A method for characterizing permeability of a subsurface hydrogen storage reservoir based on pore volume multiplication

Abstract

The application discloses a kind of underground hydrogen storage reservoir permeability characterization method based on pore volume multiple, it is related to underground hydrogen storage library technical field.The application first obtains the core sample of target depleted oil and gas reservoir, uses core sample to carry out in-situ CT displacement experiment simulation target reservoir different development history stage, obtains the rock micro-pore structure parameter under different cumulative injection pore volume multiple, then constructs the PTC-KC model and pore structure parameter two-stage competition model of target reservoir, obtains the dynamic PTC-KC permeability prediction model based on cumulative injection pore volume multiple by combining the two, for predicting the structure dynamic evolution and dynamic permeability of target reservoir full development cycle, cooperate target reservoir geological model carries out underground hydrogen storage simulation, obtains the hydrogen recovery of target reservoir and carries out evaluation, realizes the accurate prediction of underground hydrogen storage library permeability under the complex development water drive history.

Description

Technical Field

[0001] This invention relates to the field of underground hydrogen storage technology, and specifically to a method for characterizing the permeability of underground hydrogen storage reservoirs based on pore volume ratio. Background Technology

[0002] Depleted oil and gas reservoirs are ideal sites for underground hydrogen storage. However, hydrogen has extremely low viscosity and high fluidity, and its migration patterns in porous media are highly sensitive to the permeability of the reservoir structure. Accurately predicting hydrogen recovery rates in depleted oil and gas reservoirs is crucial. Currently, assessments of hydrogen recovery potential in depleted oil and gas reservoirs typically rely on static physical properties (such as porosity and permeability) from raw well logging data. However, during the long development history of depleted oil and gas reservoirs, especially during the complex interactions between fluids and reservoir rocks, continuous fluid scouring leads to clay mineral migration, pore throat widening or blockage, resulting in significant and non-monotonic dynamic evolution of reservoir permeability. Because reservoir permeability does not increase or decrease monotonically with increasing injected fluid volume, some reservoirs exhibit a non-monotonic evolution characteristic of first decreasing and then increasing, and this evolution is negatively correlated with changes in porosity. This phenomenon mainly stems from the dynamic competition between two mechanisms: pore structure damage (i.e., particle migration blocking the throat) and pore structure enhancement (i.e., particle shedding and pore throat channel expansion). Furthermore, the varying degrees of scouring experienced by different regions, such as near-wellbore and far-wellbore areas, lead to significant differences in their physical property evolution paths.

[0003] Current technologies for assessing underground hydrogen recovery rates typically rely directly on static permeability parameters obtained from original well logging. This approach completely ignores the impact of dynamic changes in pore structure and permeability caused by the long-term development history of oil and gas reservoirs on subsequent hydrogen injection, migration, storage, and production. This leads to serious misjudgments of hydrogen recovery rates in depleted oil and gas reservoirs, posing significant risks to the screening and evaluation of underground hydrogen storage facilities. Summary of the Invention

[0004] This invention aims to solve the above-mentioned problems and proposes a method for characterizing the permeability of underground hydrogen storage reservoirs based on pore volume ratio. By comprehensively considering the relationship between the cumulative injected pore volume ratio and permeability, the dynamic permeability of the reservoir is accurately predicted using the initial physical properties of the reservoir and the cumulative injected pore volume ratio. This method realistically restores the dynamic evolution of the internal structure of the reservoir during dynamic development and enables accurate prediction of the permeability of underground hydrogen storage reservoirs in depleted oil and gas reservoirs with complex development histories. This provides technical support for the evaluation of hydrogen recovery rate and site selection guidance of underground hydrogen storage reservoirs.

[0005] The present invention adopts the following technical solution: A method for characterizing the permeability of underground hydrogen storage reservoirs based on pore volume ratio includes the following steps: Step 1: Select the target reservoir in the depleted oil and gas reservoir, obtain core samples from the target reservoir, and conduct in-situ CT displacement experiments on the core samples. By controlling the cumulative injected pore volume multiple, simulate the fluid interaction process inside the target reservoir at different development stages, and obtain the rock microstructure parameters inside the target reservoir at different development stages, including porosity, average throat radius, average pore radius, average coordination number, and average tortuosity. Step 2: Construct the PTC-KC model based on the classic Kozeny-Carman equation to dynamically predict the permeability of the target reservoir; Step 3: Construct a two-stage competitive model of the pore structure parameters of the target reservoir based on the cumulative injected pore volume multiple; Step 4: Combine the two-stage competition model of the microstructure parameters of the target reservoir with the PTC-KC model to construct a dynamic PTC-KC permeability prediction model based on the cumulative injected pore volume multiple, which is used to predict the dynamic permeability of the target reservoir throughout the entire development cycle. Step 5: Based on the geometric dimensions and geological characteristics of the target reservoir, establish a geological model of the target reservoir. Substitute the cumulative injected pore volume multiples of the target reservoir's development history into the dynamic PTC-KC permeability prediction model to predict the current dynamic permeability of the target reservoir. Use the geological model of the target reservoir to perform numerical simulations of underground hydrogen storage under different injection and production cycles to obtain and evaluate the hydrogen recovery rate of the target reservoir.

[0006] Preferably, in step 1, core samples of the target depleted oil and gas reservoir are obtained, and the core samples are displaced using an in-situ displacement device built on a micron-level micro-CT scanning system. Before displacement, the core samples are saturated with a preset displaced phase. During the displacement process, the displacing phase is injected into the core samples so that it displaces the original displaced phase in the core samples, simulating the fluid interaction process inside the target reservoir at different development stages. During the in-situ CT displacement experiment, a micron-level micro-CT scanning system was used to perform high-resolution in-situ CT scans on the rock samples at each preset injection fluid node, acquiring high-resolution three-dimensional images of the rock samples and the cumulative injection pore volume multiple. After reconstructing the high-resolution 3D images of the rock samples, the pore network model of the rock samples was extracted to obtain the porosity of the core samples at each injected fluid node. Average laryngeal radius Average pore radius Mean coordination number and average tortuosity To obtain the variation patterns of microscopic pore structure parameters and permeability of target depleted oil and gas reservoirs at different stages of development.

