A method for multi-parameter quantitative classification and reservoir evaluation of salt lake carbonate rocks

Abstract

The application discloses a kind of salt lake carbonate rock multi-parameter quantitative classification and reservoir evaluation method, it is related to oil and gas exploration technical field.The application selects key well in exploration block, collects multiple salt lake carbonate rock samples and prepares analysis sample under waterless condition, quantitatively determines salinity parameter, mixing index and diagenetic intensity parameter using analysis sample respectively, determines salinity code, mixing code and diagenetic facies code, based on salinity code, mixing code, diagenetic facies code is named to salt lake carbonate rock, and according to the naming result, directly evaluate salt lake carbonate rock reservoir, and carry out single well comprehensive evaluation to salt lake carbonate rock reservoir.The application fully considers the influence of salinity, mixing, diagenetic intensity on the formation and evolution of salt lake carbonate rock, realizes the accurate division of salt lake carbonate rock type, and directly associates the reservoir naming result with reservoir evaluation, provides technical support for the standardization evaluation of salt lake carbonate rock reservoir.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration technology, specifically to a multi-parameter quantitative classification and reservoir evaluation method for carbonate rocks in salt lakes, applicable to the fine classification and reservoir evaluation of carbonate rocks in saline lake basins (salt lakes). Background Technology

[0002] Salt lake carbonate rocks differ fundamentally from marine and freshwater lacustrine carbonate rocks in terms of sedimentary environment, rock assemblages, and diagenetic evolution. First, the salinity of the sedimentary water varies widely, ranging from brackish to hypersaline, resulting in abundant evaporite minerals such as gypsum, anhydrite, celestite, and halite in their authigenic mineral assemblages, which interact and cement with carbonate minerals. Second, due to the limitations of the lake basin size and the influence of surrounding sediment sources, the incorporation of terrigenous clastic rocks (such as silt and clay) is extremely common, forming a complete sequence from pure carbonate rocks to mixed sedimentary rocks and finally to pure clastic rocks. The lithology changes rapidly both vertically and horizontally, exhibiting strong heterogeneity. Furthermore, early high-salinity pore water leads to rapid cementation of evaporite minerals, severely damaging primary porosity. Later development involves unique gypsum-dissolution and salt-dissolution processes, forming secondary porosity such as gypsum-mold pores and salt-mold pores. This results in a diagenetic evolution characterized by a game between "strong early destruction and strong later construction." In terms of lithofacies and reservoirs, it exhibits a complex symbiotic combination of "gray-cloud-gypsum-salt-mud", with strong heterogeneity of the pore system, weakened facies control and enhanced rock control, and complex distribution patterns of high-quality reservoirs.

[0003] Therefore, carbonate rocks from salt lakes cannot be classified using methods based on marine or freshwater lacustrine environments. These methods neglect the symbiotic relationship between evaporite minerals and carbonate rocks and their control over reservoirs. They cannot effectively characterize the influence of salinity, lack a quantitative description of "mixing," and are difficult to accurately describe various transitional rocks between pure carbonate rocks and pure clastic rocks. Furthermore, they mainly reflect sedimentary structures and fail to systematically incorporate "diagenetic alteration," a factor that has a decisive impact on reservoir performance, into the classification system.

[0004] Currently, researchers have conducted extensive exploratory work on classification methods for carbonate rocks in salt lakes, but the following problems still exist:

[0005] (1) Existing classification methods for salt lake carbonate rocks mostly focus on single factors such as sedimentary structure or sedimentary facies zone, without fully considering the influence of salinity, mixing, and diagenesis on the formation and evolution of salt lake carbonate rocks.

[0006] (2) Existing classification methods for salt lake carbonate rocks are mainly qualitative descriptions. For key parameters such as the degree of mixing and diagenetic strength, qualitative terms such as "small amount" and "developed" are often used. There is a lack of unified and operable quantitative standards and calculation models, resulting in classification results that vary from person to person and have poor comparability.

[0007] (3) Existing classification methods for salt lake carbonate rocks have a weak correlation with reservoir performance. The classification results of the current salt lake carbonate rock classification methods usually stop at rock naming. There is no clear and direct correspondence between the classification results and pore type and reservoir performance, which cannot effectively support reservoir evaluation and prediction.

[0008] Therefore, there is an urgent need to propose a multi-parameter quantitative classification and reservoir evaluation method for carbonate rocks in salt lakes, which should fully consider the influence of salinity, mixing, and diagenetic intensity on the formation and evolution of carbonate rocks in salt lakes, so as to achieve accurate classification of carbonate rock types in salt lake environments and provide a basis for guiding the standardized evaluation of carbonate rock reservoirs in salt lakes. Summary of the Invention

[0009] This invention aims to address the shortcomings of existing technologies by proposing a multi-parameter quantitative classification and reservoir evaluation method for salt lake carbonate rocks. It comprehensively classifies salt lake carbonate rocks based on salinity, mixing, and diagenetic strength, and integrates reservoir naming with these parameters. The method directly correlates reservoir naming results with pore type and reservoir evaluation grade, achieving precise classification of salt lake carbonate rock types. This solves a key geological problem that has long constrained oil and gas exploration in salt lake carbonate rocks, providing a basis for the refined exploration and efficient development of salt lake carbonate oil and gas reservoirs.

[0010] The present invention adopts the following technical solution:

[0011] A multi-parameter quantitative classification and reservoir evaluation method for carbonate rocks in salt lakes, which accurately classifies salt lake carbonate rock types based on salinity, mixing type, and diagenetic intensity, includes the following steps:

[0012] Step 1: Select key wells within the exploration block, collect multiple salt lake carbonate rock samples, and prepare analytical samples using each salt lake carbonate rock sample under anhydrous conditions.

[0013] Step 2: Quantitatively determine salinity parameters using rock powder from the analysis sample, identify the salinity level, and determine the salinity code;

[0014] Step 3: Calculate the mixing index using thin sections of rock in the analysis sample, classify the mixing type, and determine the mixing code;

[0015] Step 4: Analyze the rock thin sections in the sample to perform fine calculations of diagenetic strength parameters, determine the diagenetic facies, and identify the diagenetic facies code;

[0016] Step 5: Establish a three-level naming system based on salinity code, mixing code, and diagenetic facies code to name the salt lake carbonate reservoirs and evaluate the reservoir grade of the salt lake carbonate reservoirs in the exploration block.

[0017] Step 6: Based on the reservoir grade evaluation results of each salt lake carbonate reservoir in the exploration block, conduct a comprehensive evaluation of the salt lake carbonate reservoirs in the exploration block using a single well.

[0018] Preferably, step 1 includes the following sub-steps:

[0019] Step 101: Select key wells within the exploration block, collect multiple salt lake carbonate rock samples in the key wells according to the preset sampling density and sampling location, and use a four-level coding system to encode each salt lake carbonate rock sample in the order of well name, top depth, bottom depth and serial number, and obtain the macroscopic characteristics and high-resolution core images of each salt lake carbonate rock sample.

[0020] Step 102: Control the ambient temperature of the laboratory to 20±2℃ and the humidity to no more than 30%RH. Use a drying cabinet to temporarily store the salt lake carbonate rock samples and use a sealed drying box with built-in silica gel desiccant to transfer the salt lake carbonate rock samples.

[0021] Pre-treatment was performed on the carbonate rock samples of each salt lake. The cutting speed and cutting temperature were set, and the carbonate rock samples of each salt lake were cut with diamond saw blades and cooled with compressed air to obtain the pre-treated carbonate rock samples of each salt lake.

[0022] Analytical samples, including thin sections and powders, were prepared from pretreated carbonate rock samples from various salt lakes under anhydrous conditions.

[0023] In the process of preparing the rock thin sections, the pretreated salt lake carbonate rock sample is first coarsely ground with a diamond grinding disc, then finely ground with silicon carbide sandpaper, and then finely polished with diamond spray to obtain salt lake carbonate rock particles. Finally, the salt lake carbonate rock particles are bonded to epoxy resin with a water content of no more than 0.5% to obtain the rock thin sections.

[0024] In the process of preparing the rock powder, a jaw crusher is first used to crush the salt lake carbonate rock sample, and then an agate ball mill is used to grind the crushed salt lake carbonate rock sample to obtain rock powder.

[0025] Preferably, step 2 includes the following sub-steps:

[0026] Step 201: Perform salinity indicator element analysis on the analytical sample;

[0027] During the salinity indicator element analysis process, inductively coupled plasma mass spectrometry was used to perform trace element analysis on the rock powder, and the salinity of the rock powder was measured multiple times. and The content of strontium and barium in the analytical sample was determined. ;

[0028] Step 202: Identify the evaporite minerals in the analytical sample;

[0029] In the process of identifying evaporite minerals, X-ray diffractometer is used to perform semi-quantitative whole-rock mineral analysis on rock powder to determine the relative content of each evaporite mineral in the rock powder. Then, scanning electron microscope and energy dispersive spectroscopy are used to observe the micro-area morphology and analyze the composition to determine the occurrence state and symbiotic relationship of each evaporite mineral.

