Quantitative identification method of associated minerals based on elemental capture spectroscopy logging

Abstract

The present application relates to the technical field of associated mineral identification, and is a quantitative identification method of associated minerals based on element capture spectroscopy logging, which comprises establishing a quantitative relationship model according to the content of non-water-soluble associated minerals and the energy spectrum response difference of characteristic elements of corresponding non-water-soluble associated minerals; collecting early capture spectroscopy and late capture spectroscopy data of different depths of strata in a well to be identified, and then obtaining the energy spectrum response difference of characteristic elements of non-water-soluble associated minerals of different depths of strata; and substituting the energy spectrum response difference of characteristic elements of non-water-soluble associated minerals of different depths of strata into the quantitative relationship model to obtain the mass fraction of non-water-soluble associated minerals of different depths of strata. The present application establishes a quantitative relationship between the energy spectrum response difference of characteristic elements and the mass percentage of associated minerals, solves the interference problem caused by the overlapping response of different associated minerals in the capture spectroscopy curve, and improves the resolution and accuracy of associated mineral identification.

Description

Technical Field

[0001] This invention relates to the field of associated mineral identification technology, and is a quantitative identification method for associated minerals based on elemental capture energy spectrum logging. Background Technology

[0002] In existing technologies, the identification and quantitative determination of associated minerals mainly rely on core experiments, including X-ray diffraction, elemental geochemical analysis, and microscopic observation. While these methods can determine the mineral type and content relatively accurately, they have significant limitations: first, the experimental testing is costly and time-consuming; second, the test samples are discrete, making it difficult to achieve continuous identification at the wellbore scale; and third, in complex evaporative sedimentary systems, the diffraction peaks of multiple associated minerals and carbonate minerals tend to overlap, leading to significant uncertainty in the quantitative analysis results.

[0003] Patent application CN120340682A discloses a method for determining the type and relative content of mica ore. The method includes: crushing and polishing the mica ore to be tested to obtain multiple mica particles; performing in-situ micro-area analysis on each of the multiple mica particles to calculate the ratio of characteristic element content in the multiple mica particles; determining the type of mica ore to be tested based on the ratio of characteristic element content; performing scanning imaging on the multiple mica particles to measure the area of ​​different types of mica in the multiple mica particles; calculating the proportion of the target type of mica based on the area of ​​different types of mica; calculating the total mica mineral content of the mica ore to be tested; and obtaining the relative content of the target type of mica mineral based on the total mica mineral content and the proportion of the target type of mica.

[0004] Patent application CN111141734A discloses a method for rapidly locating thorium minerals and identifying their associated relationships. The method specifically includes the following steps: Step 1, identifying the geological characteristics of associated thorium deposits and collecting associated thorium mineralization samples of different mineralization types; Step 2, collecting thorium-rich ore samples from the associated thorium mineralization samples of different mineralization types obtained in Step 1, and preparing thin sections of the thorium-rich ore samples; Step 3, performing automatic mineral parameter quantitative analysis on the thin sections of the thorium mineralization samples obtained in Step 2; Step 4, based on the results of the automatic mineral parameter quantitative analysis in Step 3, rapidly obtaining the location information of thorium minerals and their associated characteristics with other minerals.

[0005] While current logging technologies can acquire physical parameters of formations such as electrical properties, density, and neutron capture spectra, they lack quantitative identification methods for associated minerals. This is especially true in evaporative sedimentary systems, where associated minerals are diverse and their occurrence complex. Existing energy dispersive spectroscopy (EDS) logging interpretation methods cannot effectively distinguish the response characteristics of different mineral types, nor can they establish reliable quantitative relationships between mineral content and logging parameters. This results in insufficient accuracy in associated mineral identification and significant uncertainty in resource assessment. Summary of the Invention

[0006] This invention provides a method for quantitative identification of associated minerals based on elemental capture energy spectrum logging, which can effectively solve the problem that experimental methods are difficult to continuously and quantitatively evaluate associated minerals in formations.

[0007] The technical solution of this invention is achieved through the following measures: a method for quantitative identification of associated minerals based on elemental trapping energy spectrum logging, comprising: Obtain rock samples and determine the types and mass fractions of the main minerals and associated minerals in the rock samples. Associated minerals include non-water-soluble associated minerals and water-soluble associated minerals. Identify the characteristic elements of non-water-soluble associated minerals; Early and late capture energy spectrum data of rock samples are obtained. The original energy spectrum response difference of the characteristic elements is obtained based on the early and late capture energy spectrum data. The original energy spectrum response difference is corrected by using the contribution ratio of the characteristic elements of non-water-soluble associated minerals to the characteristic elements of rock samples, so as to obtain the energy spectrum response difference of the characteristic elements of non-water-soluble associated minerals. A quantitative relationship model is established based on the difference in energy spectrum response between the content of non-water-soluble associated minerals and the characteristic elements of the corresponding non-water-soluble associated minerals. Early and late trapping energy spectrum data of strata at different depths in the well to be identified are collected, and then the energy spectrum response differences of characteristic elements of non-water-soluble associated minerals in strata at different depths are obtained. The energy spectrum response differences of characteristic elements of non-water-soluble associated minerals in strata at different depths are substituted into the quantitative relationship model to obtain the mass fraction of non-water-soluble associated minerals in strata at different depths.

