Method and device for rapidly determining exploration potential of oil and gas block

Abstract

The invention belongs to the technical field of geological exploration research, and particularly relates to an oil and gas block exploration potential rapid determination method and device, and the method comprises the steps: building a classification index data table based on the data of an international commercial database company, and the classification index data table comprises a plurality of large-class data and a plurality of subclass data under each large class; respectively determining weight coefficients of the first large class of data; determining a weight coefficient of a second large class of data according to the weight coefficient of the first large class of data, wherein the second large class of data is resource quantity class data; and determining the expected resource quantity of the oil and gas field according to the weight coefficient of the resource quantity type data and the resource quantity evaluated by each international commercial database company. According to the method, data of a plurality of commercial databases are comprehensively applied, comprehensive evaluation is performed by searching data of reservoir forming elements such as source rocks, reservoirs, cover stratums and traps, the overseas block exploration potential close to the real situation is quickly solved, and the evaluation precision of the overseas block exploration potential is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of geological exploration research, and in particular relates to a method and a device for quickly determining the exploration potential of an oil and gas block. Background Art

[0002] The exploration potential of an oil and gas block is a direct parameter for clarifying the commercial value of the block, determining the amount of exploration work and investment in the block, and even determining whether to acquire the block. Especially in the process of globalization of the oil and gas industry, many oil companies are in urgent need of expanding overseas business. How to quickly determine the exploration potential of an exploration block that has not been involved in or has not been deeply studied, and then decide whether to invest in the block, has become the biggest bottleneck that plagues overseas block optimization, overseas block mergers and acquisitions, and overseas block exploration deployment decisions.

[0003] At present, most researchers usually determine the exploration potential of overseas oil and gas exploration blocks based on their personal experience and the information of a certain commercial database, ignoring the uncertainty of the data source of a single commercial database. The predicted exploration potential of the blocks is often far from the actual potential after actual intervention, which directly increases the risk of overseas block mergers and acquisitions and increases the difficulty of decision-making in acquiring overseas exploration blocks. Summary of the invention

[0004] In view of the above problems, in a first aspect, the present invention proposes a method for quickly determining the exploration potential of an oil and gas block, comprising the following steps:

[0005] Establishing a classification index data table based on the data of an international commercial database company, wherein the classification index data table includes a plurality of major categories of data and a plurality of sub-categories of data under each major category;

[0006] Determine the weight coefficients of the first category of data respectively, wherein the first category of data includes oil source data, reservoir data, trap data and closed data;

[0007] Determine the weight coefficient of the second category of data according to the weight coefficient of the first category of data, where the second category of data is resource quantity data;

[0008] The expected resources of the oil and gas field are determined based on the weight coefficient of the resource data and the resources assessed by each international commercial database company.

[0009] Furthermore, the establishment of a classification index data table based on the information of the international business database company includes the following steps:

[0010] Establishing a primary classification index data table, wherein the primary classification index data table includes multiple major categories such as oil source, reservoir, trap, closure and resource;

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

[0012] Obtain information from multiple international commercial database companies, and add the commercial database information to the secondary classification index data table according to the data type, and establish a classification index data table;

[0013] Among them, the oil source category in the secondary classification index data table includes four subcategories: source rock thickness, source rock area, source rock organic carbon content and source rock maturity; the reservoir category includes four subcategories: reservoir thickness, reservoir area, reservoir porosity and reservoir permeability; the closure category includes four subcategories based on closure area, closure closure, closure type and control data; the closure category includes four subcategories: cap layer thickness, cap layer porosity, cap layer permeability and cap layer continuity; the resource category includes one subcategory: resource quantity.

[0014] Furthermore, determining the weight coefficient of oil source data includes the following steps:

[0015] The oil source parameter data set was constructed based on the four sub-categories of oil source data, namely source rock thickness, source rock area, source rock organic carbon content and source rock maturity;

[0016] Determine the oil source parameter mean Oc in the oil source parameter data set avg and the oil source company parameters Oc for each IBC company i ;

[0017] According to the mean value of oil source parameters Oc avg and oil source company parameters Oc i Determine the oil source company parameter Oc for each IBC company i and the mean value of oil source parameters Oc avg The absolute deviation between adi ;

[0018] According to the absolute deviation Oc adi Determine the weight coefficient Ow of each oil source company parameter in the international commercial database i .

[0019] Furthermore, the calculation method of a single oil source parameter in the oil source parameter data set is as follows:

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

[0021] Among them, 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 commercial database companies;

[0022] Determine the oil source parameter mean Oc in the oil source parameter data set avg and the oil source company parameters Oc for each IBC 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 is the sum of all oil source parameters in the oil source parameter data set, and m is the number of all oil source parameters in the oil source parameter data set; Oc i 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;

[0026] Oil source company parameters Oc for each IBC company i and the mean value of oil source parameters Oc avg The absolute deviation between adi The calculation formula is as follows:

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

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

[0029] Determine the weight coefficient Ow of each oil source company parameter in the 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 weight coefficient of reservoir data includes the following steps:

[0032] The reservoir parameter data set is constructed based on four sub-class reservoir data: reservoir thickness, reservoir area, reservoir porosity, and reservoir permeability;

[0033] Determine the reservoir parameter mean Rc in the reservoir parameter data set avg and the reservoir company parameter Rc for each IBC company i ;

[0034] According to the mean value of reservoir parameters Rc avg and the reservoir company parameter Rc i Determine the reservoir company parameter Rc for each IBC company i and the mean value of reservoir parameters Rc avg The absolute deviation between adi ;

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

[0036] Furthermore, the calculation method of a single reservoir parameter in the reservoir parameter data set 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, x, y, z, u = 1, 2, 3, …, i …, n, n is the number of companies in the international business database;

[0039] Determine the reservoir parameter mean Rc in the reservoir parameter data set avg and the reservoir company parameter Rc for each IBC 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 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; Rc iis the reservoir 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;

