A method for calculating oil and gas reservoir analogy similarity based on statistical testing

Abstract

The present invention relates to the technical field of petroleum data statistical testing, and discloses a method for calculating oil and gas reservoir analog similarity based on statistical testing, comprising: obtaining a number of historical mixed samples and performing data cleaning on the historical mixed samples to obtain original mixed samples, each of the original mixed samples comprising original text-type geological data and original numerical geological data; converting the original text-type geological data into intermediate numerical geological data, the intermediate numerical geological data and the original numerical geological data together constituting an original full numerical sample; grouping the data fields of the original full numerical samples to obtain groupings and establishing an initial total similarity model; and assigning an optimal weight coefficient to the similarity corresponding to each group, thereby optimizing the initial total similarity model to obtain an optimal total similarity model. The present invention provides a more accurate reference for recovery rate and reserves.

Description

Technical Field

[0001] The present invention relates to the technical field of petroleum data statistical testing, and in particular to a method for calculating oil and gas reservoir analogy similarity based on statistical testing. Background Art

[0002] Currently, the industry standard "Methods for Calculating Recoverable Petroleum Reserves" (EN 1014-1-2010, SY / T 5367-2010) establishes empirical formulas for different reservoir types for evaluating recoverable reserves in new areas. However, with the continued advancement of oilfield development and changes in development targets, the limitations of empirical formulas in reserve evaluation have become increasingly apparent. Empirical formulas use multiple samples to establish regression equations, but when applied to specific reservoirs, they suffer from poor adaptability. Furthermore, as development progresses, some parameter ranges may no longer be applicable. Consequently, in recent years, China has gradually adopted the SEC (Securities and Exchange Commission) reserve assessment rules for recoverable reserve calibration, advocating for the increased use of analogy methods in the evaluation of newly developing reserve blocks.

[0003] However, existing analogy methods have difficulties in processing text-based data such as reservoir type, drive type, and trap type, while these parameters are of great reference significance for key indicators such as recovery rate and reserves.

[0004] Therefore, how to jointly establish similarity quantification of textual geological data and numerical geological data so as to more accurately evaluate the similarity between oil and gas reservoirs is an urgent problem to be solved. Summary of the Invention

[0005] This invention provides a statistically tested method for calculating the analogical similarity of oil and gas reservoirs. This method incorporates a distribution test model to ensure the scientific nature of the similarity quantification method, while also being highly versatile and rational. This method can more accurately assess the similarity between oil and gas reservoirs, identify analogous reservoirs based on the similarity, and estimate indicators such as recoverable reserves and recovery factors. This invention helps improve the accuracy and reliability of initial oil and gas field development assessments, the design and optimization of development plans, and enhance the economic benefits and resource utilization of oil and gas field development.

[0006] The present invention is achieved through the following technical solutions:

[0007] A method for calculating oil and gas reservoir analogy similarity based on statistical testing, comprising:

[0008] Acquire a number of historical mixed samples from a general oil and gas reservoir analogy database and perform data cleaning on the historical mixed samples to obtain original mixed samples, each of the original mixed samples including original text-type geological data and original numerical-type geological data;

[0009] Converting the original text-type geological data into intermediate numerical geological data, wherein the intermediate numerical geological data and the original numerical geological data together constitute an original full numerical sample;

[0010] Grouping the data fields of the original all-numeric samples according to the static parameter categories to obtain grouping groups, and respectively calculating the similarity of each of the original all-numeric samples in each group to establish an initial overall similarity model;

[0011] An optimal weight coefficient is respectively assigned to the similarity corresponding to each group, thereby optimizing the initial total similarity model to obtain an optimal total similarity model.

[0012] As an optimization, the data fields corresponding to each of the original mixed samples include reservoir type, storage type, drive type, trap type, original formation pressure, porosity, permeability, gas saturation, gas reservoir medium depth and effective thickness.

[0013] As an optimization, the data fields corresponding to the original text-type geological data include reservoir type, reservoir type, drive type and trap type.

[0014] As an optimization, before the intermediate numerical geological data and the original numerical geological data are combined to form an original full numerical sample, the original numerical geological data is standardized or normalized.

[0015] As an optimization, data cleaning is performed on several of the historical mixed samples to obtain original mixed samples, specifically removing historical mixed samples containing null values, duplicate values and invalid values, and the remaining historical mixed samples are the original mixed samples.

