A method and model for predicting the scale of a basin containing oil and gas and related devices

Abstract

The application discloses a basin oil-gas scale prediction method and model establishment method and related devices. The basin oil-gas scale prediction method comprises the following steps: constructing a basin structure characteristic parameter set; according to the oil-gas resource quantity of each basin in a plurality of basin data and the value of each parameter in the parameter set, at least one parameter in the parameter set is selected as a sensitive parameter of the oil-gas resource quantity; and according to a set fitting condition, an optimal fitting model between the sensitive parameter and the oil-gas resource quantity is determined as a basin oil-gas scale prediction model. The basin structure characteristic parameter data which is easy to obtain is fully utilized to evaluate the basin resource potential, the oil-gas scale prediction of the low exploration degree basin can be simply, easily and quickly completed, a basis is provided for new project acquisition, overseas risk exploration and resource evaluation, and a reference is provided for subsequent exploration deployment work.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration technology, and in particular to a method for predicting the scale of oil and gas in a basin, a model establishment method, and related apparatus. Background Technology

[0002] According to domestic and international standards for classifying exploration stages, low-exploration basins refer to basins with a well density of less than 1 well per 100 km. 2 These basins are currently mainly distributed in eastern, southern, and central Africa, as well as in the African seas, northern Russia, northwestern Canada, and Antarctica. With the rapid development of the global oil and gas industry, the exploration of low-exploration basins has received increasing attention.

[0003] Oil and gas exploration begins in the low-exploration stage, and the fundamental problem to be solved in the evaluation of low-exploration basins is whether there are large-scale oil and gas reservoirs in the area. The biggest challenge facing low-exploration basin exploration is the scarcity of geological and geophysical data and the inadequacy of corresponding research methods (Liu Zhen et al., 2007; Wu Qingpeng et al., 2008). Compared with low-exploration basins in China, exploration of low-exploration basins overseas faces even more challenges (Tong Xiaoguang et al., 2009; Zhao Jiaqi et al., 2021), mainly in the following aspects: First, the block belongs to the government of the resource country, but the resource country usually does not bear any exploration investment during the exploration stage, and the exploration risk is borne entirely by the oil company. Second, exploration time is limited by the exploration period, which is usually divided into 2-3 exploration periods, each lasting between 1 and 5 years, averaging about 3 years. The total exploration time for all exploration periods generally does not exceed 10 years. Typically, only by completing the minimum required workload within the exploration period on time can one enter the next exploration period; otherwise, the resource-rich government has the right to reclaim the exploration block. Third, the effective exploration time is short. Currently, low-exploration-level rift basins on land are usually located in extremely cold, remote, or impoverished areas. The window of opportunity for exploration operations is typically only 5-8 months per year, and in some areas, simply relocating heavy equipment to the site can take more than a year. Fourth, overseas exploration projects are usually jointly operated by multiple partner companies. This shares the exploration risks but also raises the commercial threshold. In conclusion, only by implementing rapid and efficient exploration in low-exploration-level overseas basins and shortening the discovery time of large-scale oil and gas reservoirs can exploration investment be effectively reduced, and the economic benefits of the project be guaranteed.

[0004] Currently, oil and gas resource evaluation methods are broadly classified into three categories: genetic methods, geological analogy methods, and statistical methods (Bai Kunlin et al., 2021; Feng Dehao et al., 2020; Sheng Xiujie et al., 2017; Jin Zhijun et al., 2002; Sheng Xiujie et al., 2013). Among these, statistical methods are suitable for basins with a high degree of research, while genetic methods and geological analogy methods are suitable for basins with a low degree of exploration.

[0005] The existing technical solutions related to this invention mainly include: ① genetic method; ② geological analogy method.

[0006] Genetic evaluation method is a special type of volumetric resource evaluation method, also known as volumetric generation method or geochemical balance method. It determines the amount of oil and gas accumulation in oil and gas reservoirs by calculating the amount of hydrocarbons generated, discharged, adsorbed, migrated and lost in source rocks.

[0007] Geological analogy is a classic geological approach that uses known information to infer unknown information, and it is widely used. When evaluating basins with low exploration levels and where sufficient oil and gas and oil and gas-bearing structures have not yet been discovered, due to insufficient data, geological analogy is usually the primary method to calculate the resource quantity per unit area (resource quantity per unit area, average resource quantity per structure, resource quantity per unit rock volume, and resource quantity per unit reservoir volume), supplemented by other methods.

