Sandstone ratio uncertainty interval reduction method and system based on Bayesian theorem

Abstract

The present invention relates to a method and system for reducing the uncertainty interval of sandstone proportion based on Bayesian theorem, and the method comprises: selecting a distribution of sandstone phase ratio from geological data of a target reservoir, and using it as a priori probability distribution of the net-to-gross ratio of a lithofacies model of the target reservoir; based on the discretized prior probability distribution, using it and a multi-point geostatistical method to perform lithofacies simulation, and obtaining a plurality of random lithofacies models of the target reservoir with different net-to-gross ratio values; performing spatial resampling on each random lithofacies model, and obtaining a plurality of estimated values ​​of the net-to-gross ratio of sandstone; calculating the likelihood probability distribution of the net-to-gross ratio of sandstone according to the estimated values ​​of the plurality of net-to-gross ratios of sandstone; performing Bayesian transformation on the likelihood probability distribution of the net-to-gross ratio of sandstone, and obtaining the posterior probability distribution of the net-to-gross ratio of the lithofacies model of the target reservoir. The present invention combines mathematical statistics, a multi-point geostatistical model and Bayesian theorem, and reduces the uncertainty of sandstone proportion in the reservoir modeling process.

Description

Technical Field

[0001] The invention belongs to the technical field of geological exploration and mathematical statistics, relates to reservoir modeling technology, and specifically relates to a method and system for reducing the uncertainty interval of sandstone proportion based on Bayesian theorem. Background Art

[0002] In the process of reservoir modeling, due to the heterogeneity of oil and gas reservoirs and the limited data obtained, there is great uncertainty in predicting oil reservoirs with limited data, especially for oil and gas reservoirs in the early stages of exploration and development. Due to the small number of wells, the uncertainty in the understanding of its geology is more significant. These uncertainties bring huge risks to the efficient development of oil and gas reservoirs. More and more scholars at home and abroad have begun to pay attention to the study of uncertainty. Li Shaohua's monograph "Principles and Applications of Reservoir Uncertainty Modeling" published in 2020 introduces in detail the basic concepts, principles, methods and application examples of reservoir uncertainty modeling. It discusses in detail the evaluation of uncertainty parameters and methods to limit uncertainty, emphasizing that the uncertainty analysis and evaluation of each link in the modeling can more objectively characterize the current understanding of underground reservoirs. Therefore, reducing the uncertainty of sandstone ratio is of positive significance to improving the modeling effect and accurate reserve calculation and evaluation.

[0003] Uncertainty parameters are closely related to specific oil fields and the stage of development they are in. The range of values ​​of different levels of uncertainty parameters largely determines the impact of the variable on the response index. Among these variables, there are relatively more studies on the correlation of seismic attributes, the lower limit of porosity, and the oil-water interface. There are fewer studies on the parameter of different lithofacies ratios. The percentage of lithofacies directly determines the range of NTG values. NTG (Net To Gross) is an important influencing factor in reservoir modeling. It affects the connected volume of the sand body, is closely related to connectivity and fluidity, and affects the subsequent reserve calculation and evaluation.

[0004] The uncertainty study of the parameter of lithofacies ratio has become an important issue in the development of oil and gas fields. Especially under the condition of few wells, due to the limited data, the well data is not representative, so how to reasonably determine the percentage of different lithofacies and how to reduce the uncertainty of phase ratio is an urgent problem to be solved.

[0005] Therefore, it is urgent to propose a method to reasonably determine the percentages of different lithofacies and how to reduce the uncertainty of phase ratios, so as to more objectively characterize the current understanding of underground reservoirs and obtain good modeling results. Summary of the invention

