Method, device, equipment and medium for predicting water and gas saturation of tight gas reservoir
Through the nonlinear conjugation gradient inversion method and the earth electromagnetic inversion resistivity, combined with the electrical logging and the electrical characteristics of rock sample, the reservoir water saturation is calculated and the gas saturation is derived, which solves the problem of low prediction accuracy of dense reservoir parameters and realizes high-precision reservoir parameter prediction.
Patent Information
- Application Number
- CN202311568633.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-11-22
- Publication Date
- 2025-05-23
AI Technical Summary
Under complex geological conditions, the gas content detection of dense reservoirs is difficult, the reservoir parameter prediction range is small and the accuracy is low, so it cannot effectively meet the needs of high-precision tight oil and gas exploration and evaluation.
The nonlinear conjugated gradient inversion method is used, based on the earth electromagnetic inversion resistivity, combined with electrical logging, compact reservoir rock sample electrical characteristics and corrected Archie formula, the reservoir water saturation is calculated and the gas saturation is derived.
By saving computing storage space and improving computing efficiency, high-precision prediction of water and gas saturation in tight gas reservoirs is achieved, meeting the needs of high-precision exploration and evaluation.
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Abstract
Description
Technical Field
[0001] The present invention relates to the field of oil and gas geophysical exploration, and more specifically, to a method, device, equipment and medium for predicting water and gas saturation of a tight gas reservoir. Background Art
[0002] Magnetotelluric method has become one of the most widely used methods in electromagnetic exploration due to its advantages such as large detection depth, low cost and convenient construction. Magnetotelluric inversion resistivity can effectively characterize the boundary morphological characteristics of oil and gas reservoirs. Magnetotelluric resistivity inversion methods mainly include linear inversion and nonlinear inversion methods. Due to the strong nonlinearity between measured magnetotelluric data and model parameters, nonlinear inversion has been rapidly developed in magnetotelluric inversion. Commonly used magnetotelluric nonlinear inversion methods include rapid relaxation inversion (RRI), Occam inversion, Gauss-Newton inversion (GN), nonlinear conjugate gradient (NLCG) inversion, etc. Among them, the nonlinear conjugate gradient method has become one of the most important and mature two-dimensional inversion methods in magnetotelluric actual data processing due to its significant advantages of strong stability and fast convergence speed.
[0003] Therefore, it is necessary to develop a method, device, equipment and medium for predicting water and gas saturation of tight gas reservoirs based on magnetotelluric inversion resistivity.
[0004] The information disclosed in the background technology section of the present invention is only intended to deepen the understanding of the general background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention
[0005] The present invention proposes a method, device, equipment and medium for predicting water and gas saturation of tight gas reservoirs, which can save computing storage space and improve computing efficiency for low signal-to-noise ratio magnetotelluric data through a nonlinear conjugate gradient inversion algorithm, and obtain the spatial distribution of reservoir water saturation by analyzing electrical logging, electrical characteristics of tight reservoir rock samples, modified Archie formula and inverted reservoir resistivity.
[0006] In a first aspect, an embodiment of the present disclosure provides a method for predicting water and gas saturation of a tight gas reservoir, comprising:
[0007] Process the raw magnetotelluric data to obtain apparent resistivity and phase data;
[0008] Calculate the gradient vector of the objective function and obtain the conjugate gradient direction;
[0009] Based on the apparent resistivity and the phase data, searching for an optimal step length in the conjugate gradient direction to obtain reservoir inversion resistivity;
[0010] Collect tight reservoir rock samples and electrical logging resistivity data in the study area to obtain the porosity and formation factors of tight reservoir rock samples;
[0011] Based on the reservoir inversion resistivity, the porosity, and the formation factor data, the formation water saturation is calculated, and then the formation gas saturation is calculated.
[0012] As a specific implementation method of the embodiment of the present disclosure, the raw magnetotelluric data is a time series, and the time series data is converted into apparent resistivity and phase data through power spectrum calculation.
