A comprehensive evaluation method for natural gas reservoir resources rich in gas-water transition zone

Abstract

This invention relates to a comprehensive evaluation method for natural gas reservoirs rich in gas-water transition zones, belonging to the field of natural gas resource evaluation. It includes: Step S1: Determining the occurrence pattern of natural gas reservoirs rich in gas-water transition zones; Step S2: Determining the interfaces between gas layers, gas-water co-layers, and gas-water-bearing layers; Step S3: Classifying and evaluating the natural gas reservoir resources rich in gas-water transition zones; Step S4: Classifying and evaluating the natural gas reservoir resources rich in gas-water transition zones; Step S5: Obtaining key parameters, including porosity, gas saturation, maximum dissolved gas volume, pressure coefficient, actual dissolved gas volume, relative permeability, temperature, and absolute permeability; Step S6: Calculating the resource quantities for each level and category. This invention integrates cutting-edge technologies from multiple disciplines such as geology, well logging interpretation, reservoir engineering, and machine learning, striving for standardization, quantification, and refinement of the evaluation process.

Description

Technical Field

[0001] This invention relates to a comprehensive evaluation method for natural gas reservoirs rich in gas-water transition zones, belonging to the field of natural gas resource evaluation. Background Technology

[0002] Natural gas resource assessment is the process of systematically analyzing and evaluating the potential reserves, recoverable resources, and economic and technical feasibility of natural gas (including conventional natural gas and unconventional natural gas such as shale gas and coalbed methane). Specifically, it includes reservoir size, abundance, production capacity, and recoverability.

[0003] Conventional gas reservoir resource evaluation methods mainly include the volumetric method, the mass balance method, the production decline analysis method, and the analogy method. The volumetric method estimates original geological reserves using parameters such as gas-bearing area, effective thickness, porosity, and gas saturation. It is suitable for early-stage exploration but is highly dependent on geological parameters and seismic interpretation, resulting in significant uncertainty. The mass balance method inverts reserves based on the relationship between reservoir pressure and production, suitable for gas reservoirs with a production history, but relies on assumptions such as reservoir homogeneity and sealing. The production decline analysis method extrapolates recoverable reserves from historical production data, suitable for mid-to-late-stage development, but is easily affected by changes in production regimes. The analogy method estimates reserves based on experience with similar gas reservoirs, suitable for areas with limited data, but is highly subjective. Overall, all methods suffer from insufficient characterization of reservoir heterogeneity, significant uncertainty in key parameters, and limited applicability. A single method cannot meet the high-precision evaluation requirements of complex gas reservoirs. Compared to conventional gas reservoirs, the gas-water transition zone exhibits a complex underground occurrence, characterized by the coexistence of multiple forms, including free and dissolved states. Furthermore, its distribution is coupled and controlled by various factors such as reservoir properties, temperature and pressure conditions, and water properties. This complexity makes it difficult to accurately characterize natural gas reservoirs rich in the gas-water transition zone using conventional gas reservoir resource assessment methods. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention proposes a comprehensive evaluation method for natural gas reservoirs rich in gas-water transition zones. Based on different resource occurrence patterns in gas-water transition zones, resources are classified into various levels and types, and a hierarchical classification method based on quality coefficients is proposed, ultimately achieving the evaluation of the resource quantity of natural gas reservoirs rich in gas-water transition zones.

[0005] This invention systematically constructs a technical framework and methodology for evaluating natural gas reservoir resources in gas-water transition zones. It details the resource calculation methods under two typical occurrence modes: complete differentiation and transitional differentiation in the gas-water transition zone. It innovatively proposes a reservoir classification scheme based on a "quality coefficient" and clarifies the acquisition methods and technical specifications for key parameters such as porosity, gas saturation, pressure coefficient, and relative permeability. Integrating cutting-edge technologies from multiple disciplines including geology, well logging interpretation, reservoir engineering, and machine learning, it strives for standardization, quantification, and refinement in the evaluation process.

[0006] The technical solution of this invention is as follows: A comprehensive evaluation method for natural gas reservoirs rich in gas-water transition zones, including... Step S1: Identify the resource occurrence pattern of natural gas reservoirs rich in gas-water transition zones; Step S2: Determine the interfaces between the gas layer, the gas-water co-layer, and the gas-water layer; Step S3: Classify and evaluate the natural gas reservoir resources rich in the gas-water transition zone; use capillary force theory to determine the distribution of gas and water in the reservoir, thereby dividing the pure gas producing area, pure water area and gas-water co-producing area in the formation, and then classify the reservoir resources of different grades based on gas content. Step S4: Classify and evaluate the natural gas reservoir resources rich in the gas-water transition zone, construct reservoir evaluation parameters that comprehensively consider gas content and recoverability, and further classify resources at each level by utilizing their frequency and contribution distribution, based on the resource enrichment characteristics of the gas-water transition zone. Step S5: Obtain key parameters, including porosity, gas saturation, maximum dissolved gas volume, pressure coefficient, actual dissolved gas volume, relative permeability, temperature, and absolute permeability; Step S6: Calculate the amount of resources at each level and for each type.

[0007] According to a preferred embodiment of the present invention, the resource occurrence mode of a natural gas reservoir rich in a gas-water transition zone includes: There are two typical existence modes of the gas-water transition zone underground: complete differentiation type and transitional differentiation type. For complete differentiation, calculate the free gas at the gas cap, the free and dissolved gas in the gas-water co-layer, and the dissolved gas in the gas-water layer.

[0008] According to a preferred embodiment of the present invention, determining the interface between the gas layer, the gas-water co-layer, and the gas-water layer includes: The maximum dissolved gas volume of the formation sand body is positively correlated with its depth. The actual dissolved gas volume of gas-bearing, gas-water co-layers, and gas-water-bearing sand layers can be calculated. When the actual dissolved gas volume of a certain layer is less than its maximum dissolved gas volume, it indicates that the natural gas has not reached saturation and is classified as a gas-water-bearing layer. When the actual dissolved gas volume of a certain layer is greater than its maximum dissolved gas volume, it indicates that the natural gas has reached saturation and desolvation has occurred, classifying it as a gas-water co-layer or a gas-bearing layer. When the actual dissolved gas volume of a certain layer is equal to its maximum dissolved gas volume, it indicates that the natural gas has reached saturation and desolvation has just begun. The depth of this layer is the interface depth between the gas-water co-layer and the gas-water-bearing layer. If the actual dissolved gas volume of the formation sand body is less than the maximum dissolved gas volume at the corresponding depth of the sand body itself, the actual dissolved gas volume data of the gas-bearing water layer is raised to a certain depth in the shallow part so that the actual dissolved gas volume is equal to the maximum dissolved gas volume in the shallow part. The depth after the elevation is the interface depth between the gas-water layer and the gas-bearing water layer. Similarly, if the actual dissolved gas volume of the formation sand body is greater than the maximum dissolved gas volume at the corresponding depth of the sand body, the gas-water co-layer is sunk to a certain depth so that the actual dissolved gas volume is equal to the maximum dissolved gas volume at the shallow depth. The depth after the sinking is the interface depth between the gas-water co-layer and the gas-water layer. Based on the measured capillary pressure and relative permeability curves of the reservoir, the capillary pressure is converted into gas-liquid column height to obtain the relationship between gas-liquid column height and water saturation under different phases and porosities. This allows us to infer the theoretical lower limit of the gas-water transition zone thickness of a reservoir. The water saturation value corresponding to the gas phase is used as the calibration lower limit. At this point, the relative permeability of the gas phase is 0, corresponding to the boundary between the gas-water co-layer and the gas-water layer. A water saturation value higher than this indicates a gas-water layer. In the gas-liquid column height-water saturation diagram for different porosities, we find the gas-liquid column height that meets the calibration upper limit of water saturation. A thickness exceeding this value proves the presence of a gas layer. We find the gas-liquid column height that meets the calibration lower limit. A thickness lower than this value proves that there is only a water layer, with no gas-water co-layer or gas layer. The difference between the gas-liquid column heights corresponding to the calibration upper and lower limits is the theoretical thickness of the gas-water co-layer. Based on a large number of statistical and mechanistic deductions of actual reservoirs, the main controlling factors of the actual gas-water co-layer thickness are characterized by a quantitative formula, namely: theoretical thickness × pressure coefficient. The depth at which the gas-water co-layer and the gas-water layer meet is the same as the thickness of the gas-water co-layer, which is the boundary between the gas layer and the gas-water co-layer.