[0008] The geological analogy method has a wide range of applications, from the global average resource abundance of sedimentary rocks by volume or area to sedimentary basins, zones, or blocks. Especially for the evaluation of basins or regions with low exploration levels, the analogy method is the dominant approach for resource assessment. The greatest advantage of the geological analogy method lies in its accurate geological assessment, objective and reliable results, and high degree of understanding of key factors influencing resource distribution, thus greatly aiding in guiding exploration work. Summary of the Invention

[0009] The inventors discovered that neither the genetic method nor the geological analogy method, which are applicable to basins with low exploration levels, can achieve accurate and efficient evaluation of oil and gas resources.

[0010] The accuracy and reliability of the genetic method depend primarily on a comprehensive understanding of major petroleum geological issues such as hydrocarbon generation, migration, and accumulation, as well as the correct selection of geochemical parameters. All parameters are directly related to geological history processes, but our understanding of these processes is fraught with uncertainty, directly affecting the effectiveness of the genetic method.

[0011] As is well known, no two basins are exactly alike; therefore, the reasonable determination of similarity factors is crucial for oil and gas resource evaluation using the geological analogy method. Geological factors to consider in determining similarity factors include basin size, sedimentary rock thickness, hydrocarbon maturity, rock volume within the detectable depth range, main characteristics of basin lithofacies, thickness of sedimentary caprock, tectonic structure, the proportion of source, reservoir, and caprock strata, and the geological age of the oil-bearing strata. Simultaneously, the effective temporal and spatial combination of basins should also be fully considered. Therefore, the evaluation requires a large amount of geological data and experimental test results.

[0012] In order to at least partially solve the technical problems existing in the prior art, the inventors made this invention, which, through specific implementation methods, provides a method and model establishment method for predicting the oil and gas scale of a basin and related devices, which can simply, easily and quickly complete the prediction of the oil and gas scale of a basin with low exploration level.

[0013] In a first aspect, embodiments of the present invention provide a method for establishing a basin hydrocarbon scale prediction model, comprising:

[0014] Construct a set of basin structural feature parameters;

[0015] Based on the oil and gas resources of each basin in multiple basin data and the value of each parameter in the parameter set, at least one parameter is selected from the parameter set as a sensitive parameter for oil and gas resources.

[0016] The optimal fitting model between the sensitive parameters and the amount of oil and gas resources is determined based on the set fitting conditions, and used as a prediction model for the scale of oil and gas in the basin.

[0017] Secondly, embodiments of the present invention provide a method for predicting the scale of oil and gas in a basin, including:

[0018] The sensitive parameters of the oil and gas resources of the basin to be evaluated are input into the basin oil and gas scale prediction model, which is established according to the above method.

[0019] The oil and gas scale of the basin to be evaluated is determined based on the output of the model.

[0020] Thirdly, embodiments of the present invention provide an apparatus for establishing a basin oil and gas scale prediction model, comprising:

[0021] The basin structure feature parameter set construction module is used to construct the basin structure feature parameter set;

[0022] The sensitive parameter filtering module is used to filter at least one parameter from the parameter set as a sensitive parameter for oil and gas resources based on the oil and gas resource quantity of each basin in multiple basin data and the value of each parameter in the parameter set.

[0023] The basin oil and gas scale prediction model establishment module is used to determine the optimal fitting model between the sensitive parameters and the oil and gas resources based on the set fitting conditions, and serve as the basin oil and gas scale prediction model.

[0024] Fourthly, embodiments of the present invention provide a basin oil and gas scale prediction device, comprising:

[0025] The sensitive parameter input module is used to input the sensitive parameters of the oil and gas resources of the basin to be evaluated into the basin oil and gas scale prediction model. The basin oil and gas scale prediction model is established according to the above-mentioned basin oil and gas scale prediction model establishment method.

[0026] The oil and gas scale prediction and evaluation module is used to determine the oil and gas scale of the basin to be evaluated based on the output results of the model.

[0027] Fifthly, embodiments of the present invention provide a computer program product, including a computer program / instruction, wherein when the computer program / instruction is executed by a processor, it implements the above-mentioned method for establishing a basin oil and gas scale prediction model, or implements the above-mentioned method for predicting the scale of oil and gas in a basin.

[0028] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0029] (1) The method for establishing a basin hydrocarbon scale prediction model provided in this embodiment of the invention first constructs an easily obtainable set of basin structural characteristic parameters. Then, using the hydrocarbon resources of each basin from multiple basins with proven hydrocarbon resources and the values ​​of each parameter in the parameter set, sensitive parameters are selected from the parameter set through correlation analysis. Finally, based on set fitting conditions, the optimal fitting model between the sensitive parameters and the hydrocarbon resources is determined, serving as the basin hydrocarbon scale prediction model. The process of establishing the prediction model is easy to implement, and through this model, only the values ​​of the sensitive parameters of the basin's hydrocarbon resources need to be obtained to achieve rapid and accurate hydrocarbon scale prediction, saving time for resource evaluation and providing a reference for subsequent exploration and deployment work.

