Construction method of hydrate-containing porous medium resistivity model

Abstract

The invention discloses a construction method of a hydrate-containing porous medium resistivity model, and belongs to the technical field of natural gas hydrate reservoir evaluation. According to the method, based on actual rock core CT scanning data, a reservoir pore throat structure is reproduced through a 3D printing technology, a multi-channel chip model is constructed, a hydrate generation-decomposition experiment and numerical simulation are combined, and a hydrate resistivity calculation model in the porous medium is finally established. The method specifically comprises the following steps: (1) preparing a pore structure chip through CT scanning and 3D printing, and designing a resistance measuring point array; (2) generating a hydrate by adopting a water excess method, a gas excess method and the like, and monitoring saturation and spatial distribution in real time by utilizing an electron microscope; (3) decomposing the hydrate through low-frequency electric heating, and measuring the dynamic change of the resistivity of adjacent measuring points in different periods of time; (4) combining a resistivity calculation formula with an existing low-frequency electric heating simulation method, and constructing a hydrate low-frequency electric heating simulation model according to the obtained hydrate occurrence state distribution; and (5) taking the undetermined coefficient C and the saturation index (n) in the resistivity formula as optimal optimization variables, performing automatic history fitting through mixed optimization strategies such as a gradient descent method and a genetic algorithm, and establishing a hydrate resistivity calculation model.

Description

Technical Field

[0001] The invention relates to a method for constructing a resistivity model of a hydrate-containing porous medium, and belongs to the technical field of hydrate reservoir development. Background Art

[0002] Natural gas hydrates are an important potential energy source, and dynamic reservoir evaluation technology is key to their safe and efficient exploitation. In existing technologies, resistivity monitoring is widely used to characterize hydrate saturation, but its accuracy is limited by the heterogeneous distribution of hydrates. Traditional methods are mostly based on homogeneous assumptions or simplified pore models, making it difficult to truly reflect the spatial heterogeneity of the reservoir pore throat structure. Furthermore, since hydrate decomposition is a dynamic process, the calibration of the undetermined coefficient (C) and saturation index (n) in the resistivity formula is subject to large errors. This study uses a combination of CT scanning and 3D printing to reproduce the pore throat structure of actual cores. Furthermore, a combination of experiments and simulations is used to continuously correct the undetermined coefficient (C) and saturation index (n) in the resistivity formula, ensuring that the error between the theoretical calculated value and the experimental measurement value is within a reasonable range. Ultimately, a resistivity calculation model for hydrate-bearing porous media is established. Summary of the Invention

[0003] 1. The present invention discloses a method for constructing a resistivity model of a hydrate-containing porous medium. Based on actual hydrate core CT scanning data, the reservoir pore throat structure is reproduced by 3D printing technology and a multi-channel chip model is customized. The chip is used to conduct hydrate formation and decomposition experiments. The hydrate saturation evolution process is observed in real time using an electron microscope to accurately capture the hydrate saturation changes. The hydrate is then decomposed using low-frequency electric heating, and the resistivity changes between adjacent measuring points are measured. The resistivity calculation formula is then combined with an existing low-frequency electric heating simulation method. A hydrate low-frequency electric heating simulation model is constructed based on the obtained hydrate occurrence state distribution. Finally, the undetermined coefficient (C) and saturation index (n) in the resistivity formula are optimized as optimization variables. The simulated resistivity and experimental data errors are converged through automatic history fitting to establish a resistivity calculation model. The method is characterized by comprising the following steps:

[0004] Step (1): Perform CT scanning on the actual hydrate core to obtain pore structure data, use 3D printing technology to reproduce the reservoir pore throat structure and prepare a multi-channel chip model, and design the resistance measurement point array according to the chip geometry;

[0005] Step (2): Conducting a hydrate formation experiment based on the chip obtained in step (1), using an electron microscope to monitor the change in hydrate saturation in real time, terminating the experiment after the saturation reaches stability, and simultaneously recording the spatial occurrence state of the hydrate;

