Method for determining the sealing capacity of a deep-water unconsolidated sediment cap

Abstract

The present application relates to a kind of deep water unconsolidated sediment cap rock sealing capacity determination method, it is related to oil and gas field development technical field, comprising: obtaining the geological data of multiple sample wells in target area, the geological data includes the particle size of reservoir, cap rock at each sample well, mineral composition and the overburden water depth of cap rock;According to the particle size of reservoir, cap rock, mineral composition and overburden water depth, determine the particle size, mineral composition data of simulation reservoir, simulation cap rock in mud sand sealing simulation device and multiple overburden water column height;Carrying out cap rock breakthrough pressure test simulation experiment, obtains the experimental breakthrough pressure of simulation cap rock under each overburden water column height condition;According to experimental breakthrough pressure, calculate the actual breakthrough pressure of cap rock at each sample well;Based on the overburden water depth and actual breakthrough pressure of cap rock at each sample well, build the cap rock sealing capacity model of target area;According to cap rock sealing capacity model, determine the sealing capacity of cap rock at target point in target area.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of oil and gas field development technology, specifically to a method for determining the capping capacity of deep-water unformed loose sedimentary caprock. Background Technology

[0002] Unformed, loose, muddy caprock possesses a certain sealing capacity for natural gas. The main factors influencing the sealing ability of this type of caprock include its thickness, particle size, mineral composition, water saturation, and burial depth. Furthermore, changes in hydrostatic pressure with increasing overlying water depth also significantly affect the sealing performance of muddy caprocks.

[0003] However, existing technologies lack evaluation methods that can accurately quantify the impact of water depth on the sealing capacity of unformed loose mudstone caprocks. There are also relatively few quantitative evaluation methods for the sealing capacity of deep-water unformed loose sediment caprocks, making it difficult to reliably predict and assess the sealing effectiveness of this type of caprock in actual oil and gas reservoir evaluation. Summary of the Invention

[0004] The purpose of the embodiments in this specification is to provide a method for determining the sealing capacity of unformed loose sedimentary caprock in deep water, so as to overcome the problem that existing methods cannot reliably assess the sealing effectiveness of unformed loose mudstone caprock in actual oil and gas reservoir evaluation.

[0005] To address the aforementioned technical problems, this specification provides, in one aspect, a method for determining the capping capacity of deep-water undiagenetic loose sediment caprock, comprising: Geological data from multiple sample wells within the target area are obtained. The geological data includes the mineral composition and grain size of the reservoir and caprock at each sample well, as well as the overlying water depth of the caprock. Based on the mineral composition and particle size of the reservoir and caprock, as well as the overlying water depth of the caprock, the mineral composition, particle size, and height of multiple overlying water columns of the simulated reservoir and caprock in the mud and sand capping simulation device are determined. A simulated test experiment of caprock breakthrough pressure was conducted to obtain the experimental breakthrough pressure of the simulated caprock under various overlying water column heights. Based on the experimental breakthrough pressure, calculate the actual breakthrough pressure of the caprock at each sample well. Based on the overlying water depth and actual breakthrough pressure of the caprock at each sample well, a caprock sealing capacity model for the target area is constructed; the caprock sealing capacity model is used to characterize the relationship between the caprock breakthrough pressure and the overlying water depth. Based on the capping capacity model, the capping capacity of the capping layer at the target point within the target area is determined.

[0006] Furthermore, the geological data also includes the thickness of the reservoir and caprock at each sample well; Determining the simulated thickness of the reservoir and caprock under experimental conditions includes: Based on the thickness, the simulated thickness of the simulated reservoir and simulated caprock is determined under experimental conditions according to a preset geometric scaling ratio.

[0007] Furthermore, the simulated caprock breakthrough pressure test, which obtains the experimental breakthrough pressure of the simulated caprock under various overlying water column heights, includes: The mud and sand capping simulation device is controlled to conduct a natural gas charging experiment until the simulated reservoir in the mud and sand capping simulation device meets the preset breakthrough judgment condition. Based on the simulated reservoir charging pressure when the preset breakthrough judgment conditions are met, the experimental breakthrough pressure of the simulated caprock under each overlying water column height is determined.

[0008] Furthermore, the mud and sand capping simulation device includes a column for filling the simulated reservoir and simulated cap layer samples, an air pump, a pressure monitoring unit, and a resistance monitoring unit; an overlying water column of various heights can be set above the simulated cap layer; The controlled mud and sand capping simulation device conducts natural gas charging experiments until the simulated reservoir in the mud and sand capping simulation device meets the preset breakthrough judgment conditions, including: Under a preset overlying water column height, the gas pump is controlled to inject natural gas into the simulated reservoir; During the charging process, the pressure monitoring unit is controlled to acquire the pressure of the simulated reservoir, and the resistance monitoring unit is controlled to acquire the resistance of the simulated reservoir and the simulated caprock. If the pressure and the resistance meet the preset breakthrough judgment conditions, control the gas pump to stop injecting natural gas into the simulated reservoir; The step of determining the experimental breakthrough pressure of the simulated caprock under various overlying water column heights based on the simulated reservoir charging pressure when the preset breakthrough judgment conditions are met includes: Based on the simulated reservoir charging pressure when the preset breakthrough judgment conditions are met, the experimental breakthrough pressure of the simulated caprock under the preset overlying water column height is determined.

[0009] Furthermore, the method also includes: The resistivity of the reservoir and caprock is determined based on the resistance of the simulated reservoir and caprock. If the pressure and the resistance meet a preset breakthrough condition, controlling the gas pump to stop injecting natural gas into the simulated reservoir includes: If the pressure has reached its peak and the pressure curve drops sharply, and the rate of increase in resistivity increases, the gas pump is controlled to stop injecting natural gas into the simulated reservoir.

[0010] Furthermore, the calculation of the actual breakthrough pressure of the caprock at each sample well based on the experimental breakthrough pressure includes: Based on the experimental breakthrough pressure, the actual breakthrough pressure of the caprock at each sample well is calculated using the following formula: L model × Δρ model / P cmodel = L field × Δρ field / P cfield ; In the formula, L model To simulate the thickness of the cap layer, Δρ model P represents the density difference between air and water under experimental conditions. cmodel To simulate the breakthrough pressure of the caprock, L field Δρ is the thickness of the cap layer. field P represents the density difference between gas and water under geological conditions. cfield This is the pressure required to break through the cap layer.

[0011] Furthermore, the construction of the caprock sealing capacity model for the target area based on the overlying water depth and actual breakthrough pressure at each sample well includes: Using the overlying water depth of each sample well as the corresponding actual breakthrough pressure as the dependent variable, data fitting was performed to obtain the relationship curve between the overlying water depth and the critical breakthrough pressure. The formula corresponding to the relationship curve is used as the capping capacity model of the capping layer.

[0012] Further, determining the capping capacity of the cap layer at the target point within the target area based on the capping capacity model includes: Obtain the overlying water depth at the target point; Based on the capping capacity model, determine the critical breakthrough pressure corresponding to the water depth at the target point; The sealing capacity of the capping layer at the target point is determined based on the critical breakthrough pressure.

[0013] Furthermore, embodiments of this specification provide an apparatus for determining the capping capacity of deep-water undiagenetic loose sediment caprock, comprising: The first acquisition module is used to acquire geological data from multiple sample wells within the target area. The geological data includes the mineral composition and grain size of the reservoir and caprock at each sample well, as well as the overlying water depth of the caprock. The first determining module is used to determine the mineral composition, particle size, and height of multiple overlying water columns of the simulated reservoir and simulated caprock in the mud and sand sealing simulation device based on the mineral composition, particle size, and overlying water depth of the reservoir and caprock. The second acquisition module is used to conduct a simulation experiment of caprock breakthrough pressure test and to acquire the experimental breakthrough pressure of the simulated caprock under various overlying water column heights. The calculation module is used to calculate the actual breakthrough pressure of the caprock at each sample well based on the experimental breakthrough pressure. A construction module is used to construct a caprock sealing capacity model for the target area based on the overlying water depth and actual breakthrough pressure of the caprock at each sample well; the caprock sealing capacity model is used to characterize the relationship between the breakthrough pressure of the caprock and the overlying water depth. The second determining module is used to determine the sealing capacity of the cap layer at the target point within the target area based on the cap layer sealing capacity model.

[0014] Furthermore, embodiments of this specification provide a computer device, including: Memory, used to store computer programs; A processor for executing the computer program to implement the method for determining the capping capacity of deep-water unformed loose sedimentary caprock.

[0015] As can be seen from the technical solutions provided in the embodiments of this specification above, these embodiments establish the overlying water depth as the core variable and measure the breakthrough pressure of the caprock at different water column heights through simulation experiments. This directly overcomes the limitations of traditional methods that rely on well logging statistics or static core testing, which cannot dynamically reproduce and separate the environmental load effect of water depth. It achieves controllable, quantitative, and direct measurement of the causal relationship between water depth and capping capacity, giving the evaluation conclusions a solid experimental physical basis and significantly improving their credibility. Furthermore, through similarity principle conversion, the data obtained at the experimental scale are scientifically and rigorously converted to the actual geological scale, thereby obtaining the actual breakthrough pressure value reflecting real geological conditions and ensuring the macroscopic geological representativeness of the microscopic experimental conclusions. In addition, by integrating data from multiple sample wells, a regional quantitative prediction model of water depth-breakthrough pressure is constructed. For any new, untested target point within the target area, only the relatively easily obtainable parameter of its overlying water depth needs to be input to quickly and cost-effectively predict the critical capping capacity of its caprock. This represents a leap from expensive and detailed measurements of a few points to efficient and quantitative predictions across the entire area, greatly improving exploration and evaluation efficiency and providing direct and quantitative decision-making basis for well location deployment, reserve assessment, and development risk management. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments or prior art of this specification, the accompanying drawings used in the description of the embodiments or prior art will be briefly introduced below.

