A method, system, electronic device and storage medium for determining sandstone ratio

Abstract

The present invention provides a method, system, electronic device and storage medium for determining sandstone proportion, the method comprising: obtaining a selected finite sandstone data sample, calculating a characteristic value of the data sample; obtaining a selected confidence level, calculating a corresponding tolerance according to the characteristic value and the confidence level; and calculating a pessimistic value and an optimistic value of the sandstone proportion corresponding to the data sample according to the tolerance. The present invention can determine the range value of the sandstone proportion corresponding to the data sample by obtaining the characteristic value of the finite data sample, thereby realizing fast and efficient acquisition of sandstone data of the corresponding reservoir, and by using the confidence interval theory and combining the sandstone data for quantitative analysis, the probability that the true value falls within the range of the measurement result is obtained, the uncertainty between the sample and the true value is quantified, and finally the two extreme values ​​of the sandstone proportion calculated by combining the confidence interval theory are used to reduce the smoothing effect brought by the mean in reservoir geological modeling, thereby greatly improving the credibility of the modeling.

Description

Technical Field

[0001] The present invention relates to the field of reservoir geological modeling, and more specifically, to a method, system, electronic device and storage medium for determining a sandstone ratio. Background Art

[0002] The reservoir geological model is a geological model that quantitatively expresses the distribution and changes of various geological characteristics of the reservoir in three-dimensional space by comprehensively using data such as drilling, core, seismic, external survey, well testing, and development dynamics, with structural geology, reservoir sedimentology, petroleum geology, and geostatistics as the guiding ideology. The reservoir characteristics described include the geometric shape, scale, continuity, connectivity, internal structure, pore characteristics, distribution of reservoir physical properties parameters, and interlayer distribution of the reservoir body.

[0003] In reservoir geological modeling, sandstone content, also known as sandstone percentage, is a key point that everyone is very concerned about. Because sandstone is the focus of reservoir modeling, the size of the sandstone ratio determines the size of the reservoir in the model and has a controlling effect on the establishment of subsequent models.

[0004] However, since the data obtained from drilling often only contain local information about the underground and cannot summarize the overall characteristics, increasing the drilling density will enhance underground cognition but will also lead to increased costs. In the existing sandstone ratio confirmation method, the deviation between the calculated value and the actual value is not the same. Therefore, how to perform data analysis based on limited drilling data and determine the appropriate sandstone ratio for the reservoir model is an urgent problem to be solved. Summary of the invention

[0005] In view of the technical problems existing in the prior art, the present invention provides a method, system, electronic device and storage medium for determining the sandstone ratio, aiming to solve the technical problem in the prior art that it is impossible to perform data analysis based on limited drilling data and determine the appropriate sandstone ratio for use in a reservoir model.

[0006] According to a first aspect of the present invention, there is provided a method for determining a sandstone ratio, comprising:

[0007] Acquire a selected finite sandstone data sample, and calculate a characteristic value of the finite sandstone data sample;

[0008] Obtaining a selected confidence level, and calculating a corresponding tolerance according to the characteristic value and the confidence level;

[0009] A pessimistic value and an optimistic value of the sandstone ratio corresponding to the finite sandstone data sample are calculated according to the tolerance.

[0010] According to a second aspect of the present invention, there is provided a system for determining a sandstone ratio, comprising:

[0011] A characteristic calculation module, used for obtaining a selected finite sandstone data sample and calculating a characteristic value of the finite sandstone data sample;

[0012] A tolerance calculation module, used to obtain a selected confidence level and calculate a corresponding tolerance according to the characteristic value and the confidence level;

[0013] The result calculation module is used to calculate the pessimistic value and the optimistic value of the sandstone ratio corresponding to the finite sandstone data sample according to the tolerance.

[0014] According to a third aspect of an embodiment of the present invention, there is provided an electronic device, including a memory and a processor, wherein the processor is configured to implement the steps of a method for determining a sandstone ratio when executing a computer management program stored in the memory.

[0015] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which a computer management program is stored, and when the computer management program is executed by a processor, the steps of the method for determining the sandstone ratio are implemented.