[0006] In order to reduce the uncertainty of lithofacies ratio in a reservoir model, a first aspect of the present invention provides a method for reducing the uncertainty interval of sandstone ratio based on the Bayesian theorem, comprising: selecting a distribution of sandstone ratio from geological data of a target reservoir and using it as a priori probability distribution of the net-to-gross ratio of the lithofacies model of the target reservoir; based on the discretized prior probability distribution, using it and a multi-point geological statistical method to perform lithofacies simulation to obtain a plurality of random lithofacies models of the target reservoir with different net-to-gross ratios; spatially resampling each random lithofacies model to obtain a plurality of estimated values ​​of the sandstone net-to-gross ratio; calculating the likelihood probability distribution of the sandstone net-to-gross ratio based on the estimated values ​​of the plurality of sandstone net-to-gross ratios; performing a Bayesian transformation on the likelihood probability distribution of the sandstone net-to-gross ratio and obtaining a plurality of posterior probabilities obtained after the transformation; and combining the plurality of posterior probabilities to obtain a posterior probability distribution of the net-to-gross ratio of the lithofacies model of the target reservoir.

[0007] In some embodiments of the present invention, the prior probability distribution after discretization is used to perform lithofacies simulation using the prior probability distribution and a multi-point geostatistical method to obtain a plurality of random lithofacies models of the target reservoir with different net-to-gross ratio intervals, including: uniformly discretizing the distribution of the prior sandstone phase ratio from small to large into M classes: a 1 , a 2 , …, a m , corresponding to each category combined with training images, multi-point random simulation is performed with fixed sandstone phase ratio to obtain multiple random lithofacies models of the target reservoir with different net-to-gross ratio intervals.

[0008] Furthermore, the fixed sandstone phase ratio is subjected to multi-point random simulation to obtain multiple random lithofacies models of the target reservoir with different net-to-gross ratio ranges, including: using a multi-point simulation algorithm, retaining the median of the net-to-gross ratio of each category, simulating the multi-point lithofacies model, and obtaining multiple random lithofacies models of the target reservoir with different net-to-gross ratio ranges.

[0009] In some embodiments of the present invention, the spatial resampling of each random lithofacies model to obtain multiple estimated values ​​of sandstone net-to-gross ratios; calculating the likelihood probability distribution of the sandstone net-to-gross ratio based on the multiple estimated values ​​of the sandstone net-to-gross ratios includes: setting fixed sampling wells and sampling times n; after n samplings, obtaining n estimated values ​​of sandstone ratios; and calculating the likelihood probability distribution of the sandstone net-to-gross ratio based on the n estimated values ​​of sandstone ratios.

[0010] Furthermore, the likelihood probability distribution of the net-to-gross ratio of the sandstone is subjected to a Bayesian transformation, and multiple posterior probabilities are obtained after the transformation; the multiple posterior probabilities are combined to obtain the posterior probability distribution of the net-to-gross ratio of the target reservoir lithofacies model, including: substituting the calculated prior probability and likelihood probability into the Bayesian formula, calculating the posterior probability at the corresponding discrete level, and combining the posterior probabilities to obtain the posterior distribution.

[0011] In the above-mentioned embodiment, the method of selecting a distribution of sandstone phase ratio from the geological data of the target reservoir and using it as the prior probability distribution of the net-to-gross ratio of the lithofacies model of the target reservoir includes: obtaining the uncertainty range of the sandstone phase ratio according to different geological scenarios of the target reservoir; selecting a distribution of sandstone phase ratio from the existing well data, seismic data or logging data in a given geological scenario, and using it as the prior probability distribution of the net-to-gross ratio of the lithofacies model of the target reservoir.

[0012] The second aspect of the present invention provides a system for reducing the uncertainty interval of sandstone proportion based on Bayesian theorem, comprising: a selection module for selecting a distribution of sandstone phase ratio from geological data of a target reservoir and using it as a priori probability distribution of the net-to-gross ratio of a lithofacies model of the target reservoir; a simulation module for performing lithofacies simulation based on the discretized prior probability distribution and using it and a multi-point geological statistical method to obtain a plurality of random lithofacies models with different net-to-gross ratios of the target reservoir; a resampling module for spatially resampling each random lithofacies model to obtain a plurality of estimated values ​​of the net-to-gross ratio of sandstone; calculating the likelihood probability distribution of the net-to-gross ratio of sandstone based on the estimated values ​​of the plurality of net-to-gross ratios of sandstone; a conversion module for performing Bayesian conversion on the likelihood probability distribution of the net-to-gross ratio of sandstone and obtaining a plurality of posterior probabilities obtained after the conversion; and combining the plurality of posterior probabilities to obtain the posterior probability distribution of the net-to-gross ratio of the lithofacies model of the target reservoir.