[0030] Step 203: Determine the salinity level based on the salinity indicator element analysis results and the evaporite mineral identification results, and determine the salinity code of the analyzed sample;

[0031] Each salinity level is matched with a salinity code, wherein the salinity code for freshwater to brackish water is S1, the salinity code for brackish water to saline water is S2, the salinity code for salt lake is S3, and the salinity code for hypersalinity is S4.

[0032] The salinity code is determined based on the Sr / Barium ratio and evaporite mineral content of the analyzed sample. Specifically, when the Sr / Barium ratio is less than 0.6 and the evaporite mineral content is less than 1%, the salinity code is S1; when the Sr / Barium ratio is less than 1%, the salinity code is S2. And the mineral content of evaporite is taken as: When the salinity code is S2, the analysis of the sample is performed when the strontium-barium ratio is [value missing]. And the mineral content of evaporite is taken as: When the salinity ratio of the sample is greater than 2.5 and the content of evaporite minerals is greater than 30%, the salinity code is determined to be S3; when the salinity ratio of the sample is greater than 2.5 and the content of evaporite minerals is greater than 30%, the salinity code is determined to be S4.

[0033] Preferably, step 3 includes the following sub-steps:

[0034] Step 301, digital processing of rock thin sections;

[0035] Quality inspection of rock thin sections was carried out under a polarizing microscope. After repairing the rock thin sections with defects, a microscope equipped with a CCD camera was used to systematically scan the progress of the rock thin sections. First, the entire rock thin section was scanned under single polarized light, and then the key areas of the rock thin section were scanned under cross polarized light to obtain digital images of the rock thin sections.

[0036] Step 302: Set up a rock component classification system and establish a point counting system;

[0037] In the rock component classification system, rock components are divided into terrigenous clastic components, carbonate components, and evaporite components. The terrigenous clastic components include quartz, feldspar, clay minerals, and rock fragments; the carbonate components include calcite, dolomite, oolitic particles, and bioclastic fragments; and the evaporite components include gypsum, anhydrite, and halite.

[0038] A point counting system is established using point counting software. The point counting system is based on a hierarchical random sampling strategy and includes a grid system containing multiple statistical points. The counting rules of the point counting system are set.

[0039] Step 303: Using a point counting system, the number of statistical points corresponding to each rock component in the digital image of the rock thin section is counted based on the rock component classification system, and the mixing index SCI of the sample is calculated and analyzed.

[0040] Step 304: Based on the mixing index SCI, the mixing type is finely divided into C, MC, MS and S categories to determine the mixing code and depositional environment of the analysis sample.

[0041] Step 305: Statistical data verification and interpretation of lithological identification results;

[0042] Representative samples were selected from the analysis samples. After verifying the accuracy of the rock component classification of the representative samples based on the cross-validation method, the X-ray diffraction whole-rock analysis results of the representative samples and the point counting results of the point counting system were compared. The area percentage statistical verification of typical areas in the digital images of rock thin sections was carried out by image analysis software. Electron probe surface scanning analysis was performed on the difficult samples in the analysis samples.

[0043] Preferably, in step 4, the mixing index The calculation formula is:

[0044] ;

[0045] In the formula, Sample serial number; To analyze the total number of samples; For the first The number of quartz points in each sample; For the first Feldspar count of each sample; For the first Number of clay mineral points in each sample; This represents the total number of statistical points; The sample number for which the number of pore points was measured; The total number of samples used for pore point count measurement; For the first Number of pore points per sample;

[0046] when When the mixed sedimentary type is classified as Class C, with the mixed sedimentary code C, the lithology of the analyzed sample is determined to be pure carbonate rock, and the depositional environment is a lacustrine basin center far from the source or a chemically dominated sedimentary area; when At that time, the mixed sedimentary type was classified as MC, with the mixed sedimentary code MC. The lithology of the analyzed sample was determined to be mixed carbonate rock, and the depositional environment was a transitional zone. Among them, when When the mixed product type is classified into the MC1 subclass, the mixed product code is MC1. When the mixed product type is classified as MC2, the mixed product code is MC2; when At that time, the mixed sedimentary type was classified as MS, with the mixed sedimentary code MS, and the lithology of the analyzed sample was determined to be mixed clastic rock, with a near-source depositional environment. Specifically, when... When the mixed product type is classified into the MS1 subclass, the mixed product code is MS1. When the mixed product type is classified as MS2, the mixed product code is MS2; when At that time, the mixed sedimentary type was classified as S type, the mixed sedimentary code was S, and the lithology of the analyzed sample was determined to be pure clastic rock, and the sedimentary environment was near the lake shore or delta front.

[0047] Establish the correspondence between SCI values ​​and sedimentary environments, where, when When, it indicates that the depositional environment is a quiet chemical depositional environment; when When, it indicates that the depositional environment is a transitional environment; when When the time is specified, it indicates that the depositional environment is a high-energy environment.

[0048] Preferably, step 4 includes the following sub-steps:

[0049] Step 401: Acquire digital thin-section images;

[0050] After performing full-thin section scanning and image stitching on each rock thin section in the analysis sample, and then performing local high-definition scanning on key areas, at least 25 standard field-of-view images were acquired for each rock thin section, resulting in digital thin section images of the analysis sample.

[0051] Step 402: Measure the morphological parameters of the digital thin section image;

[0052] The process involves acquiring a binarized pore image of a digital thin section image, measuring the projected area and boundary perimeter of a single pore in the pore image, and calculating the shape factor and equivalent circle diameter of the pore. Next, mineral particles in the digital thin section image are identified, and their contours are extracted and labeled to obtain the particle size distribution, intergranular contact relationships, and orientation. Finally, for cements of different phases and types, the area coverage of the cement within the field of view is measured, and the spatial distribution, crystal morphology, size, and optical properties of the cement are analyzed.

[0053] Step 403: Calculate diagenetic strength parameters, including compaction to reduce porosity. Cementation reduces porosity Dissolution increases porosity ;

[0054] Step 404: Determine the diagenetic facies type based on the diagenetic strength parameters and determine the diagenetic facies code;

[0055] when At that time, the diagenetic facies type was determined to be a strongly compacted and strongly cemented facies, and the diagenetic facies code was Dc; when At that time, the diagenetic facies type was determined to be medium-compacted and medium-cemented, with the diagenetic facies code being Dp; when At that time, the diagenetic facies type was determined to be a weakly compacted and weakly cemented facies, and the diagenetic facies code was Dw;

[0056] when When the diagenetic facies type was determined to be a strongly dissolved phase, the diagenetic facies code was Dd; when At that time, the diagenetic facies type was determined to be a moderately dissolved facies, and the diagenetic facies code was Dg; when At that time, the diagenetic facies type was determined to be a weakly dissolved facies, and the diagenetic facies code was Df.

[0057] Preferably, an original porosity calculation model is established based on the particle support structure in the digital thin-section image, resulting in:

[0058] ;

[0059] In the formula, Original porosity; The volume of the particles and grains; The total volume of the rock;

[0060] Analyze the residual intergranular porosity in rocks from digital thin section images to determine how compaction reduces porosity. for:

[0061] ;

[0062] In the formula, This represents the current intergranular porosity.

[0063] Identify all authigenic cements formed during diagenesis in rock thin sections under single-polarized and crossed-polarized light, including calcite cement, dolomite cement, siliceous cement, and evaporite cement; determine the volume percentage of each type of cement; and calculate the porosity reduction caused by cementation. for:

[0064] ;

[0065] In the formula, For the first The content of autogenous cementitious material in each sample;

[0066] In digital thin section images, secondary pore spaces created by dissolution are identified, the total volume of these secondary pore spaces and the volume of authigenic minerals precipitated within the dissolution spaces are determined, and the increase in porosity due to dissolution is assessed. for:

[0067] ;

[0068] In the formula, This refers to the porosity of the solution. The degree of filling with authigenic minerals.

[0069] Preferably, in step 5, a three-level naming system is established based on salinity code, mixing code, and diagenetic facies code, and the salt lake carbonate reservoir is named in the order of salinity code, mixing code, and diagenetic facies code.

[0070] The reservoir performance, diagenetic modification and rock basis are considered to evaluate the reservoir grade of the salt lake carbonate reservoir in the exploration block, and exploration recommendations are provided for each reservoir grade.

[0071] Reservoirs with porosity greater than 15%, permeability greater than 10 mD, and pore characteristics dominated by paste pores and intergranular dissolution pores are classified as Class I reservoirs, which are high-quality reservoirs. Exploration recommendations for Class I reservoirs include prioritizing development wells and utilizing natural production capacity. Reservoirs with porosity greater than 8% but not exceeding 15%, permeability greater than 1 mD but not exceeding 10 mD, and pore characteristics characterized by a mixed pore system are classified as Class II reservoirs, which are medium-quality reservoirs. Exploration recommendations for Class II reservoirs include reservoir stimulation and optimizing well completion schemes. Reservoirs with porosity less than 8%, permeability less than 1 mD, and pore characteristics dominated by micropores are classified as Class III reservoirs, which are poor-quality reservoirs. Exploration recommendations for Class III reservoirs include temporarily suspending development and focusing on local diagenetic anomaly zones.

