Quantitative prediction method and device for quantity of oil and gas resources to be found in rift valley basin

Abstract

The invention discloses a quantitative prediction method and device for the quantity of oil and gas resources to be discovered in a rift valley basin, and the method comprises the steps: taking a rift valley basin oil and gas reservoir in a mature exploration stage as a sample oil and gas reservoir, and determining a target reservoir forming combination of the discovery workload and the main oil and gas contribution in the sample oil and gas reservoir; constructing a sample oil and gas reservoir reserve change model by taking the target reservoir-forming combination as an object unit; and predicting the to-be-discovered resource quantity of the to-be-predicted rift valley basin oil and gas reservoir based on the sample oil and gas reservoir reserve change model. According to the method, the rift valley basin in a mature exploration stage is taken as a data basis, the reserve prediction method which is suitable for the rift valley basin type and takes the reservoir forming combination as an object unit is provided, quantitative formula making and application operation flow refining are carried out, and the method provides a guidance basis for rift valley basin exploration workload deployment design and to-be-discovered resource potential prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration and resource potential evaluation, and in particular to a method and device for quantitatively predicting the amount of undiscovered oil and gas resources in a rift basin. Background Art

[0002] Understanding the undiscovered resource levels in oil and gas basins is essential for international oil and gas companies to conduct proactive strategic zoning and selection before resource-rich countries open up their territories. It also forms the basis for oil and gas companies to decide whether to enter the market and what contract prices to bid for after a resource-rich country opens up its territories. As the global competition for oil and gas resources intensifies, global oil and gas companies are extremely sensitive to information about the undiscovered resource levels in their target basins. Furthermore, given the vastly different geological conditions across different types of oil and gas basins, scientifically and quantitatively predicting the undiscovered resource levels in these basins has become a key factor influencing the survival and development of these companies.

[0003] Existing data indicate that rift basins develop extremely thick layers of source rock with extremely high organic matter abundance during the rifting phase, making them the most intensive hydrocarbon generation and the most abundantly enriched oil and gas resources among the world's six major petroliferous basin archetypes. Most rift basins are located onshore. Although these were the first to undergo oil and gas exploration, the extent of oil and gas exploration varies across rift basins worldwide due to varying geological histories and the internal environments of the resource-rich countries. This makes predicting the amount of undiscovered oil and gas resources difficult.

[0004] The existing reserve prediction methods can be roughly divided into three categories: statistical method, analogy method and genetic method. Among them, the genetic method is only applicable to the prediction of the total amount of remaining undiscovered resources; the analogy method belongs to qualitative evaluation and is only applicable to the semi-quantitative estimation of the remaining reserves in the exploration stage; the genetic method is applicable to the statistical method of quantitative prediction of reserves in future years. For example, Tong Xiaoguang (1991), Chen Yuanqian (2001) and Zhu Jie (2008) established mathematical prediction models for reserve growth based on different mathematical models (Pareto, logistic, Gompertz, Weng cycle, etc.); Gao Ruiqi (20 2002) and Chen Yuanqian (2010), who established a method for fitting the decline curve of historical production and the reserve-to-production ratio; Zheng Dewen (2004) and Zhuang Li (2012), who established a method for predicting remaining reserves using the reserve conversion rate; the United States Geological Survey (USGS) (2008) and Wu Yiping (2014), who established a global regional reserve growth prediction model based on the statistical trend line of the reserve growth coefficient along the oilfield age; Gordon Kaufman (1993), who established the discovery process method; and Li Peiran (1997), who established the scale sequence method. To summarize existing methods, they are based on the characteristics of proven recoverable reserves or production change data based on the year series or cumulative years of mature exploration areas. The method is to select an appropriate mathematical probability model for fitting, and the goal is to estimate the amount of undiscovered resources in specific oilfields, basins, and countries. Summary of the Invention

[0005] In order to quantitatively predict the amount of undiscovered oil and gas resources in rift-type basins or internal blocks at different stages of exploration and development, thereby enriching technical routes and increasing selection space, an embodiment of the present invention provides a method and device for quantitatively predicting the amount of undiscovered oil and gas resources in rift basins.

[0006] In a first aspect, an embodiment of the present invention provides a method for quantitatively predicting the amount of undiscovered oil and gas resources in a rift basin, which may include:

[0007] Taking rift basin oil and gas reservoirs in the mature exploration stage as sample oil and gas reservoirs, determine the target reservoir combination of discovery workload and major oil and gas contributions in the sample oil and gas reservoirs;

[0008] Constructing the sample oil and gas reservoir reserve change model with the target reservoir combination as the object unit;

[0009] The undiscovered resources of the oil and gas reservoirs in the to-be-predicted rift basin are predicted based on the sample oil and gas reservoir reserve change model.

[0010] Optionally, determining the target reservoir combination may include:

[0011] Taking the rift basin oil and gas reservoirs in the mature exploration stage as sample oil and gas reservoirs, the reservoir-forming assemblages of the sample oil and gas reservoirs are split based on the relationship between the cumulative number of exploration wells and the cumulative 2P reserves of the sample oil and gas reservoirs to obtain several reservoir-forming assemblages;

[0012] Comparing the stratigraphic ranges of the split several reservoir assemblages with those determined by the reservoir assembly division scheme of the sample oil and gas reservoir geological research to determine candidate reservoir assemblages;

[0013] Based on the alternative reservoir combinations, the alternative reservoir combinations are screened based on the changing trend of their cumulative 2P reserves versus the cumulative number of exploratory wells to determine the target reservoir combination; wherein the screening condition is that the trend line of the changing trend of the cumulative 2P reserves versus the cumulative number of exploratory wells is smooth and tends to a certain maximum value over the long term.

[0014] Optionally, before splitting the reservoir combination of the sample basin, the following can also be included:

[0015] A scatter plot relationship diagram was constructed based on the mining years and cumulative 2P reserves of rift basin oil and gas reservoirs. The initial sample oil and gas reservoirs for judging the mature exploration stage of rift basin oil and gas reservoirs were selected based on the stable change trend of the scatter plots of each rift basin.

[0016] The initial sample oil and gas reservoirs are used to determine whether they have entered the mature exploration stage, and the initial sample oil and gas reservoirs whose annual added 2P reserves are less than a preset percentage of the cumulative reserves or whose annual added production is less than a preset percentage of the cumulative production are regarded as rift basin oil and gas reservoirs in the mature exploration stage.

[0017] Optionally, after determining the target reservoir combination, the method may further include: judging whether the number of the target reservoir combination is one; if not, merging adjacent target reservoir combinations.

[0018] Optionally, constructing the sample oil and gas reservoir reserve change model with the target reservoir combination as the object unit may include:

[0019] Determining the underlying function model for constructing the sample oil and gas reservoir reserve change model based on the growth relationship trend of the target reservoir combination cumulative reserves versus the cumulative number of exploration wells;

[0020] The sample oil and gas reservoir reserve change model is constructed by taking the cumulative reserve abundance of the target reservoir combination as the objective function of the underlying function model and taking the cumulative exploration well density of the target reservoir combination as the model variable of the underlying function model;

[0021] Taking the scattered data of the relationship between the cumulative 2P reserves and the number of exploration wells of the target reservoir combination as statistical points, and the basic data of the exploration well controlled area and oil layer thickness in the target reservoir area, the relevant parameters in the sample oil and gas reservoir reserve change model are solved based on the least squares method to obtain the final sample oil and gas reservoir reserve change model.

[0022] Specifically, the sample oil and gas reservoir reserve change model is as follows:

[0023]

[0024] in,

[0025] m=r*A*D Formula②

[0026] n=g*A Formula ③

[0027] In the above formula, x is the cumulative number of exploratory wells, representing the cumulative intensity of drilling projects; Y is the cumulative 2P reserves of the reservoir combination; c is the drilling number threshold for reserve activation; r and g are adjustment coefficients (fixed values), where r is the adjustment coefficient of m, A, and D. In the above-mentioned reserve change attribute model in the embodiment of the present invention, the coefficient before the variable is fixed once the specific reserve change mathematical model formula is obtained by fitting the scattered data; similarly, g is similar, being the adjustment coefficient of n and A; m is a function of the exploratory well control area A and the oil layer thickness D; n is a function of the drilling control area A; k, a, b, and c are all constants to be determined, which are determined by fitting the scattered points of the geological model.

[0028] Optionally, constructing the sample oil and gas reservoir reserve change model with the target reservoir combination as the object unit may also include: judging whether the degree of fit between the scatter data and the fitting curve corresponding to the final sample oil and gas reservoir reserve change model meets a preset threshold; if not, subjecting the fitting curve to transformation processing by X and Y axis translation and scaling.

