An automatic identification and location method for high-value points of oil and gas loss based on geographic information

Abstract

The present invention discloses an automatic identification and positioning method for high-value points of oil and gas losses based on geographic information, including: extracting oil and gas loss activity data of each link in the whole process of the oilfield production database and writing it into the database; calculating the oil and gas loss activity data and writing the results into the database; selecting the threshold of the over-threshold model; fitting with the Pareto distribution function; determining the threshold of the high-value points of oil and gas losses; and positioning the nodes of the high-value points of oil and gas losses. Through the big data analysis of the oil and gas losses of each link and each node in the oil and gas production process of the oilfield, the present invention realizes the automatic identification and positioning of the high-value points of oil and gas losses with geographic information, which is beneficial to discovering the key nodes of the oil and gas losses in the whole process of the oilfield and provides an important basis for the oilfield to carry out the governance work of oil and gas losses.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas information technology, and particularly to an automatic identification and positioning method for high-value points of oil and gas loss based on geographic information. Background Art

[0002] Oil and gas loss not only causes losses of oil and gas, deterioration of oil quality, and pollution of the atmospheric environment, but also poses potential safety hazards to the safe production of oil depots. Therefore, it is necessary to study the laws of oil and gas loss and the main links where losses occur, so as to provide a basis for formulating loss reduction decisions. However, due to the large number of oil and gas loss links in the entire process of current oil and gas production and the fact that the statistics of oil and gas loss amounts in each link often form information islands, there is no way to intuitively and clearly monitor and locate the high-value points of oil and gas loss. The judgment of high-value points mainly relies on manual work, lacking a method for automatic identification and positioning of high-value points. The positioning and analysis of the main links of oil and gas loss are highly subjective and lack scientific basis, resulting in the inability to propose targeted loss reduction measures, leading to a lack of scientific basis for oil and gas loss control work and poor control effects. In view of this situation, it is necessary to establish a method for identifying and positioning high-value points of oil and gas loss based on geographic information, determine the high-value points of oil and gas loss with geographic information, and lay a foundation for the study of oil and gas loss laws and the efficient development of loss control work. Through research on relevant domestic and foreign literature and patent technologies, there is currently no effective means for automatic identification and positioning of high-loss nodes, and it is urgent to study and establish relevant methods to achieve automatic identification and positioning of high-loss nodes. Summary of the Invention

[0003] The embodiments of the present invention provide an automatic identification and positioning method for high-value points of oil and gas loss based on geographic information. To provide a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. This summary part is not a general review, nor is it intended to identify key / important constituent elements or delineate the protection scope of these embodiments. Its sole purpose is to present some concepts in a simple form as a prelude to the subsequent detailed description.

[0004] An automatic identification and positioning method for high-value points of oil and gas loss based on geographic information provided by the present invention, through big data analysis of oil and gas loss data in each link of the entire process of the oilfield, establishes an automatic identification and positioning method for high-value points of oil and gas loss based on geographic information, realizes the identification and positioning of high-loss nodes, and provides a scientific basis for formulating loss reduction decisions and the efficient development of oil and gas loss control work.

[0005] The embodiments of the present invention provide an automatic identification and positioning method for high-value points of oil and gas loss based on geographic information, which is improved in that it includes:

[0006] (1) Extract the oil and gas loss activity data in each link of the entire process of the oilfield production database and write it into the database;

[0007] (2) Calculate the oil and gas loss activity data and write the results into the database;

[0008] (3) Select the threshold of the over-threshold model;

[0009] (4) Fit the Pareto distribution function;

[0010] (5) Determine the threshold of the high-value points of oil and gas loss;

[0011] (6) Locate the nodes of the high-value points of oil and gas loss.

[0012] Preferably, step (1) includes extracting the oil and gas loss-related activity data of each link in the whole process of oilfield production from the production database of the oilfield, and writing the data into the database, where the production database includes PCS and EPBP.

[0013] Preferably, step (2) includes improving the accounting methods for oil and gas loss in each link, using the production activity data obtained from the oil and gas production site of the oilfield, and obtaining the relevant basic data for oil and gas loss accounting through cleaning, standardization, and screening. Through the accounting formulas for oil and gas loss in each link, calculate the oil and gas loss amounts of each link and each node in the process of oil and gas production, and write the results into the database.

[0014] Further, the accounting formulas for oil and gas loss in each link include the accounting formula for the joint station link, the accounting formula for the transfer station link, and the accounting formula for the wellhead link.