[0072] Preferably, in step 6, a comprehensive evaluation of each well in the salt lake carbonate reservoir is carried out, and a single-well multi-source data fusion platform is established. The single-well multi-source data fusion platform includes core description and naming results data, Sr / Ba ratio sequence, vertical variation curve of mixing index SCI, diagenetic strength parameter profile based on thin section analysis, conventional logging curves and imaging logging data, and vertical distribution data of rock physical properties.

[0073] The vertical structure of the reservoir was delineated using a combination of hierarchical analysis and pattern recognition. In the salt lake carbonate reservoir, reservoir segments were identified, interlayers were determined, dominant reservoirs were delineated, and diagenetic sequences were reconstructed. Based on the naming results and geological background of the salt lake carbonate reservoir, the reservoir development model of the salt lake carbonate reservoir was determined. The reservoir development model includes sedimentary control type, diagenetic modification type, and composite control type.

[0074] The present invention has the following beneficial effects:

[0075] (1) This invention proposes a multi-parameter quantitative classification and reservoir evaluation method for salt lake carbonate rocks. It fully considers the sedimentary environment, material source and post-modification of salt lake carbonate rocks. Based on salinity, mixing type and diagenetic strength, it accurately classifies the types of salt lake carbonate rocks. This solves the problem that existing technologies cannot combine multi-dimensional classification, and transforms the understanding of rocks from isolated static images to continuous dynamic evolution, thereby improving the ability to explain the genesis of rocks and the formation mechanism of reservoirs.

[0076] (2) This invention proposes a multi-parameter quantitative classification and reservoir evaluation method for salt lake carbonate rocks. By introducing standardized salinity codes, quantitatively calculated mixing indices and diagenetic strength parameters, the salinity code, mixing code and diagenetic facies code are determined. This completely changes the shortcomings of traditional salt lake carbonate rock classification methods that rely on qualitative descriptions and expert experience. It effectively ensures the objectivity, repeatability and comparability of salt lake carbonate rock classification results, greatly reduces subjective judgment errors due to individual differences, and enables non-senior technicians to obtain accurate salt lake carbonate rock classification results by performing analysis according to the method of this invention. It is easier to promote and apply it on a large scale in production and scientific research, and makes data exchange and comparison between different blocks and different researchers possible, which significantly improves the scientificity and reliability of salt lake carbonate rock reservoir evaluation.

[0077] (3) This invention proposes a multi-parameter quantitative classification and reservoir evaluation method for salt lake carbonate rocks. Based on a three-level naming system of salinity code, mixing code and diagenetic facies code, salt lake carbonate rocks are systematically named, and the correspondence between the naming results of salt lake carbonate rocks and reservoir performance is directly established. This enables those skilled in the art to quickly and accurately determine the genesis, key diagenetic history and reservoir potential of the rocks based solely on the naming results of the salt lake carbonate rocks. This greatly reduces the decision-making time from rock identification to reservoir evaluation, which is conducive to optimizing the exploration strategy of salt lake carbonate rock reservoirs. It guides the concentration of resources in the most favorable target area during the exploration process, avoids investment in ineffective strata in salt lake carbonate rock reservoirs during the exploration process, improves the success rate of salt lake carbonate rock reservoir development, and reduces the overall exploration and development cost of salt lake carbonate rock reservoirs.

[0078] Meanwhile, the method of this invention provides a universal key technical tool for solving the exploration problems of carbonate rocks in salt lakes. By combining a systematic research approach with quantitative technical means, it can not only be directly used to guide production, but also provides a solid foundation for further theoretical research, knowledge accumulation and the establishment of industry standards for carbonate rocks in salt lakes, which has far-reaching significance for promoting the technological progress of the entire industry. Attached Figure Description

[0079] Figure 1 This is a flowchart of a multi-parameter quantitative classification and reservoir evaluation method for salt lake carbonate rocks according to the present invention.

[0080] Figure 2 This is a flowchart of the method for determining salinity codes according to the present invention.

[0081] Figure 3 This is a flowchart of the method for determining the mixed-product code of the present invention.

[0082] Figure 4 This is a graph showing the relationship between sulfate minerals and porosity in well A.

[0083] Figure 5 This is a columnar section of the diagenetic facies of rhythm 2 in well A.

[0084] Figure 6 This is a columnar section of the diagenetic facies of well A with rhythm 5.

[0085] Figure 7 This is a columnar section of diagenetic facies in well A with a 10-rhythm pattern. Detailed Implementation

[0086] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments: Example 1

[0087] This embodiment proposes a multi-parameter quantitative classification and reservoir evaluation method for carbonate rocks in salt lakes, such as... Figure 1 As shown, it includes the following steps:

[0088] Step 1: Select key wells within the exploration block and collect multiple salt lake carbonate rock samples. Prepare analytical samples from each salt lake carbonate rock sample under anhydrous conditions. This includes the following sub-steps:

[0089] Step 101: Select key wells within the exploration block for systematic sampling. Collect multiple salt lake carbonate rock samples from the key wells according to the preset sampling density and sampling location. Use a four-level coding system to encode each salt lake carbonate rock sample in the order of well name, top depth, bottom depth, and sequence number to obtain the macroscopic characteristics and high-resolution core images of each salt lake carbonate rock sample.

[0090] Specifically, a tiered sampling strategy is adopted to set the sampling density; for cored well sections, sampling is carried out at a sampling density of 4 to 6 sampling points per meter; for important reservoir sections or sections with complex lithological changes, the sampling density of sampling points is increased to 8 to 10 sampling points per meter; for homogeneous sections, the sampling density of sampling points is reduced to 2 to 3 sampling points per meter.

[0091] Specifically, the sampling location is selected from the intact and representative parts of the core in the key well. During the sampling process, it is necessary to avoid areas with severe drilling mud intrusion, fracture development zones, and obvious areas of human contamination. In addition, in order to ensure the accuracy and traceability of the sampling location, the accurate distance between the top and bottom interfaces of the carbonate rock sample in the salt lake needs to be recorded during the sampling process.

[0092] Specifically, each salt lake carbonate rock sample was coded in four levels according to the well name, top depth, bottom depth, and serial number, for example, W1-3500.25-3500.35-01. The macroscopic characteristics of each salt lake carbonate rock sample were recorded in detail, including color, structure, texture, mineral composition, fossil content, and fracture development. High-resolution core images of each salt lake carbonate rock sample were obtained.

[0093] Step 102: Maintain a low humidity environment in the laboratory. Control the ambient temperature of the laboratory to 20±2℃ and the humidity to no more than 30%RH. Use a drying cabinet with an internal humidity of no more than 10%RH to temporarily store the salt lake carbonate rock samples, thereby ensuring that the salt lake carbonate rock samples are in a dry environment during non-experimental stages. During the transfer of salt lake carbonate rock samples, use a sealed drying box with built-in silica gel desiccant to transfer the salt lake carbonate rock samples.

[0094] Pre-treatment was performed on the carbonate rock samples from each salt lake. The cutting speed and temperature were set. To prevent the high temperature generated during the cutting process from causing mineral phase transformation in the carbonate rock samples, the cutting speed was set to not exceed 1000 rpm. Diamond saw blades were used to cut the carbonate rock samples from each salt lake, and compressed air was used for cooling. The temperature of the carbonate rock samples from each salt lake was monitored in real time during the cutting process to ensure that the temperature of the carbonate rock samples from each salt lake did not exceed 40℃. The pre-treated carbonate rock samples from each salt lake were obtained after cutting.

[0095] Analytical samples, including thin sections and powders, were prepared from pretreated carbonate rock samples from various salt lakes under anhydrous conditions.

[0096] Specifically, the preparation process of the rock thin sections includes a grinding process and a mounting process for the salt lake carbonate rock samples. The grinding process is entirely dry grinding. First, a 180-mesh diamond grinding disc is used to coarsely grind the pretreated salt lake carbonate rock samples. During coarse grinding, a wind-cooled grinding disc is used to ensure temperature stability and prevent overheating from affecting the sample's performance. Then, 400-mesh and 800-mesh silicon carbide sandpaper are used for fine grinding. Anhydrous ethanol is used to lubricate the grinding disc during fine grinding, ensuring both grinding effectiveness and preventing moisture intrusion. Finally, a 0.3μm diamond spray is used for fine polishing to obtain salt lake carbonate rock particles. The entire grinding process of the salt lake carbonate rock samples is completed in an anhydrous environment, thus ensuring the surface accuracy and dryness of the samples during grinding.