[0009] According to a preferred embodiment of the present invention, natural gas reservoirs rich in gas-water transition zones are graded and evaluated to classify reservoir resources into different grades; including: The tiered evaluation focuses on free gas as the primary objective, with gas cap, gas-water co-layer, and gas-water-bearing layer resources belonging to three different tiers. a) Divide the sand body into a large number of grids in the horizontal direction, and calculate the burial depth and thickness of each grid using the sand body scatter data; b) Statistically analyze the logging gas saturation data of the target horizon; c) Statistically analyze the distribution characteristics of gas saturation for gas zones, gas-water coexistence zones, and gas-bearing water zones, and create a frequency distribution histogram; When Sg > 35%, it is a gas zone; when 10% < Sg < 35%, it is a gas-water coexistence zone; when Sg < 10%, it is a gas-bearing water zone; Sg refers to gas saturation.

[0010] Preferably according to the present invention, classify and evaluate the natural gas reservoir resources rich in the gas-water transition zone, construct reservoir evaluation parameters comprehensively considering gas content and recoverability, and further classify each level of resources by using their frequency and contribution distribution in combination with the resource enrichment characteristics of the gas-water transition zone; including: For the classification of natural gas reservoir resources rich in the gas-water transition zone, use the quality coefficient A to conduct a detailed evaluation of each level of reservoir resources. The calculation formula of A is as follows: ; In the formula, A is the quality coefficient; S is the area, taking 1 km 2 ; h is the sand body thickness, m; φ is the sand body porosity, %; Sg is the gas saturation, %; k is the absolute permeability, mD; k r is the relative permeability, dimensionless; Cp is the pressure coefficient, dimensionless; Z is the burial depth, m; Divide the quality coefficient A into several intervals at an interval of 2, statistically analyze the data distribution of each interval to create a frequency distribution histogram. In the frequency distribution histogram, the horizontal axis is the quality coefficient A, and the vertical axis is the frequency distribution percentage; calculate the sum of the sample point data within the interval and divide it by the sum of all sample point data to obtain the contribution degree distribution histogram. In the contribution degree distribution histogram, the horizontal axis is the quality coefficient A, and the vertical axis is the contribution degree percentage; fit the trend lines of the frequency distribution histogram and the contribution degree distribution histogram, and the quality coefficient A corresponding to the intersection point formed by the two lines is used as the classification evaluation standard for each type of reservoir; First, divide the natural gas reservoir rich in the gas-water transition zone into three levels: gas zone, gas-water coexistence zone, and gas-bearing water zone. Calculate the quality coefficient for all three levels of resources, and use the quality coefficient A corresponding to the intersection point obtained according to the frequency distribution and contribution degree distribution as the classification evaluation standard for each level of resources.

[0011] Preferably according to the present invention, obtain key parameters, and the key parameters include porosity, gas saturation, maximum dissolved gas volume, pressure coefficient, actual dissolved gas volume, relative permeability, temperature, absolute permeability; including: Use the DNN and XG Boost machine learning methods to jointly predict porosity; Calculate the gas saturation using Archie's formula; water saturation The calculation formula is as follows: ; In the formula, a is the lithology coefficient (unitless); m is the cementation index (unitless); b is the lithology constant (unitless); and n is the saturation index (unitless). Rw is the porosity of the formation rock, %; Rt is the formation water resistivity, Ω·m; Sw is the formation water saturation, % Then calculate the gas saturation based on the water saturation; The maximum dissolved gas volume is a function of formation temperature, pressure, and formation water salinity; it includes: based on experimental results, using a multiple regression equation, fitting the regression equation between water dissolved gas solubility and temperature, pressure, and salinity, that is, obtaining the maximum dissolved gas volume under various conditions; The pressure coefficient is obtained using the Eaton method; pressure coefficient The formula for obtaining is: ; In the formula, point B refers to a certain depth point in the abnormal segment where the overpressure phenomenon occurs; , , The values ​​are the pore fluid pressure, overlying rock pressure, and hydrostatic pressure at point B, respectively, in MPa. , The measured time difference at point B and the normal sound wave time difference are respectively, in μs / m; c is the Eaton index; , Density of overlying rock strata and formation water, in g / cm³ 3 g is the acceleration due to gravity, taken as 9.8 m / s². 2 H B The burial depth of point B is in meters (m). The Eaton exponent c is expressed as follows: ; By combining the three core logging curves of GR, neutron, and resistivity, and analyzing their intrinsic correlation with dissolved gas volume, machine learning methods are introduced to predict the actual dissolved gas volume. Relative permeability refers to the ratio of the effective permeability of a certain phase fluid to the absolute permeability of a rock; the relationship between relative permeability and absolute permeability is expressed as: ; Among them, when the gas saturation = At that time, relative penetration rate =0; when gas saturation =1- hour, =1, at this point, the pores are almost entirely filled with gas, approaching single-phase flow; in < <1- Within the range, Follow The increase is monotonically increasing; Similarly, the absolute permeability is calculated by fitting a scatter plot of the porosity and permeability of the measured sample.

[0012] According to a preferred embodiment of the present invention, calculating the quantity of resources at each level and of each type includes: d) Divide the sand body into a large number of grids in the horizontal direction, and calculate the burial depth and thickness of each grid using the sand body scatter data; e) Based on the relationship between parameters such as porosity, gas saturation, maximum dissolved gas volume, pressure coefficient, actual dissolved gas volume, relative permeability, temperature, and absolute permeability and depth, a scatter plot is drawn and its trend line is fitted to establish the relationship curve between depth and the above parameters; where the horizontal axis of the scatter plot is the parameter value and the vertical axis is the depth value. f) Calculate the porosity, gas saturation, maximum dissolved gas volume, and actual dissolved gas volume parameters for each grid based on the relationship curve, and then obtain the grid resource quantity; g) Resource quantity is calculated using the following formula: ; In the formula, V 气顶 V 气水同层 V 含气水层 These represent the resource quantities of the gas cap, gas-water co-layer, and gas-water-bearing layer, respectively, in m. 3 S is the grid area, in meters. 2 h is the sand body thickness, in meters; φ is the sand body porosity, percentage; Sg is the gas saturation, percentage; Bg is the natural gas volume factor, the ratio of a unit mass of natural gas under surface conditions to that under geological conditions; S 最大 and S 实际 These represent the maximum dissolved gas volume and the actual dissolved gas volume of formation water, respectively, in m. 3 / m 3 ; h) The total resource quantity of the entire sand body is obtained by summing the resource quantity of each individual cell.

[0013] The beneficial effects of this invention are as follows: This invention proposes a comprehensive evaluation method for natural gas reservoirs rich in gas-water transition zones, systematically solving the problem that conventional gas reservoir evaluation methods are ill-suited to the complex occurrence of free and dissolved gas in these transition zones. The method constructs a complete technical framework, including resource occurrence pattern identification, layer boundary determination, hierarchical and classification evaluation, key parameter acquisition, and resource quantity calculation. It integrates multidisciplinary technologies such as geology, well logging, reservoir engineering, and machine learning, enabling precise quantitative evaluation of gas layers, gas-water co-containment layers, and gas-water-bearing layers.

[0014] The main advantages of the technical solution of this invention are: 1. Highly targeted, improving the accuracy of resource evaluation in the gas-water transition zone. Traditional methods are insufficient in characterizing the heterogeneity and coexistence of free and dissolved gas in the gas-water transition zone. This invention specifically addresses two typical occurrence modes: complete differentiation and transitional differentiation, and formulates differentiated resource calculation schemes for each, effectively improving the evaluation accuracy of complex gas reservoirs.

