A method for predicting and constructing mining space form of high-impurity salt mine solution cavity based on multi-source data fusion

Abstract

The application belongs to the field of salt rock underground energy storage, and discloses a high-impurity salt mine solution cavity mining space form prediction and construction method based on multi-source data fusion, so as to solve the problem that the prior art cannot directly detect the hidden space buried by sediment, and further overcome the key technical problems that it is difficult to accurately depict the solution cavity mining space form, calculate the effective gas volume of the sediment gap and the position of the gas-liquid interface. Four types of heterogeneous data sources, namely, geology, drilling, cavity measurement and production, are integrated and cooperatively used, through the fusion and iterative correction logic of "geological constraint form, cavity measurement boundary limitation and production volume verification", a complete three-dimensional mining space form containing the upper pure brine space and the lower hidden space of sediment is gradually constructed, the form is reasonable, the total volume is completely matched with the engineering practice, and a real and reliable three-dimensional geological model basis is provided for the subsequent accurate calculation of the sediment gap volume, the prediction of the gas-liquid interface and other key engineering problems.

Description

Technical Field

[0001] This invention belongs to the field of underground energy storage in salt rock, specifically involving a method for predicting and constructing the spatial morphology of high-impurity salt mine cavity based on multi-source data fusion. It is applied to the cavity formed by water dissolution in high-impurity salt mine, aiming to accurately assess the effective gas storage volume of its sediment voids and predict the dynamics of the gas-liquid interface. Background Technology

[0002] Salt caverns offer significant advantages as large-scale energy storage facilities, but the unique geological conditions of my country's salt mines make their construction and utilization difficult. my country's salt rocks are primarily lacustrine sedimentary layered salt rocks, generally characterized by thin salt layers, numerous non-salt interlayers, and high levels of insoluble impurities. This results in the generation of large amounts of insoluble sediment from salt rock impurities and interlayers within the caverns during the construction of storage facilities using water-soluble cavern technology. This sediment undergoes fragmentation and expansion during the cavern construction process, significantly occupying the effective space that should have been created. Consequently, the final cavern only retains a limited amount of clean brine space in the upper part, while the lower part is filled with loosely structured, porous sediment.

[0003] Therefore, the usable gas storage space formed by high-impurity salt mines consists of two parts: one is the clean brine space above the sediment, and the other is the "sediment voids" that exist in the form of pores within the sediment accumulation body. Traditional reservoir construction and evaluation methods usually only consider and utilize the visible clean brine space, while treating the sediment accumulation body as an ineffective entity. This results in a large amount of potential gas storage space (i.e., sediment voids) being buried and wasted, which seriously restricts the effective storage capacity and economic benefits of layered salt rock reservoirs in my country.

[0004] To maximize the utilization of high-impurity salt mine cavity resources, the key lies in accurately predicting and constructing its complete "mining space morphology." This morphology includes not only the relatively well-defined upper brine space but also the lower "hidden space" buried by sediment and difficult to detect directly. Existing cavity detection technologies such as sonar can only scan the visible boundaries of the brine space and cannot penetrate the sediment, thus failing to directly obtain the true shape and volume of the hidden space. However, the morphology of this hidden space is precisely the basis for calculating the usable void volume inside the sediment and assessing the actual gas storage capacity of the cavity; it is also crucial for accurately analyzing the migration patterns of the gas and liquid phases within the sediment during reservoir operation and predicting the depth of the gas-liquid interface.

[0005] Therefore, developing a method that integrates multi-source data to effectively predict and construct the complete mining space morphology of high-impurity salt mine cavities is of vital engineering value and scientific significance for breaking through the capacity bottleneck of salt cavern storage construction in my country and improving the safety and controllability of storage operation. Summary of the Invention

[0006] This invention aims to provide a method for predicting and constructing the spatial morphology of high-impurity salt mine cavities based on multi-source data fusion, in order to solve the key problem that existing technologies cannot directly detect the hidden spaces buried by sediment, thus making it difficult to accurately characterize the spatial morphology of cavities, calculate the effective gas storage volume of sediment voids and the position of the gas-liquid interface.

