Method for identifying pore fluid in sandstone and mudstone reservoirs, readable storage medium and device

Abstract

The present invention relates to the technical field of oil and gas exploration, and specifically to a method for identifying porous fluids in sandstone and mudstone reservoirs, comprising: step one, obtaining basic parameters of porous fluids in sandstone and mudstone reservoirs through logging data, step two, calculating new fluid factors based on a posterior probability prediction framework of reservoir parameters considering lithology constraints, specifically comprising: constructing a rock physics model suitable for sandstone and mudstone reservoirs; obtaining reservoir parameters based on the rock physics model; constructing a posterior probability prediction framework of reservoir parameters considering lithology constraints, and obtaining the maximum posterior probability solution of reservoir parameters; constructing new fluid factors, and calculating the new fluid factors based on the maximum posterior probability solution of reservoir parameters; step three, identifying porous fluids in sandstone and mudstone reservoirs based on the new fluid factors. The present invention takes into account the influence of lithology on different reservoir parameters, reduces the coupling relationship between parameters, effectively improves the prediction accuracy of reservoir parameters, and can clarify the relationship with mud content to improve fluid identification accuracy.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas exploration, and in particular to a method for identifying pore fluid in a sandstone mudstone reservoir. Background Art

[0002] Rock physics modeling theory is crucial for the exploration and development of oil and gas reservoirs. It establishes the relationship between reservoir physical parameters and rock elastic parameters and is the theoretical basis for seismic inversion research on reservoirs. Lithology differentiation, porosity prediction and fluid identification based on rock physics theory are widely used in the field of seismic exploration. In seismic exploration, oil and gas reservoirs are usually identified by constructing fluid factors, but existing theories rarely analyze the applicability of fluid factors from the perspective of rock physics modeling theory. For sandstone and mudstone reservoirs, due to the presence of brittle minerals in sandstone, it is easier to develop cracks, and cracks are usually regarded as favorable channels for oil and gas migration and reliable spaces for oil and gas storage. Therefore, in reservoir interpretation, sandstone sections are more concerned than mudstone sections. However, the law of change of fluid factors with mud content is still unclear. Targeted research is needed to construct new fluid factors so that they meet a certain relationship with mud content, thereby improving the accuracy of fluid identification.

[0003] Compared with deterministic inversion, probabilistic inversion can not only obtain the final prediction results of reservoir parameters, but also perform uncertainty analysis on the prediction results to evaluate the feasibility of prediction. However, reservoir physical properties are complex and there is a coupling relationship between reservoir parameters. In probabilistic inversion, it is usually assumed that reservoir parameters obey a certain distribution, which restricts the prediction accuracy and description reliability of reservoir parameters.

[0004] The existing fluid factors and prediction methods have the following problems: 1. The applicability of the existing fluid factors has not been analyzed from the perspective of rock physical mechanisms; 2. The existing fluid factors have not analyzed the influence of reservoir lithology, which restricts the effective identification of fluids in the target sandstone reservoir; 3. The change pattern of the existing fluid factors with mud content is unclear; 4. Deterministic inversion cannot perform uncertainty analysis on the prediction results, and probabilistic prediction is required. However, the existing probabilistic prediction framework usually assumes that the reservoir parameters obey a certain specific distribution, which restricts the prediction results and reliability. Summary of the invention

[0005] The present invention aims to provide a method for identifying pore fluid in sandstone and mudstone reservoirs to solve the problems that the existing fluid factor has unclear variation law with mud content and low prediction accuracy. The specific technical solution is as follows:

[0006] The present invention provides a method for identifying pore fluid in a sandstone-mudstone reservoir, comprising the following steps:

[0007] Step 1: Obtain basic parameters of pore fluid in sandstone and mudstone reservoirs through logging data, wherein the basic parameters include sandstone bulk modulus, sandstone shear modulus, mudstone bulk modulus, mudstone shear modulus, water bulk modulus, gas bulk modulus, shale content, porosity and water saturation;

[0008] Step 2: Calculate the new fluid factor based on the posterior probability prediction framework of reservoir parameters considering lithology constraints, including:

[0009] S2.1: Construct a rock physics model suitable for sandstone and mudstone reservoirs based on the basic parameters of step 1;

