A Pricing Method for Oil and Gas Data Assets

Abstract

The present application provides an oil and gas data asset pricing method, which relates to the technical field of data value assessment. The oil and gas data asset pricing method includes: S1: determining the data cost C, data quality coefficient M, data compliance coefficient S, data risk coefficient P and data scarcity ∈ of oil and gas data; S2: calculating the assessment value V of oil and gas data based on the data cost C, data quality coefficient M, data compliance coefficient S, data risk coefficient P and data scarcity ∈. This method fully considers the different demands and visibility of various data quality factors of both the supply and demand sides of the oil and gas data transaction, and proposes a more objective and reasonable reference value for the data value.

Description

Technical Field

[0001] The present application relates to the technical field of data value assessment, and in particular to a method for pricing oil and gas data assets. Background Art

[0002] Reasonable data asset pricing is the fundamental guarantee for promoting data transactions, ensuring the free and orderly flow and full sharing of data, and restoring the value of data commodities. Data asset pricing is a key issue that needs to be solved all over the world. In the practice of countries around the world, no mature and widely recognized data asset pricing mechanism has been formed. The price of data is mainly affected by factors such as data scale, category, quality, and real-time performance. At present, data trading platforms usually adopt pricing models, including cost method, market method, and income method. It is difficult to accurately quantify the true value of data assets, and it is impossible for data providers and data buyers to make a reasonable and mutually recognized evaluation result on the value of the transaction data set. The goal of oil and gas data is to build a production environment with ubiquitous connection, elastic configuration, and efficient supply. Its core is to promote the full sharing of data. Therefore, a data evaluation solution designed specifically for the characteristics of oil and gas data is needed.

[0003] The shortcomings of existing data pricing methods, such as the cost method, market method and income method, mainly include the neglect of the potential value of data and market demand, the difficulty in finding comparable data assets, and the uncertainty of predicting future returns. These methods usually lack the ability to dynamically adjust the value of data over time and environmental changes, and rarely consider the multi-dimensional characteristics of data assets, resulting in pricing results that may not accurately reflect the true value of the data, limiting the efficiency and fairness of data transactions. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a method and system for pricing oil and gas data assets, which fully considers the different demands and visibility of the supply and demand parties of oil and gas data transactions for various data quality factors, and proposes a more objective and reasonable reference value for data value.

[0005] In a first aspect, the present invention provides an oil and gas data asset pricing method, the oil and gas data asset pricing method comprising:

[0006] S1: Determine the data cost C, data quality coefficient M, data compliance coefficient S, data risk coefficient P and data scarcity ∈ of oil and gas data;

[0007] S2: Calculate the assessment value V of oil and gas data based on data cost C, data quality coefficient M, data compliance coefficient S, data risk coefficient P and data scarcity ∈.

[0008] In an optional embodiment, S1 includes:

[0009] S11: Determine the data cost C based on the data management cost, construction cost, and operation and maintenance cost.

[0010] In an optional embodiment, S1 includes:

[0011] S12: Determine the data quality coefficient M from the five dimensions of data accuracy, completeness, standardization, timeliness, and uniqueness.

[0012] In an optional embodiment, S12 includes:

[0013] Establish a data quality evaluation index system based on the five dimensions of data accuracy, completeness, standardization, timeliness, and uniqueness;

[0014] The quality scores of accuracy, completeness, standardization, timeliness and uniqueness were calculated using the analytic hierarchy process;

[0015] The data quality coefficient M is calculated based on the proportion of data volume and quality score.

[0016] In an optional implementation, in S12, the calculation formula of the data quality coefficient M is as follows:

[0017]

[0018] In the formula, a i is the proportion of data volume, S i is the quality score.

[0019] In an optional embodiment, S1 includes:

[0020] S13: Determine the data compliance coefficient S according to the data compliance requirements, where 0≤S≤1. If the data is completely non-compliant, S=0; if the data is completely compliant, S=1.

[0021] In an optional embodiment, S1 includes:

[0022] S14: Determine a data risk coefficient P based on data sensitivity and leakage risk.

[0023] In an optional embodiment, S1 includes:

[0024] S15: Dynamically add additional value based on current market data availability and determine data scarcity∈.

[0025] In an optional embodiment, S1 includes:

[0026] The evaluation and calculation of each parameter is completed independently on a neutral third-party platform in the data transaction process.

[0027] In an optional implementation, in S2, the calculation formula of the evaluation value V of the oil and gas data is as follows:

[0028] V=C·M·S·P+∈。

[0029] In a second aspect, the present invention provides an oil and gas data asset pricing system, the oil and gas data asset pricing system comprising:

[0030] A third-party platform is used to determine the data cost C, data quality coefficient M, data compliance coefficient S, data risk coefficient P and data scarcity ∈ of oil and gas data;

[0031] The valuation platform is used to calculate the assessed value V of oil and gas data based on data cost C, data quality coefficient M, data compliance coefficient S, data risk coefficient P and data scarcity ∈.