[0007] To achieve the above-mentioned technical objectives, the present invention provides the following technical solution: A method for predicting and constructing the spatial morphology of mining cavities in high-impurity salt mines based on multi-source data fusion includes the following specific implementation steps: Step 1: Multi-source basic data acquisition and feature parameter extraction

[0008] Collect and integrate four categories of data related to the target cavity: geological and dissolution characteristics data, drilling engineering data, historical sonar cavity measurement data, and water-soluble cavity production data, in order to obtain all the input parameters required to build and validate the model; Step 2: Preliminary 3D Modeling of the Mining Space Based on Geological and Engineering Data

[0009] By integrating the geological and dissolution characteristic data, drilling engineering data, and historical sonar cavity data obtained in step one, and using multi-source data to complement and constrain each other, an initial three-dimensional morphological model is constructed. Step 3: Model Validation Based on Production Data and Iterative Correction Based on Multi-Source Data

[0010] The water-soluble cavity production data obtained in step one is used to quantitatively verify and calibrate the initial three-dimensional morphological model constructed in step three, so as to realize the closed-loop fusion of "geology-engineering-production" data; when the model volume error meets the requirements, the final optimized and reliable three-dimensional morphology of the cavity mining space is obtained.

[0011] Preferably, step one further includes: 1.1 Geological and Dissolution Characteristics Data: Data on the distribution, thickness, dip angle, average grade, insoluble content, and mineral composition of the salt rock in the cavity-forming strata were collected. Key dissolution parameters included the upward and lateral dissolution rates of the salt rock, the lateral dissolution angle, and the fragmentation / expansion coefficient of the insoluble matter. These parameters determine the macroscopic dissolution and expansion trend of the cavity, its final morphological profile (cavity formation rate), and the amount of sediment generated.

[0012] 1.2 Drilling Engineering Data: Acquire completion data for the target salt cavity, including wellbore trajectory, wellhead coordinates, target coordinates, and cavity-building string insertion depth. If the cavity is created in a connected well, trajectory data for both wells must be included to determine the geometric reference for cavity development (such as bottom depth and well-to-well distance).

[0013] 1.3 Historical Sonar Cavity Measurement Data: Collect data from previous sonar scans and extract key geometric features, such as the depth of the cavity top, maximum radius, and depth of the sediment surface (i.e., the lower limit of the clean brine space). This is the core data source for directly probing the boundary of the clean brine space.

[0014] 1.4 Water-soluble cavity production data: Collect complete cavity production records, including cumulative water injection volume, cumulative extracted brine volume, and average extracted brine concentration. These are used to subsequently calculate the actual solid salt extraction volume, thereby inferring the "total volume of the mining space".

[0015] Preferably, step two integrates the three types of data obtained in steps 1.1, 1.2, and 1.3, using multi-source data to complement and constrain each other, and constructs an initial three-dimensional shape.

[0016] 2.1 Determining the Spatial Baseline and Main Development Direction: The lowest point of the cavity space is determined using drilling trajectory data. Based on the formation dip angle and lateral dissolution angle, the trend of preferential expansion of the cavity along the updip direction of the formation is determined, i.e., the "biased dissolution" main direction.

[0017] 2.2 Constructing a Preliminary Three-Dimensional Morphological Framework: The lateral boundaries of the solution cavity (at different depth sections) directly obtained from sonar cavity data are used as hard constraints. In sediment-buried areas (hidden spaces) not scanned by sonar data, a preliminary three-dimensional morphological model is constructed based on geological dissolution patterns. Specifically, based on parameters such as the salt layer lateral dissolution angle, the distribution of insoluble interlayers, and their overhang lengths, the dissolution boundary of the solution cavity below the interlayer is deduced. The boundary of the "net brine space" detected by sonar is merged with the boundary of the "hidden space" deduced from geological patterns to form a complete and preliminary three-dimensional morphological model of the mining space.

[0018] Preferably, step three introduces the fourth type of data (production data) obtained in step 1.4 to quantitatively verify and calibrate the preliminary model, thereby achieving closed-loop fusion of "geological-engineering-production" data.

[0019] 3.1 Calculation of Actual Mining Volume: Using water-soluble cavity creation production data, the total volume of salt rock actually dissolved and removed is calculated based on the principle of mass conservation. The calculation formula is: in, To obtain the average concentration of the brine, To calculate the cumulative volume of water injected (or brine extracted), This represents the average grade of the salt mine. This represents the density of the salt ore. This volume is the total theoretical mining space volume, including sediment voids, formed by the dissolution of salt rock and the fragmentation of insoluble matter.