[0015] Further, the accounting formula for the joint station link is as shown in Equation (1):

[0016]

[0017] In the formula: Q 标 —— Standard volume of the daily exhaled gas from the storage tank breather, m 3 / d;

[0018] S —— Total area of the breather, m 2 ;

[0019] u —— Flow rate of the breathing gas, m / s;

[0020] T s —— Absolute temperature, 273.15K;

[0021] T i —— Oil and gas temperature, °C;

[0022] P i —— Oil and gas pressure, MPa;

[0023] P s —— Standard atmospheric pressure, 0.1013MPa.

[0024] Furthermore, the accounting formula for the transfer station link is as shown in Equation (2), and the accounting formula for the wellhead link is as shown in Equation (3):;

[0025]

[0026] In the formula: Q 标 —— Standard volume of the daily exhaled gas from the storage tank breather, m 3 / d;

[0027] n —— Number of storage tank breather valves;

[0028] S —— Cross-sectional area of the gas collection pipe of the sealing device, m 2 ;

[0029] u —— Flow velocity of the breathing gas, m / s;

[0030] T s —— Absolute temperature, 273.15 K;

[0031] T i —— Temperature of oil and gas, °C;

[0032] P i —— Pressure of oil and gas, MPa;

[0033] P s —— Standard atmospheric pressure, 0.1013 MPa;

[0034] Q p =(Q / ρ)×EF#(3)

[0035] In the formula: Q p —— Volume of the casing gas vented, m 3 / d;

[0036] ρ —— Density of thermal recovery heavy oil, t / m 3 ;

[0037] Q —— Daily oil production, t / d;

[0038] EF —— Emission factor of hydrocarbons, m 3 THC / m 3 .

[0039] Preferably, step (3) includes taking out the oil and gas loss data exceeding the threshold in step (2) and subtracting the data value of the threshold to obtain the data exceeding the threshold, and obtaining the shape parameter and scale parameter of the Pareto distribution function from the data exceeding the threshold by the maximum likelihood estimation method.

[0040] Preferably, step (4) includes making a generalized Pareto distribution function of the position parameter to fit the excess threshold oil and gas loss data through shape parameters and scale parameters, and obtaining a distribution function that conforms to the statistical characteristics of the excess threshold oil and gas loss data.

[0041] Preferably, step (5) includes, according to the sigma criterion of the normal distribution, the area within the horizontal axis interval of a normal distribution is approximately 99.7%, and the area within the horizontal axis interval is approximately 95%; setting the data with the top 90% frequency distribution in the generalized Pareto distribution as normal values, and the remaining data as high values; according to the cumulative probability function of the generalized Pareto distribution, obtaining the first data point with a cumulative probability greater than 90%, and regarding the point data as the high value point of the excess threshold data; adding the data value of this point to the data value of the threshold point obtained in step (1) to obtain the high value point threshold of the oil and gas loss data, and regarding the data exceeding this threshold as the high value point data of the oil and gas loss.

[0042] Preferably, step (6) includes, after determining the data of the high value points of the oil and gas loss, using the geographical information of the links and nodes corresponding to the high value points carried by the big data of the oil and gas loss in step 1 to determine the specific positions of the high value points of the oil and gas loss.

[0043] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0044] Through the big data analysis of the oil and gas loss in each link and node during the oil and gas production process in the oilfield, the present invention realizes the automatic identification and positioning of the high value points of the oil and gas loss with geographical information, which is beneficial to discovering the key nodes of the oil and gas loss in the whole process of the oilfield, and provides an important basis for the oilfield to carry out the governance work of the oil and gas loss.

[0045] The oil and gas loss data of the present invention is calculated through a perfect accounting formula based on the actual monitoring data of the oilfield, which can objectively reflect the actual situation of the oil and gas loss at the oil and gas production site of the oilfield, has high reference value, and is of great significance for the field of governance of the oil and gas loss in the oilfield.

[0046] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present invention, and are used together with the specification to explain the principles of the present invention.

