-wave velocities of the salt rock framework minerals and gypsum rock framework minerals were analyzed. Under the salt rock framework minerals, the P-wave velocity increased linearly with increasing carnallite content, reaching 4820 m / s when the contents of halite, anhydrite, and carnallite were 78%, 16%, and 2%, respectively, indicating a significant increase in velocity when carnallite is present in the salt rock. Under the gypsum-salt framework minerals, the carnallite identification results for thin sections were 92% and 55%, respectively. With increasing proportion of halite-type carnallite, the gypsum rock velocity decreased, hovering around 5200-5500 m / s.

[0101] The sound velocity variations with pressure between gypsum rock and rock salt also differ significantly. As the pressure increases from 5 MPa to 30 MPa, the sound velocity in rock salt increases by 100-200 m / s, while that in gypsum rock increases by 300-500 m / s. Increased pressure leads to a reduction in the space between rock fissures, thus causing an increase in velocity. It is speculated that the difference in velocity variations with pressure between gypsum rock and rock salt may be due to the different degrees of fissure development; gypsum rock contains more fissures than rock salt.

[0102] Step 3) above involves establishing a seismic inversion framework model under complex tectonic deformation conditions, which is essentially establishing a rock physics model of potassium-rich strata. The specific process is as follows:

[0103] First, based on the core analysis of the potassium-rich strata, the rock layers are gypsum or salt rock, characterized by low porosity, random and sparse distribution of inclusions, and the inclusions (salt rock, gypsum, and carnallite) having bearing capacity and can be considered to have a spherical geometry. This leads to the formation of a rock physical parameter calculation model based on the KT equation:

[0104]

[0105] In equations (3-3), (3-4), and (3-5) above, K represents the bulk modulus; μ represents the shear modulus; Km is the bulk modulus of the background medium; μ m K represents the shear modulus of the background medium. i μ is the bulk modulus of the i-th inclusion material; i Let be the shear modulus of the i-th containing material; Equivalent bulk modulus; Equivalent shear modulus; coefficient P mi and Q mi It describes the effect of adding inclusion material i to the background medium m. The parameters are related to the composition and pore shape. All inclusions must be randomly distributed to make their effect isotropic.

[0106] A comparative analysis of the measured P-wave and S-wave velocities from rock cores and the calculated values ​​showed an error of no more than 5%, proving that the model is relatively reliable. The relative error in predicting the P-wave and S-wave velocities of salt rock and gypsum rock using the KT equation was within 5%.

[0107] Analysis of the impact of carnallite on the acoustic velocity of salt and gypsum rocks reveals that, based on core and well logging observations, halite-type carnallite is associated with halite. Using an established rock physics model, the influencing factors on the rock physics characteristics of potassium-rich strata were analyzed through changes in mineral content. The original P-wave velocity of the salt rock was 4580 m / s. As the carnallite content increased to 20%, the velocity increased to 4800 m / s, and when the carnallite content reached approximately 30%, the velocity increased to 4950 m / s. This indicates a significant positive correlation between carnallite content and P-wave velocity. In contrast, the P-wave velocity of conventional anhydrite-type carnallite decreases with increasing volume fraction, a change distinct from that of conventional anhydrite-associated carnallite. Analysis of shear waves and impedance shows a general trend consistent with the P-wave trend: an increase in relatively high-density, high-velocity carnallite leads to an overall increase in impedance.

[0108] The specific process of seismic attribute analysis and forward modeling in step 3) above is as follows:

[0109] 3.1) Prediction of halite-type carnallite

[0110] Statistical analysis of well logging potassium content and wave impedance curves across different layers reveals a general negative correlation between potassium-rich strata and lower wave impedance. However, due to the influence of salt rocks, the linear correlation between potassium content and wave impedance in the Jialingjiang Formation is generally weak, making it impossible to directly establish a relationship between wave impedance and potassium content. A convolutional active-limited learning mechanism combines lower-layer features to form more abstract higher-layer features; a convolution operator is used to divide the receptive field, mining useful information within the local area of ​​the potassium-rich layer, establishing the correlation between the target potassium content curve and the input wave impedance as well as various seismic attributes, thereby improving prediction accuracy.

[0111] 3.2) Inversion of sensitive factors in potassium-rich rock formations using a confined learning machine

[0112] Extreme Learning Machine (ELM) is a single-hidden-layer feedforward neural network model used for classification and regression. Unlike gradient descent-based feedforward neural networks, ELM employs a fast and novel learning mechanism that randomly assigns input weights and thresholds, and obtains the output layer connection weights using the least squares method. Compared to the backpropagation (BP) algorithm, ELM only requires setting the number of hidden layer nodes and does not require adjusting the network input weights or hidden layer thresholds during implementation, offering advantages such as fast learning speed and good generalization performance. Due to its theoretical simplicity and ease of implementation, Extreme Learning Machine has become a popular machine learning technique.