[0013] As a specific implementation method of the embodiment of the present disclosure, the vector of the objective function is:
[0014]
[0015] in, is the objective function, m is the model vector, m 0 is the initial model, m ref is the prior model, d is the data vector, λ is the regularization factor, is the forward model, J(m ref ) is the Jacobian matrix, C m is the model covariance matrix, C d is the data covariance matrix.
[0016] As a specific implementation manner of the embodiment of the present disclosure, the optimal step size makes the inversion objective function take a minimum value, and the inverted resistivity data is obtained based on the apparent resistivity and the phase data.
[0017] As a specific implementation of the embodiment of the present disclosure, the optimal step length satisfies:
[0018] V(m+α'u)<V(m)+δα'▽V(m)·v.
[0019] As a specific implementation of the embodiment of the present disclosure, the formation water saturation is calculated as:
[0020]
[0021] Among them, ρ t is the formation resistivity, a, m, n are empirical factors, ρ w is the water saturation of the reservoir, φ is the porosity, S w is the water saturation of the formation.
[0022] As a specific implementation of the embodiment of the present disclosure, the formation gas saturation is:
[0023] Sg =1-S w
[0024] Among them, S g is the gas saturation of the formation.
[0025] In a second aspect, the embodiments of the present disclosure further provide a device for predicting water and gas saturation of a tight gas reservoir, comprising:
[0026] The processing module processes the raw magnetotelluric data to obtain apparent resistivity and phase data;
[0027] Gradient calculation module, calculates the gradient vector of the objective function and obtains the conjugate gradient direction;
[0028] A step length search module, which searches for an optimal step length in the conjugate gradient direction based on the apparent resistivity and the phase data to obtain a reservoir inversion resistivity;
[0029] The collection module collects tight reservoir rock samples and electric logging resistivity data in the study area to obtain the porosity and formation factors of tight reservoir rock samples;
[0030] The gas saturation calculation module calculates the formation water saturation based on the reservoir inversion resistivity, the porosity, and the formation factor data, and then calculates the formation gas saturation.
[0031] As a specific implementation method of the embodiment of the present disclosure, the raw magnetotelluric data is a time series, and the time series data is converted into apparent resistivity and phase data through power spectrum calculation.
[0032] As a specific implementation of the embodiment of the present disclosure, the vector of the objective function is:
[0033]
[0034] in, is the objective function, m is the model vector, m 0 is the initial model, m ref is the prior model, d is the data vector, λ is the regularization factor, is the forward model, J(m ref ) is the Jacobian matrix, C m is the model covariance matrix, C d is the data covariance matrix.
[0035] As a specific implementation manner of the embodiment of the present disclosure, the optimal step size makes the inversion objective function take a minimum value, and the inverted resistivity data is obtained based on the apparent resistivity and the phase data.
[0036] As a specific implementation manner of the embodiments of the present disclosure, the optimal step size satisfies:
[0037] V(m + α'u) < V(m) + δα'▽V(m)·v.
[0038] As a specific implementation manner of the embodiments of the present disclosure, the formation water saturation is calculated as:
[0039]
[0040] where ρ t is the formation resistivity, a, m, and n are empirical factors, ρ w is the reservoir water saturation, φ is the porosity, and S w is the formation water saturation.
[0041] As a specific implementation manner of the embodiments of the present disclosure, the formation gas saturation is:
[0042] S g = 1 - S w
[0043] where S g is the formation gas saturation.
[0044] In a third aspect, the embodiments of the present disclosure further provide an electronic device, which includes:
[0045] a memory storing executable instructions;
[0046] a processor that runs the executable instructions in the memory to implement the method for predicting water and gas saturations in a tight gas reservoir.
[0047] In a fourth aspect, the embodiments of the present disclosure further provide a computer-readable storage medium, which stores a computer program that, when executed by a processor, implements the method for predicting water and gas saturations in a tight gas reservoir.