[0029] Optionally, the predicting of the undiscovered resources of the oil and gas reservoir in the to-be-predicted rift basin based on the sample oil and gas reservoir reserve change model may include:

[0030] Establishing a corresponding relationship between the reserves per unit oil layer thickness and the hydrocarbon supply of the rift basin oil and gas reservoir to be predicted and the sample oil and gas reservoir;

[0031] Based on the geological parameters and engineering parameters of the oil and gas reservoir in the rift basin to be predicted and the sample oil and gas reservoir, as well as the corresponding relationship between the reserves per unit oil layer thickness and the hydrocarbon supply, and the reserve change model of the sample oil and gas reservoir, a reserve prediction model of the basin-to-be-predicted oil and gas reservoir in the rift basin to be predicted is generated, and the reserve prediction model of the basin-to-be-predicted oil and gas reservoir in the rift basin to be predicted is used to predict the amount of undiscovered resources in the oil and gas reservoir in the rift basin to be predicted.

[0032] Specifically, based on the understanding that the reserves per unit oil layer thickness Y1 / D1 and Y2 / D2 of two reservoir combinations in two different basins are in direct proportion to the hydrocarbon supply Q1 and Q2, the relationship can be expressed as follows:

[0033]

[0034] Arranged as

[0035] Y1=D1*Y2*Q1 / (D2*Q2) Formula ⑤

[0036] If the drilling density of two reservoir combinations in different basins is the same, then the relationship between the number of drilling wells X1, X2 and the controlled area A1, A2 is as follows:

[0037]

[0038] Among them, P represents the ratio of the number of wells drilled in the target reservoir combination to the number of wells drilled in all oil and gas layers in the vertical direction.

[0039] Before determining the correlation between the above-mentioned rift basin oil and gas reservoirs to be predicted (the oil and gas reservoirs in the evaluated basin) and the sample (basin) oil and gas reservoirs, it is also necessary to obtain the key geological parameter hydrocarbon supply Q, which is referred to the following formula:

[0040] Hydrocarbon supply Q = A*H*TOC*F(Ro)*E*Ca Formula ⑦

[0041] Where: H represents the thickness of the source rock, in meters; TOC is the average organic matter abundance of the source rock, dimensionless; hydrocarbon generation efficiency F(Ro), in mg / gTOC; organic matter thermal maturity Ro, in %; hydrocarbon expulsion efficiency E, in %; Ca aggregation coefficient, in %.

[0042] Optionally, before establishing the corresponding relationship between the reserves per unit oil layer thickness and the hydrocarbon supply of the rift basin oil and gas reservoir to be predicted and the sample oil and gas reservoir, the following steps may also be included:

[0043] Determining whether the hydrocarbon reservoir combination of the rift basin to be predicted can be distinguished;

[0044] If so, prediction is performed based on the fact that the oil and gas reservoir in the rift basin to be predicted has the same reservoir-forming combination as the sample oil and gas reservoir as the main oil layer, so as to obtain the conventional oil and gas undiscovered resources of the oil and gas reservoir in the rift basin to be predicted;

[0045] If not, prediction is performed based on the reservoir thickness ratio of the rift basin oil and gas reservoir to be predicted and the sample oil and gas reservoir to obtain the unconventional oil and gas resources to be discovered in the rift basin oil and gas reservoir to be predicted.

[0046] Specifically, if the hydrocarbon reservoirs in the rift basin to be predicted can be distinguished, the reserve prediction model for the hydrocarbon reservoirs in the conventional oil and gas reserve prediction is:

[0047]

[0048] Combining formula ② and formula ③, we get

[0049] m=r*A1*D1 Formula 9

[0050] n=g*A1 formula ⑩

[0051] If the reservoir-forming assemblage of the rift basin to be predicted cannot be distinguished, the reserve prediction model for the reservoir-forming assemblage of the unconventional oil and gas reserves prediction in the basin to be predicted is:

[0052]

[0053] In a second aspect, an embodiment of the present invention provides a method for constructing a reserve change model for a rift basin oil and gas reservoir, which may include: taking a rift basin oil and gas reservoir in a mature exploration stage as a sample oil and gas reservoir, and determining a target reservoir combination of discovery workload and major oil and gas contributions in the sample oil and gas reservoir;

[0054] The reserve change model of the sample oil and gas reservoir is constructed with the target reservoir combination as the object unit.

[0055] In a third aspect, an embodiment of the present invention provides a device for quantitatively predicting the amount of undiscovered oil and gas resources in a rift basin, which may include:

[0056] A determination module is used to determine the target reservoir combination of discovery workload and major oil and gas contribution in the sample rift basin oil and gas reservoir using the oil and gas reservoir in the mature exploration stage as a sample oil and gas reservoir;

[0057] A construction module, configured to construct a reserve change model of the sample oil and gas reservoirs with the target reservoir combination as an object unit;

[0058] The prediction module is used to predict the undiscovered resources of the oil and gas reservoirs in the rift basin to be predicted based on the sample oil and gas reservoir reserve change model.

[0059] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for quantitatively predicting the amount of undiscovered oil and gas resources in a rift basin as described in the first aspect.

[0060] In a fifth aspect, an embodiment of the present invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for quantitatively predicting the amount of undiscovered oil and gas resources in a rift basin as described in the first aspect is implemented.

[0061] The beneficial effects of the above technical solutions provided by the embodiments of the present invention include at least:

[0062] The embodiment of the present invention provides a method and device for quantitatively predicting the amount of undiscovered oil and gas resources in a rift basin. The method uses a rift basin in a mature exploration stage as the data basis, proposes a reserve prediction method suitable for the rift basin type with reservoir combinations as units, makes a quantitative formula, and refines the application operation process. In specific implementation, as long as the basic geological conditions of the reservoir combination area, thickness, and source rock background are roughly distinguished, the potential for discovery reserves under the set exploration well workload conditions can be quickly obtained, regardless of whether the area is of low or high exploration degree. Furthermore, the method provides a guiding basis and guidance tool for the design of exploration workload deployment in rift basins and the prediction of the potential for undiscovered resources.

[0063] Furthermore, the above method requires the support of annual 2P reserves and exploration well number data in mature exploration areas around the world. The prediction object should be a global rift basin or a block within a rift basin, with a certain amount of continuous drilling engineering or drilling plan. The prediction object can be the risk exploration stage or the fine, rolling exploration stage. It is particularly suitable for the prediction of the entire life cycle of undiscovered resources that are currently in the medium and low exploration stages. In terms of resource types, it is suitable for the reserve prediction and evaluation of various resource types of conventional oil and gas and unconventional oil and gas. This invention has long-term application prospects in the fields of oil and gas resource evaluation and advanced area selection in basins around the world.

[0064] Furthermore, the present invention uses existing 2P reserves and drilling history data from typical mature basin reservoir combinations to establish a sample basin comparison mathematical model. Based on two custom key parameters—reservoir abundance related to reserves, reservoir volume, and hydrocarbon supply, and drilling density related to unit reservoir volume—and by analyzing the ratio of these two key parameters across reservoir combinations in different basins, a quantitative prediction model for oil and gas resources based on genetic and statistical methods is established. Clearly, once established, the comparison mathematical model and quantitative prediction model are long-term effective and applicable to other rift basins and blocks around the world at varying stages of exploration. There are no requirements for the target data being predicted; only data on reservoir combination division, reservoir thickness, and the hydrocarbon generation and expulsion capacity of the source rock are required. The resource potential of the target basin or block, from the first well to exploration exhaustion, and the corresponding drilling workload can be predicted.

[0065] This method uses reservoir combinations as units, effectively avoiding trend model distortion caused by mixed statistics. It is particularly applicable to rift basins; other types of basins require modified statistical models. It eliminates the impact of factors such as the discontinuity of exploration and development activities, the operator's activity patterns and intensity, and the degree of exploration and development on model accuracy. Geological parameter acquisition is flexible and can be selected using the thermogenic key parameter table provided in the embodiments of the present invention, or assigned based on detailed geological data.

[0066] Existing techniques rely on data on proven recoverable reserves or production changes, either sequentially or cumulatively, in mature exploration areas. These techniques employ appropriate mathematical probability models for fitting. Because they fail to account for differences in drilling intensity across regions and over time, existing techniques can only estimate the amount of exploration effort invested in adjacent blocks within the same basin over the same period, significantly limiting their scope of application and accuracy.