[0113] The extreme learning mechanism combines low-level features to form more abstract high-level features; the convolution operator is used to divide the receptive field, mine useful information in the local area of ​​the potassium-rich layer, solve the uncertainty of point-to-point calculation, and obtain potassium salt content profile map and seismic inversion wave impedance results.

[0114] By comparing the potassium salt content profile obtained by convolutional active limit learning with the seismic inversion impedance profile and the multivariate regression profile, it can be concluded that the convolutional active limit learning machine method can directly predict potassium salt content, and has a better match with the well, while also better showing the distribution range of potassium salt.

[0115] 3.3) Analysis of Sensitive Factors of Regional Carnallite and Potassium-Rich Brine Strata

[0116] The Puguang area is located in the evaporation basin, where carnallite is widely distributed. Based on the complex and folded stratigraphic interpretation of the carnallite distribution in the target section of the area, the distribution characteristics of carnallite and potassium-rich brine layers in the area are predicted by applying the inversion results of potassium-rich salt layer sensitive factors.

[0117] The prediction profiles of potassium salt sensitivity factors from the active limit learning machine in wells Puguang 8 and Puguang 10 show good agreement with the wells (e.g., Figure 9In the central part of Well Puguang 8, a thick layer of halite-type carnallite was found, with a thickness of 27.1 meters, compared to a predicted thickness of 29.8 meters, achieving a 90% agreement on the predicted thickness. Well Puguang 10 had a thickness of 2.9 meters, while the conventional inversion prediction limit is 20 meters, making effective prediction impossible. However, this method also predicted the potash salt layer. Furthermore, the distribution of potash-sensitive factors exhibited banded, speckled, and irregular cluster patterns, consistent with previous geological understanding.

[0118] Combined with regional inversion of well-connected large profile maps (such as...) Figure 10 It can be concluded that when the thickness of the potassium salt layer varies greatly, the convolutional active-limited learning machine method is consistent with the well and can be well represented in the planar distribution within the work area.

[0119] By applying the sensitive factor of the halide-bearing strata to predict the planar distribution characteristics of the halide-bearing strata, it can be seen that the thickness of the halide-bearing strata varies greatly in the lateral direction due to the strong folding characteristics. The thickness analysis results of the potassium-rich strata parameters are affected by both the strata thickness and the enrichment degree of the potassium-rich layer. The cumulative thickness of the potassium-rich layer is greatly affected by the strata thickness, which further reveals the distribution characteristics of the deformation and displacement of the potash layer under tectonic activity.

[0120] This invention combines geological prospecting methods with geophysical methods such as well logging identification and seismic prediction to establish a three-in-one prospecting model for deep halite-type carnallite, which integrates geology, well logging interpretation and seismic prediction. It also proposes scientific methods for target area selection and well location deployment, which greatly improves the success rate of prospecting for deep halite-type carnallite.

[0121] 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 above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for prospecting halite-type carnallite, characterized in that... The mineral exploration method includes the following steps: 1) Locate the center of potassium salt accumulation through lithofacies paleogeography, mineralogy, and petrology analysis; 2) By comparing geophysical logging with physical core samples, the characteristics of carnallite on the logging curves were analyzed, and logging response characteristics that are different from those of other lithologies were summarized, and a logging identification model was established. 3) Using 3D seismic data, well-seismic calibration is carried out, and forward and inverse structural evolution analysis is conducted to obtain the characteristics of tectonic deformation and potassium-bearing salt layer distribution. Then, based on rock physics testing, seismic attribute analysis and forward modeling, a seismic inversion framework model under complex tectonic deformation conditions is established to determine the distribution characteristics of heterohalite salt layers. Step 3) involves first analyzing the deformation and displacement characteristics of potassium-rich gypsum-salt rock strata under complex tectonic alteration, clarifying the deformation, displacement, and accumulation characteristics of potassium-rich gypsum-salt rocks as "early wing accumulation, later superimposed alteration," and showing a linear positive correlation between salt layer thickness and the development scale of halite-type carnallite. Based on this, a detailed seismic sequence characterization based on a global isochronous stratigraphic framework is conducted, establishing a global isochronous framework model. Combining the analysis of potassium-rich strata petrology and sensitive parameters, and through the extraction and application of sensitive parameters of potassium-rich strata primarily based on seismic inversion, and using convolutional neural networks to directly predict potassium content curves based on Bayesian wave impedance stochastic inversion, the spatial distribution characteristics of potassium-rich strata are described and predicted. The specific process of obtaining the structural deformation and potassium-bearing salt layer distribution characteristics in step 3) is as follows: by establishing a potassium-bearing rock layer description and inversion method based on seismic, geological and well logging data, with potassium-bearing rock sedimentary facies analysis and fine inversion constraint model as prior guidance, and with facies-controlled high-precision inversion as the core, a comprehensive analysis and distribution characteristic description of potassium-bearing rock strata can be achieved. The specific process of rock physics testing in step 3) is as follows: core samples are taken from actual drilled wells to measure rock physics parameters, and the rock physics parameters and seismic elastic sensitivity parameters are analyzed. Core data containing halide are collected in the halide-bearing section of the actual drilled well, and rock samples are prepared. Through rock sample analysis, the rock samples are mainly composed of gypsum, rock salt, and halite-type halide. According to the needs of rock physics analysis, laboratory tests are conducted on the rock sample parameters to analyze the velocity, density, and elastic parameter characteristics of potassium-rich salt minerals with different contents and different stratigraphic structures. Then, X-ray diffraction and thin-section microscopic tests are performed on some rock samples to obtain the rock mineral composition and structural form of the rock samples. Then, according to the two types of mineral composition, halite and gypsum, the longitudinal wave velocity of the salt rock framework minerals and gypsum rock framework minerals is analyzed.