[0008] The following are further optimizations and / or improvements to the above-mentioned technical solution: Furthermore, the acquisition of early and late capture energy spectrum data of the rock sample, and the obtaining of the original energy spectrum response difference of the characteristic element based on the early and late capture energy spectrum data, includes: (6) In the formula, Let be the late-capture spectral intensity of the characteristic element in the i-th channel of the late-capture spectral spectrum. Let be the early capture energy spectrum intensity of the i-th channel of the early capture energy spectrum of the characteristic element, and n be the number of channels contained within the characteristic energy window. The difference in the original energy spectrum response of the characteristic element.

[0009] Furthermore, the quantitative relationship model established above based on the difference in energy spectrum response between the content of non-water-soluble associated minerals and the characteristic elements of the corresponding non-water-soluble associated minerals includes: Using the content of non-water-soluble associated minerals as the y-axis and the energy spectrum response difference as the x-axis, a correlation analysis was performed on the energy spectrum response difference between the content of non-water-soluble associated minerals and the characteristic elements of the corresponding non-water-soluble associated minerals to establish a quantitative relationship model between the two.

[0010] Furthermore, the original energy spectrum response difference is corrected by utilizing the contribution ratio of the characteristic elements of the non-water-soluble associated minerals to the characteristic elements of the rock sample, resulting in the energy spectrum response difference of the characteristic elements of the non-water-soluble associated minerals, including: Multiplying the original energy spectrum response difference by the aforementioned contribution ratio yields the energy spectrum response difference of the characteristic elements of the non-water-soluble associated minerals.

[0011] Furthermore, the aforementioned non-water-soluble associated minerals include acid-soluble associated minerals and insoluble associated minerals.

[0012] Furthermore, the aforementioned acid-soluble associated minerals include sodium carbonate calcium stone and sodium carbonate magnesium stone, while the insoluble associated minerals include sodium borosilicate. The characteristic element of sodium carbonate calcium stone is calcium, the characteristic element of sodium carbonate magnesium stone is magnesium, and the characteristic element of sodium borosilicate is boron.

[0013] The beneficial effects of this invention are: This invention achieves effective coupling between laboratory test results and captured energy spectrum logging data, establishing a quantitative mapping relationship between associated mineral content and the difference in energy spectrum response. Compared with existing technologies, this invention has the following advantages: (1) This invention combines X-ray diffraction experiments with captured energy spectrum logging data to achieve continuous quantitative identification of associated minerals at the wellbore scale, especially continuous quantitative identification of non-water-soluble associated minerals, effectively overcoming the defects of discrete detection and low efficiency of traditional experimental methods.

[0014] (2) By identifying the characteristic elements of non-water-soluble associated minerals and establishing a quantitative relationship model, the content distribution of typical evaporation phase minerals (non-water-soluble associated minerals) such as sodium borosilicate, sodium carbonate calcium stone and sodium carbonate magnesium stone can be accurately inverted, so as to achieve rapid differentiation of associated mineral types, especially rapid differentiation of non-water-soluble associated mineral types.

[0015] (3) This method establishes a quantitative relationship between the energy spectrum response difference of characteristic elements and the percentage of non-water-soluble associated minerals, which solves the interference problem caused by the overlap of responses of different associated minerals in the captured energy spectrum curve, and improves the resolution and accuracy of associated mineral identification.

[0016] (4) This method can be extended to logging data from multiple wells and multiple blocks to achieve rapid evaluation of associated mineral resources within a region, and has high practicality and universality.

[0017] In summary, this invention can effectively solve the problems of relying on experiments for the identification of associated minerals (especially non-water-soluble associated minerals) and lacking continuity and quantitative evaluation in the prior art, and provides a reliable technical means for the collaborative exploration and integrated development of oil and gas and associated minerals. Attached Figure Description

[0018] Figure 1 This is a flowchart of the present invention; Figure 2 A comparison diagram of early-late capture energy spectra (early capture energy spectrum - late capture energy spectrum) between normal strata and strata with associated mineral development; Figure 3 This is a quantitative relationship diagram between sodium borosilicate and the difference in energy dispersive spectral response; the larger the difference in energy dispersive spectral response, the smaller the content of sodium borosilicate. Figure 4 This is a quantitative relationship diagram between sodium calcium carbonate and the difference in energy dispersive spectroscopy (EDS) response; the larger the EDS response difference, the smaller the sodium calcium carbonate content. Figure 5 This is a quantitative relationship diagram between sodium carbonate magnesium and the difference in energy dispersive spectroscopy (EDS) response; the larger the EDS response difference, the smaller the sodium carbonate magnesium content. Figure 6 This is one of the associated mineral content curves for example wells; Figure 7 This is the second example of the associated mineral content curves from a well. Figure 8 This is one of the total mineral content curves; Figure 9 This is the second curve showing the total mineral content.

