CN114970135A - Sandstone proportion determination method and system, electronic device and storage medium - Google Patents
Sandstone proportion determination method and system, electronic device and storage medium Download PDFInfo
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Abstract
The invention provides a method, a system, electronic equipment and a storage medium for determining sandstone proportion, wherein the method comprises the following steps: obtaining a selected limited sandstone data sample, and calculating a characteristic value of the data sample; obtaining the selected confidence level, and calculating the 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 method can determine the range value of the sandstone proportion corresponding to the data sample by obtaining the characteristic value of the limited data sample, thereby realizing the purpose of quickly and efficiently obtaining the sandstone data of the corresponding reservoir, and carrying out quantitative analysis by combining the sandstone data by utilizing a confidence interval theory so as to obtain the probability that the true value falls in the measuring result range, quantifying the uncertainty between the sample and the true value, and finally calculating two extreme values of the sandstone proportion by combining the confidence interval theory, thereby reducing the smoothing effect brought by the mean value in reservoir geological modeling and greatly improving the modeling credibility.
Description
Technical Field
The invention relates to the field of reservoir geological modeling, in particular to a method and a system for determining sandstone proportion, electronic equipment and a storage medium.
Background
The reservoir geological model is a geological model which comprehensively uses the data of well drilling, rock core, earthquake, external measurement, well testing, dynamic development and the like, takes tectonic geology, reservoir sedimentology, petroleum geology and geology statistics as guiding ideas and quantitatively expresses the distribution and the change of various geological characteristics of a reservoir in a three-dimensional space, and the described reservoir characteristics comprise the geometric form, the scale, the continuity, the connectivity, the internal structure, the pore characteristics, the distribution of reservoir physical property parameters, the interlayer distribution and the like of a reservoir body.
In reservoir geological modeling, sandstone content, also called sandstone percentage, is a key point of great concern, because sandstone is used as a focus in reservoir modeling, the size of a reservoir in a model is determined by the size of sandstone proportion, and the sandstone proportion has a control effect on the establishment of a subsequent model.
However, because the data obtained by drilling often only contains local underground data, the overall characteristics cannot be summarized, and although the underground cognition is enhanced by expanding the drilling density, the cost is increased, and in the existing sandstone proportion confirmation method, the deviation size between a calculated value and an actual value obtained by calculation is different, so that the problem that how to perform data analysis according to the limited drilling data and determine the proper sandstone proportion for a reservoir model is needed to be solved urgently is solved.
Disclosure of Invention
The invention provides a method, a system, electronic equipment and a storage medium for determining a sandstone proportion, aiming at solving the technical problems that the prior art cannot perform data analysis according to limited drilling data and determine a proper sandstone proportion for a reservoir model.
According to a first aspect of the invention, there is provided a method of determining sandstone proportion, comprising:
obtaining a selected limited sandstone data sample, and calculating a characteristic value of the limited sandstone data sample;
obtaining a selected confidence level, and calculating a corresponding tolerance according to the characteristic value and the confidence level;
and calculating pessimistic and optimistic values of the sandstone proportion corresponding to the limited sandstone data sample according to the tolerance.
According to a second aspect of the present invention, there is provided a system for determining sand proportions, comprising:
the characteristic calculation module is used for obtaining the selected limited sandstone data sample and calculating the characteristic value of the limited sandstone data sample;
the tolerance calculation module is used for acquiring the selected confidence level and calculating the corresponding tolerance according to the characteristic value and the confidence level;
and the result calculation module is used for calculating a pessimistic value and an optimistic value of the sandstone proportion corresponding to the limited sandstone data sample according to the tolerance.
According to a third aspect of the embodiments of the present invention, there is provided an electronic device, including a memory, and a processor, where the processor is configured to implement the steps of the method for determining sandstone proportions when executing a computer management-like program stored in the memory.
According to a fourth aspect of embodiments of the present invention, there is provided a computer-readable storage medium having stored thereon a computer management-like program which, when executed by a processor, implements the steps of a method of determining sand proportions.
