Method and system for predicting regional porosity

Abstract

The invention discloses a method and system for predicting the porosity of a region, and the method comprises the steps: building a prediction model which represents the relation between the porosity and a seismic wave signal based on a single-well porosity curve in a to-be-predicted region; and according to the seismic wave response signal of the to-be-evaluated region and the single well porosity curve, solving the prediction model to obtain a porosity distribution result of the to-be-evaluated region. According to the invention, quantitative prediction of the porosity parameter of the region is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil exploration, and particularly relates to a method and system for predicting regional porosity. BACKGROUND

[0002] Porosity and permeability are basic characteristics of reservoirs, and porosity, as one of important parameters of reservoir properties, has a profound influence on the distribution of underground oil and gas, oil and gas reserves and productivity, and the development plan of oil and gas fields, and thus becomes a basic parameter that must be used in the development process of oil and gas fields.

[0003] A large number of experimental and exploration research results have proved that the formation is heterogeneous, and the heterogeneity of lithologic formation is more prominent, so the response of well logging to the physical property parameters (such as porosity, saturation, permeability, etc.) is inevitably a nonlinear complex function in nature, and cannot be exactly expressed by a relational expression. With the deepening of exploration and development, it is necessary to make more accurate prediction of porosity and other physical property parameters.

[0004] Therefore, the prior art needs to provide an accurate porosity prediction scheme. SUMMARY

[0005] The purpose of the present application is to provide an accurate porosity prediction scheme.

[0006] In order to solve the above technical problems, the embodiment of the present application provides a method for predicting regional porosity, comprising: establishing a prediction model for characterizing the relationship between porosity and seismic wave signals based on the single-well porosity curve in the region to be predicted; and obtaining the porosity distribution result of the region to be evaluated by solving the prediction model according to the seismic wave response signal of the region to be evaluated and the single-well porosity curve.

[0007] Preferably, in the step of establishing the prediction model for characterizing the relationship between porosity and seismic wave signals, the step comprises: establishing a prior model about the porosity parameter; performing wave equation forward modeling on the rock physical model of the region to be evaluated and the convolution equation, and establishing a likelihood function model based on the relationship between the simulation data and the actual seismic response signal; and taking the posterior function model formed as the prediction model according to the porosity prior model and the likelihood function model.

[0008] Preferably, in the step of establishing the prior model about the porosity parameter, the step comprises: establishing a corresponding seismic noise Gaussian function by taking the seismic noise signal as a noise distribution with a mean value of 0; and converting the seismic noise Gaussian function into a porosity parameter Gaussian function according to the principle that the porosity parameter satisfies the characteristics of the seismic noise, and recording the porosity parameter Gaussian function as the prior model.

[0009] Preferably, in the step of obtaining the porosity distribution result of the region to be evaluated by solving the prediction model according to the seismic wave response signal of the region to be evaluated and the single-well porosity curve, the step comprises: solving the posterior function model by maximum a posteriori probability according to the seismic wave response signal of the region to be evaluated and the single-well porosity data to obtain a porosity expectation value; converting the posterior function model into a target function representing the relationship between the unit change of porosity and the unit change of seismic response value based on the porosity expectation value; solving the target function by using the MCMC method according to the seismic wave response signal of the region to be evaluated and the single-well porosity data to obtain a distribution prediction result of the unit change of porosity; and obtaining the porosity distribution result of the region to be evaluated based on the single-well porosity data and the distribution prediction result of the unit change of porosity.

[0010] Preferably, the posterior function model is represented by the following expression:

[0011]

[0012] wherein m represents porosity, d represents seismic response data, i represents the serial number of a porosity prediction point in the region to be evaluated, N represents the total number of the porosity prediction points, P(m|d) represents the posterior function, G represents a convolution wavelet matrix, σ represents the covariance matrix of seismic noise, σ m The target function is represented by the following expression:

[0013]

[0014] wherein I represents a prior constraint matrix.

[0015] Preferably, the single-well porosity curve is obtained by the following steps: for the region to be evaluated with logging data, calculating the single-well porosity curve based on the logging curve; and for the region without porosity, calculating the single-well porosity curve based on a rock physics model.

