Method, medium and system for predicting regional porosity
By establishing a priori models of porosity parameters and likelihood function models of seismic response signals, combined with seismic waveform indication simulation, accurate prediction of porosity in heterogeneous strata was achieved. This solved the problem of inaccurate prediction in existing technologies, reduced exploration risks, and improved the accuracy of oil and gas field development.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA PETROLEUM & CHEMICAL CORP
- Filing Date
- 2024-11-11
- Publication Date
- 2026-05-12
AI Technical Summary
Existing technologies cannot accurately predict the porosity of heterogeneous formations, especially lithological formations, leading to inaccurate predictions of oil and gas distribution and reserves.
By establishing a prior model of porosity parameters and a likelihood function model of seismic response signals, combined with seismic waveform indication simulation, and using the maximum a posteriori probability and MCMC methods, the porosity distribution results of the porosity prediction area are obtained.
It enables accurate prediction of porosity in heterogeneous strata, reduces exploration risks, provides a basis for delineating favorable reservoir areas, and improves the accuracy of oil and gas field development.
Smart Images
Figure CN122017957A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of petroleum exploration, specifically to a method, medium, and system for predicting regional porosity. Background Technology
[0002] Porosity and permeability are fundamental characteristics of reservoirs. Porosity, as one of the important parameters of reservoir properties, has a profound impact on the distribution of underground oil and gas, oil and gas reserves and production capacity, and the formulation of oil and gas field development plans. Therefore, it has become an essential parameter in the process of oil and gas field development.
[0003] Numerous experimental and exploration studies have demonstrated that geological formations are heterogeneous, especially lithological formations, where heterogeneity is even more pronounced. Therefore, the response of well logging to physical properties (such as porosity, saturation, and permeability) is inherently a complex, nonlinear function that cannot be precisely expressed by a formula. As exploration and development progress, more precise predictions of physical properties such as porosity are needed.
[0004] Therefore, a more accurate porosity prediction scheme is urgently needed. Summary of the Invention
[0005] To avoid the aforementioned problems in the prior art, the present invention aims to provide a method, medium, and system for predicting regional porosity.
[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for predicting regional porosity, comprising the following steps:
[0007] S1: Based on the porosity curve of a single well in the area to be predicted, a priori model of porosity parameters is established. The rock physics model and convolution equation of the area to be evaluated are subjected to wave equation forward modeling. Based on the relationship between the simulation data of the wave equation forward modeling and the actual seismic response signal, a likelihood function model is established.
[0008] The product of the prior model and the likelihood function model is used to form a posterior function model as a predictive model characterizing the relationship between porosity and seismic wave signals.
[0009] S2: Based on the seismic wave response signal and single-well porosity curve of the area to be evaluated, the porosity distribution of the area to be evaluated is obtained by solving the prediction model.
[0010] The present invention is further configured such that the prior model for establishing the porosity parameter is specifically established by using the seismic noise signal as a noise distribution with a mean of 0 to establish the corresponding seismic noise Gaussian function;
[0011] Based on the principle that the porosity parameter satisfies the characteristics of seismic noise, the current seismic noise Gaussian function is converted into a porosity parameter Gaussian function, and the current porosity parameter Gaussian function is recorded as the prior model.
[0012] The present invention is further configured such that the posterior function model is represented by the following expression:
[0013]
[0014] Where m represents porosity, d represents seismic response data, i represents the sequence number of the porosity prediction point in the area to be evaluated, N represents the total number of porosity prediction points, P(m|d) represents the posterior function, G represents the convolutional wavelet matrix, σ represents the covariance matrix of seismic noise, and σ m The covariance matrix representing porosity, Δm i m represents the change in porosity of the i-th element. T This represents the porosity transpose matrix.