[0016] The present invention provides a method, system, electronic device and storage medium for confirming the sandstone ratio, which obtains a selected finite sandstone data sample, calculates the characteristic value of the finite sandstone data sample; obtains a selected confidence level, calculates the corresponding tolerance according to the characteristic value and the confidence level; and calculates the pessimistic value and optimistic value of the sandstone ratio corresponding to the data sample according to the tolerance. The present invention can determine the range value of the sandstone ratio corresponding to the data sample at a certain confidence level by obtaining the characteristic value corresponding to the finite data sample, thereby realizing fast and efficient acquisition of the sandstone data of the corresponding reservoir, and by using the confidence interval theory and combining the sandstone data for quantitative analysis, the probability that the true value falls within the range of the measurement result is obtained, that is, the credibility of the measured value of the measured parameter is given, and the uncertainty between the sample and the true value is quantified. Finally, the two extreme values ​​of the sandstone ratio calculated by combining the confidence interval theory reduce the smoothing effect brought by the mean in reservoir geological modeling, and further combine with the logging interpretation, strengthen the geological interpretation, and greatly improve the credibility of the modeling. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flow chart of a method for confirming sandstone proportion provided by an embodiment of the present invention;

[0018] Figure 2 A structural diagram of a sandstone ratio confirmation system provided by an embodiment of the present invention;

[0019] Figure 3 A schematic diagram of a possible hardware structure of an electronic device provided by an embodiment of the present invention;

[0020] Figure 4 A schematic diagram of the hardware structure of a possible computer-readable storage medium provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0022] Reference Figure 1 , Figure 1 A flow chart of a method for confirming sandstone proportion provided by an embodiment of the present invention.

[0023] like Figure 1 As shown, the method for confirming the sandstone ratio includes the following steps:

[0024] Step S10: obtaining a selected finite sandstone data sample, and calculating a characteristic value of the finite sandstone data sample;

[0025] It should be noted that the execution subject of the method of this embodiment can be a computer terminal device with data processing, network communication and program running functions, such as a personal computer, a mobile phone, etc.; it can also be a server device with the same or similar functions. This embodiment and the following embodiments will be described using a personal computer as an example.

[0026] It should also be understood that the above data samples may refer to data obtained from exploration wells before formal development drilling, or logging data during formal development drilling, or relevant data during historical drilling, and this embodiment does not limit this. Among them, the above sample data may be sandstone ratio data in the rock formation, for example: see Table 1, which is sandstone ratio data on 4 sub-layers of 5 exploration wells.

[0027] Table 1 Data on sandstone proportions in each sublayer of the five wells

[0028] Small layer Well 1 Well 2 Well 3 Well 4 Well 5 H1 76.96 60.44 78.41 85.23 90.91 H2 65.13 62.33 83.25 91.33 80.36 H3 60.35 63.72 87.26 79.26 56.13 H4 92.19 60.23 74.62 81.02 73.16

[0029] It is understandable that the above-selected limited data samples may be manually selected by the user, or automatically screened by the program according to certain rules. The above-mentioned rules may be set in advance by the user, and this embodiment does not impose any limitation on this.

[0030] It should be understood that the above data samples may be data samples stored in a local and / or server-side database, data samples obtained through a third-party interface, or data samples manually input by a user, and this embodiment does not impose any restrictions on this.

[0031] It should also be understood that the above-mentioned limited data samples can be a small number of data samples required to select an average value with a significance level from the existing sample data; the above-mentioned data samples can be lithofacies ratio data samples in the same area, or lithofacies ratio data samples in different areas with the same and / or similar rock formation structure, and this embodiment does not limit this. The above-mentioned significance level refers to the probability of making an error when estimating that the overall parameter falls within a certain interval.

[0032] In a specific implementation, a small amount of sandstone data samples are obtained to calculate the characteristic values ​​of the small amount of sandstone data samples.

[0033] Step S20: obtaining a selected confidence level, and calculating a corresponding tolerance according to the characteristic value and the confidence level;

[0034] It should be understood that the above confidence level refers to the probability that the value of the population parameter falls within a certain interval of the sample statistical value; the above interval refers to the confidence interval, and the above confidence interval refers to the estimated interval of the population parameter constructed by the sample statistic. In statistics, the confidence interval of a probability sample is an interval estimate of a population parameter of this sample.

[0035] It is understandable that the above selected confidence level can be manually input by the user, obtained through a third-party interface, or automatically screened by the system according to user needs. The above confidence level can be 90%, 95%, etc., and this embodiment does not limit this.

[0036] In a specific implementation, a selected confidence level is obtained, and then the tolerance value of the sample is calculated based on the characteristic value of the sample and the confidence level.

[0037] Step S30: Calculate the pessimistic value and the optimistic value of the sandstone ratio corresponding to the finite sandstone data sample according to the tolerance.

[0038] It should be understood that the pessimistic and optimistic values ​​of the above-mentioned sandstone ratio refer to the minimum and maximum estimated values ​​of the sandstone ratio in the rock formations in the corresponding areas of the above-mentioned samples.