[0019] Figure 2 In the text, "early capture" refers to the energy spectrum of early capture, and "late capture" refers to the energy spectrum of late capture. Figure 6 and Figure 7 In the middle, from left to right: the first channel is the depth channel, the second channel is the lithology curve channel, the third channel is the three-porosity logging curve channel, the fourth channel is the resistivity curve channel, and the fifth, sixth, and seventh channels are the comparison channels between the mineral inversion results and experimental results of sodium borosilicate, sodium carbonate calcium stone, and sodium carbonate magnesium stone, respectively. Figure 8 and Figure 9In the middle, from left to right: the first channel is the depth channel, the second channel is the lithology curve channel, the third channel is the three-porosity logging curve channel, the fourth channel is the resistivity curve channel, the fifth channel is the whole rock profile, and the last ten channels are the comparison channels between the inversion results of each mineral and the experimental results. Detailed Implementation

[0020] The present invention is not limited to the following embodiments, and the specific implementation can be determined according to the technical solution of the present invention and the actual situation.

[0021] Unless otherwise specified, all chemical reagents and chemical products mentioned in this invention are known and commonly used chemical reagents and chemical products in the prior art; unless otherwise specified, all percentages and contents in this invention are mass percentages.

[0022] Unless otherwise specified, all terms in this invention are prior art. The determination of the type and mass fraction of the main minerals and associated minerals through experimental means is prior art.

[0023] Figure 8 , Figure 9 In the figure, the curves corresponding to the water-soluble associated mineral natural alkali were obtained according to the method described on page 8 of the literature (Lei Zhao, Rui Mao, Xili Deng, Ziyan Feng, Junkai Chen, Xianghua Zong and Cheng Feng. Research on Inversion Log Evaluation Method of Special Mineral in Alkali Lake Shale Oil Reservoir—A Case Study of the Fengcheng Formation in the Mahu Sag, China. Processes 2024, 12, 105).

[0024] like Figure 1 As shown, this invention provides a method for quantitative identification of associated minerals based on elemental trapping energy spectrum logging, comprising: Step 1: Obtain rock samples and determine the types and mass fractions of the main minerals and associated minerals in the rock samples. Associated minerals include non-water-soluble associated minerals and water-soluble associated minerals.

[0025] The types and mass fractions of the main minerals and associated minerals were determined using the following experimental methods: Representative rock samples from different depths were selected, crushed, and mixed uniformly. X-ray diffraction was used to identify the main minerals and their mass percentages. The main minerals (i.e., conventional minerals) included calcite, dolomite, quartz, feldspar, pyrite, and illite.

[0026] Non-water-soluble associated minerals include acid-soluble and insoluble associated minerals. The mass percentages of water-soluble, acid-soluble, and insoluble associated minerals are determined according to the following experimental method: ① The weighed rock sample powder was placed in deionized water and dissolved completely at a constant temperature of 50℃ to 70℃. After the ion concentration of the solution stabilized, solid-liquid separation was performed to obtain filtrate and residue. The concentrations of sodium ions, chloride ions, etc., in the filtrate were determined by ion chromatography, and the amount of substance of the corresponding ions was calculated based on the solution volume. According to the ionic composition and stoichiometry, the mass fractions of water-soluble minerals such as natural alkali and halite were calculated, and the residue was dried. This water-soluble treatment is mainly used to remove water-soluble salt minerals from the rock sample, eliminating interference for subsequent quantitative analysis of acid-soluble and insoluble associated minerals.

[0027] ② The dried residue is completely immersed in an excess of dilute acid (such as dilute hydrochloric acid) and dissolved under constant temperature conditions (50℃ to 70℃) until the ion concentration stabilizes. Solid-liquid separation is then performed again. The concentrations of magnesium and calcium ions in the acidic filtrate are determined by ion chromatography, and the amount of each ion is determined based on the solution volume. For example, since sodium carbonate and sodium calcium carbonate are insoluble in water but soluble in dilute hydrochloric acid, the mass fraction of acid-soluble associated minerals can be obtained based on the amounts of magnesium and calcium ions and the stoichiometric relationships of the corresponding minerals. ③ X-ray diffraction scanning was performed on the remaining residue after two rounds of dissolution to determine the relative proportion of each insoluble associated mineral in the remaining residue through characteristic diffraction peaks. Finally, based on the total mass of the rock sample, the mass of the main minerals, water-soluble associated minerals, and acid-soluble associated minerals was subtracted to obtain the mass of the insoluble associated minerals, thus obtaining the final fraction of the insoluble associated minerals.