[0048] The beneficial effects thereof are:
[0049] The present invention aims at the problem that the gas content detection of tight reservoirs under complex geological conditions is difficult, the prediction range of reservoir parameters is small, the accuracy is low, and the current high-precision tight oil and gas exploration and evaluation needs cannot be effectively met. Based on the physical characteristic that resistivity is sensitive to reservoir water saturation, a method for predicting water saturation of tight gas reservoirs based on magnetotelluric inversion resistivity is proposed. This method is based on magnetotelluric measured data, adopts nonlinear conjugate gradient inversion method, obtains reservoir inversion resistivity data body, combines the formation resistivity characteristics of the study area, completes the boundary division of tight gas reservoir, and obtains the spatial distribution of reservoir water saturation by analyzing electrical logging, electrical characteristics of tight reservoir rock samples, and reservoir resistivity based on modified Archie formula and inversion.
[0050] The methods and apparatus of the present invention have other features and advantages that will be apparent from, or will be described in detail in, the accompanying drawings and subsequent detailed descriptions incorporated herein, which together serve to explain the specific principles of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS
[0051] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present invention.
[0052] Figure 1 A flow chart showing the steps of a method for predicting water and gas saturation of a tight gas reservoir according to an embodiment of the present invention.
[0053] Figure 2 A schematic diagram of a measured magnetotelluric apparent resistivity curve according to an embodiment of the present invention is shown.
[0054] Figure 3 A schematic diagram of a measured magnetotelluric phase curve according to an embodiment of the present invention is shown.
[0055] Figure 4 A schematic diagram of tight gas reservoir boundary characterization based on magnetotelluric inversion resistivity data according to an embodiment of the present invention is shown.
[0056] Figure 5 A schematic diagram of water saturation prediction of tight gas reservoirs based on magnetotelluric inversion resistivity data according to an embodiment of the present invention is shown.
[0057] Figure 6 A block diagram of a device for predicting water and gas saturation of a tight gas reservoir according to an embodiment of the present invention is shown.
[0058] Description of reference numerals:
[0059] 201, processing module; 202, gradient calculation module; 203, step search module; 204, collection module; 205, gas saturation calculation module. DETAILED DESCRIPTION
[0060] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.
[0061] To facilitate understanding of the solutions and effects of the embodiments of the present invention, six specific application examples are given below. Those skilled in the art should understand that the examples are only for facilitating understanding of the present invention, and any specific details thereof are not intended to limit the present invention in any way.
[0062] Example 1
[0063] Figure 1 A flow chart showing the steps of a method for predicting water and gas saturation of a tight gas reservoir according to an embodiment of the present invention.
[0064] like Figure 1 As shown, the method for predicting water and gas saturation of tight gas reservoirs includes: step 101, processing the original magnetotelluric data to obtain apparent resistivity and phase data; step 102, calculating the gradient vector of the objective function, and then obtaining the conjugate gradient direction; step 103, based on the apparent resistivity and phase data, searching for the optimal step length in the conjugate gradient direction to obtain the reservoir inversion resistivity; step 104, collecting tight reservoir rock samples and electric logging resistivity data in the study area, and obtaining the porosity and formation factor of the tight reservoir rock samples; step 105, calculating the formation water saturation based on the reservoir inversion resistivity, porosity, and formation factor data, and then calculating the formation gas saturation.
[0065] In one example, the original magnetotelluric data is a time series, and the time series data is converted into apparent resistivity and phase data by power spectrum calculation.
[0066] In one example, the vector of objective functions is:
[0067]
[0068] in, is the objective function, m is the model vector, m 0 is the initial model, m ref is the prior model, d is the data vector, λ is the regularization factor, is the forward model, J(m ref ) is the Jacobian matrix, C mis the model covariance matrix, C d is the data covariance matrix.
[0069] In one example, the optimal step size makes the inversion objective function reach a minimum value, and the inverted resistivity data is obtained based on the apparent resistivity and phase data.
[0070] In one example, the optimal step size satisfies:
[0071] V(m+α'u)<V(m)+δα'▽V(m)·v.
[0072] In one example, the formation water saturation is calculated as:
[0073]
[0074] Among them, ρ t is the formation resistivity, a, m, n are empirical factors, ρ w is the water saturation of the reservoir, φ is the porosity, S w is the water saturation of the formation.
[0075] In one example, the formation gas saturation is:
[0076] S g =1-S w
[0077] Among them, S g is the gas saturation of the formation.