[0016] As an optimization, the way to convert the original text-type geological data into intermediate numerical geological data is to perform One-Hot Encoding on the original text-type geological data. The specific process is as follows:

[0017] Determine the unique value of the original text type geological data according to the text name of the original text type geological data;

[0018] Constructing an index dictionary, wherein the index dictionary stores an integer value corresponding to each unique value of the original text-type geological data;

[0019] Convert the text names corresponding to the original text-type geological data into corresponding integer values through the index dictionary;

[0020] Convert the integer value corresponding to the original text-type geological data into a One-Hot Encoding;

[0021] The original text-type geological data in the original mixed sample is replaced with the corresponding converted One-Hot Encoding, thereby converting the original text-type geological data into intermediate numerical geological data.

[0022] As an optimization, the categories of the data fields contained in each of the groups are not less than 2.

[0023] As an optimization, the specific formula of the initial total similarity model is:

[0024]

[0025] Among them, i, j are the numbers of the original full-numeric samples, i, j = 1, 2, ..., N; N is the number of the original full-numeric samples, S i,j is the total similarity between the original full-value sample i and the original full-value sample j; α k is the weight of the kth group of static parameters; is the similarity between the original all-numeric sample i and the original all-numeric sample j under the kth set of static parameters, and K is the number of groups.

[0026] As an optimization, the specific process of assigning an optimal weight coefficient to the similarity corresponding to each group is as follows:

[0027] A1. Calculate the similarity of each of the original full-value samples in each group using symmetric vector similarity

[0028] A2. Initialize the weight α of each group k Get the initial weight coefficient vector α, α=[α k ], based on the initial weight coefficient vector α, the similarity of each of the original full-value samples in each group is calculated to obtain a similarity matrix S = [S i，j ], i, j = 1, 2, ..., N, a total of N 2 The original fully numerical samples;

[0029] A3. Treat the similarity matrix S as an unordered one-dimensional array and perform normalization to obtain a normalized similarity sequence S′. Among them, μ S is the mean of the similarity matrix S, σ S is the standard deviation of the similarity matrix S;

[0030] A4. Calculate the empirical distribution function F for the standardized similarity matrix S′ n (s′ m ), the empirical distribution function F n (s′ m ) is used to represent the proportion of data less than or equal to s′ in the original full-value sample, s′ m represents the mth variable in the normalized similarity sequence S′;

[0031] A5. Calculate s′ at each standardized sample point m The theoretical standard normal distribution function F(s′ m ), where m is the sample number in S′, m=1,2,...,N 2 ;

[0032] A6. For each of the standardized sample points s′ m , calculate the empirical distribution function F n (s′ m ) and the standard normal distribution function F(s′ m ) to obtain the maximum difference D, D = sup s′ |F n (s′ m )-F(s′ m )|;

[0033] A7. Set the initial weight coefficient vector α as the variable to be solved, and use simulated annealing algorithm, particle swarm optimization algorithm or genetic algorithm to iteratively optimize and solve the optimization problem to obtain the optimal weight coefficient vector α. * , the expression of the optimization problem is:

[0034] α * =armin(D(α)).

[0035] As an optimization, the similarity of each of the original full-value samples in each group The calculation method is cosine similarity calculation method or Pearson coefficient calculation method.

[0036] As an optimization, it also includes converting the newly acquired new mixed sample into a new full numerical sample, and calculating the total similarity between the new full numerical sample and each of the original full numerical samples through the optimal total similarity model, so as to find the original full numerical sample with the highest total similarity to the new full numerical sample from each of the original full numerical samples.

[0037] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0038] 1. This paper proposes converting static parameters into numerical types through one-hot encoding, which makes it possible to uniformly process the subsequent similarity metrics, thereby providing a more accurate reference for recovery rate and reserves.

[0039] 2. The present invention groups the given static parameters and assigns different weights to different groups, thereby establishing an interpretable similarity measurement model.

[0040] 3. The present invention is based on the normal distribution assumption, combined with the optimization algorithm, and solves the parameters of the similarity measurement model according to the existing database, which has the characteristics of data-driven. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0042] Figure 1 Schematic diagram of the single-well numerical model;

[0043] Figure 2 Schematic diagram of the permeability of the near-wellbore zone obtained by logging before acidizing;

[0044] Figure 3 Waterfall diagram of DAS response during the whole water injection process (intensity is logarithmic to base 2);

[0045] Figure 4 Waterfall plot of the DAS response at the end of the water injection process (intensity logarithm to base 2);

[0046] Figure 5 Optimization of the permeability multiple during the outer layer simulated annealing algorithm (red is the final result);

[0047] Figure 6 The optimized DAS decomposition effect;

[0048] Figure 7 Interpret the water absorption profile obtained for the horizontal section. DETAILED DESCRIPTION

[0049] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with examples and drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention.

