Method for synchronously inverting parameters of five reservoirs of submarine gas reservoir based on longitudinal wave velocity and density

Abstract

The invention relates to the technical field of submarine gas reservoir exploration, and discloses a method for synchronously inverting five reservoir parameters of a submarine gas reservoir based on longitudinal wave velocity and density, which comprises the following steps: acquiring actually measured longitudinal wave velocity and density data of the submarine gas reservoir, a reservoir distribution range and reservoir parameters, and constructing a longitudinal wave velocity model and a volume density model of gas reservoir rocks; reservoir parameter constraint boundary data are obtained, five reservoir parameters are set as unknown numbers needing inversion, the correlation model and measured data construct a multi-dimensional nonlinear optimization problem of the minimum sum of squares, and a value range is set; iteratively solving by adopting an interior point method, and outputting a five-reservoir parameter inversion value and an optimal consolidation coefficient; and obtaining seismic data, carrying out inversion to obtain a two-dimensional / three-dimensional longitudinal wave velocity and a density profile, calling the optimal consolidation coefficient to carry out repeated inversion, and outputting a two-dimensional / three-dimensional reservoir parameter distribution profile for gas reservoir reserve assessment and development optimization. The method can meet the actual requirements of deepwater natural gas exploration and development.

Description

Technical Field

[0001] This application relates to the technical field of subsea gas reservoir exploration, and in particular to a method for synchronously inverting five reservoir parameters of a subsea gas reservoir based on P-wave velocity and density. Background Technology

[0002] Natural gas, as a low-pollution, high-calorific-value energy source, has attracted much attention. Subsea gas reservoirs possess enormous reserves, and free gas saturation and porosity are key parameters for assessing their reserves and development potential. Currently, exploration mainly relies on well logging and seismic exploration technologies: well logging offers high accuracy but is costly and has limited coverage; seismic exploration has a wide range and low cost but struggles to accurately quantify reservoir parameters. Therefore, there is an urgent need for methods to construct quantitative inversion of key parameters based on P-wave velocity and density data.

[0003] Existing prediction methods based on seismic rock physics have significant drawbacks: they rely on empirical coefficients with no clear physical meaning, require prior acquisition of formation mineral composition or porosity data that are difficult to fully grasp, or rely on complex elastic impedance profile data, which limits their practicality; in addition, reservoir parameters generally have strong heterogeneity, making it difficult to synchronously invert key reservoir parameters with high accuracy and efficiency, and thus failing to meet the actual needs of deepwater natural gas exploration and development.

[0004] As can be seen from the above, how to meet the actual needs of deep-sea natural gas exploration and development remains to be solved. Summary of the Invention

[0005] To meet the practical needs of deepwater natural gas exploration and development, this application provides a method for synchronously inverting five reservoir parameters of subsea gas reservoirs based on P-wave velocity and density.

[0006] Firstly, this application provides a method for simultaneously inverting five reservoir parameters of a subsea gas reservoir based on P-wave velocity and density, employing the following technical solution: A method for simultaneously inverting five reservoir parameters of a subsea gas reservoir based on P-wave velocity and density includes: The measured P-wave velocity, measured density data, and reservoir distribution range of the subsea gas reservoir were obtained by logging equipment. The actual free gas saturation, porosity, and mineral composition ratio data of the rock skeleton were measured to verify the inversion accuracy. The simplified two-phase Biot equation, the mixed fluid weighted average equation, and the bulk density equation were combined to construct the P-wave velocity model and the bulk density model of the gas reservoir rock. The reservoir parameter constraint boundary data of the subsea gas reservoir is obtained. The reservoir parameter constraint boundary data includes the upper limit of free gas saturation, the value range of porosity, and the value range of mineral component proportion. In the P-wave velocity model and the bulk density model, free gas saturation, porosity, clay content, carbonate content, and gas-water mixing coordination factor are set as unknowns to be inverted. The P-wave velocity model is correlated with the measured P-wave velocity, and the bulk density model is correlated with the measured density data. A multidimensional nonlinear correspondence optimization problem is constructed with the objective of minimizing the sum of squares of the model calculated value and the measured value. The value range of each inversion unknown is set in combination with the reservoir parameter constraint boundary data. After constructing the optimization problem, reservoir parameters and initial iteration values ​​from well logging data are simultaneously acquired. The initial iteration values ​​are randomly selected within the range corresponding to the reservoir parameter constraint boundary data. The optimization problem is iteratively solved using the interior point method. During the solution process, the consolidation coefficients of the reservoir distribution points are adjusted. The inversion accuracy is judged by calculating the error between the inversion results and the measured values ​​of the reservoir parameters until the error is minimized. The optimal inversion values ​​and optimal consolidation coefficients of the five reservoir parameters are output. The inversion values ​​of the five reservoir parameters are used to verify the inversion accuracy, and the optimal consolidation coefficients are used for two-dimensional / three-dimensional reservoir parameter prediction. In the process of predicting 2D / 3D reservoir parameters, seismic data of the subsea gas reservoir collected by seismic equipment is acquired. The seismic data of the subsea gas reservoir is inverted to obtain 2D or 3D P-wave velocity profiles and 2D or 3D density profiles. The optimal consolidation coefficient is called, and the 2D or 3D P-wave velocity profiles, 2D density profiles or 3D density profiles are substituted into the optimization problem constructed by the P-wave velocity model and the bulk density model. The inversion process is repeated to invert the five reservoir parameters at each depth point in each seismic CDP (seismic common depth point) trace. The 2D or 3D reservoir parameter distribution profile of the subsea gas reservoir is output. The 2D or 3D reservoir parameter distribution profile is used for gas reservoir reserve assessment and development optimization.

[0007] Optionally, the method further includes: the mineral component ratio data including the bulk modulus of clay. shear modulus bulk modulus of quartz shear modulus and the bulk modulus of carbonates shear modulus .

[0008] Optionally, the method further includes: the simplified two-phase Biot equation is expressed as follows: ,in, The bulk modulus of the rock. The bulk modulus of the rock's framework and mineral matrix. Shear modulus of the rock framework mineral matrix. The Biot coefficient is used to approximate the bulk modulus. Equivalent bulk modulus Porosity The bulk modulus of the gas-water mixture. The overall shear modulus of the rock. The shear modulus of the rock's framework mineral matrix. This is the approximate Biot coefficient for the shear modulus.

[0009] Optionally, the method further includes: the bulk modulus of the rock skeleton mineral matrix. With shear modulus The Hill formula, calculated using Hill's formula, is expressed as follows: ; ;in, , These represent the volume fractions of clay and carbonate in the rock skeleton.

[0010] Optionally, the method further includes: the bulk modulus approximating Biot coefficient. Biot coefficient approximating shear modulus The expression is: ; The consolidation coefficient at the target point, Consolidation coefficient at points within the reservoir distribution range, The reservoir distribution range, For the depth of the target point, Porosity.

[0011] Optionally, the method further includes: expressing the bulk modulus of the mixed fluid as follows based on the weighted average equation of the mixed fluid: ,in, The bulk modulus of the gas-water mixture. Free gas saturation The bulk modulus of liquid water, The bulk modulus of free gas. .

[0012] Optionally, the method further includes: the expression for the density formula is as follows: ; The reservoir volume density, This represents the volume fraction of clay. The density of clay, The density of quartz The density of carbonates, The density of liquid water, The density of the free gas.

[0013] Optionally, the method further includes: the calculation expression for the P-wave velocity in the P-wave velocity model is: , For reservoir P-wave velocity, The bulk modulus of the rock. This represents the overall shear modulus of the rock.

[0014] Optionally, the method further includes: the objective function expression of the multidimensional nonlinear optimization problem is as follows: ;in, Let be the objective function. To measure the longitudinal wave velocity, These are the calculated values ​​for the P-wave velocity model. These are measured density data. These are the calculated values ​​from the volume density model.

[0015] Optionally, the method further includes: the range of values ​​for each inversion unknown is: 0 , , , In the formula, This represents the upper limit of free gas saturation in the boundary data constrained by reservoir parameters.

[0016] Optionally, the method further includes: the consolidation coefficient at the points within the reservoir distribution range. The range of values ​​is 3≤ ≤11.

[0017] Optionally, the method further includes: the reservoir parameters include the free gas saturation, porosity, clay volume fraction, and carbonate volume fraction measured in the well logging data.

[0018] Optionally, the method further includes: minimizing the error by calculating the error between the inversion result and the reservoir parameters; when the error no longer decreases with the number of iterations and stabilizes within a preset range, it is determined that a convergence state has been reached; the error is calculated based on the deviation between the inversion result and the reservoir parameters.