[0028] In this invention, the mass percentage of each non-water-soluble associated mineral is obtained experimentally when establishing the quantitative relationship model. When using this quantitative relationship model in the field to predict the mass fraction of non-water-soluble associated minerals, only early and late trapping energy spectrum data are needed. The energy spectrum response differences of the corresponding characteristic elements of the non-water-soluble associated minerals are then substituted into the quantitative relationship model to obtain the mass fraction of the non-water-soluble associated minerals.

[0029] Step 2: Identify the characteristic elements of non-water-soluble associated minerals.

[0030] The term "characteristic element" refers to an element in an associated mineral that has a clear chemical composition relationship, can form an identifiable response at a characteristic energy position in the captured energy spectrum, and can be used to indicate the presence and content changes of the associated mineral.

[0031] For example, acid-soluble associated minerals include sodium carbonate calcium carbonate and sodium carbonate magnesium carbonate, while insoluble associated minerals include sodalite. The characteristic element of sodium carbonate calcium carbonate is calcium, that of sodium carbonate magnesium carbonate is magnesium, and that of sodalite is boron. In the energy capture spectrum, the characteristic peak positions of these elements are: boron: 0.478 MeV, calcium: 4.4 MeV and 6.4 MeV, and magnesium: 2.828 MeV and 3.917 MeV.

[0032] Step 3: Obtain early and late capture energy spectrum data of the rock sample. Based on the early and late capture energy spectrum data, obtain the original energy spectrum response difference of the characteristic elements. Use the contribution ratio of the characteristic elements of the non-water-soluble associated minerals to the characteristic elements of the rock sample to correct the original energy spectrum response difference, and obtain the energy spectrum response difference of the characteristic elements of the non-water-soluble associated minerals.

[0033] The LithoScanner capture spectrum logging tool can collect capture spectrum data of formations and rock samples.

[0034] like Figure 2 As shown, the main peaks of the early and late trap energy spectra in normal formations exhibit a typical and obvious separation. However, when associated minerals are present in the strata (associated mineral-developed strata), the energy spectrum evolution is significantly distorted due to the influence of the neutron trapping cross-section of their internal characteristic elements (such as boron and chlorine), causing the main peaks of the early and late trap energy spectra to tend to completely overlap. Therefore, this invention uses the LithoScanner trap energy spectrum logging tool to simultaneously acquire early and late trap energy spectrum data of formations or rock samples. By comparing and analyzing the differences in their energy spectra, the anomalous responses of characteristic elements of associated minerals can be identified.

[0035] Acquire early and late trapping energy spectrum data of the rock sample, and obtain the original energy spectrum response difference of the characteristic element based on the early and late trapping energy spectrum data, including: (6) In the formula, Let be the late-capture spectral intensity of the characteristic element in the i-th channel of the late-capture spectral spectrum. Let be the early capture energy spectrum intensity of the i-th channel of the early capture energy spectrum of the characteristic element, and n be the number of channels contained within the characteristic energy window. The difference in the original energy spectrum response of the characteristic element.

[0036] In normal formations, the intensity of the late-stage capture energy spectrum is usually higher than that of the early-stage capture energy spectrum, therefore Typically, the value is positive. When associated minerals are present in a rock, the neutron-trapping effect of their characteristic elements increases the intensity of the early-stage trapping energy spectrum, leading to a decrease in the difference between the early and late-stage trapping energy spectra, approaching zero, and in some cases, a negative value may appear. Therefore, the intensity difference between the early and late trapping energy spectra (i.e., the difference in the original energy spectrum response) can reflect the response changes of characteristic elements in the associated minerals. By averaging the intensity differences of multiple adjacent channels near the characteristic peak, the random fluctuations of a single peak point can be reduced, and the stability of the energy spectrum response parameters can be improved.

[0037] Since calcium is present in sodium calcium carbonate, calcite, and dolomite, and magnesium is present in sodium magnesia and dolomite, the difference in the original energy spectrum response of calcium and magnesium includes the combined contribution of the target associated minerals (such as sodium calcium carbonate and sodium magnesia) and common minerals (calcite and dolomite). Therefore, the mass percentage method is needed to calculate the contribution ratio of the same characteristic element in the target associated minerals and common minerals.

[0038] Under the condition that the energy spectrum response per unit mass of the same characteristic element is approximately consistent, the difference in the original energy spectrum response of the characteristic element is multiplied by the contribution ratio to obtain the difference in the energy spectrum response corresponding to the target associated mineral (that is, the difference in the original energy spectrum response is multiplied by the contribution ratio to obtain the difference in the energy spectrum response of the characteristic element of the non-water-soluble associated mineral).