[0078] Specifically, the measured raw magnetotelluric data is a time series, and the power spectrum calculation is needed to convert the raw time series data into apparent resistivity and phase data that can be used for inversion.
[0079] By obtaining the objective function gradient vector, the conjugate gradient direction is obtained. The conjugate gradient direction is the opposite direction of the gradient and is obtained by the calculated gradient.
[0080] The gradient calculation formula of the objective function is as follows:
[0081]
[0082] In the formula, is the objective function, m is the model vector, m 0 is the initial model, m ref is the prior model, d is the data vector, λ is the regularization factor, is the forward model, J(m ref ) is the Jacobian matrix, C m is the model covariance matrix, C d is the data covariance matrix.
[0083] In the process of calculating the gradient, the nonlinear conjugate gradient method only needs to calculate J(m ref ) multiplied by the model vector, and J(m ref ) T The product of the data vector is calculated instead of first calculating J(m ref ), and then multiply it with the vector, avoiding the direct calculation of J(m ref ) requires a lot of forward modeling and storage memory.
[0084] Search for the optimal step size α in the conjugate gradient direction i , so that the inversion objective function takes the minimum value, thus completing the inversion and obtaining the inverted resistivity data. The resistivity parameters are included in The initial value of the optimal step length is generally set to 1. During the inversion calculation process, the optimal step length α i Need to meet:
[0085] V(m+α'u)<V(m)+δα'▽V(m)·v
[0086] After multiple inversion iterations, the resistivity model that makes the objective function take the minimum value is finally obtained, and the inversion is completed.
[0087] Tight reservoir rock samples and measured resistivity data in the study area were collected. Through experimental and logging data analysis, the porosity, formation factors and other parameters of the tight reservoir rock samples were obtained, and the boundary characterization of the tight gas reservoir was carried out based on magnetotelluric inversion resistivity data.
[0088] Based on the reservoir inversion resistivity, porosity, and formation factor data, the modified Archie formula is used:
[0089]
[0090] Where: t is the formation resistivity, a, m, n are empirical factors, ρ w is the water saturation of the reservoir, φ is the porosity, S w is the water saturation of the formation.
[0091] Based on the formation resistivity ρ t and porosity φ, the formation water saturation S can be calculated w , since the calculation formula of gas saturation of formation is:
[0092] S g =1-S w
[0093] Based on this, water saturation prediction and gas content inspection of tight gas reservoirs are carried out.
[0094] This method uses a nonlinear conjugate gradient inversion algorithm to achieve gradient calculation for low signal-to-noise ratio magnetotelluric data by avoiding directly solving the Jacobian matrix, saving computing storage space and improving computing efficiency. Combined with the electrical characteristics of the formation in the study area, the boundary of the tight reservoir is delineated. The spatial distribution of reservoir water saturation is obtained by analyzing the electrical characteristics of electrical logging and tight reservoir rock samples, and the modified Archie formula and inverted reservoir resistivity.
[0095] Example 2
[0096] The present invention also provides a device for predicting water and gas saturation of a tight gas reservoir, comprising:
[0097] The processing module processes the raw magnetotelluric data to obtain apparent resistivity and phase data;
[0098] Gradient calculation module, calculates the gradient vector of the objective function and obtains the conjugate gradient direction;
[0099] The step search module searches for the optimal step size in the conjugate gradient direction based on the apparent resistivity and phase data to obtain the reservoir inversion resistivity;
[0100] The collection module collects tight reservoir rock samples and electric logging resistivity data in the study area to obtain the porosity and formation factors of tight reservoir rock samples;
[0101] The gas saturation calculation module calculates the formation water saturation based on the reservoir inversion resistivity, porosity and formation factor data, and then calculates the formation gas saturation.
[0102] In one example, the original magnetotelluric data is a time series, and the time series data is converted into apparent resistivity and phase data by power spectrum calculation.
[0103] In one example, the vector of objective functions is:
[0104]
[0105] in, is the objective function, m is the model vector, m 0 is the initial model, m ref is the prior model, d is the data vector, λ is the regularization factor, is the forward model, J(m ref ) is the Jacobian matrix, C m is the model covariance matrix, C d is the data covariance matrix.