[0019] Optionally, the method further includes: when iteratively solving the optimization problem using the interior point method, multiple sets of initial values ​​are randomly selected within the range of values ​​corresponding to the reservoir parameter constraint boundary data, and after solving each set, the result with the smallest objective function value is selected as the optimal solution.

[0020] Optionally, the method further includes: inversion processing of seismic data of submarine gas reservoirs, including: if it is post-stack seismic data, using post-stack seismic inversion method to obtain two-dimensional or three-dimensional P-wave velocity profiles and two-dimensional or three-dimensional density profiles; if it is pre-stack seismic data, using amplitude variation with offset inversion method to obtain two-dimensional or three-dimensional P-wave velocity profiles and two-dimensional or three-dimensional density profiles.

[0021] Optionally, during the repeated inversion process, the method further includes: repeatedly performing the following steps: constructing a P-wave velocity model and a bulk density model based on mineral property parameter data; setting free gas saturation, porosity, clay content, carbonate content, and gas-water mixing compatibility factor as unknowns to be inverted, constructing a multidimensional nonlinear optimization problem with the objective of minimizing the sum of squares of the model calculation values ​​and profile data; and using the interior point method to iteratively solve and output the five reservoir parameters at the depth points of each seismic CDP trace.

[0022] Optionally, the method further includes: the two-dimensional reservoir parameter distribution profile or the three-dimensional reservoir parameter distribution profile includes: spatial distribution information of free gas saturation, porosity, clay content, carbonate content, and gas-water mixing coordination factor in the subsea gas reservoir area, wherein the spatial distribution information corresponds one-to-one with the seismic CDP trace and depth point.

[0023] Secondly, this application provides a device for simultaneous inversion of five reservoir parameters of a subsea gas reservoir based on P-wave velocity and density, comprising: The data acquisition and physical property model construction module acquires measured P-wave velocity, measured density data, and reservoir distribution range of the subsea gas reservoir through logging equipment, and measures actual free gas saturation, porosity, and mineral composition ratio data of the rock skeleton to verify the inversion accuracy; it combines the simplified two-phase Biot equation, the mixed fluid weighted average equation, and the bulk density equation to construct the P-wave velocity model and bulk density model of the gas reservoir rock. The constraint data acquisition and optimization problem construction module acquires reservoir parameter constraint boundary data for subsea gas reservoirs. This data includes the upper limit of free gas saturation, the range of porosity values, and the range of mineral component proportions. In the P-wave velocity model and the bulk density model, free gas saturation, porosity, clay content, carbonate content, and gas-water mixing coordination factor are set as unknowns to be inverted. The P-wave velocity model is correlated with measured P-wave velocities, and the bulk density model is correlated with measured density data. A multidimensional nonlinear correspondence optimization problem is constructed with the objective of minimizing the sum of squares between the calculated and measured values. The range of values ​​for each inversion unknown is set based on the reservoir parameter constraint boundary data. The optimization solution and core parameter output module, after constructing the optimization problem, simultaneously acquires reservoir parameters and initial iteration values ​​from the well logging data. The initial iteration values ​​are randomly selected within the range corresponding to the reservoir parameter constraint boundary data. The optimization problem is iteratively solved using the interior point method. During the solution process, the consolidation coefficients of the reservoir distribution points are adjusted. The inversion accuracy is judged by calculating the error between the inversion results and the measured values ​​of the reservoir parameters until the error is minimized. The optimal inversion values ​​and optimal consolidation coefficients of the five reservoir parameters are output. The inversion values ​​of the five reservoir parameters are used to verify the inversion accuracy, and the optimal consolidation coefficients are used for two-dimensional / three-dimensional reservoir parameter prediction. The seismic data inversion and reservoir parameter distribution output module acquires seismic data of the subsea gas reservoir collected by seismic equipment during the two-dimensional / three-dimensional reservoir parameter prediction process. It then performs inversion processing on the subsea gas reservoir seismic data to obtain two-dimensional or three-dimensional P-wave velocity profiles and two-dimensional or three-dimensional density profiles. The module calls upon the optimal consolidation coefficient and substitutes the two-dimensional or three-dimensional P-wave velocity profiles, two-dimensional density profiles, or three-dimensional density profiles into the optimization problem constructed by the P-wave velocity model and the bulk density model. This inversion process is repeated to invert the five reservoir parameters at each depth point in each seismic CDP trace. Finally, it outputs a two-dimensional or three-dimensional reservoir parameter distribution profile of the subsea gas reservoir, which is used for gas reservoir reserve assessment and development optimization.

[0024] Thirdly, this application provides a system for synchronous inversion of five reservoir parameters of a subsea gas reservoir based on P-wave velocity and density, including a memory, a processor, and a program stored in the memory for synchronous inversion of five reservoir parameters of a subsea gas reservoir based on P-wave velocity and density. The program for synchronous inversion of five reservoir parameters of a subsea gas reservoir based on P-wave velocity and density is executed by the processor to implement the steps of the method described above.

[0025] Fourthly, this application provides a storage medium on which a program for synchronously inverting five reservoir parameters of a subsea gas reservoir based on P-wave velocity and density is stored. When the program for synchronously inverting five reservoir parameters of a subsea gas reservoir based on P-wave velocity and density is run by a processor, it implements the steps of the method described in any of the preceding claims.

[0026] In summary, this application includes at least one of the following beneficial technical effects: By acquiring core data such as measured P-wave velocity and density of subsea gas reservoirs, as well as the proportion of mineral components in the rock skeleton, using logging equipment, a P-wave velocity model is constructed based on the simplified two-phase Biot equation and the weighted average equation of mixed fluids. A volumetric density model is constructed using the density formula, avoiding the shortcomings of existing methods that rely on empirical coefficients. Furthermore, by combining the reservoir parameter constraint boundary data, five parameters, including free gas saturation, are set as unknowns to be inverted. A multidimensional nonlinear optimization problem is constructed by correlating the model with the measured data, solving the pain point of needing prior knowledge of mineral components or porosity. This lays the foundation for high-precision synchronous inversion and is adapted to the highly heterogeneous characteristics of reservoir parameters.

[0027] Then, the optimization problem is solved iteratively using the interior point method. By adjusting the point consolidation coefficient of the reservoir distribution range and combining it with reservoir parameters to judge error convergence, the inversion accuracy is ensured. Then, the P-wave velocity and density profiles obtained by seismic data inversion are used to call the optimal consolidation coefficient and repeat the inversion process to generate two-dimensional / three-dimensional reservoir parameter distribution profiles. This combines the high-precision advantages of well logging with the wide coverage achieved by seismic data, filling the gap in parameter prediction in areas without wells. The final output profile can be directly used for gas reservoir reserve assessment and development optimization, accurately meeting the actual needs of deepwater natural gas exploration and development. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating a method for simultaneously inverting five reservoir parameters of a subsea gas reservoir based on longitudinal wave velocity and density, according to an exemplary embodiment. Figure 2 This is a flowchart of the five-parameter synchronous inversion algorithm for a single-well subsea free gas reservoir; Figure 3 This is a schematic diagram showing the P-wave velocity and density data for Hole 1245E; Figure 4 This is a schematic diagram of the five-parameter model derived from the data of Hole 1245E; Figure 5 This is a schematic diagram of the five-parameter model inverted using data from Hole NGHP-01-14A; Figure 6 This is a schematic diagram of the five-parameter model inverted using data from Hole U1329D; Figure 7 This is a schematic diagram of the five-parameter model inverted using data from Hole 997B; Figure 8 It uses well logging data to predict the consolidation coefficient at the bottom boundary depth of the submarine free gas reservoir. The process; Figure 9 It is a process of using the "five-parameter inversion method" to invert the two-dimensional distribution profile and three-dimensional distribution structure of reservoir parameters from two-dimensional or three-dimensional seismic data; Figure 10 It is a two-dimensional distribution profile of free gas reservoir parameters retrieved from two-dimensional seismic data using the "five-parameter inversion method"; Figure 11 This is a structural block diagram of an apparatus for synchronous inversion of five reservoir parameters of a subsea gas reservoir based on longitudinal wave velocity and density, according to an exemplary embodiment. Detailed Implementation

[0029] The embodiments of this application are described in detail below, and examples of the embodiments are shown in the accompanying drawings.

[0030] In the description of this specification, the references to "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples" refer to specific features, structures, materials, or characteristics described in connection with the described embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0031] This application discloses a method for simultaneously inverting five reservoir parameters of a subsea gas reservoir based on P-wave velocity and density, referring to... Figure 1 ,include: S100 uses logging equipment to obtain measured P-wave velocity, measured density data, and reservoir distribution range of subsea gas reservoirs. It also measures actual free gas saturation, porosity, and mineral composition ratio data of the rock skeleton to verify the inversion accuracy. The simplified two-phase Biot equation, the mixed fluid weighted average equation, and the bulk density equation are combined to construct the P-wave velocity model and bulk density model of the gas reservoir rocks.