[0007] Preferably, in step 2, the average throat radius, average pore radius, and average coordination number are introduced into the classical Kozeny-Carman equation to construct the PTC-KC model, resulting in: ; In the formula, For penetration rate; Porosity; The average throat radius; The average pore radius; The average tortuosity; The average coordination number; , , , All are fitting coefficients; The fitting coefficients in the PTC-KC model , , , The micropore structure parameters and permeability of core samples from the target depleted oil and gas reservoir at different stages of development were determined by fitting analysis.

[0008] Preferably, in step 3, the type of the target reservoir is determined based on the initial permeability of the target reservoir, combined with in-situ... The micropore structure parameters of the target reservoir at each preset injection fluid node were obtained in the displacement experiment. A two-stage competition model of micropore structure parameters was established to accurately characterize the competition between particle blockage and unblocking effects at different development stages. The two-stage competition model for the microporous structure parameters is as follows: ; In the formula, This is the cumulative injected pore volume multiple; These are the microstructure parameters of the pores at the current cumulative injected pore volume ratio. The initial micropore structure parameter values ​​of the target reservoir; Damage potential coefficient; The recovery potential coefficient; Damage rate; For recovery rate; It is a natural constant.

[0009] Preferably, when the permeability of the target reservoir is greater than 10 mD and not less than 100 mD, the target reservoir is determined to be a medium-low permeability reservoir. By fitting and analyzing the micropore structure parameters of the target reservoir core samples at each preset injection fluid node with the cumulative injected pore volume multiple, a two-stage competition model for the micropore structure parameters of the medium-low permeability reservoir is obtained: ; ; ; ; ; In the formula, This indicates a medium-to-low permeability reservoir. This represents the average coordination number of low-to-medium permeability reservoirs based on the current cumulative injected pore volume multiple. The initial average coordination number of medium- and low-permeability reservoirs; The damage potential coefficient represents the average coordination number of medium- and low-permeability reservoirs. The recovery potential coefficient is the average coordination number of medium- and low-permeability reservoirs; Damage rate is the average coordination number of medium- and low-permeability reservoirs. The recovery rate of the average coordination number of medium- and low-permeability reservoirs; This represents the average throat radius of low-to-medium permeability reservoirs at the current cumulative injected pore volume multiple. The initial average throat radius of medium-low permeability reservoirs; The damage potential coefficient is the average throat radius of medium-low permeability reservoirs. The recovery potential coefficient for the average throat radius of medium-low permeability reservoirs; The damage rate is the average throat radius of medium-low permeability reservoirs. The recovery rate of the average throat radius of medium-low permeability reservoirs; The average pore radius of low-to-medium permeability reservoirs at the current cumulative injected pore volume multiple; The initial average pore radius of medium-low permeability reservoirs; The damage potential coefficient is the average pore radius of medium-low permeability reservoirs. The recovery potential coefficient of the average pore radius of medium-low permeability reservoirs; The damage rate is the average pore radius of medium-low permeability reservoirs. The recovery rate of the average pore radius of medium-low permeability reservoirs; This represents the porosity of low-to-medium permeability reservoirs based on the current cumulative injected pore volume multiple. The initial porosity of medium-low permeability reservoirs; The damage potential coefficient for medium-low permeability reservoirs; This is the potential coefficient for the recovery of porosity in medium- and low-permeability reservoirs; Damage rate for medium-low permeability reservoirs with high porosity; The recovery rate of porosity in medium- and low-permeability reservoirs; This represents the average tortuosity of low-to-medium permeability reservoirs at the current cumulative injected pore volume multiple. The initial average tortuosity of medium- and low-permeability reservoirs; The damage potential coefficient represents the average tortuosity of medium- and low-permeability reservoirs. The recovery potential coefficient for the average tortuosity of medium-low permeability reservoirs; The damage rate represents the average tortuosity of medium- and low-permeability reservoirs. This represents the recovery rate of the average tortuosity of medium- and low-permeability reservoirs.

[0010] Preferably, when the permeability of the target reservoir is greater than 100 mD and less than 500 mD, the target reservoir is determined to be a medium-to-high permeability reservoir. By fitting and analyzing the micropore structure parameters of the target reservoir core samples at each preset injection fluid node with the cumulative injected pore volume multiple, a two-stage competition model for the micropore structure parameters of the medium-to-high permeability reservoir is obtained: ; ; ; ; ; In the formula, Indicates medium-to-high permeability reservoirs; This represents the average coordination number of medium-to-high permeability reservoirs based on the current cumulative injected pore volume multiple. The initial average coordination number of medium-to-high permeability reservoirs; The damage potential coefficient represents the average coordination number of medium-to-high permeability reservoirs. The recovery potential coefficient is the average coordination number of medium-to-high permeability reservoirs; Damage rate is the average coordination number of medium-to-high permeability reservoirs; The recovery rate of the average coordination number of medium-to-high permeability reservoirs; This represents the average throat radius of medium-to-high permeability reservoirs at the current cumulative injected pore volume multiple. The initial average throat radius of medium-to-high permeability reservoirs; The damage potential coefficient is the average throat radius of medium-to-high permeability reservoirs. The recovery potential coefficient for the average throat radius of medium-to-high permeability reservoirs; The damage rate is the average throat radius of medium-to-high permeability reservoirs. The recovery rate of the average throat radius in medium-to-high permeability reservoirs; The average pore radius of medium-to-high permeability reservoirs at the current cumulative injected pore volume multiple; The initial average pore radius of medium-to-high permeability reservoirs; The damage potential coefficient is the average pore radius of medium-to-high permeability reservoirs. The recovery potential coefficient of the average pore radius of medium-to-high permeability reservoirs; The damage rate is the average pore radius of medium-to-high permeability reservoirs. The recovery rate of the average pore radius of medium-to-high permeability reservoirs; This represents the porosity of medium-to-high permeability reservoirs based on the current cumulative injected pore volume multiple. The initial porosity of medium-to-high permeability reservoirs; The damage potential coefficient for medium-to-high permeability reservoirs; It represents the potential coefficient for the recovery of porosity in medium-to-high permeability reservoirs; Damage rate for medium-to-high permeability reservoirs with high porosity; The recovery rate of porosity in medium-to-high permeability reservoirs; This represents the average tortuosity of medium-to-high permeability reservoirs at the current cumulative injected pore volume multiple. The initial average tortuosity of medium-to-high permeability reservoirs; The damage potential coefficient represents the average tortuosity of medium-to-high permeability reservoirs. The recovery potential coefficient for the average tortuosity of medium-to-high permeability reservoirs; The damage rate is the average tortuosity of medium-to-high permeability reservoirs. The recovery rate of the average tortuosity of medium-to-high permeability reservoirs.