[0013] The third aspect of the present invention provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method for reducing the uncertainty interval of sandstone proportion based on Bayesian theorem provided in the first aspect of the present invention.

[0014] A fourth aspect of the present invention provides a computer-readable medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method for reducing the uncertainty interval of sandstone proportion based on the Bayesian theorem provided in the first aspect of the present invention is implemented.

[0015] The beneficial effects of the present invention are:

[0016] 1. Use Bayesian techniques to calibrate the well core data of seismic data to obtain the best estimated NTG value;

[0017] 2. Triangular and uniform prior distributions were used for NTG, which, although different in nature, are roughly equivalent to the mean NTG. In both cases, the resulting posterior distributions show significant uncertainty reduction, and the averages of these posteriors are close to the true NTG of the training images. For the uncertainty assessment framework based on Bayesian transformation, experiments show that the updated posterior distributions reduce the uncertainty interval of the reservoir lithofacies ratio. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of a basic flow chart of a method for reducing uncertainty interval of sandstone proportion based on Bayesian theorem in some embodiments of the present invention;

[0019] Figure 2 It is a specific flow chart of a method for reducing the uncertainty interval of sandstone proportion based on Bayesian theorem in some embodiments of the present invention;

[0020] Figure 3 is a training image in some embodiments of the present invention;

[0021] Figure 4 Obtaining corresponding different probability distribution graphs by plotting the image of the probability distribution function in some embodiments of the present invention;

[0022] Figure 5 A schematic diagram of discretization of a priori NTG distribution in some embodiments of the present invention;

[0023] Figure 6 It is a schematic diagram of the effect of the lithofacies model obtained by the multi-point simulation algorithm in some embodiments of the present invention;

[0024] Figure 7 is a probability distribution diagram of NTG obtained by spatial resampling in some embodiments of the present invention;

[0025] Figure 8 is a posterior probability distribution diagram obtained by Bayesian transformation in some embodiments of the present invention;

[0026] Fig. 9 It is a schematic structural diagram of a system for reducing uncertainty interval of sandstone proportion based on Bayesian theorem in some embodiments of the present invention;

[0027] Fig.10 It is a schematic diagram of the structure of an electronic device in some embodiments of the present invention. DETAILED DESCRIPTION

[0028] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0029] In order to better understand the present invention, the following are explanations of relevant terms:

[0030] 1. Prior Distribution: It is a probability distribution of the overall distribution parameters;

[0031] 2. Training image (TI, TrainImage): A priori geological conceptual model, using the grid body G TI As a data carrier, it is a digital model that can describe the actual reservoir structure, geometry and distribution pattern;

[0032] 3. Likelihood Distribution (LD): It indicates the performance of another random variable related to it under the prior probability.

[0033] 4. Bayesian Transformation (BT): posterior probability = likelihood * prior probability / normalization constant;

[0034] 5. Posterior Distribution (PD): Before sampling, people have an understanding of the unknown parameters, which is the prior distribution. After sampling, since the samples contain information about the unknown parameters, this new information about the unknown parameters can help people correct the prior information before sampling.

[0035] 6. Multiple-Point Statistics (MPS): A reservoir geological modeling algorithm that takes spatial multi-point correlation as the core and training images as the prior geological model.