[0015] 2. The interface determination method is scientific and rigorous. Combining the comparison between the actual dissolved gas volume and the maximum dissolved gas volume, capillary force theory, relative permeability curves and pressure coefficient correction, an innovative quantitative method for dividing the three-layer interface of gas layer, gas-water co-layer, and gas-water layer is proposed. The quantitative relationship between theoretical thickness and actual thickness is also given, making the zonation more consistent with geological reality.

[0016] 3. The classification and evaluation criteria are innovative. A comprehensive evaluation parameter, "Quality Coefficient A," is proposed, which comprehensively considers multiple dimensions such as area, thickness, porosity, gas saturation, absolute / relative permeability, pressure coefficient, and burial depth. The classification threshold is determined by the intersection of the trend lines of the frequency distribution histogram and the contribution distribution histogram (e.g., gas-water co-layers are divided into Class II (A>7) and Class III (A≤7)). This approach balances gas content and recoverability, overcomes the limitations of single-parameter evaluation, and enhances the objectivity and precision of the evaluation.

[0017] 4. Technological Innovation in Key Parameter Acquisition. Machine learning (DNN+XGBoost model) is used to predict porosity, and random forests are used to predict actual dissolved gas volume. These cutting-edge methods achieve high accuracy and automation in parameter prediction, reducing reliance on human experience and uncertainty. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the comprehensive evaluation method for natural gas reservoirs rich in gas-water transition zones as described in this invention; Figure 2 This is a graph showing the relationship between the height of the gas-liquid column in different phases and the water saturation as described in the embodiments of the present invention. Figure 3 This is a graph showing the relationship between gas-liquid column height and water saturation at different porosities, as described in the embodiments of the present invention. Figure 4 This refers to the distribution characteristics of the gas-water co-layer quality coefficient A as described in the embodiments of the present invention.

[0019] Figure 5 This is the curve showing the relationship between CH4 solubility and temperature / pressure under the experimental conditions described in the embodiments of the present invention. Detailed Implementation

[0020] The present invention will be further defined below with reference to the accompanying drawings and embodiments, but is not limited thereto.

[0021] Example 1 A comprehensive evaluation method for natural gas reservoirs rich in gas-water transition zones, such as... Figure 1 As shown, including Step S1: Identify the resource occurrence pattern of natural gas reservoirs rich in gas-water transition zones; Step S2: Determine the interfaces between the gas layer, the gas-water co-layer, and the gas-water layer; Step S3: Classify and evaluate the natural gas reservoir resources rich in the gas-water transition zone; use capillary force theory to determine the distribution of gas and water in the reservoir, thereby dividing the pure gas producing area, pure water area and gas-water co-producing area in the formation, and then classify the reservoir resources of different grades based on gas content. Step S4: Classify and evaluate the natural gas reservoir resources rich in the gas-water transition zone, construct reservoir evaluation parameters that comprehensively consider gas content and recoverability, and further classify resources at each level by utilizing their frequency and contribution distribution, based on the resource enrichment characteristics of the gas-water transition zone. Step S5: Obtain key parameters, including porosity, gas saturation, maximum dissolved gas volume, pressure coefficient, actual dissolved gas volume, relative permeability, temperature, and absolute permeability; Step S6: Calculate the amount of resources at each level and for each type.

[0022] Example 2 The method for comprehensive evaluation of natural gas reservoir resources rich in the gas-water transition zone as described in Example 1 differs in that: Identify the resource occurrence patterns of natural gas reservoirs rich in gas-water transition zones, including: There are two typical existence modes of the gas-water transition zone underground: complete differentiation type and transitional differentiation type. Complete differentiation type: The strata have obvious zonation from top to bottom. The topmost layer is a gas cap, below which is a gas-water co-layer, and the bottommost layer is a gas-water layer. Complete differentiation type refers to the presence of a gas cap formed by the accumulation of free natural gas at the top of the oil and gas reservoir. Below, as the gas saturation decreases, it gradually transitions to a gas-water co-layer and a gas-water layer.

[0023] Transitional differentiation type: The formation lacks a pure gas cap and is a combination of gas-water co-layer and gas-water layer in the vertical direction; this type refers to the absence of free natural gas accumulation, with most of the underlying layer being gas-water layer, and there may be gas-water co-layer.

[0024] Different calculation methods should be used to calculate resource quantities for the two typical existence modes. For the complete differentiation mode, the free gas at the gas cap, the free and dissolved gas in the gas-water co-layer, and the dissolved gas in the gas-water layer (a small amount of free gas can be ignored) should be calculated separately. For the transitional differentiation mode, the calculation method is similar to that for the complete differentiation mode, but the resource quantity at the gas cap does not need to be calculated. The free gas is calculated using the volumetric method, while the dissolved gas is calculated by combining the volumetric method with the dissolved gas quantity.

[0025] Determine the interfaces between gas layers, gas-water co-layers, and gas-water-bearing layers, including: Determining the interface between gas-water co-layers and gas-bearing water layers requires calculating the maximum dissolved gas volume and the actual dissolved gas volume. The maximum dissolved gas volume of formation sand bodies is positively correlated with depth. The actual dissolved gas volume of gas layers, gas-water co-layers, and gas-bearing water sand layers can be calculated. When the actual dissolved gas volume of a certain segment is less than the maximum dissolved gas volume of that segment, it indicates that the natural gas has not reached saturation dissolution, and it belongs to a gas-bearing water layer. When the actual dissolved gas volume of a certain segment is greater than the maximum dissolved gas volume of that segment, it indicates that the natural gas has reached saturation dissolution, and natural gas desolution has occurred, belonging to both gas-water co-layers and gas layers. In this case, the larger the difference between the actual dissolved gas volume and the maximum dissolved gas volume, the more significant the natural gas desolution phenomenon, and the more likely that segment is a gas layer. When the actual dissolved gas volume of a certain segment is equal to the maximum dissolved gas volume of that segment, it indicates that the natural gas has reached saturation dissolution, and natural gas desolution has just begun. The depth of this segment is the interface depth between the gas-water co-layer and the gas-bearing water layer.

[0026] If the actual dissolved gas volume of the formation sand body is less than the maximum dissolved gas volume at the corresponding depth of the sand body (both are gas-bearing water layers), the actual dissolved gas volume data of the gas-bearing water layer is raised to a certain depth in the shallow part so that the actual dissolved gas volume is equal to the maximum dissolved gas volume in the shallow part. The depth after the elevation is the interface depth between the gas-water layer and the gas-bearing water layer in the ideal case. Similarly, if the actual dissolved gas volume of the formation sand body is greater than the maximum dissolved gas volume at the corresponding depth of the sand body (both are gas layers and gas-water layers), the gas-water layer is submerged to a certain depth so that the actual dissolved gas volume is equal to the maximum dissolved gas volume at the shallow depth. The depth after submersion is the interface depth between the gas-water layer and the gas-water layer in the ideal case. The ideal case here refers to the situation where, with the sand body properties and gas source conditions unchanged, the formation uplift / subsidence causes the maximum dissolved gas volume of the sand body to decrease / increase, resulting in the phenomenon that natural gas has just begun to desolvate.