[0043] Reservoir company parameter Rc for each IBC company i and the mean value of reservoir parameters Rc avg The absolute deviation between adi The calculation formula is as follows:

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

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

[0046] Determine the weight coefficient Rw of each international commercial database reservoir company parameter i The calculation formula is as follows:

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

[0048] Furthermore, determining the weight coefficient of the trap data includes the following steps:

[0049] The trap parameter data set was constructed based on four sub-categories of trap data: trap area, trap closure, trap type, and trap-control data type;

[0050] Determine the mean value Tc of the closure parameter in the closure parameter data set avg and the closed-end company parameter Tc for each IBD company i ;

[0051] According to the mean value of the trap parameter Tc avg and the closed company parameter Tc i Determine the closed-end company parameter Tc for each IBD company i and the mean value of the trap parameter Tc avg The absolute deviation between adi ;

[0052] According to the absolute deviation Tc adiDetermine the weight coefficient Tw of each closed company parameter in the international business database i .

[0053] Furthermore, the calculation method of a single trap parameter in the trap parameter data set is as follows:

[0054] Tc=TAx*THy*TTz*TDu

[0055] Among them, Tc is the trap parameter, TA is the trap area, TH is the trap closure, TT is the trap type, TD is the trap data type, x, y, z, u = 1, 2, 3, …, i …, n, n is the number of international commercial database companies;

[0056] Determine the mean value Tc of the closure parameter in the closure parameter data set avg and the closed-end company parameter Tc for each IBD 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 is the sum of all the closure parameters in the closure parameter data set, and m is the number of all the closure parameters in the closure parameter data set; Tc i is the closed 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;

[0060] The closed company parameter Tc for each IBD company i and the mean value of the trap parameter Tc avg The absolute deviation between adi The calculation formula is as follows:

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

[0062] Among them, Tc adi is the closed company parameter Oc of the i-th international business database company i and the mean value of the trap parameter Tc avg The absolute deviation between them, i = 1, 2, 3, ... n, n is the number of companies in the international business database;

[0063] Determine the weight coefficient Rw of each closed company parameter in the international business database i The calculation formula is as follows:

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

[0065] Furthermore, determining the weight coefficient of the trap data includes the following steps:

[0066] The closure parameter data set is constructed based on the four sub-class closure data of capping layer thickness, capping layer porosity, capping layer permeability, and capping layer continuity;

[0067] Determine the closed parameter mean Ec in the closed parameter data set avg and the closed class company parameter Ec for each IBD company i ;

[0068] According to the closed parameter mean Ec avg and closed company parameter Ec i Determine the closed class company parameter Ec for each IBD company i and the closed parameter mean Ec avg The absolute deviation Ec adi ;

[0069] According to the absolute deviation Ec adi Determine the weight coefficient Ew of each closed company parameter in the international business database i .

[0070] Furthermore, the calculation method of a single closure parameter in the closure parameter data set 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, x, y, z, u = 1, 2, 3, …, i …, n, n is the number of IBC companies;

[0073] Determine the closed parameter mean Ec in the closed parameter data set avg and the closed class company parameter Ec for each IBD 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 is the sum of all closed parameters in the closed parameter data set, and m is the number of all closed parameters in the closed parameter data set; Ec i is the closed 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;

[0077] Closed class company parameter Ec for each IBD company i and the closed parameter mean Ec avg The absolute deviation Ec adi The calculation formula is as follows:

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

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

[0080] Determine the weight coefficient Ew of each closed company parameter in the international business database i The calculation formula is as follows:

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

[0082] Further, determining the weight coefficient of the second category of data according to the weight coefficient of the first category of data includes the following steps:

[0083] Determine the company's resource parameter Sc based on the oil source company parameters, reservoir company parameters, trap company parameters and closed company parameters of different international commercial database companies i , the calculation formula is as follows:

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

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

[0086] According to the resource parameters Sc i Determine the resource parameter weight coefficient Sw 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 companies in different international business databases.

[0089] Furthermore, the expected resource volume of the oil and gas field is determined based on the weight coefficient of the resource volume data and the resource volume assessed by each international commercial database company according to the following formula:

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

[0091] Among them, Se is the expected resource volume, S1, S2, S3, ...S n The resource volumes evaluated by different international commercial database companies are Sw1, Sw2, Sw3, …Sw n The weight coefficients of resource data of different international commercial database companies are calculated respectively.

[0092] In a second aspect, the present invention proposes a device for quickly determining the exploration potential of an oil and gas block, comprising:

[0093] A classification index data table establishment unit, used to establish a classification index data table based on the information of the international business database company, wherein the classification index data table includes a plurality of major category data and a plurality of sub-category data under each major category;

[0094] A first category weight coefficient determination unit, used to respectively determine weight coefficients of first category data, wherein the first category data includes oil source data, reservoir data, trap data and closure data;

[0095] A second category weight coefficient determination unit, used to determine the weight coefficient of the second category data according to the weight coefficient of the first category data, the second category data being resource quantity data;

[0096] The expected resource determination unit is used to determine the expected resource volume of the oil and gas field based on the weight coefficient of the resource volume data and the resource volume evaluated by each international commercial database company.

[0097] In a third aspect, the present invention provides an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0098] a memory storing a computer program;

[0099] The processor is used to implement the method for quickly determining the exploration potential of oil and gas blocks when executing the program stored in the memory.

[0100] In a fourth aspect, the present invention proposes a computer-readable storage medium storing a computer program, which, when executed, executes the method for rapidly determining the exploration potential of an oil and gas block.

[0101] Beneficial effects of the present invention:

[0102] The present invention comprehensively applies information from multiple commercial databases, searches for data on source rocks, reservoirs, caprocks, traps and other reservoir-forming elements, comprehensively evaluates and quickly obtains the exploration potential of overseas blocks that is close to the actual situation, improves the accuracy of overseas block exploration potential evaluation, reduces the risk of overseas block mergers and acquisitions, and reduces the difficulty of overseas block acquisition decision-making.