[0017] Figure 1This is a flowchart illustrating a method for determining the capping capacity of deep-water undiagenetic loose sediment caprock, as provided in the embodiments of this specification. Figure 2 This is a schematic diagram of the overall logic flow of a method for determining the capping capacity of deep-water undiagenetic loose sediments provided in the embodiments of this specification; Figure 3 This is a schematic diagram of the structural composition of the mud and sand sealing simulation device provided in the embodiments of this specification; Figure 4 This is a graph showing the relationship between pressure and resistivity of sandy reservoirs over time under a water column height of 40 cm, as provided in the embodiments of this specification. Figure 5 This is a graph showing the relationship between pressure and resistivity of the mud cap layer over time under a water column height of 40cm, as provided in the embodiments of this specification. Figure 6 This is a graph showing the relationship between pressure and resistivity of sandy reservoirs over time under a water column height of 45 cm, as provided in the embodiments of this specification. Figure 7 This is a graph showing the relationship between pressure and resistivity of the mud cap layer over time under a water column height of 45cm, as provided in the embodiments of this specification. Figure 8 This is a graph showing the relationship between pressure and resistivity of sandy reservoirs over time under a water column height of 50 cm, as provided in the embodiments of this specification. Figure 9 This is a graph showing the relationship between pressure and resistivity of the mud cap layer over time under a water column height of 50cm, as provided in the embodiments of this specification. Figure 10 This is a graph showing the relationship between pressure and resistivity of sandy reservoirs over time under a water column height of 55 cm, as provided in the embodiments of this specification. Figure 11 This is a graph showing the relationship between pressure and resistivity of the mud cap layer over time under a water column height of 55cm, as provided in the embodiments of this specification. Figure 12 This is a graph showing the relationship between pressure and resistivity of sandy reservoirs over time under a water column height of 60 cm, as provided in the embodiments of this specification. Figure 13 This is a graph showing the relationship between pressure and resistivity of the mud cap layer over time under a water column height of 60cm, as provided in the embodiments of this specification. Figure 14 This is a graph showing the relationship between pressure and resistivity of sandy reservoirs over time under a water column height of 65 cm, as provided in the embodiments of this specification. Figure 15 This is a graph showing the relationship between pressure and resistivity of the mud cap layer over time under a water column height of 65cm, as provided in the embodiments of this specification. Figure 16 This is a graph showing the relationship between pressure and resistivity of sandy reservoirs over time under a water column height of 70 cm, as provided in the embodiments of this specification. Figure 17 This is a graph showing the relationship between pressure and resistivity of the mud cap layer over time under a water column height of 70cm, as provided in the embodiments of this specification. Figure 18 This is a fitted curve of the breakthrough pressure of the mudstone cap layer versus the water depth under actual geological conditions provided in the embodiments of this specification; Figure 19 This is a schematic diagram of the structural composition of a device for determining the capping capacity of deep-water undiagenetic loose sediments, as provided in the embodiments of this specification. Figure 20 This is a schematic diagram of the structural composition of the computer device provided in the embodiments of this specification. Detailed Implementation

[0018] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0019] It should be noted that the terms "first," "second," etc., used in this specification, claims, and the foregoing drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, apparatus, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0020] In some embodiments, the deep-water undiagenetic loose sedimentary caprock can be a fine-grained sedimentary rock layer that is developed in a deep-water environment, has not undergone significant diagenetic alteration, and is in a loose and unconsolidated state. As an overlying capping unit of an oil and gas reservoir, it effectively seals the oil and gas in the underlying reservoir by relying on capillary forces.

[0021] Deep water can refer to environments with significant sedimentary depths (typically greater than 200 meters), including but not limited to deep-sea basins, continental slopes, and deep-lake depressions. Sedimentation in these environments is dominated by pelagic mud, semi-pelagic mud, and fine-grained end-gravity deposits from deep-water gravity flows. The sedimentation rate is relatively low, and the sediments are often rich in organic matter.

[0022] Undiagenetic loose sediments may be sediments that have been buried at a shallow depth since their deposition, where the temperature and pressure of the strata have not yet reached the level required to cement and compact the particles into lithification. As a result, the sediments maintain a soft mud or paste-like physical state with high porosity, high water content, and low strength. The particles lack rigid skeletal support and exhibit plastic or rheological characteristics overall.

[0023] The sediments are mainly composed of clay (<0.0039 mm) and silt (0.0039-0.0625 mm) particles, containing siliceous biological remains (such as diatoms and radiolarians) or calcareous microfossils. The high content of clay minerals determines its fine-grained characteristics and microporous throat structure.

[0024] A caprock can be a protective layer located above a reservoir, preventing oil and gas from escaping upwards. The sealing mechanism of this type of undiagenetic caprock does not rely on the density of the rock (as in diagenetic dense caprocks) or its plastic deformation capacity (as in salt rocks and gypsum rocks), but rather on the high capillary breakthrough pressure formed by its extremely fine pore throat radius—oil and gas must overcome enormous capillary resistance to escape through this layer, thus achieving sealing. Therefore, despite its loose physical state, under deep-water, shallow-buried conditions, it can still effectively and directly cap underlying biogenic or thermogenic gas reservoirs.

[0025] In some embodiments, the target area can be a geological region where the caprock sealing capacity is to be predicted; the reservoir can include sandy reservoirs; and the caprock can include unformed argillaceous caprock. Specifically, the target area can be a block with clear geological significance and exploration potential, including but not limited to typical hydrocarbon environments such as deep-water basins, continental shelf slopes, and shallow biogenic gas-rich areas. The reservoir can mainly refer to sandy reservoirs, which possess good pore space and permeability, and are key carriers for hydrocarbon migration, accumulation, and storage. The caprock can specifically refer to unformed loose argillaceous caprocks. Due to their fine-grained structure and high capillary resistance, these caprocks can form effective physical sealing in shallow burial or deep-sea environments, and can play a key role in sealing the underlying hydrocarbon reservoirs as direct caprocks.

[0026] This specification provides an embodiment of a method for determining the capping capacity of deep-water undiagenetic loose sediment caprock, referring to... Figure 1 and Figure 2 As shown, the specific implementation may include the following steps: S101: Obtain geological data from multiple sample wells within the target area. The geological data includes the mineral composition and grain size of the reservoir and caprock at each sample well, as well as the overlying water depth of the caprock.

[0027] In some embodiments, within the target area, a certain number (N≥10, to ensure statistical significance) of representative wells with relatively uniform spatial distribution can be selected as sample wells based on geological understanding. These sample wells should penetrate the target reservoir-seal combination and, as far as possible, cover the range of variations in water depth, lithology, etc., within the target area. By collecting logging, well logging data, core testing, and regional geological survey reports from these sample wells, their corresponding geological data can be obtained and entered.

[0028] In some embodiments, sample wells can be exploratory, appraisal, or development wells drilled within the target area that have relatively complete data (possessing necessary logging and / or coring data). They serve as direct windows for obtaining firsthand subsurface geological information. As data source points, the data collected from sample wells are used for statistical analysis to represent the general characteristics of the entire target area. Selecting multiple sample wells rather than a single well aims to overcome the influence of geological heterogeneity and obtain regionally statistically significant average values ​​and ranges of variation for characteristic parameters, which form the basis for subsequent regional modeling.

[0029] In some embodiments, geological data may be the sum of a series of quantitative and qualitative parameters describing the properties of subsurface rocks, stratigraphic structure, and geological environment. Geological data may include, but is not limited to, the overlying water depth of the caprock at each sample well.

[0030] In some embodiments, the overlying water depth can be the vertical distance from sea level to the seabed. It is not simply the depth of seawater, but a direct measure of the hydrostatic pressure exerted on the caprock. The overlying water depth can be used to quantify the mechanical impact of hydrostatic pressure on the compaction state and pore throat structure of unformed, loose, muddy caprock. It serves as a bridge parameter between actual geological conditions and laboratory simulation conditions. The overlying water depth can be calculated by combining the core elevation at the wellhead of the sample well, the water depth, and the caprock top boundary depth determined from well logging data.

[0031] Systematically collecting overlying water depth as a key input parameter lays a data foundation for studying the impact of water depth on the sealing capacity of unformed caprock, overcoming the shortcomings of previous studies that focused primarily on burial depth while neglecting hydrostatic pressure. Furthermore, by collecting spatially distributed multi-point data, the distribution range and variation characteristics of overlying water depth within the target area can be effectively characterized. This ensures that the subsequently constructed caprock sealing capacity model is not based on accidental relationships at individual points, but rather reflects regional statistical patterns, significantly improving the model's reliability and extrapolation prediction capabilities.

[0032] In some embodiments, the geological data may also include the thickness of the reservoir and caprock at each sample well.

[0033] In some embodiments, based on the acquired water depth data, the true thickness of the target sandy reservoir (underlying gas layer) and the overlying unformed mudstone caprock can be accurately read or measured from the interpretation of logging curves or core data from the sample well. The thickness data must come from the same reservoir-caprock combination as the water depth data from the same well.

[0034] In some embodiments, the reservoir / caprock thickness can be the vertical thickness of a sandy rock layer with effective reservoir properties or a muddy rock layer with effective capping properties. It is a fundamental geometric parameter for evaluating the scale of the capping system.

[0035] By acquiring thickness data, a direct conversion basis is provided for scaling down the actual geological thickness to the experimental model size in subsequent steps. This allows the physical simulation experiment to more realistically reflect the thickness ratio of the actual reservoir-seal combination, which is a prerequisite for ensuring the geometric similarity between the experimental model and the geological prototype, thereby improving the fidelity of the experimental simulation and the accuracy of the conversion results.

[0036] In some embodiments, the geological data may also include the mineral composition and grain size of the reservoir and caprock at each sample well.

[0037] In some embodiments, core samples (or borehole cores) obtained from sample wells of reservoir and caprock can be analyzed in the laboratory. X-ray diffraction (XRD) analysis is used to obtain the whole-rock mineral composition and content of quartz, feldspar, clay minerals (such as illite and montmorillonite); laser particle size analysis is used to obtain the particle size distribution of rock particles (such as median particle size and percentage of clay content).

[0038] In some embodiments, the mineral composition can be the types of various minerals that constitute the rock and their relative percentage content. The clay mineral content directly affects the plasticity, expansibility, and capillary sealing capacity of the argillaceous caprock.

[0039] In some embodiments, grain size can refer to the size and distribution of clastic particles in the rock. For igneous caprock, grain size is a key factor controlling its original pore structure, permeability, and mechanical stability.

[0040] By acquiring primary physical properties such as mineral composition and grain size, subsequent experimental sample configuration can go beyond simple lithological imitation, achieving a refined reconstruction of lithological fabric. Experimental samples configured based on regional average values ​​have microscopic properties that more closely resemble the actual geological conditions of the target area. This ensures that the measured breakthrough pressure data are fundamentally geologically representative, eliminating systematic errors caused by significant differences between experimental samples and actual underground rock properties, and greatly enhancing the geological reliability of the entire evaluation method.

[0041] S102: Based on the mineral composition and particle size of the reservoir and caprock, as well as the overlying water depth of the caprock, determine the mineral composition and particle size of the simulated reservoir and caprock, and the height of multiple overlying water columns in the mud and sand capping simulation device.

[0042] In some embodiments, in order to scientifically and reasonably scale down actual geological parameters (large-scale, high-order-of-magnitude) to a laboratory-operable scale and design an experimental scheme that can effectively reflect changes in geological conditions, a similarity relationship between the geological prototype and the experimental model can be established. Specifically, this can be divided into two parts: first, determining the geometric dimensions (simulated thickness) of the model (reservoir and caprock); and second, determining the external load conditions applied to the model (simulated overlying water column height).

