To-be-discovered yield prediction method based on storage-production conversion rule

Abstract

The invention provides a to-be-discovered yield prediction method based on a storage-production conversion rule, and relates to the technical field of oil-gas exploration and development, and the method comprises the following steps: S1, determining a resource conversion rate; s2, predicting remaining reserves to be found; s3, analyzing the reserve quality of the target area; s4, carrying out statistical analysis on newly-added ascertained geological reserves of the target area over the years, and analyzing a change rule; s5, analyzing the reserve utilization rate of the target area; and S6, establishing a storage and production conversion relation model, and analyzing a key parameter rule of the target area. S7, constructing a to-be-found storage and production conversion key parameter evaluation system of the target area; and S8, constructing an intelligent prediction model of the to-be-discovered yield of the target area, and realizing prediction of the to-be-discovered yield. According to the method, a full-chain conversion model of the resource quantity, the reserves to be found, the production reserves and the yield is constructed, and the blank of systematic quantitative characterization of the oil and gas resource step-by-step conversion rule in the prior art is filled.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration and development technology, and in particular to a method for predicting the production of undiscovered resources based on the law of reservoir-production conversion. Background Technology

[0002] In the process of oil and gas exploration and development, accurate prediction of the production of undiscovered products is crucial for formulating reasonable exploration plans, resource allocation, production plans, and investment decisions. The prediction results of undiscovered production directly affect the production scale of oil companies and even relate to the strategic layout of national energy security.

[0003] However, existing methods for predicting production of undiscovered reservoirs have many limitations. Some methods rely solely on geostatistical analysis, extrapolating production from known reservoir geological characteristics to undiscovered reservoirs, ignoring the dynamic conversion relationship between production and reserves during reservoir development. Other methods rely on simple fitting predictions based on historical production data, failing to fully consider the differences in geological conditions, constraints, and extraction technologies among different oil and gas fields. As oil and gas exploration and development expands to complex oil and gas reservoirs and deep buried hills, the reservoir-production conversion patterns have become more complex and variable, and existing prediction methods can no longer meet practical needs.

[0004] Therefore, there is an urgent need for a scientific and accurate method for predicting the production of undiscovered resources based on the laws of reservoir-production conversion, in order to improve prediction accuracy and provide a reliable basis for decision-making in oil and gas exploration and development. Summary of the Invention

[0005] The purpose of this invention is to provide a method for predicting the production of discovered reserves based on the law of reserve-production transformation. This method establishes a three-tiered transformation process: from resource quantity to discovered reserves, from discovered reserves to exploitable reserves, and from exploitable reserves to production. By deeply studying the dynamic laws of reserve-production transformation, combining multi-source data, and considering reserve quality and constraints, an accurate prediction model is established to achieve scientific prediction of the production of discovered reserves, providing scientific support for oil and gas exploration and development decisions. The various technical effects of the preferred solutions among the many technical solutions provided by this invention are detailed below.

[0006] To achieve the above objectives, the present invention provides the following technical solution: This invention provides a method for predicting undiscovered yields based on the law of storage-production conversion, comprising the following steps: S1: Determine the resource conversion rate; S2: Predict the remaining undiscovered reserves; S3: Analyze the quality of reserves in the target area; S4: Statistical analysis of the newly proven geological reserves in the target area over the years, and analysis of the changing patterns; S5: Analyze the utilization rate of reserves in the target area; S6: Establish a model for the conversion of reserves and production, and analyze the patterns of key parameters in the target area.

[0007] S7: Construct an evaluation system for key parameters of undiscovered reserves and production conversion in the target area; S8: Construct an intelligent prediction model for the output to be discovered in the target area to achieve output prediction.

[0008] Preferably, in step S1, determining the resource conversion rate includes: Based on the total petroleum geological resources in the target area, the resource conversion rate is determined by combining the life cycle method and the analogy method of key oil and gas basins.

[0009] Preferably, in step S2, predicting the remaining undiscovered reserves includes: The total target for proven oil geological reserves in the target area is calculated, and the remaining undiscovered reserves are predicted based on the currently proven reserves.

[0010] Preferably, in step S3, analyzing the quality of reserves in the target area includes: identifying the distribution of reserves in restricted areas and low-grade reserves, wherein: Reserve quality is classified into conventional, heavy oil, low-permeability, buried hill, low oil column, and thin and poor layer; the distribution of newly proven reserves in the target period is analyzed to clarify the proportion and variation pattern of low-grade reserves.