[0097] In the bonding process, to minimize the impact of moisture from the source, epoxy resin with a water content of no more than 0.5% was selected as the bonding material. Unlike traditional bonding processes that use resin as the bonding material, this embodiment uses epoxy resin, effectively avoiding the impact of resin's hygroscopic properties on the quality of the rock thin sections and improving the reliability of the bonding process. Simultaneously, to enhance the stability of the rock thin sections, the curing temperature was controlled at 60℃ and the curing time at 48 hours, solidifying the salt lake carbonate rock particles in the epoxy resin to obtain the rock thin sections.

[0098] In the process of preparing the rock powder, a jaw crusher is first used to crush the salt lake carbonate rock sample, so that the particle size of the crushed salt lake carbonate rock sample is less than 2mm, which effectively avoids heavy metal pollution that may occur during the crushing process using iron instruments. Then, an agate ball mill is used to grind the crushed salt lake carbonate rock sample for 30 minutes at a material-to-ball ratio of 1:3 and a rotation speed of 300r / min, ensuring that more than 95% of the powder can pass through a 200-mesh sieve after grinding. In order to ensure the representativeness and uniformity of the rock powder, the ground powder is uniformly mixed to obtain the rock powder.

[0099] Step 2 involves quantitatively determining salinity parameters using rock powder from the analytical sample, including salinity indicator element analysis and evaporite mineral identification, to determine the salinity level and assign a salinity code, such as... Figure 2 As shown, the specific steps include the following:

[0100] Step 201: Perform salinity indicator element analysis on the analytical sample.

[0101] During the salinity indicator element analysis, inductively coupled plasma mass spectrometry (ICP-MS) was used to analyze trace elements in the rock powder. National Class I standard materials were used for quality control to ensure that the relative standard deviation of the analysis was less than 5%. An average of three measurements were performed on each sample of rock powder. and The content of strontium and barium in the analytical sample was determined. .

[0102] Step 202: Identify the evaporite minerals in the analytical sample.

[0103] In the process of identifying evaporite minerals, a Brook D8 Advance X-ray diffractometer is used to perform semi-quantitative whole-rock mineral analysis on the rock powder, mainly to determine the relative content of various evaporite minerals such as gypsum, anhydrite, and halite in the rock powder. Then, scanning electron microscopy and energy dispersive spectroscopy are used to observe the micro-area morphology and analyze the composition to accurately identify the occurrence state and symbiotic relationship of each evaporite mineral.

[0104] Step 203: Determine the salinity level based on the salinity indicator element analysis results and the evaporite mineral identification results, and determine the salinity code of the analytical sample.

[0105] Furthermore, each salinity level is matched with a salinity code, wherein the salinity code for freshwater to brackish water is S1, the salinity code for brackish water to saline water is S2, the salinity code for salt lake is S3, and the salinity code for hypersalinity is S4.

[0106] Based on the strontium-barium ratio of the analyzed sample Salinity codes are determined based on evaporite mineral content. Specifically, if the strontium-barium ratio of the analyzed sample is less than 0.6 and the evaporite mineral content is less than 1%, with no or trace amounts of evaporite minerals in the typical mineral assemblage, and calcite and dolomite being the dominant minerals, and the depositional environment is a broad lacustrine facies near the estuary with good water circulation, then the salinity code is S1. The strontium-barium ratio of the analyzed sample is... And the mineral content of evaporite is taken as: The typical mineral assemblage contains trace amounts of gypsum and ferrodolite, and authigenic quartz is visible. The sedimentary environment is primarily a lacustrine basin with salinity stratification beginning to appear, at which point the salinity code is determined to be S2. The strontium-barium ratio of the analyzed sample is [value missing]. And the mineral content of evaporite is taken as: The typical mineral assemblage is rich in gypsum and celestite, with common halite pseudomorphs and developed lamellar structures. The depositional environment is an evaporation platform or salt flat with periodic exposure, thus the salinity code is determined to be S3. The analyzed sample has a Sr-Barium ratio greater than 2.5 and an evaporite mineral content greater than 30%. The typical mineral assemblage contains abundant halite and potash layers, and gypsum occurs in nodular forms. The depositional environment is a strongly evaporating formation center and a perennial salt lake, thus the salinity code is determined to be S4.

[0107] Step 3: Calculate the mixing index using thin sections of rock from the analyzed samples, classify the mixing type, and determine the mixing code, such as... Figure 3 As shown, the specific steps include the following:

[0108] Step 301, digital processing of rock thin sections.

[0109] The rock thin sections were inspected under a polarizing microscope to ensure they were free of abrasions, air bubbles, and resin exudation. Defective sections were repaired, and the effective observation area of ​​each section was recorded, excluding areas affected by edge effects. A systematic scanning process was then performed on the rock thin sections using a microscope equipped with a DFC450 CCD camera. The microscope used a 20x objective lens with a resolution of 2588×1940 pixels. First, a full-section scan was performed under single-polarized light. Then, a local scan of key areas was performed under crossed-polarized light. During the local scan, a 100x objective lens was used to perform high-resolution local scans of special structures such as sutures, microcracks, and dissolution pores, thus completing the systematic scanning of the rock thin sections and obtaining digital images of each rock thin section in the analytical sample.

[0110] Step 302: Set up a rock component classification system and establish a point counting system.

[0111] Specifically, in the rock component classification system, rock components are divided into terrigenous clastic components, carbonate components, and evaporite components. The terrigenous clastic components include quartz, feldspar, clay minerals, and rock fragments; the carbonate components include calcite, dolomite, oolitic particles, and bioclastic fragments; and the evaporite components include gypsum, anhydrite, and halite.

[0112] A point counting system was established using a mechanical table and point counting software. The point counting system has a grid system containing 300 statistical points based on a hierarchical random sampling strategy. The hierarchical random sampling strategy is adopted in the point counting system. That is, a preset system grid is used in areas with uniform lithology, and the statistical points are denser in complex areas for sampling. The counting rules of the point counting system are set. When the pointer of the point counting system falls on the mineral boundary, the component occupying more than 50% of the area is used as the standard. When the pointer of the point counting system falls on the pore, it is recorded as a pore and an adjacent solid point is reselected.

[0113] Step 303: Use a point counting system to count the total number of points, terrigenous clastic points, carbonate points, evaporite points, and pore points in each rock thin section, and calculate and analyze the mixing index of the sample.

[0114] Specifically, the mixing index The calculation formula is:

[0115] ;

[0116] In the formula, Sample serial number; To analyze the total number of samples; For the first The number of quartz points in each sample; For the first Feldspar count of each sample; For the first Number of clay mineral points in each sample; This represents the total number of statistical points; The sample number for which the number of pore points was measured; The total number of samples used for pore point count measurement; For the first The number of pore points in each sample.

[0117] Step 304: Based on the SCI (Sedimentation Index), the sedimentation types are further divided into C, MC, MS and S types to determine the depositional environment of each sedimentation type.

[0118] when When the mixed sedimentation type was classified as Class C, with the mixed sedimentation code C, the lithology of the analyzed sample was determined to be pure carbonate rock, with rock characteristics including a carbonate mineral content greater than 90%, a clean internal rock structure, and visible oolitic grains, bioclastic debris, and other typical carbonate structures. The depositional environment was a lacustrine basin center far from the source or a chemically dominated sedimentary area. At that time, the mixed sedimentary type was classified as MC, with the mixed sedimentary code MC. The lithology of the analyzed sample was determined to be mixed carbonate rock, and the sedimentary environment was a transitional zone, influenced by periodic terrigenous inputs. Among them, when When the mixing type is classified as MC1, the mixing code is MC1. At this time, the influence of terrigenous origin is slight, and the carbonate structure remains intact; when At this time, the mixing type is classified as the MC2 subclass, with the mixing code MC2. Terrigenous influence is significant, but carbonate components still dominate. At that time, the mixed sedimentary type was classified as MS, with the mixed sedimentary code MS. The lithology of the analyzed sample was determined to be mixed clastic rock, and the depositional environment was near-source region with continuous and stable terrigenous input. Among these, when At this time, the mixed deposition type is classified into the MS1 subclass, with the mixed deposition code MS1. In this case, terrigenous clastic material is dominant, and carbonates are the important cementing material. At this time, the mixed sedimentary type is classified as the MS2 subclass, with the mixed sedimentary code MS2. This indicates a near-pure clastic rock with carbonates distributed in a patchy pattern. At that time, the mixed sedimentary type was classified as S type, the mixed sedimentary code was S, and the lithology of the analyzed sample was determined to be pure clastic rock. The rock characteristics were that terrigenous clastic rocks were absolutely dominant, the carbonate content was very low, and the sedimentary environment was near the lake shore or delta front.

[0119] Step 305: Statistical data verification and interpretation of lithological identification results.

[0120] Representative samples were selected from the analysis samples. After verifying the accuracy of the rock component classification of the representative samples based on the cross-validation method, the X-ray diffraction whole-rock analysis results of the representative samples and the point counting results of the point counting system were compared. In addition, the area percentage statistical verification of typical areas in the digital images of rock thin sections was carried out by image analysis software. Electron probe surface scanning analysis was performed on the difficult samples in the analysis samples to effectively ensure the accuracy of rock component identification.