[0036] refer to Figure 1 or Figure 2 In a first aspect of the present invention, a method for reducing the uncertainty interval of sandstone proportion based on Bayesian theorem is provided, comprising: S100. selecting a distribution of sandstone phase ratio from geological data of a target reservoir, and using it as a priori probability distribution of the net-to-gross ratio of a lithofacies model of the target reservoir; S200. based on the discretized prior probability distribution, and using it and a multi-point geological statistical method to perform lithofacies simulation, to obtain a plurality of random lithofacies models with different net-to-gross ratios of the target reservoir; S300. performing spatial resampling on each random lithofacies model to obtain a plurality of estimated values ​​of the sandstone net-to-gross ratio; S400. calculating the likelihood probability distribution of the sandstone net-to-gross ratio based on the estimated values ​​of the plurality of sandstone net-to-gross ratios; performing Bayesian transformation on the likelihood probability distribution of the sandstone net-to-gross ratio, and obtaining a plurality of posterior probabilities obtained after the transformation; combining the plurality of posterior probabilities to obtain a posterior probability distribution of the net-to-gross ratio of the lithofacies model of the target reservoir.

[0037] It can be understood that the net to gross ratio of reservoir sandstone (net to gross ratio) and the sandstone phase ratio (sandstone ratio or sandstone ratio for short) in this application can be converted to each other through simple mathematical statistics calculation, so the corresponding probability distributions can be derived or equivalent to each other. Bayesian transformation is a probability calculation based on Bayesian theorem or Bayesian formula.

[0038] In step S200 of some embodiments of the present invention, the prior probability distribution after discretization is used to perform lithofacies simulation using the prior probability distribution and a multi-point geostatistical method to obtain a plurality of random lithofacies models of the target reservoir with different net-to-gross ratio intervals, including: uniformly discretizing the distribution of the prior sandstone phase ratio from small to large into m classes: a 1 , a 2 , …, a m , corresponding to each category combined with training images, multi-point random simulation is performed with fixed sandstone phase ratio to obtain multiple random lithofacies models of the target reservoir with different net-to-gross ratio intervals.

[0039] Furthermore, the fixed sandstone phase ratio is subjected to multi-point random simulation to obtain multiple random lithofacies models with different net-to-gross ratio intervals of the target reservoir, including: using a multi-point simulation algorithm, retaining the median of the net-to-gross ratio of each category, simulating the multi-point lithofacies model, and obtaining multiple random lithofacies models with different net-to-gross ratio intervals of the target reservoir. Specifically, the prior distribution is discretized and a multi-point algorithm is implemented, that is, 10 target NTG values ​​are uniformly selected between the minimum and maximum values ​​of the prior NTG distribution, that is, discretized into m=10 classes, and the median of each class interval is taken to calculate the prior probability of the corresponding discrete level. Optionally, the m value is adjusted according to the actual distribution situation.

[0040] In step S300 of some embodiments of the present invention, spatial resampling is performed on each random lithofacies model to obtain multiple estimated values ​​of sandstone net-to-gross ratios; and the likelihood probability distribution of the sandstone net-to-gross ratio is calculated based on the multiple estimated values ​​of the sandstone net-to-gross ratios, including: setting fixed sampling wells and sampling times n; after n samplings, n estimated values ​​of sandstone ratios are obtained; and the likelihood probability distribution of the sandstone net-to-gross ratio is calculated based on the n estimated values ​​of sandstone ratios.

[0041] Specifically, the random model is spatially resampled, and a fixed sampling well is set to perform random sampling n times (for example, 200 times). Each sampling will obtain a sandstone proportion estimate, and the likelihood probability distribution of the sandstone phase proportion under n samplings is obtained, that is:

[0042] Among them, P(A * =a 0 * |A=a m ,S=SK ) represents the likelihood probability distribution of sandstone facies ratio, a 0 * Indicates that NTG estimates the set A* under condition S K When the measured value of the sandstone phase ratio is a in the measured value set A m The other probabilities are expressed in the same way.

[0043] Furthermore, in step S400, the likelihood probability distribution of the net-to-gross ratio of the sandstone is subjected to a Bayesian transformation, and multiple posterior probabilities are obtained after the transformation; the multiple posterior probabilities are combined to obtain the posterior probability distribution of the net-to-gross ratio of the target reservoir lithofacies model, including: substituting the calculated prior probability and likelihood probability into the Bayesian formula, calculating the posterior probability at the corresponding discrete level, and combining the posterior probabilities to obtain the posterior distribution.

