Oil and gas resource reserve prediction method, device, equipment, storage medium and product
By acquiring the structural characteristics and preset parameters of the depression area and combining them with the prediction model, the problem of predicting the annual proven reserves of oil and gas resources in the depression area was solved, achieving more accurate exploration parameter characterization and reserve prediction, and improving the scientific nature of exploration planning.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA PETROLEUM & CHEMICAL CORP
- Filing Date
- 2024-11-27
- Publication Date
- 2026-05-29
AI Technical Summary
Due to the scarcity of geological data, the proven oil and gas resources in the depression area are low and unevenly distributed, making it impossible to accurately predict the average annual proven oil and gas reserves, which affects medium- and long-term reserve assessment and planning.
By acquiring the structural characteristic parameters and preset parameters of the target depression area, the target exploration parameters are determined, and the annual proven oil and gas reserves are predicted using a prediction model. The initial prediction model is then trained using training data to improve prediction accuracy.
It provides data for medium- and long-term reserve planning in the depression area, improving the scientific nature and accuracy of oil and gas resource exploration planning.
Smart Images
Figure CN122114330A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of oil and gas resource exploration technology, and in particular to a method, apparatus, equipment, storage medium and product for predicting oil and gas resource reserves. Background Technology
[0002] The basin contains several depressions that may contain abundant oil and gas resources. However, the exploration progress in these depressions is low, and the distribution of oil and gas resources is uneven, leading to differences in the level of exploration. Due to the scarcity of geological data and the lack of reserve growth patterns for reference in these depressions, the exploration process in these areas faces significant challenges. The inability to predict the average annual proven oil and gas reserves after exploration efforts are initiated will severely restrict the research and development of medium- and long-term reserve assessments and planning schemes for these depressions. Summary of the Invention
[0003] This disclosure provides a method, apparatus, equipment, storage medium, and product for predicting oil and gas resource reserves, so as to predict the average annual proven oil and gas reserves in depression areas.
[0004] Firstly, this disclosure provides a method for predicting oil and gas resource reserves, including:
[0005] Obtain target structural feature parameters of the target depression region; the target structural feature parameters are used to characterize the structural features of the target depression region, and the target structural feature parameters are related to oil and gas resource reserves.
[0006] Based on the target structural feature parameters and preset parameters, the target exploration parameters of the target depression region are determined; the preset parameters are used to indicate the difficulty of exploring the target depression region.
[0007] Based on the target exploration parameters and the target prediction model, the annual average proven oil and gas reserves corresponding to the target exploration parameters are determined; the target prediction model is used to predict the annual average proven oil and gas reserves of the depression area based on the exploration parameters.
[0008] In some embodiments, obtaining the target structural feature parameters of the target depression region includes:
[0009] Obtain the maximum depth, maximum width, and maximum length of the target recessed region;
[0010] Based on the maximum depth and the maximum width, a first parameter is determined; the first parameter is used to characterize the development of the source layer in the target depression region.
[0011] A second parameter is determined based on the maximum width and the maximum length; the second parameter is used to characterize the reservoir development of the target depression region.
[0012] Based on the first parameter and the second parameter, the target structural feature parameters are determined.
[0013] In some embodiments, determining the target structural feature parameters based on the first parameter and the second parameter includes:
[0014] Calculate the average value of the first parameter and the second parameter, and determine the average value as the target structural feature parameter.
[0015] In some embodiments, the method further includes:
[0016] The preset parameters are determined based on the exploration results of the target depression area.
[0017] In some embodiments, determining the preset parameters based on the exploration results of the target depression region includes:
[0018] When the exploration results of the target depression area indicate the presence of oil and gas resources, and the oil and gas reserves are known, the preset parameter is determined as the first preset parameter; or...
[0019] When the exploration results of the target depression area indicate the presence of oil and gas resources, and the reserves of these resources are unknown, the preset parameter is determined to be the second preset parameter; or...
[0020] When the exploration results of the target depression area indicate that the probability of oil and gas resources existing in the target depression area is greater than or equal to a preset threshold, the preset parameter is determined to be the third preset parameter; or...
[0021] When the exploration results of the target depression area indicate that the probability of oil and gas resources in the target depression area is less than a preset threshold, the preset parameter is determined to be the fourth preset parameter.