[0027] Determining the interface between the gas-water co-layer and the gas layer requires considering both the thickness of the gas-water co-layer and the depth of the interface between the gas-water co-layer and the gas-water layer. The thickness of the gas-water co-layer is determined through mercury intrusion porosimetry (MIP) and gas-water interpenetration experiments. First, based on the measured capillary pressure and interpenetration curves of the reservoir, the capillary pressure is converted into gas-liquid column heights. This yields the relationship between gas-liquid column heights and water saturation under different phases and porosities, thus allowing for the inference of the theoretical lower limit of the gas-water transition zone thickness in a given reservoir. The principle is that, in the gas-liquid column height-water saturation relationship diagrams for different phases, the water saturation value corresponding to a liquid column height of 0m (converted from the capillary pressure for water) is used as the upper limit of calibration. At this point, the relative permeability of the water phase is 0, which manifests as high capillary pressure on the mercury intrusion porosimetry curve, corresponding to the boundary between the gas layer and the gas-water co-layer. Below this water saturation level, the layer is considered a gas layer. Similarly, the water saturation value corresponding to the gas phase is used as the lower limit of calibration. At this point, the relative permeability of the gas phase is 0, corresponding to the boundary between the gas-water co-layer and the gas-water layer. A water saturation value higher than this indicates a gas-water layer. In the gas-liquid column height-water saturation diagram for different porosities, find the gas-liquid column height that meets the upper limit of the water saturation calibration. A thickness exceeding this value proves the presence of a gas layer. Similarly, find the gas-liquid column height that meets the lower limit of calibration. A thickness below this value proves that only a water layer exists, without a gas-water co-layer or a gas layer. The difference between the gas-liquid column heights corresponding to the upper and lower limits of calibration is the theoretical thickness of the gas-water co-layer. (See...) Figure 2 , 3 Plot the capillary pressure and relative permeability curves of the Yinggehai Formation fine sandstone in the LD30 block, and convert the capillary pressure into gas-liquid column height, as shown below. Figure 2 , 3 It can be determined that when the thickness of a single sand body or the structural amplitude of the Yinggehai Formation is greater than 10m, the required gas column height for the gas layer can be achieved; this is the theoretical thickness.

[0028] The gas-water co-layer, a key region near the gas-water interface in gas reservoirs where gas and water coexist, is not determined by a single factor in terms of thickness. Instead, it is controlled by both reservoir pore structure and fluid pressure, and its actual thickness is generally greater than the theoretical thickness under ideal conditions. Based on statistics and mechanistic derivations from numerous actual reservoirs, a quantitative formula characterizes the main controlling factor of the actual gas-water co-layer thickness: the actual gas-water co-layer thickness is calculated as: theoretical thickness × pressure coefficient.

[0029] Theoretical thickness refers to the thickness of a reservoir with conventional physical properties, unaffected by overpressure. However, considering actual geological conditions, low-porosity reservoirs exhibit significant characteristics such as tiny pore size, narrow and highly irregular throats, poor pore connectivity, and pore heterogeneity. From a flow mechanics perspective, the small and complex pore throats directly lead to extremely high capillary pressure within the reservoir. This capillary pressure often acts in the opposite direction to the buoyancy of oil and gas migration, creating "capillary resistance" and significantly raising the threshold for gas injection—it requires overcoming not only the binding force of formation water but also the resistance of capillary pressure. Therefore, under conventional injection dynamics, gas struggles to effectively enter reservoir pores and achieve large-scale accumulation. Furthermore, the presence of formation overpressure further alters the dynamic equilibrium of gas injection. On one hand, the overpressure environment compresses the intermolecular distance of gas molecules, significantly increasing the phase density of gas within the reservoir. On the other hand, the density of formation water is relatively limited by pressure, directly reducing the density difference between gas and water. The reduction in the density difference between gas and water leads to a significant decrease in the buoyancy driving force for gas migration, making it difficult for gas to migrate upwards and fill the higher parts of the reservoir by buoyancy. This severely restricts the gas filling efficiency and accumulation scale. In summary, the actual thickness of the gas-water co-layer is often greater than the theoretical thickness.

[0030] The depth at which the gas-water co-layer and the gas-water layer meet is the same as the thickness of the gas-water co-layer, which is the boundary between the gas layer and the gas-water co-layer.

[0031] Based on the study of gas-water differentiation in the Yinggehai Formation of Well YC25-1-1, the formation water in the four sand bodies A, B, C, and D reaches its dissolution limit at depths of 3406 meters, 3461 meters, 3508 meters, and 3554 meters, respectively, where natural gas begins to precipitate and form free gas. This depth marks the boundary between the gas-water transition zone and the gas-water-bearing layer. Combining the prediction model and capillary pressure curves, the gas reservoir structure of each sand body was further determined: taking sand body A as an example, the gas-water layer is located between 3383 meters and 3406 meters; while the upper part, from 3370 meters to 3383 meters, is a pure gas layer with a thickness of 13 meters. The gas layers and gas-water interfaces of sand bodies B, C, and D were also determined accordingly.

[0032] The natural gas reservoirs rich in the gas-water transition zone are classified and evaluated, and different levels of reservoir resources are identified; including: The tiered evaluation focuses on free gas as the primary objective, with gas cap, gas-water co-layer, and gas-water-bearing layer resources belonging to three different tiers. a) Divide the sand body into a large number of grids in the horizontal direction, and calculate the burial depth and thickness of each grid using the sand body scatter data; For the work area, a natural gas exsolution prediction model for water-soluble phases is first applied, combined with the capillary pressure and relative permeability curves of sandstone, to determine the thickness range of each layer, namely, the depth of the gas layer and the gas-water co-layer interface, the thickness of the gas-water co-layer, and the depth of the interface between the gas-water co-layer and the gas-bearing water layer. Based on seismic profiles obtained from surface seismic acquisitions, the seismic reflection interface depths, i.e., the interface depths of each underground layer, are calculated. The gas layer thickness is obtained by subtracting the gas layer and gas-water co-layer interface depth from the top interface depth of the target layer (a negative value indicates the absence of a gas layer); the gas-bearing water layer thickness is obtained by subtracting the bottom interface depth of the target layer from the depth of the gas-water co-layer and the gas-bearing water layer (a negative value indicates the absence of a gas-bearing water layer). In summary, the thickness ranges of all resource layers are determined, and this thickness range data is used for calculating resource quantities.

[0033] b) Analyze the gas saturation data from well logging at the target formation; c) Statistically analyze the gas saturation distribution characteristics of gas-bearing layers, gas-water co-layers, and gas-water-bearing layers, and create frequency distribution histograms. Statistical well profile data, based on different logging interpretations for each sand body (gas layer, gas-water co-layer, gas-water-bearing layer).

[0034] The gas cap, or gas layer, refers to the resource layer located at the very top of the reservoir where natural gas accumulates in a continuous free state, forming a conventional gas reservoir. The pores in this layer are almost completely filled with natural gas, the fluid pressure is overpressured, and there is a clear gas-water interface. Below it is a gas-water transition zone or water layer, with a significant pressure gradient, where natural gas exists in a continuous gas phase (gas column).

[0035] The gas-water co-layer, or gas-water transition zone, lies between the gas and water layers. Natural gas exists in both dispersed free (microbubbles) and saturated dissolved states. The thickness of this zone is controlled by reservoir properties. The gas-water interface is clear, with significant differences in gas saturation and dissolved gas content on either side. A gas-water transition zone may appear near the interface, exhibiting a gradual change in gas saturation and dissolved gas content. A significant pressure gradient exists within this zone, allowing dispersed free gas and dissolved gas to interconvert with pressure changes. During depressurization development, dissolved gas desolvates to form free gas, which can replenish the gas reservoir reserves.

[0036] A gas-bearing water layer refers to a resource layer located below the gas-water transition zone, where natural gas mainly exists in dissolved form within the formation water. It has a low gas saturation and no significant free gas. Although the dissolved gas content is lower than that of a gas layer, it is sufficient to influence formation pressure and rock physical properties. Dissolved gas content is positively correlated with pressure and temperature and is affected by water properties such as salinity and pH. When pressure decreases, dissolved gas may desolvate, forming dispersed free gas that migrates upwards to the gas-water transition zone or the gas layer.

[0037] According to the capillary force theory, capillary pressure and saturation are in a functional relationship. The irreducible water saturation, residual gas saturation, and relative permeability at different saturations can be obtained from the relative permeability curve; from the capillary pressure curve, it can be known the height above the free water surface corresponding to different gas-water saturations. Therefore, in the case of a homogeneous reservoir, by combining the relative permeability curve with the capillary pressure curve, the distribution of gas and water in the reservoir can be determined, that is, the gas saturation at different heights of the formation, and thus the pure gas zone, pure water zone, and gas-water co-production zone in the formation can be divided. There are certain differences in the gas saturation division criteria for different work areas. Generally speaking, when Sg>35%, it is a gas layer; when 10%<Sg<35%, it is a gas-water co-layer; when Sg<10%, it is a gas-bearing water layer; Sg refers to gas saturation (Gas Saturation). The calculation of Sg uses Archie's formula;

[0038] The pure gas production zone, pure water production zone, and gas-water co-production zone are classified from the perspective of production. From the perspective of gas content, the pure gas zone refers to the gas layer, the gas-water co-production zone refers to the gas-water co-layer, and the pure water production zone refers to the gas-bearing water layer. The process of dividing the gas layer, gas-water co-layer, and water layer by capillary theory is to judge and divide according to the boundaries corresponding to the relative permeability of the water phase / gas phase being 0 mentioned above.