[0032] The specific execution process of S100 includes the following steps S101-S103: Step S101: Obtain the following core data using logging equipment (such as sonic logging tools, density logging tools, and gamma logging tools) to provide input for model construction: Measured longitudinal wave velocity ( ): Records the propagation velocity of seismic waves in the reservoir (unit: m / s) using an acoustic logging tool, reflecting the comprehensive elastic characteristics of the rock skeleton and fluids; measured density ( ): The bulk density of reservoir rock (unit: ) is measured using a density logging tool. ), reflecting the mass distribution of the rock skeleton and pore fluids; the depth of the gas reservoir floor ( ): Identify the gas reservoir distribution range (unit: m) through well logging curves, which are used for subsequent consolidation coefficient calculation.

[0033] Mineral composition data: For the main minerals in the rock framework (clay, quartz, carbonates), their elastic parameters and densities were obtained experimentally. For example, the bulk modulus of clay is 21.0 GPa, the shear modulus is 7.0 GPa, and the density is 2.60. Quartz has a bulk modulus of 37.0 GPa, a shear modulus of 44.0 GPa, and a density of 2.65. The carbonate has a bulk modulus of 63.7 GPa, a shear modulus of 31.7 GPa, and a density of 2.71. .

[0034] Fluid property data: For fluids (water, free gas) in pores, obtain their bulk modulus and density. For example, the bulk modulus of water is 2.36 GPa, and its density is 1.05. The bulk modulus of the free gas is 1. 10 -4 GPa, density 7.8 10 -4 .

[0035] Step 102, P-wave velocity model ( This is the core of describing the mapping relationship between "reservoir parameters → P-wave velocity", which requires a three-step derivation: calculation of mixed fluid elastic parameters → calculation of rock skeleton elastic parameters → calculation of overall elastic parameters. 1. Bulk modulus of the mixed fluid ( ) Calculate the weighted average equation of mixed fluid (WAEMF).

[0036] In this embodiment, the WAEMF equations refer to the methods proposed by Yan et al. in 2025 and Monachesi et al. in 2020. Specifically, see Yuning Yan, Hongbing Li, Gang Hao et al., Simultaneous inversion of five physical parameters of submarine gas reservoir from synthetic elastic impedance for high-efficiency reserve evaluation, Journal of petroleum exploration and production technology (2025) 15:68 (hereinafter referred to as Yanet al. 2025). See also Monachesi, Uri Wollner, and Jack Dvorkin; effective porefluid bulk modulus at patchy saturation an analytic study, journal of geophysical research: solid earth, published online 3 JAN 2020, 10.1029 / 2019JB018267 (hereinafter referred to as Monachesi et al., 2025).

[0037] When water and free gas coexist in the pores of a gas reservoir, the equivalent bulk modulus needs to be calculated using the Mixed Fluid Weighted Average (WAEMF) equation. The formula is as follows: (Equation 4) In Equation 4, Gas-water mixing coordination factor (inversion unknown, 0 ≤ ≤1 indicates the degree of air-water blockiness. =0 indicates that the air and water are evenly mixed. =1 indicates complete gas-liquid separation); The bulk modulus of the gas-water mixture. Free gas saturation (inversion unknown, 0 ≤ ≤1); The bulk modulus of liquid water, This represents the bulk modulus of the free gas.

[0038] 2. Elastic modulus of the rock matrix mineral skeleton ( , Calculate (Hill's formula).

[0039] In this embodiment, Hill's formula refers to the formula for calculating the elastic modulus proposed by Hill in 1952. For details, please refer to: R Hill, the elastic behaviour of a crystalline aggregate, 1952, Proc. Phys. Soc. A 65 349-354, DOI 10.1088 / 0370-1298 / 65 / 5 / 307 (hereinafter referred to as Hill, 1952).

[0040] The rock skeleton is composed of clay, quartz, and carbonates, and its equivalent bulk modulus needs to be calculated using Hill's formula. ) and shear modulus ( The formula is as follows: ; (Equation 2) In Equation 2, , These represent the volume fractions of clay and carbonate in the mineral matrix of the rock skeleton.

[0041] This represents the volume fraction of clay in the rock skeleton. The volume fraction of carbonates in the rock framework and the bulk modulus of clay. shear modulus bulk modulus of quartz shear modulus and the bulk modulus of carbonates shear modulus .

[0042] 3. Combining the elastic modulus of the rock skeleton with the bulk modulus of the pore fluid, the overall bulk modulus of the rock was calculated using the simplified two-phase Biot equation proposed by Myung Woong Lee in 2018 (Myung Woong Lee, predicting S-wave velocities for unconsolidated sediments at low effective pressure, USGS, published on August 2018 (hereinafter referred to as Lee, 2018)). ) and shear modulus ( The formula is as follows: , (Equation 1) Among them, the consolidation-related parameters are calculated as follows: , , (Equation 3).

[0043] The bulk modulus of the rock. The bulk modulus of the rock's framework and mineral matrix. Shear modulus of the rock framework mineral matrix. The Biot coefficient is used to approximate the bulk modulus. Equivalent bulk modulus Porosity The bulk modulus of the gas-water mixture. The overall shear modulus of the rock. The shear modulus of the rock's framework mineral matrix. The shear modulus is approximated by the Biot coefficient; The consolidation coefficient at the target point, Consolidation coefficient at points within the reservoir distribution range, The reservoir distribution range, For the depth of the target point, Porosity. This is determined by introducing a consolidation coefficient. This study quantifies the impact of reservoir depth on rock compaction, addressing the error problem in deep reservoirs caused by the neglect of depth effects in the traditional Biot equation. Specifically, in the embodiments of this application, the consolidation coefficient at points within the reservoir distribution range is... The range of values ​​is 3≤ ≤11.

[0044] 4. Based on the overall elastic modulus and bulk density of the rock, the longitudinal wave velocity is calculated using the formula. : (Equation 5-2) In Equation 5-2, For reservoir P-wave velocity, The bulk modulus of the rock. This represents the overall shear modulus of the rock.

[0045] Step S103, Volume density model ( The density of the rock skeleton and the density of the pore fluid are calculated by volume weighting, as shown in the following formula: (Equation 5-2) In Equation 5-2, The reservoir volume density, This represents the volume fraction of clay. The density of clay, The density of quartz The density of carbonates, The density of liquid water, The density of the free gas.

[0046] By directly linking density to mineral composition and pore fluids, the regional limitations of traditional empirical formulas can be avoided, thus improving the physical consistency of density models.

[0047] It should be noted that through the execution process of S100, namely by establishing a quantitative physical mapping between reservoir parameters and geophysical response, eliminating the dependence on empirical coefficients, providing a benchmark for comparison between measured data and theoretical models, and integrating multi-source data, the complex geological characteristics of subsea gas reservoirs are accurately characterized. This improves the physical reliability and geological adaptability of the inversion method, provides a rigorous theoretical basis and quantitative comparison basis for subsequent reservoir parameter inversion, and effectively ensures the accuracy and applicability of the inversion results.

[0048] S200, Obtain reservoir parameter constraint boundary data for the subsea gas reservoir. The reservoir parameter constraint boundary data includes the upper limit of free gas saturation, the value range of porosity, and the value range of mineral component proportions. In the P-wave velocity model and the bulk density model, free gas saturation, porosity, clay content, carbonate content, and gas-water mixing coordination factor are set as unknowns to be inverted. The P-wave velocity model is correlated with the measured P-wave velocity, and the bulk density model is correlated with the measured density data. A multidimensional nonlinear corresponding optimization problem is constructed with the objective of minimizing the sum of squares of the model calculated value and the measured value. The value range of each inversion unknown is set in conjunction with the reservoir parameter constraint boundary data.

[0049] The specific execution process of S200 includes the following steps: Step 201, Acquisition and content of reservoir parameter constraint boundary data: Reservoir parameter constraint boundary data are "geological rules" that limit the range of values ​​for inversion unknowns. They need to be obtained based on regional geological experience, well logging data statistics, or core experiment analysis, and specifically include: Upper limit of free gas saturation ( ): The maximum proportion of free gas in pore fluid in a gas reservoir (0≤ ≤ ≤1), determined by reservoir pressure, temperature conditions or core experiments (e.g., a deep-sea gas reservoir). =0.5, meaning the free gas saturation should not exceed 50%.

[0050] Porosity range ( , ): The range of the proportion of pore volume to total volume in reservoir rocks (0≤ 1. Common sandstone gas reservoirs The value is typically 0.35, determined by the porosity distribution calculated from statistics of similar reservoirs in the region or well logging curves.