2. The prospecting method for halite-type carnallite as described in claim 1, characterized in that: Step 1) involves collecting data on mineralization, potassium-forming index chlorine isotopes, and potassium and lithium concentrations, and then compiling paleogeographic maps using the residual thickness method to identify paleogeographic backgrounds of large-scale saline potassium-accumulating basins from the maps.

3. The prospecting method for halite-type carnallite as described in claim 1, characterized in that: Step 2) involves identifying the logging response characteristics of the main lithologies through well logging and core calibration logging. Based on the identification of the logging response characteristics of different lithologies, a logging identification model for the main lithologies of the strata in the study area is established.

4. The prospecting method for halite-type carnallite as described in claim 3, characterized in that, The specific process for establishing the well logging identification model for the main lithology of the stratigraphy in the study area is as follows: 2.1) High-precision identification of halite-type carnallite First, by combining the potassium and thorium curves, the sensitivity to polyhalite is expanded, and the potassium to thorium ratio is used as the sensitive curve for identifying polyhalite. Based on the first clear identification of the three sensitive parameters of halite-type polyhalite, namely "low density, high potassium, and low thorium", a potassium index KI characteristic identification curve is constructed, as shown in the following equation (3-2): In equations (3-1) and (3-2) above, K_TH is the potassium-thorium ratio, POTA is the potassium content in the formation as shown by natural gamma ray spectroscopy logging, THOR is the thorium content in the formation as shown by natural gamma ray spectroscopy logging, KI is the potassium index, and K_TH min The minimum potassium-thorium ratio, K_TH max The maximum potassium-thorium ratio is given, and DEN is the density data from the well logging data. min DEN is the minimum density data in well logging data. max This refers to the maximum density data in the well logging data; Then, by calibrating core data, the KI threshold values ​​for different lithologies and halite-type carnallites were determined. KI values ​​between 0.1 and 0.2 were defined as Class III carnallite layers; KI values ​​between 0.2 and 0.4 were defined as Class II carnallite layers; and KI values ​​greater than 0.4 were defined as Class I carnallite layers. The KI values ​​were then used to perform high-precision logging identification of carnallite quality. 2.2) Identification of carnallite logging The formation containing carnallite exhibits characteristics of "high potassium, low thorium, and low density". Based on this, the formation clay content is calculated using the thorium curve in the natural gamma ray spectroscopy logging curve. The potassium content is then calculated using the classical clay content model. Finally, the influence of potassium content in clay minerals is eliminated through gamma ray spectroscopy difference correction.

5. The prospecting method for halite-type carnallite as described in claim 1, characterized in that: In step 3), establishing the seismic inversion framework model under complex tectonic deformation conditions mainly involves establishing a rock physics model for potassium-rich strata. The specific process is as follows: First, based on the core analysis of potassium-rich strata, when the rock strata have low porosity, random and sparse distribution of inclusions, and the inclusions have bearing capacity, it can be considered as its geometric characteristics, thus forming a rock physics parameter calculation model; the measured P and S wave velocities of the core are compared and analyzed with the calculated values, and the error does not exceed 5%, proving that the model is relatively reliable, and the relative error of the predicted P and S wave velocities of salt rock and gypsum rock is within 5%.

6. The prospecting method for halite-type carnallite as described in claim 1, characterized in that, The process of seismic attribute analysis and forward modeling in step 3) is as follows: 3.1) Inversion of Sensitive Factors in Potassium-Rich Rock Strata Using Active Limitation Learning Machine The receptive field is divided by using a convolution operator to mine useful information in the local area of ​​the potassium-rich layer, solve the uncertainty of point-to-point calculation, and obtain potassium salt content profile map and seismic inversion wave impedance results. 3.2) Analysis of Sensitive Factors of Regional Carnallite and Potassium-Rich Brine Strata Based on the interpretation of complex and folded stratigraphic structures of the target strata in the region, and using the inversion results of potassium-rich salt layer sensitive factors, a study was conducted to predict the distribution characteristics of complex halides and potassium-rich brine layers in the area.

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