[0103] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0104] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0105] Figure 1 A flow chart showing the method for quickly determining the exploration potential of an oil and gas block proposed by the present invention is shown;

[0106] Figure 2A schematic diagram of a device for quickly determining the exploration potential of an oil and gas block provided in an embodiment 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 DESCRIPTION

[0108] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0109] Based on the above problems, the present invention provides a method for quickly determining the exploration potential of oil and gas blocks. The method comprehensively applies information from multiple commercial databases, searches for data on source rocks, reservoirs, cap rocks, closures and other reservoir-forming elements, comprehensively evaluates and quickly obtains the exploration potential of overseas blocks that is close to the actual situation, improves the accuracy of overseas block exploration potential evaluation, reduces the risk of overseas block mergers and acquisitions, and reduces the difficulty of overseas block acquisition decision-making.

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

[0111] S1: Establishing a classification index data table based on the data of an international business database company, wherein the classification index data table includes multiple major categories of data and several sub-category data under each major category;

[0112] S2: respectively determining weight coefficients of the first category of data, wherein the first category of data includes oil source data, reservoir data, trap data and closure data;

[0113] S3: determining a weight coefficient of a second category of data according to the weight coefficient of the first category of data, where the second category of data is resource quantity data;

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

[0115] The above steps are described in detail below using four major international business database companies as examples. In the following description, the international business database companies are referred to as companies.

[0116] Step S1 specifically includes the following steps:

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

[0118] in,

[0119] The 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] The reservoir data (R) are further subdivided into four subcategories: reservoir thickness (RH), reservoir area (RA), reservoir porosity (RP), and reservoir permeability (RK);

[0121] The trap data (T) are further subdivided into four subcategories: trap area (TA), trap closure (TH), trap type (TT), and trap control 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] According to the above classification method, we can establish an ORTES data index classification table with five categories and 17 subcategories, and query the commercial database information of different companies (there are currently four major international commercial database companies 1, 2, 3, and 4), fill in the five categories and 17 subcategories of data, and form the ORTES data tables of different companies. The general formula of the four subcategories of oil source data of different companies is 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. That is, the source rock thickness (OH1), source rock area (OA1), source rock organic carbon content (OT1), source rock maturity (OR1) of company 1, the source rock thickness (OH2), source rock area (OA2), source rock organic carbon content (OT2), source rock maturity (OR2) of company 2, and so on; the general formula of the four subcategories of reservoir data of different companies is reservoir thickness (RHx), reservoir area (RAy), reservoir porosity (RPz), and reservoir permeability (RKu). The expression method of trap data (T) and closure data (E) is the same as above; the resource data S of different companies is expressed as S i(i=1, 2, 3, 4). In one embodiment of the present invention, for a block in Central Asia, data from the commercial databases of four companies, namely, Company 1, Company 2, Company 3, and Company 4, are used to establish a classified index data table, 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, weight coefficients of the first category of data are determined respectively, including oil source weight coefficient, reservoir weight coefficient, trap weight coefficient and closure weight coefficient;

[0128] S21: Determine the oil source weight coefficient. Since the oil source data (O) of different companies come from different sources and lack basic seismic and drilling data evidence, it is difficult for data buyers to distinguish the authenticity of the data. Therefore, in order to reduce the risk of resource potential evaluation, it is necessary to determine the oil source weight coefficient (Ow) of each commercial database company.

[0129] The specific steps include:

[0130] The data set of oil source parameters is constructed based on the four sub-categories of oil source data, namely, 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 sub-categories of oil source data, namely, source rock thickness (OH), source rock area (OA), source rock organic carbon content (OT), and source rock maturity (OR) from different commercial database companies, namely, 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), thereby obtaining an oil source parameter set consisting of 256 oil source parameters;

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

[0132] According to the mean value of oil source parameters Oc avg and oil source parameter Oc iObtain the absolute deviation Oc between the parameters of different oil source companies and the mean ad , namely Oc adi =|Oc i -Oc avg |(i=1,2,3,4), for example, the absolute deviation Oc between the oil source company parameter of company 1 and the mean ad1 =|Oc1-Oc avg |.

[0133] According to the absolute deviation Oc between the oil source parameter and the mean ad Obtain the weight coefficients of the parameters of different oil source companies (Ow i ), the smaller the absolute deviation between the oil source company parameters and the mean, the greater the weight coefficient of the oil source company parameters, that is, the weight coefficient of the oil source company parameters of a certain company is Ow i =(1-Oc adi / (Oc ad1 +Oc ad2 +Oc ad3 +Oc ad4 )) / 4(i=1,2,3,4), for example, the oil source company parameter weight coefficient 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 block in Central Asia, a data set Oc consisting of 256 oil source parameters was constructed using the data from the commercial databases of four companies, namely 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, a total of 256 numbers.

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

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

[0138] Obtain the absolute deviation Oc between the parameters of different oil source companies and the mean adi , the absolute deviation between the oil source company parameters of each company and the mean value obtained in this embodiment is as follows:

[0139] 1. The absolute deviation between the oil source company parameter and the mean Oc ad1 =|Oc1-Oc avg |=|136500-72450|=64050,

[0140] 2. The absolute deviation between the oil source company parameter and the mean Oc ad2 =|Oc2-Oc avg |=|82500-72450|=10050,

[0141] The absolute deviation between the oil source company parameters of 3 companies and the mean Oc ad3 =|Oc3-Oc avg |=|27000-72450|=45450,

[0142] The absolute deviation between the oil source company parameters of 4 companies and the mean Oc ad4 =|Oc4-Oc avg |=|8400-72450|=64050.