[0043] In some embodiments, the mud and sand capping simulation device can be an experimental device specifically designed to simulate the oil and gas migration and capping process in shallow or unconsolidated reservoir capping systems, as described above. Figure 3 As shown. It may include: Column: A transparent or pressure-resistant tube used to fill simulated reservoir and caprock samples with saturated water according to simulated thickness, and is the site where physical processes occur.

[0044] Gas pump and pressure control system: used to inject natural gas (such as methane) into the simulated reservoir at a constant pressure or constant flow rate at the bottom of the column.

[0045] Pressure monitoring unit: High-precision sensor to monitor and record pressure changes inside the simulated reservoir in real time.

[0046] Resistance monitoring unit: Electrodes embedded in the column are used to monitor the resistivity changes of the simulated reservoir and simulated caprock in real time.

[0047] Data acquisition and processing system: Computer and software used to synchronously acquire, store and analyze pressure and resistivity data.

[0048] The mud and sand capping simulation device provides a physical platform that can precisely control boundary conditions (thickness, water pressure, and air injection pressure) and monitor changes in key parameters in real time. It is a tool for reproducing processes and measuring parameters.

[0049] In some embodiments, the mineral composition and particle size of the simulated reservoir and simulated caprock are determined based on the mineral composition and particle size of the reservoir and caprock corresponding to each sample well.

[0050] In some embodiments, based on the regional average values ​​of mineral composition and grain size data of each sample well obtained in S101, corresponding pure minerals (such as quartz sand, potassium feldspar, illite, calcite, etc.) can be selected and mixed in proportion to simulate the actual mineral composition. At the same time, the grain size distribution curve of the mixture is strictly controlled to make it consistent with the average grain size distribution of the geological prototype.

[0051] The breakthrough pressure of the caprock is fundamentally controlled by its microscopic pore structure, which directly depends on the mineral composition of the particles (affecting surface properties and plasticity) and particle size distribution. By accurately reproducing these parameters, the pore network, specific surface area, and capillary pressure characteristics of the experimental sample are ensured to have a high degree of similarity to the actual underground caprock. This makes the measured breakthrough pressure data fundamentally geologically representative, significantly improving the reliability and extrapolation of the experimental results.

[0052] In some embodiments, formation water from the target area's reservoir and caprock can be used to saturate the configured experimental samples with formation water. The formation water added to the column of the mud and sand capping simulation device is formation water from the target area's reservoir and caprock. In some embodiments, after sample preparation, the simulated reservoir and caprock samples are fully saturated with brine extracted from actual formation water in the target area or prepared according to formation water analysis reports to restore the true pore fluid salinity and ionic composition. In the mud and sand capping simulation apparatus, the liquid added to the column to form the overlying water column is also the same simulated formation water.

[0053] The salinity of formation water directly affects its resistivity, density, viscosity, and interfacial tension with rock particles. Using real formation water ensures that the gas-water-rock interactions (especially capillary effects) in the experiment are consistent with underground conditions. This allows the breakthrough phenomena observed and the pressure values ​​measured at the experimental scale to more realistically reflect the geological conditions, further enhancing the accuracy of data conversion.

[0054] In some embodiments, the simulated reservoir and simulated caprock can be artificially configured or reconstructed rock sample segments in a mud-sand capping simulator to represent actual sandy reservoirs and undiagenetic muddy caprocks. Their simulated thickness is the filling height of these two experimental segments within the mud-sand capping simulator. The physical surrogates of the simulated reservoir and simulated caprock geological prototypes, and their behavior and responses (such as breakthrough pressure), are used to infer actual geological conditions.

[0055] In some embodiments, the simulated thickness can be the corresponding thickness used in the mud and sand capping simulation device, which is a scaled-down version of the actual reservoir and caprock thicknesses based on the principle of geometric similarity. It is not an arbitrarily set value, but a scaled-down dimension obtained through precise calculation. The simulated thickness ensures that the experimental model is geometrically proportional to the actual geological profile, and is one of the foundations for satisfying the similarity criterion.

[0056] In some embodiments, the height of the overlying water column can be the height of the liquid column formed by the injected liquid above the simulated caprock in the mud and sand capping simulation device. The hydrostatic pressure generated at this height is used to simulate the hydrostatic load exerted on the caprock by different water depths in actual geological environments. By changing the water column height, various hydrostatic pressure conditions from the shallowest to the deepest overlying water depth in the target area can be simulated.

[0057] In some embodiments, the simulated thickness of the simulated reservoir and simulated caprock in the mud and sand capping simulation device can be determined according to a preset geometric scaling ratio based on the thickness of the reservoir and caprock at each sample well.

[0058] In some embodiments, the average reservoir thickness (H_r) and caprock thickness (H_g) of all sample wells within the target area can be calculated as the characteristic thickness of the geological prototype. A preset geometric scaling ratio (e.g., 1:100, 1:150) is selected. The selection of this ratio takes into account the total height of the experimental column, ensuring sufficient thickness of the simulated section to reflect its properties, and ease of operation. The characteristic thickness can be divided by this ratio to obtain the simulated thickness required for the experiment (h_r = H_r / scale; h_g = H_g / scale).

[0059] By using a fixed scaling factor, the experimental model was ensured to be an accurate replica of the geological prototype. This provides a rigorous mathematical and physical basis for subsequently using the principle of similarity to invert the experimental results back to the geological scale.

[0060] In some embodiments, an overlying water depth range can be determined based on the overlying water depth of the caprock at each sample well; and multiple overlying water column heights can be determined based on the overlying water depth range.

[0061] In some embodiments, the minimum (H_min) and maximum (H_max) values ​​of the overlying water depth can be determined from all the sample well overlying water depth data obtained in S101. The closed interval [H_min, H_max] formed by these two values ​​is the overlying water depth interval. This interval represents the complete range of water depth variation in the target area. Within this interval, several (M, M≥5) representative water depth values ​​are selected and converted into corresponding overlying water column heights (h_1 to h_M) using the same scaling ratio (which may be different from the thickness scaling ratio, for example, 1:1000).

[0062] By mapping the actual water depth range to a set of discrete experimental water column heights covering the entire range, it is ensured that subsequent experiments can systematically reveal the variation relationship of capping capacity across the entire water depth spectrum, rather than just focusing on individual points. This makes the final constructed water depth-breakthrough pressure relationship model have comprehensive representativeness and predictive ability.

[0063] In some embodiments, the determination of multiple overlying water column heights based on the overlying water depth range may specifically include: uniformly dividing the overlying water depth range into several sub-ranges, and using the water column height corresponding to the endpoint value of each sub-range as the multiple overlying water column heights.

[0064] For example, if the water depth range is 400-700 meters, with a preset interval of 50 meters, then the determined water depth points are 400, 450, 500…700 meters, which can then be converted into water column height. Uniformly dividing the water into sub-intervals is another equivalent expression.

[0065] Using an equal-interval design ensures that experimental data points are evenly distributed within the study interval, avoiding data concentration at one end, which is beneficial for obtaining a stable and reliable mathematical model when performing subsequent regression analysis or curve fitting.

[0066] In some embodiments, determining multiple overlying water column heights based on the overlying water depth range may further include: obtaining the rate of change of lithological parameters corresponding to the overlying water depth of each sample well; sampling the overlying water depth range based on the rate of change of lithological parameters to obtain multiple overlying water column heights; the sampling frequency is positively correlated with the rate of change of lithological parameters.

[0067] In some embodiments, sample well data can be analyzed to calculate the gradient (rate of change) of preset lithological parameters (including but not limited to clay content, median grain size, etc.) with water depth. In sections where lithological parameters change drastically with water depth (high rate of change), it means that the caprock properties may change rapidly, so more dense experimental water column height points (high sampling frequency) are set in this water depth range; in sections where lithological parameters change gently (low rate of change), sparser experimental points are set.

[0068] By focusing a limited number of experiments on key water depths where caprock capacity may be sensitive to changes, the richest geological information can be captured with the fewest experiments. This not only saves on experimental costs and time, but more importantly, it allows the final caprock capacity model to have higher resolution in key transition areas, resulting in more accurate evaluation and prediction.

[0069] S103: Conduct a simulated test of caprock breakthrough pressure to obtain the experimental breakthrough pressure of the simulated caprock under various overlying water column heights.

[0070] In some embodiments, to realistically reproduce the dynamic process of natural gas accumulating in sandy reservoirs and ultimately breaking through the overlying unformed argillaceous caprock under different overlying water pressures (simulated by water column height) under controlled experimental conditions, and to quantitatively obtain the critical pressure value at which the caprock becomes unstable, a series of well-designed physical experiments can be performed. This transforms the two sets of design parameters—the simulated thickness and the overlying water column height determined in S102—into dynamic response data describing the caprock's sealing limit—the experimental breakthrough pressure. For each preset overlying water column height, a complete breakthrough experiment can be independently completed while keeping the simulated thickness constant, thus ultimately obtaining a set of experimental breakthrough pressure data corresponding one-to-one with different water column heights.

[0071] In some embodiments, the experimental breakthrough pressure can be the minimum injection pressure that allows natural gas to begin penetrating the simulated mudstone caprock, directly measured through physical simulation experiments under specific experimental conditions (specific simulated thickness, specific overlying water column height, specific rock sample formulation). It is an inherent property of the experimental model, and its unit can be kilopascals (kPa) or megapascals (MPa). The experimental breakthrough pressure forms the basis of the raw experimental data for all subsequent calculations and modeling. Each experimental breakthrough pressure data point corresponds to a specific overlying water column height, collectively forming the raw dataset revealing the intrinsic relationship between the two.

[0072] In some embodiments, the preset breakthrough criteria can be a set of pre-defined physical quantity changes used to determine whether the caprock has been breached by natural gas during the experiment. It is not a single-moment reading, but a dynamically changing pattern. The preset breakthrough criteria can serve as an objective standard for terminating the experiment and determining the breakthrough pressure value. It replaces subjective human judgment, ensuring consistency in the standards for acquiring all experimental data, thereby guaranteeing the reliability and comparability of the data.

[0073] In some embodiments, the charging pressure can be a constant pressure of natural gas acting on the simulated reservoir, applied and maintained by a gas pump during the experiment. The charging pressure is the driving force propelling the natural gas toward the caprock. The charging pressure value recorded at the end of the experiment is determined as the experimental breakthrough pressure under those experimental conditions.

[0074] In some embodiments, a mud and sand capping simulation device can be controlled to conduct a natural gas charging experiment until the simulated reservoir in the mud and sand capping simulation device meets a preset breakthrough judgment condition; based on the charging pressure of the simulated reservoir when the preset breakthrough judgment condition is met, the experimental breakthrough pressure of the simulated capping layer under each overlying water column height condition is determined.