[0039] Taking the logging data of LD30-1-1A as an example, after calculating the gas saturation of the target layer, the distribution characteristics of the gas saturation in the gas layer, gas-water co-layer, and gas-bearing water layer are statistically analyzed, and a frequency distribution histogram is made, and it is clear that the dividing lines of gas saturation are 10% and 35%. When the gas saturation exceeds 35%, it is a gas layer; when it is between 10% and 35%, it is a gas-water co-layer; when it is less than 10%, it is a gas-bearing water layer. Based on this, the division criteria for different grades of gas content are formed, as shown in Table 1.

[0040] Table 1 Division criteria table for different grades of gas content; For the classification and evaluation of natural gas reservoir resources rich in gas-water transition zones, reservoir evaluation parameters that comprehensively consider gas content and recoverability are constructed. Combining with the resource enrichment characteristics of the gas-water transition zone, the various levels of resources are further classified using their frequency and contribution distributions; including: Regarding the classification of natural gas reservoir resources rich in gas-water transition zones, an innovative method is proposed to use the quality coefficient A to conduct a detailed evaluation of each level of reservoir resources. The calculation formula of A is as follows: ; In the formula, A is the quality coefficient; S is the area, taking 1 km 2 ; h is the sand body thickness, m; φ is the porosity of the sand body, %; Sg is the gas saturation, %; k is the absolute permeability, mD; k rCp is the relative permeability, without units; Z is the pressure coefficient, without units; Z is the burial depth, in meters. Calculate the quality coefficient for all sample points in a resource layer. Divide the quality coefficient A into several intervals with a minimum interval of 2. Plot a frequency distribution histogram based on the data distribution of each interval. In the frequency distribution histogram, the horizontal axis represents the quality coefficient A, and the vertical axis represents the percentage of frequency distribution. See [link / reference needed] Figure 4 Brown bars; calculate the sum of sample data within each interval and divide by the sum of all sample data to obtain a contribution distribution histogram for each interval. In the contribution distribution histogram, the horizontal axis represents the quality coefficient A, and the vertical axis represents the contribution percentage; see Figure 4 Green bars; trend lines from the fitted frequency distribution histogram and contribution distribution histogram, the quality coefficient A corresponding to the intersection of the two lines is used as the classification evaluation standard for each type of reservoir; Figure 4 ).

[0041] Natural gas reservoirs rich in gas-water transition zones are first classified into three levels: gas layers, gas-water co-layers, and gas-water-bearing layers. Quality coefficients are calculated for all three levels, and the quality coefficient A corresponding to the intersection of frequency distribution and contribution distribution is used as the classification evaluation standard for each level. In this example, gas layers are not classified and are collectively referred to as Class I. Gas-water co-layers are divided into two categories: those with quality coefficients higher than the classification evaluation standard for that level are classified as Class II, and those lower are classified as Class III. Gas-water-bearing layers are not classified and are collectively referred to as Class IV. The reason for not classifying gas layers and gas-water-bearing layers in this example is that the resources in the example work area are mainly gas-water co-layers, accounting for more than 50% of the total resources. Furthermore, the significant difference in shape between the fitted curves of their frequency distribution and contribution distribution indicates strong heterogeneity within the gas-water co-layer resources, necessitating further classification.

[0042] To more accurately assess reservoir characteristics, reservoir parameters were calculated for all sample points in the gas-water co-layer. Quality coefficients were calculated for all sample points in the five sets of sandstone gas-water co-layers. The quality coefficients were then divided into several intervals with a value of 2, and frequency distribution histograms were plotted for each interval. The sum of the sample point data within each interval was calculated and divided by the sum of all sample point data to obtain a contribution distribution histogram for each interval. Trend lines were fitted to the two histograms, and the intersection of the two lines was used as the classification and evaluation criteria for each type of reservoir. Figure 4 Table 2. The number of sample points to the left of the intersection point is larger, but their contribution to resource quantity is smaller; the number of sample points to the right of the intersection point is smaller, but their contribution to resource quantity is larger. Based on this analytical method, the boundary point between the gas and water layers is determined to be 7. Therefore, the gas layer is classified as a Class I reservoir; when A>7 in the gas-water layer, it can be classified as a Class II reservoir; when A≤7 in the gas-water layer, it can be classified as a Class III reservoir.

[0043] Table 2. Key Parameter Determination Scheme for 3 Levels and 4 Categories of Pressure Dissolved Gas Separation; Key parameters were obtained, including porosity, gas saturation, maximum dissolved gas content, pressure coefficient, actual dissolved gas content, relative permeability, temperature, and absolute permeability; including: Porosity was predicted by jointly employing DNN and XG Boost machine learning methods. Deep Neural Networks (DNNs) are the foundational models of deep learning. Their core lies in automatically learning complex feature representations from data through multiple layers of nonlinear transformations. A typical DNN consists of three layers connected sequentially: ① The input layer receives raw data (such as image pixels or text vectors). ② The hidden layers are the core of the network, responsible for processing and abstracting the input data layer by layer. Layers are typically fully connected, meaning each neuron in layer i is connected to every neuron in layer i+1. ③ The output layer produces the final prediction result based on the specific task (such as classification or regression). XG Boost is an efficient machine learning algorithm based on Gradient Boosting Decision Trees (GBDT), proposed by Dr. Tianqi Chen in 2016. It combines multiple weak learners (usually decision trees) into a strong learner through ensemble learning. XG Boost uses an additive training method to progressively optimize the model. In each iteration, a new decision tree is trained to fit the residual (i.e., the negative gradient direction) of the previous tree. Finally, the predictions from all trees are weighted and summed as the final output.

[0044] Porosity was calculated using a joint DNN and XGBoost machine learning approach, with the weighted sum of predictions from all XGBoost decision trees serving as the final output. During training for the LD30 block, the training loss curve at each fold exhibited a monotonically decreasing trend, eventually converging to a stable value. The validation loss curve initially decreased rapidly, then fluctuated and converged in the middle stages, ultimately differing from the training loss by less than 15%, indicating that the model had a good fit and strong generalization ability. The mean absolute error measured on the validation set was 1.13%, and the relative error was 6.7%. The predicted depth-porosity relationship was compared with the measured results, and a fitting curve was derived.

[0045] Gas saturation was calculated using Archie's formula. Archie's formula is a cornerstone theory in petroleum exploration and well logging interpretation, first proposed in 1942 by Archie, a well logging engineer at Shell Oilfield Services. It establishes empirical relationships between formation resistivity and key parameters such as porosity and water saturation, laying the foundation for quantitative evaluation of reservoir saturation. The electrical parameters of the rock samples were statistically analyzed (Table 3). The formation resistivity, formation water resistivity, and predicted porosity obtained from well logging were then used to obtain the gas saturation evaluation results (Table 4). Water saturation... The calculation formula is as follows:

[0046] ; In the formula, a is the lithology coefficient, which has no unit; m is the cementation index, which has no unit; b is the lithology constant, which in most cases is very close to 1 and has no unit; n is the saturation index, which is usually close to 2 and has no unit. Rw is the porosity of the formation rock, %; Rt is the formation water resistivity, Ω·m; Sw is the formation resistivity, which can be directly obtained from resistivity logging data, Ω·m; and Sw is the formation water saturation, % Table 3. Statistical table of electrical parameters of rock samples; Table 4. Partial evaluation results of gas saturation; Finally, the gas saturation data of the four sand bodies in the Ying 2 section of the LD30 block were statistically analyzed, and the relationship between gas saturation and depth was summarized.