[0039] The mass percentage method is used to calculate the contribution ratio of the same characteristic element in target associated minerals and common minerals, such as sodium carbonate calcium quartz and sodium carbonate magnesium quartz. The characteristic element of sodium carbonate calcium quartz is calcium, and the characteristic element of sodium carbonate magnesium quartz is magnesium. The specific calculation methods for the contribution ratios of calcium and magnesium are as follows: First, let the mass fractions of calcium in calcite, dolomite, and sodium carbonate benzene be respectively... W 方解石 , W 白云石1 , W 碳酸钠钙石 The mass fractions of magnesium in dolomite and sodium carbonate magnesia are respectively W 白云石2 , W 碳钠镁石 The molar masses of calcium and magnesium are respectively M 钙 , M 镁The molar masses of calcite, dolomite, sodium calcium carbonate, and sodium magnesium carbonate are 100.09 g / mol, 184.41 g / mol, 306.17 g / mol, and 190.31 g / mol, respectively. Therefore, the mass fractions of calcium and magnesium in each mineral (calcite, dolomite, sodium calcium carbonate, and sodium magnesium carbonate) are shown in equation (1). (1) Let the contribution ratio of calcium in sodium carbonate be... P 碳酸钠钙石 The contribution ratio of magnesium in sodium carbonate is: P 碳钠镁石 The masses of calcite, dolomite, sodium carbonate calcium stone, and sodium carbonate magnesium stone are respectively m 1, m 2, m 3, m 4. The mass fraction of magnesium in dolomite and sodium carbonate magnesia are respectively... w 白云石2 , w 碳钠镁石 The contribution ratio of calcium to sodium carbonate and the contribution ratio of magnesium to sodium carbonate are shown in equation (2): (2).

[0040] For sodium carbonate calcium carbonate, the difference in the energy spectrum response of calcium is equal to the difference in the original energy spectrum response of calcium × P 碳酸钠钙石 , For sodium carbonate magnesium, the difference in the energy spectrum response of magnesium = the difference in the original energy spectrum response of magnesium × P 碳钠镁石 .

[0041] Since boron only appears in sodium borosilicate, the contribution ratio of boron is taken as 1.

[0042] Step 4: Establish a quantitative relationship model based on the difference in energy spectrum response between the content of non-water-soluble associated minerals and the characteristic elements of the corresponding non-water-soluble associated minerals.

[0043] Using the content of insoluble associated minerals as the y-axis and the energy dispersive spectral response difference as the x-axis, a correlation analysis is performed on the energy dispersive spectral response difference between the content of insoluble associated minerals and the characteristic elements of the corresponding insoluble associated minerals. A quantitative relationship model between the two is established (e.g., a linear equation, a logarithmic function, etc., the specific form of which is obtained by fitting actual data). The fitting formula with the best correlation (i.e., the optimal coefficient of determination) is taken as the quantitative relationship model. If the function with the best correlation is a logarithmic function, then the quantitative relationship model is expressed as follows: (where a and b are fitting coefficients).

[0044] Step 5: Collect early and late trapping energy spectrum data of strata at different depths in the well to be identified, and then obtain the energy spectrum response difference of characteristic elements of non-water-soluble associated minerals in strata at different depths. Substitute the energy spectrum response difference of characteristic elements of non-water-soluble associated minerals in strata at different depths into the quantitative relationship model to obtain the mass fraction of non-water-soluble associated minerals in strata at different depths.

[0045] The present invention will be further described below with reference to embodiments: Example: A method for quantitative identification of associated minerals based on capture energy spectrum logging, the specific implementation steps are as follows: Step 1: Obtain rock samples and determine the types and mass fractions of the main minerals and associated minerals in the rock samples. Associated minerals include non-water-soluble associated minerals and water-soluble associated minerals.

[0046] Representative core samples (i.e., rock samples) from Block A of the oilfield were selected. Each rock sample was pulverized and thoroughly mixed through an 80-mesh sieve before use. X-ray diffraction was used to identify the minerals in the rock samples, obtaining the composition and mass percentage of the main minerals. The main minerals (i.e., conventional minerals) included calcite, dolomite, quartz, feldspar, pyrite, and illite.

[0047] Based on this, the composition of associated minerals is quantitatively characterized by utilizing the differences in solubility of associated minerals in different media, thereby clarifying the specific types and compositional characteristics of associated minerals. Non-water-soluble associated minerals include three categories: sodium borosilicate (Na(BSi3O8)), sodium carbonate calcium carbonate (Ca2Na2(CO3)3), and sodium carbonate magnesium carbonate (MgNa2(CO3)2). Water-soluble associated minerals include natural alkali.