[0011] Preferably, in step 4, the dynamic PTC-KC penetration rate prediction model is: ; In the formula, This is the cumulative injected pore volume multiple; This represents the dynamic permeability of the target reservoir at the current cumulative injected pore volume multiple. This represents the porosity of the target reservoir at the current cumulative injected pore volume multiple. The average pore radius of the target reservoir at the current cumulative injected pore volume multiple; This represents the average throat radius of the target reservoir at the current cumulative injected pore volume multiple. This represents the average tortuosity of the target reservoir at the current cumulative injected pore volume multiple. This represents the average coordination number of the target reservoir at the current cumulative injected pore volume multiple. , , , All are fitting coefficients.

[0012] Preferably, in step 5, a geological model of the target reservoir is established based on the geometric dimensions and geological characteristics of the target reservoir. The values ​​of various microscopic pore structure parameters in the geological model are set. The cumulative injected pore volume multiple of the target reservoir during its development history is substituted into the dynamic PTC-KC permeability prediction model. The actual permeability of the target reservoir after the historical development period is calculated using the dynamic PTC-KC permeability prediction model. The initial grid attributes of the geological model are updated based on the actual permeability of the target reservoir. Then, combined with the preset hydrogen injection and production scheme for the target reservoir's storage stages, the geological model is used to simulate the migration, distribution, and extraction processes of hydrogen in the target reservoir during different injection and production stages, obtaining and evaluating the hydrogen recovery rate of the target reservoir.

[0013] The present invention has the following beneficial effects: (1) This invention proposes a method for characterizing the permeability of underground hydrogen storage reservoirs based on pore volume ratio. Given that the hydrogen flow characteristics are significantly affected by reservoir permeability, this method can accurately predict the permeability of the reservoir after long-term development, thereby effectively improving the reliability of hydrogen recovery rate prediction during underground hydrogen storage.

[0014] (2) This invention proposes a permeability characterization method for underground hydrogen storage reservoirs based on pore volume multiples. Based on the classical Kozeny-Carman equation, it fully considers the strong correlation between average throat size and average coordination number and permeability. By introducing the average throat size and average coordination number into the permeability solution, it improves the traditional permeability prediction model based on the classical Kozeny-Carman equation, effectively overcomes the drawback of the traditional permeability prediction model failing under the action of complex fluids and rocks, and realizes accurate prediction of reservoir dynamic permeability.

[0015] (3) This invention proposes a permeability characterization method for underground hydrogen storage reservoirs based on pore volume multiples. Compared with the shortcomings of existing reservoir permeability prediction methods that rely heavily on expensive coring operations, have complex fluid experiments, and have strong lag, the dynamic PTC-KC permeability prediction model constructed in this invention can be used directly after calibration without repeated coring. It only needs to obtain the cumulative injected pore volume multiple to realize the evolution of pore structure at any development node of the target reservoir, scientifically quantify the changes in pore structure parameters and permeability in the reservoir, and realize cross-scale low-cost and non-destructive prediction of reservoir permeability. This provides a basis for the initial selection of the optimal area of ​​underground hydrogen storage reservoirs and the risk assessment of injection and production schemes. Attached Figure Description

[0016] Figure 1 This is a flowchart of a method for characterizing the permeability of underground hydrogen storage reservoirs based on pore volume ratio, according to the present invention.

[0017] Figure 2 This is a schematic diagram showing the changes in porosity and permeability of the first core sample at various cumulative injection pore volume multiples.

[0018] Figure 3 This is a schematic diagram showing the changes in porosity and permeability of the second core sample under various cumulative injected pore volume multiples.

[0019] Figure 4 This is a schematic diagram showing the hydrogen recovery rate of low-permeability reservoirs after experiencing different stages of development.

[0020] Figure 5 This is a schematic diagram illustrating the hydrogen recovery rate of medium-to-high permeability reservoirs after experiencing different stages of development. Detailed Implementation

[0021] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0022] Example 1 This invention proposes a method for characterizing the permeability of underground hydrogen storage reservoirs based on pore volume ratio, such as... Figure 1 As shown, the specific steps include: Step 1: Select the target reservoir in the depleted oil and gas reservoir, obtain core samples from the target reservoir, and conduct in-situ CT displacement experiments on the core samples. By controlling the cumulative injected pore volume multiple, simulate the fluid interaction process inside the target reservoir at different development stages, and obtain the rock microstructure parameters inside the target reservoir at different development stages, including porosity, average throat radius, average pore radius, average coordination number, and average tortuosity.

[0023] Furthermore, in this embodiment, core samples of the target reservoir are obtained, and the core samples are saturated with simulated formation oil. Using an in-situ displacement device built in a micron-level micro-CT scanning system, simulated formation water fluid is injected into the core samples at a preset constant flow rate to conduct in-situ CT displacement experiments on the core samples.

[0024] In this embodiment, multiple water injection nodes are preset, namely 0 1 5 15 30 200 During the in-situ CT displacement experiment, a micron-level micro-CT scanning system was used to perform high-resolution in-situ CT scans on the rock samples at each preset water injection node to obtain high-resolution three-dimensional images of the rock samples and the cumulative injected pore volume multiple. Threshold segmentation and watershed algorithms were used to segment high-resolution 3D images of rock samples, followed by 3D reconstruction to extract the pore network model of the rock samples and obtain the porosity of the core samples at each water injection node. Average laryngeal radius Average pore radius Mean coordination number and average tortuosity To obtain the variation patterns of micropore structure parameters and permeability of core samples from depleted oil and gas reservoirs at different stages of development.

[0025] Step 2: Construct the PTC-KC model based on the classic Kozeny-Carman equation to dynamically predict the permeability of the target reservoir in depleted oil and gas reservoirs.