[0047] The 'a' value reflects the tortuosity of the conductive path and is related to rock type and pore structure; the 'm' value reflects the tortuosity and cementation of the pore channels; 'b' and 'n' are mainly controlled by rock wettability and fluid distribution. 'a', 'b', 'm', and 'n' are all derived from rock physics experimental data reports. 'Rw' is calculated by determining the total salinity of the formation water sample and then converting it to resistivity under formation conditions based on an empirical formula. Porosity and formation resistivity data are based on known well logging data. Substituting these data into Archie's formula yields the formation water saturation. Gas saturation is then calculated based on water saturation. Strictly speaking, rock pores are filled with oil, gas, and water, therefore the sum of oil saturation, gas saturation, and water saturation is 100%. Considering the actual conditions of the work area, the resource in this example work area is natural gas, therefore oil content is not considered, and the sum of gas saturation and water saturation is 100%.

[0048] The maximum dissolved gas volume is a function of formation temperature, pressure, and formation water salinity. This includes: based on experimental results, using a multiple regression equation to fit the regression equation between water dissolved gas solubility and temperature, pressure, and salinity, thus obtaining the maximum dissolved gas volume under various conditions; the experimental principle involves slowly reducing the volume of saturated gas solution under high temperature and pressure to room temperature and atmospheric pressure, measuring the released gas and water volumes, and using the measured gas-to-water volume ratio as the gas solubility; by measuring the gas and water volume released from the solution at the current temperature and pressure to atmospheric pressure, then changing the temperature or pressure until the temperature, pressure, and gas and water re-equilibrium to form a saturated solution, and then measuring the gas and water volume released from the solution to atmospheric pressure again, calculating the difference in the two gas-to-water ratios as the dissolution amount at that temperature or pressure change. The experimental temperature range was 40–200℃, and the pressure range was 20–90 MPa. The maximum volume was 800 ml; the minimum gas volume was 0.01 ml; the minimum liquid mass was 0.01 g; the formation water data used in the experiment are shown in Table 5.

[0049] Table 5 Formation water sample data Experimental results are as follows Figure 5 Below 50 MPa, the solubility of methane increases with temperature with little change; at 50 MPa and above, the solubility of methane first decreases and then increases with increasing temperature, reaching its lowest solubility at 60℃-80℃, and the higher the pressure, the lower the temperature at which the lowest solubility occurs. When the pressure is less than 50 MPa, pressure is the dominant factor controlling the solubility of CH4 gas; when the pressure is greater than or equal to 50 MPa, the effect of temperature on the solubility of CH4 gas increases. Based on the above results, the solubility of methane under different temperature and pressure conditions was fitted using SPSS software, and the fitting equation is:

[0050] (2) In the formula R 2 =0.996, S: solubility of natural gas in water, m 3 (gas) / m 3 (Water); T: Temperature, °C; P: Pressure, MPa; M: Formation water salinity, mg / L; R: Correlation coefficient.

[0051] The actual maximum dissolved gas volume can be calculated based on the measured temperature, pressure, and salinity data, combined with the above formula.

[0052] The Eaton method is used to obtain the pressure coefficient. The Eaton method is a semi-empirical, semi-quantitative pressure prediction method. Its principle is that in clastic rock formations, the compaction curve within the overpressure zone deviates from the normal trend due to the influence of overpressure; the magnitude of this deviation is positively correlated with the strength of the overpressure. By introducing the Eaton coefficient *c*, an empirical relationship can be established between the ratio of the parameter value under normal compaction trend and the measured value at the same depth point and the formation pressure, thereby predicting formation fluid pressure. Pressure coefficient The formula for obtaining is:

[0053] ; In the formula, point B refers to a certain depth point in the abnormal segment where the overpressure phenomenon occurs; , , The values ​​are the pore fluid pressure, overlying rock pressure, and hydrostatic pressure at point B, respectively, in MPa. , The measured time difference at point B and the normal sound wave time difference are respectively, in μs / m; c is the Eaton index; , Density of overlying rock strata and formation water, in g / cm³ 3 g is the acceleration due to gravity, taken as 9.8 m / s². 2 H B The burial depth of point B is in meters (m). The Eaton exponent c is expressed as follows: ; By combining three core logging curves—GR (natural gamma), neutron, and resistivity—and analyzing their intrinsic correlation with dissolved gas volume, a machine learning method (random forest) is introduced to achieve efficient and accurate prediction of the actual dissolved gas volume. The core idea of ​​Random Forest is to construct multiple decision trees and synthesize their predictions to achieve more accurate and stable predictions than a single model. Random Forest introduces "double randomness" into traditional decision tree Bagging: random sampling of training data (Bootstrap) and random feature selection. The training data for each tree is not the entire dataset, but a subset randomly sampled with replacement from the original training set. Each decision tree's training data is different, and may contain duplicate samples. Additionally, some data serves as validation data (out-of-bag data) for model verification. When selecting a splitting feature at each node of each tree, instead of choosing the best feature from all features, a subset of features is first randomly selected, and then the optimal feature from this subset is chosen for splitting. This enhances the diversity between trees and reduces the risk of model overfitting. Secondly, the importance ranking of logging curves such as GR, neutron, and resistivity is achieved through the following mechanisms: ① When constructing each decision tree, the square root of the total number of features is randomly selected as the candidate parameter for the splitting node to avoid a single feature dominating the model; ② The importance of features is quantified through out-of-bag (OOB) error—if the model's OOB error significantly increases after a feature is removed, it indicates that the feature contributes highly to the prediction results, and vice versa. Through this mechanism, the core features most strongly correlated with dissolved gas volume can be screened from the original logging data, while irrelevant features are eliminated.

[0054] Relative permeability refers to the ratio of the effective permeability of a certain phase fluid to the absolute permeability of the rock. Its influencing factors include rock pore structure, fluid saturation, reservoir wettability, and fluid properties. The volume proportion of each fluid phase within the pores is the most critical factor; therefore, a relative permeability function relating to gas saturation can be established. Absolute permeability is an inherent property of the rock and does not directly participate in the functional relationship of relative permeability, but it affects the actual seepage capacity through effective permeability. The relationship between relative and absolute permeability is expressed as:

[0055] ; Among them, when the gas saturation = At the critical gas saturation level (the lowest saturation level at which gas begins to flow), the relative permeability =0; when gas saturation =1- ( When the water saturation level is 100%, =1, at this point, the pores are almost entirely filled with gas, approaching single-phase flow; in < <1- Within the range, Follow The increase is monotonically increasing; There is usually a direct positive correlation between formation temperature and formation depth; that is, formation temperature increases with depth. This relationship is mainly driven by the Earth's internal heat source (geothermal energy). Therefore, the relationship between formation depth and formation temperature can be obtained by studying measured sample data from the work area.

[0056] Similarly, the absolute permeability is calculated by fitting a scatter plot of the porosity and permeability of the measured sample.

[0057] The pressure coefficient was obtained using the Eaton method. The Eaton method first establishes a normal compaction trend line for the sonic transit time of mudstone. The selection principles for the normal compaction trend line data are as follows: First, relatively pure mudstone sections are screened based on natural gamma logging (GR) and shale content logging (SH) data. For wells with shale content logging data, mudstone sections with a shale content greater than 90% are selected. If SH data is unavailable, mudstone sections with a GR > 80% are selected, or the shale content is calculated using the SP and GR curves. Mudstone sections with a thickness of 2m or less are removed, as thinner mudstone sections are more affected by the surrounding rock. Mudstone sections with an enlargement rate greater than or equal to 15% are also removed, as the sonic transit time of enlarged sections differs from the true value. The average sonic transit time of the selected mudstone sections is calculated, excluding peak values ​​and "cycle jumps," and the depth is taken as the depth at the middle position of the mudstone section. Taking well YC19-1-1 as an example, the mudstone in the normally compacted section of the well was first selected, and a curve showing the relationship between sonic transit time and depth was established to determine the normal compaction trend line. Then, the Eaton index c was calculated based on the pressure value at the measured depth point, the sonic transit time, and the sonic transit time of the trend line. Table 6 shows that the Eaton index is 1.02.