[0020] 3.2 Model Volume Calculation and Error Comparison: Calculate the volume of the preliminary 3D model constructed in step two. (The model volume is then compared.) Compared with the actual calculated mining volume Compare the results and calculate the relative error δ:

[0021] 3.3 Iterative Correction Until Convergence: Set an error threshold based on actual conditions (e.g., a point value within the range of 5%-15%). If δ exceeds the threshold, it indicates that the preliminary model does not match the actual dissolution volume, and iterative correction is required. The core of the correction is to adjust the geological dissolution parameter model, especially parameters that are difficult to observe directly, such as the lateral dissolution angle, interlayer dissolution rate, and the inhibitory effect of sediment accumulation on lateral dissolution. Through multiple iterative cycles of "parameter adjustment - model reconstruction - volume comparison," the model is made infinitely close to the ideal state, thereby achieving accurate model calibration. When the model volume error meets the requirements, the final optimized and reliable three-dimensional morphology of the cavity mining space is obtained.

[0022] Compared with the prior art, the present invention has the following advantages and beneficial effects: The morphology prediction and construction method provided by this invention integrates and synergistically utilizes four types of heterogeneous data sources: geology, drilling, cavity measurement, and production. Through the fusion and iterative correction logic of "geologically constrained morphology, cavity-defined boundaries, and production-verified volume", a complete three-dimensional mining space morphology including the upper clean brine space and the lower sediment concealed space is gradually constructed.

[0023] Breaking through detection limitations and revealing hidden spaces: This effectively compensates for the inability of direct detection technologies such as sonar to penetrate sediment. By integrating directly detected clean brine spatial data (sonar data, especially historical sonar cavity data) with parameters reflecting the geological dissolution patterns of salt layers (geological data, especially geological and dissolution characteristic data, drilling engineering data) and data reflecting the total dissolution amount (production data, especially water-soluble cavity creation or brine extraction production data), the morphology of hidden spaces buried under sediment can be indirectly and with high precision deduced.

[0024] Achieving quantitative verification and calibration: The innovative introduction of production data as a "benchmark" provides an absolute volumetric verification benchmark for the geometric morphology model. This transforms model construction from traditional qualitative fitting into a quantifiable and verifiable inverse modeling and parameter inversion process, greatly improving the reliability and accuracy of prediction results.

[0025] Enhancing the reliability of the model engineering: A rigorous and mutually verifying data processing loop was formed by establishing spatial benchmarks using drilling data, guiding morphological deduction based on geological principles, constraining visible boundaries using sonar data, and finally forcing volume matching using production data. The resulting model not only has a reasonable morphology but also perfectly matches the actual engineering volume. This provides a realistic and reliable 3D geological model foundation for subsequent accurate calculations of sediment void volume and predictions of gas-liquid interfaces, offering core technical support for capacity evaluation, design optimization, and safe operation of salt cavern gas storage facilities, oil storage facilities, and compressed air storage power plants. Attached Figure Description

[0026] Figure 1 A flowchart illustrating the construction of the mining space morphology of a salt mine cavity in an embodiment of the present invention; Figure 2 A schematic diagram of the salt cavity mining space of the ZJ-XJ connected well in this embodiment of the invention; the marked values ​​are the burial depth of the salt strata at that location, i.e., the depth range. Detailed Implementation

[0027] The applicant will now clearly and completely describe the technical solution of the present invention with reference to specific embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0028] Example: This invention provides a method for predicting and constructing the morphological features of high-impurity salt mine cavity mining based on multi-source data fusion, used to demonstrate the process of predicting and constructing the morphological features of salt mine cavity mining based on the ZJ-XJ interconnected well. like Figure 1-2 As shown, this embodiment uses a working solution cavity (hereinafter referred to as the ZJ-XJ connecting well solution cavity) constructed using a dual-well interconnection method in a mining area as an example to illustrate the implementation process of the method described in this invention. Because the ZJ-XJ connecting well solution cavity contains a large amount of sediment after water-soluble cavity construction, sonar detection can only obtain a partial morphology of the top of the cavity. Its complete mining space, including the sediment-buried area, needs to be constructed and verified using the method of this invention.

[0029] 1. Multi-source basic data acquisition and feature parameter extraction The system collects and integrates four types of basic data related to the cavity: 1.1 Geological and Dissolution Characteristics Data: Geological data of the target mining area was collected. The salt-bearing strata containing the solution cavity of the ZJ-XJ connecting well are located at depths between 800m and 1600m, with the target cavity situated at a depth of 1200m to 1500m. The average grade of this strata (i.e., percentage of soluble salt by mass) is... The salt rock density is 55%. The value is 2.3 t / m³. Based on regional geological knowledge and laboratory experiments, key parameters such as stratigraphic dip angle, salt rock lateral dissolution angle, insoluble content, and its fragmentation / expansion coefficient were obtained.

