A method and device for rapidly determining exploration potential of an oil and gas block

Abstract

The present application belongs to the technical field of geological exploration and research, and particularly relates to a method and device for rapidly determining the exploration potential of an oil and gas block. A classification index data table is established based on the data of international commercial database companies, and the classification index data table includes a plurality of major category data and a plurality of subcategory data under each major category. The weight coefficients of the first major category data are determined. The weight coefficients of the second major category data are determined according to the weight coefficients of the first major category data, and the second major category data is resource quantity data. The expected resource quantity of the oil and gas field is determined according to the weight coefficients of the resource quantity data and the resource quantity obtained by each international commercial database company. The data of multiple commercial database companies are comprehensively applied to search for data of source rocks, reservoirs, cap rocks, traps and other reservoir-forming elements, to comprehensively evaluate and rapidly obtain the overseas block exploration potential close to the real situation, and to improve the evaluation accuracy of the overseas block exploration potential.

Description

Technical Field

[0001] This invention belongs to the field of geological exploration and research technology, and specifically relates to a method and apparatus for rapidly determining the exploration potential of oil and gas blocks. Background Technology

[0002] The exploration potential of an oil and gas block is crucial for determining its commercial value, the amount of exploration work and investment required, and even whether to acquire its parameters. Especially in the process of globalization in the oil and gas industry, many oil companies urgently need to expand their overseas business. How to quickly determine the exploration potential of an exploration block that has not been explored or studied in depth, and then decide whether to invest in it, has become the biggest bottleneck in overseas block selection, overseas block mergers and acquisitions, and overseas block exploration deployment decisions.

[0003] Currently, most researchers rely on personal experience and data from a single commercial database to determine the exploration potential of overseas oil and gas exploration blocks, ignoring the uncertainty of data sources from a single commercial database. As a result, the predicted exploration potential of blocks often differs significantly from the actual potential after intervention, directly increasing the risk of overseas block acquisitions and making decision-making more difficult in acquiring overseas exploration blocks. Summary of the Invention

[0004] To address the aforementioned problems, firstly, this invention proposes a method for rapidly determining the exploration potential of oil and gas blocks, comprising the following steps:

[0005] A categorized index data table is established based on data from an international business database company. The categorized index data table includes multiple major categories of data and several subcategories of data under each major category.

[0006] The weighting coefficients of the first major category of data are determined respectively. The first major category of data includes oil source data, reservoir data, trap data, and closed data.

[0007] The weight coefficients of the second category of data are determined based on the weight coefficients of the first category of data, where the second category of data is resource quantity data.

[0008] The expected resource quantity of an oil and gas field is determined based on the weighting coefficients of resource quantity data and the resource quantity assessments obtained by each international commercial database company.

[0009] Furthermore, the process of establishing a categorized index data table based on data from an international business database company includes the following steps:

[0010] A primary classification index data table is established, which includes multiple major categories such as oil source, reservoir, trap, closed structure, and resource.

[0011] Each major category is further divided into several subcategories, and a secondary classification index data table is established.

[0012] Obtain information from multiple international commercial database companies and supplement the commercial database information into the secondary category index data table according to the data type, thus creating a category index data table;

[0013] The secondary classification index data table includes four subcategories: source rock thickness, source rock area, source rock organic carbon content, and source rock maturity; reservoirs include four subcategories: reservoir thickness, reservoir area, reservoir porosity, and reservoir permeability; traps include four subcategories: trap area, trap closure degree, trap type, and control trap data; cappings include four subcategories: caprock thickness, caprock porosity, caprock permeability, and caprock continuity; and resources include one subcategory: resource quantity.

[0014] Further, determining the weighting coefficients for oil source data includes the following steps:

[0015] An oil source parameter dataset was constructed based on four subcategories of oil source data: source rock thickness, source rock area, source rock organic carbon content, and source rock maturity.

[0016] Determine the mean value Oc of the oil source parameters in the oil source parameter dataset. avg And the Oc parameters of oil source companies from each international business database company. i ;

[0017] Based on the average value of oil source parameters Oc avg And oil source company parameters Oc i Determine the Oc parameter for each international business database company's oil source companies. i Oc, the average value of oil source parameters avg The absolute deviation between Oc adi ;

[0018] According to the absolute deviation Oc adi Determine the weighting coefficients (Ow) for the parameters of oil source companies in each international commercial database. i .

[0019] Furthermore, the calculation method for a single oil source parameter in the oil source parameter dataset is as follows:

[0020] Oc=OHx*OAy*OTz*ORu

[0021] Where Oc is the source parameter, OH is the source rock thickness, OA is the source rock area, OT is the source rock organic carbon content, OR is the source rock maturity, and x, y, z, u = 1, 2, 3, ..., i, ..., n, where n is the number of international business database companies;

[0022] Determine the mean value Oc of the oil source parameters in the oil source parameter dataset. avg And the Oc parameters of oil source companies from each international business database company. i The calculation formula is as follows:

[0023] Oc avg =O 总 / m

[0024] Oc i =OH i *OA i *OT i *OR i

[0025] Among them, O 总 Oc represents the sum of all oil source parameters in the oil source parameter dataset, where m is the number of all oil source parameters in the dataset. i Let i be the oil source company parameter of the i-th international business database company, i = 1, 2, 3, ... n and i ≤ n, where n is the number of international business database companies;

[0026] The Oc parameter for oil source companies in each international business database company. i Oc, the average value of oil source parameters avg The absolute deviation between Oc adi The calculation formula is as follows:

[0027] Oc adi =|Oc i -Oc avg |

[0028] Among them, Oc adi For the oil source company parameter Oc of the i-th international business database company i Oc, the average value of oil source parameters avg The absolute deviation between them, i = 1, 2, 3, ..., n, where n is the number of international business database companies;

[0029] Determine the weighting coefficients (Ow) for the parameters of oil source companies in each international commercial database. i The calculation formula is as follows:

[0030] Ow i =(1-Oc adi / (Oc ad1 +Oc ad2 +Oc ad3 +…+Oc adn )) / n.

[0031] Further, determining the weighting coefficients for reservoir-type data includes the following steps:

[0032] A reservoir parameter dataset is constructed based on four subclasses of reservoir data: reservoir thickness, reservoir area, reservoir porosity, and reservoir permeability.

[0033] Determine the mean value Rc of the reservoir parameters in the reservoir parameter dataset. avg And the reservoir-type company parameter Rc for each international commercial database company. i ;

[0034] Based on the average reservoir parameter Rc avg And reservoir-type company parameter Rc i Determine the reservoir-type company parameter Rc for each international commercial database company. i Compared with the mean reservoir parameter Rc avg The absolute deviation Rc between adi ;

[0035] According to the absolute deviation Rc adi Determine the weighting coefficient Rw for the parameters of each international commercial database reservoir company. i .

[0036] Furthermore, the calculation method for a single reservoir parameter in the reservoir parameter dataset is as follows:

[0037] Rc=RHx*RAy*RPz*RKu

[0038] Where Rc is the reservoir parameter, RH is the reservoir thickness, RA is the reservoir area, RP is the reservoir porosity, RK is the reservoir permeability, and x, y, z, u = 1, 2, 3, ..., i, ..., n, where n is the number of international business database companies;

[0039] Determine the mean value Rc of the reservoir parameters in the reservoir parameter dataset. avg And the reservoir-type company parameter Rc for each international commercial database company. i The calculation formula is as follows:

[0040] Rc avg =R 总 / m

[0041] Rc i =RH i *RA i *RP i *RK i

[0042] Among them, R 总 Rc is the sum of all reservoir parameters in the reservoir parameter dataset, where m is the number of all reservoir parameters in the dataset; iLet i be the reservoir-type company parameter of the i-th international business database company, i = 1, 2, 3, ... n and i ≤ n, where n is the number of international business database companies;

[0043] The reservoir-type company parameter Rc for each international commercial database company. i Compared with the mean reservoir parameter Rc avg The absolute deviation Rc between adi The calculation formula is as follows:

[0044] Rc adi =|Rc i -Rc avg |

[0045] Among them, Rc adi For the i-th international commercial database company, the reservoir-type company parameter Rc i Compared with the mean reservoir parameter Rc avg The absolute deviation between them, i = 1, 2, 3, ..., n, where n is the number of international business database companies;

[0046] Determine the weighting coefficient Rw for the parameters of each international commercial database reservoir company. i The calculation formula is as follows:

[0047] Rw i =(1-Rc adi / (Rc ad1 +Rc ad2 +Rc ad3 +…+Rc adn )) / n.