[0050] The goal of the present invention is to provide a method for establishing a universal oil and gas reservoir analogy database and similarity quantification for text and numerical geological data. The text data is encoded into numerical data through One-Hot Encoding to solve the problem of text digitization; the data is reasonably classified into groups such as geological structural background, reservoir physical parameters, and reservoir reserve parameters, and the similarity is calculated for each group separately, and different weights are assigned to the similarity of each group. Based on the fact that the weighted similarity should conform to the normal distribution, the solution is obtained by introducing a normal distribution statistical test model and a simulated annealing algorithm. The similarity quantification method established by the present invention is scientific and universal, and can more accurately evaluate the similarity between oil and gas reservoirs, thereby estimating the reserves and recovery rate of unknown oil and gas reservoirs based on analogous oil and gas reservoirs. It solves the limitations of existing analogy methods, especially the difficulties in processing text data such as reservoir type, drive type, and closure type, thereby providing a more comprehensive and accurate reserve evaluation.

[0051] This paper establishes a database for oil and gas reservoir analogies and a similarity quantification method for textual and numerical geological data. By integrating one-hot encoding, statistical tests, and simulated annealing algorithms, the paper establishes a universal, quantitative, and interpretable similarity model for oil and gas reservoir analogies and provides a solution.

[0052] Next, the process of the present invention is described in detail.

[0053] This embodiment 1 provides a method for calculating the oil and gas reservoir analogy similarity based on statistical testing, such as Figure 1 As shown, including:

[0054] S1. Acquire a number of historical mixed samples from a general oil and gas reservoir analogy database and perform data cleaning on the historical mixed samples to obtain original mixed samples, each of the original mixed samples including original text-type geological data and original numerical-type geological data;

[0055] The samples (historical mixed type and original mixed type) are static data of oil and gas reservoirs. Therefore, this step is the preprocessing of static data of oil and gas reservoirs:

[0056] In some embodiments, the data fields corresponding to each original mixed sample include reservoir type, reservoir type, drive type, trap type, original formation pressure, porosity, permeability, gas saturation, intermediate depth of gas reservoir, and effective thickness. Each data field is a static parameter.

[0057] 1. Collect original geological data, which may include but are not limited to fields such as reservoir type, drive type, trap type, original formation pressure, porosity, and permeability, forming a table data of size N×F.

[0058] In this embodiment, the data fields corresponding to the original text-type geological data include reservoir type, reservoir type, drive type, and trap type. The original numerical geological data include original formation pressure, porosity, permeability, gas saturation, gas reservoir depth, and effective thickness.

[0059] 2. Clean the data, remove null values, duplicate values and invalid data, and ensure data consistency and integrity.

[0060] If it is a text type, you can consider null values as a special text. For tabular data, if there is a null value in a row, it should be filled or deleted. This is necessary for standardized data cleaning.

[0061] In some embodiments, data cleaning is performed on several of the historical mixed samples to obtain original mixed samples, specifically removing historical mixed samples containing null values, duplicate values, and invalid values, and the remaining historical mixed samples are the original mixed samples.

[0062] S2. Converting the original text-type geological data into intermediate numerical geological data, wherein the intermediate numerical geological data and the original numerical geological data together constitute an original full numerical sample;

[0063] In some embodiments, the method of converting the original text-type geological data into the intermediate numerical geological data is to perform One-Hot Encoding on the original text-type geological data (perform One-Hot Encoding on each text-type field). The specific process is as follows:

[0064] S2.1. Determine a unique value of the original text-type geological data according to the text name of the original text-type geological data;

[0065] This means determining all unique values (categories) in each text-type field. For example, reservoir types might include sandstone, carbonate rock, and so on. If "sandstone" and "carbonate rock" are the textual names of the original text-type geological data, then "sandstone" and "carbonate rock" can also be used as unique values for the corresponding original text-type geological data.

[0066] S2.2. Construct an index dictionary, wherein the index dictionary stores an integer value corresponding to each unique value of the original text-type geological data;

[0067] This step creates a dictionary mapping: assigning a unique integer index to each unique category (unique value). Build an index dictionary that maps each text category (unique value) to the corresponding integer value.