[0010] S2.2: Obtain reservoir parameters based on the rock physics model, wherein: the reservoir parameters include conventional fluid factors and shear modulus of reservoir rocks;

[0011] S2.3: Construct a posterior probability prediction framework for reservoir parameters considering lithology constraints based on Bayesian theory;

[0012] S2.4: Based on the reservoir parameter posterior probability prediction framework, the maximum posterior probability solution of reservoir parameters is obtained using the MCMC MH algorithm;

[0013] S2.5: construct a new fluid factor and calculate the new fluid factor based on the maximum a posteriori probability solution of the reservoir parameters;

[0014] Step 3: Identify the pore fluid in the sandstone-mudstone reservoir based on the new fluid factor obtained in step 2.

[0015] Optionally, the S2.1 includes:

[0016] S2.1.1. Constructing a saturated rock stiffness matrix considering the effects of various pore inclusions The specific formula is as follows:

[0017]

[0018] in: is the dry rock stiffness matrix; i refers to the i-th component in the rock, i=0 refers to background minerals, and i≥1 refers to inclusions; φ i is the content of the i-th component; N is the total number of inclusions; φ * is the content of all inclusions, i.e., the total porosity. I is the unit matrix; S i0 is the Eshelby tensor related to background minerals and inclusions, S0 is the Eshelby tensor corresponding to spherical inclusions; σ i is the pore fluid pressure; C0 is the stiffness matrix of the background matrix, C v is the equivalent stiffness matrix of the pore inclusion, and its formula is as follows:

[0019]

[0020] Among them: ζ is the minimum value that tends to 0, ζ0, k f is the bulk modulus of the fluid, and its formula is:

[0021]

[0022] Where: k w and k g are the bulk moduli of water and gas, respectively, and Sw is the water saturation;

[0023] S2.1.2. Based on one pore structure and two pore connectivity assumptions, the saturated rock stiffness matrix is ​​equivalent to obtain a rock physics model suitable for sandstone and mudstone reservoirs.

[0024] Optionally, in S2.1.1, the dry rock stiffness matrix The specific formula is as follows:

[0025]

[0026] The specific formula of the stiffness matrix C0 of the background matrix is ​​as follows:

[0027]

[0028] Where: k′ is the bulk modulus of the rock background matrix, μ′ is the shear modulus of the rock background matrix;

[0029] The specific formula for the bulk modulus k′ of the rock background matrix is ​​as follows:

[0030]

[0031] Where: k′ sand is the bulk modulus of sandstone, k s ' hale Mudstone bulk modulus; sh is the mud content;

[0032] The specific formula of the shear modulus μ′ of the rock background matrix is ​​as follows:

[0033]

[0034] Where: μ s ' and is the shear modulus of sandstone, μ s ' hale Shear modulus of mudstone.

[0035] Optionally, the S2.1.2 specifically includes:

[0036] S2.1.2.1. Divide the pore space into connected pores and unconnected pores, and then obtain Formula 1 from the saturated rock stiffness matrix:

[0037]

[0038] Where: φ c is the connected pore content, φ d are the unconnected pore content; S c0 is the Shelby tensor associated with the connected pores, S d0 Shelby tensor associated with disconnected pores; σ c is the connected pore fluid pressure, σ d is the fluid pressure in unconnected pores;

[0039] S2.1.2.2. Considering the pore structure as an equivalent pore aspect ratio, formula 2 is obtained from formula 1:

[0040]

[0041] S2.1.2.3. Simplify Formula 2 to obtain the rock physics model. The specific formula is:

[0042]

[0043] Among them, L * is the intermediate quantity, and the specific formula is:

[0044]

[0045] The specific formula of the equivalent dry rock stiffness matrix is:

[0046]

[0047] Where: φ * =φ c +φ d .

[0048] Optionally, in S2.2, the expression of the conventional fluid factor f is:

[0049]

[0050] Where: M is the longitudinal wave modulus of the reservoir rock; μ is the shear modulus of the reservoir rock; γ dry is a term related to the dry rock skeleton, and the specific formula is as follows:

[0051]

[0052] in: is the element corresponding to the first row and first column of the equivalent dry rock stiffness matrix, is the element corresponding to the fourth row and fourth column of the equivalent dry rock stiffness matrix;

[0053] The expression of the longitudinal wave modulus M of the reservoir rock is:

[0054]

[0055] in: is the saturated rock stiffness matrix The element corresponding to the first row and first column;

[0056] The expression of the shear modulus μ of the reservoir rock is:

[0057]

[0058] in: is the saturated rock stiffness matrix The element corresponding to the fourth row and fourth column.