[0030] (2) Overseas exploration blocks are characterized by short exploration cycles, limited early data, low level of understanding, and high exploration difficulty. The basin oil and gas scale prediction method provided in this embodiment of the invention adopts oil and gas resource sensitive parameters from the basin structural characteristic parameter set that are easy to obtain in the early stage and less prone to disputes. Through the above-mentioned basin oil and gas scale prediction model, accurate and efficient oil and gas scale prediction can be achieved, forming a new method for quickly screening oil and gas rich depressions in basins with low exploration degree by utilizing basin structural characteristics.

[0031] 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 particularly pointed out in the written description, claims, and drawings.

[0032] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0033] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0034] Figure 1 This is a flowchart of the method for establishing a basin oil and gas scale prediction model in Embodiment 1 of the present invention;

[0035] Figure 2 The fitting results of structural characteristic parameters of each basin and oil and gas resources in Embodiment 1 of the present invention;

[0036] Figure 3 for Figure 1 The detailed implementation flowchart of step S13 is shown below;

[0037] Figure 4 The original image of the initial fitting selection function in Embodiment 1 of the present invention;

[0038] Figure 5 This is a flowchart illustrating the specific implementation of the basin oil and gas scale prediction model establishment method in this embodiment of the invention.

[0039] Figure 6 This is a flowchart of the basin oil and gas scale prediction method in Embodiment 2 of the present invention;

[0040] Figure 7 This is a schematic diagram of the structure of the basin oil and gas scale prediction model establishment device in an embodiment of the present invention;

[0041] Figure 8 This is a schematic diagram of the basin oil and gas scale prediction device in an embodiment of the present invention. Detailed Implementation

[0042] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0043] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Every smaller range between any stated value or intermediate value within a stated range, and any other stated value or intermediate value within said range, is also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0044] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. While only preferred methods and materials have been described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0045] To address the problem of existing technologies being unable to accurately and efficiently evaluate the oil and gas scale of basins with low exploration levels, embodiments of the present invention provide a method and model for predicting the oil and gas scale of basins, as well as related apparatus, which can simply, easily, and quickly predict the oil and gas scale of basins with low exploration levels.

[0046] In this embodiment of the invention, the underlying principle is that the presence of source rocks and reaching the hydrocarbon generation threshold in a basin is a necessary but not sufficient condition for evaluating its potential. Mature source rocks are the foundation for the existence and evolution of hydrocarbon systems; their properties determine hydrocarbon generation and directly influence the migration and accumulation process. Besides the intrinsic characteristics of the source rocks themselves, such as type and abundance, the temperature and time experienced after their formation are important extrinsic factors. Both temperature and time are depth-related factors, therefore, hydrocarbon formation is significantly related to the burial depth of source rocks. Furthermore, basin size is a crucial factor determining the abundance of its hydrocarbon resources; basin size determines the scale of hydrocarbon resources, which is directly related to the basin area and the thickness of the hydrocarbon reservoir. Therefore, the structural characteristic parameters of a basin can be sensitive parameters for hydrocarbon resource quantity, allowing for rapid and reasonable prediction of its hydrocarbon scale based on basin structural characteristics.

[0047] Example 1

[0048] Embodiment 1 of the present invention provides a method for establishing a basin hydrocarbon scale prediction model, the process of which is as follows: Figure 1 As shown, it includes the following steps:

[0049] Step S11: Construct a set of basin structural feature parameters.

[0050] In some embodiments, a parameter set may be constructed that includes at least two of the following basin structural characteristic parameters:

[0051] (1) Basin area

[0052] The basin area reflects the size of the basin during the rifting period and has a significant impact on the scale of the basin's source rocks.

[0053] (2) Maximum length of the basin

[0054] (3) Maximum width of the basin

[0055] The maximum length and width of a basin determine its area and shape.

[0056] (4) Maximum depth of the basin

[0057] The depth reflects the scale of the basin rift lake, which is related to both the volume and maturity of the source rocks. The greater the depth, the thicker the rock layers that reach the hydrocarbon generation threshold.

[0058] (5) Length-to-width ratio of the basin

[0059] It is determined based on the maximum length and maximum width of the basin, specifically the ratio of the maximum length to the maximum width of the basin.

[0060] The length-to-width ratio of a basin reflects its morphological characteristics, and the sedimentary development characteristics of basins with different morphologies vary considerably.

[0061] (6) Basin type

[0062] Rift basins are classified into two main types: single-fault and double-fault.