[0121] Furthermore, the correspondence between SCI values ​​and sedimentary environments was established, where, when When, it indicates that the rock's depositional environment was a quiet chemical depositional environment; when When this occurs, it indicates that the sedimentary environment of the rocks is a transitional environment, influenced by seasonal or event-based terrigenous inputs; when... This indicates that the rock depositional environment was a high-energy environment with a continuous terrestrial supply.

[0122] Step 4 involves performing fine calculations of diagenetic strength parameters using thin sections of the analyzed samples, determining the diagenetic facies, and identifying the diagenetic facies code. This step includes the following sub-steps:

[0123] Step 401: Acquire digital thin-section images.

[0124] The single field of view size was set to 650μm×650μm. A 20x objective lens was used to perform full thin-section scanning of each rock thin section in the analysis sample. During the scanning process, different lithological regions of the thin section were covered to ensure that at least 25 standard field-of-view images were acquired for each rock thin section. In addition, 50x and 100x objectives were used for local high-definition scanning of key areas. After the full thin-section scanning was completed, the scanned images were stitched together to obtain digital thin-section images of each core thin section in the analysis sample.

[0125] Step 402: Measure the morphological parameters of the digital thin-section image.

[0126] A pore image is obtained after binarization of a digital thin section image. The projected area and boundary perimeter of a single pore in the pore image are measured. The shape factor and equivalent circle diameter of the pore are calculated. The shape factor is used to characterize the complexity of the pore morphology, and the equivalent circle diameter is the diameter of a circle with the same area as the pore. The projected area, boundary perimeter, shape factor, and equivalent circle diameter of the pore together reveal the scale, structural complexity, and geometric morphology of the pore, which are key indicators for evaluating the effectiveness of storage space and fluid permeability.

[0127] The mineral particles in the digital thin section image are then identified and their contours extracted and labeled to obtain the particle size distribution, interparticle contact relationship, and orientation of the mineral particles. The particle size distribution includes the maximum particle size, minimum particle size, average particle size, and sorting coefficient. The interparticle contact relationship includes point contact, line contact, concave-convex contact, or suture contact. The orientation is obtained through rose diagram or ellipse fitting analysis along the major axis.

[0128] Finally, for cements of different phases and types, the area coverage of cements within the field of view was measured, and the spatial distribution of cements (i.e., distribution uniformity, crust characteristics, pore filling patterns) as well as crystal morphology, size, and optical properties were analyzed, thereby systematically revealing the intensity and mode of cementation effect on pore evolution.

[0129] Step 403: Calculate diagenetic strength parameters, including compaction to reduce porosity. Cementation reduces porosity Dissolution increases porosity .

[0130] Specifically, considering that the rocks were loose sediments without diagenetic alteration in the early stages of deposition, and that their pore space was entirely controlled by the grain accumulation pattern, a model for calculating the original porosity was established based on the grain support structure in the digital thin section image, and the original porosity was determined as follows:

[0131] ;

[0132] In the formula, Original porosity; The volume of the particles and grains; This represents the total volume of the rock.

[0133] Analyze the residual intergranular porosity in rocks from digital thin section images to determine how compaction reduces porosity. for:

[0134] ;

[0135] In the formula, This represents the current interparticle porosity.

[0136] The compaction reduces porosity. It quantitatively characterizes the pore volume lost by rocks due to mechanical compaction, reflecting the destructive intensity of the reservoir space caused by physical processes such as particle rearrangement, plastic deformation, and even fracturing due to overlying strata pressure.

[0137] Identify all authigenic cements formed during diagenesis in rock thin sections under single-polarized and crossed-polarized light, including calcite cement, dolomite cement, siliceous cement, and evaporite cement; determine the volume percentage of each type of cement; and calculate the porosity reduction caused by cementation. for:

[0138] ;

[0139] In the formula, For the first The content of self-generated cementitious material in each sample.

[0140] The bonding reduces porosity. The study directly quantified the porosity loss caused by the supersaturated precipitation and filling of pores by minerals in pore water, revealing the extent of chemical damage to the reservoir caused by fluid-rock interactions.

[0141] In digital thin section images, secondary pore spaces created by dissolution are identified, the total volume of these secondary pore spaces and the volume of authigenic minerals precipitated within the dissolution spaces are determined, and the increase in porosity due to dissolution is assessed. for:

[0142] ;

[0143] In the formula, This refers to the porosity of the solution. The degree of filling with authigenic minerals.

[0144] The dissolution increases porosity. The geological significance lies in the net increase in the reservoir space of the rock, and it is a key indicator for evaluating the contribution of constructive diagenesis (such as the selective dissolution of easily soluble components by organic acids or atmospheric freshwater) to the reservoir.

[0145] Step 404: Determine the diagenetic facies type based on the diagenetic strength parameters and determine the diagenetic facies code.

[0146] Specifically, the diagenetic facies types include destructive diagenetic facies and constructive diagenetic facies.

[0147] when At that time, the geological characteristics were that the primary porosity had basically disappeared and the particles were in suture contact, which was identified as a strongly compacted and strongly cemented phase in a destructive diagenetic facies, with the diagenetic facies code being Dc, and it was determined to be a non-reservoir or tight layer; when At that time, the geological characteristics were that some primary porosity was preserved and the grain line-concave-convex contact was present. It was identified as a medium-compacted, medium-cemented facies in the destructive diagenetic facies, with the diagenetic facies code Dp, and was determined to be a potential reservoir requiring stimulation. At that time, the geological characteristics were that the primary pores were well preserved and the particles were in point-to-line contact. The weakly compacted and weakly cemented phase in the destructive diagenetic facies was identified, and the diagenetic facies code was Dw, which was determined to be a good reservoir.

[0148] when At that time, the geological characteristics included the development of numerous dissolution pores and the visible presence of casting pores and solution fractures, which identified it as a strongly dissolved phase within a constructive diagenetic facies, with the diagenetic facies code being Dd, and it was determined to be a high-quality reservoir; when At that time, the geological characteristics were moderate dissolution and good pore connectivity, which identified it as a moderately dissolved phase in a constructive diagenetic facies, with the diagenetic facies code Dg, and it was determined to be a medium-grade reservoir; when At that time, the geological characteristics were weak dissolution and poor porosity. The weak dissolution phase in the constructive diagenetic facies was identified, with the diagenetic facies code being Df, and it was determined to be a poor reservoir.

[0149] Step 5: Establish a three-level naming system based on salinity code, mixing code, and diagenetic facies code to name the salt lake carbonate reservoirs and evaluate the reservoir grade of the salt lake carbonate reservoirs in the exploration block.

[0150] Furthermore, a three-level naming system was established based on salinity code, mixing code, and diagenetic facies code. Salt lake carbonate reservoirs were named according to the format "salinity code - mixing code - diagenetic facies code," where the salinity code is the primary code, the mixing code is the secondary code, and the diagenetic facies code is the tertiary code. The primary codes use S1, S2, S3, and S4 to represent salinity levels, based on the strontium-barium ratio. The mineral content of the evaporites is determined; the secondary codes use C, MC, MS, and S to characterize the degree of mixing, and are quantitatively classified according to the mixing index SCI; the tertiary codes use Dc, Dp, Dw, Dd, Dg, and Df to characterize the diagenetic facies, and are determined according to the diagenetic strength parameters.

[0151] For rock samples with complex diagenetic sequences, supplementary naming rules are established, specifically: when multiple diagenetic processes are developed, the diagenetic facies codes are arranged in chronological order; auxiliary codes are added for special geological phenomena (such as hydrothermal alteration and tectonic fracturing); and naming priority rules are established to ensure the uniqueness and comparability of the names.

[0152] The reservoir grade of the salt lake carbonate reservoir in the exploration block is evaluated from three dimensions: reservoir performance, diagenetic modification, and rock base supply. In this embodiment, the weight coefficients for reservoir performance, diagenetic modification, and rock base supply are set to 0.45, 0.35, and 0.20, respectively.

[0153] A three-tier, six-category reservoir classification scheme was established, and exploration recommendations were provided for each level of reservoir. The specific details are as follows:

[0154] Reservoirs with porosity greater than 15%, permeability greater than 10 mD, and dominated by paste pores and intergranular dissolution pores are evaluated as Class I reservoirs. Class I reservoirs are high-quality reservoirs, typically named S3-C-Dd, which is a highly dissolved pure salt lake carbonate rock. The exploration recommendation for Class I reservoirs is to prioritize the deployment of development wells and adopt natural production capacity development.

[0155] Reservoirs with porosity greater than 8% but not exceeding 15%, permeability greater than 1 mD but not exceeding 10 mD, and exhibiting a mixed porosity system are classified as Class II reservoirs. Class II reservoirs are considered medium-sized reservoirs. Furthermore, Class II reservoirs are further divided into two subclasses: II... a Class II reservoirs and Class II reservoirs b Class II reservoir, wherein... a The typical name for this type of reservoir is S2-MC-Dp-Dd, which is a medium-compaction dissolution type. b The typical name for this type of reservoir is S3-MS-Dd, which is a strongly dissolved mixed-accretion type. For exploration of this type of reservoir, the recommendation is to implement reservoir stimulation and optimize well completion schemes.