[0106] In one example, the optimal step size makes the inversion objective function reach a minimum value, and the inverted resistivity data is obtained based on the apparent resistivity and phase data.
[0107] In one example, the optimal step size satisfies:
[0108] V(m+α'u)<V(m)+δα'▽V(m)·v.
[0109] In one example, the formation water saturation is calculated as:
[0110]
[0111] Among them, ρ t is the formation resistivity, a, m, n are empirical factors, ρ w is the water saturation of the reservoir, φ is the porosity, S w is the water saturation of the formation.
[0112] In one example, the formation gas saturation is:
[0113] S g =1-S w
[0114] Among them, S g is the gas saturation of the formation.
[0115] Specifically, the measured raw magnetotelluric data is a time series, and the power spectrum calculation is needed to convert the raw time series data into apparent resistivity and phase data that can be used for inversion.
[0116] By obtaining the objective function gradient vector, the conjugate gradient direction is obtained. The conjugate gradient direction is the opposite direction of the gradient and is obtained by the calculated gradient.
[0117] The gradient calculation formula of the objective function is as follows:
[0118]
[0119] In the formula, is the objective function, m is the model vector, m 0 is the initial model, m ref is the prior model, d is the data vector, λ is the regularization factor, is the forward model, J(m ref ) is the Jacobian matrix, C m is the model covariance matrix, C d is the data covariance matrix.
[0120] In the process of calculating the gradient, the nonlinear conjugate gradient method only needs to calculate J(m ref ) multiplied by the model vector, and J(m ref ) T The product of the data vector is calculated instead of first calculating J(mref ), and then multiply it with the vector, avoiding the direct calculation of J(m ref ) requires a lot of forward modeling and storage memory.
[0121] Search for the optimal step size α in the conjugate gradient direction i , so that the inversion objective function takes the minimum value, thus completing the inversion and obtaining the inverted resistivity data. The resistivity parameters are included in The initial value of the optimal step length is generally set to 1. During the inversion calculation process, the optimal step length α i Need to meet:
[0122] V(m+α'u)<V(m)+δα'▽V(m)·v
[0123] After multiple inversion iterations, the resistivity model that makes the objective function take the minimum value is finally obtained, and the inversion is completed.
[0124] Tight reservoir rock samples and measured resistivity data in the study area were collected. Through experimental and logging data analysis, the porosity, formation factors and other parameters of the tight reservoir rock samples were obtained, and the boundary characterization of the tight gas reservoir was carried out based on magnetotelluric inversion resistivity data.
[0125] Based on the reservoir inversion resistivity, porosity, and formation factor data, the modified Archie formula is used:
[0126]
[0127] Where: t is the formation resistivity, a, m, n are empirical factors, ρ w is the water saturation of the reservoir, φ is the porosity, S w is the water saturation of the formation.
[0128] Based on the formation resistivity ρ t and porosity φ, the formation water saturation S can be calculated w , since the calculation formula of gas saturation of formation is:
[0129] S g =1-S w
[0130] Based on this, water saturation prediction and gas content inspection of tight gas reservoirs are carried out.
[0131] Example 3
[0132] Taking a tight sandstone oil and gas block in the southwest as an example, this method is used to characterize the reservoir boundary.
[0133] Figure 2A schematic diagram of a measured magnetotelluric apparent resistivity curve according to an embodiment of the present invention is shown.
[0134] Figure 3 A schematic diagram of a measured magnetotelluric phase curve according to an embodiment of the present invention is shown.
[0135] For the original magnetotelluric time series data of the embodiment block, the apparent resistivity and phase data are obtained after power spectrum calculation. The data are the actual data of nonlinear conjugate gradient inversion, such as Figure 2 , 3 shown.
[0136] By obtaining the objective function gradient vector, the conjugate gradient direction is obtained. The gradient calculation formula of the objective function is as follows:
[0137]
[0138] In the process of calculating the gradient, the nonlinear conjugate gradient method only needs to calculate J(m ref ) multiplied by the model vector, and J(m ref ) T The product of the data vector is calculated instead of first calculating J(m ref ), and then multiply it with the vector, avoiding the direct calculation of J(m ref ) requires a lot of forward modeling and storage memory.