According to the method, the system, the electronic equipment and the storage medium for confirming the sandstone proportion, provided by the invention, the characteristic value of the limited sandstone data sample is calculated by obtaining the selected limited 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. The invention can determine the range value of the sandstone proportion corresponding to the data sample under a certain confidence level by obtaining the characteristic value corresponding to the limited data sample, thereby realizing the rapid and efficient obtaining of the sandstone data corresponding to the reservoir, and carrying out quantitative analysis by using a confidence interval theory and combining the sandstone data, thereby obtaining the probability that the true value falls in the measuring result range, namely, the credibility of the measured value of the measured parameter is given, the uncertainty between the sample and the true value is quantified, finally, two extreme values of the sandstone proportion obtained by combining the confidence interval theory calculation are combined, the smoothing effect brought by the mean value in reservoir geological modeling is reduced, the combination with logging interpretation is further carried out, the geological interpretation is strengthened, and the modeling credibility is greatly improved.
Drawings
Fig. 1 is a flowchart of a method for confirming sandstone proportion according to an embodiment of the present invention;
fig. 2 is a structural diagram of a sandstone proportion confirmation system provided in an embodiment of the present invention;
fig. 3 is a schematic diagram of a hardware structure of a possible electronic device according to an embodiment of the present invention;
fig. 4 is a schematic hardware structure diagram of a possible computer-readable storage medium according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Referring to fig. 1, fig. 1 is a flowchart of a method for confirming a sandstone proportion according to an embodiment of the present invention.
As shown in fig. 1, the method for confirming the sandstone ratio comprises the following steps:
step S10: obtaining a selected limited sandstone data sample, and calculating a characteristic value of the limited sandstone data sample;
it should be noted that the main body of the method of the embodiment may be a computer terminal device with data processing, network communication and program running functions, such as a personal computer, a mobile phone, etc.; or may be server devices having the same or similar functions. The present embodiment and the following embodiments will be described by taking a personal computer as an example.
It should be further understood that the data sample may refer to data obtained from an exploration well before formal development drilling, may also be logging data in the formal development drilling process, and may also be related data in the historical drilling process, which is not limited in this embodiment. The sample data may be sandstone proportion data in the rock formation, for example: referring to table 1, table 1 is sandstone ratio data for 4 small layers for 5 exploration wells.
TABLE 15 sandstone proportion data of wells on each sub-layer
| 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 |
It is understood that the selected limited data sample may be manually selected by a user, or may be automatically screened by a program according to a certain rule, where the rule may be set by the user in advance, and this embodiment is not limited thereto.
It should be understood that the data sample may be a data sample stored in a local and/or server-side database, or a data sample acquired through a third-party interface, or a data sample manually input by a user, which is not limited in this embodiment.
It is also to be understood that the limited data samples described above may be a small number of data samples required to select an average of significance levels among the existing sample data; the data sample may be a facies proportion data sample in the same region, or a facies proportion data sample in different regions under the condition of the same and/or similar rock structure, which is not limited in this embodiment. The significance level refers to the probability of making a mistake in estimating that the overall parameter falls within a certain interval.
In a specific implementation, a small number of sandstone data samples are obtained, so that characteristic values in the small number of sandstone data samples are calculated.
Step S20: obtaining a selected confidence level, and calculating a corresponding tolerance according to the characteristic value and the confidence level;
it should be understood that the confidence level refers to the probability that the overall parameter value falls within a certain interval of the sample statistics; the interval refers to a confidence interval, the confidence interval refers to an estimation interval of the overall parameter constructed by the sample statistic, and in statistics, the confidence interval of a probability sample is an interval estimation of a certain overall parameter of the sample.
It is understood that the selected confidence level may be manually input by the user, obtained through a third-party interface, or automatically filtered by the system according to the user's requirement, and the confidence level may be 90%, 95%, etc., which is not limited in this embodiment.
In a specific implementation, the tolerance value of the sample is obtained by obtaining a selected confidence level and then calculating according to the characteristic value of the sample and the confidence level.
Step S30: and calculating pessimistic and optimistic values of the sandstone proportion corresponding to the limited sandstone data sample according to the tolerance.
It should be understood that the pessimistic value and the optimistic value of the sandstone proportion refer to the minimum estimation value and the maximum estimation value of the sandstone proportion in the rock formation of the area corresponding to the sample.
In specific implementation, according to the tolerance of the data samples, the minimum estimation value and the maximum estimation value of the sandstone ratio of the corresponding rock stratum are calculated, and then the reservoir geological model is optimized according to the maximum estimation value and the minimum estimation value of the sandstone ratio.