[0039] In a specific implementation, the minimum and maximum estimated values ​​of the sandstone ratio of the corresponding rock formation are calculated according to the tolerance of the above data samples, and then the reservoir geological model is optimized according to the maximum and minimum estimated values ​​of the sandstone ratio.

[0040] The embodiment of the present invention confirms the sandstone proportion in the reservoir geological modeling process by obtaining a selected finite sandstone data sample and calculating the characteristic value of the finite sandstone data sample; obtaining a selected confidence level and calculating the corresponding tolerance according to the characteristic value and the confidence level; and then calculating the pessimistic value and optimistic value of the sandstone proportion corresponding to the finite sandstone data sample according to the tolerance. The present invention calculates the tolerance value under the corresponding confidence level according to the selected confidence level and the characteristic value of the sample, and then calculates the pessimistic value and the optimistic value of the sandstone ratio of the above sample to the stratum according to the tolerance value, thereby realizing the quantitative analysis combined with the sandstone data using the confidence interval theory, and then giving the probability that the true value falls within the range of the measurement result, that is, giving the credibility of the measured value of the measured parameter, quantifying the "uncertainty" between the sample and the true value, and finally combining the two extreme values ​​of the sandstone ratio calculated by the confidence interval theory to reduce the smoothing effect brought by the mean in reservoir geological modeling, further combining with the logging interpretation, strengthening the geological interpretation, and greatly improving the credibility of the modeling, avoiding the errors caused by subjective judgment, thereby minimizing cognitive bias, improving the reliability of subsequent modeling, and reducing development risks.

[0041] In a possible implementation manner, the characteristic value includes the number of samples, the mean, and the standard deviation, and the corresponding tolerance is calculated according to the characteristic value and the confidence level, including:

[0042] Step S201: obtaining a τ statistic corresponding to the selected confidence level according to the confidence level;

[0043] It should be noted that the above characteristic values ​​can be calculated from the sandstone proportions in different exploration wells under the same rock formation, for example: the number of samples, the average value of the sandstone proportion in the samples, and the standard deviation of the sandstone proportion in the samples; referring to the above Table 1, among the H1 layer samples, the number of samples is 5, the average value of the sandstone proportion in the samples is 78.3900, and the standard deviation of the sandstone proportion in the samples is 11.4868.

[0044] In the specific implementation, the τ value corresponding to the above confidence level is obtained by looking up the table according to the selected confidence level. For example: in a sample data, when the number of samples is 5 and the selected confidence level is 95%, the corresponding degree of freedom when the number of samples is 5 is the number of samples minus one, so the corresponding degree of freedom is 4. We can get the corresponding τ value of approximately 2.78 by looking up the table; in another sample data, when the number of samples is 50 and the selected confidence level is 95%, we can get the corresponding τ value of approximately 1.96 by looking up the table.

[0045] Step S202: Calculate the tolerance according to the sample number, the mean, the standard deviation and the τ statistic.

[0046] In a specific implementation, based on the existing sample data, the tolerance of the sample data can be calculated based on the mean, standard deviation and τ statistic of the existing sample data at a selected confidence level.

[0047] The embodiment of the present invention can quickly determine the tolerance value of sandstone sample data through simple lithofacies ratio data, thereby determining the data range of the sandstone ratio, and further can quickly and efficiently provide a reasonable parameter range for reservoir geological uncertainty modeling, thereby quantifying the "uncertainty".

[0048] In a possible embodiment, the tolerance is:

[0049]

[0050] Where n is the number of selected finite samples, S is the standard deviation of the samples (obtained by taking the square root of the unbiased estimator of the variance), is the mean of the average sandstone percentage calculated for the sample, and τ is the τ statistic corresponding to the confidence level in the two-sided confidence interval at the selected confidence level.

[0051] For ease of understanding, the principle of the above formula can be:

[0052] Assuming that the sandstone ratio follows a normal distribution, the sample data is from a normal population N(μ, σ 2 ), where N is a normal distribution, μ is the expected value, σ is the standard deviation, and σ 2 is the variance.

[0053] Assuming n is the number of samples, the sample mean is The sample variance is Then there is It follows the standard normal distribution N(0,1), Follows chi-square distribution 2 (n-1), and S 2 Independent of each other, according to the definition of t distribution:

[0054]

[0055] To better describe the interval length, use Where p% is the percentage of tolerance, that is, the product of the mean and the tolerance percentage is used to characterize the difference between the sample mean and the population mean, and the t distribution is symmetric about the Y axis.