[0026] In this embodiment, based on the variation patterns of micropore structure parameters and permeability of core samples from depleted oil and gas reservoirs at different development stages, the relationship between various micropore structure parameters and permeability of the target depleted oil and gas reservoir at different development stages is analyzed. The pore structure damage mechanism caused by particle migration blocking the throat and the pore structure enhancement mechanism caused by the expansion of the pore throat channel and the improvement of connectivity are identified. The dominant mechanism of the target depleted oil and gas reservoir at different development stages is clarified, which is used to determine the critical point of permeability transformation in the target reservoir.

[0027] Furthermore, based on the Pearson correlation coefficient, micropore structure parameters that are strongly correlated with permeability were identified. Analysis revealed a correlation between permeability and the average throat radius. and mean coordination number The strong correlation confirms the average larynx radius. Average pore radius Mean coordination number Its role in controlling permeability.

[0028] In this embodiment, the average throat radius is... With average pore radius The ratio and average coordination number are introduced into the classical Kozeny-Carman equation, which is: ,in, For penetration rate, For porosity, For tortuosity, To determine the equivalent capillary bundle radius, a PTC-KC model is constructed.

[0029] The PTC-KC model is as follows: ; In the formula, For penetration rate; Porosity; The average throat radius; The average pore radius; The average tortuosity; The average coordination number; , , , All are fitting coefficients, where the fitting coefficients are... , , , The micropore structure parameters and permeability of core samples from the target depleted oil and gas reservoir at different stages of development were determined by fitting analysis.

[0030] Step 3: Construct a two-stage competitive model of the pore structure parameters of the target reservoir based on the cumulative injected pore volume multiple.

[0031] In this embodiment, the influence of the cumulative injected pore volume ratio on the micropore structure is highly nonlinear and grade-dependent. To further align with engineering practice, the target reservoir type is determined based on its initial permeability, combined with in-situ... The microstructure parameters of the target reservoir at each preset water injection node were obtained during the displacement experiment, and a two-stage competition model for the microstructure parameters was established. Considering the entire development history of the depleted oil and gas reservoir, there are both particle blockage and unblocking effects, and the pore structure parameters change complexly with the cumulative injected pore volume ratio.

[0032] To accurately characterize the competitive effects of microparticle blockage and unblocking in target reservoirs during development stages, a two-stage competition model of micropore structure parameters was established, yielding: ; In the formula, This is the cumulative injected pore volume multiple; These are the microstructure parameters of the pores at the current cumulative injected pore volume ratio. The initial micropore structure parameter values ​​of the target reservoir; Damage potential coefficient; The recovery potential coefficient; Damage rate; For recovery rate; It is a natural constant.

[0033] Furthermore, when the permeability of the target reservoir is greater than 10 mD and not less than 100 mD, the target reservoir is determined to be a medium-low permeability reservoir. By fitting and analyzing the microscopic pore structure parameters of the target reservoir core samples at each preset water injection node with the cumulative injected pore volume multiple, a two-stage competition model for the microscopic pore structure parameters of the medium-low permeability reservoir is obtained: ; ; ; ; ; In the formula, This indicates a medium-to-low permeability reservoir. This represents the average coordination number of low-to-medium permeability reservoirs based on the current cumulative injected pore volume multiple. The initial average coordination number of medium- and low-permeability reservoirs; The damage potential coefficient represents the average coordination number of medium- and low-permeability reservoirs. The recovery potential coefficient is the average coordination number of medium- and low-permeability reservoirs; Damage rate is the average coordination number of medium- and low-permeability reservoirs. The recovery rate of the average coordination number of medium- and low-permeability reservoirs; This represents the average throat radius of low-to-medium permeability reservoirs at the current cumulative injected pore volume multiple. The initial average throat radius of medium-low permeability reservoirs; The damage potential coefficient is the average throat radius of medium-low permeability reservoirs. The recovery potential coefficient for the average throat radius of medium-low permeability reservoirs; The damage rate is the average throat radius of medium-low permeability reservoirs. The recovery rate of the average throat radius of medium-low permeability reservoirs; The average pore radius of low-to-medium permeability reservoirs at the current cumulative injected pore volume multiple; The initial average pore radius of medium-low permeability reservoirs; The damage potential coefficient is the average pore radius of medium-low permeability reservoirs. The recovery potential coefficient of the average pore radius of medium-low permeability reservoirs; The damage rate is the average pore radius of medium-low permeability reservoirs. The recovery rate of the average pore radius of medium-low permeability reservoirs; This represents the porosity of low-to-medium permeability reservoirs based on the current cumulative injected pore volume multiple. The initial porosity of medium-low permeability reservoirs; The damage potential coefficient for medium-low permeability reservoirs; This is the potential coefficient for the recovery of porosity in medium- and low-permeability reservoirs; Damage rate for medium-low permeability reservoirs with high porosity; The recovery rate of porosity in medium- and low-permeability reservoirs; This represents the average tortuosity of low-to-medium permeability reservoirs at the current cumulative injected pore volume multiple. The initial average tortuosity of medium- and low-permeability reservoirs; The damage potential coefficient represents the average tortuosity of medium- and low-permeability reservoirs. The recovery potential coefficient for the average tortuosity of medium-low permeability reservoirs; The damage rate represents the average tortuosity of medium- and low-permeability reservoirs. This represents the recovery rate of the average tortuosity of medium- and low-permeability reservoirs.