In the embodiment of the invention, for the confirmation of the sandstone proportion in the reservoir geological modeling process, the characteristic value of the limited sandstone data sample is calculated by obtaining the selected limited sandstone data sample; obtaining a selected confidence level, and calculating a corresponding tolerance according to the characteristic value and the confidence level; and calculating the pessimistic value and the optimistic value of the sandstone proportion corresponding to the limited sandstone data sample according to the tolerance. The invention calculates the tolerance value under the corresponding confidence level according to the selected confidence level and the characteristic value of the sample, then calculates the pessimistic value and the optimistic value of the sandstone ratio of the sample to the bed rock according to the tolerance value, thereby realizing the quantitative analysis by combining the confidence interval theory with the sandstone data, further providing the probability that the true value falls in the measuring result range, namely providing the credibility of the measured parameter measured value, quantizing the uncertainty between the sample and the true value, finally combining the two extreme values of the sandstone ratio calculated by the confidence interval theory, reducing the smoothing effect brought by the mean value in the reservoir geological modeling, further combining with the logging interpretation, strengthening the geological interpretation, greatly improving the modeling credibility, avoiding the errors caused by subjective judgment, and reducing the cognitive deviation to the maximum extent, the reliability of subsequent modeling is improved, and the development risk is reduced.
In one possible embodiment, the characteristic values include a sample number, a mean value and a standard deviation, and calculating corresponding tolerances according to the characteristic values and the confidence levels includes:
step S201: acquiring tau statistic corresponding to the confidence level according to the selected confidence level;
it should be noted that the above characteristic values may be calculated from sandstone proportions in different exploratory wells in 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 table 1 above, where H1 layers of the sample, the number of samples was 5, the average value of the sandstone proportion in the sample was 78.3900, and the standard deviation of the sandstone proportion in the sample was 11.4868.
In a specific implementation, the τ value corresponding to the confidence level is obtained according to a selected confidence level lookup table, for example: in a sample data, the number of samples is 5, when the selected confidence level is 95%, the corresponding degree of freedom is one less than the number of samples when the number of samples is 5, so that the corresponding degree of freedom is 4, and the corresponding value of tau can be obtained by table lookup to be approximately 2.78; 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 to approximately 1.96 according to the table lookup.
Step S202: calculating the tolerance from the number of samples, the mean, the standard deviation, and the τ statistic.
In a specific implementation, the tolerance of the sample data can be calculated according to the mean value and standard deviation of the existing sample data and the tau statistic of the selected confidence level under the condition of the existing sample data.
According to the embodiment of the invention, the tolerance value of the sandstone sample data can be rapidly determined through the simple lithofacies proportion data, so that the data range of the sandstone proportion is determined, a reasonable parameter range can be rapidly and efficiently provided for reservoir geological uncertainty modeling, and uncertainty is quantized.
In a possible embodiment, the tolerance is:
where n is the selected finite number of samples, S is the standard deviation of the samples (derived by 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.
For ease of understanding, the principle of the above formula may be:
assuming that the sandstone proportions follow a normal distribution, the sample data is from a normal population N (μ, σ) 2 ) Where N is a normal distribution, μ is an expected value, σ is a standard deviation, and σ is a normal distribution 2 Is the variance.
Let n be the number of samples and the mean of the samples be
Sample variance of
Then there is
Subject to a standard normal distribution N (0, 1),
obey chi-square distribution 2 (n-1)，
And S 2 Independently of each other, according to the t distribution definition:
for better description of interval length, use
Where p% is the percentage of tolerance, i.e., the difference between the mean and the overall mean is plotted as the product of the mean and percentage of tolerance, and the t distribution is symmetric about the Y axis.
Then there are:
the transformation may result in:
wherein tau is a corresponding value in t distribution under a fixed confidence level, and when n is less than 45, looking up a corresponding value in a t distribution fractional number table n-1. When n is greater than 45, the value corresponding to the standard normal distribution quantile table can be looked up according to the confidence level to be approximate.