[0063] (7) Ratio of basin rift thickness to total stratigraphic thickness

[0064] (8) Basin size

[0065] The size of a basin is determined based on its area, maximum depth, and the ratio of rift layer thickness to total stratigraphic thickness.

[0066] Furthermore, the basin size is determined using the following formula (1):

[0067] x=S×D max ×H rift ÷H all ÷1000 (1)

[0068] In formula (1), x is the basin size, S is the basin area, and D is the basin size. max H is the maximum depth of the basin. rift ÷H all H represents the thickness of the basin rift layer. rift With the total stratigraphic thickness H of the basin all Compare.

[0069] (9) Standard basin size.

[0070] Basin size is linearly correlated with the volume of source rocks during the rifting period and indirectly correlated with basin structure type. Therefore, basin size is corrected using a basin size coefficient to obtain the standard basin size.

[0071] The above basin size coefficients are determined based on the basin type.

[0072] Different types of basins have different oil and gas potentials. Linear correlation analysis of reserves and scale coefficients of different types of basins in the West-Central African Rift System shows that the scale coefficient of a double-faulted basin is three times that of a single-faulted basin. That is, the reserves of a double-faulted basin with a scale of three times that of a single-faulted basin are equivalent to the resource potential of a single-faulted basin with a scale of one time.

[0073] Therefore, if the basin type is double-faulted, the basin size coefficient is determined to be 1; if the basin type is single-faulted, the basin size coefficient is determined to be 3.

[0074] This study used data from over forty basins / depressions within the West-Central African Rift System. Each basin's data included the aforementioned nine basin structural parameters and its hydrocarbon resources (proven recoverable resources), as shown in Table 1 below.

[0075] Table 1 Source Data Table for Basin Structure Analysis

[0076]

[0077]

[0078] The size of a rift basin determines the total amount of oil and gas resources. If the basin is too small, the total amount of hydrocarbons it accumulates may not reach the commercial threshold. Therefore, a lower limit for the size of the basin can be set to exclude basins with less potential that have not reached the commercial threshold.

[0079] In this embodiment, the basin is a basin with a depth greater than a set depth threshold. Furthermore, the basin's time thickness is greater than 2 seconds; it is generally believed that only basins with a depth below 2 seconds are likely to have large-scale oil and gas accumulations.

[0080] Step S12: Based on the oil and gas resources of each basin in multiple basin data and the value of each parameter in the parameter set, select at least one parameter from the parameter set as a sensitive parameter for oil and gas resources.

[0081] Linear correlation analysis was conducted on various structural characteristic parameters of the basin and their correlation with hydrocarbon resources. The results showed that the maximum length, maximum width, area, and ratio of fault-bounded strata thickness to total strata thickness were all positively correlated with hydrocarbon resources within the basin, but the correlation coefficients were low, with R values ​​ranging from 0.173 to 0.43. The length-to-width ratio showed a skewed normal distribution with hydrocarbon resources, with a better ratio between 2 and 4 (see...). Figure 2 ).

[0082] Considering that parameters such as length, width, area, and the ratio of rift basin thickness to total stratum thickness show a linear relationship with the standard basin size, and that their correlation with hydrocarbon resources is also positive (same as the standard basin size), the standard basin size can be used for comprehensive representation. This means the standard basin size already includes geological information regarding length, width, area, and the ratio of rift basin thickness. The relationship between the length-to-width ratio and hydrocarbon resources differs from the others; therefore, the length-to-width ratio and the standard basin size are selected as sensitive parameters for hydrocarbon resources as independent variables, with hydrocarbon resources as the dependent variable for comprehensive fitting.

[0083] Step S13: Determine the optimal fitting model between sensitive parameters and oil and gas resources based on the set fitting conditions, and use it as the prediction model for the scale of oil and gas in the basin.

[0084] In some embodiments, refer to Figure 3 As shown, determining the optimal fitting model can include the following steps:

[0085] Step S131: For each candidate fitting model, obtain the fitting relationship between the sensitive parameters and the amount of oil and gas resources based on the candidate fitting model.

[0086] Formula (alternative fitting model) selection: You can select all 928 formulas in the 1stOpt software library for fitting.

[0087] Optimization algorithm: Levenberg-Marquardt method + general global optimization method.

[0088] Step S132: Select the fitting relationship that meets the set fitting conditions as the candidate fitting model.

[0089] The fitting criteria should include at least one of the following conditions:

[0090] (1) When the size of a standard basin tends to be at its minimum, the amount of oil and gas resources converges to 0.

[0091] Because the basin is very small, the amount of oil and gas resources should be close to zero.

[0092] (2) When the size of a standard basin approaches its maximum value, the amount of oil and gas resources expands toward infinity.

[0093] (3) As the size of a standard basin increases, the amount of oil and gas resources also increases accordingly.