[0156] Reservoirs with porosity less than 8%, permeability less than 1 mD, and dominated by micropores are evaluated as Class III reservoirs. Class III reservoirs are poor reservoirs, typically named S3-MC-Dc, which is a strongly compacted cemented type. The exploration recommendation for Class III reservoirs is to postpone development and pay attention to local diagenetic anomaly zones.

[0157] Step 6: Based on the reservoir grade evaluation results of each salt lake carbonate reservoir in the exploration block, conduct a comprehensive evaluation of the salt lake carbonate reservoirs in the exploration block using a single well.

[0158] Furthermore, a comprehensive evaluation of each well within the salt lake carbonate reservoir was conducted, and a multi-source data fusion platform for each well was established. This platform includes core description and naming results, Sr / Ba ratio sequences determined by continuous sampling, vertical variation curves of the SCI (Segregation Index), diagenetic strength parameter profiles based on thin section analysis, conventional logging curves (including natural gamma curves, sonic transit time curves, neutron porosity curves, and resistivity curves), imaging logging data, and vertical distribution data of rock properties (i.e., porosity and permeability).

[0159] The vertical structure of the reservoir is further divided based on a combination of hierarchical analysis and pattern recognition.

[0160] Specifically, with and Based on this, reservoir sections were identified in the carbonate reservoirs of the salt lake; or Based on this, identify dense interlayers in the carbonate reservoirs of salt lakes; comprehensively consider porosity, and Targeting high-quality reservoirs with porosity greater than 12% , Based on this, dominant reservoirs were delineated in the carbonate reservoirs of the salt lake; the diagenetic sequence was reconstructed by obtaining the vertical combination characteristics of diagenetic strength parameters.

[0161] Based on the naming results and geological background of the salt lake carbonate reservoirs, the reservoir development models of the salt lake carbonate reservoirs are determined. Specifically, the reservoir development models are sedimentary-controlled, diagenetic-modified, and composite-controlled. The sedimentary-controlled model develops in high-energy grain shoal facies, the diagenetic-modified model is formed in the dissolution and porosification zone under a mixed sedimentary background, and the composite-controlled model is controlled by both sedimentary facies zones and subsequent dissolution.

[0162] Example 2

[0163] In this embodiment, the 2240-2245m depth range of well A in a certain salt lake carbonate reservoir with rhythm 5 is taken as the exploration block. The multi-parameter quantitative classification and reservoir evaluation method for salt lake carbonate rocks proposed in Example 1 is adopted, including the following steps:

[0164] Step 1: Collect multiple salt lake carbonate rock samples from the 2240-2245m depth range of well A to carry out systematic sample collection and standardized preparation.

[0165] In this embodiment, sampling was preferentially conducted on sections of well A with intact cores and no obvious fragmentation within the 2240-2245m section, avoiding areas with severe brown contamination from drilling mud intrusion. Considering the high-precision reservoir evaluation requirement for this block and the fact that this section is a reservoir with complex lithological variations, sampling was conducted at a density of 20 sampling points per meter, resulting in multiple samples of salt lake carbonate rocks.

[0166] Each salt lake carbonate rock sample was coded in four levels according to the well name, top depth, bottom depth, and serial number. The macroscopic characteristics of each salt lake carbonate rock sample were recorded in detail, including color, structure, texture, mineral composition, fossil content, and fracture development. High-resolution core images of each salt lake carbonate rock sample were obtained.

[0167] The analytical samples used for subsequent analysis were prepared under anhydrous conditions using carbonate rock samples from various salt lakes, including rock thin sections and rock powder.

[0168] Step 2: Quantitatively determine salinity parameters using rock powder from the analysis sample, identify the salinity level, and determine the salinity code.

[0169] In this embodiment, salinity indicator element analysis was performed on the analytical samples to obtain the salinity indicator elements of the analytical samples collected within the 5-degree range of well A. Content and Content, determining the strontium-barium ratio of the analytical sample. Next, the evaporite minerals in the analyzed samples were identified to determine the relative contents of various evaporite minerals such as gypsum, anhydrite, and halite in the rock powder, as shown in Table 1.

[0170] Table 1. Statistical table of Sr and Ba ratios in samples

[0171] .

[0172] As can be seen from Table 1, the SrB ratio in this well section is greater than 2, which indicates that the salinity is that of a salt lake or hypersalinity. Combined with the mineral content of the evaporites in Table 2, the salinity codes are S3 and S4, which correspond to the observations from the core that indicate that the well is susceptible to moisture and has a high salt content.

[0173] Table 2. Statistical table of relative content of minerals in evaporite.

[0174] .

[0175] Step 3: Calculate the mixing index and classify the mixing type using thin sections of rock in the analysis sample, and determine the mixing code.

[0176] In this embodiment, the mixing index was calculated for the analytical samples collected from the 2240-2245m section of well A with rhythm 5. The mixing index SCI of each analytical sample was calculated and is shown in Table 3.

[0177] Table 3. Statistical table of point counts for each analytical sample

[0178] .

[0179] Based on Tables 1, 2, and 3, it can be seen that the carbonate rock content in this area is relatively high. According to the SCI (Segregation Index), the lithology is determined to be pure carbonate rock and mixed carbonate rock, and the segregation codes are C and MC.

[0180] Step 4: Analyze the rock thin sections in the sample to perform fine calculations of diagenetic strength parameters, determine the diagenetic facies, and identify the diagenetic facies code.

[0181] In this embodiment, due to the significant destructive effect of cementation on porosity, complex and multi-stage diagenetic fluid alteration was observed, primarily involving sulfate cementation, calcite cementation, and dolomite cementation. Statistical analysis of the distribution and types of cementation in the characteristic rhythm of well A revealed that sulfate cementation was the most widespread, present in all types of lithology. Furthermore, sulfate cementation in the study area was dominated by anhydrite and glaucophane cementation, exhibiting multiple stages, which can be broadly categorized into early and late sulfate cementation. Early sulfate cementation mainly cemented within intergranular pores or was distributed in the strata as euhedral granular sulfate minerals. The contact relationship between sample particles was predominantly point contact or suspension contact, reflecting its formation in the early stages of diagenetic evolution, at a shallow stratum depth, and without prior compaction. Late-stage sulfate cementation is mainly cemented in intergranular pores, dissolution pores, casting pores, and structural cracks. The particle contact relationship is mainly line contact or concave-convex contact, reflecting its development in the residual pores after calcite cementation.

[0182] Taking the 5-rhythm pattern of Well A as an example, this pattern shows well-developed grainy carbonate rocks with significant sulfate cementation. A cross-sectional diagram of sulfate content and porosity reveals that porosity gradually decreases with increasing sulfate mineral content. Particularly after the sulfate content exceeds 15%, sulfate cementation plays a dominant role in disrupting the reservoir space, resulting in a significant decrease in porosity. Sulfate cementation also has a certain impact on grainy carbonate rocks. In Well A, the average sulfate content in the upper slope zone far from the center of the salt lake, according to elemental logging, is 7.41%, and the average total porosity according to nuclear magnetic resonance (NMR) logging is 7.15%. In the lower slope zone closer to the center of the salt lake, the average sulfate content in elemental logging is 15.77%, and the average total porosity according to NMR logging is 5.94%. Figure 4 The diagram shown illustrates the relationship between sulfate minerals and porosity in well A. Figure 4 It can be seen that sulfate cementation has a destructive effect on the reservoir space of grainy carbonate rocks. However, because the organic acid dissolution and alteration of grainy carbonate rocks is more intense, the destructive effect of sulfate cementation on them is weaker than that on granular carbonate rocks.

[0183] Diagenetic facies identification was conducted based on the relationships between constructive diagenetic processes (dissolution), destructive diagenetic processes (compaction), and cementation) in the study area, establishing a diagenetic facies classification standard applicable to the Qianjiang Formation. Based on qualitative and quantitative characterization from numerous rock thin sections, the compaction-reduced porosity was calculated by subtracting the intergranular porosity after compaction from the original porosity. The porosity represented by the amount of cementitious material is used to indicate the reduction in porosity due to cementation. The increase in porosity due to dissolution is calculated by subtracting the decrease in porosity due to authigenic minerals from the increase in porosity due to dissolution. The diagenetic strength parameters of each analytical sample were calculated and are shown in Table 4.

[0184] Table 4. Statistical table of diagenetic strength parameters for each analyzed sample

[0185] .