In one possible embodiment, τ statistic is:
when the selected number of samples is equal to or greater than 45, the τ statistic approximates a standard normal distribution:
τ 1-α ≈φ 1-α/2 ；
when the selected number of samples is less than 45, the τ statistic satisfies t-distribution:
T 1-α ＝t 1-α/2 (n-1)；
where 1- α is the selected confidence level, 1- α/2 is the corresponding confidence level in the bilateral confidence interval at the selected confidence level, φ is the normal distribution, t is the t distribution, and n-1 is the degree of freedom corresponding to the selected number of samples.
In a specific implementation, assuming that the selected confidence level is 95%, the number of the selected samples is 5, and since the number of the selected samples is less than 45, the τ value corresponding to the t distribution can be found by directly looking up the t distribution fractional number table according to the bilateral confidence intervals, wherein τ is 0.975 (4) At 2.7764, the τ statistic is approximately 2.78, i.e.:
if the tolerance p under other confidence levels needs to be calculated, the tau value under the corresponding confidence level only needs to be obtained by table lookup and substituted into a formula to obtain the tolerance p.
In a possible embodiment, the pessimistic value of the sandstone proportion is:
the optimistic value of the sandstone proportion is as follows:
wherein the content of the first and second substances,
is the mean of the average sandstone percentage calculated for the sample, and p is the tolerance for the confidence interval.
It should be noted that the pessimistic value may be a minimum value of the sandstone proportion in the limited sandstone sample, the optimistic value may be a maximum value of the sandstone proportion in the limited sandstone sample, the minimum value and the maximum value may be used for reducing a smoothing effect influence caused by a mean value of the sandstone proportion in reservoir geological modeling, and the maximum value, the minimum value and the mean value may provide an estimation range for uncertainty in a modeling parameter, provide a reasonable parameter range for subsequent reservoir uncertainty modeling, and further quantify uncertainty in a modeling process.
In a specific implementation, assuming the selected confidence level is 95%, see table 2, where table 2 is a pessimistic, well average, and optimistic estimate of the sandstone percentage for the different formations in table 1 at the 95% confidence level.
TABLE 2 optimistic and pessimistic values for sandstone phase percentages on sub-layers H1-H4
The embodiment of the invention can quickly determine the tolerance value of sandstone sample data through simple rock-facies proportion data, thereby determining the data range of sandstone proportion, further realizing the purpose of quickly and efficiently providing a reasonable parameter range for reservoir geological uncertainty modeling, quantifying uncertainty, and ensuring that a true value is in a given range by a given estimation range with greater confidence in geological sandstone proportion evaluation. And the pessimistic value, the optimistic value and the mean value of the sandstone proportion are combined with well logging interpretation, so that the method has practical significance for strengthening geological interpretation and improving modeling effect.
In a possible embodiment, the characteristic value further includes a variation system number:
where, S is the standard deviation of the sample,
is the average of the average sandstone percentage calculated for the sample.
In one embodiment, the variance coefficient may be used as a reference for comparison between the data samples, together with the mean, the optimistic value, and the pessimistic value. Therefore, the smoothing effect brought by the mean value in the rock stratum sandstone ratio estimation is further avoided, an objective and reasonable lithofacies ratio range is given, the grasp on the edge value is enhanced, and errors caused by subjective judgment are avoided, so that the cognitive deviation is reduced to the maximum extent, the reliability of subsequent modeling is improved, and the development risk is reduced.
Referring to fig. 2, fig. 2 is a structural diagram of a sandstone proportion confirmation system according to an embodiment of the present invention, and as shown in fig. 2, the sandstone proportion confirmation system includes a feature calculation module 100, a tolerance calculation module 200, and a result calculation module 300, where:
the characteristic calculation module 100 is configured to obtain a selected limited sandstone data sample, and calculate a characteristic value of the limited sandstone data sample;
a tolerance calculation module 200 for obtaining a selected confidence level and calculating a corresponding tolerance according to the feature value and the confidence level;
and the result calculating module 300 is configured to calculate a pessimistic value and an optimistic value of the sandstone proportion corresponding to the limited sandstone data sample according to the tolerance.
It can be understood that the sandstone proportion confirmation system provided by the embodiment of the present invention corresponds to the sandstone proportion confirmation method provided by each of the foregoing embodiments, and the relevant technical features of the sandstone proportion confirmation system may refer to the relevant features of the sandstone proportion confirmation method of each of the foregoing embodiments, and are not described herein again.