[0011] Preferably, in step S4, the newly discovered geological reserves in the target area over the years are statistically analyzed to analyze the changing patterns. The statistical unit is the year, and the statistical period covers at least the past 10 years. The annual average, maximum and minimum values ​​of the newly discovered reserves are calculated.

[0012] Preferably, in step S5, the utilization rate of reserves in the target area is analyzed, including: distinguishing the utilization characteristics of conventional reserves and low-grade / restricted area reserves, clarifying the total geological reserves proven in production and the reserves currently utilized in the target area, and calculating the utilization rate 5 and 10 years after the discovery of reserves on a 5-year cycle; and developing low-grade reserves and restricted area reserves in stages.

[0013] Preferably, in step S6, a model for the conversion of reserves and production is established, and the patterns of key parameters in the target area are analyzed, including: Based on collected historical data, a mathematical model of the relationship between reserves and production was constructed using time series analysis and grey relational analysis. The analysis of the target area includes the patterns of key parameters such as production cycle, peak production rate, stable production cycle, well-controlled reserves, natural decline rate, and rated recovery rate.

[0014] Preferably, the historical data includes annual reserves, production, and development technical parameters of the target area.

[0015] Preferably, in step S7, constructing an evaluation system for key parameters of the target area's undiscovered reserves and production conversion specifically includes: Based on the composition of remaining resources in the target area and the conversion patterns of proven reserves, the preprocessed data is divided into training and testing sets. The selected model is trained using training set data, and the parameters are adjusted to optimize it with the goal of minimizing the prediction error. The trained model is validated using test set data to evaluate prediction accuracy and generalization ability, and a system evaluation framework for key parameters of the target area's undiscovered reserves and production conversion is constructed.

[0016] Preferably, in step S8, a smart prediction model for the yield to be discovered in the target area is constructed to achieve the prediction of the yield to be discovered, including: Based on the evaluation system of key parameters for the conversion of undiscovered reserves and production, VBA programming is used to construct an intelligent prediction model for undiscovered production, enabling rapid and accurate prediction of undiscovered production.

[0017] This invention provides a method for predicting the production of discovered resources based on the law of reserve-production conversion. By employing the life cycle method and the analogy method of key oil and gas basins, it determines the resource conversion rate and then calculates the final proven oil geological reserves in the target area. Based on this, it analyzes the quality of reserves in the target area and calculates the proportion of various reserve qualities in the future discovered reserves. Furthermore, it analyzes the utilization patterns by statistically analyzing the newly discovered geological reserves in the target area over the years. Simultaneously, it establishes a model of the reserve-production conversion relationship in the target area, systematically constructs an evaluation system for key parameters of the discovered reserve-production conversion, and finally uses VBA programming to construct an intelligent prediction model for the production of discovered resources. This model quickly and accurately predicts the production of discovered resources by establishing a three-level conversion method: from resources to discovered reserves, from discovered reserves to utilized reserves, and from utilized reserves to production. This constructs a full-chain conversion model of resources, discovered reserves, utilized reserves, and production, filling the gap in the existing technology for the systematic quantitative characterization of the step-by-step conversion law of oil and gas resources. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of the method for predicting the output to be discovered based on the storage-production conversion law of the present invention; Figure 2 This is a map showing the extent of proven oil and liquid reserves in the target area; Figure 3 This is a diagram showing the key parameters for converting the utilization of reserves in the target area into production. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0021] Figure 1 This is a flowchart of this embodiment, as shown below. Figure 1 As shown, this embodiment provides a method for predicting the yield to be discovered based on the storage-production conversion law, including the following steps: S1: Determine the resource conversion rate; specifically, use the life cycle method and the key oil and gas basin analogy method to determine the resource conversion rate.

[0022] like Figure 2 As shown, the resource conversion rate is determined by combining the total petroleum geological resources of the target area with the life cycle method and the analogy method of key oil and gas basins.

[0023] In this embodiment, the total petroleum geological resources of the Bohai Sea A region were selected as the benchmark. Based on the combined life cycle method and the analogy method of key oil and gas basins, the resource conversion rate of the Bohai Sea A region in this embodiment was determined to be 62.4%.

[0024] S2: Predict remaining undiscovered reserves; including: calculating the total target of proven geological reserves of oil in the target area, and predicting the remaining undiscovered reserves based on the currently proven reserves.

[0025] This embodiment estimates the total proven geological reserves of oil in the Bohai Sea A region to be 1.5 billion tons. Based on the current proven reserves of 800 million tons, the remaining undiscovered reserves are estimated to be 700 million tons.