[0143] According to the above absolute deviation, the weight coefficients of the parameters of different oil source companies (Ow i ), the smaller the absolute deviation between the oil source company parameters and the mean, the greater the weight coefficient of the oil source company parameters. In this embodiment, the weight coefficient of the oil source company parameters of each company is calculated as follows:

[0144] The weight coefficient of the oil source company parameter 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] 2. The weight coefficient of the oil source company parameter 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 weight coefficient of the oil source company parameters of the 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 weight coefficient of the oil source company parameters of 4 companies 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 weight coefficient (Rw). Since the reservoir data (R) of different companies come from different sources and lack basic seismic and drilling data evidence, it is difficult for data buyers to distinguish the authenticity of the data. Therefore, in order to reduce the risk of resource potential evaluation, it is necessary to determine the reservoir weight coefficient (Rw) of each commercial database company.

[0149] The specific steps are as follows:

[0150] The reservoir parameter data set is constructed based on the four sub-reservoir data of 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 sub-reservoir data of reservoir thickness (RH), reservoir area (RA), reservoir porosity (RP), and reservoir permeability (RK) from different commercial database companies, that is, 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), thus a reservoir parameter set consisting of 256 reservoir parameters can be obtained.

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

[0152] According to the mean value Rc of reservoir parameters avg and the reservoir company parameter Rc i Obtain 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 company parameter of company 1 and the mean ad1 =|Rc1-Rc avg |.

[0153] Obtain the weight coefficients of reservoir parameters of different companies (Rw i ), the smaller the absolute deviation between the reservoir company parameter and the mean, the larger the weight coefficient of the reservoir company parameter, that is, the weight coefficient of the reservoir company parameter of a certain company Rw i =(1-Rc adi / (Rc ad1 +Rc ad2 +Rc ad3 +Rc ad4 )) / 4(i=1,2,3,4), for example, the weight coefficient of the reservoir company parameter of company 1 is Rw1=(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 block in Central Asia, a data set Rc consisting of 256 reservoir parameters was constructed using data from the commercial databases of four companies, namely 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, a total of 256 numbers.

[0156] Obtain the mean Rc of 256 reservoir parameters in the reservoir parameter set avg =(the sum of 256 parameters in the above Rc data set) / 256=103750000, and obtain the reservoir company parameters Rc of different companies i In this embodiment, the reservoir company parameters of each company are as follows:

[0157] The reservoir company parameter of company 1 is Rc1=RH1*RA1*RP1*RK1=31500000,

[0158] The reservoir company parameter of company 2 is Rc2=RH2*RA2*RP2*RK2=61200000,

[0159] The reservoir company parameter of company 3 is Rc3=RH3*RA3*RP3*RK3=113400000,

[0160] The reservoir company parameter of Company 4 is Rc4=RH4*RA4*RP4*RK4=176000000.

[0161] According to the reservoir parameters of each company, the absolute deviation Rc between the parameters of different reservoir companies and the mean is obtained. adi In this example, the absolute deviations between the reservoir parameters of different companies and the mean are as follows:

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

[0163] 2. The absolute deviation Rc between the reservoir company parameter and the mean of the companyad2 =|Rc2-Rc avg |=|61200000-103750000|=42550000,

[0164] The absolute deviation Rc between the reservoir company parameters of the 3 companies and the mean ad3 =|Rc3-Rc avg |=|113400000-103750000|=9650000,

[0165] The absolute deviation Rc between the reservoir parameters of 4 companies and the mean ad4 =|Rc4-Rc avg |=|176000000-103750000|=72250000.

[0166] According to the above absolute deviation, the weight coefficients of the parameters of different reservoir companies (Rw i ), the smaller the absolute deviation between the reservoir company parameter and the mean, the greater the weight coefficient of the reservoir company parameter. In this embodiment, the weight coefficient of the reservoir company parameter of each company is as follows:

[0167] The weight coefficient of the reservoir company parameter of company 1 is Rw1 = (1-72250000 / (72250000+42550000+9650000+72250000)) / 4 = (1-0.367) / 4 = 0.158,

[0168] The weight coefficient of the reservoir company parameter of company 2 is Rw2 = (1-42550000 / (72250000+42550000+9650000+72250000)) / 4 = (1-0.216) / 4 = 0.196,

[0169] The weight coefficient of the reservoir company parameter of Company 3 is Rw3 = (1-9650000 / (72250000+42550000+9650000+72250000)) / 4 = (1-0.049) / 4 = 0.238,

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

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

[0172] The specific steps are as follows:

[0173] The data set of trap parameters is constructed based on four sub-categories of reservoir data: trap area (TA), trap closure (TH), trap type (TT), and trap-controlling data type (TD). The trap parameter Tc is a parameter formed by cross-producting four sub-class reservoir data of trap area (TA), trap closure (TH), trap type (TT), and trap-controlling data type (TD) from different commercial database companies, 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). Thus, a trap parameter set consisting of 256 trap parameters can be obtained, wherein the trap type TT and the trap-controlling data type TD are assigned values. In one embodiment of the present invention, the TT value of the anticline trap type is 1, the TT value of the fault anticline trap type is 0.8, and the TT value of other trap types is 0.6; the TD value of a trap controlled by three-dimensional data is 1, the TD value of a trap controlled by a two-dimensional survey network with a survey network spacing of less than or equal to 2 km is 0.8, and the TD value of a trap controlled by a two-dimensional survey network but with a survey network spacing of more than 2 km is 0.6.

[0174] Obtain the mean value Tc of 256 closure parameters in the closure parameter set avg , and obtain the closed-end company parameter Tc of different companies i , that is, Tc i =TA i *TH i *TT i *TD i (i=1,2,3,4), for example, the closed-end company parameter of company 1 is Tc1=TA1*TH1*TT1*TD1.