[0048] Figure 1 It is a schematic flow chart of an automatic identification and positioning method for high value points of oil and gas loss based on geographical information provided by an embodiment of the present invention;

[0049] Figure 2 is a schematic diagram of fitting the Pareto distribution function in the method for automatically identifying and locating high-value points of oil and gas loss based on geographic information provided by an embodiment of the present invention;

[0050] Figure 3 Figure 4 is a schematic diagram of the CDF curves of the super-threshold data and the fitting function in the method for automatically identifying and locating high-value points of oil and gas loss based on geographic information provided by an embodiment of the present invention; Detailed implementation manners

[0051] The following description and drawings fully disclose specific embodiments of the present invention, enabling those skilled in the art to practice them. The embodiments merely represent possible variations. Unless explicitly required, individual components and functions are optional, and the order of operations can vary. Parts and features of some embodiments can be included in or substituted for parts and features of other embodiments. The scope of the embodiments of the present invention includes the entire scope of the claims and all available equivalents of the claims. In this document, the embodiments can be individually or collectively referred to by the term "invention" for convenience only, and if in fact more than one invention is disclosed, it is not intended to automatically limit the scope of the application to any single invention or inventive concept. In this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method or device comprising a series of elements not only includes those elements but also includes other elements not explicitly listed. The embodiments in this document are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For the structures, products, etc. disclosed in the embodiments, since they correspond to the parts disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0052] The present invention will be further described below in conjunction with the drawings and embodiments:

[0053] The object of the present invention is to provide a method for automatically identifying and locating high-value points of oil and gas loss based on geographic information in view of the deficiencies in the existing technologies for monitoring high-value points of oil and gas loss in oil fields. The distribution function established by the present invention through mathematical statistics methods is determined according to the measured data of oil fields, which can reflect the distribution and development trend of oil and gas loss in oil fields. The method for identifying and locating high-value points of oil and gas loss established based on this can effectively realize the identification and location of high-value points of oil and gas loss, which is of great significance for the proposed loss reduction decisions of important nodes. The specific implementation process of this method is as follows:

[0054] ①Extract the activity data related to oil and gas losses in each link of the entire oilfield production process from the oilfield production databases (such as PCS and EPBP), and write the data into the database.

[0055] ②Clean and standardize the activity data of oil and gas losses; improve the accounting methods for oil and gas losses in each link, screen the oil and gas loss activity database, extract the activity data of oil and gas losses required by the accounting methods for oil and gas losses in each link, calculate the oil and gas loss amounts in each link through the accounting methods for oil and gas losses, and write the results into the database.

[0056] ③Analyze the oil and gas loss data in each link through a threshold model, fit the data exceeding the threshold with a generalized Pareto function, and calculate the specific parameters of the fitted distribution function by the maximum likelihood estimation method; analyze the fitted Pareto distribution, consider the data within the first 90% range in the distribution as normal values, and the remaining data as high values, so as to determine the threshold of the high-value points of oil and gas losses, and screen out the high-value points from each link and each node of the entire oilfield production process; use the geographical information carried in the oil and gas loss activity data to locate the nodes of the high-value points of oil and gas losses.

[0057] Embodiment

[0058] The present invention provides an automatic identification and location method for high-value points of oil and gas losses based on geographical information, specifically including the following steps:

[0059] 1. Calculate the activity data of oil and gas losses and write the results into the database

[0060] Improve the accounting methods for oil and gas losses in each link, extract the activity data related to oil and gas losses in each link of the entire oilfield production process from the oilfield production databases (such as PCS and EPBP), write the data into the database, and obtain the relevant basic data for oil and gas loss accounting through cleaning, standardization, and screening. Calculate the oil and gas loss amounts in each link and each node during the oil and gas production process through the accounting formulas for oil and gas losses in each link. The accounting formula for the joint station link is as shown in Equation (1), the accounting formula for the transfer station link is as shown in Equation (2), and the accounting formula for the wellhead link is as shown in Equation (3). Among them, some data are shown in Tables 1, 2, and 3 respectively.

[0061]

[0062] In the formula: Q 标 —— Standard volume of the daily exhaled gas from the storage tank breather, m 3 / d;

[0063] S —— Total area of the breather, m 2 ;

[0064] u —— Flow velocity of the breathing gas, m / s;

[0065] Ts —— Absolute temperature, 273.15 K;

[0066] T i —— Temperature of oil and gas, °C;

[0067] P i —— Pressure of oil and gas, MPa;

[0068] P s —— Standard atmospheric pressure, 0.1013 MPa.