[0056] Then we have:

[0057]

[0058] Transformation yields:

[0059]

[0060] Among them, τ is the corresponding value under the fixed confidence level in the t distribution. When n is less than 45, the corresponding value of n-1 in the t distribution quantile table is checked. When n is greater than 45, the corresponding value in the standard normal distribution quantile table can be approximated according to the confidence level.

[0061] In one possible implementation manner, the τ statistic is:

[0062] When the selected number of samples is greater than or equal to 45, the τ statistic approximates a standard normal distribution:

[0063] τ 1-α ≈φ 1-α / 2 ;

[0064] When the selected sample number is less than 45, the τ statistic satisfies the t distribution:

[0065] T 1-α =t 1-α / 2 (n-1);

[0066] Among them, 1-α is the selected confidence level, 1-α / 2 is the corresponding confidence level in the two-sided confidence interval under the selected confidence level, φ is the normal distribution, t is the t distribution, and n-1 is the degrees of freedom corresponding to the selected number of samples.

[0067] In the specific implementation, assuming that the selected confidence level is 95% and the selected sample size is 5, since the selected sample size is less than 45, the τ value corresponding to the t distribution can be found by directly looking up the t distribution quantile table and according to the two-sided confidence interval, where τ 0.975 (4) = 2.7764, the τ statistic is approximately 2.78, that is:

[0068]

[0069] If you need to calculate the tolerance p at other confidence levels, you only need to look up the table to get the τ value at the corresponding confidence level and substitute it into the formula to get it.

[0070] In a possible embodiment, the pessimistic value of the above sandstone ratio is:

[0071]

[0072] The optimistic values ​​for the above sandstone ratios are:

[0073]

[0074] in, is the mean of the average sandstone percentage calculated for the sample, and p is the tolerance corresponding to the confidence interval.

[0075] It should be noted that the above pessimistic value can be the minimum value of the sandstone proportion in the above limited sandstone samples, and the above optimistic value can be the maximum value of the sandstone proportion in the above limited sandstone samples. The above minimum and maximum values ​​can be used to reduce the smoothing effect brought by the mean of the sandstone proportion in reservoir geological modeling, and the above maximum, minimum and mean values ​​can provide an estimated range for the uncertainty in the modeling parameters, provide a reasonable parameter range for subsequent reservoir uncertainty modeling, and then quantify the uncertainty in the modeling process.

[0076] In a specific implementation, assuming that the selected confidence level is 95%, see Table 2, which shows the pessimistic estimated value, well average value and optimistic estimated value of the sandstone percentage of different rock formations in Table 1 at the 95% confidence level.

[0077] Table 2 Optimistic and pessimistic values ​​of the percentage of sandstone phases in each sublayer H1-H4

[0078]

[0079] The embodiment of the present invention can quickly determine the tolerance value of sandstone sample data through simple lithofacies ratio data, thereby determining the data range of sandstone ratio, and then can quickly and efficiently provide a reasonable parameter range for reservoir geological uncertainty modeling, quantify the "uncertainty", and the given estimated interval can be more confident in determining the true value within the given interval in the geological sandstone ratio assessment. Due to the addition of possible deviations, the calculation results are more credible and more practical. And by combining the pessimistic value, optimistic value and mean of the above sandstone ratio with well logging interpretation, it has practical significance for strengthening geological interpretation and improving modeling effect.

[0080] In a possible implementation manner, the above-mentioned characteristic value also includes a mutation system number:

[0081]

[0082] Where S is the standard deviation of the sample, is the mean of the average sandstone percentage calculated for the sample.

[0083] In specific implementation, the above coefficient of variation can be used together with the above mean value and optimistic value and pessimistic value as a reference for comparison between the above data samples. This further avoids the smoothing effect brought by the mean value in the estimation of the proportion of rock formation sandstone, and gives an objective and reasonable range of lithofacies proportions, strengthens the grasp of edge values, avoids errors caused by subjective judgment, and thus minimizes cognitive bias, improves the reliability of subsequent modeling, and reduces development risks.

[0084] See also Figure 2 , Figure 2 A structural diagram of a sandstone ratio confirmation system provided by an embodiment of the present invention, such as Figure 2 As shown, a sandstone proportion confirmation system includes a feature calculation module 100, a tolerance calculation module 200, and a result calculation module 300, wherein:

[0085] The feature calculation module 100 is used to obtain the selected finite sandstone data sample and calculate the feature value of the finite sandstone data sample;

[0086] A tolerance calculation module 200, used to obtain a selected confidence level and calculate a corresponding tolerance according to the characteristic value and the confidence level;

[0087] The result calculation module 300 is used to calculate the pessimistic value and the optimistic value of the sandstone ratio corresponding to the finite sandstone data sample according to the tolerance.