[0094] (4) When the standard basin size is in the set high value range, the correlation coefficient between oil and gas resources and each sensitivity coefficient is the lowest.

[0095] As the size of the basin increases, the shape of the basin has a smaller and smaller impact on the amount of resources.

[0096] (5) When the standard basin size is in the set low value range, the correlation coefficient between oil and gas resources and each sensitivity coefficient is the highest.

[0097] When the basin is small, the development of source rocks and reservoirs varies greatly depending on the basin shape.

[0098] Step S133: Select the optimal fitting model from all candidate fitting models based on the correlation coefficient and / or determination coefficient of the candidate fitting models.

[0099] The correlation coefficient reflects the correlation between the fitted model and the sample data points; the coefficient of determination is a statistical indicator that reflects the reliability of the regression model in explaining the changes in the dependent variable, that is, the regression formula can reflect the degree of data change.

[0100] If the correlation coefficients and / or determination coefficients of the candidate fitting models are not significantly different, a simpler candidate fitting model can be selected as the optimal fitting model.

[0101] Based on the set fitting conditions, the optimal fitting model between the sensitive parameters and the oil and gas resource quantity is determined as follows:

[0102]

[0103] In formula (2), Z represents the oil and gas resources of the basin, and x s The standard basin size is given by y, which represents the length-to-width ratio of the basin. p1, p2, p3, p4, and p5 are influencing parameters: p1 = -101.772494558364, p2 = 7.97539366630681, p3 = 1168852.01221794, p4 = 2.75603409808345, and p5 = 0.00244659422528741.

[0104] Its correlation coefficient R is 0.89, and the square of the correlation coefficient R0.89 2 The coefficient of determination (DC) is 0.792, and the initial fitting selection function is 0.761. The original image of the above initial fitting selection function is attached. Figure 4 As shown.

[0105] Furthermore, the influence parameters p1, p2, p3, p4, and p5 in the above formula (2) can be optimized to obtain the optimized best-fit model as shown in the following formula (3):

[0106]

[0107] A specific simplification method can be to keep four of the influencing parameters unchanged, and then change the number of decimal places of another influencing parameter. The influencing parameter with the fewest decimal places, which meets the set conditions, and whose correlation coefficient and determination coefficient meet the requirements, can be used as the optimized influencing parameter.

[0108] Formula (3) further simplifies the model while ensuring its accuracy.

[0109] The method for establishing a basin hydrocarbon scale prediction model provided in Embodiment 1 of this invention first constructs an easily obtainable set of basin structural characteristic parameters. Then, using data from multiple basins with proven hydrocarbon resources, the hydrocarbon resources of each basin and the values ​​of each parameter in the parameter set are analyzed to select sensitive parameters from the parameter set. Finally, based on set fitting conditions, the optimal fitting model between the sensitive parameters and the hydrocarbon resources is determined, serving as the basin hydrocarbon scale prediction model. The model establishment process is easy to implement, and with this model, only the values ​​of the sensitive parameters for the basin's hydrocarbon resources are needed to achieve rapid and accurate hydrocarbon scale prediction, saving time for resource evaluation and providing a reference for subsequent exploration and deployment work.

[0110] See Figure 5 The diagram shown is a flowchart illustrating the specific implementation of the method for establishing a prediction model for the scale of oil and gas in a basin.

[0111] Example 2

[0112] Embodiment 2 of the present invention provides a method for predicting the scale of oil and gas in a basin, the process of which is as follows: Figure 6 As shown, it includes the following steps:

[0113] Step S61: Input the sensitive parameters of oil and gas resources of the basin to be evaluated into the basin oil and gas scale prediction model.

[0114] The basin's oil and gas scale prediction model was established using any of the methods described in Example 1.

[0115] The length-to-width ratio of the basin to be evaluated and the size of a standard basin can be input into the basin's hydrocarbon scale prediction model.

[0116] Furthermore, if the basin's hydrocarbon scale prediction model is formula (2) or formula (3) in Example 1, the hydrocarbon resource-sensitive parameters of the basin to be evaluated are input into the basin's hydrocarbon scale prediction model, specifically including:

[0117] Based on the value subtracted from the length-width ratio in formula (2) or formula (3), determine the length-width ratio sensitive range; determine whether the length-width ratio of the basin to be evaluated is within the sensitive range; if so, correct the length-width ratio of the basin to be evaluated according to the set rules, and input the standard oil and gas scale of the basin to be evaluated and the corrected length-width ratio into the basin oil and gas scale prediction model; if not, directly input the standard oil and gas scale and length-width ratio of the evaluated basin into the basin oil and gas scale prediction model.