[0030] 1.2 Drilling Engineering Data: Completement data for wells ZJ (vertical well, 1480m deep) and XJ (build-up well, 1490m deep) were obtained, including detailed wellbore structure, wellhead coordinates, and well trajectory data (especially build-up point, build-up rate, and target point data). These data precisely define the three-dimensional spatial framework of the cavity development and the wellbore connectivity.

[0031] 1.3 Historical Sonar Cavity Measurement Data: Data from previous sonar cavity measurements were collected. The data shows that the sonar detected the cavity top profile at depths of 1293m-1323m in the ZJ wellbore and at depths of 1261m-1306m in the XJ wellbore. This data directly defines part of the detectable boundary of the "clean brine space" above the cavity.

[0032] 1.4 Water-soluble cavity construction production data: Complete production records were collected during the cavity construction period. Data shows the cumulative water injection volume. The total volume was 7.019 million m³, with a cumulative average concentration of brine extracted. It is 300g / L.

[0033] 2. Preliminary 3D Modeling of Mining Space Based on Multi-Source Data Fusion By combining the above data, an initial three-dimensional morphological model is constructed: 2.1 Establishing the spatial benchmark and main framework: The precise three-dimensional well trajectories of wells ZJ and XJ are used as the spatial skeleton and development axis of the model.

[0034] 2.2 Integrating Detection Data and Geological Laws to Construct a Morphology: The cavity top contour data obtained from sonar measurements in the 1293m-1323m section of Well ZJ and the 1261m-1306m section of Well XJ were used as hard constraints to define the model boundaries for the corresponding well sections. For well sections below this depth, buried by sediment (especially the connecting section between the two wells and most of the build-up sections), a preliminary 3D model of the mining space was constructed based on geological dissolution characteristics parameters (such as lateral dissolution angle and interlayer influence). Ultimately, a preliminary, complete 3D model of the mining space was generated, integrating the measured boundaries and the geologically extrapolated boundaries. Figure 2 As shown.

[0035] 3. Model Validation and Iterative Correction Based on Production Data This step is crucial for achieving accurate predictions, as it involves quantitatively calibrating the model using production data.

[0036] 3.1 Calculate the total volume of the actual mining space: Using the principle of conservation of mass, the comprehensive proportion of sodium chloride is determined by the average grade of the formation, and the theoretical total volume of the underground space formed by the dissolved salt rock is calculated from the production data. Calculations show that the total volume of the theoretical mining space is approximately 1.66 million cubic meters.

[0037] 3.2 Model Validation and Error Analysis: Calculate the volume of the preliminary 3D model constructed in step 2, which is 1.72 million cubic meters in this example. Compare the results and calculate the relative error δ:

[0038] 3.3 Result Confirmation: The calculation error δ=3.6% is less than the preset 10% tolerance. This slight difference is likely within a reasonable range, stemming from minor uncertainties in production data statistics, average concentration values, or local parameter estimations. Therefore, the initially constructed model does not require further iterative correction and passes verification on the first attempt. The final confirmed 3D model has a volume of 1.72 million cubic meters, and its shape clearly shows a complex 3D cavity composed of a vertical shaft, a directional section, and horizontal connecting channels.

[0039] This embodiment overcomes the limitations of detection technology: where sonar can only reveal local morphology within a limited depth range (ZJ well: ~1292-1323m; XJ well: ~1261-1306m), by integrating geological dissolution patterns and drilling trajectories, it successfully predicted and constructed the morphology of a completely buried, concealed space, achieving a complete characterization of the mining space. Introducing cavity-building production data as an objective and quantifiable verification benchmark ensures that the constructed geometric model volume highly matches the actual dissolution volume (error 3.6%), significantly improving the reliability and accuracy of the prediction results. It provides crucial engineering foundation data: the final accurate three-dimensional model provides a reliable spatial morphological basis for subsequent assessment of the effective gas storage volume of the cavity's sediment voids and prediction of key engineering issues such as gas-liquid interface dynamics during operation, strongly supporting the capacity evaluation and reuse feasibility study of old cavities in high-impurity salt mines.

Claims

1. A method for predicting and constructing the spatial morphology of mining cavities in high-impurity salt mines based on multi-source data fusion, characterized in that, Includes the following steps: Step 1: Multi-source basic data acquisition and feature parameter extraction Collect and integrate four categories of data related to the target cavity: geological and dissolution characteristics data, drilling engineering data, historical sonar cavity measurement data, and water-soluble cavity production data, in order to obtain all the input parameters required to build and validate the model; Step 2: Preliminary 3D modeling of the mining space based on geological and engineering data By integrating the geological and dissolution characteristic data, drilling engineering data, and historical sonar cavity data obtained in step one, and using multi-source data to complement and constrain each other, an initial three-dimensional morphological model is constructed. Step 3: Model Validation Based on Production Data and Iterative Correction Based on Multi-Source Data The water-soluble cavity production data obtained in step one is used to quantitatively verify and calibrate the initial three-dimensional morphological model constructed in step three, so as to realize the closed-loop fusion of "geology-engineering-production" data; when the model volume error meets the requirements, the final optimized and reliable three-dimensional morphology of the cavity mining space is obtained.