[0088] It can be understood that the sandstone ratio confirmation system provided in the embodiment of the present invention corresponds to the sandstone ratio confirmation method provided in the aforementioned embodiments. The relevant technical features of the sandstone ratio confirmation system can refer to the relevant features of the sandstone ratio confirmation method in the aforementioned embodiments, which will not be repeated here.

[0089] See also Figure 3 , Figure 3 A possible hardware structure diagram of an electronic device provided by an embodiment of the present invention. Figure 3 As shown, an embodiment of the present application provides an electronic device, including a memory 410, a processor 420, and a computer program 411 stored in the memory 420 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented: obtaining a selected finite sandstone data sample, and calculating a characteristic value of the finite sandstone data sample; obtaining a selected confidence level, and calculating a corresponding tolerance based on the characteristic value and the confidence level; and calculating a pessimistic value and an optimistic value of the sandstone ratio corresponding to the data sample based on the tolerance.

[0090] See also Figure 4 , Figure 4 A possible hardware structure diagram of a computer-readable storage medium provided in an embodiment of the present application. Figure 4 As shown, this embodiment provides a computer-readable storage medium 500 on which a computer program 511 is stored. When the computer program 511 is executed by a processor, the following steps are implemented: obtaining a selected finite sandstone data sample, and calculating a characteristic value of the finite sandstone data sample; obtaining a selected confidence level, and calculating a corresponding tolerance according to the characteristic value and the confidence level; and calculating a pessimistic value and an optimistic value of the sandstone proportion corresponding to the data sample according to the tolerance.

[0091] It should be noted that, in the embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0092] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0093] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0094] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0095] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0096] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0097] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for determining the proportion of sandstone, characterized in that: include: Acquire a selected finite sandstone data sample, and calculate a characteristic value of the finite sandstone data sample; The characteristic values ​​include sample size, mean and standard deviation; Obtaining a selected confidence level, and calculating a corresponding tolerance according to the characteristic value and the confidence level; Calculate a pessimistic value and an optimistic value of the sandstone ratio corresponding to the finite sandstone data sample according to the tolerance; The calculating the corresponding tolerance according to the characteristic value and the confidence level includes: According to the selected confidence level, the corresponding Statistics; According to the sample size, the mean, the standard deviation and the The statistic is calculated with said tolerance; The tolerances are: ； in, is the number of samples selected, S is the standard deviation of the samples, obtained by taking the square root of the unbiased estimator of the variance, is the mean of the average sandstone percentage calculated for the sample, is the confidence level in the two-sided confidence interval corresponding to Statistics; The pessimistic value of the sandstone ratio is: ； The optimistic value of the sandstone ratio is: ； in, is the mean of the average sandstone percentage calculated for the sample, is the tolerance corresponding to the confidence interval.

2. The method for determining the sandstone ratio according to claim 1, characterized in that: Said The statistics are: When the number of selected samples is greater than or equal to 45, the The statistic approximates a standard normal distribution: ； When the number of selected samples is less than 45, the The statistic follows a t distribution: ； in, is the selected confidence level, is the corresponding confidence level in the two-sided confidence interval under the selected confidence level, φ is the normal distribution, t is the t distribution, n -1 is the degree of freedom corresponding to the selected number of samples.

3. The method for determining the sandstone ratio according to claim 1, characterized in that: The eigenvalues ​​also include the coefficient of variation: ； Where S is the standard deviation of the sample, is the mean of the average sandstone percentage calculated for the sample.

4. A sandstone ratio determination system, used to implement a sandstone ratio determination method as claimed in any one of claims 1 to 3, characterized in that: include: A characteristic calculation module, used for obtaining a selected finite sandstone data sample and calculating a characteristic value of the finite sandstone data sample; A tolerance calculation module, used to obtain a selected confidence level and calculate a corresponding tolerance according to the characteristic value and the confidence level; The result calculation module is used to calculate the pessimistic value and the optimistic value of the sandstone ratio corresponding to the finite sandstone data sample according to the tolerance.

5. An electronic device, characterized in that: It comprises a memory and a processor, wherein the processor is used to implement the steps of the method for determining the sandstone proportion as described in any one of claims 1 to 3 when executing a computer management program stored in the memory.

6. A computer-readable storage medium, characterized in that: A computer management program is stored thereon, and when the computer management program is executed by a processor, the steps of the method for determining the sandstone proportion as described in any one of claims 1 to 3 are implemented.

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