[0118] For example, taking the prediction model as formula (3), the value subtracted by the length-to-width ratio in formula (3) is 2.756. When the standard basin size value is fixed, the correlation between basin potential and length-to-width ratio reaches its peak when the length-to-width ratio is 2.756. The overall distribution is close to normal. Therefore, smoothing is required within the range of [2, 3], and the sensitive range of length-to-width ratio is determined to be [2, 3]. If the length-to-width ratio of the basin to be evaluated is in the range [2, 2.756], then the length-to-width ratio of the basin to be evaluated is corrected to 2. If the length-to-width ratio of the basin to be evaluated is in the range (2.756, 3), then the length-to-width ratio of the basin to be evaluated is corrected to 3.

[0119] When the length-to-width ratio of a basin is constant, the basin's resource quantity is linearly correlated with the standard basin size.

[0120] When the standard basin size is fixed, the correlation between basin resources and length-to-width ratio reaches its peak when the length-to-width ratio is around 2.756, and the overall distribution is close to normal.

[0121] Under different scale values, the width of the peak decreases as the standard basin size increases, reflecting that the influence of the length-to-width ratio on the basin's resource potential decreases as the standard basin size increases.

[0122] Based on the above analysis, it is believed that the fitting formula meets the constraints, its functional performance is consistent with the geological significance, and it can be used as the fitting output function.

[0123] Step S62: Determine the oil and gas scale of the basin to be evaluated based on the output results of the model.

[0124] Considering the differences in geological conditions of basins in different regions, the output of the model, i.e. the calculated value of the formula, only takes its relative size to indicate the relative size of oil and gas.

[0125] It is possible to determine the amount of oil and gas resources in the basin to be evaluated based on the output of the model; and to determine the scale of oil and gas in the basin to be evaluated based on the correspondence between the amount of oil and gas resources and the scale of oil and gas.

[0126] Overseas exploration blocks are characterized by short exploration cycles, limited prior data, low levels of understanding, and high exploration difficulty. The basin hydrocarbon scale prediction method provided in Embodiment 2 of this invention uses hydrocarbon resource-sensitive parameters from a set of basin structural characteristic parameters that are readily available and less prone to controversy. Through the aforementioned basin hydrocarbon scale prediction model, accurate and efficient hydrocarbon scale prediction can be achieved, forming a new method for rapidly screening hydrocarbon-rich depressions in low-exploration basins using basin structural characteristics.

[0127] Considering the geological and data availability, the XX Basin in China was selected for testing and verification. (In the fitting process, the depth parameter correction was performed using a factor of 1.5. Sixteen depressions within the XX Basin were analyzed. First, the parameters were corrected and standardized based on paleothermal data. Then, depressions with no potential were removed based on the depth cutoff value, which was 3S in this case. The resource potential of the remaining basins was calculated and ranked. The agreement rate with current understanding reached 87.51% (Table 2).)

[0128] Table 2XX Basin Data Detection Table

[0129]

[0130] It should be noted that since no reserve data for the XX Basin has been collected, the known resource potential is only determined based on its known distribution, and the resource potential is calculated by taking its relative value.

[0131] Based on the inventive concept of this invention, embodiments of this invention also provide a device for establishing a basin oil and gas scale prediction model, the structure of which is as follows: Figure 7 As shown, it includes:

[0132] Basin structure feature parameter set construction module 71, used to construct basin structure feature parameter set;

[0133] Sensitive parameter filtering module 72 is used to filter at least one parameter from the parameter set as a sensitive parameter for oil and gas resources based on the oil and gas resources of each basin in multiple basin data and the value of each parameter in the parameter set;

[0134] The basin oil and gas scale prediction model establishment module 73 is used to determine the optimal fitting model between the sensitive parameters and the oil and gas resources based on the set fitting conditions, and to serve as the basin oil and gas scale prediction model.

[0135] Based on the inventive concept of this invention, embodiments of this invention also provide a basin oil and gas scale prediction device, the structure of which is as follows: Figure 8 As shown, it includes:

[0136] Sensitive parameter input module 81 is used to input sensitive parameters of oil and gas resources of the basin to be evaluated into the basin oil and gas scale prediction model, which is established according to the above method;

[0137] The oil and gas scale prediction and evaluation module 82 is used to determine the oil and gas scale of the basin to be evaluated based on the output results of the model.

[0138] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0139] Based on the inventive concept of the present invention, embodiments of the present invention also provide a computer program product, including a computer program / instruction, wherein, when the computer program / instruction is executed by a processor, it implements the above-mentioned method for establishing a basin oil and gas scale prediction model, or implements the above-mentioned method for predicting the scale of oil and gas in a basin.