[0048] Further, determining the weighting coefficients for closed-loop data includes the following steps:

[0049] A trap parameter dataset is constructed based on four subclasses of trap data: trap area, trap closure degree, trap type, and control trap data type.

[0050] Determine the mean Tc of the trapping parameters in the trapping parameter dataset. avg And the closed-loop company parameter Tc of each international business database company i ;

[0051] Based on the mean value of the trapping parameter Tc avg And closed-loop company parameter Tc i Determine the closed-loop company parameter Tc for each international business database company. i With the mean of the trapping parameters Tc avg The absolute deviation Tc between adi ;

[0052] According to the absolute deviation Tc adiDetermine the weighting coefficient Tw for the parameters of each international business database closed-loop company. i .

[0053] Furthermore, the calculation method for a single trapping parameter in the trapping parameter dataset is as follows:

[0054] Tc = TAX * THY * TTz * TDu

[0055] Where Tc is the trapping parameter, TA is the trapping area, TH is the trapping closure degree, TT is the trapping type, TD is the control loop data type, and x, y, z, u = 1, 2, 3, ..., i, ..., n, where n is the number of international business database companies;

[0056] Determine the mean Tc of the trapping parameters in the trapping parameter dataset. avg And the closed-loop company parameter Tc of each international business database company i The calculation formula is as follows:

[0057] Tc avg =T 总 / m

[0058] Tc i =OH i *OA i *OT i *OR i

[0059] Among them, T 总 Tc is the sum of all trapping parameters in the trapping parameter dataset, where m is the number of trapping parameters in the dataset; i Let i be the closed-loop company parameter of the i-th international business database company, i = 1, 2, 3, ... n and i ≤ n, where n is the number of international business database companies;

[0060] The Tc parameter for each international business database company's closed-loop company category. i With the mean of the trapping parameters Tc avg The absolute deviation Tc between adi The calculation formula is as follows:

[0061] Tc adi =|Tc i -Tc avg |

[0062] Among them, Tc adi For the i-th international business database company, the closed-loop company parameter Oc i With the mean of the trapping parameters Tc avg The absolute deviation between them, i = 1, 2, 3, ..., n, where n is the number of international business database companies;

[0063] Determine the weighting coefficient Rw for the parameters of each international business database closed-loop company. i The calculation formula is as follows:

[0064] Tw i =(1-Tc) adi / (Tc ad1 +Tc ad2 +Tc ad3 +…+Tc adn )) / n.

[0065] Further, determining the weighting coefficients for closed-loop data includes the following steps:

[0066] A closed parameter dataset is constructed based on four subclasses of closed data: capping layer thickness, capping layer porosity, capping layer permeability, and capping layer continuity.

[0067] Determine the mean value Ec of the closing parameter in the closed parameter dataset. avg And the closed-class company parameter Ec for each international business database company i ;

[0068] Based on the mean of the closure parameter Ec avg And closed-end company parameter Ec i Determine the closed-class company parameter Ec for each international business database company. i With the mean of the closure parameter Ec avg The absolute deviation between Ec adi ;

[0069] According to the absolute deviation Ec adi Determine the weighting coefficient Ew for the parameters of each closed-class international business database company. i .

[0070] Furthermore, the calculation method for a single closed parameter in the closed parameter dataset is as follows:

[0071] Ec = EHx * (1 / EPy) * (1 / EKz) * ECu

[0072] Where Ec is the closure parameter, EH is the capping layer thickness, EP is the capping layer porosity, EK is the capping layer permeability, EC is the capping layer continuity, and x, y, z, u = 1, 2, 3, ..., i, ..., n, where n is the number of international business database companies;

[0073] Determine the mean value Ec of the closing parameter in the closed parameter dataset. avg And the closed-class company parameter Ec for each international business database company i The calculation formula is as follows:

[0074] Ec avg =E 总 / m

[0075] Ec i =EH i *(1 / EP i )*(1 / EK i EC i

[0076] Among them, E 总 Ec represents the sum of all closed parameters in the closed parameter dataset, where m is the number of closed parameters in the dataset. i Let be the closed-class company parameter of the i-th international business database company, i = 1, 2, 3, ..., n and i ≤ n, where n is the number of international business database companies;

[0077] The closed-class company parameter Ec for each international business database company i With the mean of the closure parameter Ec avg The absolute deviation between Ec adi The calculation formula is as follows:

[0078] Ec adi =|Ec i -Ec avg |

[0079] Among them, Ec adi For the i-th international business database company, the closed-class company parameter Oc i With the mean of the closure parameter Ec avg The absolute deviation between them, i = 1, 2, 3, ..., n, where n is the number of international business database companies;

[0080] Determine the weighting coefficient Ew for the parameters of each closed-class international business database company. i The calculation formula is as follows:

[0081] Ew i =(1-Ec) adi / (Ec ad1 +Ec ad2 +Ec ad3 +…+Ec adn )) / n.

[0082] Further, determining the weight coefficients of the second category of data based on the weight coefficients of the first category of data includes the following steps:

[0083] The resource quantity parameter Sc is determined based on the parameters of oil source companies, reservoir companies, trap companies, and closed-loop companies from different international commercial database companies. i The calculation formula is as follows:

[0084] Sc i =Ow i *Rw i *Tw i *Ew i

[0085] Where i = 1, 2, 3, ..., n, and n is the number of different international business database companies;

[0086] Based on the resource quantity parameter Sc of each company i Determine the weighting coefficients Sw for resource quantity parameters of different international commercial database companies. i The calculation formula is as follows:

[0087] Sw i =Sc i / (Sc1+Sc2+Sc3+…+Sc n )

[0088] Where i = 1, 2, 3, ..., n, and n is the number of different international business database companies.

[0089] Furthermore, the expected resource quantity of the oil and gas field is determined based on the weighting coefficients of the resource quantity data and the resource quantity assessed by each international commercial database company, using the following formula:

[0090] Se=S1*Sw1+S2*Sw2+S3*Sw3+…+S3*Sw n

[0091] Where Se is the expected resource quantity, and S1, S2, S3, ... S n These are the resource quantities assessed by different international business database companies, Sw1, Sw2, Sw3, ... Sw n These are the weighting coefficients for resource data from different international business database companies, respectively.

[0092] Secondly, this invention proposes a device for rapidly determining the exploration potential of oil and gas blocks, comprising:

[0093] The classification index data table creation unit is used to create a classification index data table based on data from an international business database company. The classification index data table includes multiple major categories of data and several subcategories of data under each major category.

[0094] The first major category weight coefficient determination unit is used to determine the weight coefficients of the first major category of data, which includes oil source data, reservoir data, trap data, and closed data.

[0095] The second category weight coefficient determination unit is used to determine the weight coefficient of the second category of data based on the weight coefficient of the first category of data, where the second category of data is resource quantity data.

[0096] The expected resource quantity determination unit is used to determine the expected resource quantity of an oil and gas field based on the weighting coefficients of resource quantity data and the resource quantity assessments obtained by each international commercial database company.

[0097] Thirdly, the present invention proposes an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

[0098] Memory, which stores computer programs;

[0099] The processor, when executing the program stored in the memory, implements the method for rapidly determining the exploration potential of oil and gas blocks.

[0100] Fourthly, the present invention proposes a computer-readable storage medium storing a computer program, which, when run, executes the method for rapidly determining the exploration potential of oil and gas blocks.

[0101] The beneficial effects of this invention are:

[0102] This invention comprehensively utilizes data from multiple commercial databases, searching for data on hydrocarbon accumulation elements such as source rocks, reservoirs, caprocks, and traps to comprehensively evaluate and quickly determine the exploration potential of overseas blocks that approximates the actual situation. This improves the accuracy of overseas block exploration potential evaluation, reduces the risk of overseas block acquisitions, and simplifies the decision-making process for acquiring overseas blocks.

[0103] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0104] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0105] Figure 1 A flowchart of the method for rapidly determining the exploration potential of oil and gas blocks proposed in this invention is shown;

[0106] Figure 2A schematic diagram of the device for rapidly determining the exploration potential of oil and gas blocks provided in the embodiments of the present invention is shown;

[0107] Figure 3 A schematic diagram of an electronic device provided in an embodiment of the present invention is shown. Detailed Implementation

[0108] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0109] To address the aforementioned issues, this invention provides a method for rapidly determining the exploration potential of oil and gas blocks. This method comprehensively utilizes data from multiple commercial databases, searching for data on reservoir, caprock, trap, and other hydrocarbon accumulation elements to comprehensively evaluate and rapidly determine the exploration potential of overseas blocks that approximates the actual situation. This improves the accuracy of overseas block exploration potential evaluation, reduces the risk of overseas block acquisitions, and decreases the difficulty of overseas block acquisition decisions.