[0051] Range of mineral component proportions: Clay content ( ): (For example, 0.4, meaning the clay volume fraction does not exceed 40%) Carbonate content ( ): (For example, 0.3, which means the volume fraction of carbonate does not exceed 30%) ; Quartz content ( Derived from the conservation of total mineral quantity: =1- - Therefore, it needs to meet the following conditions. + (Ensure the quartz content is non-negative).

[0052] Step 202, Determining the inversion unknowns, identifies the five core reservoir parameters (unknowns) that need to be solved through inversion. These parameters directly affect the P-wave velocity model constructed by S100. ) and volume density model ( Specifically: Free gas saturation ( ): The volume percentage of free gas in the pores controls the fluid elastic characteristics; Porosity ( ): The proportion of rock pore volume affects the contribution weight of the skeleton and fluid; Clay content ( The volume fraction of clay minerals in the rock skeleton affects the elasticity of the skeleton. Carbonate content ( The volume fraction of carbonate minerals in the rock framework affects the elasticity of the framework. Air-water mixing coordination factor ( Characterizes the uniformity of gas-water distribution in pores (0≤ ≤1), controlling the equivalent elastic parameters of the mixed fluid.

[0053] These five unknowns encompass the three core reservoir characteristics: fluid properties, pore structure, and framework composition. By solving these unknowns through inversion, the physical properties of the gas reservoir can be fully characterized.

[0054] Step 203: The optimization problem is to describe the "deviation between the model calculated value and the measured value" in a mathematical way, and to achieve the process of inferring reservoir parameters from the geophysical response (P-wave velocity, density) with the goal of "minimizing the deviation".

[0055] 1. Objective function (deviation quantization), calculated using the P-wave velocity model ( ) and measured values ​​( The deviation of the volume density model calculation value () ) and measured values ​​( Taking the deviation as the core, we construct the objective function of "minimizing the sum of squared errors", as shown in the following formula: (Equation 6) In Equation 6, Let be the objective function. To measure the longitudinal wave velocity, These are the calculated values ​​for the P-wave velocity model. These are measured density data. These are the calculated values ​​from the volume density model.

[0056] It should be noted here that the objective function The smaller the value, the better the model calculation results match the measured data, and the fewer the corresponding unknowns. The closer to the actual reservoir parameters; due to the P-wave velocity model in S100 ( ) and volume density model ( ) includes nonlinear operations such as multiplication and square root of unknowns (e.g. and Coupling, For the objective function (nonlinear effects), objective function It is about Since it is a multidimensional nonlinear function, this problem belongs to the category of multidimensional nonlinear constrained optimization problems.

[0057] Here, for reservoir parameter constraint boundary data, a value range (inequality constraint) is set for each unknown to ensure that the inversion results conform to geological and physical laws. The formula is as follows: The value range of each inversion unknown is: 0 , , , In the formula, This serves as the upper limit for free gas saturation in the boundary data of reservoir parameters. The constraint functions in this way: excluding physically impossible solutions (such as negative porosity or mineral content exceeding 100%); narrowing the solution space, reducing the computational load of inversion, and avoiding iteration getting trapped in meaningless local optima; and introducing prior geological knowledge (such as the maximum gas saturation of regional gas reservoirs) to improve the geological reliability of the inversion results.

[0058] S200 constructs a multidimensional nonlinear optimization problem with the objective of minimizing the sum of squares between the calculated and measured values. It clarifies five core inversion unknowns and geological constraint boundaries, realizing the transformation from the forward modeling relationship of "reservoir parameters → geophysical response" to the inversion problem of "geophysical response → reservoir parameters," providing a mathematically solvable framework for geological problems. Furthermore, it avoids physically anomalous solutions through constraints, focuses on key parameters to reduce the ambiguity of inversion, and provides a clear convergence criterion for subsequent iterations with a quantified deviation target. Ultimately, it builds a rigorous bridge connecting theoretical models and actual parameters for reservoir parameter inversion, ensuring the rationality of the inversion process and the reliability of the results.

[0059] S300: After constructing the optimization problem, reservoir parameters and initial iteration values ​​from the well logging data are simultaneously acquired. The initial iteration values ​​are randomly selected within the range corresponding to the reservoir parameter constraint boundary data. Specifically, the reservoir parameters include the measured free gas saturation, porosity, clay volume fraction, and carbonate volume fraction from the well logging data. These reservoir parameters are used for error determination. The initial iteration values ​​are randomly selected within the range corresponding to the reservoir parameter constraint boundary data. The interior point method is used to iteratively solve the optimization problem. During the solution process, the consolidation coefficients at points within the reservoir distribution range are adjusted. The results are calculated by comparing the inversion results with the reservoir parameters. The error of the measured values ​​is used to determine the inversion accuracy. The error here needs to be calculated based on the deviation between the inversion results and the reservoir parameters. For example, the deviation between the free gas saturation obtained by inversion and the free gas saturation measured by well logging, the deviation between the porosity obtained by inversion and the porosity measured by well logging, and the deviation between the clay / carbonate volume fraction obtained by inversion and the measured value by well logging, etc. When the error no longer decreases with the number of iterations and stabilizes within the preset range, it is determined that the convergence state has been reached, and the error is minimized at this time. The optimal inversion values ​​and optimal consolidation coefficients of the five reservoir parameters are output. The inversion values ​​of the five reservoir parameters are used to verify the inversion accuracy, and the optimal consolidation coefficients are used for two-dimensional / three-dimensional reservoir parameter prediction.

[0060] It should be noted that when using the interior point method to iteratively solve the optimization problem, multiple sets of initial values ​​are randomly selected from the range of values ​​corresponding to the reservoir parameter constraint boundary data. After solving each set, the result with the smallest objective function value is selected as the optimal solution. For details on the interior point method, please refer to Richard H. Byrd, Mary E. Hribar, and Jorge Nocedal, An Interior Point Algorithm for Large-Scale Nonlinear Programming, 1999 Society for Industrial and Applied Mathematics, Volume 9, No. 4, Pages 877–900 (hereinafter referred to as Byrd et al., 1999).

[0061] It should be noted that S300 solves the multidimensional nonlinear constraint optimization problem constructed by S200 using the interior point method, and judges the convergence state by combining reservoir parameters. Finally, it obtains the inversion values ​​of five reservoir parameters and the optimal consolidation coefficient that conform to the geological reality, solving the problems of "how to efficiently find the optimal" and "how to ensure the reliability of the results" in the inversion process, and providing computational support for the quantification of reservoir parameters.

[0062] The specific execution process of S300 includes the following steps: Step 301: Identify three key input types to provide a foundation for the inversion solution: The optimization problem is a multidimensional nonlinear constrained optimization problem directly constructed using S200, including the objective function (minimizing the sum of squares of the calculated and measured values) and reservoir parameter constraint boundaries (upper limit of free gas saturation, porosity range, etc.); the objective function formula is as follows: (Equation 6) Reservoir parameters: Known reference values ​​extracted from well logging data (such as porosity from core analysis, free gas saturation obtained from well testing, etc.) are used to determine the error of the inversion results. These benchmark values ​​correspond one-to-one with the inversion unknowns (free gas saturation, porosity, clay content, carbonate content, gas-water mixing compatibility factor), forming a benchmark value vector, which serves as the "true value" reference for error judgment.

[0063] Initial values ​​for iteration: Initial solutions are randomly selected within the reservoir parameter constraint boundaries defined by S200 (such as free gas saturation between 0 and the upper limit, porosity between its minimum and maximum values, etc.) to ensure that the initial values ​​are geologically and physically reasonable and to provide a starting point for iterative solutions.

[0064] Step 302, iteratively solve the optimization problem using the interior point method: The interior-point method is used to iteratively solve the optimization problem constructed by S200. The core of this process is to continuously approach the optimal solution through mathematical iteration. The specific operation focuses on two key steps: Iterative solution process: Using the interior point method as the calculation tool, the iteration starts from the initial value. The algorithm continuously adjusts the values ​​of the inversion unknowns (free gas saturation, porosity, etc.) to gradually reduce the objective function value (the sum of the squares of the model calculated value and the measured value) until it approaches the minimum value.

[0065] Consolidation coefficient adjustment: During the iteration process, the consolidation coefficient (a parameter in S100 used to describe the degree of rock compaction) at points within the reservoir distribution range is adjusted synchronously. By dynamically adjusting this coefficient, the calculation accuracy of the rock elastic parameters and density model is optimized, ensuring that the model can better match the measured data at different depths (such as the variation characteristics of P-wave velocity and density with depth).