[0075] In some embodiments, as designed in S102, a simulated reservoir and a simulated caprock of saturated water, reaching a simulated thickness, are sequentially filled into the column of the device, and a water column of a specific height is injected above the caprock. At the start of the experiment, natural gas is injected from the bottom of the column in a constant-pressure mode. During the charging process, the data system continuously collects the pressure of the simulated reservoir and the resistivity of the reservoir / caprock. It is determined whether these data meet the preset breakthrough criteria. If the criteria are met, gas injection is stopped, and the charging pressure value at this moment is recorded as the experimental breakthrough pressure at that water column height. This process is repeated for each overlying water column height determined in S102 to obtain a complete dataset of multiple experimental breakthrough pressures.

[0076] Through physical simulation experiments, the entire process of natural gas accumulation in the reservoir, pressure buildup, and eventual breakthrough pressure overcoming the capillary resistance of the caprock was visually demonstrated, maximizing the reproduction of underground geological processes and ensuring the high geological authenticity and reliability of the obtained breakthrough pressure data. Furthermore, by precisely controlling the single variable of the overlying water column height and conducting a series of experiments under constant conditions (thickness, lithology), the independent influence of hydrostatic pressure (water depth) on the caprock breakthrough pressure can be clearly and quantitatively separated and revealed, which is something that well logging calculations or statistical models cannot achieve.

[0077] S104: Calculate the actual breakthrough pressure of the caprock at each sample well based on the experimental breakthrough pressure.

[0078] In some embodiments, the experimental breakthrough pressure data obtained in S103 under specific overlying water column height conditions can be converted into actual breakthrough pressure values ​​that truly reflect the sealing capacity of the mudstone caprock in the actual geological environment of the target area, through a preset similarity principle or prediction model. This conversion is not a simple unit conversion, but involves the equivalent processing of the core physical mechanisms controlling caprock breakthrough under different physical scales and environmental conditions. Through this step, the understanding gained from physical experiments can be projected onto the broad and complex geological reality, providing directly applicable quantitative indicators for regional assessment.

[0079] In some embodiments, the actual breakthrough pressure can be predicted or calculated. It is the minimum pressure that the target unformed mudstone caprock can withstand to seal the underlying oil and gas under actual geological conditions in the target area (specific burial depth, temperature, pressure, formation water salinity, etc.). Its unit can be megapascals (MPa). The actual breakthrough pressure is a direct basis for determining the effectiveness of the caprock and is used to compare it with the actual reservoir pressure, thereby making a risk assessment of the preservation conditions of the oil and gas reservoir.

[0080] In some embodiments, the similarity principle is the fundamental theory for the design and result conversion of physical simulation experiments. It requires that the model and prototype maintain consistency on key dimensionless parameters controlling certain phenomena. In the gas-water two-phase seepage and breakthrough problem, a dimensionless combination controlling caprock breakthrough is the (capillary force) / (driving force) related parameter, which can be simplified to (L × Δρ / P) c The similarity criterion is proportional. The principle of similarity provides a rigorous theoretical formula and physical basis for the quantitative conversion from laboratory model data to geological prototype data, ensuring the scientific validity and reliability of the conversion.

[0081] In some embodiments, the predictive model can be a function or algorithm built using mathematical methods (such as multiple regression or machine learning algorithms) that describes the complex nonlinear relationship between experimental breakthrough pressure and a range of geological parameters. When the simple assumptions of the similarity principle cannot fully encompass geological complexity, the predictive model can provide a data-driven, more flexible conversion tool. It can incorporate more actual geological information, thereby improving conversion accuracy and adaptability to complex situations.

[0082] In some embodiments, the actual breakthrough pressure of the caprock at each sample well is calculated based on the experimental breakthrough pressure. Specifically, this may include: calculating the actual breakthrough pressure of the caprock at each sample well using the following formula based on the plurality of experimental breakthrough pressures: L model × Δρ model / P cmodel = L field × Δρ field / P cfield In the formula, L model To simulate the thickness of the cap layer, Δρ model P represents the density difference between air and water under experimental conditions. cmodel To simulate the breakthrough pressure of the caprock, L field Δρ is the thickness of the cap layer. field P represents the density difference between gas and water under geological conditions. cfield This is the pressure required to break through the cap layer.

[0083] In some embodiments, formula L can be obtained. model × Δρ model / P cmodel = L field × Δρ field / P cfield All parameters in the model. Among them, the model-side parameters (model): L model (Simulated cap layer thickness, which can be determined by S102), Δρ model (The density difference between gas and water under experimental conditions can be determined by the experimental temperature, pressure, and the properties of the gas and water used.) Pcmodel (Experimental breakthrough pressure, determined by S103). Field parameters: L field (Actual thickness of the target caprock, which can be the regional average or a specific sample well value, from S101), Δρ field (The actual gas-water density difference underground can be calculated based on formation temperature, pressure, natural gas composition, and formation water salinity.) The goal is to solve for P. cfield (Actual breakthrough pressure).

[0084] In some embodiments, for each P cmodel (Corresponding to the height of an experimental water column), the known model end and field end can be divided by P. cfield By substituting all external parameters into the formula, the P value under the corresponding geological conditions can be directly calculated. cfield By iterating through all experimental breakthrough pressures, a set of corresponding actual breakthrough pressures is obtained.

[0085] The complex caprock breaching process is abstracted into a physical phenomenon controlled by key dimensionless numbers. The formula is simple, and the required parameters (thickness, density difference) have clear physical meanings and are relatively easy to obtain. The calculation process is direct and clear, facilitating the rapid and batch conversion of large amounts of experimental data into geological data. It has high computational efficiency and good engineering applicability. By forcing the model and prototype to be similar in core control parameters, the regularities observed at a scale of a few centimeters in the laboratory are ensured, which can effectively represent geological behavior at a scale of tens to hundreds of meters underground, solving the most fundamental scale effect problem in physical simulation.

[0086] In some embodiments, the actual breakthrough pressure of the caprock at each sample well is calculated based on the experimental breakthrough pressure. Specifically, this may include: establishing a prediction model between the experimental breakthrough pressure and the geological parameters of the corresponding sample well, wherein the geological parameters include burial depth, formation temperature, and formation pressure; and, based on the prediction model, using the experimental breakthrough pressure and the geological parameters of the corresponding sample well as inputs, obtaining the actual breakthrough pressure under the geological conditions of each sample well.

[0087] In some embodiments, a training dataset can be constructed. Each data sample in this dataset contains: an experimental breakthrough pressure (result S103) and its corresponding key geological parameters of the well represented by that simulated condition obtained from S101, such as burial depth (affecting compaction and stress), formation temperature (affecting fluid properties and rock physical properties), and formation pressure (affecting phase state and seepage). An algorithm (such as support vector machine, random forest, or neural network) is selected to train this dataset, enabling the model to learn the mapping relationship from experimental breakthrough pressure and geological parameters to actual breakthrough pressure. After the model training and validation are completed, new data pairs can be input for conversion.

[0088] In actual geological conditions, the relationship between breakthrough pressure and various parameters can be complex and nonlinear. Data-driven modeling can automatically learn and capture these complex relationships without specifying the exact form of the physical equations beforehand, potentially achieving higher conversion accuracy than simple similarity criteria. Integrating experimental data with various geological parameters (temperature, pressure, etc.) obtained from well logging and geochemical analysis essentially incorporates more comprehensive geological background information into the conversion process. This results in a final actual breakthrough pressure that not only reflects the influence of water depth but also implicitly includes comprehensive information such as temperature and pressure effects, leading to a more comprehensive evaluation result.

[0089] S105: Based on the overlying water depth and actual breakthrough pressure of the caprock at each sample well, construct a caprock sealing capacity model for the target area; the caprock sealing capacity model is used to characterize the relationship between the caprock breakthrough pressure and the overlying water depth.

[0090] In some embodiments, the discrete actual breakthrough pressure data obtained in S104, corresponding to different overlying water depths, can be refined and constructed into a quantitative model that can continuously describe the universal law of the change in capping capacity with overlying water depth throughout the target area through mathematical modeling.

[0091] In some embodiments, the caprock sealing capacity model can be a mathematical expression, algorithm, or graph, taking as input the overlying water depth at any location in the target area and outputting the predicted critical breakthrough pressure of the caprock at that location. It encapsulates the intrinsic water depth-pressure relationship revealed through the aforementioned experiments and conversions. The caprock sealing capacity model simplifies complex physical processes and geological understanding into a queryable and computable tool for quickly and quantitatively evaluating the caprock sealing capacity at any unknown point within the target area.

[0092] Caprock sealing capacity models can quantitatively describe and reflect the trends and patterns of how caprock breakthrough pressure changes in response to changes in overlying water depth (e.g., linear growth, exponential growth, inflection points, etc.). Caprock sealing capacity models clarify that their predictive function goes beyond simply providing a single value; more importantly, they reveal a continuous and extrapolable physical and geological law, thus giving the predictions scientific basis and credibility.

[0093] In some embodiments, a capping capacity model for the target area is constructed based on the overlying water depth and the actual breakthrough pressure of the caprock at each sample well. Specifically, this may include: using the overlying water depth of each sample well as the corresponding actual breakthrough pressure as the dependent variable, performing data fitting to obtain a relationship curve between the overlying water depth and the critical breakthrough pressure; and using the formula corresponding to the relationship curve as the capping capacity model.

[0094] In some embodiments, the overlying water depth of each sample well (from S101) can be used as the X-axis data, and the corresponding actual breakthrough pressure calculated in S104 can be used as the Y-axis data, forming a series of discrete data points in the coordinate system. Mathematical methods (such as least squares) are used to perform curve fitting on these data points. The goal of the fitting is to find a continuous relationship curve that best approximates the overall trend of these data points (e.g., a linear equation Pc = aH + b, or a logarithmic equation Pc = aln(H) + b, etc.). This curve is the relationship curve between the overlying water depth and the critical breakthrough pressure, and the corresponding mathematical equation is defined as the caprock sealing capacity model.

[0095] The model, presented graphically and with simple mathematical formulas, is highly intuitive. Predicted values ​​can be directly read from the graphs or obtained through simple calculations, making it easy to understand and apply. The fitted functional relationships require minimal computation and can be easily embedded into any evaluation software or spreadsheet, enabling batch, rapid, and automated evaluation, significantly improving efficiency in regional exploration site selection or well location justification. The fitted curves smoothly summarize the main trends in the data, filtering out possible individual measurement errors. They clearly demonstrate whether the breakthrough pressure monotonically increases or decreases with water depth, or whether other patterns exist, providing direct evidence for deepening geological understanding (such as determining the dominant role of hydrostatic compaction).