[0034] When the permeability of the target reservoir is greater than 100 mD and less than 500 mD, the target reservoir is determined to be a medium-to-high permeability reservoir. By fitting and analyzing the microstructure parameters of the core samples of the target reservoir at each preset water injection node with the cumulative injected pore volume multiple, a two-stage competition model for the microstructure parameters of the medium-to-high permeability reservoir is obtained: ; ; ; ; ; In the formula, Indicates medium-to-high permeability reservoirs; This represents the average coordination number of medium-to-high permeability reservoirs based on the current cumulative injected pore volume multiple. The initial average coordination number of medium-to-high permeability reservoirs; The damage potential coefficient represents the average coordination number of medium-to-high permeability reservoirs. The recovery potential coefficient is the average coordination number of medium-to-high permeability reservoirs; Damage rate is the average coordination number of medium-to-high permeability reservoirs; The recovery rate of the average coordination number of medium-to-high permeability reservoirs; This represents the average throat radius of medium-to-high permeability reservoirs at the current cumulative injected pore volume multiple. The initial average throat radius of medium-to-high permeability reservoirs; The damage potential coefficient is the average throat radius of medium-to-high permeability reservoirs. The recovery potential coefficient for the average throat radius of medium-to-high permeability reservoirs; The damage rate is the average throat radius of medium-to-high permeability reservoirs. The recovery rate of the average throat radius in medium-to-high permeability reservoirs; The average pore radius of medium-to-high permeability reservoirs at the current cumulative injected pore volume multiple; The initial average pore radius of medium-to-high permeability reservoirs; The damage potential coefficient is the average pore radius of medium-to-high permeability reservoirs. The recovery potential coefficient of the average pore radius of medium-to-high permeability reservoirs; The damage rate is the average pore radius of medium-to-high permeability reservoirs. The recovery rate of the average pore radius of medium-to-high permeability reservoirs; This represents the porosity of medium-to-high permeability reservoirs based on the current cumulative injected pore volume multiple. The initial porosity of medium-to-high permeability reservoirs; The damage potential coefficient for medium-to-high permeability reservoirs; It represents the potential coefficient for the recovery of porosity in medium-to-high permeability reservoirs; Damage rate for medium-to-high permeability reservoirs with high porosity; The recovery rate of porosity in medium-to-high permeability reservoirs; This represents the average tortuosity of medium-to-high permeability reservoirs at the current cumulative injected pore volume multiple. The initial average tortuosity of medium-to-high permeability reservoirs; The damage potential coefficient represents the average tortuosity of medium-to-high permeability reservoirs. The recovery potential coefficient for the average tortuosity of medium-to-high permeability reservoirs; The damage rate is the average tortuosity of medium-to-high permeability reservoirs. The recovery rate of the average tortuosity of medium-to-high permeability reservoirs.

[0035] Step 4: Combine the two-stage competition model of the micropore structure parameters of the target reservoir with the PTC-KC model, and construct a dynamic PTC-KC permeability prediction model based on the cumulative injected pore volume multiple to predict the dynamic permeability of the target reservoir throughout the entire development cycle.

[0036] In this embodiment, the dynamic PTC-KC penetration prediction model is: ; In the formula, This is the cumulative injected pore volume multiple; This represents the dynamic permeability of the target reservoir at the current cumulative injected pore volume multiple. This represents the porosity of the target reservoir at the current cumulative injected pore volume multiple. The average pore radius of the target reservoir at the current cumulative injected pore volume multiple; This represents the average throat radius of the target reservoir at the current cumulative injected pore volume multiple. This represents the average tortuosity of the target reservoir at the current cumulative injected pore volume multiple. This represents the average coordination number of the target reservoir at the current cumulative injected pore volume multiple. , , , All are fitting coefficients.

[0037] Step 5: Establish a geological model of the target reservoir based on its geometric dimensions and geological characteristics. First, substitute the cumulative injected pore volume multiples from the historical development stages of the target reservoir into the dynamic PTC-KC permeability prediction model to predict the current permeability of the target reservoir. Then, use the geological model of the target reservoir to perform numerical simulations of underground hydrogen storage under different injection and production cycles to obtain and evaluate the hydrogen recovery rate of the target reservoir.

[0038] Example 2 This embodiment applies the underground hydrogen storage reservoir permeability characterization method based on pore volume ratio described in Embodiment 1 to a sandstone oil and gas reservoir. The hydrogen recovery rate of this sandstone oil and gas reservoir is evaluated using the underground hydrogen storage reservoir permeability characterization method based on pore volume ratio described in Embodiment 1. The specific steps include: Step 1: Select the target reservoir in the depleted oil and gas reservoir, obtain core samples from the target reservoir, conduct in-situ CT displacement experiments on the core samples, and obtain the microstructure parameters of the target reservoir at different development stages, including porosity, average throat radius, average pore radius, average coordination number, and average tortuosity.

[0039] In this embodiment, a micro-CT scanning system is used in conjunction with an in-situ displacement device made of polyetheretherketone (PEEK). The core sample is placed in the core holder, and a confining pressure of 6 MPa is applied to the core sample. Then, simulated formation oil is first saturated, and simulated formation water is injected into the core sample at a constant flow rate of 0.015 mL / min. The cumulative injected pore volume multiple of the core sample is recorded in real time, and the values ​​are recorded at 0... 1 5 15 30 200 High-resolution in-situ CT scans were performed on the rock samples to obtain high-resolution three-dimensional images of the rock samples.

[0040] High-resolution 3D images of rock samples from each water injection node were imported into the image processing software Avizo. Based on existing 3D digital core reconstruction methods, image registration, nonlocal mean filtering for noise reduction, and watershed algorithms were performed sequentially. Threshold segmentation was used to separate the rock skeleton from the pore space, and the pore network model of the rock samples was extracted to obtain the porosity of core samples from target depleted oil and gas reservoirs at different development stages at each water injection node. Average laryngeal radius Average pore radius Mean coordination number and average tortuosity .

[0041] In this embodiment, two core samples are selected, namely the first core sample and the second core sample. Taking the first core sample and the second core sample as examples, the porosity and permeability of the first core sample at each water injection node are as follows: Figure 2 As shown, the porosity and permeability of the second core sample at each water injection node are as follows: Figure 3 As shown, Figure 2 The initial permeability of the first core sample was 92.43 mD, and its permeability was still lower than the initial permeability even after water was injected to 200 PV. Figure 3 The initial permeability of the second core sample was 453.40 mD. After long-term water injection to 200 PV, the permeability of the target reservoir was higher than the initial permeability.

[0042] Step 2: Construct the PTC-KC model based on the classic Kozeny-Carman equation for dynamically predicting the permeability of target reservoirs in depleted oil and gas reservoirs.

[0043] In this embodiment, by fitting analysis of the micropore structure parameters and permeability of core samples from the target depleted oil and gas reservoir at different development stages, the average throat radius is determined. With average pore radius The ratio and average coordination number are introduced into the classical Kozeny-Carman equation to obtain the PTC-KC model: ; In the formula, For penetration rate; Porosity; The average throat radius; The average pore radius; The average tortuosity; This represents the average coordination number.