[0048] Quantitative characterization of associated mineral components based on solubility differences involves the following steps: ① Weighed rock sample powder is placed in deionized water and fully dissolved at a constant temperature of 50℃ to 70℃. After the ion concentration in the solution stabilizes, solid-liquid separation is performed to obtain filtrate and residue. The concentrations of sodium ions, chloride ions, etc., in the filtrate are determined by ion chromatography, and the amount of substance of the corresponding ions is calculated based on the solution volume. The mass fractions of water-soluble minerals such as natural alkali and halite are calculated according to the ion composition and stoichiometry. The residue is then dried. This water-soluble treatment is mainly used to remove water-soluble salt minerals from the rock sample, eliminating interference for subsequent quantitative analysis of acid-soluble and insoluble associated minerals.

[0049] ② The dried residue after water-soluble treatment is completely immersed in excess dilute hydrochloric acid and reacted fully under constant temperature conditions of 50℃ to 70℃. After the ion concentration in the acidic solution stabilizes, solid-liquid separation is performed again to obtain an acidic filtrate and a fixed residue. The concentrations of magnesium and calcium ions in the acidic filtrate are determined by ion chromatography, and the amount of substance of the corresponding ions is determined based on the solution volume. Since sodium carbonate and sodium calcium carbonate are insoluble in water but soluble in dilute hydrochloric acid, the amount of substance and mass fraction of sodium carbonate and sodium calcium carbonate are calculated based on the amount of magnesium and calcium ions and the stoichiometric relationship of the corresponding minerals.

[0050] ③ The solid residue remaining after dissolution in water and dilute hydrochloric acid was dried, weighed, and subjected to X-ray diffraction. Sodium borosilicate is insoluble in both water and dilute hydrochloric acid, and therefore remains in the final solid residue. Its relative mass fraction in the final residue was determined based on the characteristic diffraction peaks of sodium borosilicate. This relative mass fraction was then calculated by combining the ratio between the mass of the final residue and the mass of the original rock sample.

[0051] Step 2: Identify the characteristic elements of non-water-soluble associated minerals.

[0052] Based on the identification of the main types of associated minerals, the characteristic elements of each non-water-soluble associated mineral were identified. In this embodiment, boron, calcium, and magnesium were used as characteristic elements for sodalite, sodium carbonate, and sodium carbonate, respectively. Boron exhibits a large thermal neutron capture cross-section; calcium and magnesium can form identifiable capture spectral responses at their respective characteristic energy positions. Furthermore, compared to the control sample without the corresponding associated minerals, the intensity difference between the early and late capture spectra within the characteristic energy window exceeded the background fluctuation range.

[0053] First, the characteristic peak positions of the characteristic elements are identified as follows: boron: 0.478 MeV, calcium: 4.4 MeV and 6.4 MeV, magnesium: 2.828 MeV and 3.917 MeV.

[0054] Step 3: Obtain early and late capture energy spectrum data of the rock sample. Based on the early and late capture energy spectrum data, obtain the original energy spectrum response difference of the characteristic elements. Use the contribution ratio of the characteristic elements of the non-water-soluble associated minerals to the characteristic elements of the rock sample to correct the original energy spectrum response difference, and obtain the energy spectrum response difference of the characteristic elements of the non-water-soluble associated minerals.

[0055] Formation capture spectrum data were acquired using the LithoScanner capture spectrum logging tool.

[0056] By combining the early and late trapping energy spectra at corresponding depths in the core sample, the original energy spectral response difference of the characteristic element is obtained based on the early and late trapping energy spectrum data. The calculation formula is as follows: (6) In the formula, Let be the late-capture spectral intensity of the characteristic element in the i-th channel of the late-capture spectral spectrum. Let be the early capture energy spectrum intensity of the i-th channel of the early capture energy spectrum of the characteristic element, and n be the number of channels contained within the characteristic energy window. The difference in the original energy spectrum response of the characteristic element.

[0057] Step 4: Establish a quantitative relationship model based on the difference in energy spectrum response between the content of non-water-soluble associated minerals and the characteristic elements of the corresponding non-water-soluble associated minerals.

[0058] Quantitative relationship models were established between three associated minerals—sodium borosilicate, sodium carbonate calcium aluminate, and sodium carbonate magnesium aluminate—and the differences in energy dispersive spectral responses, respectively. Figures 3 to 5 As shown, the correlation coefficients are all above 0.66, with the correlation coefficient for sodium carbonate magnesium reaching 0.92, indicating a stable logarithmic response relationship between the energy dispersive spectral response difference and the content of associated minerals.

[0059] For sodium borosilicate, the energy spectrum response difference of its characteristic elements is the energy spectrum response difference of boron. The energy spectrum response difference of boron = the original energy spectrum response difference × the contribution ratio of boron. The original energy spectrum response difference is calculated according to formula (6).

[0060] like Figure 3 As shown, with the content of sodium borate as the y-axis and the energy spectrum response difference of boron as the x-axis, the fitting formula is: y = -5.704ln(x) + 52.198, R 2 =0.7262.