[0068] S2.3, converting the text names corresponding to the original text-type geological data into corresponding integer values through the index dictionary;

[0069] This step is encoding conversion: use the index dictionary to convert the text data into integer index data, and each text category will be converted into a corresponding integer value.

[0070] S2.4, converting the integer value corresponding to the original text-type geological data into a One-Hot Encoding;

[0071] This step is the One-Hot Encoding conversion: convert the integer index data into a One-Hot Encoding. For each data point, create a vector with a length equal to the number of possible categories, set the corresponding category position to 1, and set the remaining positions to 0.

[0072] S2.5. Replace the original text-type geological data in the original mixed sample with the corresponding converted One-Hot Encoding, thereby converting the original text-type geological data into intermediate numerical geological data.

[0073] This step generates the encoding matrix: the one-hot encoding vectors of all data points (the integer values corresponding to the text names of the original text-based geological data) are combined into a matrix to form the final encoded dataset. That is, after converting the text names corresponding to the original text-based geological data into one-hot encodings, the corresponding one-hot encodings are filled into the cells corresponding to the text names of the original text-based geological data.

[0074] In some embodiments, before the intermediate numerical geological data and the original numerical geological data are combined to form an original full numerical sample, the original numerical geological data is standardized or normalized, which may be but is not limited to using standard processing procedures such as Min-Max and Standardization.

[0075] S3. Grouping the data fields of the original all-numeric samples according to the static parameter categories to obtain group groups, and respectively calculating the similarity of each of the original all-numeric samples in each group to establish an initial overall similarity model;

[0076] This step mainly involves grouping static parameters and establishing an initial overall similarity model.

[0077] Without grouping, only one similarity can be calculated to get the total similarity, and its distribution is fixed and most likely not normally distributed. Since multiple similarity components can be calculated after grouping, it is possible to optimize the weight of each group in the total similarity, thereby adjusting the distribution of the total similarity.

[0078] In some embodiments, each of the groups includes data fields of no less than 2 categories.

[0079] The fully numerical data processed in step S2 (the original fully numerical samples) are rationally classified into K groups, ensuring that each group contains at least two parameter classes. For reference, for example, ['Trap Type', 'Lithology', 'Reservoir Space Type'] is combined into "Geological Background Parameters," ['Porosity', 'Permeability', 'Original Gas Saturation'] is combined into "Reservoir Physical Parameters," and ['Effective Thickness', 'Original Formation Pressure'] is combined into "Geological Quantitative Parameters," for a total of three categories (three groups, grouped into "Geological Background Parameters," "Reservoir Physical Parameters," and "Geological Quantitative Parameters").

[0080] Construct an initial total similarity model. In some embodiments, the specific formula of the initial total similarity model is:

[0081]

[0082] Among them, i, j are the numbers of the original full-numeric samples, i, j = 1, 2, ..., N; N is the number of the original full-numeric samples, S i,j is the total similarity between the original full-value sample i and the original full-value sample j; α k is the weight of the kth group of static parameters; is the similarity between the original all-numeric sample i and the original all-numeric sample j under the kth set of static parameters, and K is the number of groups.

[0083] Here, an original fully numerical sample is an oil / gas reservoir data, and an oil / gas reservoir data includes but is not limited to fields such as reservoir type, drive type, trap type, original formation pressure, porosity, and permeability.

[0084] S4. Assigning an optimal weight coefficient to the similarity corresponding to each group, thereby optimizing the initial total similarity model to obtain an optimal total similarity model.

[0085] In some embodiments, an optimal weight coefficient α is given to the similarity corresponding to each group, and the Kolmogorov-Smirnov statistic is calculated. The specific process is:

[0086] A1. Calculate the similarity of each of the original full-value samples in each group using symmetric vector similarity

[0087] Here, it can be cosine similarity, Pearson coefficient, etc. That is, the similarity of each of the original full-value samples in each group The calculation method is cosine similarity calculation method or Pearson coefficient calculation method.

[0088] A2. Initialize the weight α of each group k Get the initial weight coefficient vector α, α=[α k ], based on the initial weight coefficient vector α, the similarity of each of the original full-value samples in each group is calculated to obtain a similarity matrix S = [S i，j ], i, j = 1, 2, ..., N, a total of N 2 The original fully numerical samples;

[0089] S is the NxN similarity matrix obtained by calculating the similarity between each two of N samples.