[0044] Specifically, the likelihood distribution is transformed by Bayesian to obtain each corresponding posterior probability, namely:

[0045]

[0046] Then, the posterior probabilities after Bayesian transformation are combined to obtain the posterior NTG distribution, namely:

[0047]

[0048] Finally, the reduced uncertainty interval is obtained through the posterior distribution.

[0049] In step S100 of the above-mentioned embodiment, the method of selecting a distribution of sandstone phase ratio from the geological data of the target reservoir and using it as the prior probability distribution of the net-to-gross ratio of the lithofacies model of the target reservoir includes: obtaining the uncertainty range of the sandstone phase ratio according to different geological scenarios of the target reservoir; selecting a distribution of sandstone phase ratio from the existing well data, seismic data or logging data in a given geological scenario, and using it as the prior probability distribution of the net-to-gross ratio of the lithofacies model of the target reservoir.

[0050] Specifically, several different geological scenarios are combined to obtain the uncertainty range of sandstone phase ratio. For a given geological scenario, a distribution of sandstone phase ratio is selected in combination with the existing well data, seismic data, logging data, etc. in the work area, and this distribution is used as the prior sandstone probability distribution in the Bayesian formula; when the well encounters a high-yield area (high-oil and gas production area), in order to give a reliable initial best estimate, the geological data of the reservoir model in the work area is declustered, and combined with data calibration, the original observed well data (sandstone ratio in the proven observation well) d is obtained. 0 For example, based on the well observation data D = d 0 , the best initial NTG estimate A* = a0 *, and the geological scenario S inferred from the actual available well data k (e.g., 1, 2, ..., K) to form a priori NTG distribution, that is, NTG probability distribution based on geological scenarios, namely: P(A|S=S K ).

[0051] refer to Figure 3 In one embodiment, a 3D river channel training image is provided by using a target-based modeling method. The river channel width is set at 1000-2000 meters, the thickness is 10-20 meters, the training image grid size is divided into 100*100*40, each grid size is 50m×50m×1m, and the lithofacies model NTG=0.42. In the present invention, the diversion channel sand and the center bar are classified as a single sandstone phase, and the river channel mud is classified as a mudstone phase, that is, the light color (yellow) is sandstone, and the dark color (black) is mudstone.

[0052] Figure 4 The corresponding triangular distribution and triangular cumulative probability distribution are obtained by using the probability distribution function drawn in the simulation software, and the value after data declustering correction is used as the initial optimal NTG estimate, and then the prior distribution of NTG is updated given this value. Figure 5 To select 10 target NTG values ​​evenly between the minimum and maximum values ​​of the prior NTG distribution, that is, discretize into m = 10 classes. Figure 6 To simulate the multi-point lithofacies model using the multi-point simulation algorithm snesim (Strebelle, 2002), the median NTG value of each category was retained, and 10 lithofacies models were obtained. Figure 7 To spatially resample random realizations of these 10 multi-point simulations, 4 wells, and 200 simulations were sampled to obtain the likelihood probability distribution of the NTG estimates. Figure 8 Substitute the calculated prior probability and likelihood probability into the Bayesian transformation formula, calculate the posterior probability at the corresponding discrete level, and combine the posterior probabilities to obtain the posterior distribution. The conclusion of this example is that the updated posterior distribution does reduce uncertainty compared to the previous distribution: after the study, the initial [p10, p90] probability interval [0.31, 0.59] is reduced to [0.35, 0.55]. In addition, both distributions are centered on the same value, which shows that the prior distribution is quite consistent with the observed quantitative data.

[0053] The embodiment of the present invention takes NTG, an important modeling parameter of multi-point geostatistics, as an example, and reduces the uncertainty interval of sandstone ratio based on Bayes' theorem; compared with the previous distribution, the updated posterior distribution indeed reduces the uncertainty.