[0006] Step (3): Decompose the hydrate using low-frequency electric heating technology and measure the resistivity values ​​between adjacent measuring points in different time periods;

[0007] Step (4): combining the resistivity calculation formula with the existing low-frequency electric heating simulation method, and constructing a hydrate low-frequency electric heating simulation model based on the hydrate occurrence state distribution obtained in step 2;

[0008] Step (5): Using the undetermined coefficient (C) and saturation index (n) in the resistivity formula as optimization variables, the parameters are adjusted by the automatic history fitting algorithm so that the error between the simulated resistivity and the experimental measurement value converges to a preset threshold, and finally a resistivity calculation model for hydrate-containing porous media is established;

[0009] 2. The method for constructing a resistivity model of a hydrate-containing porous medium according to claim 1, wherein in step 2, the hydrate formation method includes a water excess method, a gas excess method, a dissolved gas method, and an ice powder method;

[0010] 3. The method for constructing a resistivity model of a hydrate-containing porous medium according to claim 1, wherein in step 3, the low-frequency electric heating parameters are designed with reference to Natural Gas Hydrate - Arctic Ocean Deepwater Resource Potential, by Michael D. Max et al.

[0011] 4. The method for constructing a resistivity model of a hydrate-containing porous medium according to claim 1, wherein in step 4, the resistivity simulation method is based on "Natural Gas Hydrate Reservoir Well Logging Evaluation" by Zhang Jian, Liu Jiangping, et al.

[0012] 5. The method for constructing a resistivity model of a hydrate-containing porous medium according to claim 1, wherein in step 4, the resistivity is calculated by referring to the following formula:

[0013]

[0014] Where ρ τ is the resistivity of the porous medium containing hydrate, Ω·m; C is the coefficient to be determined; S w is the water saturation, a decimal; n is the saturation index; It is the resistivity value when the hydrate saturation is 100% (that is, the resistivity measured when all the pore channels of the porous medium are saturated with hydrates).

[0015] 6. The method for constructing a resistivity model of a hydrate-containing porous medium according to claim 1, wherein in step 5, the automatic history fitting method includes a gradient descent method, a genetic algorithm, a Bayesian inversion, an ensemble Kalman filter, and a hybrid optimization strategy of the gradient descent method and the genetic algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of a method for constructing a resistivity model of a hydrate-containing porous medium;

[0017] Figure 2 Microfluidic chips designed based on key parameters;

[0018] Figure 3 Microscopic images of methane hydrate formation at different times in some areas;

[0019] Figure 4 Comparison of the experimentally measured resistivity with the theoretical value calculated by the model;

[0020] The following specific embodiments are given to further illustrate the content of the present invention: DETAILED DESCRIPTION

[0021] The present invention will be further described below with reference to the accompanying drawings, but the scope of implementation of the present invention is not limited thereto.

[0022] 7. The present invention discloses a method for constructing a resistivity model of a hydrate-containing porous medium. Based on actual hydrate core CT scanning data, the reservoir pore throat structure is reproduced through 3D printing technology and a multi-channel chip model is customized. The chip is used to conduct hydrate formation and decomposition experiments. The hydrate saturation evolution process is observed in real time with an electron microscope to accurately capture the hydrate saturation changes. The hydrate is then decomposed using low-frequency electric heating, and the resistivity changes between adjacent measuring points are measured. The resistivity calculation formula is then combined with an existing low-frequency electric heating simulation method. A hydrate low-frequency electric heating simulation model is constructed based on the obtained hydrate occurrence state distribution. Finally, the undetermined coefficient (C) and saturation index (n) in the resistivity formula are optimized as optimization variables. The simulated resistivity and experimental data errors are converged through automatic history fitting to establish a resistivity calculation model. The method is characterized by comprising the following steps:

[0023] Step (1): Perform CT scanning on the actual hydrate core to obtain pore structure data, use 3D printing technology to reproduce the reservoir pore throat structure and prepare a multi-channel chip model, and design the resistance measurement point array according to the chip geometry;