[0090] This step calculates the weighted similarity matrix S between each sample based on the given α = [S i，j ], i, j=1, 2,...,N.

[0091] A3. Treat the similarity matrix S as an unordered one-dimensional array and perform normalization to obtain a normalized similarity sequence S′. Among them, μ S is the mean of the similarity matrix S, σ S is the standard deviation of the similarity matrix S;

[0092] A4. Calculate the empirical distribution function F for the standardized similarity sequence S′ n (s′ m ), the empirical distribution function F n (s′ m ) is used to represent the proportion of data less than or equal to s′ in the original full-value sample, s′ m represents the mth variable in the normalized similarity sequence S′;

[0093] A5. Calculate s′ at each standardized sample point m The theoretical standard normal distribution function F(s′ m ), where m is the sample number in S′ (the number of the original fully numerical sample after normalization), m = 1, 2, ..., N 2 ;

[0094] F(s′ m ) Refer to the standard normal distribution function for the specific formula.

[0095] A6. For each of the standardized sample points s′ m , calculate the empirical distribution function F n (s′) and the standard normal distribution function F(s′) m) to obtain the maximum difference D, D = sup s′ |F n (s′ m )-F(s′ m )|;

[0096] The maximum difference D is the Kolmogorov-Smimov statistic.

[0097] A7. Set the initial weight coefficient vector α as the variable to be solved, and use simulated annealing algorithm, particle swarm optimization algorithm or genetic algorithm to iteratively optimize and solve the optimization problem to obtain the optimal weight coefficient vector α. * , the expression of the optimization problem is:

[0098] α * =armin(D(α)).

[0099] That is, α is set as the variable to be solved, and the simulated annealing algorithm (optionally, particle swarm optimization algorithm, genetic algorithm can also be used) is used for the following optimization problem. k Random perturbation is performed, and the search range of each element is set in the interval [10 - 3 1e-, 1], repeat steps A1-A6 and perform iterative optimization to solve:

[0100] α * =armin(D(α)) to obtain the optimal weight coefficient vector α for each group * , so that in a given N samples, the calculated N 2 The Kolmogorov-Smimov statistic of the similarity sequence S composed of the total similarities between each pair is the smallest. In this case, the similarity sequence S is closest to the normal distribution.

[0101] The optimal total similarity model is specifically:

[0102]

[0103] In some embodiments, it also includes S5, converting the newly acquired new mixed sample into a new full numerical sample, and calculating the total similarity between the new full numerical sample and each of the original full numerical samples through the optimal total similarity model, so as to find the original full numerical sample with the highest total similarity to the new full numerical sample from each of the original full numerical samples.

[0104] Based on α * , is the optimal weight coefficient of the kth group. When an unknown sample is given, the N total similarities between it and the N samples (original full numerical samples) in the known database in step S2 are calculated based on the optimal total similarity model:

[0105]

[0106] X i Represents the total similarity between the unknown sample and the i-th original full-value sample.

[0107] To X i The greater the total similarity, the more suitable it is as an analogy oil and gas reservoir for unknown samples, in order to refer to indicators such as reserves and recoverable rate.

[0108] Next, the present invention will be described by taking a gas reservoir database as an example.

[0109] 1. Data preprocessing

[0110] The database (general oil and gas reservoir analogy database) contains 342 samples in total, such as Figure 2 As shown in the figure, it includes 10 features: reservoir lithology, reservoir type, trap type, drive type, porosity, permeability, gas saturation, medium gas reservoir depth, effective thickness, and original formation pressure. The first four features are text data, and the rest are numerical data.

[0111] Follow step A to obtain the full numeric table data after one-hot encoding.

[0112] 2. Group static parameters and establish a total similarity model:

[0113] Taking into account the attributes of the given data, the full numerical data tables are rationally grouped into the following three groups: [‘trap type’, ‘lithology’, ‘reservoir space type’] are combined into “geological background parameters”, [‘porosity’, ‘permeability’, ‘original gas saturation’] are combined into “reservoir physical parameters”, and [‘effective thickness’, ‘original formation pressure’] are combined into “geological quantitative parameters”.

[0114] Follow step A to obtain the full numeric table data after one-hot encoding.