[0061] For sodium carbonate calcium stone, the energy spectrum response difference of its characteristic elements is the energy spectrum response difference of calcium. The energy spectrum response difference of calcium = the original energy spectrum response difference × the contribution ratio of calcium. The original energy spectrum response difference is calculated according to formula (6), and the contribution ratio of calcium is calculated according to formulas (1) and (2).

[0062] like Figure 4 As shown, with the sodium carbonate calcium carbonate content as the y-axis and the energy spectrum response difference of calcium as the x-axis, the fitting formula is: y = -6.433ln(x) + 40.311, R 2 =0.6639.

[0063] For sodium carbonate magnesium, the energy spectrum response difference of its characteristic elements is the energy spectrum response difference of magnesium. The energy spectrum response difference of magnesium = the original energy spectrum response difference × the contribution ratio of magnesium. The original energy spectrum response difference is calculated according to formula (6), and the contribution ratio of magnesium is calculated according to formulas (1) and (2).

[0064] like Figure 5 As shown, with the sodium carbonate content as the y-axis and the energy spectrum response difference of magnesium as the x-axis, the fitting formula is: y = -4.549ln(x) + 28.742, R0 2 =0.9287.

[0065] Step 5: Collect early and late trapping energy spectrum data of strata at different depths in the well to be identified, and then obtain the energy spectrum response difference of characteristic elements of non-water-soluble associated minerals in strata at different depths. Substitute the energy spectrum response difference of characteristic elements of non-water-soluble associated minerals in strata at different depths into the quantitative relationship model to obtain the mass fraction of non-water-soluble associated minerals in strata at different depths.

[0066] When the non-water-soluble associated minerals are borosilicate, sodium carbonate, and sodium magnesium carbonate, the mass fraction of the non-water-soluble associated minerals (borosilicate, sodium carbonate, and sodium magnesium carbonate) at different depths is calculated according to the quantitative relationship model described in step 4. The calculation results are then plotted as an associated mineral content curve, such as... Figures 6 to 7 As shown, the comparison with the core experiment results (red dots) indicates that the error of the inversion results of associated mineral content is within ±3%, with an average error of 2.56%, which verifies the reliability and applicability of the quantitative relationship model.

[0067] Based on this, and combining the calculation results of the content of non-water-soluble associated minerals (mass fraction of non-water-soluble associated minerals), an optimization method is used to invert the content of all minerals (common minerals + associated minerals), forming the content curve of all minerals as shown below. Figures 8 to 9 As shown, the content inversion results fit well with the experimental data (red dots), indicating that this method can achieve continuous quantitative prediction in well sections without core data. This method can effectively improve the resolution of associated mineral identification and data interpretation efficiency, providing reliable technical support for the quantitative evaluation of minerals in complex evaporative sedimentary systems.

[0068] To achieve unified inversion of common minerals and associated minerals, the optimization calculation process of this invention includes the following steps: Establish mineral content constraints. Assume the sum of associated mineral content and common mineral content is 1. The calculation formula is as follows: (3) In the formula, X iThe mass fraction of the i-th common mineral is expressed as % (%). Y i The mass fraction of the i-th associated mineral (including water-soluble and non-water-soluble associated minerals) is expressed in % (%). m and k These represent the number of common minerals and associated minerals, respectively.

[0069] Given the determined mineral content fractions, the contents of common minerals are normalized using the following formula: (4) In the formula, W i denoted as the normalized mineral content of the i-th common mineral.

[0070] Optimization inversion is performed based on the normalized results. A system of linear equations is established between mineral content and logging response parameters, as shown below: (5) In the formula, α 11 , α 12 , α 13 ... α nm Let be the mass fraction of the m-th element in n common minerals, % respectively. W 1. W 2. W 3, ... W m The mass fractions of m common minerals, respectively, are %; b 1. b 2. b 3, ... b n The values ​​are the original dry weights (%) of the nth element measured by the Litho Scanner. The mass fractions of common minerals can be obtained by solving the equations using constrained optimization methods.

[0071] Full mineral content curve reconstruction: The mass fractions of common minerals obtained from the inversion are recombined with the mass fractions of associated minerals to obtain the full mineral content curve. This curve can be directly used for quantitative analysis of stratigraphic mineral distribution and associated mineral resources.

[0072] The above technical features constitute various embodiments of the present invention, which have strong adaptability and implementation effect. Unnecessary technical features can be added or removed according to actual needs to meet the needs of different situations.