[0054] Example 2

[0055] refer to Fig. 9According to a second aspect of the present invention, a system 1 for reducing the uncertainty interval of sandstone proportion based on Bayesian theorem is provided, comprising: a selection module 11 for selecting a distribution of sandstone phase ratio from geological data of a target reservoir and using it as a priori probability distribution of the net-to-gross ratio of a lithofacies model of the target reservoir; a simulation module 12 for performing lithofacies simulation based on the discretized prior probability distribution and using it and a multi-point geological statistical method to obtain a plurality of random lithofacies models of the target reservoir with different net-to-gross ratios; a resampling module 13 for spatially resampling each random lithofacies model to obtain a plurality of estimated values ​​of the net-to-gross ratio of sandstone; and calculating the likelihood probability distribution of the net-to-gross ratio of sandstone according to the estimated values ​​of the plurality of net-to-gross ratios of sandstone; and a conversion module 14 for performing Bayesian conversion on the likelihood probability distribution of the net-to-gross ratio of sandstone and converting the plurality of posterior probabilities obtained after the conversion; and combining the plurality of posterior probabilities to obtain the posterior probability distribution of the net-to-gross ratio of the lithofacies model of the target reservoir.

[0056] Furthermore, the resampling module 13 includes: a setting unit for setting a fixed sampling well and a sampling number n; a sampling unit for obtaining n sandstone proportion estimation values ​​after n samplings; and a calculation unit for calculating the likelihood probability distribution of the sandstone net-to-gross ratio based on the n sandstone proportion estimation values.

[0057] Example 3

[0058] refer to Fig.10 According to a third aspect of the present invention, an electronic device is provided, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method of the first aspect of the present invention.

[0059] The electronic device 500 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0060] Typically, the following devices may be connected to the I / O interface 505: an input device 506 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 508 including, for example, a hard disk, etc.; and a communication device 509. The communication device 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. Although Fig.10 The electronic device 500 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Fig.10 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0061] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by the processing device 501, the above functions defined in the method of the embodiment of the present disclosure are executed. It should be noted that the computer-readable medium described in the embodiment of the present disclosure can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more conductors, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In an embodiment of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In an embodiment of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wire, optical cable, RF (radio frequency), etc., or any suitable combination of the foregoing.

[0062] The computer-readable medium may be included in the electronic device, or may exist independently without being installed in the electronic device. The computer-readable medium carries one or more computer programs. When the one or more programs are executed by the electronic device, the electronic device:

[0063] Computer program code for performing the operations of embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, Python, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on a user's computer, partially on a user's computer, as a separate software package, partially on a user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0064] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0065] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method to reduce the uncertainty interval of sandstone proportion based on Bayesian theorem, It is characterized in that include: Selecting a distribution of sandstone facies ratio from geological data of the target reservoir and using it as a priori probability distribution of net-to-gross ratio of the target reservoir lithofacies model; Based on the discretized prior probability distribution, and using it and multi-point geostatistical methods to perform lithofacies simulation, multiple random lithofacies models with different net-to-gross ratios of the target reservoir are obtained; Performing spatial resampling on each random lithofacies model to obtain multiple estimated values ​​of the net-to-gross ratio of sandstone; calculating the likelihood probability distribution of the net-to-gross ratio of sandstone according to the multiple estimated values ​​of the net-to-gross ratio of sandstone; The likelihood probability distribution of the sandstone net-to-gross ratio is subjected to Bayesian transformation, and multiple posterior probabilities obtained after the transformation are combined to obtain the posterior probability distribution of the net-to-gross ratio of the target reservoir lithofacies model.

2. The method for reducing the uncertainty interval of sandstone proportion based on Bayesian theorem according to claim 1, It is characterized in that The prior probability distribution after discretization is used to perform lithofacies simulation using the prior probability distribution and a multi-point geostatistical method to obtain multiple random lithofacies models of the target reservoir with different net-to-gross ratio intervals, including: The distribution of the prior sandstone phase ratio is evenly discretized into M categories from small to large: a 1 , a 2 , …, a m , corresponding to each category combined with training images, multi-point random simulation is performed with fixed sandstone phase ratio to obtain multiple random lithofacies models of the target reservoir with different net-to-gross ratio intervals.