[0115] The similarity model is constructed as follows:

[0116]

[0117] Where i, j are oil / gas reservoir numbers, i, j = 1, 2, ..., N; S i，j is the total similarity between oil / gas reservoir i and oil / gas reservoir j; α k is the weight of the kth group of static parameters; is the similarity between oil / gas reservoir i and oil / gas reservoir j under the kth set of parameters; K=3.

[0118] 3. Solve α based on statistical tests

[0119] a. Use symmetric vector similarity to calculate the similarity between samples calculated by the initial total similarity model within each group As an example, cosine similarity is used here. According to the given α, the weighted similarity sequence S between each sample is calculated = [S i，j ], i, j = 1, 2, ..., N, a total of N 2 samples.

[0120] b. Standardize the similarity sequence S and get Among them, μS is the mean of sequence S, σ S is the standard deviation of S.

[0121] c. Calculate the empirical distribution function F for S′ n (s′) is expressed as the proportion of samples that are less than or equal to s′.

[0122] d. Considering the standard normal distribution, calculate s′ at each standardized sample point m The theoretical distribution function value F(s′) m ), where m is the sample number in S′, m=1,2,...,N 2 .

[0123] e. For each standardized sample point s′ m , calculate the empirical distribution function F n (s′) and the standard normal distribution function F(s′) m ) difference.

[0124] f. Get the Kolmogorov-Smimov statistic, that is, the maximum difference:

[0125]

[0126] g. Set α as the variable to be solved, use the simulated annealing algorithm (optionally, particle swarm optimization algorithm, genetic algorithm can also be used) for the following optimization problem, perform random perturbations on the variable α, set the search range of each element in the interval [1e-3, 1], and use α as the initial solution. # =[1.0, 1.0, 1.0], the distribution obtained based on the initial solution is as follows Figure 3 As shown, it is obviously far from the normal distribution. Repeat steps a to f and perform iterative optimization to solve:

[0127] α * =armin(D(α))

[0128] That is to obtain the optimal weight coefficient vector α * , so that in a given N samples, the calculated N 2 The Kolmogorov-Smirnov statistic of the similarity sequence S composed of the total similarities between each pair is the smallest. In this case, S is closest to the normal distribution.

[0129] Through iterative solution, it finally converges to:

[0130] α * =[0.79222765, 0.70382911, 0.21127358]

[0131] According to the optimal weight coefficient vector α * The calculated similarity distribution is as follows Figure 4 As shown, it can be seen that it is very close to the normal distribution and achieves the expected effect.

[0132] 4. Calculate similarity and make analogies for unknown gas reservoir samples:

[0133] Given the static geological parameters used as samples of unknown gas reservoirs (such as Figure 5 As shown in the figure, the same data preprocessing (including one-hot encoding, standardization or normalization, etc.) is performed as with the training database.

[0134] Based on the optimized vector and the established total similarity measurement model, the similarity between the sample and N=342 samples in the database is calculated. The similarity distribution is as follows: Figure 6 As shown, it is also relatively close to the normal distribution and is more ideal.

[0135] Among them, the five database samples with the closest similarity to the unknown sample are as follows: Figure 7 As shown in the figure, three of the samples that can be used as analogous gas reservoirs have a similarity exceeding 85%. The recovery factors of these three reservoirs range from 66% to 70%. Based on this, it is reasonable to infer that the recovery factor of the unknown gas reservoir should be 66% to 70%. The similarity of these three reservoirs can also be used as a weight to calculate the weighted recovery factor.