[0110] like Figure 1 As shown, the determination method includes the following steps:

[0111] S1: Establish a categorized index data table based on data from an international business database company. The categorized index data table includes multiple major categories of data and several subcategories of data under each major category.

[0112] S2: Determine the weight coefficients of the first major category of data, which includes oil source data, reservoir data, trap data, and closed data.

[0113] S3: Determine the weight coefficient of the second category of data based on the weight coefficient of the first category of data. The second category of data is resource quantity data.

[0114] S4: Determine the expected resource quantity of the oil and gas field based on the weighting coefficients of resource quantity data and the resource quantity assessed by each international commercial database company.

[0115] The following will use four major international business database companies as examples to explain the above steps in detail. In the following description, international business database companies will be abbreviated as "the Company".

[0116] Step S1 specifically includes the following steps:

[0117] A primary classification index data table is established, which is divided into five major categories of data. These five categories are further subdivided: oil source data (O), reservoir data (R), trap data (T), closed data (E), and resource quantity data (S). This table is referred to as the ORTES data index classification table (i.e., the primary classification index data table).

[0118] in,

[0119] Oil source data are further subdivided into four subcategories: source rock thickness (OH), source rock area (OA), source rock organic carbon content (OT), and source rock maturity (OR).

[0120] Reservoir data (R) is further subdivided into four subcategories: reservoir thickness (RH), reservoir area (RA), reservoir porosity (RP), and reservoir permeability (RK).

[0121] Trap data (T) is further subdivided into four subcategories: trap area (TA), trap closure degree (TH), trap type (TT), and control trap data type (TD);

[0122] Resource quantity data (S) refers to the overall resource potential data of the study area provided by various commercial databases.

[0123] Following the above classification method, an ORTES data index classification table with five categories and 17 subcategories can be established. Commercial database data from different companies (currently four major international commercial database companies 1, 2, 3, and 4) can be queried and used to populate the five categories and 17 subcategories, forming ORTES data tables for different companies. The general formulas for the four subcategories of oil source data from different companies are source rock thickness (OHx), source rock area (OAy), source rock organic carbon content (OTz), and source rock maturity (ORu). Since there are currently four major international commercial database companies 1, 2, 3, and 4, the values ​​of x, y, z, and u are all 1, 2, 3, and 4. Specifically, the source rock thickness (OH1), source rock area (OA1), source rock organic carbon content (OT1), and source rock maturity (OR1) are represented for Company 1; the source rock thickness (OH2), source rock area (OA2), source rock organic carbon content (OT2), and source rock maturity (OR2) are represented for Company 2, and so on. The four subcategories of reservoir data from different companies are represented by the general formulas for reservoir thickness (RHx), reservoir area (RAy), reservoir porosity (RPz), and reservoir permeability (RKu). The representation methods for trap data (T) and closed data (E) are the same. Resource quantity data S from different companies is represented as S... i(i = 1, 2, 3, 4). In one embodiment of the present invention, for a certain block in Central Asia, a categorized index data table was established using data from the commercial databases of four companies: Company 1, Company 2, Company 3, and Company 4, as shown in Table 1. The data representation format is as shown in the oil source data.

[0124] Table 1

[0125]

[0126]

[0127] In step S2, the weighting coefficients of the first major category of data are determined, including the weighting coefficients of oil source, reservoir, trap, and closure.

[0128] S21: Determine the oil source weighting coefficient. Because different companies source their oil source data (O) from different sources, and in the absence of basic seismic and drilling data evidence, it is difficult for the data purchaser to distinguish the authenticity of the data. Therefore, to reduce the risk of resource potential assessment, it is necessary to determine the oil source weighting coefficient (Ow) for each commercial database company.

[0129] Specifically, the following steps are included:

[0130] A dataset of oil source parameters is constructed based on four subclasses of oil source data: source rock thickness (OH), source rock area (OA), source rock organic carbon content (OT), and source rock maturity (OR). The oil source parameter Oc is a parameter formed by cross-multiplying the four subclasses of oil source data (OH, OA, OT, and OR) from different commercial database companies, i.e., Oc = OHx * OAy * OTz * ORu (x = 1, 2, 3, 4; y = 1, 2, 3, 4; z = 1, 2, 3, 4; u = 1, 2, 3, 4). This results in an oil source parameter set containing 256 oil source parameters.

[0131] Calculate the mean value Oc of 256 oil source parameters in the oil source parameter set. avg And obtain the Oc parameter of oil source companies for different companies. i Oc i =OH i *OA i *OT i *OR i (i = 1, 2, 3, 4); For example, the oil source company parameter Oc1 = OH1 * OA1 * OT1 * OR1 of Company 1.

[0132] Based on the average value Oc of the oil source parameters avg and oil source parameters Oc iCalculate the absolute deviation Oc between the parameters and the mean of different oil source companies. ad Oc adi =|Oc i -Oc avg |(i=1,2,3,4), for example, the absolute deviation Oc between the parameters of oil source companies of Company 1 and the mean. ad1 =|Oc1-Oc avg |

[0133] Based on the absolute deviation Oc between the oil source parameters and the mean. ad Calculate the weighting coefficients (Ow) of parameters for different types of oil source companies. i The smaller the absolute deviation between the parameters of oil-source companies and the mean, the larger the weighting coefficient of the oil-source company parameters. That is, the weighting coefficient of a company's oil-source company parameters Ow i =(1-Oc adi / (Oc ad1 +Oc ad2 +Oc ad3 +Oc ad4 )) / 4 (i=1,2,3,4), for example, the weighting coefficient of the oil source company parameter Ow1=(1-Oc ad1 / (Oc ad1 +Oc ad2 +Oc ad3 +Oc ad4 )) / 4.

[0134] In one embodiment of the present invention, the above steps are specifically performed as follows:

[0135] For a certain block in Central Asia, a dataset Oc consisting of 256 oil source parameters was constructed using data from the commercial databases of four companies: Company 1, Company 2, Company 3, and Company 4. Oc includes: OH1*OA1*OT1*OR1, OH1*OA1*OT1*OR2, OH1*OA1*OT1*OR3, OH1*OA1*OT1*OR4, OH1*OA1*OT2*OR1, OH1*OA1*OT2*OR2, OH1*OA1*OT2*OR3, OH1*OA1*OT2*OR4, ..., OH4*OA4*OT4*OR4, totaling 256 numbers.

[0136] Calculate the mean value Oc of 256 oil source parameters in the oil source parameter set. avg= (Sum of 256 parameters in the Oc dataset above) / 256 = 72450. In this embodiment, the oil source company parameter Oc1 for Company 1 is OH1*OA1*OT1*OR1 = 136500, the oil source company parameter Oc2 for Company 2 is OH2*OA2*OT2*OR2 = 82500, and the oil source company parameter Oc3 for Company 3 is OH3*OA3*OT3*OR3.

[0137] =27000, 4 Company's oil source company parameter Oc4 = OH4 * OA4 * OT4 * OR4 = 8400.

[0138] Calculate the absolute deviation Oc between the parameters and the mean of different oil source companies. adi The absolute deviations between the parameters of each oil-source company obtained in this embodiment and the mean are as follows:

[0139] The absolute deviation (Oc) between the parameters of Company 1's oil source companies and the mean ad1 =|Oc1-Oc avg |=|136500-72450|=64050,

[0140] The absolute deviation (Oc) between the parameters of the oil source companies of Company 2 and the mean ad2 =|Oc2-Oc avg |=|82500-72450|=10050,

[0141] The absolute deviation (Oc) between the parameters of the oil source companies of the three companies and the mean ad3 =|Oc3-Oc avg |=|27000-72450|=45450,

[0142] The absolute deviation (Oc) between the parameters of the oil source companies of the four companies and the mean ad4 =|Oc4-Oc avg |=|8400-72450|=64050.