[0016] Preferably, the method further comprises: performing standard pretreatment on the seismic wave response signal and the single-well porosity curve, wherein the standard pretreatment comprises but is not limited to: outlier processing, inter-well consistency correction, and well-seismic calibration.

[0017] In another aspect, the embodiments of the present application provide a computer readable storage medium containing a series of instructions for performing the method steps as described above.

[0018] In addition, the embodiment of the present application further provides a system for predicting regional porosity, comprising: a prediction model establishing module configured to establish a prediction model representing the relationship between porosity and seismic wave signals based on single-well porosity curves in a region to be predicted; and a porosity prediction module configured to obtain a porosity distribution result of the region to be evaluated by solving the prediction model according to seismic wave response signals of the region to be evaluated and the single-well porosity curves.

[0019] Preferably, the system further comprises a data preprocessing module configured to perform standard preprocessing on the seismic wave response signals and the single-well porosity curves, the standard preprocessing including but not limited to outlier processing, inter-well consistency correction and well-seismic calibration.

[0020] Compared with the prior art, one or more embodiments of the above scheme can have the following advantages or beneficial effects:

[0021] The present application provides a method and system for predicting regional porosity. The method and system are suitable for predicting the porosity of a clastic rock region, specifically by calculating the porosity through logging data and rock physics models, and after the processes of outlier processing, inter-well consistency correction and well-seismic calibration on the porosity curve and seismic data, developing seismic waveform indication simulation porosity. The present application realizes quantitative prediction of the porosity parameters of the region, has a wide application prospect, and can make geophysicists predict the favorable area of the reservoir according to the predicted porosity plan, providing an important basis for geological research and reserve calculation. In addition, since the regional porosity has a favorable limit, the prediction results of the present application can also be used to further divide the favorable area of the reservoir, analyze the favorable range of the reservoir and reduce the exploration risk.

[0022] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. The objects and other advantages of the present application can be realized and achieved by the structure particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0023] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, illustrate embodiments of the present application and are used to explain the present application together with the description. The drawings do not limit the present application.

[0024] Figure 1 A step schematic diagram of the method for predicting regional porosity of the embodiment of the present application.

[0025] Figure 2 A module block diagram of the system for predicting regional porosity of the embodiment of the present application. DETAILED DESCRIPTION

[0026] The embodiments of the present application will be described in detail hereinafter with reference to the drawings and examples, so that the technical means by which the present application solves the technical problems and achieves the technical effects can be fully understood and implemented. It should be noted that, as long as there is no conflict, each embodiment in the present application and each feature in each embodiment can be combined with each other, and the technical solutions formed thereby are all within the protection scope of the present application.

[0027] In addition, the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer-executable instructions. Moreover, although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0028] The terms used herein are merely used to describe specific embodiments and are not intended to limit exemplary embodiments. Unless the context clearly indicates otherwise, as used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well. It will be further understood that the terms "comprises" and / or "comprising," when used herein, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0029] Porosity and permeability are basic characteristics of reservoirs, and porosity, as one of important parameters of reservoir properties, has a profound influence on the distribution of underground oil and gas, oil and gas reserves and productivity, and the development plan of oil and gas fields, and thus becomes a basic parameter that must be determined in the development process of oil and gas fields.

[0030] A large number of experimental and exploration research results have proved that the formation is heterogeneous, especially the lithologic formation, and the heterogeneity is more prominent, so the response of well logging to the physical property parameters (such as porosity, saturation, permeability, etc.) is inevitably a nonlinear complex function in nature, and cannot be exactly expressed by a relational expression. With the deepening of exploration and development, more accurate prediction of porosity and other physical parameters is needed. Therefore, the prior art needs to provide an accurate porosity prediction scheme.

[0031] In order to solve the technical problems in the background art, the embodiments of the present application propose a method and system for predicting regional porosity. The method and system calculate the porosity parameters based on logging accuracy based on a rock physics model or a logging curve, and use the calculated porosity parameters as the basis to use seismic waveform classification technology to establish the relationship between the seismic waveform and the high-frequency logging porosity curve, and to realize the prediction of the porosity parameters in the regional range by using the waveform indication simulation process. The present application realizes high-resolution porosity prediction.