[0059] Optionally, the calculation formula of the new fluid factor f1 in S2.5 is as follows:

[0060]

[0061] Optionally, the specific expression of the reservoir parameter posterior probability prediction framework P(m, lith|d) considering lithology constraints in S2.3 is:

[0062] P(m,lith|d)∝P(d|m,lith)P(lith|m)P(m);

[0063] Where: m is the reservoir parameter, lith is the lithology, and the observed data d is the formation reflection coefficient after removing the wavelet from the seismic angle gather data; P(d|m,lith) is the conditional probability of the reservoir parameter m and the lithology lith to the observed data d; P(lith|m) is the relationship between the reservoir parameter and the lithology obtained by logging data; P(m) is the prior probability of the reservoir parameter m; P(d|m,lith) is the likelihood function composed of the forward operator G(m) and the observed data d under the Gaussian noise assumption, and the specific formula is as follows:

[0064]

[0065] Where: σ is the variance of seismic noise; the forward operator G(m) is a linear AVO expression constructed by conventional fluid factors, shear modulus and density of reservoir rocks. The specific formula is as follows:

[0066]

[0067] Where: Vp is the longitudinal wave velocity, Vs is the shear wave velocity; ρ is the density, θ is the incident angle; Δf is the fluid factor difference between the upper and lower layers of the reflection interface, Δμ is the shear modulus difference between the reservoir rocks above and below the reflection interface, and Δρ is the density difference between the upper and lower layers of the reflection interface.

[0068] Optionally, the S2.4 includes:

[0069] S2.4.1, randomly generate new initial reservoir parameters m0 and initial lithology lith0, take t = 1, and set m = m0, lith = lith0;

[0070] S2.4.2. Calculate the posterior probability P(m,lith|d) based on the reservoir parameter posterior probability prediction framework;

[0071] S2.4.3, using the discrimination rule of the MCMCMH algorithm, if m and lith under the posterior probability P(m, lith|d) condition meet the probability requirements, then keep the current m and lith, set t=t+1, return to S2.4.1 until t=n ends the cycle, and obtain n posterior probability density solutions of the reservoir parameter m; if the probability requirements are not met, use the update formula to update m and lith, and return to S2.4.2;

[0072] S2.4.4. Perform statistical analysis on the n posterior probability density solutions of the reservoir parameter m. The posterior probability density solution with the highest probability is the maximum posterior probability solution of the reservoir parameter m.

[0073] The present invention also provides a computer-readable storage medium having computer program instructions stored thereon, and when the computer program instructions are executed by a processor, the above-mentioned method for identifying pore fluid in sandstone and mudstone reservoirs is implemented.

[0074] The present invention also provides an electronic device, comprising: at least one processor, at least one memory and computer program instructions stored in the memory, when the computer program instructions are executed by the processor, the above-mentioned method for identifying pore fluid in sandstone and mudstone reservoirs is implemented.

[0075] By applying the technical solution of the present invention, by constructing a sandstone-mudstone rock physics model, conventional fluid factors can be analyzed theoretically, and the influence of different reservoir parameters can be analyzed; the new fluid factor in the present invention is the ratio of the conventional fluid factor to the shear modulus, wherein the shear modulus is not affected by the pore fluid but only by the rock background skeleton, and can eliminate the influence of background minerals to a certain extent, thereby better indicating the fluid; the present invention also adopts a priori probability distribution of lithology constraints, considers the influence of lithology on different reservoir parameters, reduces the coupling relationship between parameters, effectively improves the prediction accuracy of reservoir parameters, and can clarify the relationship with mud content to improve the fluid identification accuracy; and the nonlinear inversion method adopted is the MCMC MH algorithm, which can perform uncertainty analysis on the prediction results, which is beneficial to improving the reliability of evaluation prediction.