[0139] Search for the optimal step size α in the conjugate gradient direction i , so that the inversion objective function takes the minimum value, thus completing the inversion and obtaining the inversion resistivity data. The initial value of the optimal step length is generally set to 1. During the inversion calculation process, the optimal step length α i Need to meet:
[0140] V(m+α'u)<V(m)+δα'▽V(m)·v
[0141] After multiple inversion iterations, the resistivity model that makes the objective function take the minimum value is finally obtained, and the inversion is completed.
[0142] Figure 4 A schematic diagram of tight gas reservoir boundary characterization based on magnetotelluric inversion resistivity data according to an embodiment of the present invention is shown.
[0143] Collect tight reservoir rock samples and measured resistivity data in the study area, obtain parameters such as porosity and formation factors of tight reservoir rock samples through experimental and logging data analysis, and carry out tight gas reservoir boundary characterization based on magnetotelluric inversion resistivity data, such as Figure 4 shown.
[0144] Figure 5A schematic diagram of water saturation prediction of tight gas reservoirs based on magnetotelluric inversion resistivity data according to an embodiment of the present invention is shown.
[0145] Based on the reservoir inversion resistivity, porosity, and formation factor data, the modified Archie formula is used: Based on the formation resistivity ρ t and porosity φ, the formation water saturation S can be calculated w , since the gas saturation calculation formula of the formation is: S g =1-S w Based on this, the water saturation of tight gas reservoirs is predicted and the gas content is checked. Figure 5 shown.
[0146] Example 4
[0147] Figure 6 A block diagram of a device for predicting water and gas saturation of a tight gas reservoir according to an embodiment of the present invention is shown.
[0148] like Figure 6 As shown, the device for predicting water and gas saturation of tight gas reservoirs comprises:
[0149] Processing module 201 processes the raw magnetotelluric data to obtain apparent resistivity and phase data;
[0150] A gradient calculation module 202 calculates the gradient vector of the objective function to obtain the conjugate gradient direction;
[0151] A step length search module 203 searches for an optimal step length in the conjugate gradient direction based on the apparent resistivity and phase data to obtain the reservoir inversion resistivity;
[0152] The collection module 204 collects tight reservoir rock samples and electric logging resistivity data in the study area to obtain the porosity and formation factors of the tight reservoir rock samples;
[0153] The gas saturation calculation module 205 calculates the formation water saturation based on the reservoir inversion resistivity, porosity and formation factor data, and then calculates the formation gas saturation.
[0154] As an optional solution, the original magnetotelluric data is a time series, and the time series data is converted into apparent resistivity and phase data through power spectrum calculation.
[0155] As an alternative, the vector of the objective function is:
[0156]
[0157] in, is the objective function, m is the model vector, m0 is the initial model, m ref is the prior model, d is the data vector, λ is the regularization factor, is the forward model, J(m ref ) is the Jacobian matrix, C m is the model covariance matrix, C d is the data covariance matrix.
[0158] As an optional solution, the optimal step size makes the inversion objective function take a minimum value, and the inverted resistivity data is obtained based on the apparent resistivity and phase data.
[0159] As an alternative, the optimal step size satisfies:
[0160] V(m+α'u)<V(m)+δα'▽V(m)·v.
[0161] As an alternative, calculate the formation water saturation as:
[0162]
[0163] Among them, ρ t is the formation resistivity, a, m, n are empirical factors, ρ w is the water saturation of the reservoir, φ is the porosity, S w is the water saturation of the formation.
[0164] As an alternative, the formation gas saturation is:
[0165] S g =1-S w
[0166] Among them, S g is the gas saturation of the formation.
[0167] Example 5
[0168] The present disclosure provides an electronic device, which includes: a memory storing executable instructions; a processor running the executable instructions in the memory to implement the above-mentioned method for predicting water and gas saturation of tight gas reservoirs.
[0169] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.
[0170] The memory is used to store non-temporary computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.
[0171] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of the present disclosure, the processor is used to run the computer-readable instructions stored in the memory.