[0175] According to the mean value Tc of the closure parameter avg and the closed company parameter Tc i Obtain the absolute deviation Tc between the parameters of different types of companies and the mean adi , that is, Tc adi =|Tc i -Tc avg |(i=1,2,3,4), for example, the absolute deviation Tc between the closed-end company parameter of company 1 and the mean ad1 =|Tc1-Tc avg |.

[0176] Calculate the weight coefficients of different closed-end company parameters (Tw 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 of the closed-end company parameter of a certain company is Tw i =(1-Tc adi / (Tc ad1 +Tc ad2 +Tc ad3 +Tc ad4 )) / 4(i=1,2,3,4), for example, the weight coefficient of the closed 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 block in Central Asia, a data set Tc consisting of 256 closure parameters is constructed using data from the commercial databases of four companies, namely 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] Obtain the mean value Tc of 256 closure parameters in the closure parameter set avg = (the sum of 256 parameters in the above Tc data set) / 256 = 12680, and obtain the closed-end company parameters Tc of different companies i In this embodiment, the closure parameters of each company are as follows:

[0180] The closed company parameter of company 1 is Tc1=TA1*TH1*TT1*TD1=18000,

[0181] The closed company parameter of company 2 is Tc2 = TA2*TH2*TT2*TD2 = 10000,

[0182] The closed company parameter of company 3 is Tc3=TA3*TH3*TT3*TD3=21000,

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

[0184] According to the parameters of each company's closed-end company, the absolute deviation Tc between the parameters of different closed-end companies and the mean is obtained. adi In this example, the absolute deviations between the parameters of each company's closed-end company and the mean are as follows:

[0185] The absolute deviation Tc between the closed-end company parameter of company 1 and the mean ad1 =|Tc1-Tc avg |=|18000-12680|=5320,

[0186] The absolute deviation Tc between the closed-end company parameter of the company 2 and the mean ad2 =|Tc2-Tc avg |=|10000-12680|=2680,

[0187] The absolute deviation Tc between the parameters of the 3-company closed-end company and the mean ad3 =|Tc3-Tc avg |=|21000-12680|=8320,

[0188] The absolute deviation Tc between the parameters of the 4-company closed-end company and the mean ad4 =|Tc4-Tc avg |=|15360-12680|=2680.

[0189] The weight coefficients of different closed-loop company parameters (Tw 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. In this embodiment, the calculation results of the weight coefficient of the closed-end company parameter of each company are as follows:

[0190] The weight coefficient of the closed company parameter of company 1 is 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] 2 The weight coefficient of the closed company parameter of the company 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 weight coefficient of the closed company parameter of the 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 weight coefficient of the closed company parameter of the company 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 weight coefficient (Ew). Since different companies have different sources of closed data (E), it is difficult for data buyers to distinguish the authenticity of data in the absence of basic seismic and drilling data evidence. Therefore, in order to reduce the risk of resource potential evaluation, it is necessary to determine the closure weight coefficient (Ew) of each commercial database company.

[0195] The specific steps are as follows:

[0196] The closed parameter data set is constructed based on the four sub-reservoir data of cap layer thickness (EH), cap layer porosity (EP), cap layer permeability (EK), and cap layer continuity (EC). The closed parameter Ec is a parameter formed by cross-multiplying the four sub-reservoir data of cap layer thickness (EH), cap layer porosity (EP), cap layer permeability (EK), and cap layer continuity (EC) from different commercial database companies, that is, 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), thus a closed parameter set consisting of 256 closed parameters can be obtained (Note: the cap layer porosity EP and the cap layer permeability EK are inversely proportional to the capping capacity, so the inverse is multiplied).

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

[0198] According to the mean value Ec of the closure parameter avg and closed company parameter Ec i Find the absolute deviation Ec between the two adi , that is, Ec adi =|Ec i -Ec avg |(i=1,2,3,4), for example, the absolute deviation Ec between the closed company parameter of company 1 and the mean ad1 =|Ec1-Ec avg |.

[0199] Obtain the weight coefficients (Ew i ), the smaller the absolute deviation between the closed company parameter and the mean, the larger the weight coefficient of the closed company parameter, that is, the weight coefficient of the closed company parameter of a certain company Ew i =(1-Ec adi / (Ec ad1 +Ec ad2 +Ec ad3 +Ec ad4 )) / 4(i=1,2,3,4), for example, the closed company parameter weight coefficient 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 block in Central Asia, a data set Ec consisting of 256 closed parameters is constructed using data from the commercial databases of four companies, namely, 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*( 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, a total of 256 numbers.

[0202] Find the mean value Ec of 256 closed parameters in the closed parameter set avg =(the sum of 256 parameters in the above Ec data set) / 256=0.648, and obtain the closed company parameters Ec of different companies i In this embodiment, the calculation results of the closed parameters of each company are as follows:

[0203] The closed company parameter of company 1 is Ec1 = EH1*(1 / EP1)*(1 / EK1)*EC1 = 0.375,

[0204] The closed company parameter of company 2 is Ec2 = EH2*(1 / EP2)*(1 / EK2)*EC2 = 0.185,

[0205] The closed company parameter of company 3 is Ec3 = EH3*(1 / EP3)*(1 / EK3)*EC3 = 0.533.

[0206] The closed company parameter of Company 4 is Ec4=EH4*(1 / EP4)*(1 / EK4)*EC4=1.111.

[0207] According to the closed company parameters of each company, the absolute deviation Ec between the closed company parameters of different companies and the mean is obtained. adi In this example, the absolute deviations between the closed company parameters of each company and the mean are as follows:

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

[0209] The absolute deviation Ec between the closed company parameter of 2 companies and the mean ad2=|Ec2-Ec avg |=|0.185-0.648|=0.463,

[0210] The absolute deviation Ec between the closed company parameter of 3 companies and the mean ad3 =|Ec3-Ec avg |=|0.533-0.648|=0.115,

[0211] The absolute deviation Ec between the closed company parameter of 4 companies and the mean ad4 =|Ec4-Ec avg |=|1.111-0.648|=0.463.