[0058] Table 6. Calculation of Eaton Index at YC19-1-1 Measured Points; Finally, by substituting the Eaton exponent c into the Eaton formula to obtain the formation pressure, the relationship between the pressure coefficient and depth in the LD30 block can be derived. As the burial depth increases, the pressure coefficient gradually increases. At shallower depths, the pressure coefficient increases slowly; after reaching a certain depth, the pressure coefficient increases rapidly; and as the burial depth continues to increase, the rate of increase in the pressure coefficient decreases.

[0059] Actual Dissolved Gas Volume. This invention calculates the actual dissolved gas volume by combining three core logging curves: GR (natural gamma), neutron, and resistivity. By analyzing their intrinsic correlation with dissolved gas volume, a machine learning method (random forest) is introduced to achieve efficient and accurate prediction of the actual dissolved gas volume. As underground probes reflecting the physicochemical properties of the formation, logging curves have a close intrinsic relationship with the occurrence state and content of dissolved gas in the reservoir. In this invention, three logging curve parameters—natural gamma, neutron, and resistivity—are used for modeling. Neutron and resistivity, with their sensitivity to formation pore fluid properties and strong correlation with dissolved gas volume, are core parameters for constructing the dissolved gas volume prediction model. The natural gamma curve, by identifying formation lithology, is a key parameter for auxiliary modeling.

[0060] The Random Forest algorithm, based on ensemble learning, reduces the randomness and overfitting risk of individual decision trees by constructing multiple decision trees and integrating voting results. It demonstrates strong robustness in handling issues such as local outliers and uneven data distribution in parameter samples. The optimized water-soluble phase natural gas dissolution prediction model was applied to the LD30 block, achieving accurate predictions from quantitative evaluation of actual dissolved gas volume in formation water to the vertical distribution of gas-bearing water layers and gas-water co-layers.

[0061] Relative permeability. Relative permeability refers to the ratio of the effective permeability of a certain phase fluid to the absolute permeability of the rock. Influencing factors include rock pore structure, fluid saturation, reservoir wettability, and fluid properties. The volume ratio of each fluid phase within the pores is the most critical factor; therefore, a relative permeability function relating to gas saturation can be established by statistically analyzing measured data.

[0062] Temperature and absolute permeability. There is usually a direct positive correlation between formation temperature and formation depth; that is, formation temperature increases with depth. This relationship is mainly driven by geothermal heat sources within the Earth. Therefore, the relationship between formation depth and formation temperature can be obtained by studying measured sample data from the work area. Similarly, absolute permeability is calculated by fitting a scatter plot of the porosity and permeability of measured samples.

[0063] Calculate the quantity of resources at all levels and in all categories, including: d) Divide the sand body into a large number of grids in the horizontal direction, and calculate the burial depth and thickness of each grid using the sand body scatter data; e) Based on the relationship between the parameters such as porosity, gas saturation, maximum dissolved gas volume, pressure coefficient, actual dissolved gas volume, relative permeability, temperature, and absolute permeability and depth, a scatter plot is drawn and its trend line is fitted to establish the relationship curve between depth and the above parameters; where the horizontal axis of the scatter plot is the parameter value and the vertical axis is the depth value; this setting can conveniently and quickly show the changing pattern of depth and each parameter, which is in line with visual intuition.

[0064] f) Calculate the porosity, gas saturation, maximum dissolved gas volume, and actual dissolved gas volume parameters for each grid based on the relationship curve, and then obtain the grid resource quantity; g) Resource quantity is calculated using the following formula: ; In the formula, V 气顶 V 气水同层 V 含气水层 These represent the resource quantities of the gas cap, gas-water co-layer, and gas-water-bearing layer, respectively, in m. 3 S is the grid area, in meters. 2 h is the sand body thickness, in meters; φ is the sand body porosity, percentage; Sg is the gas saturation, percentage; Bg is the natural gas volume factor, the ratio of a unit mass of natural gas under surface conditions to that under geological conditions; S 最大 and S 实际 These represent the maximum dissolved gas volume and the actual dissolved gas volume of formation water, respectively, in m. 3 (gas) / m 3 (water); h) The total resource quantity of the entire sand body is obtained by summing the resource quantity of each individual cell.

[0065] Resource Calculation. Overall, gas-bearing reservoirs account for the highest proportion of total resources, followed by gas-bearing water-bearing reservoirs. The gas-water transition zone has the smallest calculated resource quantity due to its smaller area and thickness compared to gas-bearing and gas-bearing water-bearing reservoirs, as shown in Table 7.

[0066] Table 7. Resource Quantities of Each Sand Body Set (×10) 8 m 3 )surface;

Claims

1. A comprehensive evaluation method for natural gas reservoirs rich in gas-water transition zones, characterized in that, include: Step S1: Identify the resource occurrence pattern of natural gas reservoirs rich in gas-water transition zones; Step S2: Determine the interfaces between the gas layer, the gas-water co-layer, and the gas-water layer; Step S3: Classify and evaluate the natural gas reservoir resources rich in the gas-water transition zone; use capillary force theory to determine the distribution of gas and water in the reservoir, thereby dividing the pure gas producing area, pure water area and gas-water co-producing area in the formation, and then classify the reservoir resources of different grades based on gas content. Step S4: Classify and evaluate the natural gas reservoir resources rich in the gas-water transition zone, construct reservoir evaluation parameters that comprehensively consider gas content and recoverability, and further classify resources at each level by utilizing their frequency and contribution distribution, based on the resource enrichment characteristics of the gas-water transition zone. Step S5: Obtain key parameters, including porosity, gas saturation, maximum dissolved gas volume, pressure coefficient, actual dissolved gas volume, relative permeability, temperature, and absolute permeability; Step S6: Calculate the amount of resources at each level and for each type.

2. The comprehensive evaluation method for natural gas reservoirs rich in gas-water transition zones according to claim 1, characterized in that, Identify the resource occurrence patterns of natural gas reservoirs rich in gas-water transition zones, including: There are two typical existence modes of the gas-water transition zone underground: complete differentiation type and transitional differentiation type. For complete differentiation, calculate the free gas at the gas cap, the free and dissolved gas in the gas-water co-layer, and the dissolved gas in the gas-water layer.

3. The comprehensive evaluation method for natural gas reservoir resources rich in gas-water transition zones according to claim 1, characterized in that, Determine the interfaces between gas layers, gas-water co-layers, and gas-water-bearing layers, including: The maximum dissolved gas volume of the formation sand body is positively correlated with its depth. The actual dissolved gas volume of gas-bearing, gas-water co-layers, and gas-water-bearing sand layers can be calculated. When the actual dissolved gas volume of a certain layer is less than its maximum dissolved gas volume, it indicates that the natural gas has not reached saturation and is classified as a gas-water-bearing layer. When the actual dissolved gas volume of a certain layer is greater than its maximum dissolved gas volume, it indicates that the natural gas has reached saturation and desolvation has occurred, classifying it as a gas-water co-layer or a gas-bearing layer. When the actual dissolved gas volume of a certain layer is equal to its maximum dissolved gas volume, it indicates that the natural gas has reached saturation and desolvation has just begun. The depth of this layer is the interface depth between the gas-water co-layer and the gas-water-bearing layer. If the actual dissolved gas volume of the formation sand body is less than the maximum dissolved gas volume at the corresponding depth of the sand body itself, the actual dissolved gas volume data of the gas-bearing water layer is raised to a certain depth in the shallow part so that the actual dissolved gas volume is equal to the maximum dissolved gas volume in the shallow part. The depth after the elevation is the interface depth between the gas-water layer and the gas-bearing water layer. Similarly, if the actual dissolved gas volume of the formation sand body is greater than the maximum dissolved gas volume at the corresponding depth of the sand body, the gas-water co-layer is sunk to a certain depth so that the actual dissolved gas volume is equal to the maximum dissolved gas volume at the shallow depth. The depth after the sinking is the interface depth between the gas-water co-layer and the gas-water layer. According to the capillary pressure curve and relative permeability curve of the measured reservoir, convert the capillary pressure into the height of the gas-liquid column, obtain the relationship between the height of the gas-liquid column and the water saturation under different phases and different porosities, and thus infer the theoretical lower limit of the thickness of the gas-water transition zone in a certain reservoir; take the water saturation value corresponding to the gas phase as the calibration lower limit. At this time, the relative permeability of the gas phase is 0, corresponding to the boundary between the gas-water layer and the gas-bearing water layer. Above this water saturation is the gas-bearing water layer; find the height of the gas-liquid column that meets the calibration upper limit of the water saturation in the gas-liquid column height-water saturation diagram with different porosities. If the thickness exceeds this value, it proves the occurrence of a gas layer; find the height of the gas-liquid column that meets the calibration lower limit. If the thickness is lower than this value, it proves that there is only a water layer, no gas-water layer and gas layer; the difference in the height of the gas-liquid column corresponding to the calibration upper and lower limits is the theoretical thickness of the gas-water layer. Based on the statistics and mechanism derivation of a large number of actual reservoirs, the main controlling factors of the actual thickness of the gas-water layer are characterized by a quantitative formula, that is, the actual thickness of the gas-water layer is: theoretical thickness × pressure coefficient. The depth of the boundary between the gas-water layer and the gas-bearing water layer, which raises the thickness of the gas-water layer upward, is the boundary between the gas layer and the gas-water layer.