[0067] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0068] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0070] Figure 1 A simplified flow chart of a method for quantitatively predicting undiscovered oil and gas resources in a rift basin provided in an embodiment of the present invention;

[0071] Figure 2 A detailed flow chart of a method for quantitatively predicting undiscovered oil and gas resources in a rift basin provided in an embodiment of the present invention;

[0072] Figure 3 for Figure 1 Specific flow chart of step S11;

[0073] Figure 4 for Figure 1 Specific flow chart of step S12;

[0074] Figure 5 for Figure 1 Specific flow chart of step S13;

[0075] Figure 6A graph showing the cumulative 2P recoverable reserves of oil and gas in a typical rift basin (Bohai Bay Rift Basin) over time, provided in an embodiment of the present invention;

[0076] Figure 7 A graph showing the cumulative recoverable 2P reserves of oil and gas in a typical rift basin (West Siberian Rift Basin) around the world as a function of years, provided in an embodiment of the present invention;

[0077] Figure 8 A graph showing the change trend of oil and gas reserves in the West Siberian Basin as a function of the number of wells drilled is provided in an embodiment of the present invention;

[0078] Figure 9 A comparison chart of the reserves abundance per unit area of ​​oil and gas resources in different types of basins around the world provided in an embodiment of the present invention;

[0079] Figure 10 A graph showing the relationship between the cumulative number of exploratory wells drilled in the West Siberia Basin and time provided in an embodiment of the present invention;

[0080] Figure 11 A mathematical simulation fitting curve diagram for predicting reserves changes in the Lower Cretaceous of the West Siberian Basin provided in an embodiment of the present invention;

[0081] Figure 12 for Figure 11 Scatter fitting effect diagram after transformation (dashed line is before transformation, solid line is after transformation);

[0082] Figure 13 A comparison diagram of the predicted reserves curve and actual reserves of the Songliao Basin Quantou Formation reservoir combination provided in an embodiment of the present invention;

[0083] Figure 14 A statistical chart showing the cumulative number of exploratory wells drilled in the Lower Cretaceous Quantou Formation of the Songliao Basin over the years provided in an embodiment of the present invention;

[0084] Figure 15 This is a schematic diagram of the structure of a device for quantitatively predicting the amount of undiscovered oil and gas resources in a rift basin provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0085] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0086] Technical term explanation:

[0087] A rift basin refers to a basin in which the source rock strata develop during the basin rifting period and the hydrocarbon reservoir combination is finalized during the rift prototype period. A mature rift basin refers to a rift-type basin that has entered the rolling exploration stage, and its annual newly added 2P reserves (usually refers to recoverable reserves, that is, recoverable oil reserves calculated based on geological exploration results and engineering technology evaluation through a reasonable mining plan; while 1P reserves refer to oil reserves that have been mined) or production is less than 2% of the cumulative reserves or cumulative production. This embodiment of the present invention defines this type of basin as a rift-type basin in the rolling exploration stage or a mature rift basin. In this embodiment of the present invention, the cumulative discovered reserves are required to be consistent with the predicted target, either as production or as 2P technically recoverable reserves, so as to avoid differences in the definitions of reserves between China and abroad.

[0088] A target reservoir combination (suitable reservoir combination) refers to the discovery workload and the primary source of discoveries in a basin or block. This suitable reservoir combination is characterized by complete records of reserves, geology, engineering, and other data. Discovery workload and discovery are professional terms in oil and gas exploration and development. Discovery workload refers to the geological, seismic, and drilling engineering workload required for the initial discovery of oil and gas resources. Discovered reservoirs refer to discovered reservoirs or oil and gas field targets that make the primary oil and gas contribution to the basin's reservoirs. A reservoir combination can be a single reservoir combination or two or even three reservoir combinations with identical sedimentary and tectonic backgrounds and evolutionary processes, adjacent spatial overlap, and simultaneously explored and developed as a single set of exploration and development targets.

[0089] The number of exploratory wells refers to the number of all exploratory wells and appraisal wells drilled to discover and control oil and gas field reserves, including wildcat wells. If the accumulated reserves are production, the number of development wells must also be included.

[0090] In actual work, the inventors found that the above existing technical methods have at least one or more of the following problems: (1) The statistical method requires that the evaluation object must be within the statistical sample area, which limits the scope of application of the method. (2) The statistical method does not take into account the vertical differentiation of the strata, that is, the differences between multiple layers or multiple reservoir combinations of the exploration object. (3) The statistical method has too high requirements for the temporal continuity and integrity of the data. For example, if the exploration and development activities in the statistical sample area are continuous, the time series data cannot be missing, and it must enter the medium-to-high exploration maturity stage of the rolling development period to meet the needs of the method to establish a complete prediction curve model. Obviously, such example areas are rare in the world, and the method is not applicable to the medium-to-low exploration degree areas and new areas that are dominant in the world. (4) The statistical method and the analogy method lack consideration of factors affecting reserve changes, such as the size of the evaluation area, the fluctuation of exploration and development methods and activity intensity in different periods. For example, the patent "A method for predicting oil and gas reserve growth in complex fault basins" (201710740073.8) states that as the number of wells drilled increases, the reserves will increase indefinitely, which is obviously not in line with reality. (5) Statistical and analogical methods lack consideration of geological factors such as the differences in resource abundance between different basin types, different sedimentary lithologies in different reservoir combinations, and different trap types. This problem often results in the evaluation object being at a point with high discreteness in sample statistics, that is, the reserve change pattern of the evaluation object is seriously inconsistent with the statistical model, which seriously affects the reliability of the prediction results. (6) Existing statistical methods, analogical methods, and genetic methods do not consider scenarios involving unconventional oil and gas. In view of the above problems, the present invention is proposed to provide a method and device for quantitatively predicting the amount of undiscovered oil and gas resources in a rift basin that overcomes the above problems or at least partially solves the above problems.

[0091] The embodiment of the present invention provides a quantitative prediction method for the amount of oil and gas resources to be discovered in a rift basin, referring to Figure 1 and Figure 2 As shown, the prediction method may include the following steps:

[0092] Step S11: Taking the rift basin oil and gas reservoirs in the mature exploration stage as sample oil and gas reservoirs, determine the target reservoir combination of the discovery workload and the main oil and gas contribution in the sample oil and gas reservoirs.

[0093] Step S12: constructing a sample oil and gas reservoir reserve change model with the target reservoir combination as the object unit.

[0094] Step S13: predicting the undiscovered resources of the oil and gas reservoirs in the rift basin to be predicted based on the sample oil and gas reservoir reserve change model.

[0095] The quantitative prediction method for the amount of undiscovered oil and gas resources in the rift basin provided in the embodiment of the present invention uses the data of the rift basin in the mature exploration stage as the basis, proposes a reserve prediction method suitable for the rift basin type with the reservoir combination as the unit, creates a quantitative formula, and refines the application process. In specific implementation, as long as the basic geological conditions of the reservoir combination area, thickness, and source rock background are roughly distinguished, the potential for discovery reserves under the conditions of the set exploration well workload can be quickly obtained, regardless of whether the exploration level is low or high. Furthermore, this method provides a guiding basis and guidance tool for the deployment design of exploration workload in rift basins and the prediction of the potential for undiscovered resources.

[0096] In an alternative embodiment, referring to Figure 2 and Figure 3 As shown, determining the target reservoir combination in the above step S11 may specifically include the following steps:

[0097] Step S112: Taking the rift basin oil and gas reservoirs in the mature exploration stage as sample oil and gas reservoirs, the reservoir-forming assemblages of the sample oil and gas reservoirs are split based on the relationship between the cumulative number of exploration wells and the cumulative 2P reserves of the sample oil and gas reservoirs to obtain several reservoir-forming assemblages.

[0098] In the embodiment of the present invention, the cumulative 2P reserves of the sample basin N are analyzed as a function of the cumulative number of exploratory wells. The reservoir combination is split based on the sudden increase of the 2P reserves as the standard, until the trend line of the cumulative 2P reserves of each split reservoir combination as a function of the cumulative number of wells has no sudden change points. For example, the sudden change point can be combined with the graph, and the increase rate of two adjacent points is more than 1.5 times the increase rate of the previous multiple points as the standard to determine whether it is a sudden change point. For example, referring to the specific example Figure 8 As shown in the figure, the location where cumulative 2P reserves suddenly increase with engineering intensity (cumulative number of exploratory wells) is the mutation point. Because the entire basin may have multiple reservoir combinations, this step is split from left to right until the remaining right portion no longer has a mutation point.

[0099] Step S114: Compare the stratigraphic ranges of the split several reservoir assemblages with those determined by the reservoir assembly division scheme of the sample oil and gas reservoir geological research to determine candidate reservoir assemblages.