[0143] The weighting coefficients (Ow) for parameters of different oil source companies are calculated based on the absolute deviations mentioned above. i The smaller the absolute deviation between the parameters of oil source companies and the mean, the larger the weighting coefficient of the oil source company parameters. In this embodiment, the weighting coefficients of the oil source company parameters of each company are calculated as follows:

[0144] The weighting coefficient of the oil source company parameters of Company 1 is Ow1 = (1 - Oc) ad1 / (Oc ad1 +Oc ad2 +Oc ad3 +Oc ad4)) / 4=(1-64050 / (64050+10050+45450+64050)) / 4=(1-0.349) / 4=0.163,

[0145] The weighting coefficient of the oil source company parameters for Company 2 is Ow2 = (1 - Oc) ad2 / (Oc ad1 +Oc ad2 +Oc ad3 +Oc ad4 )) / 4=(1-10050 / (64050+10050+45450+64050)) / 4=(1-0.055) / 4=0.236,

[0146] The weighting coefficient for the oil source company parameters of Company 3 is Ow3 = (1 - Oc) ad3 / (Oc ad1 +Oc ad2 +Oc ad3 +Oc ad4 )) / 4=(1-45450 / (64050+10050+45450+64050)) / 4=(1-0.248) / 4=0.188,

[0147] The weighting coefficient for the oil source company parameters of Company 4 is Ow4 = (1 - Oc) ad4 / (Oc ad1 +Oc ad2 +Oc ad3 +Oc ad4 )) / 4=(1-64050 / (64050+10050+45450+64050)) / 4=(1-0.349) / 4=0.163.

[0148] S22: Determine the reservoir weighting coefficient (Rw). Because different companies source reservoir data (R) from different sources, and in the absence of basic seismic and drilling data evidence, it is difficult for the data purchaser to distinguish the authenticity of the data. Therefore, to reduce the risk of resource potential assessment, it is necessary to determine the reservoir weighting coefficient (Rw) for each commercial database company.

[0149] The specific steps are as follows:

[0150] A reservoir parameter dataset is constructed based on four subclasses of reservoir data: reservoir thickness (RH), reservoir area (RA), reservoir porosity (RP), and reservoir permeability (RK). The oil source parameter Rc is a parameter formed by cross-multiplying the four subclasses of reservoir thickness (RH), reservoir area (RA), reservoir porosity (RP), and reservoir permeability (RK) data from different commercial database companies, i.e., Rc = RHx * RAy * RPz * RKu (x = 1, 2, 3, 4; y = 1, 2, 3, 4; z = 1, 2, 3, 4; u = 1, 2, 3, 4). This yields a reservoir parameter set consisting of 256 parameters.

[0151] Calculate the mean value Rc of 256 reservoir parameters in the reservoir parameter set. avg And obtain the reservoir type company parameter Rc for different companies. i That is, Rc i =RH i *RA i *RP i *RK i (i = 1, 2, 3, 4), for example, the reservoir type company parameter Rc1 of company 1 is RH1*RA1*RP1*RK1.

[0152] Based on the mean value Rc of the reservoir parameters avg And reservoir-type company parameter Rc i Calculate the absolute deviation Rc between the parameters of different reservoir types and the mean. adi That is, Rc adi =|Rc i -Rc avg |(i=1,2,3,4), for example, the absolute deviation Rc between the reservoir parameters of company 1 and the mean. ad1 =|Rc1-Rc avg |

[0153] Calculate the weighting coefficients (Rw) of reservoir-type company parameters for different companies. i The smaller the absolute deviation between the reservoir-type company parameters and the mean, the larger the weighting coefficient of the reservoir-type company parameters, i.e., the weighting coefficient Rw of a company's reservoir-type company parameters. i =(1-Rc adi / (Rc ad1 +Rc ad2 +Rc ad3 +Rc ad4 )) / 4 (i=1,2,3,4), for example, the weighting coefficient Rw1 of the reservoir-type company parameter of company 1 is (1-Rc ad1 / (Rc ad1 +Rc ad2 +Rc ad3 +Rcad4 )) / 4.

[0154] In one embodiment of the present invention, the above steps are specifically performed as follows:

[0155] For a certain block in Central Asia, a dataset Rc consisting of 256 reservoir parameters was constructed using data from the commercial databases of four companies: Company 1, Company 2, Company 3, and Company 4. Rc includes: RH1*RA1*RP1*RK1, RH1*RA1*RP1*RK2, RH1*RA1*RP1*RK3, RH1*RA1*RP1*RK4, RH1*RA1*RP2*RK1, RH1*RA1*RP2*RK2, RH1*RA1*RP2*RK3, RH1*RA1*RP2*RK4, ..., RH4*RA4*RP4*RK4, totaling 256 numbers.

[0156] Calculate the mean value Rc of 256 reservoir parameters in the reservoir parameter set. avg = (Sum of 256 parameters in the Rc dataset above) / 256 = 103750000, and calculate the reservoir-type company parameters Rc for different companies. i In this embodiment, the reservoir-type company parameters for each company are as follows:

[0157] Company 1's reservoir parameters Rc1 = RH1 * RA1 * RP1 * RK1 = 31,500,000

[0158] Company 2's reservoir parameters Rc2 = RH2 * RA2 * RP2 * RK2 = 61200000

[0159] 3. Reservoir parameters of Company Rc3 = RH3 * RA3 * RP3 * RK3 = 113400000

[0160] The reservoir parameters of Company 4 are: Rc4 = RH4 * RA4 * RP4 * RK4 = 176000000.

[0161] The absolute deviation Rc between the reservoir parameters of each company and the mean value is calculated based on the reservoir parameters of each company. adi In this embodiment, the absolute deviations between the reservoir-type company parameters and the mean values ​​from different companies are as follows:

[0162] The absolute deviation Rc between the reservoir parameters of Company 1 and the mean ad1 =|Rc1-Rc avg |=|31500000-103750000|=72250000,

[0163] The absolute deviation Rc between the reservoir parameters of Company 2 and the meanad2 =|Rc2-Rc avg |=|61200000-103750000|=42550000,

[0164] The absolute deviation Rc between the reservoir parameters of the three companies and the mean ad3 =|Rc3-Rc avg |=|113400000-103750000|=9650000,

[0165] The absolute deviation Rc between the reservoir parameters of the four companies and the mean ad4 =|Rc4-Rc avg |=|176000000-103750000|=72250000.

[0166] The weighting coefficients (Rw) for different reservoir types are calculated based on the aforementioned absolute deviation. i The smaller the absolute deviation between the reservoir company parameters and the mean, the larger the weighting coefficient of the reservoir company parameters. In this embodiment, the weighting coefficients of the reservoir company parameters for each company are as follows:

[0167] The weighting coefficient Rw1 for the reservoir parameters of Company 1 is: Rw1 = (1 - 72250000 / (72250000 + 42550000 + 9650000 + 72250000)) / 4 = (1 - 0.367) / 4 = 0.158.

[0168] The weighting coefficient Rw2 for the reservoir parameters of Company 2 is: Rw2 = (1 - 42550000 / (72250000 + 42550000 + 9650000 + 72250000)) / 4 = (1 - 0.216) / 4 = 0.196.

[0169] The weighting coefficient Rw3 for the reservoir parameters of Company 3 is: Rw3 = (1 - 9650000 / (72250000 + 42550000 + 9650000 + 72250000)) / 4 = (1 - 0.049) / 4 = 0.238.

[0170] The weighting coefficient Rw4 for the reservoir-type company parameters of Company 4 is (1-72250000 / (72250000+42550000+9650000+72250000)) / 4=(1-0.367) / 4=0.158.

[0171] S23: Determine the trap weighting coefficient (Tw). Because the trap data (T) from different companies comes from different sources, and in the absence of basic seismic and drilling data evidence, it is difficult for the data purchaser to distinguish the authenticity of the data. Therefore, to reduce the risk of resource potential assessment, it is necessary to determine the trap weighting coefficient (Tw) for each commercial database company.

[0172] The specific steps are as follows:

[0173] A dataset of trap parameters is constructed based on four subclasses of reservoir data: trap area (TA), trap closure degree (TH), trap type (TT), and control trap data type (TD). The trap parameter Tc is a parameter formed by cross-multiplying four subclasses of reservoir data from different commercial database companies: trap area (TA), trap closure degree (TH), trap type (TT), and control trap data type (TD). That is, Tc = TAx * THy * TTz * TDu (x = 1, 2, 3, 4; y = 1, 2, 3, 4; z = 1, 2, 3, 4; u = 1, 2, 3, 4). This yields a trap parameter set consisting of 256 trap parameters, where the trap type TT and control trap data type TD are assigned values. In one embodiment of the present invention, the TT value for anticline trap type is 1, the TT value for faulted anticline trap type is 0.8, and the TT value for other trap types is 0.6; the TD value for traps controlled by three-dimensional data is 1, the TD value for traps controlled by two-dimensional survey network with a survey network spacing of less than or equal to 2 km is 0.8, and the TD value for traps controlled by two-dimensional survey network with a survey network spacing of greater than 2 km is 0.6.