[0212] The weight coefficients of the closed-type company parameters of different companies (Ew i ), the smaller the absolute deviation between the closed company parameter and the mean, the greater the weight coefficient of the closed company parameter. In this embodiment, the weight of the closed company parameter of each company is as follows:

[0213] The weight coefficient of the closed company parameter of company 1 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] 2 The weight coefficient of the closed company parameter of the company 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 weight coefficient of the closed company parameter of the 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 weight coefficient of the closed company parameter of the company 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 weight coefficients (Sw) of different companies are determined according to the oil source weight coefficient (Ow), reservoir weight coefficient (Rw), trap weight coefficient (Tw), and closure weight coefficient (Ew); because the resource data (S) of different companies come from different sources and lack basic seismic and drilling data evidence, it is difficult for data buyers to distinguish the authenticity of the data. Therefore, in order to reduce the risk of resource potential evaluation, the resource weight coefficient of each commercial database company needs to be determined.

[0218] The specific steps include:

[0219] The resource parameters Sc of different companies are determined according to the oil source weight coefficient (Ow), reservoir weight coefficient (Rw), trap weight coefficient (Tw), and closure weight coefficient (Ew) of each company; the resource parameter Sc is the product of the oil source weight coefficient (Ow), reservoir weight coefficient (Rw), trap weight coefficient (Tw), and closure weight coefficient (Ew) of different commercial database companies, that is, Sci = Ow i *R i *Tw i *Ew i (i=1,2,3,4), for example, the resource parameter Sc1 of company 1=Ow1*Rw1*Tw1*Ew1.

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

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

[0222] 1. The resource quantity parameter of the company Sc1=Ow1*Rw1*Tw1*Ew1=0.163*0.158*0.180*0.198=0.000918,

[0223] 2. The resource quantity parameter of the company Sc2 = Ow2*Rw2*Tw2*Ew2 = 0.236*0.196*0.215*0.162 = 0.001611,

[0224] 3. The resource quantity parameter of the company Sc3=Ow3*Rw3*Tw3*Ew3=0.188*0.238*0.141*0.228=0.001438,

[0225] 4 Company’s resource quantity parameter Sc4=Ow4*Rw4*Tw4*Ew4=0.163*0.158*0.215*0.162=0.000897.

[0226] Determine the resource parameter weight coefficient Sw of different companies based on the resource parameters of each company i In this embodiment, the calculation results of the resource quantity parameter weight parameters of each company are as follows:

[0227] 1. The resource parameter weight coefficient of the company Sw1 = Sc1 / (Sc1+Sc2+Sc3+Sc4) = 0.000918 / (0.000918+0.001611+0.001438+0.000897) = 0.000918 / 0.004864 = 0.188691,

[0228] 2. The resource parameter weight coefficient of the company Sw2 = Sc2 / (Sc1+Sc2+Sc3+Sc4) = 0.001611 / (0.000918+0.001611+0.001438+0.000897) = 0.001611 / 0.004864 = 0.331201,

[0229] 3. The resource parameter weight coefficient of the company Sw3 = Sc3 / (Sc1+Sc2+Sc3+Sc4) = 0.001438 / (0.000918+0.001611+0.001438+0.000897) = 0.001438 / 0.004864 = 0.295705.

[0230] 4 Company’s resource parameter weight coefficient 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 weight coefficient (Sw) and the resource volume (S i ) Determine the expected resource volume (Se) of the oil and gas field;

[0232] The specific steps include:

[0233] According to the resource weight coefficient (Sw) of each company and the resource volume (S i ) calculate the expected resources of the oil and gas field;

[0234] The expected resource volume (Se) is based on the existing commercial database data. The expected exploration potential of the oil and gas block is the resource volume (S) estimated by different companies. i ) multiplied by the resource parameter weight coefficient (Sw i ) is the sum of the products,

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

[0236] In this embodiment, the expected resource volume (Se) calculation result is 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 quickly determining the exploration potential of an oil and gas block, such as Figure 2 As shown, including:

[0239] A classification index data table establishment unit 201 is used to establish a classification index data table based on the data of an international business database company, wherein the classification index data table includes a plurality of major category data and a plurality of subcategory data under each major category;

[0240] A first category weight coefficient determination unit 202, used to respectively determine weight coefficients of first category data, wherein the first category data includes oil source data, reservoir data, trap data and closure data;

[0241] A second category weight coefficient determination unit 203, used to determine the weight coefficient of the second category data according to the weight coefficient of the first category data, the second category data being resource quantity data;

[0242] The expected resource volume determination unit 204 is used to determine the expected resource volume of the oil and gas field according to the weight coefficient of the resource volume data and the resource volume evaluated by each international commercial database company.

[0243] Based on the same inventive concept, another exemplary embodiment of the present invention provides an electronic device. 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, the communication interface 302 and the memory 303 communicate with each other through the communication bus 304;

[0244] Memory 303, storing computer programs;

[0245] The processor 301 is used to implement the method for quickly determining the exploration potential of oil and gas blocks when executing the program stored in the memory 303.

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

[0247] Based on the same inventive concept, the embodiment of the present application further provides a computer-readable storage medium storing a computer program, which implements part or all of the above method embodiments when the computer program is executed. Optionally, the storage medium may be a non-temporary computer-readable storage medium, for example, the non-temporary computer-readable storage medium may be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

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

Claims

1. A method for quickly determining the exploration potential of an oil and gas block, characterized in that: The following steps are involved: Establishing a classification index data table based on the data of an international commercial database company, wherein the classification index data table includes a plurality of major categories of data and a plurality of sub-categories of data under each major category; Determine the weight coefficients of the first category of data respectively, wherein the first category of data includes oil source data, reservoir data, trap data and closed data; Determine the weight coefficient of the second category of data according to the weight coefficient of the first category of data, where the second category of data is resource quantity data; The expected resources of the oil and gas field are determined based on the weight coefficient of the resource data and the resources assessed by each international commercial database company.