[0096] In some embodiments, a caprock sealing capacity model for the target area can be constructed based on the overlying water depth and actual breakthrough pressure of the caprock at each sample well. Specifically, this may further include: obtaining caprock lithological characteristic parameters and formation pressure parameters corresponding to each sample well; using the overlying water depth, the lithological characteristic parameters, and the formation pressure parameters as input features, and the actual breakthrough pressure as the prediction target, to construct a caprock sealing capacity model; the caprock sealing capacity model is used to characterize the relationship between the breakthrough pressure of the caprock in the target area and the overlying water depth and geological conditions.

[0097] In some embodiments, more dimensional features can be further extracted or calculated from the geological data of S101. Caprock lithological parameters may include clay mineral content, silt content, median grain size, etc.; formation pressure parameters may include hydrostatic pressure, reservoir pressure, etc. A structured dataset is constructed: each record (corresponding to a sample well) contains a set of input features (overlying water depth H, lithological feature parameters X1, X2…, formation pressure parameters P1, P2…) and a prediction target (actual breakthrough pressure Pc). Using this dataset, a machine learning algorithm (such as random forest, gradient boosting decision tree, or neural network) is selected for model training. The training process aims to allow the algorithm to automatically learn the mapping relationship from complex multidimensional features to the target pressure. After training is completed and validated, the resulting algorithm is a caprock sealing capacity model that comprehensively considers the influence of multiple factors such as water depth, lithology, and pressure.

[0098] This model overcomes the limitations of relying solely on water depth. By incorporating key control factors such as lithology and formation pressure, it can more precisely characterize the differences in caprock sealing capacity under different geological backgrounds. For target areas with strong heterogeneity, its prediction accuracy is expected to be significantly higher than simple models based solely on water depth. The model itself (especially tree-based models) can provide feature importance ranking, thereby quantitatively revealing which factors, such as overlying water depth, clay content, and formation pressure, have the most critical impact on caprock sealing capacity. This deepens the understanding of caprock sealing mechanisms from a data-driven perspective. Furthermore, such models possess a strong ability to handle nonlinear and high-dimensional relationships, adapting to complex geological conditions. With the addition of more sample well data, the model can continuously optimize and upgrade itself through iterative training, forming a continuously learning intelligent evaluation system.

[0099] S106: Determine the capping capacity of the capping layer at the target point within the target area based on the capping capacity model.

[0100] In some embodiments, the capping capacity model constructed in S105, which reflects the general laws of the region, can be applied to any specific location (target point) within the target area that needs to be evaluated, thereby generating a quantitative and operable evaluation conclusion on the capping effectiveness of that point.

[0101] In some embodiments, the target point can be a specific geographical location within a target area where the caprock sealing capacity needs to be evaluated. This can include, but is not limited to: a drilled but uncorked or untested well; a planned new well location; a specific location on a gas reservoir structure (such as a high point or flank); or an exploration block to be evaluated.

[0102] In some embodiments, the critical breakthrough pressure can be the maximum fluid pressure that the caprock can withstand at a target point, calculated or predicted based on a caprock sealing capacity model and the specific overlying water depth. It is the theoretical limit of the caprock's own sealing capacity. The critical breakthrough pressure serves as a benchmark for evaluation. All subsequent evaluations revolve around comparing the actual pressure in the reservoir with this critical value.

[0103] In some embodiments, actual formation pressure can be the real pressure exerted by fluids (oil, gas, water) in the reservoir pore space at the target point. It can be obtained through drill pipe testing, repeated formation testing, or calculated from sonic and density logging data. Actual formation pressure is the measured object being evaluated, representing the actual load borne by the caprock during its actual geological history.

[0104] In some embodiments, the capping capability level can be an evaluation system that categorizes quantitative pressure comparison results into several qualitative or semi-qualitative levels (e.g., strong, medium, weak, ineffective; or safe, critical, risky). The capping capability level can transform continuous numerical differences into more intuitive and easier-to-manage hierarchical and risk-determining categorical information, which is particularly suitable for rapid ranking in block selection or regional evaluation.

[0105] In some embodiments, the overlying water depth at the target point can be obtained; the critical breakthrough pressure corresponding to the overlying water depth at the target point can be determined according to the caprock sealing capacity model; and the caprock sealing capacity at the target point can be determined according to the critical breakthrough pressure.

[0106] In some embodiments, the overlying water depth of the target point can be obtained: if the target point is an existing well, it can be calculated from its logging and well site data; if it is a planned well location, it needs to be predicted based on regional water depth maps or seismic data. The obtained water depth value can be used as input and substituted into the caprock sealing capacity model constructed in S105. If the model is a curve, the corresponding critical breakthrough pressure value is calculated by looking up the map or substituting it into the formula; if the model is a machine learning model, the model is called for prediction. The evaluation logic is—as long as the critical breakthrough pressure value exists and is a reasonable positive number, it indicates that the caprock has a certain sealing capacity under the water depth conditions. Its value can directly and quantitatively reflect the strength of the sealing capacity.

[0107] For target points that have not undergone expensive core testing or specialized logging, only their water depth information is needed to quickly estimate the theoretical limit of their caprock sealing capacity using an established regional model, greatly saving exploration and evaluation time and financial costs. The output critical breakthrough pressure is a specific pressure value, which allows for precise quantitative comparison of the sealing capacity between different target points, providing direct numerical basis for well location optimization and risk coefficient determination in reserve calculation.

[0108] In some embodiments, the actual formation pressure of the reservoir at the target point can be obtained; the difference or ratio between the actual formation pressure and the critical breakthrough pressure can be calculated; and the capping capacity level of the caprock at the target point can be determined based on the preset interval in which the difference or ratio is located.

[0109] In some embodiments, the water depth at the target point can be obtained, and its critical breakthrough pressure (Pc_critical) can be obtained through a model. The actual formation pressure (P_actual) of the reservoir at the target point can be obtained. The difference ΔP = Pc_critical - P_actual (safety margin) or the ratio R = Pc_critical / P_actual (safety factor) is calculated. Based on geological experience or industry standards, the capping capacity level corresponding to the preset range of the difference or ratio is defined in advance. For example, ΔP > 3MPa or R > 1.5 indicates strong capping capacity (low risk); 0 < ΔP ≤ 3MPa or 1 < R ≤ 1.5 indicates medium capping capacity (medium risk); ΔP ≤ 0 or R ≤ 1 indicates weak or failed capping capacity (high risk). By comparing the calculated ΔP or R value with this standard, the capping capacity level of the target point can be determined.

[0110] By introducing actual formation pressure, the evaluation system transforms from a static assessment of caprock attributes to a dynamic assessment of the reservoir-caprock pressure system balance. This makes the evaluation results more closely reflect the actual situation of oil and gas reservoir preservation, since caprock failure is essentially a pressure imbalance. The output risk ratings (e.g., high, medium, low risk) are very intuitive, making them particularly suitable for creating exploration risk zoning maps, guiding drilling sequences, or optimizing development plans.

[0111] In some embodiments, the mud and sand capping simulation device includes a column for filling the simulated reservoir and simulated capping samples, an air pump, a pressure monitoring unit, and a resistance monitoring unit; an overlying water column of various heights can be set above the simulated capping.

[0112] In some embodiments, the column is a reaction vessel used to fill simulated reservoir and caprock samples saturated with water in layers of designed thickness. The column is made of transparent or pressure-resistant material to allow for observation or pressure resistance. A gas pump and its connected pressure control system are used to inject natural gas into the bottom of the column and precisely maintain a constant charging pressure. A pressure monitoring unit (such as a high-precision pressure sensor) monitors and records the pressure evolution within the simulated reservoir in real time. A resistance monitoring unit (such as an electrode array deployed within the column) monitors the resistivity changes of the reservoir and caprock sections in real time. Furthermore, the device must have the capability to form and precisely adjust the height of the overlying water column at the top of the column to simulate different water depths.

[0113] This device enables simultaneous, real-time dynamic monitoring of multiple parameters during the breakthrough process. Pressure monitoring directly reflects the accumulation and release of driving force, while resistivity monitoring is highly sensitive to the movement of the displacement front (gas-water interface). The combination of the two provides a dual and complementary chain of evidence for accurately determining the moment of breakthrough.

[0114] In some embodiments, the mud and sand sealing simulation device includes multiple columns of varying heights.

[0115] In some embodiments, the mud and sand capping simulation device can be equipped with a series of columns of different fixed heights (e.g., 40cm, 60cm, 100cm, etc.). When conducting a series of experiments, the column closest to the overlying water column height value designed in S102 can be directly selected for the experiment, without having to repeatedly set different heights on a single column through precise liquid level adjustment.

[0116] For each preset water column height, using the corresponding column allows for the rapid and accurate establishment of initial experimental conditions, avoiding errors and time consumption caused by adjusting the liquid level. Furthermore, different columns can be used in parallel or sequentially, accelerating the data acquisition process. This demonstrates optimized design from methodological principles to engineering practice.

[0117] In some embodiments, the controlled mud and sand capping simulation device performs a natural gas charging experiment until the simulated reservoir in the mud and sand capping simulation device meets a preset breakthrough judgment condition. Specifically, this may include: controlling the gas pump to charge the simulated reservoir with natural gas under a preset overlying water column height; during the charging process, controlling the pressure monitoring unit to obtain the pressure of the simulated reservoir and controlling the resistance monitoring unit to obtain the resistance of the simulated reservoir and the simulated cap; if the pressure and the resistance meet the preset breakthrough judgment condition, controlling the gas pump to stop charging the simulated reservoir with natural gas.

[0118] In some embodiments, determining the experimental breakthrough pressure of the simulated caprock under each overlying water column height based on the charging pressure of the simulated reservoir when the preset breakthrough determination condition is met may specifically include: determining the experimental breakthrough pressure of the simulated caprock under a preset overlying water column height based on the charging pressure of the simulated reservoir when the preset breakthrough determination condition is met.

[0119] In some embodiments, under a set overlying water column height, a gas pump is activated to perform constant-pressure natural gas charging. During the charging process, the system synchronously and continuously collects pressure data of the simulated reservoir and resistivity data of the simulated reservoir / caprock. When the monitored data meets a preset, objective breakthrough condition, charging is automatically or manually stopped. The constant charging pressure value recorded at this time is defined as the experimental breakthrough pressure of the simulated caprock under that specific overlying water column height. Each water column height corresponds to an independent experiment, yielding one breakthrough pressure value.

[0120] By clearly mapping the experimental breakthrough pressure to the preset overlying water column height, a clear input (water pressure condition)-output (breakthrough pressure) relationship was established, providing clean and orderly data pairs for the subsequent construction of the water depth-pressure model. Automated monitoring and control reduced human intervention errors, ensuring the efficiency and reliability of the entire data acquisition process.

[0121] In some embodiments, the preset breakthrough determination condition may specifically include: the pressure of the simulated reservoir has reached its peak and the pressure drop rate is greater than or equal to a preset drop threshold, while the resistance rise rate of the simulated reservoir and the simulated caprock is greater than or equal to a preset rise threshold.