[0174] Calculate the mean Tc of the 256 trap parameters in the trap parameter set. avg And obtain the closed-loop company parameter Tc for different companies. i That is, Tc i =TA i *TH i *TT i *TD i (i = 1, 2, 3, 4), for example, the closed-loop company parameter Tc1 of company 1 is TA1*TH1*TT1*TD1.

[0175] Based on the mean value Tc of the trapping parameter avg And closed-loop company parameter Tc i Calculate the absolute deviation Tc between the parameters and the mean of companies in different closed-loop types. adi That is, Tc adi =|Tc i -Tc avg |(i=1,2,3,4), for example, the absolute deviation Tc between the parameters of closed-loop companies of company 1 and the mean. ad1 =|Tc1-Tc avg |

[0176] Calculate the weighting coefficients (Tw) of the parameters for companies in different closed-loop categories. i The smaller the absolute deviation between the parameters of closed-loop companies and the mean, the larger the weight coefficient of the closed-loop company parameters. That is, the weight coefficient Tw of a company's closed-loop company parameters. i =(1-Tc) adi / (Tc ad1 +Tc ad2 +Tc ad3 +Tc ad4 )) / 4 (i=1,2,3,4), for example, the weighting coefficient Tw1 of the closed-loop company parameter of company 1 is Tw1=(1-Tc ad1 / (Tc ad1 +Tc ad2 +Tc ad3 +Tc ad4 )) / 4.

[0177] In one embodiment of the present invention, the above steps are specifically performed as follows:

[0178] In this embodiment, for a certain block in Central Asia, a dataset Tc consisting of 256 trap parameters was constructed using data from the business databases of four companies: Company 1, Company 2, Company 3, and Company 4. Tc includes: TA1*TH1*TT1*TD1, TA1*TH1*TT1*TD2, TA1*TH1*TT1*TD3, TA1*TH1*TT1*TD4, TA1*TH1*TT2*TD1, TA1*TH1*TT2*TD2, TA1*TH1*TT2*TD3, TA1*TH1*TT2*TD4, ..., TA1*TH1*TT4*TD4, a total of 256 numbers.

[0179] Calculate the mean Tc of the 256 trap parameters in the trap parameter set. avg = (Sum of 256 parameters in the Tc dataset above) / 256 = 12680, and calculate the Tc parameter for closed-loop companies of different companies. i In this embodiment, the trap class parameters for each company are as follows:

[0180] Company 1's closed-loop company parameter Tc1 = TA1 * TH1 * TT1 * TD1 = 18000,

[0181] 2. Company's closed-loop company parameter Tc2 = TA2 * TH2 * TT2 * TD2 = 10000.

[0182] The parameter Tc3 for the closed-loop company of Company 3 is Tc3 = TA3 * TH3 * TT3 * TD3 = 21000.

[0183] The parameter Tc4 for the closed-loop company of Company 4 is TA4*TH4*TT4*TD4=15360.

[0184] The absolute deviation Tc between the parameters of different types of closed-loop companies and the mean is calculated based on the parameters of each company. adi In this embodiment, the absolute deviations between the parameters of each company's closed-loop company and the mean are as follows:

[0185] The absolute deviation Tc between the parameters of the closed-loop companies and the mean of Company 1 ad1 =|Tc1-Tc avg |=|18000-12680|=5320,

[0186] The absolute deviation Tc between the parameters of the closed-loop companies and the mean of the two companies. ad2 =|Tc2-Tc avg |=|10000-12680|=2680,

[0187] The absolute deviation Tc between the parameters of the closed-loop companies and the mean of the three companies. ad3 =|Tc3-Tc avg |=|21000-12680|=8320,

[0188] The absolute deviation Tc between the parameters of the closed-loop companies and the mean of the four companies. ad4 =|Tc4-Tc avg |=|15360-12680|=2680.

[0189] The weighting coefficients (Tw) of the parameters for different types of companies are calculated based on the absolute deviations between the parameters of each type of company and the mean. i The smaller the absolute deviation between the closed-loop company parameters and the mean, the larger the weighting coefficient of the closed-loop company parameters. In this embodiment, the calculation results of the weighting coefficients of the closed-loop company parameters for each company are as follows:

[0190] The weighting coefficient of the closed-loop company parameter Tw1 = (1-Tc) ad1 / (Tc ad1 +Tc ad2 +Tc ad3 +Tc ad4 )) / 4=(1-5320 / (5320+2680+8320+2680)) / 4=(1-0.280) / 4=0.180,

[0191] The weighting coefficient of the closed-loop company parameter for Company 2 is Tw2 = (1 - Tc) ad2 / (Tc ad1 +Tc ad2 +Tc ad3+Tc ad4 )) / 4=(1-2680 / (5320+2680+8320+2680)) / 4=(1-0.141) / 4=0.215,

[0192] The weighting coefficient of the closed-loop company parameter for Company 3 is Tw3 = (1 - Tc) ad3 / (Tc ad1 +Tc ad2 +Tc ad3 +Tc ad4 )) / 4=(1-8320 / (5320+2680+8320+2680)) / 4=(1-0.438) / 4=0.141,

[0193] The weighting coefficient of the closed-loop company parameter for Company 4 is Tw4 = (1-Tc) ad4 / (Tc ad1 +Tc ad2 +Tc ad3 +Tc ad4 )) / 4=(1-2680 / (5320+2680+8320+2680)) / 4=(1-0.141) / 4=0.215.

[0194] S24: Determine the closure weighting coefficient (Ew). Because the sources of closure-type data (E) differ between companies, and in the absence of basic seismic and drilling data evidence, it is difficult for the data purchaser to distinguish the authenticity of the data. Therefore, to reduce the risk of resource potential assessment, it is necessary to determine the closure weighting coefficient (Ew) for each commercial database company.

[0195] The specific steps are as follows:

[0196] A dataset of sealing parameters was constructed based on reservoir data from four subclasses: caprock thickness (EH), caprock porosity (EP), caprock permeability (EK), and caprock continuity (EC). The sealing parameter Ec is a parameter formed by cross-multiplying the data from different commercial database companies for the four subclasses of reservoir data: EH, EP, EK, and EC. Specifically, Ec = EHx * (1 / EPy) * (1 / EKz) * ECu (x = 1, 2, 3, 4; y = 1, 2, 3, 4; z = 1, 2, 3, 4; u = 1, 2, 3, 4). This yields a set of 256 sealing parameters (Note: caprock porosity EP and caprock permeability EK are inversely proportional to sealing capacity, hence the reciprocals are multiplied).

[0197] Find the mean Ec of the 256 closed parameters in the closed parameter set. avg And obtain the closed-class company parameter Ec for different companies. iEc i =EH i *(1 / EP i )*(1 / EK i EC i (i = 1, 2, 3, 4), for example, the closed parameter of company 1 is Ec1 = EH1 * (1 / EP1) * (1 / EK1) * EC1.

[0198] Based on the mean of the closure parameter Ec avg And closed-end company parameter Ec i Find the absolute deviation Ec between the two. adi Ec adi =|Ec i -Ec avg |(i=1,2,3,4), for example, the absolute deviation Ec between the parameters of the closed-end companies of company 1 and the mean. ad1 =|Ec1-Ec avg |

[0199] Calculate the weighting coefficients (Ew) of the closed-end company parameter for different companies. i The smaller the absolute deviation between the closed-end company parameter and the mean, the larger the weight coefficient of the closed-end company parameter. That is, the weight coefficient Ew of a company's closed-end company parameter. i =(1-Ec) adi / (Ec ad1 +Ec ad2 +Ec ad3 +Ec ad4 )) / 4 (i=1,2,3,4), for example, the weight coefficient of the closed-end company parameter Ew1=(1-Ec ad1 / (Ec ad1 +Ec ad2 +Ec ad3 +Ec ad4 )) / 4.