[0044] Step 3: Construct a two-stage competitive model of the pore structure parameters of the target reservoir based on the cumulative injected pore volume multiple.

[0045] In this embodiment, the influence of the cumulative injected pore volume ratio on the micropore structure is highly nonlinear and grade-dependent. To further align with engineering practice, the target reservoir type is determined based on its initial permeability, combined with in-situ... The microstructure parameters of the target reservoir at each preset water injection node were obtained during the displacement experiment, and a two-stage competition model for the microstructure parameters was established. Considering the entire development history of the depleted oil and gas reservoir, there are both particle blockage and unblocking effects, and the pore structure parameters change complexly with the cumulative injected pore volume ratio.

[0046] To accurately characterize the competitive effects of microparticle blockage and unblocking in target reservoirs during development stages, a two-stage competitive model of micropore structure parameters was established.

[0047] Specifically, in this embodiment, the two-stage competition model for micropore structure parameters constructed for medium- and low-permeability reservoirs is as follows: ; ; ; ; ; In the formula, This indicates a medium-to-low permeability reservoir. This is the cumulative injected pore volume multiple; This represents the average coordination number of low-to-medium permeability reservoirs based on the current cumulative injected pore volume multiple. This represents the average throat radius of low-to-medium permeability reservoirs at the current cumulative injected pore volume multiple. The average pore radius of low-to-medium permeability reservoirs at the current cumulative injected pore volume multiple; This represents the porosity of low-to-medium permeability reservoirs based on the current cumulative injected pore volume multiple. This represents the average tortuosity of low-to-medium permeability reservoirs at the current cumulative injected pore volume multiple.

[0048] The two-stage competition model for micropore structure parameters constructed for medium-to-high permeability reservoirs is as follows: ; ; ; ; ; In the formula, Indicates medium-to-high permeability reservoirs; This is the cumulative injected pore volume multiple; This represents the average coordination number of medium-to-high permeability reservoirs based on the current cumulative injected pore volume multiple. This represents the average throat radius of medium-to-high permeability reservoirs at the current cumulative injected pore volume multiple. The average pore radius of medium-to-high permeability reservoirs at the current cumulative injected pore volume multiple; This represents the porosity of medium-to-high permeability reservoirs based on the current cumulative injected pore volume multiple. This represents the average tortuosity of medium-to-high permeability reservoirs at the current cumulative injected pore volume multiple.

[0049] Because the initial throat size is large in medium-to-high permeability reservoirs, the final pore structure unblocking effect is stronger than the blocking effect during the entire water drive process, and the permeability is greater than the initial value. Furthermore, considering that the initial pore throat size is large in high-permeability reservoirs, most of the detached particles are directly carried out without forming a main channel blockage, and the tortuosity remains relatively stable. Therefore, in this embodiment, the tortuosity of medium-to-high permeability reservoirs is set to a constant value.

[0050] Step 4: Combine the two-stage competition model of the micropore structure parameters of the target reservoir with the PTC-KC model, and construct a dynamic PTC-KC permeability prediction model based on the cumulative injected pore volume multiple to predict the dynamic permeability of the target reservoir throughout the entire development cycle.

[0051] In this embodiment, the two-stage competition model of micropore structure parameters of medium- and low-permeability reservoirs is substituted into the PTC-KC model to obtain the dynamic PTC-KC permeability prediction model for medium- and low-permeability reservoirs as follows: ;

[0052] in, ; ; ; ; ; In the formula, This represents the dynamic permeability of low-to-medium permeability reservoirs based on the current cumulative injected pore volume multiple.

[0053] Substituting the two-stage competition model of micropore structure parameters of medium-to-high permeability reservoirs into the PTC-KC model, the dynamic PTC-KC permeability prediction model for medium-to-high permeability reservoirs is obtained as follows: ;

[0054] in, ; ; ; ; ; In the formula, This represents the dynamic permeability of medium-to-high permeability reservoirs based on the current cumulative injected pore volume multiple.

[0055] In this embodiment, the dynamic PTC-KC permeability prediction model is used to accurately predict the dynamic permeability of the target reservoir throughout its entire life cycle. Through calibration and verification using measured data, it is determined that the dynamic PTC-KC permeability prediction model constructed in this invention has a permeability prediction error of 8% to 10% for core samples of different grades, thus achieving accurate prediction of the dynamic permeability of the target reservoir at any water drive stage in history.

[0056] Step 5: Establish a geological model of the target reservoir based on its geometric dimensions and geological characteristics. First, substitute the cumulative injected pore volume multiples from the target reservoir's development history into the prediction model to accurately predict the current reservoir permeability. Then, use the geological model of the target reservoir to perform numerical simulations of underground hydrogen storage under different injection and production cycles to obtain the hydrogen recovery rate of the target reservoir and evaluate its hydrogen recovery rate.

[0057] In this embodiment, in DuMu x In the software, a two-dimensional sandstone geological model with dimensions of 2100 m × 1000 m is established based on the geometric dimensions and geological characteristics of the target reservoir. The cumulative injected pore volume multiple of the target reservoir is substituted into the dynamic PTC-KC permeability prediction model constructed in step 3. The dynamic permeability of the target reservoir at each stage of the depleted oil and gas reservoir development history is obtained using the dynamic PTC-KC permeability prediction model and input as a grid attribute into the sandstone geological model, thereby eliminating the evaluation distortion caused by the original static logging permeability data. Furthermore, an injection and production scheme for the target reservoir is set. In this embodiment, the injection and production scheme is to inject hydrogen at a constant rate for one month and then produce it for two months to simulate the hydrogen injection and production process in the target reservoir. After the simulation, the hydrogen recovery rate of low-to-medium permeability reservoirs and high-to-medium permeability reservoirs after a complete injection and production cycle is obtained, such as... Figure 4 and Figure 5 As shown.

[0058] Depend on Figure 4It can be seen that for medium-low permeability reservoirs, when the original static logging permeability measured before the reservoir was developed is used directly, the hydrogen recovery rate is 71%. However, by using the method of this invention, the cumulative injected pore volume multiple during the historical development period of the reservoir is substituted into the dynamic PTC-KC permeability model, and the actual hydrogen recovery rate of the reservoir after 200 PV water drive development is 47%.