[0022] Wherein, the first preset parameter is greater than the second preset parameter, the second preset parameter is greater than the third preset parameter, and the third preset parameter is greater than the fourth preset parameter.
[0023] In some embodiments, the training process of the target prediction model includes:
[0024] Acquire training data, which includes multiple exploration parameters and the annual proven reserves of oil and gas resources corresponding to each exploration parameter;
[0025] Based on the training data, an initial prediction model is trained to obtain the target prediction model.
[0026] Secondly, this disclosure provides an oil and gas resource reserve prediction device, comprising:
[0027] The acquisition module is configured to acquire target structural feature parameters of the target depression region; the target structural feature parameters are used to characterize the structural features of the target depression region and are related to oil and gas resource reserves.
[0028] The determination module is configured to determine the target exploration parameters of the target depression region based on the target structural feature parameters and preset parameters; the preset parameters are used to indicate the difficulty of exploring the target depression region.
[0029] The determination module is also configured to determine the average annual proven oil and gas reserves corresponding to the target exploration parameters based on the target exploration parameters and the target prediction model; the target prediction model is used to predict the average annual proven oil and gas reserves of the depression area based on the exploration parameters.
[0030] Thirdly, this disclosure provides an electronic device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the preceding aspects.
[0031] Fourthly, this disclosure provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the methods described in the above aspects.
[0032] Fifthly, this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the methods described in the above aspects.
[0033] This disclosure provides a method, apparatus, equipment, storage medium, and product for predicting oil and gas reserves. By using structural characteristic parameters and preset parameters of a target depression area, it can determine the target exploration parameters of the depression area and, through these parameters, determine the average annual proven oil and gas reserves. This solves the problem of being unable to predict the average annual proven oil and gas reserves of a depression area after exploration work has commenced, providing data support for medium- and long-term reserve planning in depression areas and improving the scientific rigor of oil and gas exploration planning. Furthermore, the inclusion of preset parameters allows the target exploration parameters to characterize the difficulty of exploring the target depression area, thereby determining a more accurate average annual proven oil and gas reserves. Attached Figure Description
[0034] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:
[0035] Figure 1This is a flowchart illustrating a method for predicting oil and gas resource reserves provided in an embodiment of this disclosure.
[0036] Figure 2 This is a schematic diagram illustrating the relationship between exploration parameters and the average annual proven reserves of oil and gas resources, provided as an embodiment of this disclosure.
[0037] Figure 3 This is a flowchart illustrating a method for predicting oil and gas resource reserves provided in an embodiment of this disclosure.
[0038] Figure 4 This is a block diagram of an oil and gas resource reserve prediction device provided in an embodiment of the present disclosure.
[0039] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation
[0040] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort should fall within the protection scope of this disclosure.
[0041] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0042] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.
[0043] The basin contains multiple depressions, which may contain abundant oil and gas resources. However, these depressions have low exploration levels and uneven distribution of oil and gas resources, resulting in varying exploration progress. Due to the needs of strategic zoning and mineral rights bidding, it is necessary to conduct medium- to long-term reserve assessments for low-exploration depressions where proven reserves have not yet been submitted, thereby enabling exploration planning for these areas. However, due to the scarcity of geological data and the lack of reserve growth patterns for reference, it is difficult to conduct planning studies for certain depressions within the basin using conventional methods. Thus, during the exploration process in these depressions, the inability to predict the average annual proven oil and gas reserves after exploration efforts have commenced significantly restricts the research on medium- to long-term reserve assessments and planning schemes.
[0044] To address this technical problem, this disclosure provides a method for predicting oil and gas reserves. Based on structural characteristic parameters and preset parameters of the target depression area, this disclosure determines the target exploration parameters for the depression area, and based on these target exploration parameters, determines the average annual proven oil and gas reserves of the depression area. This solves the problem of being unable to predict the average annual proven oil and gas reserves of a depression area after exploration work has commenced, providing data support for medium- and long-term reserve planning in depression areas and improving the scientific rigor of oil and gas exploration planning. Furthermore, the inclusion of preset parameters allows the target exploration parameters to characterize the difficulty of exploring the target depression area, thereby determining a more accurate average annual proven oil and gas reserves.