[0140] Unless otherwise specifically stated, terms such as processing, calculation, operation, determination, display, etc., may refer to the actions and / or processes of one or more processing or computing systems or similar devices that represent the manipulation and conversion of data representing physical (e.g., electronic) quantities within the registers or memory of the processing system into other data similarly representing physical quantities within the memory, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals can be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0141] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process may be rearranged without departing from the scope of this disclosure. The appended method claims provide elements of various steps in an exemplary order and are not intended to limit the scope to the specific order or hierarchy described.

[0142] In the detailed description above, various features are combined together in a single embodiment to simplify this disclosure. This approach to disclosure should not be construed as reflecting an intention that embodiments of the claimed subject matter require more features than are explicitly stated in each claim. Rather, as reflected in the appended claims, the invention is presented with fewer features than all of the features in a single disclosed embodiment. Therefore, the appended claims are hereby explicitly incorporated into the detailed description, with each claim representing a separate preferred embodiment of the invention.

[0143] Those skilled in the art will also understand that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments herein can be implemented as electronic hardware, computer software, or a combination thereof. To clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art can implement the described functionality in alternative ways for each specific application; however, such implementation decisions should not be construed as departing from the scope of this disclosure.

[0144] The steps of the methods or algorithms described in conjunction with the embodiments herein can be directly embodied in hardware, software modules executed by a processor, or a combination thereof. The software modules can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. The ASIC can reside in a user terminal. Alternatively, the processor and storage medium can exist as discrete components in the user terminal.

[0145] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. This software code can be stored in memory units and executed by a processor. The memory units can be implemented within the processor or outside the processor; in the latter case, they are communicatively coupled to the processor via various means, as is well known in the art.

[0146] The foregoing description includes examples of one or more embodiments. It is certainly impossible to describe all possible combinations of components or methods in order to describe the above embodiments, but those skilled in the art will recognize that further combinations and arrangements of the various embodiments are possible. Therefore, the embodiments described herein are intended to cover all such changes, modifications, and variations that fall within the scope of the appended claims. Furthermore, the term "comprising" as used in the specification or claims is interpreted in a manner similar to the term "including," as interpreted when used as a conjunction in the claims. Additionally, the use of any term "or" in the specification of the claims is intended to mean "non-exclusive or."

Claims

1. A method for establishing a basin hydrocarbon scale prediction model, characterized in that, include: Construct a parameter set that includes at least two of the following basin structural characteristic parameters: basin area; Maximum length of the basin; Maximum width of the basin; Maximum depth of the basin; The length-to-width ratio of the basin, which is determined based on the maximum length and maximum width of the basin; Basin type; ratio of basin rift thickness to total stratigraphic thickness; basin size, which is determined based on basin area, maximum depth, and ratio of rift thickness to total stratigraphic thickness; standard basin size obtained by correcting the basin size according to a basin size coefficient, which is determined based on the basin type. Based on the oil and gas resources of each basin in multiple basin data and the value of each parameter in the parameter set, at least one parameter is selected from the parameter set as a sensitive parameter for oil and gas resources, where the oil and gas resources are the proven recoverable oil and gas resources. For each candidate fitting model, the fitting relationship between the sensitive parameters and the oil and gas resources is obtained based on the candidate fitting model; the fitting relationship that meets the set fitting conditions is taken as the candidate fitting model; based on the correlation coefficient and / or determination coefficient of the candidate fitting models, the optimal fitting model is selected from all candidate fitting models as the prediction model for the oil and gas scale of the basin, and the optimal fitting model is: （1）； In formula (1), Z represents the amount of oil and gas resources in the basin. The standard basin size is given by y, which represents the length-to-width ratio of the basin. p1, p2, p3, p4, and p5 are the influencing parameters.

2. The method as described in claim 1, characterized in that, The size of the basin is determined in the following manner: The basin size is determined by the following formula (2) based on the basin area, maximum depth, and the ratio of rift layer thickness to total stratigraphic thickness: x= S×D max ×H rift ÷H all ÷1000 (2); In formula (2), x is the basin size, S is the basin area, and D is the basin size. max H is the maximum depth of the basin. rift ÷H all H represents the thickness of the basin rift layer. rift With the total stratigraphic thickness H of the basin all Compare.

3. The method as described in claim 1, characterized in that, The basin types include single-fault and double-fault basins; correspondingly, The basin size coefficient is determined in the following manner: If the basin type is double-faulted, the basin size coefficient is set to 1. If the basin type is single-faulted, the basin size coefficient is determined to be 3.