[0076] In addition to the above-described purposes, features and advantages, the present invention has other purposes, features and advantages. The present invention will be further described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] The drawings constituting a part of this application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0078] Figure 1 is a comparison chart of the conventional fluid factor f and the new fluid factor f1 based on the construction of the rock physics model with the change of the mud content in the embodiment of the present invention, wherein: Figure 1 (a) is a schematic diagram of the conventional fluid factor f changing with the mud content. Figure 1 (b) is a schematic diagram of the change of the new fluid factor f1 with the mud content, sh is the mud content, and Sw is the water saturation;

[0079] Figure 2 is the probabilistic prediction result of reservoir parameters based on not considering lithology constraints in the embodiment of the present invention, where f is the conventional fluid factor, μ is the shear modulus, and Den is the density;

[0080] Figure 3 is the probabilistic prediction result of reservoir parameters based on lithology constraints in the embodiment of the present invention, where f is the conventional fluid factor, μ is the shear modulus, and Den is the density;

[0081] Figure 4 is a comparison diagram of the fluid factor prediction results based on well logging data and the well logging curve in an embodiment of the present invention, wherein: Figure 4 (a) is the prediction result of conventional fluid factor f; Figure 4 (b) is the predicted result of shear modulus μ; Figure 4 (c) in the figure is the density Den prediction result; Figure 4 (d) in the figure is the prediction result of the new fluid factor f1; Figure 4 (e) is the gas saturation Sg curve; Figure 4 (f) is the porosity Por curve; Figure 4 (g) in the figure is the lithology prediction result, where sand is sandstone and shale is mudstone. DETAILED DESCRIPTION

[0082] The embodiments of the present invention are described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0083] In one embodiment, a method for identifying pore fluid in a sandstone-mudstone reservoir comprises the following steps:

[0084] Step 1: Obtain basic parameters of pore fluid in sandstone and mudstone reservoirs through logging data, wherein the basic parameters include sandstone bulk modulus, sandstone shear modulus, mudstone bulk modulus, mudstone shear modulus, water bulk modulus, gas bulk modulus, shale content, porosity and water saturation;

[0085] Step 2: Calculate the new fluid factor based on the posterior probability prediction framework of reservoir parameters considering lithology constraints, including:

[0086] S2.1: Construct a rock physics model suitable for sandstone and mudstone reservoirs based on the basic parameters of step 1;

[0087] S2.1 includes:

[0088] S2.1.1. Constructing a saturated rock stiffness matrix considering the effects of various pore inclusions The specific formula is as follows:

[0089]

[0090] in: is the dry rock stiffness matrix; i refers to the i-th component in the rock, i=0 refers to background minerals, and i≥1 refers to inclusions; φ i is the content of the i-th component; N is the total number of inclusions; φ * is the content of all inclusions, i.e., the total porosity. I is the unit matrix; S i0 is the Eshelby tensor related to background minerals and inclusions, S0 is the Eshelby tensor corresponding to spherical inclusions; σ i is the pore fluid pressure; C0 is the stiffness matrix of the background matrix, C v is the equivalent stiffness matrix of the pore inclusion, and its formula is as follows:

[0091]

[0092] Among them: ζ is the minimum value that tends to 0, ζ0, k f is the bulk modulus of the fluid, and its formula is:

[0093]

[0094] Where: k w and k g are the bulk moduli of water and gas respectively, and Sw is the water saturation.

[0095] Dry rock stiffness matrix The specific formula is as follows:

[0096]

[0097] The specific formula of the stiffness matrix C0 of the background matrix is ​​as follows:

[0098]

[0099] Where: k′ is the bulk modulus of the rock background matrix, μ′ is the shear modulus of the rock background matrix;

[0100] The specific formula for the bulk modulus k′ of the rock background matrix is ​​as follows:

[0101]

[0102] Where: k s ' and is the bulk modulus of sandstone, k s ' hale Mudstone bulk modulus; sh is the mud content;

[0103] The specific formula of the shear modulus μ′ of the rock background matrix is ​​as follows:

[0104]

[0105] Where: μ s ' and is the shear modulus of sandstone, μ s ' hale Shear modulus of mudstone.

[0106] S2.1.2. Based on one pore structure and two pore connectivity assumptions, the saturated rock stiffness matrix is ​​equivalent to obtain a rock physics model suitable for sandstone and mudstone reservoirs.