[0045] Example 1
[0046] Figure 1 This is a flowchart illustrating a method for predicting oil and gas resource reserves provided in an embodiment of this disclosure. This embodiment is applied to an oil and gas resource exploration scenario and employs... Figure 1 The method shown can be used to predict oil and gas reserves. For example... Figure 1 As shown, a method for predicting oil and gas resource reserves includes:
[0047] S101, Obtain the target structural feature parameters of the target concave region.
[0048] The target depression region can refer to a topographically characterized area that is high around the edges and low in the center. In some embodiments, the target depression region can refer to any depression region within a basin, and the basin includes at least one depression region. Target structural characteristic parameters are used to characterize the structural features of the target depression region. These parameters may include the maximum length, maximum width, and maximum depth of the target depression region, as well as parameters determined based on these maximum length, maximum width, and maximum depth. The target structural characteristic parameters are related to oil and gas reserves.
[0049] In some embodiments, a geological map of the target depression area can be obtained, and based on the geological map, the maximum length, maximum width, and maximum depth of the target depression area can be determined. Optionally, the geological map may include a contour map of the target depression area, a basement structural map of the target depression area, a basement contour map of the target depression area, and a cross-sectional map, such as a geological cross-section. The contour map can be used to characterize the undulation of the depression area, thereby determining the boundary and size of the depression area; the basement structural map can characterize the structural features of the basement of the depression area, thereby determining the length and width of the depression area; the basement contour map can be used to characterize the undulation and morphology of the depression area, thereby determining the length and width of the depression area; and the cross-section can be used to display strata, rocks, and structures, thereby determining the depth of the depression area.
[0050] It should be noted that the greater the maximum length, maximum width, and maximum depth, the thicker or larger the area containing oil and gas resources. Therefore, the target structural characteristic parameters are related to the oil and gas reserves. For example, a greater maximum depth indicates a thicker source layer in the target depression area, thus suggesting greater oil and gas reserves. Similarly, a greater maximum width indicates a longer reservoir extension in the target depression area, also suggesting greater oil and gas reserves.
[0051] In some embodiments, after determining the maximum length, maximum width, and maximum depth, the units of measurement for these parameters can be standardized to facilitate subsequent calculations. Optionally, the units of measurement for these parameters can be standardized to kilometers or meters.
[0052] In some embodiments, the maximum depth, maximum width, and maximum length of the target depression region can be obtained, and a first parameter can be determined based on the maximum depth and maximum width; a second parameter can be determined based on the maximum width and maximum length. The first parameter characterizes the development of the source layer in the target depression region; the second parameter characterizes the reservoir development in the target depression region. Optionally, the method for obtaining the maximum depth, maximum width, and maximum length of the target depression region can refer to the above embodiments, and will not be repeated here. The first parameter can be the ratio of the maximum depth to the maximum width, and the second parameter can be the ratio of the maximum width to the maximum length.
[0053] In one example, the average of the first and second parameters can be calculated, and this average can be used as the target structural feature parameter. In another example, a weighted average of the first and second parameters can be calculated, and this weighted average can be used as the target structural feature parameter. The weights of the first and second parameters can be set and selected based on actual needs.
[0054] S102, Based on the target structural characteristic parameters and preset parameters, determine the target exploration parameters for the target depression area.
[0055] The preset parameters are used to indicate the difficulty of exploring the target depression area, and can also be used to indicate the exploration results of the target depression area. In some embodiments, the preset parameters can be constant parameters, and the magnitude of the preset parameters is directly proportional to the difficulty of exploring the target depression area. That is, when the preset parameters are larger, it is easier to explore the target depression area; conversely, when the preset parameters are smaller, it is more difficult to explore the target depression area.
[0056] Since the target exploration parameters are determined based on both the target structural characteristic parameters and preset parameters, they can simultaneously characterize the oil and gas reserves of the target depression area and the ease or difficulty of exploring the target depression area. In some embodiments, the product between the target structural characteristic parameters and the preset parameters can be calculated, and this product can be determined as the target exploration parameter.
[0057] It should be noted that when the designed depression area of the target depression region is large, the calculated values of the target structural characteristic parameters will also be large. In subsequent processes, the target exploration parameters calculated based on the target structural characteristic parameters and preset parameters will also be large. This increases the computational workload, thus increasing the computational difficulty and time, when determining the corresponding annual proven oil and gas reserves based on the target exploration parameters. Optionally, to simplify the calculation process and reduce the computational difficulty, the exploration parameters can be normalized to obtain normalized exploration parameters.