[0200] In one embodiment of the present invention, the above steps are specifically performed as follows:

[0201] In this embodiment, for a certain block in Central Asia, a dataset Ec consisting of 256 closed parameters was constructed using data from the business databases of four companies: Company 1, Company 2, Company 3, and Company 4. Ec includes: EH1*(1 / EP1)*(1 / EK1)*EC1, EH1*(1 / EP1)*(1 / EK1)*EC2, EH1*(1 / EP1)*(1 / EK1)*EC3, EH1*( There are 256 numbers in total: 1 / EP1)*(1 / EK1)*EC4, EH1*(1 / EP1)*(1 / EK2)*EC1, EH1*(1 / EP1)*(1 / EK2)*EC2, EH1*(1 / EP1)*(1 / EK2)*EC3, EH1*(1 / EP1)*(1 / EK2)*EC4, ..., EH4*(1 / EP4)*(1 / EK4)*EC4.

[0202] Find the mean Ec of the 256 closed parameters in the closed parameter set. avg = (Sum of 256 parameters in the Ec dataset above) / 256 = 0.648, and calculate the Ec parameter for closed-class companies of different companies. i In this embodiment, the calculation results of the closure parameters for each company are as follows:

[0203] For Company 1, the closed-end company parameter Ec1 = EH1 * (1 / EP1) * (1 / EK1) * EC1 = 0.375.

[0204] For Company 2, the closed-end company parameter Ec2 = EH2 * (1 / EP2) * (1 / EK2) * EC2 = 0.185.

[0205] For the closed-end company of Company 3, the parameter Ec3 = EH3 * (1 / EP3) * (1 / EK3) * EC3 = 0.533.

[0206] The closed-end company parameter Ec4 = EH4 * (1 / EP4) * (1 / EK4) * EC4 = 1.111.

[0207] The absolute deviation Ec between the closed-end company parameters and the mean of each company is calculated based on the closed-end company parameters of each company. adi In this embodiment, the absolute deviations between the parameters of each company's closed-end companies and the mean are as follows:

[0208] 1. The absolute deviation Ec between the parameters of the closed-end companies and the mean ad1 =|Ec1-Ec avg |=|0.375-0.648|=0.273,

[0209] The absolute deviation Ec between the parameters of the closed-end companies and the mean of the two companies. ad2=|Ec2-Ec avg |=|0.185-0.648|=0.463,

[0210] The absolute deviation Ec between the parameters of the closed-end companies and the mean of the three companies ad3 =|Ec3-Ec avg |=|0.533-0.648|=0.115,

[0211] The absolute deviation Ec between the parameters of the closed-end companies and the mean of the four companies ad4 =|Ec4-Ec avg |=|1.111-0.648|=0.463.

[0212] The weighting coefficients (Ew) of the closed-end company parameters for each company are calculated based on the absolute deviation between the closed-end company parameters and the mean. i The smaller the absolute deviation between the closed-end company parameters and the mean, the larger the weight coefficient of the closed-end company parameters. In this embodiment, the results of the closed-end company parameter weights for each company are as follows:

[0213] The weighting coefficient of the closed-end company parameter is Ew1 = (1 - Ec) ad1 / (Ec ad1 +Ec ad2 +Ec ad3 +Ec ad4 )) / 4=(1-0.273 / (0.273+0.463+0.115+0.463)) / 4=(1-0.208) / 4=0.198,

[0214] The weighting coefficient of the closed-end company parameter for Company 2 is Ew2 = (1 - Ec) ad2 / (Ec ad1 +Ec ad2 +Ec ad3 +Ec ad4 )) / 4=(1-0.463 / (0.273+0.463+0.115+0.463)) / 4=(1-0.352) / 4=0.162,

[0215] The weighting coefficient of the closed-end company parameter for Company 3 is Ew3 = (1 - Ec) ad3 / (Ec ad1 +Ec ad2 +Ec ad3 +Ec ad4 )) / 4=(1-0.115 / (0.273+0.463+0.115+0.463)) / 4=(1-0.088) / 4=0.228,

[0216] The weighting coefficient of the closed-end company parameter for Company 4 is Ew4 = (1 - Ec) ad4 / (Ec ad1 +Ec ad2 +Ec ad3 +Ec ad4 )) / 4=(1-0.463 / (0.273+0.463+0.115+0.463)) / 4=(1-0.352) / 4=0.162.

[0217] In step S3, the resource weighting coefficient (Sw) for different companies is determined based on the source weighting coefficient (Ow), reservoir weighting coefficient (Rw), trap weighting coefficient (Tw), and sealing weighting coefficient (Ew). Because the resource data (S) from different companies comes from different sources, and in the absence of basic seismic and drilling data evidence, it is difficult for the data purchaser to distinguish the authenticity of the data. Therefore, to reduce the risk of resource potential assessment, it is necessary to determine the resource weighting coefficient for each commercial database company.

[0218] Specifically, the following steps are included:

[0219] The resource quantity parameter Sc for each company is determined based on its source oil weight coefficient (Ow), reservoir weight coefficient (Rw), trap weight coefficient (Tw), and closure weight coefficient (Ew). The resource quantity parameter Sc is the product of the source oil weight coefficient (Ow), reservoir weight coefficient (Rw), trap weight coefficient (Tw), and closure weight coefficient (Ew) for each commercial database company, i.e., Sci = Ow. i *Rw i *Tw i *Ew i (i = 1, 2, 3, 4), for example, the resource quantity parameter Sc1 of company 1 = Ow1 * Rw1 * Tw1 * Ew1.

[0220] Based on the resource quantity parameters of different companies, determine the weighting coefficient Sw for the resource quantity parameters of different companies. i Sw i =Sc i / (Sc1+Sc2+Sc3+Sc4)(i=1,2,3,4), for example, the resource quantity parameter weight coefficient Sw1=Sc1 / (Sc1+Sc2+Sc3+Sc4) for Company 1.

[0221] In this embodiment, the resource quantity parameters for each company are as follows:

[0222] Company 1's resource quantity parameter Sc1 = Ow1 * Rw1 * Tw1 * Ew1 = 0.163 * 0.158 * 0.180 * 0.198 = 0.000918.

[0223] Company 2's resource quantity parameter Sc2 = Ow2 * Rw2 * Tw2 * Ew2 = 0.236 * 0.196 * 0.215 * 0.162 = 0.001611.

[0224] Company 3's resource quantity parameter Sc3 = Ow3 * Rw3 * Tw3 * Ew3 = 0.188 * 0.238 * 0.141 * 0.228 = 0.001438.

[0225] The resource quantity parameter Sc4 of Company 4 is: Ow4*Rw4*Tw4*Ew4 = 0.163*0.158*0.215*0.162 = 0.000897.

[0226] Based on the resource quantity parameters of each company, determine the weighting coefficient Sw for the resource quantity parameters of different companies. i The calculation results of the resource quantity parameter weight parameters for each company in this embodiment are as follows:

[0227] Company 1's resource quantity parameter weighting coefficient Sw1=Sc1 / (Sc1+Sc2+Sc3+Sc4)=0.000918 / (0.000918+0.001611+0.001438+0.000897)=0.000918 / 0.004864=0.188691,

[0228] Company 2's resource quantity parameter weighting coefficient Sw2=Sc2 / (Sc1+Sc2+Sc3+Sc4)=0.001611 / (0.000918+0.001611+0.001438+0.000897)=0.001611 / 0.004864=0.331201,

[0229] The resource quantity parameter weighting coefficient Sw3 for Company 3 is calculated as follows: Sw3 = Sc3 / (Sc1+Sc2+Sc3+Sc4) = 0.001438 / (0.000918+0.001611+0.001438+0.000897) = 0.001438 / 0.004864 = 0.295705.

[0230] The weighting coefficient of the resource quantity parameter of Company 4 is Sw4=Sc4 / (Sc1+Sc2+Sc3+Sc4)=0.000897 / (0.000918+0.001611+0.001438+0.000897)=0.000897 / 0.004864=0.184403.

[0231] In step S4, the resource quantity weighting coefficient (Sw) and the resource quantity (S) obtained from assessments by different existing companies are used as the basis for the calculation. i Determine the expected resource quantity (Se) of the oil and gas field;

[0232] Specifically, the following steps are included:

[0233] Based on each company's resource weighting coefficient (Sw) and the resource quantity (S) assessed for each company i Calculate the expected resources of the oil and gas field;

[0234] Expected resources (Se) are the anticipated exploration potential of oil and gas blocks identified based on existing commercial database data. These resources (S) are assessed by different companies. i Multiply by the resource quantity parameter weighting coefficient (Sw) i The sum of products,

[0235] That is, Se=S1*Sw1+S2*Sw2+S3*Sw3+S4*Sw4.