[0100] It should be noted that the reservoir combination determined by the geological research reservoir combination classification scheme in the embodiments of the present invention is a reservoir combination classification scheme unanimously recognized by geological researchers. This is obtained by researchers combining geological research, oilfield companies, and other expert literature. Alternative reservoir combinations are considered to comprehensively reflect and represent the characteristics of a typical rift basin, and are therefore temporarily considered as alternatives. (In actual operation, the target reservoir combination may change with the increase in the number of exploratory wells, the discovery workload, and the main oil and gas contributions.) The final selection process requires that the screening conditions in step S116 below are met.

[0101] In the embodiment of the present invention, when determining the alternative reservoir combination, when the stratigraphic range of the reservoir combination after splitting is smaller than the range of the geological research reservoir combination division plan, the former is selected as the alternative reservoir combination; when the stratigraphic range of the reservoir combination after splitting is larger than the range of the geological research reservoir combination division plan, the latter is selected as the alternative reservoir combination.

[0102] Step S116: Based on the alternative reservoir combinations, the alternative reservoir combinations are screened according to the changing trend of their cumulative 2P reserves with the cumulative number of exploratory wells to determine the target reservoir combination; wherein the screening condition is that the trend line of the changing trend of the cumulative 2P reserves with the cumulative number of exploratory wells is smooth and tends to a certain maximum value in the long term.

[0103] During the specific implementation of this step in the embodiment of the present invention, a rectangular coordinate diagram is drawn for each candidate reservoir combination to show the relationship between the cumulative 2P reserves and the cumulative number of exploratory wells. A single reservoir combination with a smooth trend line and a cumulative 2P reserve value that tends to a maximum value in the long term as the cumulative number of wells drilled increases is selected as the final suitable reservoir combination.

[0104] In another optional embodiment, also referring to Figure 3 As shown, before splitting the reservoir combination of the sample basin, the following steps may be included:

[0105] Step S110: construct a scatter plot based on the mining years and cumulative 2P reserves of the rift basin oil and gas reservoirs, and select initial sample oil and gas reservoirs for judging the mature exploration stage of the rift basin oil and gas reservoirs based on the stable change trend of the scatter plots of each rift basin.

[0106] In the embodiment of the present invention, the global oil and gas basin database provided by S&P (Standard & Poor's, abbreviated as S&P) is used as the data source. Several typical rift basins {A, B, ...} are selected, and scatter plots are prepared with year as the horizontal axis and total hydrocarbon cumulative 2P reserves as the vertical axis. Then, based on the scatter plot trends of each basin, basins N with rapid growth in the early stage, stable growth in the middle stage, and long-term slow growth in the late stage that is basically stable and close to a certain extreme value are selected as samples for judging whether they have entered the exploration maturity stage, and these are used as basin samples. It can also be used to judge whether the selected typical rift basins have entered the exploration maturity stage. For basins that have entered the exploration maturity stage, the annual 2P reserves data is also required to be complete before the next analysis.

[0107] Step S111: Determine whether the initial sample oil and gas reservoir has entered the mature exploration stage, and define the initial sample oil and gas reservoir whose annual added 2P reserves are less than a preset percentage of the cumulative reserves or whose annual added production is less than a preset percentage of the cumulative production as a rift basin oil and gas reservoir in the mature exploration stage.

[0108] In an embodiment of the present invention, the preset percentage may be 2%. For example, an initial sample oil and gas reservoir whose annual added 2P reserves is less than 2% of the cumulative reserves or whose annual added production is less than 2% of the cumulative production is regarded as a rift basin oil and gas reservoir in the mature exploration stage.

[0109] In another optional embodiment, also referring to Figure 3 As shown, after determining the target reservoir combination, the following steps may also include: Step S117: Determining whether there is only one target reservoir combination; if not, executing Step S118: Merging adjacent target reservoir combinations. In this step, if more than one target reservoir combination remains within the final selection, adjacent layers may be merged to facilitate the subsequent establishment of a stable sample reservoir reserve change model.

[0110] In the embodiment of the present invention, in the above-mentioned step S11, when determining the target reservoir combination of the discovery workload and the main oil and gas contribution in the sample oil and gas reservoir, the selected reserves account for a large proportion in the entire basin, the curve characteristics of the reserves change with the number of exploration wells are relatively smooth and continuous, and the cumulative 2P recoverable reserves value in the middle and late stages tends to a certain maximum value. This can represent that the data records are relatively complete and true, and can reflect the entire process of each exploration stage such as risk exploration, fine exploration, and rolling exploration experienced by the representative exploration area. Such a reservoir combination provides a data basis for the subsequent step of constructing a sample oil and gas reservoir reserve change model.

[0111] In another optional embodiment, the target reservoir combination is used as the object unit to construct the sample oil and gas reservoir reserve change model in the above step S12, that is, the target reservoir combination finally selected in the above step S11 is used as the object unit to construct the sample oil and gas reservoir reserve change model of the embodiment of the present invention. Figure 2 and Figure 4 As shown, the following steps may be specifically included:

[0112] Step S121: Determine the underlying function model for constructing the sample oil and gas reservoir reserve change model based on the growth relationship trend of the cumulative reserves of the target reservoir combination with the cumulative number of exploration wells.

[0113] In this step, a suitable curve model is selected for the target reservoir assemblage, and then used as the underlying function to construct the innovative statistical mathematical model proposed in the embodiments of the present invention. In the embodiments of the present invention, a suitable curve model is required to fully fit the growth trend of cumulative reserves as a function of the cumulative number of exploratory wells. A logistic curve model, a Gompertz curve model, a modified exponential curve model, an Ong cycle model, or other models can be selected. Based on the growth trend of cumulative reserves as a function of the cumulative number of exploratory wells in the target reservoir assemblage of rift basin oil and gas reservoirs, the embodiments of the present invention preferably use a logistic curve model as the underlying function.

[0114] Step S122: Using the cumulative reserve abundance of the target reservoir combination as the objective function of the underlying function model and the cumulative exploration well density of the target reservoir combination as the model variable of the underlying function model, a sample oil and gas reservoir reserve change model is constructed.

[0115] Through research on rift basin oil and gas reservoirs, the inventors discovered that, given sufficient exploration effort, the maximum reserves discovered in a given reservoir assemblage are related to the reservoir thickness and the area under evaluation. However, once a block is identified and before all reserves are discovered, the annual drilling volume (number of exploratory wells) varies with investment decisions. Exploration time is not a direct factor influencing reserve discovery; the true determining factor is the cumulative intensity of exploratory (drilling) projects. Based on this assumption, a mathematical model for reserve variation, with reservoir assemblage as the underlying function, was constructed as follows:

[0116]

[0117] in,

[0118] m=r*A*D Formula②

[0119] n=g*A Formula ③

[0120] In the above formula, x is the cumulative number of exploratory wells, representing the cumulative intensity of drilling projects; Y is the cumulative 2P reserves of the reservoir combination; c is the drilling number threshold for reserve activation; r and g are adjustment coefficients (fixed values), where r is the adjustment coefficient of m, A, and D. In the above-mentioned reserve change attribute model in the embodiment of the present invention, the coefficient before the variable is fixed once the specific reserve change mathematical model formula is obtained by fitting the scattered data; similarly, g is similar, being the adjustment coefficient of n and A; m is a function of the exploratory well control area A and the oil layer thickness D; n is a function of the drilling control area A; k, a, b, and c are all constants to be determined, which are determined by fitting the scattered points of the geological model.

[0121] Step S123: Using the scattered data of the relationship between the cumulative 2P reserves and the number of exploration wells of the target reservoir combination as statistical points, and the basic data of the exploration well controlled area and oil layer thickness in the target reservoir area, the relevant parameters in the sample oil and gas reservoir reserve change model are solved based on the least squares method to obtain the final sample oil and gas reservoir reserve change model.

[0122] In this embodiment of the present invention, the controlled area A of all exploration wells and the oil layer thickness D of the reservoir combination in the region corresponding to the target reservoir combination of the sample oil and gas reservoir can be obtained by querying the mining rights information of the oilfield company. In this step, the scattered data of the relationship between the cumulative 2P reserves and exploration wells of the target reservoir combination of the sample oil and gas reservoir is used as statistical points. Formulas 1, 2, and 3 of the mathematical model of reserve change are used as fitting formulas. The above geological parameters A and D are substituted into the fitting, and the least squares method is used to directly obtain the optimal solution for k, a, b, and c. The optimal solution for r and g is indirectly obtained by solving for m and n.