[0186] According to At that time, the diagenetic facies was determined to be a strongly compacted and strongly cemented facies. At that time, the diagenetic facies was determined to be a medium-compacted, medium-cemented phase; when At that time, the diagenetic facies was determined to be a weakly compacted and weakly cemented facies; when At that time, the diagenetic facies was determined to be a strongly dissolved phase; when At that time, the diagenetic facies was determined to be a moderately dissolved phase; when At that time, the diagenetic facies was determined to be a weakly dissolved facies. Based on this, a total of 6 diagenetic facies types were identified in the study area, which are ranked from best to worst in terms of reservoir performance as follows: weakly compacted and strongly dissolved facies, moderately compacted and strongly dissolved facies, moderately compacted and weakly cemented facies, moderately compacted and moderately cemented facies, weakly compacted and strongly cemented facies, and strongly compacted and strongly cemented facies.

[0187] Through comprehensive analysis of diagenetic facies and lithofacies, and comprehensive identification of stratigraphic reservoir space, the following diagenetic facies were identified: Weakly compacted, strongly dissolved diagenetic facies are mainly distributed in massive / layered granular carbonate facies, layered / laminated crystalline carbonate facies, and layered granular mixed sedimentary facies. The pore types are mainly intergranular pores and dissolution pores, exhibiting excellent reservoir performance. Medium-compacted, strongly dissolved diagenetic facies are mainly distributed in massive / layered granular carbonate facies, layered / laminated crystalline carbonate facies, and layered granular mixed sedimentary facies. The pore types are mainly intergranular pores and dissolution pores, exhibiting good reservoir performance. Medium-compacted, weakly cemented diagenetic facies are mainly distributed in… In massive / layered granular carbonate facies, layered granular mixed sedimentary rocks, and massive / layered fine-grained mixed sedimentary rocks, the pore type is mainly primary intergranular pores, and the reservoir performance is good. Medium-compacted and medium-cemented diagenetic facies are mainly distributed in massive / layered granular carbonate facies and layered granular mixed sedimentary rocks, with the pore type mainly residual intergranular pores, and the reservoir performance is poor. Weakly compacted and strongly cemented diagenetic facies and strongly compacted and strongly cemented diagenetic facies are mainly developed in layered granular carbonate facies and layered granular mixed sedimentary rocks, and the reservoir space is not well developed.

[0188] Step 5: Establish a three-level naming system based on salinity code, mixing code, and diagenetic facies code to name the salt lake carbonate reservoirs and evaluate the reservoir grade of the salt lake carbonate reservoirs in the exploration block.

[0189] In this embodiment, due to the strontium-barium ratio A salinity greater than 2.5 and an evaporite mineral content greater than 30% indicate a salinity code of S4. The calculated average SCI (Segregation Index) is 12%, classifying the sedimentation type as MC1 subclass of MC, indicating slight terrigenous influence and intact carbonate structure, thus classifying the sedimentation code as MC1. The diagenetic strength parameter shows that compaction reduces porosity. 32%, cementation reduces porosity 25%, dissolution increases porosity The early diagenetic facies was determined to be 3.8%, with the code being Dc. The porosity of the carbonate rocks at this depth was relatively low, and the late diagenetic facies was a weakly dissolved facies.

[0190] The rocks in the study well section were named according to the format of "salinity code-mixing code-diagenetic facies code", as shown in Table 5.

[0191] Table 5. Naming results of each analytical sample

[0192] .

[0193] According to the three-level, six-category reservoir classification scheme, the porosity of this well section is between 8% and 15%, the permeability is low, the cementation degree is high, and the pore development is poor. The reservoir evaluation result is Class II reservoir, and the exploration recommendation is to postpone development, implement reservoir stimulation, and optimize the well completion plan.

[0194] Step 6: Based on the reservoir grade evaluation results of each salt lake carbonate reservoir in the exploration block, conduct a comprehensive evaluation of the salt lake carbonate reservoirs in the exploration block using a single well.

[0195] Based on the diagenetic facies identification scheme, diagenetic facies identification was performed on the 2-tone, 5-tone, and 10-tone rhythms in well A, and diagenetic facies bar charts were drawn for each rhythm, such as... Figures 5-7 As shown. Through comprehensive analysis of diagenetic and lithofacies, and comprehensive identification of stratigraphic reservoir space, it was found that the 10-tone sequence of well A contains massive / layered granular carbonate rocks with medium compaction and strong dissolution, and weak compaction and strong dissolution. These massive / layered granular carbonate rocks are of high-quality lithofacies-diagenetic facies integrated type, with a small amount of cementation, a large number of dissolution pores, and well-developed reservoir space.

[0196] This invention provides a systematic naming system for salt lake carbonate rocks based on a three-level naming system of salinity code, mixing code, and diagenetic facies code. This system enables precise classification of salt lake carbonate rock types and directly correlates the naming results with reservoir performance. Without relying on expert experience, the system can quickly and accurately determine the rock's genesis, key diagenetic history, and reservoir potential solely based on the naming results. This provides a basis for accurately evaluating salt lake carbonate rock reservoirs and guiding their exploration and development.