[0032] The oil and gas data asset pricing method and system provided by the embodiments of the present invention have the following beneficial effects:

[0033] 1. The evaluation method is completely quantitative, and all calculation parameters can be uniquely determined based on data analysis, avoiding the subjective influence of human participation and ensuring the consistency and relative objectivity of the evaluation results;

[0034] 2. The evaluation method fully considers the characteristics and application scenarios of oil and gas data, and designs a value evaluation algorithm that covers the relevant weights that affect the value of data to ensure the integrity and scientificity of the data value evaluation;

[0035] 3. Introduced universal evaluation and data association integration evaluation indicators to ensure that only data with strong promotion and reference value, reasonable organization and convenient query can obtain higher value assessment;

[0036] 4. The introduction of scarcity parameters enables all evaluation results to be adjusted in real time according to the actual status of the current market, thereby ensuring the objectivity and effectiveness of value evaluation over a longer time frame and in a more complex economic environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 A flowchart of the oil and gas data asset pricing method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0040] Oil and gas are usually highly specialized, involving knowledge from multiple fields such as geology, engineering, and chemistry; secondly, oil and gas data are spatial and temporal, and data collection and analysis need to consider changes in geographic location and time series; thirdly, the real-time nature of oil and gas data is crucial for decision-making, because oil and gas exploration and production activities need to respond quickly to market and technological changes; in addition, the scale and complexity of data are also large, covering the entire life cycle data from exploration, development to production. In data evaluation, the following business characteristics should be considered: first, the accuracy and reliability of data, ensuring that the data can truly reflect the actual situation of oil and gas reservoirs; second, the integrity of data, covering data from various stages of oil and gas exploration, development, and production; third, the availability of data, that is, whether the data is easy to access and analyze, and whether it can support the decision-making process; fourth, the sharing of data, considering the degree of sharing of data between different organizations and departments; fifth, the frequency of data updates, the dynamic nature of the oil and gas industry requires that data can be updated in a timely manner to reflect the latest exploration and production conditions.

[0041] Please refer to Figure 1 The embodiment of the present application provides an oil and gas data asset pricing method (hereinafter referred to as: method). The method defines a pricing calculation formula for oil and gas data and clearly designs the calculation method of its various parameters to ensure the operability, rationality and effectiveness of the method.

[0042] Specifically, the method includes the following steps:

[0043] S1: Determine the data cost C, data quality coefficient M, data compliance coefficient S, data risk coefficient P and data scarcity ε of oil and gas data.

[0044] In this way, by comprehensively considering factors such as data costs, reasonable pricing of oil and gas data can be achieved.

[0045] S1 specifically includes the following steps:

[0046] S11: Determine the data cost C based on the data management cost, construction cost, and operation and maintenance cost.

[0047] S12: Determine the data quality coefficient M from the five dimensions of data accuracy, completeness, standardization, timeliness, and uniqueness.

[0048] Among them, a data quality evaluation index system is established from the five dimensions of data accuracy, completeness, standardization, timeliness and uniqueness; the analytic hierarchy process (AHP) is used to calculate the quality scores of accuracy, completeness, standardization, timeliness and uniqueness; based on the proportion of data volume and quality score, the data quality coefficient M is calculated.

[0049] The calculation formula of data quality coefficient M is as follows:

[0050]

[0051] In the formula, a i is the proportion of data volume, S i is the quality score.

[0052] Based on the fuzzy comprehensive evaluation method, a scoring standard for data quality evaluation dimensions is established, and the quality score of system data is S i This can be determined using the following table:

[0053]

[0054] S13: Determine the data compliance coefficient S according to the data compliance requirements, where 0≤S≤1. If the data is completely non-compliant, S=0; if the data is completely compliant, S=1.

[0055] Specifically, the data compliance coefficient S is that the data passes the data legality and compliance review in data management, data application, and data circulation, complies with the relevant provisions of the "Data Security Law", "Network Safety Law", "Personal Information Protection Law", etc., ensures data security, and does not infringe on the legitimate rights of other subjects. If the data is fully compliant, S=1.

[0056] S14: Determine a data risk coefficient P based on data sensitivity and leakage risk.

[0057] Specifically, the data risk coefficient P is determined based on the data sensitivity and leakage risk. Specifically, the data is divided into levels according to sensitivity, for example, data is divided into three levels: high, medium, and low, and the proportion of data at each level to the total data is calculated. Sensitive data access frequency: record the number and frequency of accesses to each type of sensitive data, and quantify the leakage risk based on the frequency of data access.