[0123] In another alternative embodiment, referring to Figure 4 As shown, the above step S12 may further include the following steps:

[0124] Step S124: Determine whether the degree of fit between the scattered data and the fitting curve corresponding to the final sample oil and gas reservoir reserve change model meets a preset threshold. If not, proceed to step S125, which transforms the fitting curve by performing X and Y axis translation and scaling.

[0125] In this embodiment, when the sample basin oil and gas reservoir data is partially unreasonable, for example, referring to the specific example Figure 11 As shown in the figure, after fitting the above model, although the correlation coefficient is high, there is a large deviation (greater than a certain preset threshold) in the shoulder of the graph where the increase is rapid or the increase is rapid. Therefore, in order to achieve a high degree of consistency between the fitting curve and the scatter points, the transformation processing of X-axis translation and scaling can be performed to ensure high consistency from the low-value area to the high-value area until a large mutation point in the high-value area is reached (the mutation point threshold is set during specific implementation), and the final mathematical model for reserve change prediction is obtained.

[0126] In another optional embodiment, in the above step S13, the amount of undiscovered resources of the oil and gas reservoir to be predicted in the rift basin is predicted based on the reserve change model of the sample oil and gas reservoir, referring to Figure 2 and Figure 5 As shown, the following steps may be specifically included:

[0127] Step S133: establishing a corresponding relationship between the reserves per unit oil layer thickness and the hydrocarbon supply of the rift basin oil and gas reservoir to be predicted and the sample oil and gas reservoir.

[0128] This step determines the relationship between the predicted rift basin reservoir (the evaluated basin reservoir) and the sample (basin) reservoir. It should also be noted that in the present embodiment, the parameters in the formulas defining the sample (basin) reservoir assemblage are assigned a subscript of 1, while the parameters in the formulas defining the predicted rift basin reservoir (the evaluated basin reservoir) assemblage are assigned a subscript of 2.

[0129] Specifically, it is based on the understanding that the reserves per unit oil layer thickness Y1 / D1 and Y2 / D2 of two reservoir combinations in two different basins are in direct proportion to the hydrocarbon supply Q1 and Q2, which can be expressed as follows:

[0130]

[0131] Arranged as

[0132] Y1=D1*Y2*Q1 / (D2*Q2) Formula ⑤

[0133] If the drilling density of two reservoir combinations in different basins is the same, then the relationship between the number of drilling wells X1, X2 and the controlled area A1, A2 is as follows:

[0134]

[0135] Among them, P represents the ratio of the number of wells drilled in the target reservoir combination to the number of wells drilled in all oil and gas layers in the vertical direction.

[0136] Before determining the correlation between the above-mentioned rift basin oil and gas reservoirs to be predicted (the oil and gas reservoirs in the evaluated basin) and the sample (basin) oil and gas reservoirs, it is also necessary to obtain the key geological parameter hydrocarbon supply Q, which is referred to the following formula:

[0137] Hydrocarbon supply Q = A*H*TOC*F(Ro)*E*Ca Formula ⑦

[0138] Where: H represents the thickness of the source rock, in meters; TOC is the average organic matter abundance of the source rock, dimensionless; hydrocarbon generation efficiency F(Ro), in mg / gTOC; organic matter thermal maturity Ro, in %; hydrocarbon expulsion efficiency E, in %; Ca aggregation coefficient, in %.

[0139] The exploration well's controlled area, A, can be determined from the oilfield company's mining rights information. The thickness, H, organic matter abundance TOC, organic matter thermal maturity, Ro, and organic matter type can all be determined from geological evaluation results. Other parameters can be determined based on the source rock's organic matter type and maturity classification, as shown in Table 1 below. If in-depth research data on hydrocarbon generation and expulsion efficiency and aggregation coefficient is available for the sample basin and the basin being evaluated, such as thermal simulation data for hydrocarbon generation and expulsion, the values ​​of these parameters can be determined based on this research data.

[0140] Parameter Table 1 Reference table for input values ​​of parameters for different types of organic matter source rocks

[0141]

[0142] Step S134: Based on the geological parameters and engineering parameters of the oil and gas reservoir in the rift basin to be predicted and the sample oil and gas reservoir, as well as the correspondence between the reserves per unit oil layer thickness and the hydrocarbon supply, and the reserve change model of the sample oil and gas reservoir, a reserve prediction model of the reservoir-forming combination in the rift basin to be predicted is generated, and the amount of undiscovered resources in the oil and gas reservoir in the rift basin to be predicted is predicted using the reserve prediction model of the reservoir-forming combination in the rift basin to be predicted.

[0143] This step first determines the reserve prediction model for the reservoir combination in the basin to be predicted. In the specific implementation, X1 and A1 are used as the cumulative reserves and cumulative number of exploration wells of a single reservoir combination in the sample basin, and X2 and A2 are used as the cumulative reserves and cumulative number of exploration wells of any reservoir combination in the evaluated basin (the rift basin oil and gas reservoir to be predicted). Substituting formulas ⑤ and ⑥ into formula ①, we can get

[0144]

[0145] Combined with formula ⑦, it can be organized into

[0146]

[0147] Combining formula ② and formula ③, we get

[0148] m=r*A1*D1 Formula 9

[0149] n=g*A1 formula ⑩

[0150] In another alternative embodiment, referring to Figure 5 As shown, before establishing the corresponding relationship between the reserves per unit oil layer thickness and the hydrocarbon supply of the rift basin oil and gas reservoir to be predicted and the sample oil and gas reservoir, the following steps may also be included:

[0151] Step S130: Determine whether the hydrocarbon reservoir combination of the rift basin to be predicted can be distinguished.

[0152] If yes, step S131 is executed, i.e. prediction is performed based on the same reservoir combination of the rift basin oil and gas reservoir to be predicted and the sample oil and gas reservoir as the main oil layer to obtain the conventional oil and gas undiscovered resources of the rift basin oil and gas reservoir to be predicted.

[0153] Conventional oil and gas reserve prediction: (1) When the reservoir combinations of the evaluated rift basin can be distinguished, find and obtain the geological parameters and constant parameters of the main reservoir combinations of the sample basin and each reservoir combination of the evaluated basin required by Formula ⑧, Formula ⑨ and Formula ⑩; (2) According to the continuation or planned arrangement of the exploration drilling workload of the evaluated rift basin, set the number of exploration wells in each year, substitute the cumulative number of exploration wells in different years into Formula ⑧, and calculate the cumulative reserves of each reservoir combination in each year; (3) Add up the cumulative reserves of each reservoir combination in each year, which is the cumulative reserves of the entire evaluated basin in each year; (4) For basins with discoveries, subtract the existing discovered reserves from the cumulative reserves in future years to obtain the amount of resources to be discovered.

[0154] If not, step S132 is executed, that is, prediction is performed based on the reservoir thickness ratio of the rift basin oil and gas reservoir to be predicted and the sample oil and gas reservoir to obtain the unconventional oil and gas undiscovered resources of the rift basin oil and gas reservoir to be predicted.

[0155] Unconventional oil and gas resource prediction: The other steps and formulas are the same. It is necessary to replace the single reservoir combination of the sample basin and the reservoir combination of the evaluated basin with unconventional oil and gas layers. The relevant geological parameters are also selected as the geological parameters of unconventional oil and gas layers. The formula ⑧ of the reserve prediction model of the reservoir combination of the predicted basin is changed to formula as follows:

[0156]

[0157] In a specific example, the Songliao Basin oil and gas reservoir is used as an evaluation basin (rift basin oil and gas reservoir to be predicted) for illustration:

[0158] 1. Using mature rift basins as samples, select suitable reservoir combinations as target reservoir combinations

[0159] (1) Selecting samples from mature rift basins for exploration

[0160] The relationship between the change of oil and gas 2P reserves over time in typical rift basins around the world is statistically analyzed using years as the horizontal axis and cumulative 2P recoverable reserves as the vertical axis. According to general knowledge, in the process of oil and gas exploration in a basin, as the exploration workload is implemented, the amount of reserves discovered will experience a process from rapid growth to stable growth and then to slow growth. The long-term slow growth of reserves represents that the exploration discovery has entered the mature exploration stage. Figure 6 and Figure 7As shown, the West Siberian Basin, which has a smooth overall change trend, slow growth in the past 20 years and tends to be stable, is selected as a representative for the next analysis.

[0161] (2) Separation of reservoir-forming assemblages in the West Siberian Basin

[0162] According to the current geological conditions of oil and gas discovery in the West Siberian Basin, the 2P reserves are divided according to single reservoir combination, and the trend of the cumulative oil and gas 2P reserves of a single reservoir combination with the total number of exploratory wells, appraisal wells and wildcat wells drilled is statistically analyzed. Figure 8 As shown in Figure 2, in this example, the West Siberian Basin is divided into five reservoir-forming assemblages: Upper Cretaceous, Lower Cretaceous, Upper Jurassic, Middle Jurassic, and Lower Jurassic (Pre-Jurassic). Figure 8 The relationship between the cumulative 2P recoverable reserves of each reservoir combination and the number of wildcat wells drilled is statistically analyzed.