[0015] The present invention is further configured such that step S2 specifically includes the following steps:
[0016] S21: Based on the seismic wave response signal of the area to be evaluated and the porosity data of the single well in the single well porosity curve, the maximum a posteriori probability is solved for the posterior function model to obtain the expected porosity value;
[0017] S22: Based on the expected porosity value, the posterior function model is converted into an objective function that characterizes the relationship between the unit change in porosity and the unit change in seismic response value;
[0018] S23: Based on the seismic wave response signal and single-well porosity data of the area to be evaluated, the MCMC method is used to solve the objective function and obtain the distribution prediction results of the unit change in porosity;
[0019] S24: Based on the porosity data of the single well and the distribution prediction results of the unit change in porosity, the porosity distribution results of the area to be evaluated are obtained.
[0020] The present invention is further configured such that the objective function in step S22 is expressed by the following formula:
[0021]
[0022] Where I represents the prior constraint matrix, G T Δd is the transformation matrix of the convolutional wavelet matrix, and Δd is the unit change in the actual seismic response value.
[0023] The present invention is further configured to obtain the single-well porosity curve through the following steps:
[0024] For the evaluation area with well logging data, calculate the porosity curve of a single well based on the well logging curve;
[0025] For areas without porosity, the porosity curve of a single well is calculated based on a rock physics model.
[0026] The present invention is further configured such that, in step S1, the seismic wave response signal and the single-well porosity curve are subjected to standard preprocessing, the standard preprocessing including outlier processing, inter-well consistency correction and well-seismic calibration.
[0027] The present invention also includes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for predicting regional porosity.
[0028] The present invention also includes a system for predicting regional porosity, applicable to the aforementioned method for predicting regional porosity, comprising:
[0029] The prediction model building module is configured to build a prediction model characterizing the relationship between porosity and seismic wave signals based on the porosity curve of a single well in the area to be predicted.
[0030] The porosity prediction module is configured to obtain the porosity distribution results of the area to be evaluated by solving the prediction model based on the seismic wave response signal of the area to be evaluated and the porosity curve of the single well.
[0031] The present invention is further configured such that the system also includes a data preprocessing module, which is configured to perform standard preprocessing on the seismic wave response signal and the single-well porosity curve, the standard preprocessing including outlier processing, inter-well consistency correction and well-seismic calibration.
[0032] In summary, the beneficial effects of the above-mentioned technical solution of the present invention are as follows:
[0033] This invention is applicable to porosity prediction in clastic rock areas. Specifically, it calculates porosity using well logging data and a rock physics model. After outlier processing, well-to-well consistency correction, and well-to-seismic calibration of the porosity curve and seismic data, it simulates porosity using seismic waveform indicators. This invention enables quantitative prediction of regional porosity parameters and has broad application prospects. Based on the predicted porosity plan, geophysicists can predict favorable reservoir areas, providing important data for geological research and reserve calculation. Furthermore, since there are favorable boundaries for regional porosity, the prediction results of this invention can be used to further delineate favorable reservoir areas, analyze the favorable reservoir range, and reduce exploration risks. Attached Figure Description
[0034] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0035] Figure 1 This is a schematic diagram illustrating the steps of a method for predicting regional porosity according to an embodiment of the present invention.
[0036] Figure 2 This is a block diagram of a system for predicting regional porosity according to an embodiment of the present invention. Detailed Implementation
[0037] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments of the present invention, other similar embodiments obtained by those skilled in the art without creative effort should all fall within the scope of protection of the present invention.
[0038] Furthermore, the steps illustrated in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in a different order than that shown here.
[0039] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments. Unless the context clearly indicates otherwise, the singular forms “a” and “an” as used herein are also intended to include the plural. It should also be understood that the terms “comprising” and / or “including” as used herein specify the presence of the stated features, integers, steps, operations, units, and / or components, without excluding the presence or addition of one or more other features, integers, steps, operations, units, components, and / or combinations thereof.
[0040] The present invention will be further described below with reference to the accompanying drawings and preferred embodiments.