[0059] Depend on Figure 5 Therefore, for medium-to-high permeability reservoirs, the hydrogen recovery rate is 75% during the initial development stage. After 5PV of waterflooding development, the hydrogen recovery rate is 56%. After 200PV of waterflooding development, the hydrogen recovery rate is 78%.

[0060] Furthermore, the optimal strategy for hydrogen storage sites in low-to-medium permeability reservoirs and medium-to-high permeability reservoirs is clarified. For low-to-medium permeability reservoirs, priority should be given to selecting areas in the far-well zone where water drive is less intense. For medium-to-high permeability reservoirs, mature areas that have undergone long-term water drive development have better underground hydrogen storage potential.

[0061] Of course, the above description is not intended to limit the present invention, and the present invention is not limited to the examples given above. Any changes, modifications, additions or substitutions made by those skilled in the art within the scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for characterizing the permeability of underground hydrogen storage reservoirs based on pore volume ratio, characterized in that, Includes the following steps: Step 1: Select the target reservoir in the depleted oil and gas reservoir, obtain core samples from the target reservoir, and conduct in-situ CT displacement experiments on the core samples. By controlling the cumulative injected pore volume multiple, simulate the fluid interaction process inside the target reservoir at different development stages, and obtain the rock microstructure parameters inside the target reservoir at different development stages, including porosity, average throat radius, average pore radius, average coordination number, and average tortuosity. Step 2: Construct the PTC-KC model based on the classic Kozeny-Carman equation to dynamically predict the permeability of the target reservoir; Step 3: Construct a two-stage competitive model of the pore structure parameters of the target reservoir based on the cumulative injected pore volume multiple; Step 4: Combine the two-stage competition model of the microstructure parameters of the target reservoir with the PTC-KC model to construct a dynamic PTC-KC permeability prediction model based on the cumulative injected pore volume multiple, which is used to predict the dynamic permeability of the target reservoir throughout the entire development cycle. Step 5: Based on the geometric dimensions and geological characteristics of the target reservoir, establish a geological model of the target reservoir. Substitute the cumulative injected pore volume multiples of the target reservoir's development history into the dynamic PTC-KC permeability prediction model to predict the current dynamic permeability of the target reservoir. Use the geological model of the target reservoir to perform numerical simulations of underground hydrogen storage under different injection and production cycles to obtain and evaluate the hydrogen recovery rate of the target reservoir.

2. The method for characterizing the permeability of underground hydrogen storage reservoirs based on pore volume ratio according to claim 1, characterized in that, In step 1, core samples of the target depleted oil and gas reservoir are obtained, and the core samples are displaced using an in-situ displacement device built on a micron-level micro-CT scanning system. Before displacement, the core samples are saturated with a preset displaced phase. During the displacement process, the displacing phase is injected into the core samples so that it displaces the original displaced phase in the core samples, simulating the fluid interaction process inside the target reservoir at different development stages. During the in-situ CT displacement experiment, a micron-level micro-CT scanning system was used to perform high-resolution in-situ CT scans on the rock samples at each preset injection fluid node, acquiring high-resolution three-dimensional images of the rock samples and the cumulative injection pore volume multiple. After reconstructing the high-resolution 3D images of the rock samples, the pore network model of the rock samples was extracted to obtain the porosity of the core samples at each injected fluid node. Average laryngeal radius Average pore radius Mean coordination number and average tortuosity To obtain the variation patterns of microscopic pore structure parameters and permeability of target depleted oil and gas reservoirs at different stages of development.

3. The method for characterizing the permeability of underground hydrogen storage reservoirs based on pore volume ratio according to claim 1, characterized in that, In step 2, the average throat radius, average pore radius, and average coordination number are introduced into the classical Kozeny-Carman equation to construct the PTC-KC model, resulting in: ； In the formula, For penetration rate; Porosity; The average throat radius; The average pore radius; The average tortuosity; The average coordination number; , , , All are fitting coefficients; The fitting coefficients in the PTC-KC model , , , The micropore structure parameters and permeability of core samples from the target depleted oil and gas reservoir at different stages of development were determined by fitting analysis.

4. The method for characterizing the permeability of underground hydrogen storage reservoirs based on pore volume ratio according to claim 1, characterized in that, In step 3, the type of the target reservoir is determined based on its initial permeability, combined with in-situ... The micropore structure parameters of the target reservoir at each preset injection fluid node were obtained in the displacement experiment. A two-stage competition model of micropore structure parameters was established to accurately characterize the competition between particle blockage and unblocking effects at different development stages. The two-stage competition model for the microporous structure parameters is as follows: ； In the formula, This is the cumulative injected pore volume multiple; These are the microstructure parameters of the pores at the current cumulative injected pore volume ratio. The initial micropore structure parameter values ​​of the target reservoir; This is the damage potential coefficient; The recovery potential coefficient; Damage rate; For recovery rate; It is a natural constant.