[0122] In some embodiments, the preset breakthrough criteria include two criteria that must be met simultaneously: 1) Pressure criterion: After the simulated reservoir pressure reaches its historical high (peak), its rate of decline exceeds a preset decline threshold (e.g., the pressure drop exceeds a certain value per unit time). This indicates that the accumulated natural gas has begun to break through the caprock on a large scale, causing the pressure system to depressurize. 2) Resistance criterion: The rate of increase in resistance of the simulated reservoir and / or caprock exceeds a preset increase threshold. This is because the high-resistivity natural gas displaces the conductive formation water, causing a sharp increase in overall resistance.

[0123] This approach avoids the uncertainty of relying on visual observation of bubbles (which is lagging and subjective), and instead makes judgments based on the abrupt changes in two key parameters that reflect the essence of the physical process. The simultaneous satisfaction of both criteria improves the accuracy of the judgment (strong anti-interference capability). The preset rate threshold ensures a unified and repeatable judgment standard, allowing different operators or different experiments to obtain consistent results, greatly guaranteeing the accuracy and comparability of the experimental pressure breakthrough.

[0124] In some embodiments, the resistivity of the reservoir and the caprock is determined based on the resistance of the simulated reservoir and the simulated caprock. Based on this, the step of controlling the gas pump to stop charging the simulated reservoir with natural gas if the pressure and the resistance meet the preset breakthrough judgment conditions includes: if the pressure has reached its peak and the pressure curve drops sharply, and the rate of increase of the resistivity increases, controlling the gas pump to stop charging the simulated reservoir with natural gas.

[0125] In some embodiments, peak pressure can refer to the maximum point on the pressure-time curve within the simulated reservoir during constant-pressure natural gas charging. Before this point, the pressure continuously increases with the ongoing injection of natural gas; afterwards, the pressure begins to decrease. The peak pressure marks the limit of the capillary barrier's ability to seal off natural gas. At this point, the reservoir pressure driving the upward migration of natural gas is exactly equal to the capillary breakthrough pressure of the capillary. Once the pressure surpasses this peak and begins to decline, it means that the capillary barrier of the capillary has been partially breached, gas begins to macroscopically escape, and the reservoir begins to depressurize. Peak pressure can serve as a zero-point reference for determining whether a breakthrough is imminent or already occurring.

[0126] In some embodiments, a steep drop in the pressure curve can mean that after reaching its peak, the pressure value no longer maintains a steady or slow decline, but instead drops rapidly at a rate significantly greater than that during the charging phase. This is represented on the pressure-time curve as a steep downward segment with an increasing absolute slope. A rate of decline (pressure drop per unit time) exceeding a preset threshold (e.g., several times greater than the background fluctuation rate) indicates that the depressurization process of the pressure system has transitioned from micro-leakage to a macro-level breakthrough. A steep drop in the pressure curve serves as dynamic evidence confirming substantial caprock sealing failure, rather than merely an isolated peak point.

[0127] In some embodiments, the increase in the rate of resistivity rise can be a significant and sustained increase in the calculated resistivity values ​​of the simulated reservoir and / or simulated caprock over time (dρ / dt). This is manifested as a steepening slope on the resistivity-time curve. The increased rate of resistivity rise directly reflects the dynamic process of high-resistivity natural gas rapidly displacing low-resistivity formation water in the porous medium. When a breakthrough occurs, the gas forms a continuous escape channel, leading to a sharp decrease in water saturation within the affected area, thereby triggering an accelerated increase in resistivity. The increased rate of resistivity rise can serve as an independent and sensitive criterion for confirming the occurrence of a breakthrough from the perspective of fluid phase change.

[0128] In some embodiments, under a preset overlying water column height, a gas pump is activated to inject natural gas into the simulated reservoir at constant pressure. The experimental control system synchronously acquires two data streams at a fixed high frequency (e.g., 10 times per second): the simulated reservoir pressure value P(t) from the pressure monitoring unit, and the original resistance values ​​Rr(t) and Rg(t) of the simulated reservoir and simulated caprock from the resistance monitoring unit, and calculates the corresponding resistivity ρr(t) and ρg(t). The pressure curve P(t) and resistivity curve ρ(t) can be analyzed. Specifically, the maximum points on the pressure curve can be identified, and the derivative of pressure with respect to time (i.e., the rate of decrease) can be calculated. When it is detected that the pressure exceeds the peak value and its rate of decrease continues to exceed a preset decrease threshold (e.g., dP / dt < -0.5 kPa / s), the condition for a steep decrease in the pressure curve is determined to be met. The slope dρ / dt of the resistivity curve can also be calculated. When the dρ / dt ratio changes from a stable or slow increase to a sustained increase above a preset threshold (e.g., dρ / dt > 0.1 Ω·m / s), the resistivity increase rate condition is considered met. If, simultaneously, the pressure curve shows a peak and a steep decline, and the resistivity increase rate increases significantly, the preset breakthrough condition is considered met. Once the condition is met, a command is issued to stop the gas pump from charging the simulated reservoir with natural gas, and the current charging pressure value is locked as the simulated breakthrough pressure for this experiment. The complete pressure and resistivity change curves are then recorded.

[0129] By transforming the vague concept of a breakthrough into the synchronous changes of two quantifiable and real-time monitorable physical indicators—the peak pressure and its sharp drop, and the increase in resistivity—the subjectivity and lag inherent in relying on researchers' visual observation of bubbles or their experience-based judgments are completely eliminated. This ensures that the simulated breakthrough pressure data obtained from each experiment have a unified, objective, and repeatable standard, fundamentally guaranteeing the accuracy and reliability of core experimental data.

[0130] The pressure drop reflects the decompression of the macroscopic mechanical system, while the accelerated increase in resistivity reflects the evolution of the fluid phase in the microscopic pores. Simultaneously using these two independent yet complementary physical processes as the basis for judgment forms a strong chain of evidence. Even if one signal experiences a brief fluctuation due to unforeseen factors, the system will not misjudge as long as the other signal fails to meet the standard. This multi-parameter integrated decision-making mechanism greatly improves the anti-interference capability and fault tolerance of experimental criteria, avoiding experimental termination due to noise from a single sensor or local anomalies.

[0131] In some embodiments, the resistivity of the reservoir can be calculated using the following formula based on the simulated reservoir resistance: ρ r = R r 1000 0.036; where ρ r R is the resistivity of the reservoir. r To simulate the reservoir resistance under experimental conditions.

[0132] In some embodiments, the resistivity of the capping layer can be calculated using the following formula based on the resistance of the simulated capping layer: ρ g = R g 1000 0.036; where ρ g R is the resistivity of the capping layer. g To simulate the resistance of the capping layer under experimental conditions.

[0133] In some embodiments, during mud and sand capping simulation experiments, the resistance monitoring unit in the device directly measures the resistance (R) of the simulated reservoir and caprock samples. To convert the monitoring data into a more universal, geologically interpretable parameter comparable to well logging data, the resistance value can be converted into resistivity (ρ).

[0134] In some embodiments, the resistance value can be a physical quantity directly measured by electrodes embedded in the mud and sand capping simulation device, reflecting the overall conductivity of a simulated reservoir or capping sample with a specific geometry under specific water-saturated and pressurized conditions. Its unit can be ohms (Ω). The reservoir resistance value and capping resistance value are raw electrical signals acquired in real time by the resistance monitoring unit during the experiment. Their magnitude is affected by multiple factors such as sample mineral composition, pore structure, pore fluid salinity, and saturation. The resistance value is the direct input data for calculating resistivity, and its dynamic changes (increasing trend and rate of increase) are one of the key criteria for determining whether natural gas has breached the capping layer.

[0135] In some embodiments, resistivity is a physical parameter characterizing the electrical conductivity of the rock or material itself, excluding the influence of sample geometry and size. Its unit is ohm-meter (Ω·m). ρ r and ρ g These represent the resistivity of simulated sandy reservoirs and simulated muddy caprock materials, respectively. Resistivity is a derived parameter calculated from measured values. Its value directly reflects the electrical conductivity of the rock skeleton, the electrical conductivity of pore fluids, and the connectivity of pores. The converted resistivity is a more standardized physical property parameter. Its time-varying curve (resistivity increase) more clearly indicates the displacement process of conductive formation water by high-resistivity natural gas in the pore space, serving as a basis for identifying breakthrough events.

[0136] In some embodiments, the conversion factor (1000) 0.036 (36) can be a dimensionless constant [Ω·m / Ω] used to convert dimensionless resistance ratios or directly measured resistance values ​​into resistivity values ​​with standard meaning. Its specific value (36) depends on the specific geometric factors of the mud and sand capping simulation device (cylinder), which is usually determined by the arrangement of the electrodes (such as the quadrupole method), the spacing, and the effective cross-sectional area of ​​the sample.

[0137] In some embodiments, during the constant-pressure natural gas charging experiment, the resistance monitoring unit synchronously acquires the raw resistance signals of the simulated reservoir section and the simulated caprock section at a fixed frequency (e.g., once per second). For each data point at each acquisition time, R is respectively... r and R g Substitute the values ​​into their respective formulas to perform the calculations. Then, calculate a series of ρ values. r and ρ g By mapping data points to the time axis, dynamic curves showing the resistivity evolution of the simulated reservoir and caprock over time can be plotted. These resistivity curves are then combined with synchronously monitored pressure curves, and preset breakthrough criteria (such as a resistivity rise rate greater than or equal to a preset threshold) are applied to jointly determine whether a natural gas breakthrough has occurred.

[0138] By introducing a fixed conversion formula based on the device's geometric factors, the original resistance readings, which are tied to specific experimental settings, are transformed into standardized material resistivity parameters with clear physical meaning. This allows for direct and effective comparison and analysis of experimental results obtained from different batches and devices (provided the geometric factors are fixed), greatly enhancing the standardization and universality of the data. Resistivity is more sensitive to changes in the phase state of pore fluids than the original resistance. The displacement of formation water (low resistivity) by natural gas (high resistivity) leads to a sharp increase in resistivity. This can be achieved by calculating ρ in real time. r and ρ g By observing the rate of change, the advance and breakthrough signs of the gas front can be detected earlier and more sensitively than by observing only pressure changes. Using this calculated parameter as one of the core criteria for breakthrough significantly improves the accuracy and objectivity of determining the moment of breakthrough.