[0058] In some embodiments, the ratio of the target exploration parameter to a preset exploration parameter can be used to determine the normalized target exploration parameter. The preset exploration parameter can refer to the largest exploration parameter among all the exploration parameters of all depressions included in the basin where the target depression is located. For example, if the basin where the target depression is located includes depression a, depression b, and the target depression, where the exploration parameter corresponding to depression a is X1, the exploration parameter corresponding to depression b is X2, and the target exploration parameter corresponding to the target depression is X3, and X1>X2>X3, then the ratio of X3 to X1 can be calculated, and this ratio can be determined as the normalized target exploration parameter.
[0059] S103, based on the target exploration parameters and the target prediction model, determines the average annual proven reserves of oil and gas resources corresponding to the target exploration parameters.
[0060] The target prediction model is used to predict the average annual proven oil and gas reserves in the depression area based on exploration parameters. Correspondingly, the average annual proven oil and gas reserves corresponding to the normalized target exploration parameters can be determined based on the normalized target exploration parameters and the target prediction model.
[0061] In some embodiments, the target prediction model is a pre-trained prediction model. In step S103, the target prediction model can be directly invoked, and the average annual proven oil and gas reserves corresponding to the target exploration parameters can be determined based on the target exploration parameters and the target prediction model. Specifically, the target exploration parameters can be input into the target prediction model to obtain the average annual proven oil and gas reserves corresponding to the target exploration parameters.
[0062] In some embodiments, the training process of the target prediction model is briefly described below:
[0063] Acquire training data, which includes multiple exploration parameters and the annual proven reserves of oil and gas resources corresponding to each exploration parameter; based on the training data, train an initial prediction model to obtain a target prediction model.
[0064] Here, "multiple exploration parameters" refers to the exploration parameters of each of the other depressions within the basin containing the target depression. These other depressions refer to the proven depressions within the basin containing the target depression. Additionally, the average annual proven oil and gas reserves corresponding to these other depressions can be obtained. It should be noted that when the target exploration parameters are normalized, the exploration parameters in the training data are also normalized; conversely, when the target exploration parameters are non-normalized, the exploration parameters in the training data are also non-normalized. In other words, the type of exploration parameters in the target exploration parameters remains consistent with the type of exploration parameters in the training data.
[0065] In some embodiments, for a depression region, the time when the proven oil and gas reserves of the depression region were first submitted can be obtained, and the sum of the proven oil and gas reserves submitted during the number of exploration years from the time of the first submission to the present time can be calculated. Based on the number of exploration years and the sum of the proven oil and gas reserves, the average annual proven oil and gas reserves of the depression region can be calculated. For example, if depression region a first submitted its proven oil and gas reserves in 2018, and the total proven oil and gas reserves submitted from 2018 to 2022 is 4.6 million tons, and the number of exploration years is 6 years, then the average annual proven oil and gas reserves of depression region a can be determined to be 1.15 million tons. Correspondingly, the average annual proven oil and gas reserves of each depression region in other depression regions can be calculated in the same way, and the specific process will not be elaborated here.
[0066] In some embodiments, the initial prediction model can refer to a regression model. A regression model is a type of model used to predict and analyze the relationship between variables. A regression model can determine the relationship between a dependent variable and one or more independent variables. Regression models can be classified into different types based on the number and type of independent variables. For example, when there is only one independent variable and one dependent variable, the regression model can be a simple linear regression model, which approximates a straight line. Alternatively, when there are multiple independent variables and one dependent variable, the regression model can be a multiple linear regression model.
[0067] Correspondingly, the regression model can be trained using exploration parameters as independent variables and the average annual proven oil and gas reserves as dependent variables. In this case, the regression model can be a simple linear regression model, which yields the relationship between exploration parameters and the average annual proven oil and gas reserves.