Claims

1. A method for quantitative identification of associated minerals based on elemental trapping energy spectrum logging, characterized in that, include: Obtain rock samples and determine the types and mass fractions of the main minerals and associated minerals in the rock samples. Associated minerals include non-water-soluble associated minerals and water-soluble associated minerals. Identify the characteristic elements of non-water-soluble associated minerals; Early and late capture energy spectrum data of rock samples are obtained. The original energy spectrum response difference of the characteristic elements is obtained based on the early and late capture energy spectrum data. The original energy spectrum response difference is corrected by using the contribution ratio of the characteristic elements of non-water-soluble associated minerals to the characteristic elements of rock samples, so as to obtain the energy spectrum response difference of the characteristic elements of non-water-soluble associated minerals. A quantitative relationship model is established based on the difference in energy spectrum response between the content of non-water-soluble associated minerals and the characteristic elements of the corresponding non-water-soluble associated minerals. Early and late trapping energy spectrum data of strata at different depths in the well to be identified are collected, and then the energy spectrum response differences of characteristic elements of non-water-soluble associated minerals in strata at different depths are obtained. The energy spectrum response differences of characteristic elements of non-water-soluble associated minerals in strata at different depths are substituted into the quantitative relationship model to obtain the mass fraction of non-water-soluble associated minerals in strata at different depths.

2. The method for quantitative identification of associated minerals based on elemental trapping energy spectrum logging according to claim 1, characterized in that, Acquire early and late trapping energy spectrum data of the rock sample, and obtain the original energy spectrum response difference of the characteristic element based on the early and late trapping energy spectrum data, including: （6） In the formula, Let be the late-capture spectral intensity of the characteristic element in the i-th channel of the late-capture spectral spectrum. Let be the early capture energy spectrum intensity of the i-th channel of the early capture energy spectrum of the characteristic element, and n be the number of channels contained within the characteristic energy window. The difference in the original energy spectrum response of the characteristic element.

3. The method for quantitative identification of associated minerals based on elemental trapping energy spectrum logging according to claim 1 or 2, characterized in that, A quantitative relationship model is established based on the difference in energy spectrum response between the content of insoluble associated minerals and the characteristic elements of the corresponding insoluble associated minerals, including: Using the content of non-water-soluble associated minerals as the y-axis and the energy spectrum response difference as the x-axis, a correlation analysis was performed on the energy spectrum response difference between the content of non-water-soluble associated minerals and the characteristic elements of the corresponding non-water-soluble associated minerals to establish a quantitative relationship model between the two.

4. The method for quantitative identification of associated minerals based on elemental trapping energy spectrum logging according to claim 1 or 2, characterized in that, The original energy spectrum response difference was corrected by utilizing the contribution ratio of characteristic elements of insoluble associated minerals to the characteristic elements of the rock sample, resulting in the energy spectrum response difference of characteristic elements of insoluble associated minerals, including: Multiplying the original energy spectrum response difference by the aforementioned contribution ratio yields the energy spectrum response difference of the characteristic elements of the non-water-soluble associated minerals.

5. The method for quantitative identification of associated minerals based on elemental trapping energy spectrum logging according to claim 3, characterized in that, The original energy spectrum response difference was corrected by utilizing the contribution ratio of characteristic elements of insoluble associated minerals to the characteristic elements of the rock sample, resulting in the energy spectrum response difference of characteristic elements of insoluble associated minerals, including: Multiplying the original energy spectrum response difference by the aforementioned contribution ratio yields the energy spectrum response difference of the characteristic elements of the non-water-soluble associated minerals.

6. The method for quantitative identification of associated minerals based on elemental trapping energy spectrum logging according to claim 1, 2, or 5, characterized in that, Non-water-soluble associated minerals include acid-soluble associated minerals and insoluble associated minerals.

7. The method for quantitative identification of associated minerals based on elemental trapping energy spectrum logging according to claim 3, characterized in that, Non-water-soluble associated minerals include acid-soluble associated minerals and insoluble associated minerals.

8. The method for quantitative identification of associated minerals based on elemental trapping energy spectrum logging according to claim 4, characterized in that, Non-water-soluble associated minerals include acid-soluble associated minerals and insoluble associated minerals.

9. The method for quantitative identification of associated minerals based on elemental trapping energy spectrum logging according to claim 6, characterized in that, Acid-soluble associated minerals include sodium carbonate calcium stone and sodium carbonate magnesium stone, while insoluble associated minerals include siliceous borosilicate. The characteristic element of sodium carbonate calcium stone is calcium, the characteristic element of sodium carbonate magnesium stone is magnesium, and the characteristic element of siliceous borosilicate is boron.

10. The method for quantitative identification of associated minerals based on elemental trapping energy spectrum logging according to claim 7 or 8, characterized in that, Acid-soluble associated minerals include sodium carbonate calcium stone and sodium carbonate magnesium stone, while insoluble associated minerals include siliceous borosilicate. The characteristic element of sodium carbonate calcium stone is calcium, the characteristic element of sodium carbonate magnesium stone is magnesium, and the characteristic element of siliceous borosilicate is boron.

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