[0139] As can be seen from the technical solutions provided in the embodiments of this specification above, these embodiments establish the overlying water depth as the core variable and measure the breakthrough pressure of the caprock at different water column heights through simulation experiments. This directly overcomes the limitations of traditional methods that rely on well logging statistics or static core testing, which cannot dynamically reproduce and separate the environmental load effect of water depth. It achieves controllable, quantitative, and direct measurement of the causal relationship between water depth and capping capacity, giving the evaluation conclusions a solid experimental physical basis and significantly improving their credibility. Furthermore, through similarity principle conversion, the data obtained at the experimental scale are scientifically and rigorously converted to the actual geological scale, thereby obtaining the actual breakthrough pressure value reflecting real geological conditions and ensuring the macroscopic geological representativeness of the microscopic experimental conclusions. In addition, by integrating data from multiple sample wells, a regional quantitative prediction model of water depth-breakthrough pressure is constructed. For any new, untested target point within the target area, only the relatively easily obtainable parameter of its overlying water depth needs to be input to quickly and cost-effectively predict the critical capping capacity of its caprock. This represents a leap from expensive and detailed measurements of a few points to efficient and quantitative predictions across the entire area, greatly improving exploration and evaluation efficiency and providing direct and quantitative decision-making basis for well location deployment, reserve assessment, and development risk management.

[0140] The following is a specific embodiment of this specification: (1) Lithological logging data from 10 wells in Block 36 of Area A were collected to obtain the thickness of sandy reservoir and muddy caprock. The average thickness of sandy reservoir and muddy caprock was calculated, and the average thickness of sandy reservoir and muddy caprock was found to be 15m and 15m, respectively. After conversion at a ratio of 1:100, the thickness of sandy reservoir and muddy caprock under experimental conditions was found to be 15cm and 15cm, respectively. (2) Collect water depth data from 10 wells in area 36 of location A, with a water depth range of 400m to 700m. Seven water depth data points were obtained at 50m intervals, as shown in Table 1.

[0141] Table 1

[0142] The water column height data points under the experimental conditions were obtained by converting the data at a ratio of 1:1000, as shown in Table 2.

[0143] Table 2

[0144] (3) Collect core (or wall core) samples of sandy reservoirs and clay caprock from 10 wells in Block 36 of Area A. Whole-rock mineral X-ray diffraction and laser particle size analysis were performed on the collected core (or wall core) samples of the sandy reservoirs and clay caprock from the 10 wells to obtain the particle size and mineral composition of the sandy reservoirs and clay caprock from the 10 well core or wall core samples, and the average values ​​of the particle size and mineral composition of the sandy reservoirs and clay caprock were calculated. The particle size of the sandy reservoir was as follows: fine sand (>0.0625 mm) accounted for 7%, coarse silt (0.0625-0.0310 mm) accounted for 2%, fine silt (0.0310-0.0039 mm) accounted for 65%, and clay (<0.0039 mm) accounted for 26%. The particle size distribution of the argillaceous caprock is as follows: fine sand (>0.0625 mm) 2%, coarse silt (0.0625-0.0310 mm) 1%, fine silt (0.0310-0.0039 mm) 68%, and mud (<0.0039 mm) 29%. The mineral composition of the sandy reservoir is: quartz 33%, feldspar 13%, carbonate minerals 23%, and clay minerals 31%. The mineral composition of the argillaceous caprock is: quartz 33%, feldspar 12%, carbonate minerals 21%, and clay minerals 34%.

[0145] (4) Based on the calculated average values ​​of mineral composition and particle size of sandy reservoir and muddy caprock, loose experimental samples of sandy reservoir and muddy caprock were prepared for the experiment. The minerals used for preparation were quartz sand, potassium feldspar, calcite and illite, with particle sizes of 0.065 mm, 0.05 mm, 0.0078 mm and 0.002 mm, respectively.

[0146] (5) The prepared loose experimental samples of sandy reservoir and muddy caprock were saturated with formation water. The formation water was standard seawater with a salinity of 35 g / L and a NaCl type. The loose experimental samples of sandy reservoir and muddy caprock after water saturation were thoroughly stirred to ensure that minerals of different types and particle sizes were mixed evenly.

[0147] (6) Fill the saturated sandy reservoir with formation water into a 100cm column in the loose mud and sand capping and storage capacity simulation mud and sand capping simulation device (e.g. Figure 3 During the filling process, the sandy reservoir was kept saturated with water. After filling a 15cm layer of sandy reservoir experimental sample, a 15cm layer of muddy caprock experimental sample with saturated water was added. Simultaneously, the muddy caprock experimental sample was kept saturated with water during the filling process. Finally, standard seawater was added to a height of 40cm (45cm, 50cm, 55cm, 60cm, 65cm, 70cm).

[0148] (7) Start constant pressure natural gas injection at the bottom of the 100cm column to conduct a breakthrough pressure test experiment of the unformed loose muddy caprock. At the same time, record the pressure Pr and resistance value Rr of the reservoir, as well as the pressure Pg and resistance value Rg of the caprock during the experiment, until the reservoir pressure curve drops sharply and the rate of increase of the resistivity curve increases, and the experiment ends.

[0149] (8) Using the formula ρr = Rr 1000 0.036, the resistivity value of the sandy reservoir during the experiment is calculated; where ρr is the calculated resistivity value of the sandy reservoir, and Rr is the resistance value of the sandy reservoir measured during the experiment. (9) Using the formula ρg = Rg 1000 0.036, calculate the resistivity value of the mud cover layer during the experiment; where ρg is the calculated resistivity value of the mud cover layer, and Rg is the resistance value of the mud cover layer measured during the experiment. (10) Based on the pressure and resistivity data of the sandy reservoir and the clay cap during the experiment, obtain the time-varying curves of the sandy reservoir and the clay cap, such as... Figures 4-17 .

[0150] (11) When the reservoir pressure curve reaches its maximum value and then drops sharply, and the rate of increase of the resistivity curve increases, the corresponding reservoir pressure value is defined as the breakthrough pressure value of the undiagenetic loose argillaceous caprock under experimental conditions. For example, when the water column height is 40 cm, the caprock breakthrough pressure is 3.39 kPa (e.g., Figure 18 In this embodiment, by completing experiments with different water column heights, different breakthrough pressure values ​​of the mud cap layer corresponding to the breakthrough pressure test experiments with different water column heights were obtained, as shown in Table 3.

[0151] Table 3

[0152] (12) Using formula L model × Δρmodel / P cmodel = L field × Δρ field / P cfield The breakthrough pressure values ​​of the mudstone cap layer at different water depths under actual geological conditions were calculated, as shown in Table 4. Where L... model To simulate the thickness of the cap layer, Δρ model P represents the density difference between air and water under experimental conditions. cmodel To simulate the breakthrough pressure of the caprock, L field Δρ is the thickness of the cap layer. field P represents the density difference between gas and water under geological conditions. cfield The breakthrough pressure of the caprock; where Δρ model It is 1.02 kg / m 3 ,Δρ field It is 0.8 kg / m 3 .

[0153] Table 4

[0154] (13) The water depth data is fitted with the breakthrough pressure of the mudstone caprock under actual geological conditions to obtain the actual water depth (H) - caprock breakthrough pressure (Pc) curve (e.g. Figure 4 ).

[0155] (14) Obtain the breakthrough pressure value of the muddy cap under actual geological conditions based on the actual water depth (H) and the curve of actual water depth (H) - cap layer breakthrough pressure (Pc).

[0156] Based on the above-described method for determining the capping capacity of deep-water unformed loose sediment caprock, this specification also provides an embodiment of a device for determining the capping capacity of deep-water unformed loose sediment caprock. For example... Figure 19 As shown, the device 1900 for determining the capping capacity of deep-water undiagenetic loose sediment caps may specifically include the following modules: The first acquisition module 1901 is used to acquire geological data from multiple sample wells within the target area. The geological data includes the mineral composition and grain size of the reservoir and caprock at each sample well, as well as the overlying water depth of the caprock. The first determining module 1902 is used to determine the mineral composition, particle size, and height of multiple overlying water columns of the simulated reservoir and simulated caprock in the mud and sand sealing simulation device based on the mineral composition, particle size, and overlying water depth of the reservoir and caprock. The second acquisition module 1903 is used to conduct a simulation experiment of caprock breakthrough pressure test and acquire the experimental breakthrough pressure of the simulated caprock under various overlying water column heights. Calculation module 1904 is used to calculate the actual breakthrough pressure of the caprock at each sample well based on the experimental breakthrough pressure. Module 1905 is used to construct a caprock sealing capacity model for the target area based on the overlying water depth and actual breakthrough pressure of the caprock at each sample well; the caprock sealing capacity model is used to characterize the relationship between the breakthrough pressure of the caprock and the overlying water depth. The second determining module 1906 is used to determine the sealing capacity of the cap layer at the target point within the target area based on the cap layer sealing capacity model.

[0157] In some embodiments, the geological data may also include the thickness of the reservoir and caprock at each sample well.

[0158] Based on this, the first determining module 1902 mentioned above can be specifically used for: Based on the thickness, the simulated thickness of the simulated reservoir and simulated caprock is determined under experimental conditions according to a preset geometric scaling ratio.

[0159] In some embodiments, the second acquisition module 1903 described above can be specifically used for: The mud and sand capping simulation device is controlled to conduct a natural gas charging experiment until the simulated reservoir in the mud and sand capping simulation device meets the preset breakthrough judgment condition. Based on the simulated reservoir charging pressure when the preset breakthrough judgment conditions are met, the experimental breakthrough pressure of the simulated caprock under each overlying water column height is determined.

[0160] In some embodiments, the above-mentioned mud and sand capping simulation device includes a column for filling the simulated reservoir and simulated capping samples, an air pump, a pressure monitoring unit, and a resistance monitoring unit; an overlying water column of various heights can be set above the simulated capping.

[0161] Based on this, the second acquisition module 1903 mentioned above can also be used for: Under a preset overlying water column height, the gas pump is controlled to inject natural gas into the simulated reservoir; During the charging process, the pressure monitoring unit is controlled to acquire the pressure of the simulated reservoir, and the resistance monitoring unit is controlled to acquire the resistance of the simulated reservoir and the simulated caprock. If the pressure and the resistance meet the preset breakthrough judgment conditions, control the gas pump to stop injecting natural gas into the simulated reservoir; Based on the simulated reservoir charging pressure when the preset breakthrough judgment conditions are met, the experimental breakthrough pressure of the simulated caprock under the preset overlying water column height is determined.

[0162] In some embodiments, the preset breakthrough determination conditions include: the pressure of the simulated reservoir has reached its peak and the pressure drop rate is greater than or equal to a preset drop threshold, while the resistivity rise rate of the simulated reservoir and the simulated caprock is greater than or equal to a preset rise threshold.

[0163] In some embodiments, the above-described calculation module 1904 can be specifically used for: Based on the experimental breakthrough pressure, the actual breakthrough pressure of the caprock at each sample well is calculated using the following formula: L model × Δρ model / P cmodel = L field × Δρ field / P cfield ; In the formula, L model To simulate the thickness of the cap layer, Δρ model P represents the density difference between air and water under experimental conditions. cmodel To simulate the breakthrough pressure of the caprock, L field Δρ is the thickness of the cap layer. field P represents the density difference between gas and water under geological conditions. cfield This is the pressure required to break through the cap layer.