[0066] Step 303, Convergence Judgment and Result Output: The iterative process must terminate through a clear convergence criterion, ultimately outputting the core result: convergence status judgment. This is determined by calculating the error between the inversion result obtained in each iteration and the reservoir parameters (such as the deviation of each parameter's inversion value from its corresponding baseline value). When the error decreases to a preset minimum threshold (i.e., error is minimized), or when the error no longer changes significantly, the iteration is considered converged, and the calculation stops.

[0067] Output results and their uses: Five reservoir parameter inversion values: these are the optimal solutions for free gas saturation, porosity, clay content, carbonate content, and gas-water mixing compatibility factor. The accuracy of the inversion method can be verified by comparing these values ​​with data from core experiments and well tests. The optimal consolidation coefficient: this is the consolidation coefficient value that best matches the measured data during the iteration process. It is used for subsequent two-dimensional / three-dimensional reservoir parameter prediction, eliminating the influence of depth differences on reservoir characteristic characterization (e.g., ...). Figure 2 (As shown).

[0068] The optimization problem was solved iteratively using the interior point method. The convergence state was determined by combining reservoir parameters and the consolidation coefficient was dynamically adjusted. The final output of the five reservoir parameter inversion values ​​verified the inversion accuracy. The optimal consolidation coefficient provided key support for subsequent two-dimensional / three-dimensional reservoir parameter prediction. This not only realized the transformation from mathematical model to quantitative reservoir parameters, ensuring the reliability and geological rationality of the inversion results, but also connected single-well inversion with regional scale prediction, laying a precise parameter foundation for gas reservoir reserve assessment and development optimization.

[0069] In the embodiments of this application, the core logic of the method for synchronously inverting five reservoir parameters of subsea gas reservoirs based on P-wave velocity and density has been described in detail above (including physical property model construction, optimization problem design, interior point method solution, and optimal consolidation coefficient output). To further verify the inversion accuracy, parameter setting rationality, and engineering applicability of this method under actual geological conditions, inversion tests were conducted at multiple publicly available logging sites (including Ocean Drilling Program (IODP) and special exploration sites) to quantitatively demonstrate the reliability of the method. At the same time, the connection between "single-well logging verification → two-dimensional / three-dimensional seismic profile inversion process" provides experimental support for the "seismic data inversion and reservoir parameter distribution output" in the following section, ensuring the integrity of the technical solution from "single-point theoretical verification" to "regional engineering application".

[0070] In this embodiment of the application, the data provided by IODP includes: Sample data for Hole 1245E (Hydrate Ridge-Cascadia margin) (Data File Summary - ODP Leg 204 - Hole 1245E (columbia.edu)), referenced in Tréhu, AM, Bohrmann, G., Rack, FR, Torres, ME, et al., 2003 proceedings of the ocean drilling program, initial reports, volume 204, site 1245 (hereinafter referred to as Tréhu et al.,2003)); and Data for Hole NGHP-01-14 (Krishna-Godavari Basin), see T. Collett, M. Riedel, J. Cochran, R. Boswell, J. Presley, P. Kumar, A. Sathe, A. Sethi, M. Lall, the national gas hydrate program expedition 01 scientists, 2015, sitesNGHP-01-14 (hereinafter referred to as Collett et al., 2015); and Hole U1329D (Cascadia Margin), see Riedel, M., Collett, TS, Malone, MJ, the expedition 311 scientists proceedings of the integrated oceandrilling program, volume 311, site U1329, 2006 (hereinafter referred to as Collett et al., 2006); and Hole 997B (Blake Ridge), see Paull, CK, Matsumoto, R., Wallace, PJ, et al, 1996 proceedings of the ocean drilling program, initial reports, volume 164, site 997, (hereinafter referred to as Paull et al., 1996).

[0071] An inversion experiment and effect evaluation were conducted using a set of unpublished seismic and well logging data.

[0072] The following are the specific verification contents of the inversion effect: In the inversion, the main rock physical parameters and the range of the five parameters are shown in Table 1 and Table 2, respectively.

[0073] Table 1. Physical properties of sedimentary layer components In addition, the data in Table 1 also references: Lee, 2008; and Rafael Amaral Cataldo, Impact of mineralogy on rock physics modeling: a Brazilian pre-salt case study, Emilson Pereira Leite, 2022 (hereinafter referred to as Cataldo, 2022).

[0074] Table 2. Range of values ​​for inversion variables When performing a "five-parameter inversion test" using the logging data (P-wave velocity, density), the initial model for the five parameters is selected as [0,0,0,0,0]. The setting was 4, and the remaining rock physical parameters were the same as in Table 1. The results inverted by Lee and Collett in 2006 based on Hole 1245E data were added for comparison. (Refer to MW Lee and TS Collett, gas hydrate and free gas saturations estimated from velocity logs on hydrate ridge, off shore oregon, USA proceedings of the ocean drilling program, scientific results, volume 204, web publication: 13 January 2006, hereinafter referred to as Lee and Collett, 2006). Well logging data are as follows: Figure 3 As shown.

[0075] like Figure 4 As shown, Figure 4 This is a schematic diagram of the five-parameter model inverted from Hole 1245E data. From left to right, the parameters are free gas saturation, porosity, clay content, carbonate content, and gas-water mixing compatibility factor. The gray areas mark the depth regions where thin layers with high carbonate content are distributed, while the yellow areas represent regions where thin layers with high quartz (volcanic glass) content are distributed. Although the root mean square error (RMSE) of the free gas saturation inversion result (0.0598) is slightly higher than that of the univariate inversion method based on the BGTL-Brie equation proposed by Lee and Collett, 2006 (0.0502), it can still accurately invert porosity and mineral composition ratios simultaneously. Several thin layers with high quartz and high carbonate content are too thin (~1m) to be distinguishable in P-wave velocity and density logging, making accurate inversion difficult.

[0076] When performing a "five-parameter inversion test" using well logging data (P-wave velocity, density) from Hole NGHP-01-14A (Krishna-Godavari Basin) (Collett et al., 2015), the following will be used: Set to 5. Set to 2.73, and The values ​​were set to 74.9 GPa and 49.8 GPa respectively, with the remaining rock physical parameters set the same as in Table 1. The inversion results are as follows: Figure 5 As shown, the inversion results of free gas saturation, porosity, and mineral component ratios fit well with the well logging data. It should be noted that... Figure 5 In the middle, from left to right, are free gas saturation, porosity, clay content, carbonate content, and gas-water mixing coordination factor.

[0077] When performing a "five-parameter inversion test" using well logging data (P-wave velocity, density) from Hole U1329D (Cascadia Margin) (Collett et al., 2006), Set to 5. Set to 2.72, and The values ​​were set to 74.9 GPa and 49.8 GPa respectively, with the remaining rock physical parameters set the same as in Table 1. The inversion results are as follows: Figure 6 As shown, the inversion results for free gas saturation and porosity fit well with the logging data. The inversion results for mineral composition ratios are slightly higher because the thickness of locally high-quartz and high-carbonate thin layers is lower than the resolution of the P-wave velocity and density logging data. It should be noted that... Figure 6 In the middle, from left to right, are free gas saturation, porosity, clay content, carbonate content, and gas-water mixing coordination factor.

[0078] When performing a "five-parameter inversion test" using well logging data (P-wave velocity, density) from Hole 997B (Blake Ridge) (Paull et al., 1996), Set to 8, The value was set to 2.58, and the remaining rock physical parameters were set the same as in Table 1. The inversion results are as follows: Figure 7 As shown, the inversion results of free gas saturation, porosity, and mineral component ratio fit well with the well logging data. Figure 7 The study also included a comparison with the results of Tinivella et al. 1999, referencing U Tinivella, a method for estimating gas hydrate and free gas concentrations in marine sediments, bollettino di geofisica teorica ed applocata, volume 40, N. 1, pp.19-30 (hereinafter Tinivella et al. 1999). It should be noted here that... Figure 7 In the middle, from left to right, are free gas saturation, porosity, clay content, carbonate content, and gas-water mixing coordination factor.