[0107] S2.1.2 specifically includes:

[0108] S2.1.2.1. Divide the pore space into connected pores and unconnected pores, and then obtain Formula 1 from the saturated rock stiffness matrix:

[0109]

[0110] Where: φ c is the connected pore content, φ d are the unconnected pore content; S c0 is the Shelby tensor associated with the connected pores, S d0 Shelby tensor associated with disconnected pores; σ c is the connected pore fluid pressure, σ d is the fluid pressure in unconnected pores;

[0111] S2.1.2.2. Considering the pore structure as an equivalent pore aspect ratio, formula 2 is obtained from formula 1:

[0112]

[0113] S2.1.2.3. Simplify Formula 2 to obtain the rock physics model. The specific formula is:

[0114]

[0115] Among them, L * is the intermediate quantity, and the specific formula is:

[0116]

[0117] The specific formula of the equivalent dry rock stiffness matrix is:

[0118]

[0119] Where: φ * =φ c +φ d .

[0120] S2.2: Obtain reservoir parameters based on the rock physics model, wherein: the reservoir parameters include conventional fluid factors and shear modulus of reservoir rocks;

[0121] The expression of the conventional fluid factor f is:

[0122]

[0123] Where: M is the longitudinal wave modulus of the reservoir rock; μ is the shear modulus of the reservoir rock; γ dry is a term related to the dry rock skeleton, and the specific formula is as follows:

[0124]

[0125] in: is the element corresponding to the first row and first column of the equivalent dry rock stiffness matrix, is the element corresponding to the fourth row and fourth column of the equivalent dry rock stiffness matrix;

[0126] The expression of the longitudinal wave modulus M of the reservoir rock is:

[0127]

[0128] in: is the saturated rock stiffness matrix The element corresponding to the first row and first column;

[0129] The expression of the shear modulus μ of the reservoir rock is:

[0130]

[0131] in: is the saturated rock stiffness matrix The element corresponding to the fourth row and fourth column.

[0132] S2.3: Construct a posterior probability prediction framework for reservoir parameters considering lithology constraints based on Bayesian theory;

[0133] According to Bayesian theory, the posterior probability P(m|d) of the model parameter m based on the observed data d can be expressed as:

[0134]

[0135] Among them, P(d|m) is the conditional probability, P(m) is the prior probability, P(d) is the marginal probability, and m is the reservoir parameter including the fluid factor f, shear modulus μ and density Den. The marginal probability is usually a constant, so the marginal probability P(d) is omitted, and the posterior probability formula is transformed into:

[0136] P(m|d)∝P(d|m)P(m);

[0137] Considering the lithology constraint, we can get:

[0138] P(m,lith|d)∝P(d|m,lith)P(m,lith);

[0139] The prior probability P(m,lith) can be further expressed as:

[0140] P(m,lith)=P(lith|m)P(m);

[0141] The specific expression of the posterior probability prediction framework P(m,lith|d) of reservoir parameters considering lithology constraints in S2.3 is:

[0142] P(m,lith|d)∝P(d|m,lith)P(lith|m)P(m);

[0143] Where: m is the reservoir parameter, lith is the lithology, and the observed data d is the formation reflection coefficient after removing the wavelet from the seismic angle gather data; P(d|m,lith) is the conditional probability of the reservoir parameter m and the lithology lith to the observed data d; P(lith|m) is the relationship between the reservoir parameter and the lithology obtained by logging data; P(m) is the prior probability of the reservoir parameter m; P(d|m,lith) is the likelihood function composed of the forward operator G(m) and the observed data d under the Gaussian noise assumption, and the specific formula is as follows:

[0144]

[0145] Where: σ is the variance of seismic noise; the forward operator G(m) is a linear AVO expression constructed by conventional fluid factors, shear modulus and density of reservoir rocks. The specific formula is as follows:

[0146]

[0147] Where: Vp is the longitudinal wave velocity, Vs is the shear wave velocity; ρ is the density, θ is the incident angle; Δf is the fluid factor difference between the upper and lower layers of the reflection interface, Δμ is the shear modulus difference between the reservoir rocks above and below the reflection interface, and Δρ is the density difference between the upper and lower layers of the reflection interface.