[0041] Example 1:
[0042] like Figure 1 As shown in the preferred embodiment of the present invention, a method for predicting regional porosity includes the following steps:
[0043] S1: Based on the porosity curve of a single well in the area to be predicted, a prediction model characterizing the relationship between porosity and seismic wave signal is established.
[0044] First, collect the porosity curves of at least one well in the area to be predicted.
[0045] In one embodiment, for the area to be evaluated with well logging data, the porosity curve of a single well is calculated directly based on the well logging curve.
[0046] In another embodiment, for the region to be evaluated that has no porosity, at least one single-well porosity curve based on logging accuracy can be calculated based on a rock physics model.
[0047] For wells with similar seismic reflection structures, their corresponding lithology curves are also comparable. Therefore, embodiments of the present invention can use porosity parameters based on logging accuracy to simulate regional seismic waveform indications.
[0048] After obtaining the porosity curve based on well logging accuracy, a predictive model characterizing the intrinsic correlation between porosity and seismic wave signals is established. Specifically, firstly, a priori model of porosity parameters is established; then, the rock physics model and convolution equation of the area to be evaluated are subjected to wave equation forward modeling, and a likelihood function model is established based on the relationship between the simulated data and the actual seismic response signal; finally, based on the porosity prior model and the likelihood function model, a posterior function model is formed by multiplying the two models, and the formed posterior function model is used as the predictive model.
[0049] The specific method for establishing the prior model for porosity parameters is to establish a corresponding seismic noise Gaussian function by treating the seismic noise signal as a noise distribution with a mean of 0.
[0050] Based on the principle that the porosity parameter satisfies the characteristics of seismic noise, the current seismic noise Gaussian function is converted into a porosity parameter Gaussian function, and the current porosity parameter Gaussian function is recorded as the prior model.
[0051] In this embodiment of the invention, if the seismic noise signal n is regarded as a Gaussian function with a noise distribution of mean 0, the seismic noise Gaussian function can be represented by the following expression:
[0052]
[0053] Where p(n) represents the seismic noise Gaussian function, n represents the seismic noise signal, σ represents the covariance matrix of the seismic noise, and T represents the transpose sign.
[0054] Since the actual seismic response signal d is noisy, the seismic waveform indication simulation can be performed by representing the noise-free seismic response signal Gm using the convolution equation, where the seismic noise signal n is: n = d - Gm.
[0055] Assuming that the porosity parameter m to be calculated is also a function of a Gaussian distribution, the prior function can be expressed by the following expression:
[0056]
[0057] Where p(m) represents the Gaussian function of the porosity parameter, m represents porosity, and σ m The covariance matrix representing porosity.
[0058] Furthermore, based on the model parameters G*Δm i To establish the relationship between the actual seismic signal parameter di and the actual seismic signal parameter di, a likelihood function model is constructed. The likelihood function model is expressed by the following expression:
[0059]
[0060] Where P(d|m) represents the likelihood function, i represents the index of the porosity prediction point in the region to be evaluated (i.e., the iteration number), N represents the total number of porosity prediction points (i.e., the total number of iterations), and d represents the actual seismic response data. Where G*Δm i This represents the forward simulation data mentioned above.
[0061] Finally, according to the Bayesian function, the posterior information is equal to the product of the prior information and the likelihood function. Therefore, in this embodiment of the invention, the posterior function model is represented by the following expression:
[0062]
[0063] Where P(m|d) represents the posterior function, G represents the convolutional wavelet matrix, and Δm i m represents the change in porosity of the i-th element. T This represents the porosity transpose matrix.
[0064] Furthermore, in order to ensure the accuracy of the solution results of the prediction model, the regional porosity prediction method described in this embodiment of the invention also includes: standard preprocessing of the collected seismic wave response signals of the area to be evaluated and at least one single-well porosity curve.
[0065] In this embodiment of the invention, standard preprocessing includes, but is not limited to: sequentially performing outlier processing, inter-well consistency correction, and well-seismic calibration on the collected seismic wave response signal data and single-well porosity curve data. It should be noted that the standard preprocessing steps described in this embodiment can be completed before step S1 or before step S2. This invention does not impose specific limitations on this, and those skilled in the art can choose the appropriate timing for implementing the standard preprocessing steps based on actual circumstances.