[0032] Example One

[0033] Figure 1 The steps of the method for predicting the regional porosity according to the embodiments of the present application are shown in the following. The specific steps of the method for predicting the regional porosity (also referred to as the "regional porosity prediction method") according to the embodiments of the present application are described below. Figure 1 The specific steps of the method for predicting the regional porosity (also referred to as the "regional porosity prediction method") according to the embodiments of the present application are described below.

[0034] In step S110, a prediction model representing the relationship between the porosity and the seismic wave signal is established based on the single-well porosity curve in the region to be predicted.

[0035] In step S110, first, the single-well porosity curve of at least one well in the region to be predicted is collected.

[0036] In one embodiment, for the region to be evaluated with the logging data, the single-well porosity curve is directly calculated based on the logging curve.

[0037] In another embodiment, for the region to be evaluated without the porosity, at least one single-well porosity curve based on the logging accuracy can be calculated based on the rock physics model.

[0038] For the wells with similar seismic reflection structures, the corresponding lithology curves also have certain comparability, and therefore, the porosity parameter based on the logging accuracy can be used by the embodiments of the present application to perform the regional seismic waveform indication simulation.

[0039] After obtaining the porosity curve based on the logging accuracy, step S110 further establishes a prediction model representing the inherent correlation between the porosity and the seismic wave signal.

[0040] Specifically, first, a prior model about the porosity parameter is established; then, the rock physics model of the region to be evaluated is subjected to the wave equation forward modeling with the convolution equation, and a likelihood function model is established based on the relationship between the simulation data and the actual seismic response signal; finally, the product of the prior model of the porosity and the likelihood function model is used to form a posterior function model, and the formed posterior function model is taken as the prediction model.

[0041] In one embodiment, the prior model can be established according to the following steps:

[0042] The corresponding seismic noise Gaussian function is established by taking the seismic noise signal as the noise distribution with the mean value of 0;

[0043] The current seismic noise Gaussian function is converted into the porosity parameter Gaussian function according to the principle that the porosity parameter satisfies the seismic noise characteristics, and the current porosity parameter Gaussian function is recorded as the prior model.

[0044] In the embodiment of the present application, if the seismic noise signal n is regarded as a Gaussian function with noise distribution of mean value 0, the seismic noise Gaussian function can be expressed as follows:

[0045]

[0046] Wherein, 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 symbol.

[0047] In the embodiment of the present application, since the actual seismic response signal d is noisy, the seismic waveform indication simulation can be performed by convolving equation representing the seismic response signal Gm without noise, wherein the seismic noise signal n is: n=d-Gm.

[0048] Suppose that the porosity parameter m to be calculated is also a Gaussian distribution function, the prior function can be expressed as follows:

[0049]

[0050] Wherein, p(m) represents the Gaussian function of the porosity parameter, m represents the porosity, σ m represents the covariance matrix of the porosity.

[0051] Further, according to the relationship between the model parameter G*Δm i and the actual seismic signal parameter di, the likelihood function model is constructed. The likelihood function model is expressed as follows:

[0052]

[0053] Wherein, P(d|m) represents the likelihood function, i represents the serial number of the porosity prediction point in the evaluation area (i.e. the serial number of the iteration), N represents the total number of the porosity prediction points (i.e. the total number of iterations), d represents the actual seismic response data. Wherein, G*Δm i represents the above forward simulation data.

[0054] Finally, according to the Bayes function, the posterior information is equal to the product of the prior information and the likelihood function. Therefore, in the embodiment of the present application, the posterior function model is expressed as follows:

[0055]

[0056] Wherein, P(m|d) represents the posterior function, and G represents the convolution wavelet matrix.

[0057] Further, in order to guarantee the accuracy of the solution result of the prediction model, the area porosity prediction method provided in the embodiments of the present application further comprises: performing standard preprocessing on the collected seismic wave response signal and at least one single-well porosity curve of the area to be evaluated.

[0058] In the embodiments of the present application, the standard preprocessing comprises 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.

[0059] Thus, after the standard preprocessing of the completed data, the step S120 is entered.

[0060] It should be noted that the standard preprocessing step provided in the embodiments of the present application can be completed before the step S110 is implemented or before the step S120 is implemented, and the present application does not make a specific limitation thereon, and a person skilled in the art can select the implementation time of the standard preprocessing step according to the actual situation.