[0163] (3) Screening of suitable reservoir combinations (target reservoir combinations)

[0164] Based on the trend of 2P recoverable reserves of a single reservoir assemblage as a function of the number of exploratory wells drilled, suitable reservoirs were selected as units for statistical analysis of cumulative 2P reserves and cumulative well counts. A suitable reservoir assemblage is one that represents a significant proportion of reserves within the entire basin, exhibits a relatively smooth and continuous curve of reserves versus exploratory well count, and exhibits a tendency toward a maximum in the middle and later stages of the cumulative 2P recoverable reserves. This indicates that the data record is relatively complete and authentic, reflecting the entire exploration process of the exploration area, including risk exploration, detailed exploration, and rolling exploration. Therefore, based on the above-mentioned criteria for suitable reservoirs, the Lower Cretaceous reservoir assemblage was selected as the reservoir assemblage analysis unit.

[0165] 2. Establish a mathematical model of oil and gas reserves changes in sample basins using the Cretaceous reservoir-forming assemblage as the object unit

[0166] (1) Selection of underlying functions

[0167] According to the statistics of the reserves abundance per unit area of ​​discovered oil and gas resources in different types of basins around the world, Figure 9 As shown, rift basins have more developed source rocks, higher reserves per unit area, and greater differentiation of oil and gas enrichment than other basins. Consequently, the rapid growth phase of exploration and discovery is less pronounced, while the stable growth phase is dominated by the discovery of structural trap-type oil and gas reservoirs with large reserves. Therefore, among mathematical models representing reserve growth, such as the logistic curve model, the Gompertz curve model, the modified exponential curve model, and the Weng cycle model, the logistic curve model, which is suitable for the characteristics of rift basins, was selected as the underlying function between cumulative 2P reserves of oil and gas and the cumulative number of exploratory wells.

[0168] (2) Establishment of mathematical model for predicting reserve changes in sample basins

[0169] Statistics on the relationship between the cumulative number of exploratory wells drilled in the West Siberian Basin and time, refer to Figure 10 As shown in the figure, it can be seen that during the basin exploration process, the intensity of exploration drilling wells implemented in each year is not the same, and the differences are huge. Figure 8 The horizontal axis selects the number of drilled wells instead of the year.

[0170] Taking the cumulative reserve abundance Y / m as the function of the underlying model and the cumulative exploration well density (Xc) / n as the variable of the underlying model, the mathematical model for predicting reserve changes proposed by the core innovation of the embodiment of the present invention is established as follows:

[0171]

[0172] in,

[0173] m=r*A*D Formula②

[0174] n=g*A Formula ③

[0175] In the above formula, c is the drilling number threshold for reserve activation; r and g are adjustment coefficients, which are indirect constants to be determined; m is a function of the exploration well control area A and the reservoir thickness D of the reservoir; n is a function of the drilling control area A; k, a, b, and c are all constants to be determined, k>0, a>0, and 0<b<1.

[0176] (3) Determination of geological parameters in the mathematical model for predicting reserve changes in sample basins

[0177] According to comparative oil and gas source analysis, the oil and gas in the Lower Cretaceous reservoir of the West Siberian Basin originate from the asphaltene-bearing shale of the Bazhenov Formation, a marine shale formation from the Tithonian at the top of the Jurassic to the bottom of the Lower Cretaceous in the Berriasian. Its organic matter type is I-II1, with a current maturity of 0.6-1.3%, an average TOC of 7%, an average effective thickness of 35m, and a distribution area exceeding 160×10 4 km 2 Overall, the source rock layer is thicker in the north and thinner in the south, with higher maturity in the north and lower in the south. Based on the organic matter type and maturity, combined with Table 1, it is determined that the average hydrocarbon generation efficiency G of the source rock layer is approximately 400 mg / g TOC. The average stratigraphic thickness of the source rock layer is 35 m, and the thickness of the oil layer D in the Lower Cretaceous reservoir assemblage ranges from 18.7 to 92.5 m, with an average thickness of 55.6 m. The average organic matter abundance TOC is 7%. The hydrocarbon expulsion coefficient E is 65%, and the aggregation coefficient Ca is 10%.

[0178] According to Q=H*TOC*F2(Ro)*E formula⑧

[0179] Q = 35 * 7 * 400 * 65 = 6370000

[0180] (4) Determination of the constants to be determined in the mathematical model for predicting reserve changes in sample basins

[0181] After selecting the Lower Cretaceous reservoir combination as the appropriate reservoir combination for the basin sample, the cumulative reserves and cumulative exploration well data groups were used as statistical points, and the above-mentioned reserve change prediction mathematical model was used for fitting, and the value of the constant to be determined was obtained by the least squares method. The fitting results are shown in Figure 2. Figure 11 As shown,

[0182] m=64.9719

[0183] n=0.205243

[0184] a=0.00653196

[0185] b=0.999216

[0186] c=14.0101

[0187] Fitting correlation coefficient R 2 =0.977935.

[0188] (5) Determine the preliminary mathematical model for predicting reserve changes in sample basins

[0189] Therefore, the preliminary mathematical model for rift basin reserves prediction is obtained as follows:

[0190]

[0191] in,

[0192] m=64.9719=r*A*D=r*160*55.6

[0193] n=0.205243=g*A=g*160

[0194] (6) Determine the final mathematical model for predicting reserve changes in sample basins

[0195] Reference Figure 11 As shown in the figure, errors in the selection of reservoir assemblages or data limitations in the S&P database (incomplete statistics on well counts or reserves, and possibly inconsistent calibers in the historical statistical splitting of reserves between different reservoir assemblages) can result in a high correlation but poor agreement between the fitted line and the scattered points. Therefore, it is necessary to transform the X and Y axes to maintain high agreement from low-value areas to high-value areas, until the high-value areas are dominated by large mutation points, thus obtaining the final mathematical model for predicting reserve changes. Figure 12 The transformed image is obviously more consistent than Figure 11 There is a great improvement, and the final mathematical model for reserve change prediction is:

[0196]

[0197] m=64.9719=r*A*D=r*1600000*55.6 Formula ②

[0198] n=0.205243=g*A=g*1600000 Formula ③

[0199] 3. Using the rift basin reservoir-forming assemblage reserve prediction model (sample oil and gas reservoir reserve change model), verify and predict the reserves of the Quantou Formation (100-112 Ma) in the Songliao Basin

[0200] In this example, the parameters of the Lower Cretaceous typical hydrocarbon reservoir of the West Siberian Basin, which is the sample basin, are marked with a subscript of 1, and the parameters of the Quantou Formation hydrocarbon reservoir of the Songliao Basin, which is the predicted basin, are marked with a subscript of 2. Substituting formulas ⑤ and ⑥ into formula 01, the result is:

[0201]

[0202] According to existing research results, the Songliao Basin covers an area of ​​26×10 4 km 2 . The main source rock layer of the Quantou Formation is the first member of the Qingshankou Formation. The thickness of the source rock layer is 27-164m, with an average of 90m; the organic matter abundance is generally distributed in the range of 2%-3.6%, with an average of 2.8%; the maturity Ro is 0.8%-1.0%, and the current hydrogen index is 600mg / gTOC. Based on the type and maturity of organic matter, combined with Table 1, it can be judged that the average hydrocarbon generation efficiency G of the source rock layer is about 310mg / gTOC. The average thickness of the source rock layer is 90m, and the reservoir area is 137277.08km 2 The average TOC of organic matter is 2.8%. The hydrocarbon expulsion coefficient E is 65%, and the aggregation coefficient Ca is determined to be 4% based on the number of reservoirs corresponding to the source rock. The reservoir thickness of the Quantou formation reservoir assemblage is 20-60m, with an average of 30m. Based on the various parameter values ​​of the Lower Cretaceous reservoir assemblage in the West Siberian Basin and the Quantou formation reservoir assemblage in the Songliao Basin, as well as formula ⑦, and substituted into formula ⑤, we get

[0203]

[0204]

[0205] According to the parameter values ​​of the Lower Cretaceous reservoir combination in the West Siberian Basin and the Quantou Formation reservoir combination in the Songliao Basin, considering that the number of wells drilled with the Quantou Formation as the target layer is not the main oil layer, according to the reservoir thickness ratio, P1 is taken as 1.0, P2 is taken as 0.11, and substituted into formula ⑥, we get

[0206]

[0207]

[0208] Substituting formulas ④ and ⑤ into formulas ① and ②, we obtain the following predicted relationship between the cumulative 2P reserves Y2 of the Quantou Formation and the cumulative number of wells drilled X2:

[0209]

[0210] Reference Figure 13 As shown in the figure, the predicted curve of the reserves of the Quantou Formation (100-112Ma) in the Songliao Basin along with the number of wells drilled is determined. It can be seen that the actual 2P reserves before 2022 are consistent with the predicted values. Figure 14 As shown in the figure, since 2000, the annual drilling project investment has decreased significantly. If the number of drilling wells increases in the future, the cumulative 2P reserves will not increase significantly. The increase in reserves in the latter stage may be related to the investment in unconventional oil and gas horizontal well development technology. In this example, Figure 14 The vertical axis and Figure 13 The horizontal axis is consistent, both graphs are monotonic, combined Figure 13 and Figure 14 That is, the changes in reserves and the number of wells drilled over time.