[0069]

[0070] Table 1 Oil and gas loss data of the combined station

[0071]

[0072] In the formula: Q 标 —— Standard volume of the daily exhaled gas from the storage tank breathing port, m 3 / d;

[0073] n —— Number of breathing valves of the storage tank;

[0074] S —— Cross-sectional area of the gas collection pipe of the sealing device, m 2 ;

[0075] u —— Flow rate of breathing gas, m / s;

[0076] T s —— Absolute temperature, 273.15 K;

[0077] T i —— Temperature of oil and gas, °C;

[0078] P i —— Pressure of oil and gas, MPa;

[0079] P s —— Standard atmospheric pressure, 0.1013 MPa.

[0080]

[0081] Table 2 Oil and gas loss data of the transfer station

[0082] Q p =(Q / ρ)×EF#(3)

[0083] In the formula: Q p —— Volume of the gas discharged from the casing gas, m 3 / d;

[0084] ρ —— Density of thermal recovery heavy oil, t / m 3 ;

[0085] Q —— Daily oil production, t / d;

[0086] EF —— Emission factor of hydrocarbons, m 3 THC / m 3

[0087]

[0088] Table 3 Wellhead oil and gas loss data

[0089] 2. Select the threshold of the over-threshold model

[0090] Use the over-threshold model to analyze the oil and gas loss data, and fit the data before and after the threshold with different functions to obtain the most suitable fitting function. Here, the kernel goodness-of-fit statistic method is used. Based on the Hill estimate on the basis of minimizing the mean square error method, the threshold of the over-threshold model is determined by inversely deriving from the fitting perspective to minimize the asymptotic mean square error, as shown in Equation (4). The threshold kopt of the over-threshold model for the oil and gas loss data in Step 1 is calculated using the kernel goodness-of-fit statistic method.

[0091]

[0092] 3. Determine the parameters of the fitting function

[0093] The present invention determines the high-value points by studying the statistical characteristics of the data exceeding the threshold in the over-threshold model. The distribution of the over-threshold data conforms to the generalized Pareto distribution, and the form of the generalized Pareto distribution function is shown in Equation (5). The oil and gas loss data exceeding the threshold kopt in Step 2 are taken out and subtracted by the data value of the threshold kopt to obtain the over-threshold data. The over-threshold data are approximated by the maximum likelihood estimation method to obtain the shape parameter ζ and scale parameter β of the Pareto distribution function.

[0094]

[0095] 4. Fit the Pareto distribution function

[0096] Using the shape parameter ζ and scale parameter β obtained in Step 3, a generalized Pareto distribution function with the location parameter u = 0 is made to fit the over-threshold oil and gas loss data, and a distribution function conforming to the statistical characteristics of the over-threshold oil and gas loss data is obtained. Taking a set of full-process oil and gas loss data containing 191 data as an example, as shown in Figure 2, the gray histogram in the figure is the relative frequency of the over-threshold oil and gas loss data with a range of 50 as the interval, and the blue curve is the Pareto distribution function curve obtained in Step 3.

[0097] 5. Determine the threshold of the high-value point of oil and gas loss

[0098] The generalized Pareto distribution in Step 4 can represent the statistical characteristics of the exceedance threshold data. According to the sigma criterion of the normal distribution: the area within the horizontal axis interval (μ - 3σ, μ + 3σ) of a normal distribution is approximately 99.7%, and the area within the horizontal axis interval (μ - 2σ, μ + 2σ) is approximately 95%. The data with the top 90% of the frequency distribution in the generalized Pareto distribution can be considered normal values, and the remaining data can be considered high values. According to the CDF (cumulative probability function) of the generalized Pareto distribution, the first data point i with a cumulative probability greater than 90% is obtained, and the data at point i is considered the high value point of the exceedance threshold data, as Figure 3 shown. Add the data value at point i to the data value of the threshold point kopt obtained in Step 1 to obtain the threshold of the high value point of the oil and gas loss data. The data exceeding this threshold is considered the high value point data of the oil and gas loss.

[0099] 6. Locate the nodes of the high value points of the oil and gas loss

[0100] After determining the data of the high value points of the oil and gas loss, use the geographical information of the links and nodes corresponding to the high value points carried by the big data of the oil and gas loss in Step 1 to determine the specific locations of the high value points of the oil and gas loss, as shown in Table 4.