[0061] In step S120, the porosity distribution result of the area to be evaluated is obtained by solving the prediction model constructed in step S110 according to the seismic wave response signal of the area to be evaluated and the at least one single-well porosity curve collected in step S110.

[0062] In step S120, first, the prediction model representing the posterior function model is solved by maximum a posteriori probability according to the seismic wave response signal and the single-well porosity data of the area to be evaluated, and the porosity expectation value is obtained.

[0063] In the actual process, the actual seismic wave response signal of the area to be evaluated is brought into di in expression (4), and the porosity parameter based on the logging accuracy is brought into m in expression (4). Since the actual posterior function is in accordance with the normal distribution, the mean value solved is the maximum a posteriori probability value. The maximum likelihood probability value of the solved posterior function is taken as the posterior solution, that is, the mean value (porosity expectation value) of m of expression (4) can be solved as the maximum a posteriori probability solution.

[0064] Then, secondly, based on the solved porosity expectation value, the posterior function model is converted into a target function representing the relationship between the unit change of porosity and the unit change of seismic response value.

[0065] In the embodiments of the present application, the target function is represented by the following expression:

[0066]

[0067] Wherein, I represents the prior constraint matrix.

[0068] Next, in a third step, according to the actual seismic wave response signal of the region to be evaluated and at least one single-well porosity data, the MCMC method is used to solve the above objective function, and a distribution prediction result of a porosity unit change amount is obtained.

[0069] Finally, based on at least one single-well porosity data and the distribution prediction result of the porosity unit change amount obtained in the third step, a porosity distribution result of different position points in the region to be evaluated is further calculated, so that the porosity value distribution prediction for the entire region to be evaluated is completed.

[0070] Since the lateral variation of the seismic waveform can reflect the change of the sedimentary environment, and the lithological combination is the manifestation form of the sedimentary facies or the seismic facies, therefore, the embodiment of the present application realizes the seismic waveform indication simulation process based on the Markov chain Monte Carlo random simulation by using the above steps S110 to S120, so as to quantitatively deduce the porosity distribution state of the entire region based on the porosity curve under the logging accuracy condition.

[0071] Example Two

[0072] Based on the above-mentioned regional porosity prediction method, the embodiment of the present application also provides a computer readable storage medium, and the storage medium stores a computer program. The computer program can run computer instructions, and the computer instructions include computer program codes which can be in the form of source codes, object codes, executable files or some intermediate forms, etc.

[0073] The computer readable storage medium can include any entity or device capable of carrying computer program codes, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0074] It should be noted that the content contained in the computer readable storage 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 the requirements of legislation and patent practice, the computer readable storage medium does not include electric carrier signals and telecommunication signals.

[0075] Example Three

[0076] Based on the above regional porosity prediction method, the embodiment of the present application further provides a system for predicting regional porosity (also referred to as "regional porosity prediction system"). The regional porosity prediction system is used to implement the above regional porosity prediction method.

[0077] Figure 2 The module block diagram of the system for predicting regional porosity of the embodiment of the present application is shown in FIG. 1. As shown in FIG. 1, the regional porosity prediction system of the embodiment of the present application comprises a prediction model establishing module 21 and a porosity prediction module 22. Figure 2

[0078] Specifically, the prediction model establishing module 21 is implemented according to the method as described in the above step S110, and is configured to establish a prediction model representing the relationship between porosity and seismic wave signals based on the single-well porosity curve in the region to be predicted; the porosity prediction module 22 is implemented according to the method as described in the above step S120, and is configured to obtain the porosity distribution result of the region to be evaluated by solving the prediction model according to the seismic wave response signal of the region to be evaluated and the single-well porosity curve.

[0079] In one embodiment, the porosity prediction module 22 of the embodiment of the present application further comprises a data preprocessing module 23.

[0080] The data preprocessing module 23 is configured to perform standard preprocessing on the seismic wave response signal and the single-well porosity curve. In the embodiment of the present application, the standard preprocessing includes but is not limited to sequentially performing outlier processing, inter-well consistency correction and well-seismic calibration.