[0079] Based on the above process, single-well inversion tests at multiple publicly available logging well locations (Hole 1245E, NGHP-01-14A, etc.) have confirmed the accuracy of the "five-parameter inversion method" in predicting single-point reservoir parameters (free gas saturation, porosity, etc.). To further verify the applicability of this method from "single-well local data" to "regional large-scale data," and to achieve quantitative characterization of the two-dimensional reservoir parameter distribution of subsea gas reservoirs, the following three procedures are proposed in this application embodiment to test the applicability of the "five-parameter inversion method" inverting two-dimensional subsea free gas reservoir parameters from seismic and logging data: (1) Let the program first calculate the differences within the range of variables. The "free gas saturation" inverted from the consolidation coefficient value at points within the reservoir distribution range ( ), "porosity" ), and well logging data ( , The sum of the root mean squares between () ), and find the one that minimizes the sum of the mean squares. Value (e.g.) Figure 8 (as shown) (2) Determine the depth points of the upper boundary (BSR, bottom-simulating reflector) and lower boundary (interface of dramatic increase in wave impedance) of the submarine gas reservoir through geological experience in the post-stack seismic profile, and obtain the P-wave velocity and wave impedance profiles and calculate the density profile using seismic inversion methods. In addition, P-wave velocity and density profiles can also be obtained using high-precision pre-stack seismic data through methods such as amplitude variation with offset and full waveform inversion; Will Set as the consolidation coefficient of the lower boundary of the subsea gas reservoir, and use the P-wave velocity and density profile obtained by inversion ( or By combining rock physics equations and the interior point method, the free gas saturation and porosity at each depth point in each CDP channel are calculated, thereby inverting two-dimensional or three-dimensional saturation and porosity distribution profiles. , or , )(like Figure 9 (As shown).

[0080] To test the effectiveness of the "five-parameter inversion method" in predicting the two-dimensional distribution profile of submarine free gas reservoir parameters, this application embodiment used well logging data and post-stack seismic data from an undisclosed location for testing. The test results are as follows: Figure 10As shown, the free gas saturation is relatively high (0.15-0.3) within 1-2 m below the BSR (3151-3153 m), while it is relatively low (0.05-0.15) in other layers (3153-3170 m), which is consistent with the well logging data. There is a region with high porosity (3153-3160 m) within 1-10 m below the BSR. The inverted two-dimensional profile of free gas saturation and porosity in the near-wellbore region is basically consistent with the well logging data, thus meeting industrial requirements.

[0081] First, through single-well inversion tests at multiple publicly available logging well locations (Hole 1245E, NGHP-01-14A, etc.), the accuracy of this method in predicting reservoir parameters such as free gas saturation and porosity at single points has been accurately verified, laying a reliable foundation for subsequent regional applications. To further extend "single-well local data" to "regional large-scale data," this application proposes a three-stage core process that plays a crucial role: First, by calculating the sum of the root mean square errors of the inversion parameters of the consolidation coefficient at the bottom of different reservoirs within the variable range and the logging data, the optimal consolidation coefficient (such as...) is selected. Figure 8 As shown), this effectively eliminates the influence of depth differences on the rock elastic model, ensuring the consistency of regional inversion parameters; secondly, it identifies the gas reservoir boundary (BSR and the interface of dramatic increase in wave impedance) based on geological experience, and obtains P-wave velocity and density profiles through post-stack / pre-stack seismic inversion, providing large-scale, high-coverage input data for regional reservoir parameter inversion; thirdly, it sets the optimal consolidation coefficient as the consolidation coefficient of the lower boundary of the gas reservoir, and calculates reservoir parameters per CDP trace and per depth point by combining the P-wave velocity / density profile with rock physics equations and the interior point method, inverting to obtain two-dimensional / three-dimensional saturation and porosity distribution profiles (such as...). Figure 9 As shown), regional quantitative characterization of subsea gas reservoir parameters is achieved; finally, through well logging and post-stack seismic data testing at undisclosed locations (results are shown in...). Figure 10 As shown in the figure, the inverted two-dimensional profile is highly consistent with the logging data in the near-well region, meeting the industrial needs of gas reservoir reserve assessment and development well location deployment. It integrates the advantages of high precision of single wells and wide coverage of seismic data, fills the gap in reservoir parameter prediction in areas without wells, and provides accurate and efficient technical support for the exploration and development of subsea gas reservoirs.

[0082] In the S400 process of 2D / 3D reservoir parameter prediction, seismic data of subsea gas reservoirs collected by seismic equipment is acquired. The subsea gas reservoir seismic data is inverted to obtain 2D or 3D P-wave velocity profiles and 2D or 3D density profiles. The optimal consolidation coefficient is then used to substitute the 2D or 3D P-wave velocity profiles, 2D density profiles, or 3D density profiles into the optimization problem constructed by the P-wave velocity model and the bulk density model. In this repeated inversion process, the method also includes repeatedly executing the following steps: constructing P-wave velocity and bulk density models based on mineral component proportion data; setting free gas saturation, porosity, clay content, carbonate content, and gas-water mixing compatibility factor as unknowns to be inverted, and constructing a multidimensional nonlinear optimization problem with the objective of minimizing the sum of squares between the model calculation values ​​and the profile data; and using the interior point method to iteratively solve and output the seismic common depth (CDP) for each earthquake. Five reservoir parameters at depth points (common depth points); invert five reservoir parameters at each depth point in each seismic CDP trace; output two-dimensional or three-dimensional reservoir parameter distribution profiles for subsea gas reservoirs; the two-dimensional or three-dimensional reservoir parameter distribution profiles include: spatial distribution information of free gas saturation, porosity, clay content, carbonate content, and gas-water mixing coordination factor within the subsea gas reservoir area, with the spatial distribution information corresponding one-to-one with the seismic CDP traces and depth points; the two-dimensional or three-dimensional reservoir parameter distribution profiles are used for gas reservoir reserve assessment and development optimization.

[0083] It should be noted that, based on seismic data and the optimal consolidation coefficient output by S300, the reservoir parameter inversion at the single-well scale is extended to the two-dimensional / three-dimensional regional scale, systematically characterizing the spatial distribution features of reservoir parameters for the entire gas reservoir, and providing quantitative spatial distribution basis for accurate assessment of gas reservoir reserves and optimization of development schemes.

[0084] The specific execution process of S400 includes the following steps: Step 401: Acquire seismic data and invert it to obtain the P-wave velocity and density profiles: Seismic data acquisition: Seismic reflection data of seafloor gas reservoirs are acquired using seismic exploration equipment (such as towed seismographs and seafloor seismographs) (recording the reflection signals of seismic waves at the interfaces of different rock layers underground). The data covers the planar and vertical range of the gas reservoir area (two-dimensional or three-dimensional coverage).

[0085] Seismic data inversion processing: After performing conventional processing on the raw seismic data (such as denoising, stacking, and migration), seismic inversion is performed to convert seismic amplitude and other responses into geophysical parameter profiles with physical dimensions, ultimately yielding: Two-dimensional / three-dimensional P-wave velocity profile: using planar coordinates (x, y) and depth (z) as dimensions, it characterizes the P-wave velocity at different locations and depths within the gas reservoir area. Distribution, in m / s; two-dimensional / three-dimensional density profile: corresponding to the spatial range of the P-wave velocity profile, characterizing the volume density at different locations and depths ( Distribution, unit is .

[0086] Step 402: Call the optimal consolidation coefficient and repeat the inversion process to calculate the five reservoir parameters.

[0087] Core input integration: Calling the optimal consolidation coefficient output by S300 ( This coefficient has been optimized using single-well data and can uniformly correct for the compaction effect of reservoirs at different depths, ensuring the vertical consistency of the model in regional inversion; substitute the two-dimensional / three-dimensional P-wave velocity profile obtained in step 1 (replacing the single-well data). ) and two-dimensional / three-dimensional density profiles (replacing single wells) ), which serves as the geophysical response input for regional inversion.

[0088] Regional inversion calculation unit: The CDP trace (common depth point trace) in the seismic data is used as the basic calculation unit. Each CDP trace corresponds to the vertical depth sequence (i.e., "each depth point") at a certain lateral position underground. Through trace-by-trace and depth-by-depth point calculation, the entire region is covered.

[0089] Repeat the inversion process, strictly repeating the S200-S300 inversion logic for each depth point of each CDP trace: 1. Construct the optimization problem, with the objective function (same as S200): ; in, and At this point, replace them with the P-wave velocity profile and density profile data of the current CDP trace depth point.

[0090] 2. Interior point method iterative solution: Within the reservoir parameter constraint boundary (same as S200), the above objective function is solved by interior point method iteration (same as S300) to obtain the five reservoir parameters at this depth point: free gas saturation ( ), porosity ( ), clay content ( ), carbonate content ( ), air-water mixing coordination factor ( ).

[0091] Step 403: Output the two-dimensional / three-dimensional reservoir parameter distribution profile and its application.

[0092] Results Integration: The calculation results of the five reservoir parameters at all depth points of all CDP lines are integrated according to spatial coordinates (x, y, z) to form: a two-dimensional reservoir parameter distribution profile: displaying the reservoir parameters along a certain exploration profile (such as the direction of the survey line). , The distribution characteristics of reservoir parameters in the vertical and horizontal directions (such as the planar distribution of high porosity areas and high gas content areas); three-dimensional reservoir parameter distribution profile: the spatial distribution of each reservoir parameter in the entire gas reservoir range is displayed in the form of a three-dimensional grid, and the three-dimensional aggregation characteristics of the parameters can be presented through slicing, three-dimensional rendering and other methods.