[0236] In this embodiment, the expected resource quantity (Se) is calculated as follows:

[0237] Se=S1*Sw1+S2*Sw2+S3*Sw3+S4*Sw4=3500*0.188691+6000*0.331201+200 0*0.295705+1000*0.184403=660.42+1987.20+591.41+184.40=3423.44.

[0238] Based on the same concept of the present invention, another exemplary embodiment of the present invention provides a device for rapidly determining the exploration potential of oil and gas blocks, such as... Figure 2 As shown, it includes:

[0239] The classification index data table creation unit 201 is used to create a classification index data table based on data from an international business database company. The classification index data table includes multiple major categories of data and several subcategories of data under each major category.

[0240] The first category weight coefficient determination unit 202 is used to determine the weight coefficients of the first category of data, which includes oil source data, reservoir data, trap data and closed data.

[0241] The second category weight coefficient determination unit 203 is used to determine the weight coefficient of the second category data based on the weight coefficient of the first category data, where the second category data is resource quantity data.

[0242] The expected resource quantity determination unit 204 is used to determine the expected resource quantity of an oil and gas field based on the weighting coefficients of resource quantity data and the resource quantity assessed by each international commercial database company.

[0243] Based on the same inventive concept, another exemplary embodiment of the present invention provides an electronic device. For example... Figure 3 As shown, the electronic device includes at least one processor 301, at least one communication interface 302, at least one memory 303, and at least one communication bus 304; wherein the processor 301, communication interface 302, and memory 303 communicate with each other through the communication bus 304.

[0244] Memory 303 stores computer programs;

[0245] The processor 301, when executing the program stored in the memory 303, implements the method for rapidly determining the exploration potential of oil and gas blocks.

[0246] Optionally, the communication interface can be an interface of a communication module, such as the interface of a GSM module; the processor may be a CPU, an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The memory may include high-speed RAM and may also include non-volatile memory, such as at least one disk storage device. The memory stores a program, and the processor calls the program stored in the memory to execute some or all of the above-described method embodiments.

[0247] Based on the same inventive concept, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed, implements some or all of the above-described method embodiments. Optionally, the storage medium may be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.

[0248] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for rapidly determining the exploration potential of an oil and gas block, characterized in that, The method comprises the following steps: a classification index data table is established based on the data of international commercial database companies, and the classification index data table comprises a plurality of major categories and a plurality of subcategories under each major category; a classification index data table is established by the following steps: a primary classification index data table is established, which comprises a plurality of major categories of oil source category, reservoir category, trap category, sealing category and resource category; each major category is further divided into a plurality of subcategories, and a secondary classification index data table is established; data of a plurality of international commercial database companies are obtained, and the data of the commercial database are supplemented into the secondary classification index data table according to the types of the data to establish the classification index data table; in the secondary classification index data table, the oil source category comprises four subcategories of source rock thickness, source rock area, source rock organic carbon content and source rock maturity; the reservoir category comprises four subcategories of reservoir thickness, reservoir area, reservoir porosity and reservoir permeability; the trap category comprises four subcategories of trap area, trap closure, trap type and trap control data; the sealing category comprises four subcategories of sealing layer thickness, sealing layer porosity, sealing layer permeability and sealing layer continuity; and the resource category comprises one subcategory of resource amount; weight coefficients of first major category data are determined, the first major category data being oil source category data, reservoir category data, trap category data and sealing category data; weight coefficients of second major category data are determined according to the weight coefficients of the first major category data, the second major category data being resource amount category data, comprising the following steps: resource amount parameters Sc of different international commercial database companies are determined according to the oil source weight coefficient Ow, the reservoir weight coefficient Rw, the trap weight coefficient Tw and the sealing weight coefficient Ew of each international commercial database company, and the calculation formula is as follows: ； wherein i=1, 2, 3, …n, and n is the number of different international commercial database companies; According to the resource amount parameter Sc of each company i Determine the weight coefficient Sw of the resource amount parameter of different international business database companies i The calculation formula is as follows: Sw i = Sc i (Sc1+ Sc2+ Sc3+... + Sc n ). wherein i=1, 2, 3, …n, and n is the number of different international commercial database companies; expected resource amount of the oil and gas field is determined according to the weight coefficients of the resource amount category data and the resource amount evaluated by each international commercial database company, and the calculation formula of the expected resource amount is as follows: ； where Se is the expected resource amount, S1, S2, S3,... S n are the resource amounts assessed by different international business database companies, respectively, and Sw1, Sw2, Sw3,... Sw n are the weight coefficients of the resource amount category data of the different international business database companies calculated, respectively.

2. The method for rapidly determining exploration potential of oil and gas blocks according to claim 1, characterized in that, the determination of the weight coefficients of the oil source category data comprises the following steps: an oil source parameter data set is constructed according to the four subcategories of oil source category data, i.e. source rock thickness, source rock area, source rock organic carbon content and source rock maturity; an average value Ocavg of the oil source parameters in the oil source parameter data set and the oil source category company parameters Oci of each international commercial database company are determined; an absolute deviation Ocadi between the oil source category company parameters Oci of each international commercial database company and the average value Ocavg of the oil source parameters is determined according to the average value Ocavg of the oil source parameters and the oil source category company parameters Oci; the weight coefficients Owi of the oil source category company parameters of each international commercial database are determined according to the absolute deviation Ocadi.

3. The method according to claim 2, wherein the calculation of a single oil source parameter in the oil source parameter data set is as follows: ； Wherein, Oc is the oil source parameter, OH is the source rock thickness, OA is the source rock area, OT is the source rock organic carbon content, OR is the source rock maturity, x, y, z, u = 1, 2, 3, …, i…, n, n is the number of international business database companies; The calculation formula of the oil source parameter average value Ocavg in the oil source parameter data set and the oil source company parameter Oci of each international business database company is as follows: Ocavg=Ototal / m; ； Wherein, Ototal is the sum of all oil source parameters in the oil source parameter data set, m is the number of all oil source parameters in the oil source parameter data set; Oci is the oil source company parameter of the i-th international business database company, i = 1, 2, 3, … n and i ≤ n, n is the number of international business database companies; The calculation formula of the absolute deviation Ocadi between the oil source company parameter Oci of each international business database company and the oil source parameter average value Ocavg is as follows: Oc adi =|Oc i -Oc avg |; wherein, Oc adi is the absolute deviation between the oil source parameter of the i-th International Business Database Company and the average value of the oil source parameter of all International Business Database Companies, i = 1, 2, 3, … n, n is the number of International Business Database Companies. i is the absolute deviation between the oil source parameter of the i-th International Business Database Company and the average value of the oil source parameter of all International Business Database Companies, i = 1, 2, 3, … n, n is the number of International Business Database Companies. avg is the absolute deviation between the oil source parameter of the i-th International Business Database The calculation formula of the weight coefficient Owi of each international business database oil source company parameter is as follows: Ow i =(1-Oc adi / (Oc ad1 +Oc ad2 +Oc ad3 +…+Oc adn )) / n.

4. The method for rapidly determining exploration potential of oil and gas blocks according to claim 1, characterized in that, The determination of the weight coefficient of the reservoir class data includes the following steps: The reservoir parameter data set is constructed according to the four sub-class reservoir class data of reservoir thickness, reservoir area, reservoir porosity and reservoir permeability; The calculation formula of the reservoir parameter average value Rcavg in the reservoir parameter data set and the reservoir class company parameter Rci of each international business database company is as follows: The absolute deviation Rcadi between the reservoir class company parameter Rci of each international business database company and the reservoir parameter average value Rcavg is determined according to the reservoir parameter average value Rcavg and the reservoir class company parameter Rci; The weight coefficient Rwi of each international business database reservoir class company parameter is determined according to the absolute deviation Rcadi.