[0211] The above-mentioned method provided in the embodiments of the present invention, based on data from well-explored basins, proposes a reserve prediction method for rift basins, using reservoir-forming assemblages as units. The method is expressed in quantitative formulas, and its application process is concise. By roughly understanding the area, thickness, and basic geological conditions of the source rock background of the reservoir-forming assemblages, the potential for discovered reserves can be quickly determined under a given exploration well workload, regardless of whether the exploration level is low or high. This method provides a basis and tool for planning exploration workload deployment in rift basins and predicting the potential for undiscovered resources.

[0212] Furthermore, the above method requires the support of annual 2P reserves and exploration well number data in mature exploration areas around the world. The prediction object should be a global rift basin or a block within a rift basin, with a certain amount of continuous drilling engineering or drilling plan. The prediction object can be the risk exploration stage or the fine, rolling exploration stage. It is particularly suitable for the prediction of the entire life cycle of undiscovered resources that are currently in the medium and low exploration stages. In terms of resource types, it is suitable for the reserve prediction and evaluation of various resource types of conventional oil and gas and unconventional oil and gas. This invention has long-term application prospects in the fields of oil and gas resource evaluation and advanced area selection in basins around the world.

[0213] Furthermore, the present invention uses existing 2P reserves and drilling history data from typical mature basin reservoir combinations to establish a sample basin comparison mathematical model. Based on two custom key parameters—reservoir abundance related to reserves, reservoir volume, and hydrocarbon supply, and drilling density related to unit reservoir volume—and by analyzing the ratio of these two key parameters across reservoir combinations in different basins, a quantitative prediction model for oil and gas resources based on genetic and statistical methods is established. Clearly, once established, the comparison mathematical model and quantitative prediction model are long-term effective and applicable to other rift basins and blocks around the world at varying stages of exploration. There are no requirements for the target data being predicted; only data on reservoir combination division, reservoir thickness, and the hydrocarbon generation and expulsion capacity of the source rock are required. The resource potential of the target basin or block, from the first well to exploration exhaustion, and the corresponding drilling workload can be predicted.

[0214] The prediction method provided in the embodiments of the present invention uses reservoir combinations as units, effectively avoiding trend model distortion caused by mixed statistics. It is particularly applicable to rift basins; other types of basins require modified statistical models. The impact of factors such as the discontinuity of exploration and development activities, the operator's activity patterns and intensity, and the degree of exploration and development on model accuracy is eliminated. Geological parameter acquisition is flexible and can be selected using the thermogenic key parameter table provided in the embodiments of the present invention, or assigned based on detailed geological data.

[0215] Existing techniques rely on data on proven recoverable reserves or production changes, either sequentially or cumulatively, in mature exploration areas. These techniques employ appropriate mathematical probability models for fitting. Because they fail to account for differences in drilling intensity across regions and over time, existing techniques can only estimate the amount of exploration effort invested in adjacent blocks within the same basin over the same period, significantly limiting their scope of application and accuracy.

[0216] Based on the same inventive concept, an embodiment of the present invention further provides a device for quantitatively predicting the amount of undiscovered oil and gas resources in a rift basin, comprising: a determination module 11, a construction module 12, and a prediction module 13. The working principle of the device is as follows:

[0217] The determination module 11 is used to determine the target reservoir combination of the discovery workload and the main oil and gas contribution in the sample oil and gas reservoir by taking the rift basin oil and gas reservoir in the mature exploration stage as the sample oil and gas reservoir;

[0218] The construction module 12 is used to construct a sample oil and gas reservoir reserve change model with the target reservoir combination as the object unit;

[0219] The prediction module 13 is used to predict the undiscovered resources of the oil and gas reservoirs in the rift basin to be predicted based on the sample oil and gas reservoir reserve change model.

[0220] In an optional embodiment, the determining module 11 is specifically configured to:

[0221] Taking the rift basin oil and gas reservoirs in the mature exploration stage as sample oil and gas reservoirs, the reservoir-forming assemblages of the sample oil and gas reservoirs are split based on the relationship between the cumulative number of exploration wells and the cumulative 2P reserves of the sample oil and gas reservoirs to obtain several reservoir-forming assemblages;

[0222] Comparing the stratigraphic ranges of the split several reservoir assemblages with those determined by the reservoir assembly division scheme of the sample oil and gas reservoir geological research to determine candidate reservoir assemblages;

[0223] Based on the alternative reservoir combinations, the alternative reservoir combinations are screened based on the changing trend of their cumulative 2P reserves versus the cumulative number of exploratory wells to determine the target reservoir combination; wherein the screening condition is that the trend line of the changing trend of the cumulative 2P reserves versus the cumulative number of exploratory wells is smooth and tends to a certain maximum value over the long term.

[0224] In another optional embodiment, the determining module 11 is further configured to:

[0225] A scatter plot relationship diagram was constructed based on the mining years and cumulative 2P reserves of rift basin oil and gas reservoirs. The initial sample oil and gas reservoirs for judging the mature exploration stage of rift basin oil and gas reservoirs were selected based on the stable change trend of the scatter plots of each rift basin.

[0226] The initial sample oil and gas reservoirs are used to determine whether they have entered the mature exploration stage, and the initial sample oil and gas reservoirs whose annual added 2P reserves are less than a preset percentage of the cumulative reserves or whose annual added production is less than a preset percentage of the cumulative production are regarded as rift basin oil and gas reservoirs in the mature exploration stage.

[0227] In another optional embodiment, the determination module 11 is further configured to: determine whether the number of the target reservoir combination is one; if not, merge adjacent target reservoir combinations.

[0228] In another optional embodiment, the construction module 12 is specifically configured to:

[0229] Determining the underlying function model for constructing the sample oil and gas reservoir reserve change model based on the growth relationship trend of the target reservoir combination cumulative reserves versus the cumulative number of exploration wells;

[0230] The sample oil and gas reservoir reserve change model is constructed by taking the cumulative reserve abundance of the target reservoir combination as the objective function of the underlying function model and taking the cumulative exploration well density of the target reservoir combination as the model variable of the underlying function model;

[0231] Taking the scattered data of the relationship between the cumulative 2P reserves and the number of exploration wells of the target reservoir combination as statistical points, and the basic data of the exploration well controlled area and oil layer thickness in the target reservoir area, the relevant parameters in the sample oil and gas reservoir reserve change model are solved based on the least squares method to obtain the final sample oil and gas reservoir reserve change model.

[0232] In another optional embodiment, the construction module 12 is further specifically used to: determine whether the degree of fit between the scattered data and the fitting curve corresponding to the final sample oil and gas reservoir reserve change model meets a preset threshold; if not, the fitting curve is transformed by X and Y axis translation and scaling.

[0233] In another optional embodiment, the prediction module 13 is specifically configured to:

[0234] Establishing a corresponding relationship between the reserves per unit oil layer thickness and the hydrocarbon supply of the rift basin oil and gas reservoir to be predicted and the sample oil and gas reservoir;

[0235] Based on the geological parameters and engineering parameters of the oil and gas reservoir in the rift basin to be predicted and the sample oil and gas reservoir, as well as the corresponding relationship between the reserves per unit oil layer thickness and the hydrocarbon supply, and the reserve change model of the sample oil and gas reservoir, a reserve prediction model of the basin-to-be-predicted oil and gas reservoir in the rift basin to be predicted is generated, and the reserve prediction model of the basin-to-be-predicted oil and gas reservoir in the rift basin to be predicted is used to predict the amount of undiscovered resources in the oil and gas reservoir in the rift basin to be predicted.