[0068] Additionally, test data can be set, consisting of exploration parameters and actual annual proven oil and gas reserves from other depressions within the same basin as the training data. After determining the target prediction model, it can be tested based on the test data to determine its accuracy. Optionally, the exploration parameters from the test data can be input into the target prediction model to obtain the predicted annual proven oil and gas reserves corresponding to the test data. Furthermore, when the accuracy parameters such as the mean square error, mean absolute error, and mean absolute percentage error between the predicted and actual annual proven oil and gas reserves are within a preset range, the accuracy of the target prediction model is determined to meet user requirements. Conversely, if the accuracy parameters do not meet the preset range, the accuracy of the target prediction model is determined to not meet user requirements, and further adjustments to the target prediction model are needed, such as adjusting the parameters of the target prediction model. This embodiment does not limit the specific type of accuracy parameters or the specific value of the preset range; the accuracy parameters and preset range can be set and selected based on actual needs.
[0069] This disclosure allows for the determination of the average annual proven oil and gas reserves corresponding to exploration parameters and a pre-trained target prediction model. This addresses the current difficulty in predicting the average annual proven oil and gas reserves in depression areas, providing data support for medium- and long-term reserve planning in these areas and improving the scientific rigor of oil and gas exploration planning. Furthermore, the inclusion of preset parameters allows the target exploration parameters to characterize the difficulty of exploring the target depression area, thereby determining a more accurate average annual proven oil and gas reserves.
[0070] Example 2
[0071] Based on the above embodiments, in some embodiments, preset parameters can be determined based on the exploration status of the target depression area. Here, "exploration status" can refer to the already explored area. The methods for determining preset parameters are categorized into four cases based on the different exploration statuses of the target depression area.
[0072] First scenario:
[0073] When the exploration results of the target depression area indicate that there are oil and gas resources in the target depression area, and the oil and gas resource reserves are known, the preset parameter is determined as the first preset parameter.
[0074] Currently, drilling methods can be used to determine the oil and gas reserves in a depression area. For example, drilling, logging, and well logging can be used to determine the oil and gas reserves in the depression area. Correspondingly, the current situation can also be characterized as the presence of a successful well in the target depression area, and the specific reserves of oil and gas resources being known. For example, the target depression area has a successful well, and the specific reserves of oil and gas resources are 200 million tons. Here, a successful well refers to a well discovered and confirmed to contain significant oil and gas resources after drilling in the field of oil and gas exploration and development; a successful well possesses high commercial exploitation value. That is, when a successful well is found in the target depression area, and the reserves of oil and gas resources are known, the preset parameters can be determined as the first preset parameters.
[0075] Given that the target depression area contains oil and gas resources, and the reserves of these resources are known, exploring the target depression area is easier and more commercially viable. Based on the relationship between the first preset parameter and the difficulty of exploring the target depression area, the first preset parameter can be determined to be a larger value. This embodiment does not limit the value of the first preset parameter; it can be a constant such as 1.5, 1.2, or 1.
[0076] The second scenario:
[0077] When exploration in the target depression area indicates the presence of oil and gas resources, but the reserves are unknown, the second preset parameter is determined. That is, when a successful well exists in the target depression area, and the oil and gas reserves are unknown, the second preset parameter is used. The discussion of successful wells can refer to the above situation and will not be repeated here.
[0078] It should be noted that although the presence of oil and gas resources in the target depression area has been confirmed under the current circumstances, the specific reserves of these resources are unknown. Therefore, the difficulty of exploring the target depression area will be greater than that in the first scenario. Accordingly, the second preset parameter is less than the first preset parameter. Optionally, the second preset parameter can be 0.9, 0.8, 0.7, etc.
[0079] The third scenario:
[0080] When the exploration results of the target depression area indicate that the probability of oil and gas resources in the target depression area is greater than or equal to a preset threshold, the preset parameter is determined as the third preset parameter.
[0081] In some embodiments, a probability that the target depression area contains oil and gas resources greater than or equal to a preset threshold indicates that the target depression area may contain oil and gas resources during drilling, but the reserves of these resources may be relatively low. A show well refers to a well that exhibits signs of oil and gas during drilling. These signs may include the smell of oil and gas, oil and gas displays, oil and gas water samples, etc. Show wells do not necessarily have commercial development value, but they are helpful for further geological assessment and exploration strategy formulation. In other words, the current situation can be that a show well exists in the target depression area, and the oil and gas resources in the target depression area are relatively scarce.
[0082] Under the current circumstances, the difficulty of exploring the target depression area is greater than that corresponding to the second scenario. Therefore, the third preset parameter is less than the second preset parameter. Optionally, the third preset parameter can be 0.6, 0.5, 0.4, etc.