5. The method for quickly determining the exploration potential of an oil and gas block according to claim 4, characterized in that, The calculation method of a single reservoir parameter in the reservoir parameter data set is as follows: ； Wherein, Rc is the reservoir parameter, RH is the reservoir thickness, RA is the reservoir area, RP is the reservoir porosity, RK is the reservoir permeability, x, y, z, u = 1, 2, 3, …, i…, n, n is the number of international business database companies; The calculation formula of the reservoir parameter average value Rcavg in the reservoir parameter data set and the reservoir class company parameter Rci of each international business database company is as follows: Rcavg=Rtotal / m; ； Wherein, Rtotal is the sum of all reservoir parameters in the reservoir parameter data set, m is the number of all reservoir parameters in the reservoir parameter data set; Rci is the reservoir class company parameter of the i-th international business database company, i = 1, 2, 3, … n and i ≤ n, n is the number of international business database companies; The calculation formula of the absolute deviation Rcadi between the reservoir class company parameter Rci of each international business database company and the reservoir parameter average value Rcavg is as follows: Rc adi =|Rc i -Rc avg |; wherein Rc adi is the absolute deviation between the reservoir class company parameter Rc i and the reservoir parameter mean Rc avg , i = 1, 2, 3,... n, n being the number of international business database companies. The calculation formula of the weight coefficient Rwi of each international business database reservoir class company parameter is as follows: Rw i = (1 - Rc adi / (Rc ad1 + Rc ad2 + Rc ad3 + … + Rc adn )) / n.

6. The method for rapid determination of exploration potential of oil and gas blocks as claimed in claim 1 wherein, The determination of the weight coefficient of the trap class data includes the following steps: According to the four sub-class trap class data of trap area, trap closure, trap type and control trap data, a trap parameter data set is constructed; Determine the trap parameter average value Tcavg in the trap parameter data set and the trap class company parameter Tci of each international commercial database company; According to the trap parameter average value Tcavg and the trap class company parameter Tci, the absolute deviation Tcadi between the trap class company parameter Tci of each international commercial database company and the trap parameter average value Tcavg is determined. According to the absolute deviation Tcadi, the weight coefficient Twi of the trap class company parameter of each international commercial database is determined.

7. The method for rapidly determining the exploration potential of an oil and gas block according to claim 6, characterized in that, The calculation method of a single trap parameter in the trap parameter data set is as follows: ； Wherein, Tc is the trap parameter, TA is the trap area, TH is the trap closure, TT is the trap type, TD is the control trap data, x, y, z, u=1, 2, 3, …, i…, n, n is the number of international commercial database companies; The calculation formula of the trap parameter average value Tcavg in the trap parameter data set and the trap class company parameter Tci of each international commercial database company is as follows: Tcavg=Ttotal / m; ； Wherein, Ttotal is the sum of all trap parameters in the trap parameter data set, m is the number of all trap parameters in the trap parameter data set; Tci is the trap class company parameter of the i th international commercial database company, i=1, 2, 3, … n and i≤n, n is the number of international commercial database companies; The calculation formula of the absolute deviation Tcadi between the trap class company parameter Tci of each international commercial database company and the trap parameter average value Tcavg is as follows: Tc adi =|Tc i -Tc avg |; wherein Tc adi is the absolute deviation between the i-th International Business Machines Corporation's trap class company parameter Tc i and the trap parameter mean Tc avg i = 1, 2, 3,... n, n being the number of International Business Machines Corporation's. The calculation formula of the weight coefficient Twi of the trap class company parameter of each international commercial database is as follows: Tw i = (1 - Tc adi / (Tc ad1 + Tc ad2 + Tc ad3 +... + Tc adn )) / n.

8. The method for rapid determination of exploration potential of oil and gas blocks as claimed in claim 1 wherein, The weight coefficient of the sealing class data is determined, including the following steps: According to the four sub-class sealing class data of caprock thickness, caprock porosity, caprock permeability and caprock continuity, a sealing parameter data set is constructed; Determine the sealing parameter average value Ecavg in the sealing parameter data set and the sealing class company parameter Eci of each international commercial database company; According to the sealing parameter average value Ecavg and the sealing class company parameter Eci, the absolute deviation Ecadi between the sealing class company parameter Eci of each international commercial database company and the sealing parameter average value Ecavg is determined. According to the absolute deviation Ecadi, the weight coefficient Ewi of the sealing class company parameter of each international commercial database is determined.

9. The method for quickly determining the exploration potential of oil and gas blocks according to claim 8, characterized in that, The calculation method of a single sealing parameter in the sealing parameter data set is as follows: ； Wherein, Ec is the sealing parameter, EH is the caprock thickness, EP is the caprock porosity, EK is the caprock permeability, EC is the caprock continuity, x, y, z, u=1, 2, 3, …, i…, n, n is the number of international commercial database companies; The calculation formula of the sealing parameter average value Ecavg in the sealing parameter data set and the sealing class company parameter Eci of each international commercial database company is as follows: Ecavg = Etot / m; ； wherein Etot is the sum of all the trap parameters in the closed parameter dataset, m is the number of all the trap parameters in the closed parameter dataset; Eci is the trap parameter of the i-th international business database company, i = 1, 2, 3, … n and i ≤ n, n is the number of the international business database companies; The calculation formula of the absolute deviation Ecadi between the trap parameter Eci of each international business database company and the trap parameter average value Ecavg is as follows: Ec adi =|Ec i -Ec avg |; where Ec adi is the closed class company parameter Ec i of the i-th Dun & Bradstreet company avg and the absolute deviation between the closed parameter Ec avg and the average Ec avg , i = 1, 2, 3,... n, n is the number of Dun & Bradstreet companies. The calculation formula of the weight coefficient Ewi of each international business database trap parameter is as follows: Ew i =(1-Ec adi / (Ec ad1 +Ec ad2 +Ec ad3 +…+Ec adn )) / n.

10. A device for rapidly determining the exploration potential of an oil and gas block, characterized in that it comprises: It comprises: The classification index data table establishing unit is used for establishing a classification index data table based on the information of the international business database companies, wherein the classification index data table comprises a plurality of major category data and a plurality of subcategory data under each major category; The classification index data table is established according to the following steps: A primary classification index data table is established, which comprises a plurality of major category categories of source class, reservoir class, trap class, trap class and resource class; Each major category category is further divided into a plurality of subcategories under the respective category to establish a secondary classification index data table; The information of a plurality of international business database companies is obtained, and the business database information is supplemented into the secondary classification index data table according to the type of the data to establish a classification index data table; The source rock thickness, source rock area, source rock organic carbon content and source rock maturity are four subcategories of the source class in the secondary classification index data table; the reservoir thickness, reservoir area, reservoir porosity and reservoir permeability are four subcategories of the reservoir class; the trap area, trap closure, trap type and trap control data are four subcategories of the trap class; the caprock thickness, caprock porosity, caprock permeability and caprock continuity are four subcategories of the trap class; and the resource amount is one subcategory of the resource class; The first major category weight coefficient determining unit is used for determining the weight coefficients of the first major category data, which are the source class data, the reservoir class data, the trap class data and the trap class data; The second major category weight coefficient determining unit is used for determining the weight coefficients of the second major category data according to the weight coefficients of the first major category data, which are the resource amount class data, comprising the following steps: The resource amount parameters Sc of different international business database companies are determined according to the source weight coefficients Ow, the reservoir weight coefficients Rw, the trap weight coefficients Tw and the trap weight coefficients Ew of each international business database company, and the calculation formula is as follows: ； wherein i = 1, 2, 3, … n, and n is the number of different international business database companies; According to the resource amount parameter Sc of each company i Determine the weight coefficient Sw of the resource amount parameter of different international business database companies i The calculation formula is as follows: Sw i =Sc i / (Sc1+Sc2+Sc3+…+Sc n ). wherein i = 1, 2, 3, … n, and n is the number of different international business database companies; The expected resource amount determining unit is used for determining the expected resource amount of the oil and gas field according to the weight coefficients of the resource amount class data and the resource amount evaluated by each international business database company; the expected resource amount is calculated according to the following formula: ； where Se is the expected resource amount, S1, S2, S3,... S n are the resource amounts assessed by different international business database companies, respectively, and Sw1, Sw2, Sw3,... Sw n are the weight coefficients of the calculated resource amount category data of different international business database companies, respectively.

11. An electronic device, comprising: It comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete communication with each other through the communication bus; a memory, which stores a computer program; a processor, which is configured to implement the method for rapidly determining exploration potential of an oil and gas block according to any one of claims 1-9 when executing the program stored in the memory.

12. A computer readable storage medium storing a computer program, wherein the computer program comprises program instructions configured to cause a processor to perform the method according to any one of claims 1 to 11. The computer program is executed to implement the method for rapidly determining exploration potential of an oil and gas block according to any one of claims 1-9.

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