2. The method for predicting and constructing mining spatial morphology according to claim 1, characterized in that, Step one specifically includes: 1.1 Geological and dissolution characteristic data: collect data on the distribution, thickness, dip angle, average grade, insoluble content, and mineral composition of the salt rock in the cavity-forming strata; collect key dissolution parameters: including the rate of upper and lateral dissolution of the salt rock, the lateral dissolution angle, and the fragmentation / expansion coefficient of the insoluble matter; 1.2 Drilling Engineering Data: Acquire completion data for the target salt cavity, including wellbore trajectory, wellhead coordinates, target coordinates, and depth of the cavity-building string. 1.3 Historical sonar cavity measurement data: Collect data from all previous sonar scans, extract key geometric features, and obtain contour information; 1.4 Water-soluble cavity production data: Collect complete cavity production records, including cumulative water injection volume, cumulative extracted brine volume, and average extracted brine concentration; used for subsequent calculation of the actual solid salt extraction volume, thereby inferring the total volume of the mining space.

3. The method for predicting and constructing mining spatial morphology according to claim 2, characterized in that, If the cavity is created in a connecting well in step 1.2, the drilling engineering data should include the trajectory data of the two wells to determine the geometric benchmark for cavity development, including bottom depth and distance between wells.

4. The method for predicting and constructing mining spatial morphology according to claim 2 or 3, characterized in that, The contour information mentioned in step 1.3 includes the depth of the top of the cavity, the maximum radius, and the depth of the sediment surface.

5. The method for predicting and constructing mining spatial morphology according to claim 1 or 2, characterized in that, Step 2 includes: 2.1 Determining the spatial benchmark and main development direction: Determining the lowest point of the cavity space using drilling trajectory data; Determining the tendency of the cavity to preferentially expand along the updip direction of the formation, i.e., the "partial dissolution" main direction, based on the formation dip angle and lateral dissolution angle; 2.2 Constructing a preliminary three-dimensional morphological framework: using the transverse boundaries of the cavity at different depths and directly obtained sonar cavity measurement data as hard constraints; and speculatively constructing the framework based on geological dissolution laws in the sediment burial area not scanned by sonar data.

6. The method for predicting and constructing mining spatial morphology according to claim 5, characterized in that, The implementation method of step 2.2 is as follows: based on the salt layer side dissolution angle, the distribution of insoluble interlayers and their overhang length, the dissolution boundary of the cavity below the interlayer is deduced; the net brine space boundary detected by sonar is integrated with the hidden space boundary inferred by geological laws to form a complete and preliminary three-dimensional morphological model of the mining space.

7. The method for predicting and constructing mining spatial morphology according to claim 5, characterized in that, Step 3 also includes 3.1 Calculating the actual mining volume: Using the water-soluble cavity production data, the total volume of salt rock actually dissolved and removed is calculated based on the principle of mass conservation. ; 3.2 Model Volume Calculation and Error Comparison: Calculate the volume of the preliminary 3D model constructed in step two. (The model volume is then compared.) Compared with the actual calculated mining volume Compare the results and calculate the relative error δ: ; 3.3 Iterative correction until convergence: Set an error threshold based on the actual situation. It is recommended that the error threshold be between 5% and 15%. If δ exceeds the threshold, it indicates that the preliminary model does not match the actual dissolution amount and iterative correction is required. Adjust the geological dissolution parameter model. Through multiple iterative cycles of "parameter adjustment - model reconstruction - volume comparison", the model can be made to approach the ideal state infinitely, thereby achieving accurate calibration of the model. When the model volume error meets the requirements, the final optimized and reliable three-dimensional morphology of the cavity mining space is obtained.

8. The method for predicting and constructing mining spatial morphology according to claim 7, characterized in that, The formula for calculating the total volume of salt rock is: in, To obtain the average concentration of the brine, To accumulate the volume of water injected or brine extracted, This represents the average grade of the salt mine. The density of the salt ore is given; the total volume of the salt rock is the theoretical mining space volume composed of the dissolution and insoluble matter expansion of the salt rock, including the voids in the sediment.

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