Referring to fig. 3, fig. 3 is a schematic diagram of a hardware structure of a possible electronic device according to an embodiment of the present invention. As shown in fig. 3, the embodiment of the present application provides an electronic device, which includes a memory 410, a processor 420, and a computer program 411 stored on the memory 420 and executable on the processor 420, and when the processor 420 executes the computer program 411, the following steps are implemented: obtaining a selected limited sandstone data sample, and calculating a characteristic value of the limited sandstone data sample; obtaining a selected confidence level, and calculating a corresponding tolerance according to the characteristic value and the confidence level; and calculating pessimistic values and optimistic values of the sandstone proportions corresponding to the data samples according to the tolerance.
Referring to fig. 4, fig. 4 is a schematic hardware structure diagram of a possible computer readable storage medium according to an embodiment of the present disclosure. As shown in fig. 4, the present embodiment provides a computer-readable storage medium 500 having a computer program 511 stored thereon, the computer program 511 implementing the following steps when executed by a processor: obtaining a selected limited sandstone data sample, and calculating a characteristic value of the limited 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.
It should be noted that, in the embodiments, the description of each embodiment has a respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to the related description of other embodiments.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such 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 such modifications and variations.
Claims (9)
1. A method for determining sandstone proportion is characterized by comprising the following steps:
obtaining a selected limited sandstone data sample, and calculating a characteristic value of the limited 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 limited sandstone data sample according to the tolerance.
2. The method of determining sandstone proportions of claim 1 wherein the characteristic values include sample number, mean, and standard deviation, and wherein calculating the corresponding tolerance from the characteristic values and the confidence levels comprises:
acquiring tau statistic corresponding to the confidence level according to the selected confidence level;
calculating the tolerance from the number of samples, the mean, the standard deviation, and the τ statistic.
3. The method of determining sandstone proportions of claim 2, wherein the tolerances are:
where n is the number of samples selected, S is the standard deviation of the samples (derived by the unbiased estimator of the variance),
is the mean of the average sandstone percentage calculated for the sample, and τ is the τ statistic for the confidence level corresponding to the confidence level in the two-sided confidence interval.
4. A method of determining sandstone proportions as claimed in claim 3, wherein the τ statistic is:
when the selected number of samples is equal to or greater than 45, the τ statistic approximates a standard normal distribution:
τ 1-α ≈φ 1-α/2 ；
when the selected number of samples is less than 45, the τ statistic satisfies t-distribution:
τ 1-α ＝t 1-α/2 (n-1)；
where 1- α is the selected confidence level, 1- α/2 is the corresponding confidence level in the bilateral confidence interval at the selected confidence level, φ is the normal distribution, t is the t distribution, and n-1 is the degree of freedom corresponding to the selected number of samples.
5. The method of determining sandstone proportions of claim 1, wherein the characteristic values further comprise coefficients of variation:
where, S is the standard deviation of the sample,
is the average of the average sandstone percentage calculated for the sample.
6. The method for determining the sandstone proportion of claim 1, wherein the pessimistic value of the sandstone proportion is:
the optimistic value of the sandstone proportion is as follows:
wherein the content of the first and second substances,
is the mean of the average sandstone percentage calculated for the sample, and p is the tolerance for the confidence interval.
7. A sandstone ratio determination system, comprising:
the characteristic calculation module is used for obtaining the selected limited sandstone data sample and calculating the characteristic value of the limited sandstone data sample;
the tolerance calculation module is used for acquiring the selected confidence level and calculating the corresponding tolerance according to the characteristic value and the confidence level;
and the result calculation module is used for calculating a pessimistic value and an optimistic value of the sandstone proportion corresponding to the limited sandstone data sample according to the tolerance.
8. An electronic device comprising a memory, a processor for implementing the steps of the method of determining sand proportions as claimed in any one of claims 1 to 6 when executing a computer management like program stored in the memory.
9. A computer-readable storage medium, characterized in that it has stored thereon a computer management-like program which, when executed by a processor, carries out the steps of the method for determining sandstone proportions as claimed in any of the claims 1-6.
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| CN116911072B (en) * | 2023-09-07 | 2024-01-26 | 长江三峡集团实业发展(北京)有限公司 | Method, device, computer equipment and medium for determining distribution duty ratio of lens body |
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