4. The method as described in claim 1, characterized in that, The step of selecting at least one parameter from the parameter set as a sensitive parameter for oil and gas resources based on the oil and gas resource quantity of each basin in multiple basin data and the value of each parameter in the parameter set specifically includes: Based on the oil and gas resources of each basin in multiple basin data and the value of each parameter in the parameter set, the standard basin size and aspect ratio are selected from the parameter set as sensitive parameters for oil and gas resources.

5. The method as described in claim 4, characterized in that, The set fitting conditions include at least one of the following conditions: When the size of a standard basin approaches its minimum, the amount of oil and gas resources converges to zero. When the size of a standard basin approaches its maximum, the amount of oil and gas resources expands towards infinity. As the size of a standard basin increases, the amount of oil and gas resources also increases accordingly. When the standard basin size is within the set high value range, the correlation coefficient between oil and gas resources and each sensitivity coefficient is the lowest. When the standard basin size is within the set low range, the correlation coefficient between oil and gas resources and various sensitivity coefficients is the highest.

6. The method as described in claim 1, characterized in that, After selecting the optimal fitting model from all candidate fitting models, the process further includes: By optimizing the influence parameters p1, p2, p3, p4, and p5 in formula (1), the optimized best-fit model is obtained as shown in formula (3): （3）。 7. The method according to any one of claims 1 to 6, characterized in that, The depth of the basin is greater than a set depth threshold.

8. A method for predicting the scale of oil and gas in a basin, characterized in that, include: The sensitive parameters of the oil and gas resources of the basin to be evaluated are input into the basin oil and gas scale prediction model, which is established according to the method of any one of claims 1 to 7. The oil and gas scale of the basin to be evaluated is determined based on the output of the model.

9. The method as described in claim 8, characterized in that, If the basin's hydrocarbon scale prediction model is as described in formula (1), the step of inputting the sensitive parameters of the hydrocarbon resources of the basin to be evaluated into the basin's hydrocarbon scale prediction model specifically includes: Based on the value subtracted from the aspect ratio in the formula (1), determine the aspect ratio sensitive range; Determine whether the aspect ratio of the basin to be evaluated is within the sensitive range; If so, the aspect ratio of the basin to be evaluated is corrected according to the set rules, and the standard oil and gas scale of the basin to be evaluated and the corrected aspect ratio are input into the basin oil and gas scale prediction model.

10. The method as described in claim 8 or 9, characterized in that, The determination of the oil and gas scale of the basin to be evaluated based on the model output specifically includes: The amount of oil and gas resources in the basin to be evaluated is determined based on the output of the model. Based on the correspondence between oil and gas resources and oil and gas scale, and the oil and gas resources of the basin to be evaluated, the oil and gas scale of the basin to be evaluated is determined.

11. A device for establishing a basin oil and gas scale prediction model, characterized in that, include: The basin structure feature parameter set construction module is used to construct a parameter set containing at least two of the following basin structure feature parameters: basin area; Maximum length of the basin; maximum width of the basin; Maximum depth of the basin; length-to-width ratio of the basin, which is determined based on the maximum length and maximum width of the basin; Basin type; ratio of basin rift thickness to total stratigraphic thickness; basin size, which is determined based on basin area, maximum depth, and ratio of rift thickness to total stratigraphic thickness; standard basin size obtained by correcting the basin size according to a basin size coefficient, which is determined based on the basin type. The sensitive parameter screening module is used to screen at least one parameter from the parameter set as a sensitive parameter for oil and gas resources based on the oil and gas resources of each basin in multiple basin data and the value of each parameter in the parameter set. The oil and gas resources are the proven recoverable oil and gas resources. The basin hydrocarbon scale prediction model establishment module is used to, for each candidate fitting model, obtain the fitting relationship between the sensitive parameters and the hydrocarbon resources based on the candidate fitting model; select the fitting relationship that meets the set fitting conditions as the candidate fitting model; and select the optimal fitting model from all candidate fitting models based on the correlation coefficient and / or determination coefficient of the candidate fitting models as the basin hydrocarbon scale prediction model. The optimal fitting model is: （1）； In formula (1), Z represents the amount of oil and gas resources in the basin. The standard basin size is given by y, which represents the length-to-width ratio of the basin. p1, p2, p3, p4, and p5 are the influencing parameters.

12. A basin oil and gas scale prediction device, characterized in that, include: A sensitive parameter input module is used to input sensitive parameters of the oil and gas resources of the basin to be evaluated into the basin oil and gas scale prediction model, wherein the basin oil and gas scale prediction model is established according to the method of any one of claims 1 to 7; The oil and gas scale prediction and evaluation module is used to determine the oil and gas scale of the basin to be evaluated based on the output results of the model.

13. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the basin oil and gas scale prediction model establishment method according to any one of claims 1 to 7, or implements the basin oil and gas scale prediction method according to any one of claims 8 to 10.

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