[0093] Gas reservoir reserve assessment: The effective reservoir volume is calculated based on the three-dimensional porosity distribution, and the gas saturation is calculated by combining the free gas saturation distribution. Finally, the geological reserves of the gas reservoir are obtained through the formula "Reserves = Gas reservoir distribution area × Free gas saturation × Porosity". Development optimization: Based on the reservoir parameter distribution characteristics (such as the location of high permeability zones and high gas-bearing zones), the deployment of development wells and the design of fracturing schemes are guided to improve the extraction efficiency.

[0094] The solution in this application embodiment does not require relying on known porosity and mineral composition ratio data in rock physics modeling, nor does it require setting the gas-water mixing coordination factor as a fixed constant. It can dynamically adapt to changes in the distribution of gas-water mixed fluids in the reservoir, greatly improving its applicability in complex geological and data-deficient scenarios, and solving the data dependency problem of existing methods.

[0095] For the first time, a joint optimization framework for five parameters—free gas saturation, porosity, clay content, carbonate content, and gas-water mixing coordination factor—was constructed and integrated into the objective function for simultaneous inversion, breaking through the accuracy bottleneck of traditional separate parameter prediction. Verification with actual drilling and logging data showed that the inversion results had a high degree of fit with the measured values, and the quantitative prediction capability was superior to traditional methods.

[0096] It is applicable to the inversion of logging point parameters in a single well, and can also directly utilize two-dimensional / three-dimensional seismic P-wave velocity and density profiles to achieve regional inversion, filling the gap in parameter prediction in areas without wells. The interior point method optimization strategy has strong stability and low sensitivity to initial values. Combined with reasonable parameter constraints, it can converge quickly in diverse geological environments and is suitable for industrial applications.

[0097] Deepwater gas reservoirs generally face challenges such as complex geology, difficulty in data acquisition, and high accuracy requirements for key parameters. This case addresses these challenges by eliminating data dependence and adapting to complex scenarios. The simultaneous inversion of five parameters ensures the accuracy of key parameters such as free gas saturation and porosity for accurate reserve calculation. Regional inversion covers wellless areas to guide well location deployment. Industrial adaptability ensures stable application and comprehensively meets the core needs of deepwater natural gas exploration and development: "accurate reserve assessment and efficient optimized development".

[0098] This application discloses a device for simultaneous inversion of five reservoir parameters of a subsea gas reservoir based on P-wave velocity and density, referring to... Figure 11 ,include: The data acquisition and physical property model construction module 001 acquires measured P-wave velocity, measured density data, and reservoir distribution range of the subsea gas reservoir through logging equipment, and measures actual free gas saturation, porosity, and mineral composition ratio data of the rock skeleton to verify the inversion accuracy; it combines the simplified two-phase Biot equation, the mixed fluid weighted average equation, and the bulk density equation to construct the P-wave velocity model and bulk density model of the gas reservoir rock. The constraint data acquisition and optimization problem construction module 002 acquires the reservoir parameter constraint boundary data of the subsea gas reservoir. The reservoir parameter constraint boundary data includes the upper limit of free gas saturation, the value range of porosity, and the value range of mineral component proportions. In the P-wave velocity model and the bulk density model, free gas saturation, porosity, clay content, carbonate content, and gas-water mixing coordination factor are set as unknowns to be inverted. The P-wave velocity model is correlated with the measured P-wave velocity, and the bulk density model is correlated with the measured density data. A multidimensional nonlinear correspondence optimization problem is constructed with the objective of minimizing the sum of squares of the model calculated value and the measured value. The value range of each inversion unknown is set in combination with the reservoir parameter constraint boundary data. The optimization solution and core parameter output module 003, after constructing the optimization problem, simultaneously acquires reservoir parameters and initial iteration values ​​from the well logging data. The initial iteration values ​​are randomly selected within the range corresponding to the reservoir parameter constraint boundary data. The interior point method is used to iteratively solve the optimization problem. During the solution process, the consolidation coefficient of the reservoir distribution points is adjusted. The inversion accuracy is judged by calculating the error between the inversion results and the measured values ​​of reservoir parameters until the error is minimized. The optimal inversion values ​​and optimal consolidation coefficients of the five reservoir parameters are output. The inversion values ​​of the five reservoir parameters are used to verify the inversion accuracy, and the optimal consolidation coefficients are used for two-dimensional / three-dimensional reservoir parameter prediction. The seismic data inversion and reservoir parameter distribution output module 004 acquires seismic data of the subsea gas reservoir collected by seismic equipment during the two-dimensional / three-dimensional reservoir parameter prediction process. It performs inversion processing on the subsea gas reservoir seismic data to obtain two-dimensional or three-dimensional P-wave velocity profiles and two-dimensional or three-dimensional density profiles. It calls the optimal consolidation coefficient and substitutes the two-dimensional or three-dimensional P-wave velocity profiles, two-dimensional density profiles, or three-dimensional density profiles into the optimization problem constructed by the P-wave velocity model and the bulk density model. It repeats the inversion process to invert the five reservoir parameters at each depth point in each seismic CDP trace. It outputs the two-dimensional or three-dimensional reservoir parameter distribution profile of the subsea gas reservoir. The two-dimensional or three-dimensional reservoir parameter distribution profile is used for gas reservoir reserve assessment and development optimization.

[0099] This application also discloses a system for synchronous inversion of five reservoir parameters of a subsea gas reservoir based on P-wave velocity and density, including a memory, a processor, and a program stored in the memory for synchronous inversion of five reservoir parameters of a subsea gas reservoir based on P-wave velocity and density. The program for synchronous inversion of five reservoir parameters of a subsea gas reservoir based on P-wave velocity and density is executed by the processor to implement the steps of the method described above.

[0100] This application also discloses a storage medium on which a program for synchronously inverting five reservoir parameters of a subsea gas reservoir based on P-wave velocity and density is stored. When the program for synchronously inverting five reservoir parameters of a subsea gas reservoir based on P-wave velocity and density is run by a processor, it implements the steps of the method described above.

[0101] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for simultaneously inverting five reservoir parameters of a subsea gas reservoir based on P-wave velocity and density, characterized in that, include: The measured P-wave velocity, measured density data, and reservoir distribution range of the subsea gas reservoir were obtained by logging equipment. The actual free gas saturation, porosity, and mineral composition ratio data of the rock skeleton were measured to verify the inversion accuracy. The simplified two-phase Biot equation, the mixed fluid weighted average equation, and the bulk density equation were combined to construct the P-wave velocity model and the bulk density model of the gas reservoir rock. The reservoir parameter constraint boundary data of the subsea gas reservoir is obtained. The reservoir parameter constraint boundary data includes the upper limit of free gas saturation, the value range of porosity, and the value range of mineral component proportion. In the P-wave velocity model and the bulk density model, free gas saturation, porosity, clay content, carbonate content, and gas-water mixing coordination factor are set as unknowns to be inverted. The P-wave velocity model is correlated with the measured P-wave velocity, and the bulk density model is correlated with the measured density data. A multidimensional nonlinear correspondence optimization problem is constructed with the objective of minimizing the sum of squares of the model calculated value and the measured value. The value range of each inversion unknown is set in combination with the reservoir parameter constraint boundary data. After constructing the optimization problem, reservoir parameters and initial iteration values ​​from well logging data are simultaneously acquired. The initial iteration values ​​are randomly selected within the range corresponding to the reservoir parameter constraint boundary data. The optimization problem is iteratively solved using the interior point method. During the solution process, the consolidation coefficients of the reservoir distribution points are adjusted. The inversion accuracy is judged by calculating the error between the inversion results and the measured values ​​of the reservoir parameters until the error is minimized. The optimal inversion values ​​and optimal consolidation coefficients of the five reservoir parameters are output. The inversion values ​​of the five reservoir parameters are used to verify the inversion accuracy, and the optimal consolidation coefficients are used for two-dimensional / three-dimensional reservoir parameter prediction. In the process of predicting 2D / 3D reservoir parameters, seismic data of the subsea gas reservoir collected by seismic equipment is acquired. The seismic data of the subsea gas reservoir is inverted to obtain 2D or 3D P-wave velocity profiles and 2D or 3D density profiles. The optimal consolidation coefficient is called, and the 2D or 3D P-wave velocity profiles, 2D density profiles or 3D density profiles are substituted into the optimization problem constructed by the P-wave velocity model and the bulk density model. The inversion process is repeated to invert the five reservoir parameters at each depth point in each seismic CDP (seismic common depth point) trace. The 2D or 3D reservoir parameter distribution profile of the subsea gas reservoir is output. The 2D or 3D reservoir parameter distribution profile is used for gas reservoir reserve assessment and development optimization.