[0083] The fourth scenario:
[0084] When the exploration results of the target depression area indicate that the probability of oil and gas resources in the target depression area is less than the preset threshold, the preset parameter is determined to be the fourth preset parameter.
[0085] In some embodiments, the probability that the target depression area has oil and gas resources is less than a preset threshold may include: first, no wells are found during the drilling process in the target depression area, that is, no wells showing signs of oil and gas are found during the drilling process in the target depression area, and the target depression area may not have oil and gas resources; or, second, before the drilling process in the target depression area, it is determined by assessment that the target depression area may not have oil and gas resources.
[0086] Under the current circumstances, the difficulty of exploring the depression area is greater than that corresponding to the third scenario. Therefore, the fourth preset parameter is less than the third preset parameter. Optionally, the fourth preset parameter can be 0.3, 0.2, 0.1, etc.
[0087] This disclosure provides four scenarios for determining preset parameters. Based on different exploration conditions of the target depression area, preset parameters corresponding to the current exploration conditions can be determined in a targeted manner, so that the target exploration parameters are more consistent with the characteristics of the target depression area and the accuracy of the annual proven reserves of oil and gas resources can be improved.
[0088] Example 3
[0089] Based on the above embodiments, this embodiment provides an application example.
[0090] In one example, the following uses 10 depressions in a basin (i.e. Figure 2 Taking the AJ depression shown in the figure as an example, the initial prediction model is trained to obtain the target prediction model.
[0091] The specific training process includes: first, obtaining the maximum length, maximum width, and maximum depth of each depression region in the A-J depressions as shown in Table 1; then, determining the first parameter corresponding to the maximum depth and maximum width, the second parameter corresponding to the maximum width and maximum length, the preset parameter, the initial exploration parameter, and the exploration parameter (normalized exploration parameter) for each depression region as shown in Table 2; then, based on the total proven oil and gas reserves and exploration years of each depression region as shown in Table 3, determining the average annual proven oil and gas reserves of each depression region; finally, based on the exploration parameters and average annual proven oil and gas reserves of each depression region, fitting and establishing a mathematical relationship between the exploration parameters and the average annual proven oil and gas reserves, resulting in... Figure 2 The relationship between exploration parameters and average annual proven oil and gas reserves in this basin is shown as: y = 720.84x - 169.98, where y represents the average annual proven oil and gas reserves, x represents the exploration parameters, and the unit of average annual proven oil and gas reserves is 10,000 tons. That is, the target prediction model for this basin can be determined as: Average annual proven oil and gas reserves = 720.84 × exploration parameters - 169.98. Furthermore, using data from 10 depression areas, the correlation coefficient between the predicted and actual average annual proven oil and gas reserves is determined to be 0.809.
[0092] Table 1 Basic parameters for each depression region
[0093] Serial Number Depressed area Maximum length Maximum width Maximum depth 1 A depression 80 18 3.5 2 B depression 110 46 3.6 3 C-depression 67 20 3.3 4 D-depression 80 35 3.8 5 E-depression 110 27 3.5 6 F-depression 100 25 3.5 7 G depression 120 25 4.5 8 H-shaped depression 75 20 3.5 9 I dent 55 12 3.5 10 J-depression 150 28 5.2
[0094] Table 2 Exploration Parameters
[0095]
[0096]
[0097] Table 3. Average Annual Proven Oil and Gas Reserves
[0098] Serial Number Depressed area Proven reserves years of exploration Average annual proven oil and gas reserves 1 A depression 2145.67 24 89.40 2 B depression 1486.45 9 165.16 3 C-depression 2754.7 31 88.86 4 D-depression 4535.08 11 412.28 5 E-depression 1768.2 38 46.53 6 F-depression 1150.1 32 35.94 7 G depression 164.85 10 16.49 8 H-shaped depression 3950.5 21 188.12 9 I dent 2382.54 9 264.73 10 J-depression 1617.4 26 62.21
[0099] For the target prediction model corresponding to this basin, a test can be conducted using test data with an exploration parameter of 0.42 and an actual annual proven oil and gas reserves of 1.15 million tons. The test process includes: inputting the exploration parameter into the formula: Annual proven oil and gas reserves = 720.84 × exploration parameter - 169.98, which yields a predicted annual proven oil and gas reserves of 1.33 million tons. When the accuracy parameter between 1.15 million tons and 1.33 million tons meets the preset range, the target prediction model can be determined to meet the requirements, and no further adjustments to the target prediction model are necessary.