[0148] S2.4: Based on the reservoir parameter posterior probability prediction framework, the maximum posterior probability solution of reservoir parameters is obtained using the MCMCMH algorithm;

[0149] S2.4 includes:

[0150] S2.4.1. Randomly generate new initial reservoir parameters m0 and initial lithology lith0, take t = 1, and set m = m0,

[0151] lith = lith0;

[0152] S2.4.2. Calculate the posterior probability P(m,lith|d) based on the reservoir parameter posterior probability prediction framework;

[0153] S2.4.3, using the discrimination rule of the MCMCMH algorithm, if m and lith under the posterior probability P(m, lith|d) condition meet the probability requirements, then keep the current m and lith, set t=t+1, return to S2.4.1 until t=n ends the cycle, and obtain n posterior probability density solutions of the reservoir parameter m; if the probability requirements are not met, use the update formula to update m and lith, and return to S2.4.2;

[0154] S2.4.4. Perform statistical analysis on the n posterior probability density solutions of the reservoir parameter m. The posterior probability density solution with the highest probability is the maximum posterior probability solution of the reservoir parameter m.

[0155] S2.5: Construct a new fluid factor and calculate the new fluid factor based on the maximum a posteriori probability solution of the reservoir parameters.

[0156] The calculation formula of the new fluid factor f1 is as follows:

[0157]

[0158] Step 3: Identify the pore fluid in the sandstone-mudstone reservoir based on the new fluid factor obtained in step 2.

[0159] In this embodiment, Figure 1 As shown in the figure, the new fluid factor f1 and the conventional fluid factor f are compared and analyzed with the change of water saturation Sw. Figure 2 From (a) in the figure, we can see that the conventional fluid factor f is affected by the shale content sh, and as the shale content increases, the conventional fluid factor f shows a trend of first increasing and then decreasing, that is, there is no monotonically increasing or decreasing relationship between the two, which affects the accuracy of reservoir fluid identification and may cause pore fluid prediction deviations due to different reservoir lithology. Figure 2 (b) in the figure can give the monotonic trend of the new fluid factor f1 changing with the mud content, and the smaller the mud content, the smaller the value of f1, which is helpful in finding favorable oil and gas-bearing areas in sandstone reservoirs.

[0160] like Figure 2 and Figure 3 As shown in the figure, the prediction results of reservoir parameters without considering lithology constraints and with considering lithology constraints are compared, including the prediction results of fluid factor f, shear modulus μ and density Den. By comparison, it can be found that if lithology constraints are not considered ( Figure 3 ), the prediction results are less effective in describing reservoir details, while considering lithology constraints ( Figure 4 ), the prediction accuracy of reservoir parameters is higher and more consistent with the true logging values.

[0161] like Figure 4 As shown in the figure, the differences between conventional fluid factors and new fluid factors in identifying gas-bearing reservoirs are analyzed by comparing well logging data. Figure 4 (a) and Figure 4 In (e), we can find that Figure 4 The conventional fluid factor indication result at the gas-bearing reservoir position shown in (e) is not obvious. Figure 4 The low value area indicated by the arrow below (a) can reflect Figure 4 (e) The gas reservoir below, but the arrow above cannot effectively indicate Figure 4 (e) gas reservoir above, and Figure 4 The position indicated by the middle arrow in (a) is unclear from the position indicated by the upper arrow, which can easily lead to errors in gas reservoir identification. Figure 4 The low value areas indicated by arrows in the new fluid factor shown in (d) can effectively identify Figure 4 The two sets of gas reservoirs in (e) are shown in Figure 3. Through analysis, it is found that the new fluid factor f1 can effectively identify gas reservoirs, and its identification accuracy is higher than that of the conventional fluid factor f.

[0162] The new fluid factor constructed in this embodiment shows certain advantages in terms of both rock physics mechanism analysis and actual data application. The new fluid factor has a clear relationship with the mud content and is more sensitive to pore fluid. This embodiment analyzes the shortcomings of conventional fluid factors from the perspective of rock physics modeling. The prior distribution considering lithology constraints includes the inherent coupling relationship between different reservoir parameters, which can more reasonably describe the statistical laws of reservoir parameters. The probabilistic inversion of fluid factors is carried out based on the Bayesian prediction framework. On the basis of obtaining the inversion results of reservoir parameters, the posterior probability of the inversion results can also be obtained, thereby performing uncertainty analysis. This embodiment combines theoretical analysis with predictive inversion, ensures the rationality and effectiveness of the application of new fluid factors, and improves the recognition accuracy.