[0081] The present application discloses a method and system for predicting regional porosity. The method and system are suitable for predicting the porosity of a clastic rock region, and specifically calculate the porosity through logging data and rock physics model, and then perform the process of outlier processing, inter-well consistency correction and well-seismic calibration on the porosity curve and seismic data, and then develop the simulated porosity indicated by the seismic waveform. The present application realizes the quantitative prediction of the porosity parameter of a region, has a wide application prospect, and can make geophysicists predict the favorable area of a reservoir according to the predicted porosity plan, thereby providing an important basis for geological research and reserve calculation. In addition, since the regional porosity has a favorable limit, the prediction result of the present application can also be used to further divide the favorable area of the reservoir, analyze the favorable range of the reservoir, and reduce the exploration risk.

[0082] The above description is only the preferred embodiment of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims. ​

[0083] In the description of the application, unless otherwise specified and limited, the meaning of "a plurality of" is two or more; the orientation or positional relationship indicated by the terms "upper", "lower", "left", "right", "inner", "outer", "front end", "rear end", "head", "tail" and the like is based on the orientation or positional relationship shown in the drawings, and is only for the purpose of facilitating the description of the application and simplifying the description, and does not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the application. In addition, the terms "first", "second", "third" and the like are only for the purpose of description and cannot be understood as indicating or implying relative importance.

[0084] In the description of the application, it should be noted that, unless otherwise specified and limited, the terms "connected", "connected" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0085] It should be understood that the embodiments disclosed in the present application are not limited to the specific structure, processing steps or materials disclosed herein, but should extend to equivalent alternatives of these features understood by those skilled in the relevant art. It should also be understood that the terms used herein are only for the purpose of describing the specific embodiments and do not mean limitation.

[0086] The phrase "one embodiment" or "an embodiment" appearing in the specification means that the specific feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the application. Therefore, the phrase "one embodiment" or "an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment.

[0087] Although the embodiments disclosed in the present application are as described above, the content described is only the embodiments adopted for the purpose of facilitating the understanding of the present application, and is not intended to limit the present application. Any person skilled in the art of the present application can make any modification and change in the form and details without departing from the spirit and scope of the present application, but the patent protection scope of the present application shall be subject to the scope defined by the appended claims.

Claims

1. A method for predicting regional porosity, characterized in that, include: Based on the porosity curves of single wells in the area to be predicted, a predictive model characterizing the relationship between porosity and seismic wave signals is established. Based on the seismic wave response signal of the area to be evaluated and the porosity curve of the single well, the porosity distribution of the area to be evaluated is obtained by solving the prediction model.

2. The method according to claim 1, characterized in that, The steps in establishing a predictive model characterizing the relationship between porosity and seismic wave signals include: Establish a priori model for porosity parameters; The rock physics model and convolution equation of the area to be evaluated are used for wave equation forward modeling, and a likelihood function model is established based on the relationship between the simulation data and the actual seismic response signal. Based on the porosity prior model and the likelihood function model, the resulting posterior function model is used as the prediction model.

3. The method according to claim 2, characterized in that, The steps in establishing a priori models of porosity parameters include: A corresponding Gaussian function for seismic noise is established 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 seismic noise Gaussian function is converted into a porosity parameter Gaussian function, which is denoted as the prior model.

4. The method according to claim 3, characterized in that, The step of obtaining 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 includes: Based on the seismic wave response signal and single-well porosity data of the area to be evaluated, the maximum a posteriori probability is solved for the posterior function model to obtain the expected porosity value. 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. 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. Based on the single-well porosity data and the distribution prediction results of the unit change in porosity, the porosity distribution results of the area to be evaluated are obtained.

5. The method according to claim 4, 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; The objective function is expressed by the following expression: Where I represents the prior constraint matrix.

6. The method according to any one of claims 1 to 5, characterized in that, The single-well porosity curve 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. The method according to any one of claims 1 to 6, characterized in that, The method further includes: The seismic wave response signal and the single-well porosity curve are subjected to standard preprocessing, which includes, but is not limited to, outlier processing, well-to-well consistency correction, and well-to-seismic calibration.

8. A computer-readable storage medium, characterized in that, It includes a series of instructions for performing the method steps as described in any one of claims 1 to 7.

9. A system for predicting regional porosity, characterized in that, include: 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. The system according to claim 9, characterized in that, The system 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, but is not limited to, outlier processing, well-to-well consistency correction, and well-to-seismic calibration.

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