5. The method for characterizing the permeability of underground hydrogen storage reservoirs based on pore volume ratio according to claim 4, characterized in that, When the permeability of the target reservoir is greater than 10 mD and not less than 100 mD, the target reservoir is determined to be a medium-low permeability reservoir. By fitting and analyzing the micropore structure parameters of the target reservoir core samples at each preset injection fluid node with the cumulative injected pore volume multiple, a two-stage competition model for the micropore structure parameters of medium-low permeability reservoirs is obtained: ； ； ； ； ； In the formula, This indicates a medium-to-low permeability reservoir. This represents the average coordination number of low-to-medium permeability reservoirs based on the current cumulative injected pore volume multiple. The initial average coordination number of medium- and low-permeability reservoirs; The damage potential coefficient represents the average coordination number of medium- and low-permeability reservoirs. The recovery potential coefficient is the average coordination number of medium- and low-permeability reservoirs; Damage rate is the average coordination number of medium- and low-permeability reservoirs. The recovery rate of the average coordination number of medium- and low-permeability reservoirs; This represents the average throat radius of low-to-medium permeability reservoirs at the current cumulative injected pore volume multiple. The initial average throat radius of medium-low permeability reservoirs; The damage potential coefficient is the average throat radius of medium-low permeability reservoirs. The recovery potential coefficient for the average throat radius of medium-low permeability reservoirs; The damage rate is the average throat radius of medium-low permeability reservoirs. The recovery rate of the average throat radius of medium-low permeability reservoirs; The average pore radius of low-to-medium permeability reservoirs at the current cumulative injected pore volume multiple; The initial average pore radius of medium-low permeability reservoirs; The damage potential coefficient is the average pore radius of medium-low permeability reservoirs. The recovery potential coefficient of the average pore radius of medium-low permeability reservoirs; The damage rate is the average pore radius of medium-low permeability reservoirs. The recovery rate of the average pore radius of medium-low permeability reservoirs; This represents the porosity of low-to-medium permeability reservoirs based on the current cumulative injected pore volume multiple. The initial porosity of medium-low permeability reservoirs; The damage potential coefficient for medium-low permeability reservoirs; This is the potential coefficient for the recovery of porosity in medium- and low-permeability reservoirs; Damage rate for medium-low permeability reservoirs with high porosity; The recovery rate of porosity in medium- and low-permeability reservoirs; This represents the average tortuosity of low-to-medium permeability reservoirs at the current cumulative injected pore volume multiple. The initial average tortuosity of medium- and low-permeability reservoirs; The damage potential coefficient represents the average tortuosity of medium- and low-permeability reservoirs. The recovery potential coefficient for the average tortuosity of medium-low permeability reservoirs; The damage rate represents the average tortuosity of medium- and low-permeability reservoirs. This represents the recovery rate of the average tortuosity of medium- and low-permeability reservoirs.

6. The method for characterizing the permeability of underground hydrogen storage reservoirs based on pore volume ratio according to claim 4, characterized in that, When the permeability of the target reservoir is greater than 100 mD and less than 500 mD, the target reservoir is determined to be a medium-to-high permeability reservoir. By fitting and analyzing the micropore structure parameters of the target reservoir core samples at each preset injection fluid node with the cumulative injected pore volume multiple, a two-stage competition model for the micropore structure parameters of medium-to-high permeability reservoirs is obtained: ； ； ； ； ； In the formula, Indicates medium-to-high permeability reservoirs; This represents the average coordination number of medium-to-high permeability reservoirs based on the current cumulative injected pore volume multiple. The initial average coordination number of medium-to-high permeability reservoirs; The damage potential coefficient represents the average coordination number of medium-to-high permeability reservoirs. The recovery potential coefficient is the average coordination number of medium-to-high permeability reservoirs; Damage rate is the average coordination number of medium-to-high permeability reservoirs; The recovery rate of the average coordination number of medium-to-high permeability reservoirs; This represents the average throat radius of medium-to-high permeability reservoirs at the current cumulative injected pore volume multiple. The initial average throat radius of medium-to-high permeability reservoirs; The damage potential coefficient is the average throat radius of medium-to-high permeability reservoirs. The recovery potential coefficient for the average throat radius of medium-to-high permeability reservoirs; The damage rate is the average throat radius of medium-to-high permeability reservoirs. The recovery rate of the average throat radius in medium-to-high permeability reservoirs; The average pore radius of medium-to-high permeability reservoirs at the current cumulative injected pore volume multiple; The initial average pore radius of medium-to-high permeability reservoirs; The damage potential coefficient is the average pore radius of medium-to-high permeability reservoirs. The recovery potential coefficient of the average pore radius of medium-to-high permeability reservoirs; The damage rate is the average pore radius of medium-to-high permeability reservoirs. The recovery rate of the average pore radius of medium-to-high permeability reservoirs; This represents the porosity of medium-to-high permeability reservoirs based on the current cumulative injected pore volume multiple. The initial porosity of medium-to-high permeability reservoirs; The damage potential coefficient for medium-to-high permeability reservoirs; It represents the potential coefficient for the recovery of porosity in medium-to-high permeability reservoirs; Damage rate for medium-to-high permeability reservoirs with high porosity; The recovery rate of porosity in medium-to-high permeability reservoirs; This represents the average tortuosity of medium-to-high permeability reservoirs at the current cumulative injected pore volume multiple. The initial average tortuosity of medium-to-high permeability reservoirs; The damage potential coefficient represents the average tortuosity of medium-to-high permeability reservoirs. The recovery potential coefficient for the average tortuosity of medium-to-high permeability reservoirs; The damage rate is the average tortuosity of medium-to-high permeability reservoirs. The recovery rate of the average tortuosity of medium-to-high permeability reservoirs.

7. The method for characterizing the permeability of underground hydrogen storage reservoirs based on pore volume ratio according to claim 4, characterized in that, In step 4, the dynamic PTC-KC penetration prediction model is: ； In the formula, This is the cumulative injected pore volume multiple; This represents the dynamic permeability of the target reservoir at the current cumulative injected pore volume multiple. This represents the porosity of the target reservoir at the current cumulative injected pore volume multiple. The average pore radius of the target reservoir at the current cumulative injected pore volume multiple; This represents the average throat radius of the target reservoir at the current cumulative injected pore volume multiple. This represents the average tortuosity of the target reservoir at the current cumulative injected pore volume multiple. This represents the average coordination number of the target reservoir at the current cumulative injected pore volume multiple. , , , All are fitting coefficients.

8. The method for characterizing the permeability of underground hydrogen storage reservoirs based on pore volume ratio according to claim 1, characterized in that, In step 5, a geological model of the target reservoir is established based on its geometric dimensions and geological characteristics. The values ​​of various microscopic pore structure parameters in the geological model are set. The cumulative injected pore volume multiple of the target reservoir during its development history is substituted into the dynamic PTC-KC permeability prediction model. The actual permeability of the target reservoir after the historical development period is calculated using the dynamic PTC-KC permeability prediction model. The initial grid attributes of the geological model are updated based on the actual permeability of the target reservoir. Then, combined with the preset hydrogen injection and production scheme for the target reservoir's storage stages, the geological model is used to simulate the migration, distribution, and extraction processes of hydrogen in the target reservoir during different injection and production stages, obtaining and evaluating the hydrogen recovery rate of the target reservoir.

Images