[0163] This embodiment also includes a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the above-mentioned method for identifying pore fluid in sandstone and mudstone reservoirs is implemented.

[0164] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0165] This embodiment also includes an electronic device, including: at least one processor, at least one memory, and computer program instructions stored in the memory. When the computer program instructions are executed by the processor, the mining slope stability analysis method as described above is performed.

[0166] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0167] The electronic device may be a computing device such as a mobile phone, a desktop computer, a notebook, a PDA, a cloud server, etc. The electronic device may include, but is not limited to, a processor and a memory. For example, the electronic device may also include an input / output device, a network access device, a bus, etc.

[0168] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0169] The memory can be used to store the computer program and / or module, and the processor implements the computer program by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0170] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0171] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for identifying pore fluid in sandstone and mudstone reservoirs, characterized in that: The steps include: Step 1: Obtain basic parameters of pore fluid in sandstone and mudstone reservoirs through logging data, wherein the basic parameters include sandstone bulk modulus, sandstone shear modulus, mudstone bulk modulus, mudstone shear modulus, water bulk modulus, gas bulk modulus, shale content, porosity and water saturation; Step 2: Calculate the new fluid factor based on the posterior probability prediction framework of reservoir parameters considering lithology constraints, including: S2.1: Construct a rock physics model suitable for sandstone and mudstone reservoirs based on the basic parameters of step 1; S2.2: Obtain reservoir parameters based on the rock physics model, wherein: the reservoir parameters include conventional fluid factors and shear modulus of reservoir rocks; S2.3: Construct a posterior probability prediction framework for reservoir parameters considering lithology constraints based on Bayesian theory; S2.4: Based on the reservoir parameter posterior probability prediction framework, the MCMC MH algorithm is used to obtain the maximum posterior probability solution of the reservoir parameters; S2.5: construct a new fluid factor and calculate the new fluid factor based on the maximum a posteriori probability solution of the reservoir parameters; Step 3: Identify the pore fluid of the sandstone-mudstone reservoir based on the new fluid factor obtained in step 2; In S2.2, the expression of the conventional fluid factor f is: Where: M is the longitudinal wave modulus of the reservoir rock; μ is the shear modulus of the reservoir rock; γ dry is a term related to the dry rock skeleton, and the specific formula is as follows: in: is the element corresponding to the first row and first column of the equivalent dry rock stiffness matrix, is the element corresponding to the fourth row and fourth column of the equivalent dry rock stiffness matrix; The expression of the longitudinal wave modulus M of the reservoir rock is: in: is the saturated rock stiffness matrix The element corresponding to the first row and first column; The expression of the shear modulus μ of the reservoir rock is: in: is the saturated rock stiffness matrix The element corresponding to the fourth row and fourth column; The calculation formula of the new fluid factor f1 in S2.5 is as follows:

2. The method for identifying pore fluid in sandstone and mudstone reservoirs according to claim 1, characterized in that: The S2.1 includes: S2.1.

1. Constructing a saturated rock stiffness matrix considering the effects of various pore inclusions The specific formula is as follows: in: is the dry rock stiffness matrix; i refers to the i-th component in the rock, i=0 refers to background minerals, and i≥1 refers to inclusions; φ i is the content of the i-th component; N is the total number of inclusions; φ * is the content of all inclusions, i.e., the total porosity; I is the unit matrix; S i0 is the Eshelby tensor related to background minerals and inclusions, S0 is the Eshelby tensor corresponding to spherical inclusions; σ i is the pore fluid pressure; C0 is the stiffness matrix of the background matrix, C v is the equivalent stiffness matrix of the pore inclusion, and its formula is as follows: Among them: ζ is the minimum value that tends to 0, ζ~0, k f is the bulk modulus of the fluid, and its formula is: Where: k w and k g are the bulk moduli of water and gas, respectively, and Sw is the water saturation; S2.1.

2. Based on one pore structure and two pore connectivity assumptions, the saturated rock stiffness matrix is ​​equivalent to obtain a rock physics model suitable for sandstone and mudstone reservoirs.