[0236] In another optional embodiment, the prediction module 13 is further configured to:

[0237] Determining whether the hydrocarbon reservoir combination of the rift basin to be predicted can be distinguished;

[0238] If so, prediction is performed based on the fact that the oil and gas reservoir in the rift basin to be predicted has the same reservoir-forming combination as the sample oil and gas reservoir as the main oil layer, so as to obtain the conventional oil and gas undiscovered resources of the oil and gas reservoir in the rift basin to be predicted;

[0239] If not, prediction is performed based on the reservoir thickness ratio of the rift basin oil and gas reservoir to be predicted and the sample oil and gas reservoir to obtain the unconventional oil and gas resources to be discovered in the rift basin oil and gas reservoir to be predicted.

[0240] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for quantitatively predicting the amount of undiscovered oil and gas resources in a rift basin.

[0241] Based on the same inventive concept, an embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for quantitatively predicting the amount of undiscovered oil and gas resources in the rift basin is implemented.

[0242] Based on the same inventive concept, embodiments of the present invention also provide a method for constructing a rift basin oil and gas reservoir reserve change model, which may include: using a rift basin oil and gas reservoir in the mature exploration stage as a sample oil and gas reservoir, determining a target reservoir combination for discovery workload and major oil and gas contributions in the sample oil and gas reservoir; and constructing a reserve change model for the sample oil and gas reservoir using the target reservoir combination as an object unit. The specific construction process of this method can refer to steps S11 and S12 of the aforementioned method for quantitatively predicting undiscovered oil and gas resources in a rift basin, and the aforementioned rift basin oil and gas reservoir reserve change model is pre-constructed for use in subsequent applications. This embodiment of the present invention will not be further described here.

[0243] The principles of the problems solved by the above-mentioned devices, media, and related equipment in the embodiments of the present invention are similar to the aforementioned quantitative prediction method for the amount of undiscovered oil and gas resources in the rift basin. Therefore, their implementation can refer to the implementation of the aforementioned method, and the repeated parts will not be repeated.

[0244] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage, etc.) containing computer-usable program code.

[0245] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0246] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0247] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0248] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A quantitative prediction method for the amount of undiscovered oil and gas resources in a rift basin, characterized by: include: Taking rift basin oil and gas reservoirs in the mature exploration stage as sample oil and gas reservoirs, determine the target reservoir combination of discovery workload and major oil and gas contributions in the sample oil and gas reservoirs; Constructing the sample oil and gas reservoir reserve change model with the target reservoir combination as the object unit; The undiscovered resources of the oil and gas reservoirs in the to-be-predicted rift basin are predicted based on the sample oil and gas reservoir reserve change model.

2. The method according to claim 1, characterized in that Determining the target reservoir combination includes: Taking the rift basin oil and gas reservoirs in the mature exploration stage as sample oil and gas reservoirs, the reservoir-forming assemblages of the sample oil and gas reservoirs are split based on the relationship between the cumulative number of exploration wells and the cumulative 2P reserves of the sample oil and gas reservoirs to obtain several reservoir-forming assemblages; Comparing the stratigraphic ranges of the split several reservoir assemblages with those determined by the reservoir assembly division scheme of the sample oil and gas reservoir geological research to determine candidate reservoir assemblages; Based on the alternative reservoir combinations, the alternative reservoir combinations are screened based on the changing trend of their cumulative 2P reserves versus the cumulative number of exploratory wells to determine the target reservoir combination; wherein the screening condition is that the trend line of the changing trend of the cumulative 2P reserves versus the cumulative number of exploratory wells is smooth and tends to a certain maximum value over the long term.

3. The method according to claim 2, characterized in that Before splitting the reservoir combination of the sample basin, it also includes: A scatter plot relationship diagram was constructed based on the mining years and cumulative 2P reserves of rift basin oil and gas reservoirs. The initial sample oil and gas reservoirs for judging the mature exploration stage of rift basin oil and gas reservoirs were selected based on the stable change trend of the scatter plots of each rift basin. The initial sample oil and gas reservoirs are used to determine whether they have entered the mature exploration stage, and the initial sample oil and gas reservoirs whose annual added 2P reserves are less than a preset percentage of the cumulative reserves or whose annual added production is less than a preset percentage of the cumulative production are regarded as rift basin oil and gas reservoirs in the mature exploration stage.

4. The method according to claim 2, characterized in that After determining the target reservoir combination, the method further includes: judging whether the number of the target reservoir combination is one; if not, merging adjacent target reservoir combinations.

5. The method according to claim 1, wherein The step of constructing the sample oil and gas reservoir reserve change model with the target reservoir combination as the object unit includes: Determining the underlying function model for constructing the sample oil and gas reservoir reserve change model based on the growth relationship trend of the target reservoir combination cumulative reserves versus the cumulative number of exploration wells; The sample oil and gas reservoir reserve change model is constructed by taking the cumulative reserve abundance of the target reservoir combination as the objective function of the underlying function model and taking the cumulative exploration well density of the target reservoir combination as the model variable of the underlying function model; Taking the scattered data of the relationship between the cumulative 2P reserves and the number of exploration wells of the target reservoir combination as statistical points, and the basic data of the exploration well controlled area and oil layer thickness in the target reservoir area, the relevant parameters in the sample oil and gas reservoir reserve change model are solved based on the least squares method to obtain the final sample oil and gas reservoir reserve change model.

6. The method according to claim 5, characterized in that Also includes: Determining whether the degree of fit between the scattered point data and the fitting curve corresponding to the final sample oil and gas reservoir reserve change model meets a preset threshold; If not, the fitting curve is transformed by translation and scaling of the X and Y axes.

7. The method according to claim 1, characterized in that The method of predicting the undiscovered resources of the oil and gas reservoir in the rift basin to be predicted based on the sample oil and gas reservoir reserve change model includes: Establishing a corresponding relationship between the reserves per unit oil layer thickness and the hydrocarbon supply of the rift basin oil and gas reservoir to be predicted and the sample oil and gas reservoir; Based on the geological parameters and engineering parameters of the oil and gas reservoir in the rift basin to be predicted and the sample oil and gas reservoir, as well as the corresponding relationship between the reserves per unit oil layer thickness and the hydrocarbon supply, and the reserve change model of the sample oil and gas reservoir, a reserve prediction model of the basin-to-be-predicted oil and gas reservoir in the rift basin to be predicted is generated, and the reserve prediction model of the basin-to-be-predicted oil and gas reservoir in the rift basin to be predicted is used to predict the amount of undiscovered resources in the oil and gas reservoir in the rift basin to be predicted.

8. The method according to claim 7, characterized in that Before establishing the corresponding relationship between the reserves per unit oil layer thickness and the hydrocarbon supply of the rift basin oil and gas reservoir to be predicted and the sample oil and gas reservoir, the method further includes: Determining whether the hydrocarbon reservoir combination of the rift basin to be predicted can be distinguished; If so, prediction is performed based on the fact that the oil and gas reservoir in the rift basin to be predicted has the same reservoir-forming combination as the sample oil and gas reservoir as the main oil layer, so as to obtain the conventional oil and gas undiscovered resources of the oil and gas reservoir in the rift basin to be predicted; If not, prediction is performed based on the reservoir thickness ratio of the rift basin oil and gas reservoir to be predicted and the sample oil and gas reservoir to obtain the unconventional oil and gas resources to be discovered in the rift basin oil and gas reservoir to be predicted.

9. A method for constructing a reserve change model of oil and gas reservoirs in a rift basin, characterized in that: include: Taking rift basin oil and gas reservoirs in the mature exploration stage as sample oil and gas reservoirs, determine the target reservoir combination of discovery workload and major oil and gas contributions in the sample oil and gas reservoirs; The reserve change model of the sample oil and gas reservoir is constructed with the target reservoir combination as the object unit.

10. A quantitative prediction device for the amount of undiscovered oil and gas resources in a rift basin, characterized in that: include: A determination module is used to determine the target reservoir combination of discovery workload and major oil and gas contribution in the sample rift basin oil and gas reservoir using the oil and gas reservoir in the mature exploration stage as a sample oil and gas reservoir; A construction module, configured to construct a reserve change model of the sample oil and gas reservoirs with the target reservoir combination as an object unit; The prediction module is used to predict the undiscovered resources of the oil and gas reservoirs in the rift basin to be predicted based on the sample oil and gas reservoir reserve change model.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for quantitatively predicting the amount of undiscovered oil and gas resources in a rift basin according to any one of claims 1 to 8 is implemented.

12. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method for quantitatively predicting the amount of undiscovered oil and gas resources in a rift basin according to any one of claims 1 to 8 is implemented.

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