[0101]

[0102] Table 4 Information of the high value points of the oil and gas loss

[0103] It should be understood that the present invention is not limited to the processes and structures already described and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. An automatic identification and location method for high-value points of oil and gas loss based on geographic information, characterized in that, Including: (1) Extract the oil and gas loss activity data of each link in the whole process of the oilfield production database and write it into the database; (2) Calculate the oil and gas loss activity data and write the result into the database; (3) Select the data exceeding the threshold of the threshold model; (4) Fit the Pareto distribution function; (5) Determine the threshold of the high-value points of oil and gas loss; (6) Locate the nodes of the high-value points of oil and gas loss; Among them, the step (3) includes taking out the data of the oil and gas loss activity data in the step (2) that exceeds the threshold of the threshold model and subtracting the threshold of the threshold model to obtain the over-threshold data, and obtaining the shape parameter and scale parameter of the Pareto distribution function from the over-threshold data by the maximum likelihood estimation method; The step (4) includes making a generalized Pareto distribution function of the location parameter to fit the over-threshold oil and gas loss data through the shape parameter and scale parameter to obtain a distribution function that conforms to the statistical characteristics of the over-threshold oil and gas loss data; The step (5) includes according to the sigma criterion of the normal distribution, the area within the horizontal axis interval of a normal distribution is 99.7%, and the area within the horizontal axis interval is 95%; the data with the first 90% of the frequency distribution in the generalized Pareto distribution is set as the normal value, and the remaining data is the high value; according to the cumulative probability function of the generalized Pareto distribution, obtain the first data point with a cumulative probability greater than 90%, and regard the point data as the high-value point of the over-threshold data; add the data value of this point to the threshold in the threshold model obtained in the step (3) to obtain the threshold of the high-value points of the oil and gas loss data, and regard the data exceeding this threshold as the high-value point data of the oil and gas loss.

2. The automatic identification and positioning method for high-value points of oil and gas loss based on geographic information according to claim 1, wherein The step (1) includes extracting the oil and gas loss-related activity data of each link in the whole process of oilfield production from the production database of the oilfield and writing the data into the database, where the production database includes PCS and EPBP.

3. The automatic identification and positioning method for high-value points of oil and gas loss based on geographic information according to claim 1, characterized in that The step (2) includes improving the oil and gas loss accounting methods for each link, using the production activity data obtained from the oil and gas production site of the oilfield, and obtaining the relevant basic data for oil and gas loss accounting through cleaning, standardization, and screening. Through the accounting formulas for oil and gas loss in each link, calculate the oil and gas loss amounts of each link and each node in the process of oil and gas production, and write the results into the database.

4. The automatic identification and location method for high-value points of oil and gas loss based on geographic information according to claim 3, characterized in that The accounting formulas for oil and gas loss in each link include the accounting formula for the joint station link, the accounting formula for the transfer station link, and the accounting formula for the wellhead link.

5. The automatic identification and location method for high-value points of oil and gas loss based on geographic information according to claim 3, characterized in that The accounting formula for the joint station link is as shown in formula (1): In the formula: —— Standard volume of daily exhaled gas from the storage tank breather, m 3 / d; —— Total breathing area, m 2 ; —— Breathing gas flow rate, m / s; ——Absolute temperature, 273.15 K; —— Oil and gas temperature, °C; —— Oil and gas pressure, MPa; —— Standard atmospheric pressure, 0.1013 MPa.

6. The automatic identification and positioning method for high-value points of oil and gas loss based on geographic information according to claim 3, characterized in that, The accounting formula for the transfer station link is as shown in formula (2), and the accounting formula for the wellhead link is as shown in formula (3): Where: —— Standard volume of daily exhaled gas from the storage tank breather, m 3 / d; —— Number of storage tank breather valves; —— Cross-sectional area of the gas collection pipe of the sealing device, m 2 ; —— Breathing gas flow rate, m / s; ——Absolute temperature, 273.15 K; —— oil and gas temperature, °C; ——Oil and gas pressure, MPa; ——Standard atmospheric pressure, 0.1013 MPa; In the formula: —— The volume of the casing gas vent gas, m 3 / d; ρ——Density of thermal recovery heavy oil, t / m³; ——Daily oil production, t / d; ——Emission factor of hydrocarbons, m³THC / m³.

7. The automatic identification and positioning method for high-value points of oil and gas loss based on geographic information according to claim 1, characterized in that The step (6) includes after determining the data of the high-value points of oil and gas loss, using the geographical information of the links and nodes corresponding to the high-value points carried by the big data of oil and gas loss in the step 1 to determine the specific location of the high-value points of oil and gas loss.

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