[0136] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for calculating oil and gas reservoir analogy similarity based on statistical testing, characterized in that: include: Acquire a number of historical mixed samples from a general oil and gas reservoir analogy database and perform data cleaning on the historical mixed samples to obtain original mixed samples, each of the original mixed samples including original text-type geological data and original numerical-type geological data; Converting the original text-type geological data into intermediate numerical geological data, wherein the intermediate numerical geological data and the original numerical geological data together constitute an original full numerical sample; Grouping the data fields of the original all-numeric samples according to the static parameter categories to obtain grouping groups, and respectively calculating the similarity of each of the original all-numeric samples in each group to establish an initial overall similarity model; Assigning an optimal weight coefficient to the similarity corresponding to each group, thereby optimizing the initial total similarity model to obtain an optimal total similarity model; The specific process of assigning an optimal weight coefficient to the similarity corresponding to each group is as follows: A1. Calculate the similarity of each of the original full-value samples in each group using symmetric vector similarity A2. Initialize the weight α of each group k Get the initial weight coefficient vector α, α=[α k ], based on the initial weight coefficient vector α, the similarity of each of the original full-value samples in each group is calculated to obtain a similarity matrix S = [S i,j ],i,j=1,2,…,N, total N 2 The original fully numerical samples; A3. Treat the similarity matrix S as an unordered one-dimensional array and perform normalization to obtain a normalized similarity sequence S′. Among them, μ S is the mean of the similarity matrix S, σ S is the standard deviation of the similarity matrix S; A4. Calculate the empirical distribution function F for the standardized similarity sequence S′ n (s′ m ), the empirical distribution function F n (s′ m ) is used to indicate that the original full-value sample is less than or equal to s′ m The proportion of data, s′ m represents the mth variable in the normalized similarity sequence S′; A5. Calculate s′ at each standardized sample point m The theoretical standard normal distribution function F(s′ m ), where m is the sample number in S′, m=1,2,…,N 2 ; A6. For each of the standardized sample points s′ m , calculate the empirical distribution function F n (s′ m ) and the standard normal distribution function F(s′ m ) to obtain the maximum difference D, D = sup s′ |F n (s′ m )-F(s′ m )|; A7. Set the initial weight coefficient vector α as the variable to be solved, and use simulated annealing algorithm, particle swarm optimization algorithm or genetic algorithm to iteratively optimize and solve the optimization problem to obtain the optimal weight coefficient vector α. * , the expression of the optimization problem is: a * = armin(D(α)).

2. The method for calculating oil and gas reservoir analogy similarity based on statistical testing according to claim 1, characterized in that: The data fields corresponding to each of the original mixed-type samples include reservoir type, storage type, drive type, trap type, original formation pressure, porosity, permeability, gas saturation, medium depth of gas reservoir and effective thickness, and the data fields corresponding to the original text-type geological data include reservoir type, storage type, drive type and trap type.

3. The method for calculating oil and gas reservoir analogy similarity based on statistical testing according to claim 1, characterized in that: Before the intermediate numerical geological data and the original numerical geological data are combined to form an original full numerical sample, the original numerical geological data is standardized or normalized.

4. The method for calculating oil and gas reservoir analogy similarity based on statistical testing according to claim 1, characterized in that: Data cleaning is performed on several of the historical mixed samples to obtain original mixed samples, specifically removing historical mixed samples containing null values, duplicate values and invalid values, and the remaining historical mixed samples are the original mixed samples.

5. The method for calculating oil and gas reservoir analogy similarity based on statistical testing according to claim 1, characterized in that: The method of converting the original text geological data into intermediate numerical geological data is to perform One-Hot Encoding on the original text geological data. The specific process is as follows: Determine the unique value of the original text type geological data according to the text name of the original text type geological data; Constructing an index dictionary, wherein the index dictionary stores an integer value corresponding to each unique value of the original text-type geological data; Convert the text names corresponding to the original text-type geological data into corresponding integer values through the index dictionary; Convert the integer value corresponding to the original text-type geological data into a One-Hot Encoding; The original text-type geological data in the original mixed sample is replaced with the corresponding converted One-HotEncoding, thereby converting the original text-type geological data into intermediate numerical geological data.

6. The method for calculating oil and gas reservoir analogy similarity based on statistical testing according to claim 1, characterized in that: Each of the groups contains at least 2 categories of data fields.

7. The method for calculating oil and gas reservoir analogy similarity based on statistical testing according to claim 1, characterized in that: The specific formula of the initial total similarity model is: Among them, i, j are the numbers of the original full-numeric samples, i, j = 1, 2, ..., N; N is the number of the original full-numeric samples, S i,j is the total similarity between the original full-value sample i and the original full-value sample j; α k is the weight of the kth group of static parameters; is the similarity between the original all-numeric sample i and the original all-numeric sample j under the kth set of static parameters, and K is the number of groups.

8. The method for calculating oil and gas reservoir analogy similarity based on statistical testing according to claim 1 or 7, characterized in that: The similarity of each original full-value sample in each group The calculation method is cosine similarity calculation method or Pearson coefficient calculation method.

9. The method for calculating oil and gas reservoir analogy similarity based on statistical testing according to claim 1, characterized in that: It also includes converting the newly acquired new mixed sample into a new full numerical sample, and calculating the total similarity between the new full numerical sample and each of the original full numerical samples through the optimal total similarity model, so as to find the original full numerical sample with the highest total similarity to the new full numerical sample from each of the original full numerical samples.

Images