[0172] Those skilled in the art should be able to understand that in order to solve the technical problem of how to obtain a good user experience, the present embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the protection scope of the present disclosure.
[0173] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.
[0174] Example 6
[0175] An embodiment of the present disclosure provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the method for predicting the water and gas saturation of a tight gas reservoir is implemented.
[0176] According to the computer-readable storage medium of the embodiment of the present disclosure, non-transitory computer-readable instructions are stored thereon. When the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the above-mentioned methods of each embodiment of the present disclosure are executed.
[0177] The above-mentioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or mobile hard disk), media with built-in rewritable non-volatile memory (e.g., memory card) and media with built-in ROM (e.g., ROM box).
[0178] Those skilled in the art should understand that the purpose of the above description of the embodiments of the present invention is only to exemplarily illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any given examples.
[0179] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.
Claims
1. A method for predicting water and gas saturations in a tight gas reservoir, characterized in that, it includes: Processing the original magnetotelluric data to obtain apparent resistivity and phase data; Calculating the gradient vector of the objective function, and then obtaining the conjugate gradient direction; Based on the apparent resistivity and the phase data, searching for the optimal step size in the conjugate gradient direction to obtain the reservoir inversion resistivity; Collecting tight reservoir rock samples and resistivity data from electric logging in the study area to obtain the porosity and formation factor of the tight reservoir rock samples; Based on the reservoir inversion resistivity, the porosity, and the formation factor data, calculating the formation water saturation, and then calculating the formation gas saturation.
2. The method for predicting water and gas saturations in a tight gas reservoir according to claim 1, wherein, The original magnetotelluric data is a time series, and through power spectrum calculation, the time series data is converted into apparent resistivity and phase data.
3. The method for predicting water and gas saturations in a tight gas reservoir according to claim 1, wherein, The vector of the objective function is: in, is the objective function, m is the model vector, m 0 is the initial model, m ref is the prior model, d is the data vector, λ is the regularization factor, is the forward model, J(m ref ) is the Jacobian matrix, C m is the model covariance matrix, C d is the data covariance matrix.
4. The method for predicting water and gas saturations in a tight gas reservoir according to claim 1, wherein, The optimal step size makes the objective function of the inversion take the minimum value, and based on the apparent resistivity and the phase data, the inversion resistivity data is obtained.
5. The method for predicting water and gas saturations in a tight gas reservoir according to claim 1, wherein, The optimal step size satisfies: V(m + α'u) < V(m) + δα'▽V(m)·v.
6. The method for predicting water and gas saturations in a tight gas reservoir according to claim 1, wherein, Calculating the formation water saturation as: Among them, ρ t is the formation resistivity, a, m, n are empirical factors, ρ w is the water saturation of the reservoir, φ is the porosity, S w is the water saturation of the formation.
7. The method for predicting water and gas saturations in a tight gas reservoir according to claim 1, wherein, The formation gas saturation is: S g =1-S w Among them, S g is the gas saturation of the formation.
8. A device for predicting water and gas saturations in a tight gas reservoir, characterized in that, it includes: A processing module that processes the original magnetotelluric data to obtain apparent resistivity and phase data; A gradient calculation module that calculates the gradient vector of the objective function and then obtains the conjugate gradient direction; A step size search module that searches for the optimal step size in the conjugate gradient direction based on the apparent resistivity and the phase data to obtain the reservoir inversion resistivity; A collection module that collects tight reservoir rock samples and resistivity data from electric logging in the study area to obtain the porosity and formation factor of the tight reservoir rock samples; A gas saturation calculation module that calculates the formation water saturation based on the reservoir inversion resistivity, the porosity, and the formation factor data, and then calculates the formation gas saturation.
9. An electronic device, characterized in that, the electronic device includes: A memory that stores executable instructions; A processor that runs the executable instructions in the memory to implement the method for predicting water and gas saturations in a tight gas reservoir according to any one of claims 1 - 7.
10. A computer-readable storage medium, characterized in that, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the method for predicting water and gas saturations in a tight gas reservoir according to any one of claims 1 - 7.