2. The method for synchronously inverting five reservoir parameters of a subsea gas reservoir based on P-wave velocity and density according to claim 1, characterized in that, The method also includes: The mineral component ratio data includes the bulk modulus of clay. shear modulus bulk modulus of quartz shear modulus and the bulk modulus of carbonates shear modulus ; The simplified two-phase Biot equation is expressed as follows: (Equation 1) In Equation 1, The bulk modulus of the rock. The bulk modulus of the rock's framework and mineral matrix. Shear modulus of the rock framework mineral matrix. The Biot coefficient is used to approximate the bulk modulus. Equivalent bulk modulus Porosity The bulk modulus of the gas-water mixture. The overall shear modulus of the rock. The shear modulus of the rock's framework mineral matrix. The shear modulus is approximated by the Biot coefficient; The bulk modulus of the rock skeleton mineral matrix With shear modulus The Hill formula, calculated using Hill's formula, is expressed as follows: ； (Equation 2) In Equation 2, , These represent the volume fractions of clay and carbonate in the rock skeleton.

3. The method for synchronously inverting five reservoir parameters of a subsea gas reservoir based on P-wave velocity and density according to claim 2, characterized in that, The method also includes: The bulk modulus is approximately the Biot coefficient. Biot coefficient approximating shear modulus The expression is: (Equation 3) In Equation 2, The consolidation coefficient at the target point, Consolidation coefficient at points within the reservoir distribution range, The reservoir distribution range, For the depth of the target point, Porosity; The bulk modulus of the mixed fluid is expressed as follows, based on the weighted average equation for the mixed fluid: (Equation 4) In Equation 4, The bulk modulus of the gas-water mixture. Free gas saturation The bulk modulus of liquid water, The bulk modulus of the free gas, ( ; The expression for the density formula is: (Equation 5-1) In Equation 5-1, The reservoir volume density, This represents the volume fraction of clay. The density of clay, The density of quartz The density of carbonates, The density of liquid water, The density of the free gas.

4. The method for synchronously inverting five reservoir parameters of a subsea gas reservoir based on P-wave velocity and density according to claim 3, characterized in that, The method also includes: The expression for calculating the P-wave velocity in the P-wave velocity model is as follows: (Equation 5-2) In Equation 5-2, For reservoir P-wave velocity, The bulk modulus of the rock. The overall shear modulus of the rock; The objective function expression for the multidimensional nonlinear optimization problem is: (Equation 6) In Equation 6, Let be the objective function. To measure the longitudinal wave velocity, These are the calculated values ​​from the P-wave velocity model. These are measured density data. These are the calculated values ​​from the bulk density model; The range of values ​​for each inversion unknown is: 0 , , , In the formula, This represents the upper limit of free gas saturation in the boundary data constrained by reservoir parameters.

5. The method for synchronously inverting five reservoir parameters of a subsea gas reservoir based on P-wave velocity and density according to claim 3, characterized in that, The method also includes: The consolidation coefficient at the points within the reservoir distribution range The range of values ​​is 3≤ ≤11; The reservoir parameters include the free gas saturation, porosity, clay volume fraction, and carbonate volume fraction measured in the well logging data. Error minimization is achieved by calculating the error between the inversion result and the reservoir parameters. When the error no longer decreases with the number of iterations and stabilizes within a preset range, convergence is determined. The error is calculated based on the deviation between the inversion result and the reservoir parameters.

6. The method for synchronously inverting five reservoir parameters of a subsea gas reservoir based on P-wave velocity and density according to claim 1, characterized in that, The method also includes: When using the interior point method to iteratively solve the optimization problem, multiple sets of initial values ​​are randomly selected within the range of values ​​corresponding to the reservoir parameter constraint boundary data. After solving each set, the result with the smallest objective function value is selected as the optimal solution. Inversion processing of seismic data for submarine gas reservoirs includes: if it is post-stack seismic data, using post-stack seismic inversion methods to obtain two-dimensional or three-dimensional P-wave velocity profiles and two-dimensional or three-dimensional density profiles; If the data is pre-stack seismic data, the amplitude variation with offset inversion method is used to obtain two-dimensional or three-dimensional P-wave velocity profiles and two-dimensional or three-dimensional density profiles.

7. The method for synchronously inverting five reservoir parameters of a subsea gas reservoir based on P-wave velocity and density according to claim 1, characterized in that, The method also includes the following steps during the repeated inversion process: Repeat the following steps: Construct a P-wave velocity model and a bulk density model based on mineral property parameter data; set free gas saturation, porosity, clay content, carbonate content, and gas-water mixing compatibility factor as unknowns to be inverted, and construct a multidimensional nonlinear optimization problem with the objective of minimizing the sum of squares between the model calculation values ​​and the profile data; use the interior point method to iteratively solve and output the five reservoir parameters at the depth points of each seismic CDP trace; The two-dimensional or three-dimensional reservoir parameter distribution profile includes: spatial distribution information of free gas saturation, porosity, clay content, carbonate content, and gas-water mixing coordination factor in the subsea gas reservoir area. The spatial distribution information corresponds one-to-one with the seismic CDP trace and depth point.

8. A device for simultaneous inversion of five reservoir parameters of subsea gas reservoirs based on P-wave velocity and density, characterized in that, include: The data acquisition and physical property model construction module acquires measured P-wave velocity, measured density data, and reservoir distribution range of the subsea gas reservoir through logging equipment, and measures actual free gas saturation, porosity, and mineral composition ratio data of the rock skeleton to verify the inversion accuracy; it combines the simplified two-phase Biot equation, the mixed fluid weighted average equation, and the bulk density equation to construct the P-wave velocity model and bulk density model of the gas reservoir rock. The constraint data acquisition and optimization problem construction module acquires reservoir parameter constraint boundary data for subsea gas reservoirs. This data includes the upper limit of free gas saturation, the range of porosity values, and the range of mineral component proportions. In the P-wave velocity model and the bulk density model, free gas saturation, porosity, clay content, carbonate content, and gas-water mixing coordination factor are set as unknowns to be inverted. The P-wave velocity model is correlated with measured P-wave velocities, and the bulk density model is correlated with measured density data. A multidimensional nonlinear correspondence optimization problem is constructed with the objective of minimizing the sum of squares between the calculated and measured values. The range of values ​​for each inversion unknown is set based on the reservoir parameter constraint boundary data. The optimization solution and core parameter output module, after constructing the optimization problem, simultaneously acquires reservoir parameters and initial iteration values ​​from the well logging data. The initial iteration values ​​are randomly selected within the range corresponding to the reservoir parameter constraint boundary data. The optimization problem is iteratively solved using the interior point method. During the solution process, the consolidation coefficients of the reservoir distribution points are adjusted. The inversion accuracy is judged by calculating the error between the inversion results and the measured values ​​of the reservoir parameters until the error is minimized. The optimal inversion values ​​and optimal consolidation coefficients of the five reservoir parameters are output. The inversion values ​​of the five reservoir parameters are used to verify the inversion accuracy, and the optimal consolidation coefficients are used for two-dimensional / three-dimensional reservoir parameter prediction. The seismic data inversion and reservoir parameter distribution output module acquires seismic data of the subsea gas reservoir collected by seismic equipment during the two-dimensional / three-dimensional reservoir parameter prediction process. It then performs inversion processing on the subsea gas reservoir seismic data to obtain two-dimensional or three-dimensional P-wave velocity profiles and two-dimensional or three-dimensional density profiles. The module calls upon the optimal consolidation coefficient and substitutes the two-dimensional or three-dimensional P-wave velocity profiles, two-dimensional density profiles, or three-dimensional density profiles into the optimization problem constructed by the P-wave velocity model and the bulk density model. This inversion process is repeated to invert the five reservoir parameters at each depth point in each seismic CDP trace. Finally, it outputs a two-dimensional or three-dimensional reservoir parameter distribution profile of the subsea gas reservoir, which is used for gas reservoir reserve assessment and development optimization.

9. A system for simultaneous inversion of five reservoir parameters of subsea gas reservoirs based on P-wave velocity and density, characterized in that, The method includes a memory, a processor, and a program stored in the memory for synchronously inverting five reservoir parameters of a subsea gas reservoir based on P-wave velocity and density. The program for synchronously inverting five reservoir parameters of a subsea gas reservoir based on P-wave velocity and density is executed by the processor to implement the steps of the method as described in any one of claims 1-7.

10. A computer storage medium, characterized in that, The computer storage medium stores a program for synchronously inverting the parameters of five subsea gas reservoirs based on P-wave velocity and density. When the processor runs the program for synchronously inverting the parameters of five subsea gas reservoirs based on P-wave velocity and density, it implements the steps of the method as described in any one of claims 1-7.

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