[0100] This disclosure provides specific examples of determining the target prediction model. These examples make the training process of the target prediction model clearer and easier to understand, thereby improving the convenience of this disclosure.
[0101] Example 4
[0102] In one example, refer to Figure 3 The diagram shows a flowchart of a method for predicting oil and gas resource reserves. Figure 3 The process shown includes the following steps:
[0103] Step S301: Obtain the maximum depth, maximum width, and maximum length of the target recessed area.
[0104] Step S302: Determine the first parameter based on the maximum depth and maximum width.
[0105] Step S303: Determine the second parameter based on the maximum width and maximum length.
[0106] Step S304: Calculate the average value of the first parameter and the second parameter, and determine the average value as the target structural feature parameter.
[0107] Step S305: Determine preset parameters based on the exploration results of the target depression area.
[0108] Step S306: Based on the target structural feature parameters and preset parameters, determine the target exploration parameters for the target depression area.
[0109] Step S307: Based on the target exploration parameters and the target prediction model, determine the average annual proven reserves of oil and gas resources corresponding to the target exploration parameters.
[0110] Example 5
[0111] Based on the above embodiments, Figure 4 This is a block diagram of an oil and gas resource reserve prediction device provided in an embodiment of this disclosure. Figure 4 As shown, an oil and gas resource reserve prediction device includes:
[0112] The acquisition module 401 is configured to acquire target structural feature parameters of the target depression region; the target structural feature parameters are used to characterize the structural features of the target depression region and are related to oil and gas reserves.
[0113] The determination module 402 is configured to determine the target exploration parameters of the target depression area based on the target structural feature parameters and preset parameters; the preset parameters are used to indicate the difficulty of exploring the target depression area.
[0114] The determination module 402 is also configured to determine the average annual proven oil and gas reserves corresponding to the target exploration parameters based on the target exploration parameters and the target prediction model; the target prediction model is used to predict the average annual proven oil and gas reserves of the depression area based on the exploration parameters.
[0115] In some embodiments, the acquisition module 401 is configured to:
[0116] Obtain the maximum depth, maximum width, and maximum length of the target depression region;
[0117] The first parameter is determined based on the maximum depth and maximum width; the first parameter is used to characterize the development of the source layer in the target depression region.
[0118] The second parameter is determined based on the maximum width and maximum length; the second parameter is used to characterize the reservoir development in the target depression region.
[0119] Based on the first and second parameters, the target structural feature parameters are determined.
[0120] In some embodiments, the acquisition module 401 is configured to:
[0121] Calculate the average value of the first and second parameters, and determine the average value as the target structural feature parameter.
[0122] In some embodiments, the determining module 402 is further configured to:
[0123] Based on the exploration results of the target depression area, preset parameters are determined.
[0124] In some embodiments, the determining module 402 is configured to:
[0125] When the exploration results of the target depression area indicate the presence of oil and gas resources, and the oil and gas reserves are known, the preset parameter is determined as the first preset parameter; or...
[0126] When the exploration results of the target depression area indicate the presence of oil and gas resources, but the reserves of these resources are unknown, the preset parameter is determined as the second preset parameter; or...
[0127] When the exploration results of the target depression area indicate that the probability of oil and gas resources existing in the target depression area is greater than or equal to a preset threshold, the preset parameter is determined as the third preset parameter; or,
[0128] When the exploration results of the target depression area indicate that the probability of oil and gas resources in the target depression area is less than the preset threshold, the preset parameter is determined to be the fourth preset parameter.
[0129] Among them, the first preset parameter is greater than the second preset parameter, the second preset parameter is greater than the third preset parameter, and the third preset parameter is greater than the fourth preset parameter.
[0130] In some embodiments, the determining module 402 is configured to:
[0131] Acquire training data, which includes multiple exploration parameters and the annual proven reserves of oil and gas resources corresponding to each exploration parameter;
[0132] Based on the training data, an initial prediction model is trained to obtain the target prediction model.
[0133] Example 6
[0134] Based on the above embodiments, this embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above embodiments.
[0135] In some embodiments of this example, a computer-readable storage medium is provided, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the steps of the method described in the above embodiments.