[0024] Step (2): Conducting a hydrate formation experiment based on the chip obtained in step (1), using an electron microscope to monitor the change in hydrate saturation in real time, terminating the experiment after the saturation reaches stability, and simultaneously recording the spatial occurrence state of the hydrate;

[0025] Step (3): Decompose the hydrate using low-frequency electric heating technology and measure the resistivity values ​​between adjacent measuring points in different time periods;

[0026] Step (4): combining the resistivity calculation formula with the existing low-frequency electric heating simulation method, and constructing a hydrate low-frequency electric heating simulation model based on the hydrate occurrence state distribution obtained in step 2;

[0027] Step (5): Using the undetermined coefficient (C) and saturation index (n) in the resistivity formula as optimization variables, the parameters are adjusted by the automatic history fitting algorithm so that the error between the simulated resistivity and the experimental measurement value converges to a preset threshold, and finally a resistivity calculation model for hydrate-containing porous media is established;

[0028] 8. The method for constructing a resistivity model of a hydrate-containing porous medium according to claim 1, wherein in step 2, the hydrate formation method includes a water excess method, a gas excess method, a dissolved gas method, and an ice powder method;

[0029] 9. The method for constructing a resistivity model of a hydrate-containing porous medium according to claim 1, wherein in step 3, the low-frequency electric heating parameters are designed with reference to Natural Gas Hydrate - Arctic Ocean Deepwater Resource Potential, by Michael D. Max et al.

[0030] 10. The method for constructing a resistivity model of a hydrate-containing porous medium according to claim 1, wherein in step 4, the resistivity simulation method is based on "Natural Gas Hydrate Reservoir Well Logging Evaluation" by Zhang Jian, Liu Jiangping, et al.

[0031] 11. The method for constructing a resistivity model of a hydrate-containing porous medium according to claim 1, wherein in step 4, the resistivity is calculated by referring to the following formula:

[0032]

[0033] Where ρ τ is the resistivity of the porous medium containing hydrate, Ω·m; C is the coefficient to be determined; S w is the water saturation, a decimal; n is the saturation index; It is the resistivity value when the hydrate saturation is 100% (that is, the resistivity measured when all the pore channels of the porous medium are saturated with hydrates).

[0034] 12. The method for constructing a resistivity model of a hydrate-containing porous medium according to claim 1, wherein in step 5, the automatic history fitting method includes a gradient descent method, a genetic algorithm, a Bayesian inversion, an ensemble Kalman filter, and a hybrid optimization strategy of the gradient descent method and the genetic algorithm.

[0035] The embodiment of the present invention provides an experimental scheme for establishing a resistivity model considering the heterogeneous distribution of hydrates. The specific process is as follows: first, based on the CT scanning data of the sandstone hydrate core in the Shenhu area of ​​the South China Sea, the reservoir pore throat structure is reproduced using high-precision 3D printing technology, and a multi-channel chip (20mm×20mm×5mm) with a porosity of 35% and a permeability of 150mD is prepared, and an 8×8 gold-plated copper electrode measurement point array is designed. Figure 2 ; Subsequently, hydrates were generated in a high-pressure reactor using the gas excess method (CH4 pressure 8 MPa, temperature 2°C, salinity 3.5 wt%). The spatial distribution of hydrates was captured in real time using an electron microscope combined with a low-temperature sample stage, and the resistivity evolution was simultaneously monitored until the saturation stabilized. 50 Hz low-frequency electric heating was then applied to decompose the hydrates, and the dynamic resistivity response was measured in stages (recorded every 10 minutes). The resistivity calculation formula was further combined with the existing low-frequency electric heating simulation method, and a hydrate low-frequency electric heating simulation model was constructed based on the obtained hydrate occurrence state distribution. A genetic algorithm was used to automatically fit the Archie formula parameters, and finally a heterogeneous hydrate resistivity calculation model was established (RMSE = 3.8%, R 2 >0.95), and select three measurement points to verify the model, such as Figure 3 , it can be seen that the fitting effect is good, so the obtained model has practical application significance.