[0164] In some embodiments, the above-described building module can be specifically used for: Using the overlying water depth of each sample well as the corresponding actual breakthrough pressure as the dependent variable, data fitting was performed to obtain the relationship curve between the overlying water depth and the critical breakthrough pressure. The formula corresponding to the relationship curve is used as the capping capacity model of the capping layer.

[0165] In some embodiments, the second determining module described above can be specifically used for: Obtain the overlying water depth at the target point; Based on the capping capacity model, determine the critical breakthrough pressure corresponding to the water depth at the target point; The sealing capacity of the capping layer at the target point is determined based on the critical breakthrough pressure.

[0166] As can be seen from the technical solutions provided in the embodiments of this specification above, these embodiments establish the overlying water depth as the core variable and measure the breakthrough pressure of the caprock at different water column heights through simulation experiments. This directly overcomes the limitations of traditional methods that rely on well logging statistics or static core testing, which cannot dynamically reproduce and separate the environmental load effect of water depth. It achieves controllable, quantitative, and direct measurement of the causal relationship between water depth and capping capacity, giving the evaluation conclusions a solid experimental physical basis and significantly improving their credibility. Furthermore, through similarity principle conversion, the data obtained at the experimental scale are scientifically and rigorously converted to the actual geological scale, thereby obtaining the actual breakthrough pressure value reflecting real geological conditions and ensuring the macroscopic geological representativeness of the microscopic experimental conclusions. In addition, by integrating data from multiple sample wells, a regional quantitative prediction model of water depth-breakthrough pressure is constructed. For any new, untested target point within the target area, only the relatively easily obtainable parameter of its overlying water depth needs to be input to quickly and cost-effectively predict the critical capping capacity of its caprock. This represents a leap from expensive and detailed measurements of a few points to efficient and quantitative predictions across the entire area, greatly improving exploration and evaluation efficiency and providing direct and quantitative decision-making basis for well location deployment, reserve assessment, and development risk management.

[0167] This specification also provides a computer device for determining the capping capacity of deep-water unformed loose sediment caprock, including a processor and a memory for storing processor-executable instructions. Specifically, the processor can perform the following tasks according to the instructions: acquiring geological data from multiple sample wells within a target area, including the mineral composition, grain size, and overlying water depth of the reservoir and caprock at each sample well; determining the mineral composition, grain size, and multiple overlying water column heights of the simulated reservoir and caprock in a mud-sand capping simulation device based on the mineral composition, grain size, and overlying water depth of the reservoir, caprock, and caprock; conducting a caprock breakthrough pressure test simulation experiment to obtain the experimental breakthrough pressure of the simulated caprock under each overlying water column height condition; calculating the actual breakthrough pressure of the caprock at each sample well based on the experimental breakthrough pressure; constructing a caprock capping capacity model for the target area based on the overlying water depth and actual breakthrough pressure of the caprock at each sample well; the caprock capping capacity model characterizes the relationship between the caprock breakthrough pressure and the overlying water depth; and determining the capping capacity of the caprock at a target point within the target area based on the caprock capping capacity model.

[0168] To execute the above instructions more accurately, please refer to... Figure 20 As shown in the embodiments of this specification, another specific computer device 2000 is also provided, wherein the computer device 2000 includes a network communication port 2001, a processor 2002 and a memory 2003, and the above structures are connected by internal cables so that the various structures can perform specific data interaction.

[0169] The processor 2002 can be specifically used to: acquire geological data from multiple sample wells within a target area, including the mineral composition and particle size of the reservoir and caprock at each sample well, as well as the overlying water depth of the caprock; determine the mineral composition and particle size of the simulated reservoir and caprock, and the height of multiple overlying water columns in the mud-sand capping simulation device based on the mineral composition, particle size, and overlying water depth of the reservoir and caprock; conduct a caprock breakthrough pressure test simulation experiment to obtain the experimental breakthrough pressure of the simulated caprock under each overlying water column height; calculate the actual breakthrough pressure of the caprock at each sample well based on the experimental breakthrough pressure; construct a caprock sealing capacity model for the target area based on the overlying water depth and the actual breakthrough pressure of the caprock at each sample well; the caprock sealing capacity model is used to characterize the relationship between the caprock breakthrough pressure and the overlying water depth; and determine the sealing capacity of the caprock at the target point within the target area based on the caprock sealing capacity model.

[0170] The memory 2003 can be used to store the corresponding instruction program.

[0171] In this embodiment, the network communication port 2001 can be a virtual port bound to different communication protocols, thereby enabling the sending or receiving of different data. For example, the network communication port can be a port responsible for web data communication, a port responsible for FTP data communication, or a port responsible for email data communication. Furthermore, the network communication port can also be a physical communication interface or communication chip. For example, it can be a wireless mobile network communication chip, such as GSM or CDMA; it can also be a Wi-Fi chip; or it can be a Bluetooth chip.

[0172] In this embodiment, the processor 2002 can be implemented in any suitable manner. For example, the processor can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers, etc. This specification is not limiting.

[0173] In this embodiment, the memory 2003 includes volatile memory and non-volatile memory. The memory 2003 can include multiple layers. In digital systems, anything that can store binary data can be a memory; in integrated circuits, a circuit with storage function but no physical form is also called a memory, such as RAM, FIFO, etc.; in a system, a storage device with a physical form is also called a memory, such as a memory stick, TF card, etc.

[0174] Furthermore, embodiments of this specification also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described... Figure 1 Instructions for determining the capping capacity of deep-water undiagenetic loose sedimentary caprocks.

[0175] It should be understood that in the various embodiments of this specification, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this specification.

[0176] It should also be understood that, in the embodiments of this specification, the term "and / or" merely describes the relationship between the associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this specification generally indicates that the preceding and following associated objects are in an "or" relationship.

[0177] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0178] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0179] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0180] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational tasks to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The task is a function specified in one or more boxes.

[0181] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for determining the capping capacity of deep-water unformed loose sedimentary caprock, characterized in that, include: Geological data from multiple sample wells within the target area are obtained. The geological data includes the mineral composition and grain size of the reservoir and caprock at each sample well, as well as the overlying water depth of the caprock. Based on the mineral composition and particle size of the reservoir and caprock, as well as the overlying water depth of the caprock, the mineral composition, particle size, and height of multiple overlying water columns of the simulated reservoir and caprock in the mud and sand capping simulation device are determined. A simulated test experiment of caprock breakthrough pressure was conducted to obtain the experimental breakthrough pressure of the simulated caprock under various overlying water column heights. Based on the experimental breakthrough pressure, calculate the actual breakthrough pressure of the caprock at each sample well. Based on the overlying water depth and actual breakthrough pressure of the caprock at each sample well, a caprock sealing capacity model for the target area is constructed. The cap layer sealing capacity model is used to characterize the relationship between cap layer breakthrough pressure and overlying water depth. Based on the capping capacity model, the capping capacity of the capping layer at the target point within the target area is determined.

2. The method according to claim 1, characterized in that, The geological data also includes the thickness of the reservoir and caprock at each sample well; The method further includes: Based on the thickness, the simulated thickness of the simulated reservoir and simulated caprock in the mud and sand capping simulation device is determined according to a preset geometric scaling ratio.

3. The method according to claim 1, characterized in that, The simulation experiment of caprock breakthrough pressure test, which obtains the experimental breakthrough pressure of the simulated caprock under various overlying water column heights, includes: The mud and sand capping simulation device is controlled to conduct a natural gas charging experiment until the simulated reservoir in the mud and sand capping simulation device meets the preset breakthrough judgment condition. Based on the simulated reservoir charging pressure when the preset breakthrough judgment conditions are met, the experimental breakthrough pressure of the simulated caprock under each overlying water column height is determined.

4. The method according to claim 3, characterized in that, The mud and sand capping simulation device includes a column for filling the simulated reservoir and simulated capping samples, an air pump, a pressure monitoring unit, and a resistance monitoring unit; an overlying water column of various heights can be set above the simulated capping layer; The controlled mud and sand capping simulation device conducts natural gas charging experiments until the simulated reservoir in the mud and sand capping simulation device meets the preset breakthrough judgment conditions, including: Under a preset overlying water column height, the gas pump is controlled to inject natural gas into the simulated reservoir; During the charging process, the pressure monitoring unit is controlled to acquire the pressure of the simulated reservoir, and the resistance monitoring unit is controlled to acquire the resistance of the simulated reservoir and the simulated caprock. If the pressure and the resistance meet the preset breakthrough judgment conditions, control the gas pump to stop injecting natural gas into the simulated reservoir; The step of determining the experimental breakthrough pressure of the simulated caprock under various overlying water column heights based on the simulated reservoir charging pressure when the preset breakthrough judgment conditions are met includes: Based on the simulated reservoir charging pressure when the preset breakthrough judgment conditions are met, the experimental breakthrough pressure of the simulated caprock under the preset overlying water column height is determined.

5. The method according to claim 4, characterized in that, The method further includes: The resistivity of the reservoir and caprock is determined based on the resistance of the simulated reservoir and caprock. If the pressure and the resistance meet a preset breakthrough condition, controlling the gas pump to stop injecting natural gas into the simulated reservoir includes: If the pressure has reached its peak and the pressure curve drops sharply, and the rate of increase in resistivity increases, the gas pump is controlled to stop injecting natural gas into the simulated reservoir.

6. The method according to claim 1, characterized in that, The calculation of the actual breakthrough pressure of the caprock at each sample well based on the experimental breakthrough pressure includes: Based on the experimental breakthrough pressure, the actual breakthrough pressure of the caprock at each sample well is calculated using the following formula: L model × Dr. model / P cmodel = L field × Dr. field / P cfield ; In the formula, L model To simulate the thickness of the cap layer, Δρ model P represents the density difference between air and water under experimental conditions. cmodel To simulate the breakthrough pressure of the caprock, L field Δρ is the thickness of the cap layer. field P represents the density difference between gas and water under geological conditions. cfield This is the pressure required to break through the cap layer.

7. The method according to claim 1, characterized in that, The caprock sealing capacity model for the target area is constructed based on the overlying water depth and actual breakthrough pressure of the caprock at each sample well, including: Using the overlying water depth of each sample well as the corresponding actual breakthrough pressure as the dependent variable, data fitting was performed to obtain the relationship curve between the overlying water depth and the critical breakthrough pressure. The formula corresponding to the relationship curve is used as the capping capacity model of the capping layer.

8. The method according to claim 1, characterized in that, Determining the capping capacity of the cap layer at a target point within the target area based on the capping capacity model includes: Obtain the overlying water depth at the target point; Based on the capping capacity model, determine the critical breakthrough pressure corresponding to the water depth at the target point; The sealing capacity of the capping layer at the target point is determined based on the critical breakthrough pressure.

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