[0136] In some embodiments of this example, a computer program product is provided, including a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the method described in the above embodiments.
[0137] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for executing the methods described in the above embodiments.
[0138] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof. Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (e.g., hard disks, floppy disks, solid-state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).
[0139] Computer-readable storage media may also store at least one computer-executable program / instruction, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.
[0140] In addition, the electronic device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., a keyboard, a mouse, a speaker, etc.).
[0141] The processor can communicate with external devices via the I / O bus through wired or wireless networks.
[0142] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.
[0143] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.
[0144] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0145] While the embodiments disclosed herein are as described above, the foregoing content is merely for the purpose of facilitating understanding of this disclosure and is not intended to limit this disclosure. Any person skilled in the art to which this disclosure pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope of this disclosure; however, the scope of patent protection of this disclosure shall still be determined by the scope defined in the appended claims.
Claims
1. A method for predicting oil and gas resource reserves, characterized in that, include: Obtain the target structural feature parameters of the target concave region; The target structural feature parameters are used to characterize the structural features of the target depression region, and the target structural feature parameters are related to the oil and gas resource reserves. Based on the target structural feature parameters and preset parameters, the target exploration parameters of the target depression region are determined; The preset parameters are used to indicate the difficulty of exploring the target depression area; Based on the target exploration parameters and the target prediction model, the annual proven reserves of oil and gas resources corresponding to the target exploration parameters are determined. The target prediction model is used to predict the average annual proven oil and gas reserves in the depression area based on exploration parameters.
2. The method according to claim 1, characterized in that, The acquisition of target structural feature parameters of the target concave region includes: Obtain the maximum depth, maximum width, and maximum length of the target recessed region; Based on the maximum depth and the maximum width, a first parameter is determined; the first parameter is used to characterize the development of the source layer in the target depression region. A second parameter is determined based on the maximum width and the maximum length; the second parameter is used to characterize the reservoir development of the target depression region. Based on the first parameter and the second parameter, the target structural feature parameters are determined.
3. The method according to claim 2, characterized in that, The step of determining the target structural feature parameters based on the first parameter and the second parameter includes: Calculate the average value of the first parameter and the second parameter, and determine the average value as the target structural feature parameter.
4. The method according to claim 1, characterized in that, The method further includes: The preset parameters are determined based on the exploration results of the target depression area.
5. The method according to claim 4, characterized in that, The determination of the preset parameters based on the exploration results of the target depression area includes: When the exploration results of the target depression area indicate the presence of oil and gas resources, and the oil and gas reserves are known, the preset parameter is determined as the first preset parameter; or... When the exploration results of the target depression area indicate the presence of oil and gas resources, and the reserves of these resources are unknown, the preset parameter is determined to be the second preset parameter; or... When the exploration results of the target depression area indicate that the probability of oil and gas resources existing in the target depression area is greater than or equal to a preset threshold, the preset parameter is determined to be the third preset parameter; or... When the exploration results of the target depression area indicate that the probability of oil and gas resources in the target depression area is less than a preset threshold, the preset parameter is determined to be the fourth preset parameter. Wherein, the first preset parameter is greater than the second preset parameter, the second preset parameter is greater than the third preset parameter, and the third preset parameter is greater than the fourth preset parameter.
6. The method according to claim 1, characterized in that, The training process of the target prediction model includes: Acquire training data, which includes multiple exploration parameters and the annual proven reserves of oil and gas resources corresponding to each exploration parameter; Based on the training data, an initial prediction model is trained to obtain the target prediction model.
7. An oil and gas resource reserve prediction device, characterized in that, include: The acquisition module is configured to acquire target structural feature parameters of the target concave region. The target structural feature parameters are used to characterize the structural features of the target depression region, and the target structural feature parameters are related to the oil and gas resource reserves. The determination module is configured to determine the target exploration parameters of the target depression region based on the target structural feature parameters and preset parameters; The preset parameters are used to indicate the difficulty of exploring the target depression area, and the target exploration parameters are used to indicate the oil and gas reserves of the target depression area; The determination module is also configured to determine the average annual proven oil and gas reserves corresponding to the target exploration parameters based on the target exploration parameters and the target prediction model; the target prediction model is used to predict the average annual proven oil and gas reserves of the depression area based on the exploration parameters.
8. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.
10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 6.