[0066] S2: Based on the seismic wave response signal and single-well porosity curve of the area to be evaluated, the porosity distribution of the area to be evaluated is obtained by solving the prediction model.
[0067] S21: Based on the seismic wave response signal of the area to be evaluated and the porosity data of the single well in the single well porosity curve, the maximum a posteriori probability is solved for the posterior function model to obtain the expected porosity value;
[0068] In practice, it is necessary to input the actual seismic wave response signal of the area to be evaluated into the posterior function model d. i In this process, the porosity parameter based on logging accuracy is substituted into m in the posterior function model. Since the actual posterior function conforms to a normal distribution, the mean obtained is the maximum posterior probability value. The maximum likelihood probability value of the obtained posterior function is used as the posterior solution. That is, for the posterior function model, the mean of m (expected porosity value) can be obtained as the maximum posterior probability solution.
[0069] S22: Based on the expected porosity value, the posterior function model is converted into an objective function that characterizes the relationship between the unit change in porosity and the unit change in seismic response value;
[0070] The objective function is expressed by the following formula:
[0071]
[0072] Where I represents the prior constraint matrix, G T Δd is the transformation matrix of the convolutional wavelet matrix, and Δd is the unit change in the actual seismic response value.
[0073] S23: Based on the seismic wave response signal and single-well porosity data of the area to be evaluated, the MCMC method is used to solve the objective function and obtain the distribution prediction results of the unit change in porosity;
[0074] S24: Based on the porosity data of the single well and the distribution prediction results of the unit change in porosity, the porosity distribution results of the area to be evaluated are obtained.
[0075] Since lateral variations in seismic waveforms can reflect changes in the sedimentary environment, and lithological assemblages are manifestations of sedimentary or seismic facies, this embodiment of the invention utilizes the aforementioned steps S1-S2 to implement a seismic waveform indication simulation process based on Markov chain Monte Carlo stochastic simulation. This allows for the quantitative derivation of the porosity distribution across the entire region based on porosity curves under well logging accuracy conditions.
[0076] Example 2:
[0077] This invention also provides a computer-readable storage medium storing a computer program, which is executed to run the method for predicting regional porosity described in Embodiment 1.
[0078] Computer programs can execute computer instructions, which include computer program code. Computer program code can be in the form of source code, object code, executable files, or some intermediate form.
[0079] Computer-readable storage media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.
[0080] It should be noted that the contents of computer-readable storage media may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, the contents may be appropriately increased or decreased according to the requirements of legislation and patent practice. In other jurisdictions, computer-readable storage media may not include electrical carrier signals and telecommunication signals.
[0081] Example 3:
[0082] like Figure 2 As shown, this embodiment of the invention also provides a system for predicting regional porosity, which implements the method for predicting regional porosity described in Embodiment 1.
[0083] The regional porosity prediction system described in this embodiment of the invention includes: a prediction model establishment module and a porosity prediction module.
[0084] Specifically, the prediction model building module is configured to build a prediction model characterizing the relationship between porosity and seismic wave signals based on the porosity curve of a single well in the area to be predicted.
[0085] The porosity prediction module is configured to obtain the porosity distribution results of the area to be evaluated by solving the prediction model based on the seismic wave response signal and single-well porosity curve of the area to be evaluated.
[0086] In one embodiment, the porosity prediction module of this invention further includes a data preprocessing module, configured to perform standard preprocessing on the seismic wave response signal and the single-well porosity curve. In this embodiment, the standard preprocessing includes, but is not limited to, sequentially performing outlier processing, inter-well consistency correction, and well-seismic calibration.
[0087] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.