[0197] Of course, the above description is not intended to limit the present invention, and the present invention is not limited to the examples given above. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for multi-parameter quantitative classification and reservoir evaluation of salt lake carbonate rock, characterized in that, The method comprises the following steps of: Step 1, selecting key wells in the exploration block, collecting multiple salt lake carbonate rock samples, and preparing analysis samples under waterless conditions respectively by using the salt lake carbonate rock samples; Step 2, quantitatively determining the salinity parameter by using the rock powder in the analysis sample, determining the salinity level and determining the salinity code; Step 3, calculating the mixing index by using the rock thin section in the analysis sample, dividing the mixing type and determining the mixing code; Step 4, finely calculating the diagenetic intensity parameter by using the rock thin section in the analysis sample, determining the diagenetic facies and determining the diagenetic facies code; Step 5, establishing a three-level naming system according to the salinity code, the mixing code and the diagenetic facies code, naming the salt lake carbonate rock reservoir, and evaluating the reservoir grade of the salt lake carbonate rock reservoir in the exploration block; Step 6, according to the reservoir grade evaluation result of each salt lake carbonate rock reservoir in the exploration block, the salt lake carbonate rock reservoir in the exploration block is comprehensively evaluated by single well; In step 2, the following sub-steps are included: Step 201, analyzing the salinity indicating elements of the analysis sample; The salinity indicating element analysis process adopts inductively coupled plasma mass spectrometer to analyze trace elements of the rock powder, measures the content of Sr and Ba in the rock powder for multiple times, and determines the Sr / Ba ratio of the analysis sample and ;​ Step 202, evaporite mineral identification is performed on the analysis sample; In the process of evaporite mineral identification, the X-ray diffractometer is used for whole rock mineral semi-quantitative analysis of the rock powder, the relative content of each evaporite mineral in the rock powder is determined, and then the scanning electron microscope and the energy spectrometer are used for micro-area morphology observation and composition analysis to determine the occurrence state and paragenetic relationship of each evaporite mineral; Step 203, determining the salinity level according to the salinity indicating element analysis result and the evaporite mineral identification result, and determining the salinity code of the analysis sample; Each salinity level is matched with a salinity code, wherein the salinity code of the salinity of fresh water-micro-salinity water is S1, the salinity code of the salinity of semi-salinity water-salinity water is S2, the salinity code of the salinity of salt lake is S3, and the salinity code of the salinity of super-salinity is S4; According to the strontium barium ratio and evaporite mineral content of the analysis sample, the salinity code is determined, wherein when the strontium barium ratio of the analysis sample is less than 0.6 and the evaporite mineral content is less than 1%, the salinity code is determined as S1; when the strontium barium ratio of the analysis sample is and the evaporite mineral content is , the salinity code is determined as S2; when the strontium barium ratio of the analysis sample is and the evaporite mineral content is , the salinity code is determined as S3; when the strontium barium ratio of the analysis sample is greater than 2.5 and the evaporite mineral content is greater than 30%, the salinity code is determined as S4. In step 3, the mixing type is finely classified into C, MC, MS, and S categories based on the mixing index SCI, thus determining the mixing code and depositional environment of the analyzed sample; the mixing index... The calculation formula is: ； wherein is the sample number; is the total number of samples analyzed; is the number of quartz points for the sample; is the number of feldspar points for the sample; is the number of clay mineral points for the sample; is the total number of points; is the sample number for the pore point measurement; is the total number of samples for the pore point measurement; is the number of pore points for the sample; When , the mixing type is divided into C class, the mixing code is C, the lithology of the analyzed sample is determined as pure carbonate rock, and the sedimentary environment is the center of the lake basin far away from the source or the area dominated by chemical deposition; when , the mixing type is divided into MC class, the mixing code is MC, the lithology of the analyzed sample is determined as mixed carbonate rock, and the sedimentary environment is the transition zone, wherein, when , the mixing type is divided into MC1 subclass, the mixing code is MC1, when , the mixing type is divided into MC2 subclass, the mixing code is MC2; when , the mixing type is divided into MS class, the mixing code is MS, the lithology of the analyzed sample is determined as mixed clastic rock, and the sedimentary environment is the proximal source area, wherein, when , the mixing type is divided into MS1 subclass, the mixing code is MS1, when , the mixing type is divided into MS2 subclass, the mixing code is MS2; when , the mixing type is divided into S class, the mixing code is S, the lithology of the analyzed sample is determined as pure clastic rock, and the sedimentary environment is close to the lake shore or the delta front. establishing a corresponding relationship between the SCI value and the sedimentary environment, wherein, when SCI < 0.5, it indicates that the sedimentary environment is a quiet chemical sedimentary environment; when 0.5 < SCI < 1, it indicates that the sedimentary environment is a transitional environment; and when SCI > 1, it indicates that the sedimentary environment is a high-energy environment. In step 4, the following sub-steps are included: Step 401, collecting digital thin section images; After scanning each rock thin section in the analysis sample and image stitching, local high-definition scanning is performed on the key areas, so that at least 25 standard field images are collected for each rock thin section, and the digital thin section images of the analysis sample are obtained; Step 402, measuring the morphological parameters of the digital thin section images; After binarization of the digital thin section images, the pore images are obtained, the projection area and boundary perimeter of a single pore in the pore image are measured, the shape factor and equivalent circle diameter of the pore are calculated, the mineral particle size distribution, inter-particle contact relationship and directionality are obtained by recognizing and extracting the contour and marking the mineral particles in the digital thin section images, and finally the area coverage of the cement in the field of view is measured for different periods and types of cement, and the spatial distribution of the cement and the crystal morphology, size and optical property characteristics are analyzed; Step 403, calculating diagenetic intensity parameters, including compaction reducing porosity , cementation reducing porosity , dissolution increasing porosity ; Step 404, determining the diagenetic facies type according to the diagenetic intensity parameter, and determining the diagenetic facies code; When , the lithofacies type is determined as strong compaction and strong cementation, and the lithofacies code is Dc; when , the lithofacies type is determined as medium compaction and medium cementation, and the lithofacies code is Dp; when , the lithofacies type is determined as weak compaction and weak cementation, and the lithofacies code is Dw. When , the diagenetic facies type is determined to be a strong dissolution facies, and the diagenetic facies code is Dd; when , the diagenetic facies type is determined to be a moderate dissolution facies, and the diagenetic facies code is Dg; when , the diagenetic facies type is determined to be a weak dissolution facies, and the diagenetic facies code is Df; In step 5, a three-level naming system is established according to the salinity code, the sedimentary code and the diagenetic facies code, and the salt lake carbonate reservoir is named in the order of the salinity code, the sedimentary code and the diagenetic facies code; The reservoir grade of the salt lake carbonate reservoir in the exploration block is evaluated by comprehensively considering the reservoir performance, the diagenetic reconstruction and the rock foundation, and exploration suggestions are provided for each grade of reservoir; The reservoir with the porosity greater than 15%, the permeability greater than 10 mD and the pore characteristics mainly being gypsum mold pores and intergranular dissolved pores is evaluated as a type I reservoir, the type I reservoir is a high-quality reservoir, and the exploration suggestion for the type I reservoir is to preferentially deploy a development well and adopt natural productivity development; the reservoir with the porosity greater than 8% and not more than 15%, the permeability greater than 1 mD and not more than 10 mD and the pore characteristics being a mixed pore system is evaluated as a type II reservoir, the type II reservoir is a medium-quality reservoir, and the exploration suggestion for the type II reservoir is to implement reservoir reconstruction and optimize a well completion scheme; the reservoir with the porosity less than 8%, the permeability less than 1 mD and the pore characteristics being mainly micro-pores is evaluated as a type III reservoir, the type III reservoir is a poor-quality reservoir, and the exploration suggestion for the type III reservoir is to temporarily suspend development and pay attention to local diagenetic abnormal zones; In step 6, a single-well comprehensive evaluation is performed on each well in the salt lake carbonate reservoir, and a single-well multi-source data fusion platform is established, the single-well multi-source data fusion platform including core description and naming result data, Sr / Ba ratio sequence, sedimentary index SCI vertical variation curve and diagenetic intensity parameter profile based on thin section analysis; The reservoir longitudinal structure is divided by using the method combining the analytic hierarchy process and pattern recognition, the reservoir section is identified, the interlayer is distinguished, the dominant reservoir is delineated, the diagenetic sequence is reconstructed in the salt lake carbonate reservoir, and the reservoir development mode of the salt lake carbonate reservoir is determined in combination with the naming result of the salt lake carbonate reservoir and the geological background, the reservoir development mode including a sedimentation control type, a diagenetic reconstruction type and a composite control type.

2. The method according to claim 1, wherein, In step 1, the following sub-steps are included: In step 101, a plurality of salt lake carbonate rock samples are collected in the key well in the exploration block according to a preset sampling density and sampling position, and each salt lake carbonate rock sample is coded in the order of well name, top depth, bottom depth and sequence number by using a four-level coding system, and the macroscopic characteristics and high-resolution core photos of each salt lake carbonate rock sample are obtained; In step 102, the environmental temperature of the laboratory is controlled to be 20±2℃ and the humidity is not more than 30% RH, the salt lake carbonate rock samples are temporarily stored by using a drying cabinet, and the salt lake carbonate rock samples are transferred by using a sealed drying box with built-in silica gel desiccant; Each salt lake carbonate rock sample is pretreated, the cutting speed and the cutting temperature are set, each salt lake carbonate rock sample is cut by using a diamond saw blade and cooled by using compressed air, and each pretreated salt lake carbonate rock sample is obtained; Each pretreated salt lake carbonate rock sample is used to prepare an analysis sample, including a rock thin section and a rock powder, under anhydrous conditions; In step 103, the rock thin section and the rock powder are prepared by using each pretreated salt lake carbonate rock sample under anhydrous conditions; The rock slice is prepared by the following steps: firstly, rough grinding the pretreated salt lake carbonate rock sample by using a diamond grinding disc, then fine grinding by using a silicon carbide sandpaper, and finally, obtaining the salt lake carbonate rock particles by using a diamond spray for fine polishing; and finally, fixing the salt lake carbonate rock particles in the epoxy resin with a water content of not more than 0.5% to obtain the rock slice; The rock powder is prepared by the following steps: firstly, crushing the salt lake carbonate rock sample by using a jaw crusher, and then grinding the crushed salt lake carbonate rock sample by using a agate ball mill to obtain the rock powder.

3. The method of claim 1, wherein the method is characterized by, In step 3, the following sub-steps are included: Step 301, digital processing of the rock slice; After quality inspection of the rock slice under a polarizing microscope, the rock slice with defects is repaired, and then the rock slice is systematically scanned by using a microscope equipped with a CCD camera; firstly, the whole rock slice is scanned under single polarized light, and then the key area of the rock slice is locally scanned under crossed polarized light to obtain the digital image of the rock slice; Step 302, setting a rock component classification system and establishing a point counting system; In the rock component classification system, the rock components are divided into terrigenous clastic components, carbonate components and evaporite components; the terrigenous clastic components include quartz, feldspar, clay minerals and rock debris; the carbonate components include calcite, dolomite, oolitic particles and bioclasts; and the evaporite components include gypsum, anhydrite and halite; The point counting system is established by using point counting software; the point counting system includes a grid system containing a plurality of statistical points based on a hierarchical random sampling strategy, and the counting rules of the point counting system are set; Step 303, based on the rock component classification system, the statistical points corresponding to each rock component are counted in the digital image of the rock slice by using the point counting system, and the sedimentary index SCI of the sample is calculated and analyzed; Step 304, the mixing type is finely divided into C type, MC type, MS type and S type according to the sedimentary index SCI, and the mixing code and the sedimentary environment of the analysis sample are determined; Step 305, statistical data verification and lithology discrimination result interpretation; Representative samples are selected from the analysis samples, the accuracy of the rock component division of the representative samples is verified based on the cross-validation method, the X-ray diffraction whole rock analysis results of the representative samples and the point counting results of the point counting system are compared, the area percentage statistical verification of the typical area in the digital image of the rock slice is verified by using the image analysis software, and the electron probe area scanning analysis of the difficult samples in the analysis samples is performed.

4. The method of claim 1, wherein the method is characterized by, Based on the particle support structure in the digital slice image, an original porosity calculation model is established to obtain: ； wherein is the original porosity; is the volume of the grains and the crystals; is the total volume of the rock; analyzing the digital thin section images for residual intergranular porosity in the rock to determine the compaction loss porosity is: ； In the formula, is the current intergranular porosity; Identify all authigenic cements formed during diagenesis in the rock thin section under single and crossed polarized light, including calcite cements, dolomite cements, siliceous cements, and evaporite cements, determine the volume percent of each type of cement, and calculate cement reduction porosity To: ； wherein is the authigenic cement content of the nth sample; and is the authigenic cement content of the nth sample; and identifying the secondary pore space resulting from the dissolution in the digital thin section image, determining the total volume of the secondary pore space and the volume of the precipitated authigenic minerals in the dissolution space, determining the dissolution-induced porosity increase is: ； wherein is the dissolution porosity; is the authigenic mineral fill.

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