2. The method for quickly determining the exploration potential of an oil and gas block according to claim 1, characterized in that: The establishment of a classification index data table based on the information of the international business database company includes the following steps: Establishing a primary classification index data table, wherein the primary classification index data table includes multiple major categories such as oil source, reservoir, trap, closure and resource; Each major category is further divided into several subcategories under each category, and a secondary classification index data table is established; Obtain information from multiple international commercial database companies, and add the commercial database information to the secondary classification index data table according to the data type, and establish a classification index data table; Among them, the oil source category in the secondary classification index data table includes four subcategories: source rock thickness, source rock area, source rock organic carbon content and source rock maturity; the reservoir category includes four subcategories: reservoir thickness, reservoir area, reservoir porosity and reservoir permeability; the closure category includes four subcategories based on closure area, closure closure, closure type and control data; the closure category includes four subcategories: cap layer thickness, cap layer porosity, cap layer permeability and cap layer continuity; the resource category includes one subcategory: resource quantity.

3. The method for quickly determining the exploration potential of an oil and gas block according to claim 2, characterized in that: Determining the weight coefficient of oil source data includes the following steps: The oil source parameter data set was constructed based on four sub-categories of oil source data, namely source rock thickness, source rock area, source rock organic carbon content and source rock maturity; Determine the oil source parameter mean Oc in the oil source parameter data set avg and the oil source company parameters Oc for each IBC company i ; According to the mean value of oil source parameters Oc avg and oil source company parameters Oc i Determine the oil source company parameter Oc for each IBC company i and the mean value of oil source parameters Oc avg The absolute deviation between adi ; According to the absolute deviation Oc adi Determine the weight coefficient Ow of each oil source company parameter in the international commercial database i .

4. The method for quickly determining the exploration potential of an oil and gas block according to claim 3, characterized in that: The calculation method of a single oil source parameter in the oil source parameter data set is as follows: Oc=OHx*OAy*OTz*ORu; Among them, 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 commercial database companies; Determine the oil source parameter mean Oc in the oil source parameter data set avg and the oil source company parameters Oc for each IBC company i The calculation formula is as follows: Oc avg =O 总 / m; Oc i =OH i *OA i *OT i *OR i ; Among them, O 总 is the sum of all oil source parameters in the oil source parameter data set, and m is the number of all oil source parameters in the oil source parameter data set; Oc i 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; Oil source company parameters Oc for each IBC company i and the mean value of oil source parameters Oc avg The absolute deviation between adi The calculation formula is as follows: Oc adi =|Oc i -Oc avg |; Among them, Oc adi is the oil source company parameter Oc of the i-th international business database company i and the mean value of oil source parameters Oc avg The absolute deviation between them, i = 1, 2, 3, ... n, n is the number of companies in the international business database; Determine the weight coefficient Ow of each oil source company parameter in the international commercial database i The calculation formula is as follows: Ow i =(1-Oc adi / (Oc ad1 +Oc ad2 +Oc ad3 +…+Oc adn )) / n。 5. The method for quickly determining the exploration potential of an oil and gas block according to claim 2, characterized in that: Determining the weight coefficient of reservoir data includes the following steps: The reservoir parameter data set was constructed based on four sub-class reservoir data: reservoir thickness, reservoir area, reservoir porosity, and reservoir permeability; Determine the reservoir parameter mean Rc in the reservoir parameter data set avg and the reservoir company parameter Rc for each IBC company i ; According to the mean value of reservoir parameters Rc avg and the reservoir company parameter Rc i Determine the reservoir company parameter Rc for each IBC company i and the mean value of reservoir parameters Rc avg The absolute deviation between adi ; According to the absolute deviation Rc adi Determine the weight coefficient Rw of each international commercial database reservoir company parameter i .

6. The method for quickly determining the exploration potential of an oil and gas block according to claim 3, characterized in that: The calculation method of a single reservoir parameter in the reservoir parameter data set is as follows: Rc=RHx*RAy*RPz*RKu; 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, x, y, z, u = 1, 2, 3, …, i …, n, n is the number of companies in the international business database; Determine the reservoir parameter mean Rc in the reservoir parameter data set avg and the reservoir company parameter Rc for each IBC company i The calculation formula is as follows: Rc avg =R 总 / m; Rc i =RH i *RA i *RP i *RK i ; Among them, R 总 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; Rc i is the reservoir 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; Reservoir company parameter Rc for each IBC company i and the mean value of reservoir parameters Rc avg The absolute deviation between adi The calculation formula is as follows: RC adi =|Rc i -Rc avg |; Among them, Rc adi is the reservoir company parameter Rc of the i-th international commercial database company i and the mean value of reservoir parameters Rc avg The absolute deviation between them, i = 1, 2, 3, ... n, n is the number of companies in the international business database; Determine the weight coefficient Rw of each international commercial database reservoir company parameter i The calculation formula is as follows: Rw i =(1-Rc adi / (Rc ad1 +Rc ad2 +Rc ad3 +…+Rc adn )) / n。 7. The method for quickly determining the exploration potential of an oil and gas block according to claim 2, characterized in that: Determining the weight coefficient of the trap data includes the following steps: The trap parameter dataset was constructed based on four sub-categories of trap data: trap area, trap closure, trap type, and trap-control data type; Determine the mean value Tc of the closure parameter in the closure parameter data set avg and the closed-end company parameter Tc for each IBD company i ; According to the mean value of the trap parameter Tc avg and the closed company parameter Tc i Determine the closed company parameter Tc for each IBD company i and the mean value of the trap parameter Tc avg The absolute deviation between adi ; According to the absolute deviation Tc adi Determine the weight coefficient Tw of each closed company parameter in the international business database i .