[0036] The final result shows that the optimized parameters C are 0.945 and n is 1.92 (S w ≤76.02%) and 1.69(S w >76.02%), the quantitative relationship model between hydrate saturation and resistivity in porous media is obtained as follows: S w ≤76.02%.

Claims

1. The present invention discloses a method for constructing a resistivity model of a hydrate-containing porous medium. Based on actual hydrate core CT scanning data, the reservoir pore throat structure is reproduced by 3D printing technology and a multi-channel chip model is customized. The chip is used to carry out hydrate generation and decomposition experiments. The hydrate saturation evolution process is observed in real time with an electron microscope to accurately capture the hydrate saturation change. Subsequently, low-frequency electric heating is used to decompose the hydrate, and the resistivity change between adjacent measuring points is measured. Subsequently, the resistivity calculation formula is combined with the existing low-frequency electric heating simulation method, and a hydrate low-frequency electric heating simulation model is constructed based on the obtained hydrate occurrence state distribution. Finally, the undetermined coefficient (C) and saturation index (n) in the resistivity formula are optimized as optimization variables. The simulated resistivity and experimental data errors are converged through automatic history fitting to establish a resistivity calculation model. The method is characterized by: The following steps are involved: Step (1): Perform CT scanning on the actual hydrate core to obtain pore structure data, use 3D printing technology to reproduce the reservoir pore throat structure and prepare a multi-channel chip model, and design the resistance measurement point array according to the chip geometry; Step (2): Conducting a hydrate formation experiment based on the chip obtained in step (1), using an electron microscope to monitor the change in hydrate saturation in real time, terminating the experiment after the saturation reaches stability, and simultaneously recording the spatial occurrence state of the hydrate; Step (3): Decompose the hydrate using low-frequency electric heating technology and measure the resistivity values ​​between adjacent measuring points in different time periods; Step (4): combining the resistivity calculation formula with the existing low-frequency electric heating simulation method, and constructing a hydrate low-frequency electric heating simulation model based on the hydrate occurrence state distribution obtained in step 2; Step (5): The undetermined coefficient (C) and saturation index (n) in the resistivity formula are used as optimization variables. The parameters are adjusted through the automatic history fitting algorithm so that the error between the simulated resistivity and the experimental measurement value converges to the preset threshold, and finally a resistivity calculation model for hydrate-containing porous media is established.

2. The method for constructing a resistivity model of a hydrate-containing porous medium according to claim 1, wherein: In step 2, hydrate formation methods include water excess method, gas excess method, dissolved gas method and ice powder method.

3. The method for constructing a resistivity model of a hydrate-containing porous medium according to claim 1, wherein: In step 3, the low-frequency electric heating parameter design refers to "Natural Gas Hydrate-Arctic Ocean Deepwater Resource Potential" by Michael D. Max et al.

4. The method for constructing a resistivity model of a hydrate-containing porous medium according to claim 1, wherein: In step 4, the resistivity simulation method is based on "Natural Gas Hydrate Reservoir Logging Evaluation", written by Zhang Jian, Liu Jiangping, etc.

5. The method for constructing a resistivity model of a hydrate-containing porous medium according to claim 1, wherein: In step 4, the resistivity calculation method refers to the following formula: Where ρ τ is the resistivity of the porous medium containing hydrate, Ω·m; C is the coefficient to be determined; S w is the water saturation, a decimal; n is the saturation index; It is the resistivity value when the hydrate saturation is 100% (that is, the resistivity measured when all the pore channels of the porous medium are saturated with hydrates).

6. The method for constructing a resistivity model of a hydrate-containing porous medium according to claim 1, wherein: In step 5, the automatic history fitting method includes gradient descent method, genetic algorithm, Bayesian inversion, ensemble Kalman filtering and a hybrid optimization strategy of gradient descent method and genetic algorithm.

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