4. The comprehensive evaluation method for natural gas reservoirs rich in gas-water transition zones according to claim 1, characterized in that, Conduct a hierarchical evaluation of the natural gas reservoir resources rich in the gas-water transition zone, and classify the reservoir resources at different levels; including: The hierarchical evaluation takes free gas as the main target. The resources of the gas cap, gas-water layer, and gas-bearing water layer belong to three levels. a) Horizontally divide the sand body into a large number of grids, and calculate the burial depth and thickness corresponding to each grid through the scattered point data of the sand body. b) Statistically analyze the logging gas saturation data of the target horizon. c) Statistically analyze the distribution characteristics of the gas saturation in the gas layer, gas-water layer, and gas-bearing water layer respectively, and make a frequency distribution histogram. When Sg>35%, it is a gas layer; when 10%<Sg<35%, it is a gas-water layer; when Sg<10%, it is a gas-bearing water layer; Sg refers to the gas saturation.

5. The comprehensive evaluation method for natural gas reservoir resources rich in gas-water transition zones according to claim 1, characterized in that, Conduct a classification evaluation of the natural gas reservoir resources rich in the gas-water transition zone, construct reservoir evaluation parameters that comprehensively consider gas content and recoverability, and combine the resource enrichment characteristics of the gas-water transition zone. Further classify the resources at each level by using their frequency and contribution distribution; including: For the classification of the natural gas reservoir resources rich in the gas-water transition zone, use the quality coefficient A to conduct a detailed evaluation of each level of reservoir resources. The calculation formula of A is as follows: ； In the formula, A is the quality coefficient; S is the area, taken as 1 km². 2 h represents the thickness of the sand body, in meters. φ Sg is the porosity of the sand body, %; Sg is the gas saturation, %; k is the absolute permeability, mD; k r Cp is the relative permeability, without units; Z is the pressure coefficient, without units; Z is the burial depth, in meters. Divide the quality coefficient A into several intervals at an interval of 2, statistically analyze the data distribution in each interval and make a frequency distribution histogram. In the frequency distribution histogram, the horizontal axis is the quality coefficient A, and the vertical axis is the percentage of frequency distribution; calculate the sum of the sample point data within the interval and divide it by the sum of all sample point data to obtain the contribution degree distribution histogram. In the contribution degree distribution histogram, the horizontal axis is the quality coefficient A, and the vertical axis is the percentage of contribution degree; fit the trend lines of the frequency distribution histogram and the contribution degree distribution histogram. The quality coefficient A corresponding to the intersection point formed by the two lines is used as the classification evaluation standard for each type of reservoir. Natural gas reservoirs rich in gas-water transition zones are first divided into three levels: gas layer, gas-water co-layer, and gas-water layer. Quality coefficients are calculated for each of these three levels of resources, and the quality coefficient A corresponding to the intersection point is obtained according to the frequency distribution and contribution distribution as the classification and evaluation standard for each level of resources.

6. The comprehensive evaluation method for natural gas reservoir resources rich in gas-water transition zones according to claim 1, characterized in that, Key parameters were obtained, including porosity, gas saturation, maximum dissolved gas content, pressure coefficient, actual dissolved gas content, relative permeability, temperature, and absolute permeability; including: Porosity was predicted by jointly employing DNN and XG Boost machine learning methods. Gas saturation and water saturation were calculated using Archie's formula. The calculation formula is as follows: ； In the formula, a is the lithology coefficient (unitless); m is the cementation index (unitless); b is the lithology constant (unitless); and n is the saturation index (unitless). Rw is the porosity of the formation rock, %; Rt is the formation water resistivity, Ω·m; Sw is the formation water saturation, % Then calculate the gas saturation based on the water saturation; The maximum dissolved gas volume is a function of formation temperature, pressure, and formation water salinity; it includes: based on experimental results, using a multiple regression equation, fitting the regression equation between water dissolved gas solubility and temperature, pressure, and salinity, that is, obtaining the maximum dissolved gas volume under various conditions; The pressure coefficient is obtained using the Eaton method; pressure coefficient The formula for obtaining is: ； In the formula, point B refers to a certain depth point in the abnormal segment where the overpressure phenomenon occurs; , , The values ​​are the pore fluid pressure, overlying rock pressure, and hydrostatic pressure at point B, respectively, in MPa. , The measured time difference at point B and the normal sound wave time difference are respectively, in μs / m; c is the Eaton index. , Density of overlying rock strata and formation water, in g / cm³ 3 g is the acceleration due to gravity, taken as 9.8 m / s². 2 H B The burial depth of point B is in meters (m). The Eaton exponent c is expressed as follows: ； By combining the three core logging curves of GR, neutron, and resistivity, and analyzing their intrinsic correlation with dissolved gas volume, machine learning methods are introduced to predict the actual dissolved gas volume. Relative permeability refers to the ratio of the effective permeability of a certain phase fluid to the absolute permeability of a rock; the relationship between relative permeability and absolute permeability is expressed as: ； Among them, when the gas saturation = At that time, relative penetration rate =0; when gas saturation =1- hour, =1, at this point, the pores are almost entirely filled with gas, approaching single-phase flow; in < <1- Within the range, Follow The increase is monotonically increasing; Similarly, the absolute permeability is calculated by fitting a scatter plot of the porosity and permeability of the measured sample.

7. A comprehensive evaluation method for natural gas reservoir resources rich in gas-water transition zones according to any one of claims 1-6, characterized in that, Calculate the quantity of resources at all levels and in all categories, including: d) Divide the sand body into a large number of grids in the horizontal direction, and calculate the burial depth and thickness of each grid using the sand body scatter data; e) Based on the relationship between parameters such as porosity, gas saturation, maximum dissolved gas volume, pressure coefficient, actual dissolved gas volume, relative permeability, temperature, and absolute permeability and depth, a scatter plot is drawn and its trend line is fitted to establish the relationship curve between depth and the above parameters; where the horizontal axis of the scatter plot is the parameter value and the vertical axis is the depth value. f) Calculate the porosity, gas saturation, maximum dissolved gas volume, and actual dissolved gas volume parameters for each grid based on the relationship curve, and then obtain the grid resource quantity; g) Resource quantity is calculated using the following formula: ； In the formula, V 气顶 V 气水同层 V 含气水层 These represent the resource quantities of the gas cap, gas-water co-layer, and gas-water-bearing layer, respectively, in m. 3 S is the grid area, in meters. 2 h is the sand body thickness, in meters; φ is the sand body porosity, percentage; Sg is the gas saturation, percentage; Bg is the natural gas volume factor, the ratio of a unit mass of natural gas under surface conditions to that under geological conditions; S 最大 and S 实际 These represent the maximum dissolved gas volume and the actual dissolved gas volume of formation water, respectively, in m. 3 / m 3 ; h) The total resource quantity of the entire sand body is obtained by summing the resource quantity of each individual cell.

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