8. The method for quickly determining the exploration potential of an oil and gas block according to claim 3, characterized in that: The calculation method of a single trap parameter in the trap parameter data set is as follows: Tc=TAx*THy*TTz*TDu; Among them, Tc is the trap parameter, TA is the trap area, TH is the trap closure, TT is the trap type, TD is the trap data type, x, y, z, u = 1, 2, 3, …, i …, n, n is the number of international commercial database companies; Determine the mean value Tc of the closure parameter in the closure parameter data set avg and the closed-end company parameter Tc for each IBD company i The calculation formula is as follows: Tc avg =T 总 / m; Tc i =OH i *OA i *OT i *OR i ; Among them, T 总 is the sum of all the closure parameters in the closure parameter data set, and m is the number of all the closure parameters in the closure parameter data set; Tc i is the closed 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 closed company parameter Tc for each IBD company i and the mean value of the trap parameter Tc avg The absolute deviation between adi The calculation formula is as follows: Tc adi =|Tc i -Tc avg |; Among them, Tc adi is the closed company parameter Oc of the i-th international business database company i and the mean value of the trap parameter Tc avg The absolute deviation between them, i = 1, 2, 3, ... n, n is the number of companies in the international business database; Determine the weight coefficient Rw of each closed company parameter in the international business database i The calculation formula is as follows: Tw i =(1-Tc adi / (Tc ad1 +Tc ad2 +Tc ad3 +…+Tc adn )) / n。 9. The method for quickly determining the exploration potential of an oil and gas block according to claim 2, characterized in that: Determining the weight coefficient of the trap data includes the following steps: The closure parameter data set is constructed based on the four sub-class closure data of cap layer thickness, cap layer porosity, cap layer permeability and cap layer continuity; Determine the closed parameter mean Ec in the closed parameter data set avg and the closed class company parameter Ec for each IBD company i ; According to the closed parameter mean Ec avg and closed company parameter Ec i Determine the closed class company parameter Ec for each IBD company i and the closed parameter mean Ec avg The absolute deviation Ec adi ; According to the absolute deviation Ec adi Determine the weight coefficient Ew of each closed company parameter in the international business database i .

10. The method for quickly determining the exploration potential of an oil and gas block according to claim 3, characterized in that: The calculation method of a single closure parameter in the closure parameter dataset is as follows: Ec=EHx*(1 / EPy)*(1 / EKz)*ECu; 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, x, y, z, u = 1, 2, 3, …, i …, n, n is the number of IBC companies; Determine the closed parameter mean Ec in the closed parameter data set avg and the closed class company parameter Ec for each IBD company i The calculation formula is as follows: Ec avg =E 总 / m; Ec i =EH i *(1 / EP i )*(1 / EC i )*EC i 4 Among them, E 总 is the sum of all closed parameters in the closed parameter data set, and m is the number of all closed parameters in the closed parameter data set; Ec i is the closed 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; Closed class company parameter Ec for each IBD company i and the closed parameter mean Ec avg The absolute deviation Ec adi The calculation formula is as follows: Ec adi =|Ec i -Ec avg |; Among them, Ec adi is the closed company parameter Oc of the i-th international business database company i and the closed parameter mean Ec avg The absolute deviation between them, i = 1, 2, 3, ... n, n is the number of companies in the international business database; Determine the weight coefficient Ew of each closed company parameter in the international business database i The calculation formula is as follows: Yes i =(1-Ec adi / (Ec ad1 +Ec ad2 +Ec ad3 +…+Ec adn )) / n。 11. The method for quickly determining the exploration potential of an oil and gas block according to claim 1, characterized in that: Determining the weight coefficient of the second category of data according to the weight coefficient of the first category of data comprises the following steps: Determine the company's resource parameter Sc based on the oil source company parameters, reservoir company parameters, trap company parameters and closed company parameters of different international commercial database companies i , the calculation formula is as follows: Sc i =Ow i *Rw i *Tw i *Ew i ; Where i = 1, 2, 3, ... n, n is the number of companies in different international business databases; According to the resource parameters Sc i Determine the resource parameter weight coefficient Sw of different international commercial database companies i , the calculation formula is as follows: Sw i =Sc i / (Sc1+Sc2+Sc3+…+Sc n ); Where i = 1, 2, 3, ... n, and n is the number of companies in different international business databases.

12. The method for quickly determining the exploration potential of an oil and gas block according to claim 1, characterized in that: The expected resource volume of an oil and gas field is calculated based on the weight coefficient of the resource volume data and the resource volume estimated by each international commercial database company according to the following formula: <h2 style=";text-align:left;direction:ltr">Se = S1*Sw1+S2*Sw2+S3*Sw3+…+S3*Sw<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> ; Among them, Se is the expected resource volume, S1, S2, S3, ...S n The resource volumes evaluated by different international commercial database companies are Sw1, Sw2, Sw3, …Sw n The weight coefficients of resource data of different international commercial database companies are calculated respectively.

13. A device for quickly determining the exploration potential of an oil and gas block, characterized in that: include: A classification index data table establishment unit, used to establish a classification index data table based on the information of the international business database company, wherein the classification index data table includes a plurality of major category data and a plurality of sub-category data under each major category; A first category weight coefficient determination unit, used to respectively determine weight coefficients of first category data, wherein the first category data includes oil source data, reservoir data, trap data and closure data; A second category weight coefficient determination unit, used to determine the weight coefficient of the second category data according to the weight coefficient of the first category data, the second category data being resource quantity data; The expected resource determination unit is used to determine the expected resource volume of the oil and gas field based on the weight coefficient of the resource volume data and the resource volume evaluated by each international commercial database company.

14. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; a memory storing a computer program; The processor is used to implement the method for quickly determining the exploration potential of an oil and gas block according to any one of claims 1 to 12 when executing the program stored in the memory.

15. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed, the method for quickly determining the exploration potential of an oil and gas block according to any one of claims 1 to 12 is executed.

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