Claims
1. A method for predicting regional porosity, characterized in that, Includes the following steps: S1: Based on the porosity curve of a single well in the area to be predicted, a priori model of porosity parameters is established. The rock physics model and convolution equation of the area to be evaluated are subjected to wave equation forward modeling. Based on the relationship between the simulation data of the wave equation forward modeling and the actual seismic response signal, a likelihood function model is established. The posterior function model formed by the product of the prior model and the likelihood function model is used as a predictive model characterizing the relationship between porosity and seismic wave signals. S2: Based on the seismic wave response signal and single-well porosity curve of the area to be evaluated, the porosity distribution of the area to be evaluated is obtained by solving the prediction model.
2. The method for predicting regional porosity according to claim 1, characterized in that, The specific method for establishing the prior model for porosity parameters is to establish a corresponding seismic noise Gaussian function by treating the seismic noise signal as a noise distribution with a mean of 0. Based on the principle that the porosity parameter satisfies the characteristics of seismic noise, the current seismic noise Gaussian function is converted into a porosity parameter Gaussian function, and the current porosity parameter Gaussian function is recorded as the prior model.
3. The method for predicting regional porosity according to claim 1, characterized in that, The posterior function model is represented by the following expression: Where m represents porosity, d represents seismic response data, i represents the sequence number of the porosity prediction point in the area to be evaluated, N represents the total number of porosity prediction points, P(m|d) represents the posterior function, G represents the convolutional wavelet matrix, σ represents the covariance matrix of seismic noise, and σ m The covariance matrix representing porosity, Δm i m represents the change in porosity of the i-th element. T This represents the porosity transpose matrix.
4. The method for predicting regional porosity according to claim 3, characterized in that, Step S2 specifically includes the following steps: S21: Based on the seismic wave response signal of the area to be evaluated and the porosity data of the single well in the single well porosity curve, the maximum a posteriori probability is solved for the posterior function model to obtain the expected porosity value; S22: Based on the expected porosity value, the posterior function model is converted into an objective function that characterizes the relationship between the unit change in porosity and the unit change in seismic response value; S23: Based on the seismic wave response signal and single-well porosity data of the area to be evaluated, the MCMC method is used to solve the objective function and obtain the distribution prediction results of the unit change in porosity; S24: Based on the porosity data of the single well and the distribution prediction results of the unit change in porosity, the porosity distribution results of the area to be evaluated are obtained.
5. A method for predicting regional porosity according to claim 4, characterized in that, The objective function described in step S22 is expressed by the following formula: Where I represents the prior constraint matrix, G T Δd is the transformation matrix of the convolutional wavelet matrix, and Δd is the unit change in the actual seismic response value.
6. A method for predicting regional porosity according to any one of claims 1-5, characterized in that, In step S1, the porosity curve of the single well is obtained through the following steps: For the evaluation area with well logging data, calculate the porosity curve of a single well based on the well logging curve; For areas without porosity, the porosity curve of a single well is calculated based on a rock physics model.
7. A method for predicting regional porosity according to any one of claims 1-5, characterized in that, In step S1, the seismic wave response signal and the single-well porosity curve are subjected to standard preprocessing, which includes outlier processing, inter-well consistency correction, and well-seismic calibration.
8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements a method for predicting regional porosity according to any one of claims 1-7.
9. A system for predicting regional porosity, characterized in that, A method for predicting regional porosity applicable to any one of claims 1-7, comprising: The prediction model building module is configured to build a prediction model characterizing the relationship between porosity and seismic wave signals based on the porosity curve of a single well in the area to be predicted. The porosity prediction module is configured to obtain the porosity distribution results of the area to be evaluated by solving the prediction model based on the seismic wave response signal of the area to be evaluated and the porosity curve of the single well.
10. A system for predicting regional porosity according to claim 9, characterized in that, The system for predicting regional porosity also includes: The data preprocessing module is configured to perform standard preprocessing on the seismic wave response signal and the single-well porosity curve. The standard preprocessing includes outlier processing, inter-well consistency correction, and well-seismic calibration.