[0058] S15: Dynamically add additional value based on current market data availability and determine data scarcity∈.

[0059] Specifically, data scarcity∈ can be determined by dynamically adding additional value based on the availability of such data in the current market.

[0060] S2: Calculate the assessed value V of oil and gas data based on data cost C, data quality coefficient M, data compliance coefficient S, data risk coefficient P and data scarcity ε.

[0061] Specifically, the calculation formula for the evaluation value V of oil and gas data is as follows:

[0062] V=C·M·S·P+∈。

[0063] Based on the same inventive concept, an oil and gas data asset pricing system (hereinafter referred to as “system”) is also provided in the embodiment of the present application. The system includes:

[0064] The third-party platform is used to determine the data cost C, data quality coefficient M, data compliance coefficient S, data risk coefficient P and data scarcity ∈ of oil and gas data; that is, it is used to execute S1 in the above method.

[0065] The valuation platform is used to calculate the assessed value V of oil and gas data based on data cost C, data quality coefficient M, data compliance coefficient S, data risk coefficient P and data scarcity ∈, that is, to execute S2 in the above method.

[0066] The oil and gas data asset pricing method and system provided in this embodiment have the following beneficial effects:

[0067] 1. The evaluation method is completely quantitative, and all calculation parameters can be uniquely determined based on data analysis, avoiding the subjective influence of human participation and ensuring the consistency and relative objectivity of the evaluation results;

[0068] 2. The evaluation method fully considers the characteristics and application scenarios of oil and gas data, and designs a value evaluation algorithm that covers the relevant weights that affect the value of data to ensure the integrity and scientificity of the data value evaluation;

[0069] 3. Introduced universal evaluation and data association integration evaluation indicators to ensure that only data with strong promotion and reference value, reasonable organization and convenient query can obtain higher value assessment;

[0070] 4. The introduction of scarcity parameters enables all evaluation results to be adjusted in real time according to the actual status of the current market, thereby ensuring the objectivity and effectiveness of value evaluation over a longer time frame and in a more complex economic environment.

[0071] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0072] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0073] Furthermore, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0074] It should be noted that if the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0075] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0076] The above are only embodiments of the present application and are not intended to limit the scope of protection of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for pricing oil and gas data assets, characterized in that: The oil and gas data asset pricing method includes: S1: Determine the data cost C, data quality coefficient M, data compliance coefficient S, data risk coefficient P and data scarcity ∈ of oil and gas data; S2: Calculate the assessment value V of oil and gas data based on data cost C, data quality coefficient M, data compliance coefficient S, data risk coefficient P and data scarcity ∈.

2. The oil and gas data asset pricing method according to claim 1, characterized in that: S1 includes: S11: Determine the data cost C according to the management cost, construction cost and operation and maintenance cost of the data.

3. The oil and gas data asset pricing method according to claim 1, characterized in that: S1 includes: S12: Determine the data quality coefficient M from five dimensions: accuracy, completeness, standardization, timeliness, and uniqueness of the data.

4. The oil and gas data asset pricing method according to claim 2, characterized in that: S12 includes: Establish a data quality evaluation index system based on the five dimensions of data accuracy, completeness, standardization, timeliness, and uniqueness; The quality scores of accuracy, completeness, standardization, timeliness and uniqueness were calculated using the analytic hierarchy process; The data quality coefficient M is calculated based on the proportion of the data volume and the quality score.

5. The oil and gas data asset pricing method according to claim 4, characterized in that: In S12, the calculation formula of the data quality coefficient M is as follows: In the formula, a i is the proportion of data volume, S i is the quality score.

6. The oil and gas data asset pricing method according to claim 1, characterized in that: S1 includes: S13: Determine the data compliance coefficient S according to the data compliance requirements, where 0≤S≤1. If the data is completely non-compliant, S=0; if the data is completely compliant, S=1.

7. The oil and gas data asset pricing method according to claim 1, characterized in that: S1 includes: S14: Determine the data risk coefficient P according to data sensitivity and leakage risk.

8. The oil and gas data asset pricing method according to claim 1, characterized in that: S1 includes: S15: Dynamically add additional value based on the current market data availability and determine the data scarcity∈.

9. The oil and gas data asset pricing method according to claim 1, characterized in that: S1 includes: The evaluation and calculation of each parameter is completed independently on a neutral third-party platform in the data transaction process.

10. The oil and gas data asset pricing method according to claim 1, characterized in that: In S2, the calculation formula of the evaluation value V of the oil and gas data is as follows: V=C·M·S·P+∈。

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