3. The method for reducing the uncertainty interval of sandstone proportion based on Bayesian theorem according to claim 2, It is characterized in that The fixed sandstone phase ratio is subjected to multi-point random simulation to obtain multiple random lithofacies models of the target reservoir with different net-to-gross ratio ranges, including: A multi-point simulation algorithm is used to retain the median value of the net-to-gross ratio of each category, and a multi-point lithofacies model is simulated to obtain multiple random lithofacies models of the target reservoir with different net-to-gross ratio intervals.

4. The method for reducing the uncertainty interval of sandstone proportion based on Bayesian theorem according to claim 1, It is characterized in that The spatial resampling of each random lithofacies model is performed to obtain multiple estimated values ​​of the net-to-gross ratio of sandstone; Calculating the likelihood probability distribution of the sandstone net-to-gross ratio according to the plurality of estimated values ​​of the sandstone net-to-gross ratio comprises: Set fixed sampling wells and sampling times n; After n samplings, n estimated values ​​of sandstone proportions are obtained; and the likelihood probability distribution of the net-to-gross ratio of sandstone is calculated based on the n estimated values ​​of sandstone proportions.

5. The method for reducing the uncertainty interval of sandstone proportion based on Bayesian theorem according to claim 4, It is characterized in that The likelihood probability distribution of the net-to-gross ratio of the sandstone is subjected to Bayesian transformation, and a plurality of posterior probabilities obtained after the transformation are obtained; Combining the multiple posterior probabilities to obtain the posterior probability distribution of the net-to-gross ratio of the target reservoir lithofacies model includes: Substitute the calculated prior probability and likelihood probability into the Bayesian formula, calculate the posterior probability at the corresponding discrete level, and combine the posterior probabilities to obtain the posterior distribution.

6. The method for reducing the uncertainty interval of sandstone proportion based on Bayesian theorem according to any one of claims 1 to 5, It is characterized in that The method of selecting a distribution of sandstone phase ratio from the geological data of the target reservoir and using it as the prior probability distribution of the net-to-gross ratio of the target reservoir lithofacies model includes: Obtain the uncertainty range of sandstone phase ratio according to different geological scenarios of the target reservoir; A distribution of sandstone facies ratio is selected from existing well data, seismic data or logging data in a given geological scenario and used as the prior probability distribution of the net-to-gross ratio of the target reservoir lithofacies model.

7. A system for reducing the uncertainty interval of sandstone proportion based on Bayesian theorem, It is characterized in that include: A selection module is used to select a distribution of sandstone phase ratio from geological data of a target reservoir and use it as a priori probability distribution of net-to-gross ratio of a lithofacies model of the target reservoir; A simulation module is used to perform lithofacies simulation based on the discretized prior probability distribution and the multi-point geostatistical method to obtain multiple random lithofacies models of the target reservoir with different net-to-gross ratios; A resampling module is used to perform spatial resampling on each random lithofacies model to obtain multiple estimated values ​​of the net-to-gross ratio of sandstone; and calculate the likelihood probability distribution of the net-to-gross ratio of sandstone according to the multiple estimated values ​​of the net-to-gross ratio of sandstone; The conversion module is used to perform Bayesian transformation on the likelihood probability distribution of the sandstone net-to-gross ratio, and combine the multiple posterior probabilities obtained after the transformation to obtain the posterior probability distribution of the net-to-gross ratio of the target reservoir lithofacies model.

8. The system for reducing uncertainty interval of sandstone proportion based on Bayesian theorem according to claim 7, It is characterized in that The resampling module comprises: A setting unit is used to set a fixed sampling well and sampling times n; The sampling unit is used to obtain n estimated values ​​of sandstone proportion after n samplings; A calculation unit is used to calculate the likelihood probability distribution of the sandstone net-to-gross ratio according to the n sandstone ratio estimation values.

9. An electronic device, include: one or more processors; A storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method for reducing the uncertainty interval of sandstone proportion based on Bayesian theorem as described in any one of claims 1 to 6.

10. A computer readable medium having a computer program stored thereon, in, When the computer program is executed by a processor, the method for reducing the uncertainty interval of sandstone proportion based on Bayesian theorem as described in any one of claims 1 to 6 is implemented.

Images