3. The method for identifying pore fluid in sandstone and mudstone reservoirs according to claim 2, characterized in that: In S2.1.1, the dry rock stiffness matrix The specific formula is as follows: The specific formula of the stiffness matrix C0 of the background matrix is ​​as follows: Where: k′ is the bulk modulus of the rock background matrix, μ′ is the shear modulus of the rock background matrix; The specific formula for the bulk modulus k′ of the rock background matrix is ​​as follows: Where: k s ' and is the bulk modulus of sandstone, k s ' hale Mudstone bulk modulus; sh is the mud content; The specific formula of the shear modulus μ′ of the rock background matrix is ​​as follows: Where: μ s ' and is the shear modulus of sandstone, μ s ' hale Shear modulus of mudstone.

4. The method for identifying pore fluid in sandstone and mudstone reservoirs according to claim 3, characterized in that: S2.1.2 specifically includes: S2.1.2.

1. Divide the pore space into connected pores and unconnected pores, and then obtain Formula 1 from the saturated rock stiffness matrix: Where: φ c is the connected pore content, φ d are the unconnected pore content; S c0 is the Shelby tensor associated with the connected pores, S d0 Shelby tensor associated with disconnected pores; σ c is the connected pore fluid pressure, σ d is the fluid pressure in the unconnected pores; S2.1.2.

2. Considering the pore structure as an equivalent pore aspect ratio, formula 2 is obtained from formula 1: S2.1.2.

3. Simplify Formula 2 to obtain the rock physics model. The specific formula is: Among them, L * is the intermediate quantity, and the specific formula is: The specific formula of the equivalent dry rock stiffness matrix is: Among them: f * =φ c +φ d 。 5. The method for identifying pore fluid in sandstone and mudstone reservoirs according to any one of claims 1 to 4, characterized in that: The specific expression of the reservoir parameter posterior probability prediction framework P(m,lith|d) considering lithology constraints in S2.3 is: P(m,lith|d)∝P(d|m,lith)P(lithm)P(m); Where: m is the reservoir parameter, lith is the lithology, and the observed data d is the formation reflection coefficient after removing the wavelet from the seismic angle gather data; P(d|m,lith) is the conditional probability of the reservoir parameter m and the lithology lith to the observed data d; P(lith|m) is the relationship between the reservoir parameter and the lithology obtained by logging data; P(m) is the prior probability of the reservoir parameter m; P(d|m,lith) is the likelihood function composed of the forward operator G(m) and the observed data d under the Gaussian noise assumption, and the specific formula is as follows: Where: σ is the variance of seismic noise; the forward operator G(m) is a linear AVO expression constructed by conventional fluid factors, shear modulus and density of reservoir rocks. The specific formula is as follows: Where: Vp is the longitudinal wave velocity, Vs is the shear wave velocity; ρ is the density, θ is the incident angle; Δf is the fluid factor difference between the upper and lower layers of the reflection interface, Δμ is the shear modulus difference between the reservoir rocks above and below the reflection interface, and Δρ is the density difference between the upper and lower layers of the reflection interface.

6. The method for identifying pore fluid in sandstone and mudstone reservoirs according to any one of claims 1 to 4, characterized in that: The S2.4 includes: S2.4.1, randomly generate new initial reservoir parameters m0 and initial lithology lith0, take t = 1, and set m = m0, lith = lith0; S2.4.

2. Calculate the posterior probability P(m,lith|d) based on the reservoir parameter posterior probability prediction framework; S2.4.3, using the discrimination rule of the MCMCMH algorithm, if m and lith under the posterior probability P(m, lith|d) condition meet the probability requirements, then keep the current m and lith, set t=t+1, return to S2.4.1 until t=n ends the cycle, and obtain n posterior probability density solutions of the reservoir parameter m; if the probability requirements are not met, use the update formula to update m and lith, and return to S2.4.2; S2.4.

4. Perform statistical analysis on the n posterior probability density solutions of the reservoir parameter m. The posterior probability density solution with the highest probability is the maximum posterior probability solution of the reservoir parameter m.

7. A computer-readable storage medium, characterized in that: Computer program instructions are stored thereon, and when the computer program instructions are executed by a processor, the method for identifying pore fluid in a sandstone-mudstone reservoir as claimed in any one of claims 1 to 6 is implemented.

8. An electronic device, characterized in that: include: At least one processor, at least one memory and computer program instructions stored in the memory, when the computer program instructions are executed by the processor, the method for identifying pore fluid in a sandstone-mudstone reservoir according to any one of claims 1 to 6 is provided.

Images