[0026] S3: Analyze the quality of reserves in the target area; including: clarifying the distribution of reserves in restricted areas and low-grade reserves, of which: reserve quality is classified into conventional, heavy oil, low-permeability, buried hill, low oil column, and thin and poor layer; analyze the distribution of newly discovered reserves in the target period, and clarify the proportion and changing pattern of low-grade reserves.

[0027] In this embodiment, the distribution of newly discovered oil reserves is analyzed in detail to determine the reserves in restricted areas and unrestricted areas.

[0028] Specifically, firstly, the number, reserves, and proportion of oilfields affected by the environment are determined. Secondly, the reserves in unrestricted areas are clarified, and the reserve quality is classified in detail, mainly including conventional, heavy oil, low-permeability, buried hills, low oil column, and thin and poor layers. The proportion and changing pattern of low-grade reserves such as low-grade and heavy-permeability oil are clarified. From the changing pattern, the proportion of low-grade reserves has been increasing year by year, as shown in Table 1.

[0029] Table 1. Analysis of the proportion of newly proven reserves by quality in Bohai Sea Region A, 2019-2023 Of the remaining oil resources, Paleogene lithology and buried hill reserves account for 70%. Comprehensive analysis suggests that low-grade reserves will account for over 50% of the future undiscovered reserves in the region.

[0030] S4: Statistical analysis of the newly proven geological reserves in the target area over the years, and analysis of the changing patterns; Specifically, statistical analysis is conducted on the newly discovered geological reserves in the target area over the years to analyze the changing patterns. The statistical unit is the year, and the statistical period covers at least the past 10 years. The annual average, maximum, and minimum values ​​of newly discovered reserves are calculated.

[0031] In this embodiment, the newly discovered geological reserves in the Bohai Sea A area over the years are statistically analyzed. The Bohai Sea A area has been continuously explored and developed, and the average annual increase in reserves has been rising over the past ten years.

[0032] S5: Analyze the utilization rate of reserves in the target area; including: distinguishing the utilization characteristics of conventional reserves and low-grade / restricted area reserves, clarifying the total proven geological reserves and currently utilized reserves in the target area, and calculating the utilization rate 5 and 10 years after the discovery of reserves on a 5-year cycle; and developing low-grade reserves and restricted area reserves in stages.

[0033] Specifically, the total proven geological reserves and currently utilized reserves in the target area are clearly defined. Using a 5-year cycle, the utilization rate of reserves is defined for the first 5 years after discovery and the utilization rate after 10 years. Low-grade reserves are affected by development models, technological limitations, and economic benefits; reserves in restricted areas pose environmental risks. These two types of reserves are generally developed in phases. The utilization rate is low in the first phase, and subsequent phases utilize reserves gradually through enhanced technological breakthroughs and innovative development models.

[0034] This embodiment specifically analyzes the reserve utilization rate in the Bohai Sea A region, including conventional reserves and low-grade / restricted area reserves. It clarifies the total proven geological reserves and currently utilized reserves in the Bohai Sea A region, highlighting the continuously improving reserve utilization rate and the orderly implementation of production projects through rolling potential tapping in the producing oilfield.

[0035] Based on a 5-year cycle, the reserve utilization rate is defined for 5 years after discovery and 10 years after discovery, as shown in Table 2.

[0036] Table 2. Statistics on the utilization rate of proven reserves after discovery in Area A of the Bohai Sea. Low-grade reserves are affected by development models, technological limitations, and economic benefits, while reserves in restricted areas pose environmental risks. Both types of reserves are generally developed in phases. The utilization rate of reserves in the first phase is low, averaging 35%. Subsequent phases involve strengthening technological breakthroughs and innovating development models to gradually utilize these reserves. To address the challenges of utilizing reserves in restricted areas, two development models are being explored in the Bohai Oilfield: subsea production system development and extended reach well development. A technological breakthrough and field experimentation plan has been formulated to steadily advance the development of oil and gas fields in restricted areas of the Bohai Oilfield and release the reserves in these areas.

[0037] S6: Establish a model for the conversion of reserves and production, and analyze the patterns of key parameters in the target area. This includes: Based on the collected historical data, a mathematical model of the conversion relationship between reserves and production was constructed using time series analysis and grey relational analysis. The historical data includes the annual reserves, production, and development technical parameters of the target area.

[0038] The analysis of the target area includes the patterns of key parameters such as production cycle, peak production rate, stable production cycle, well-controlled reserves, natural decline rate, and rated recovery rate.

[0039] Specifically, such as Figure 3 As shown, based on collected historical data, this study employs time series analysis and grey relational analysis to investigate the dynamic conversion relationship between production and reserves under different mining stages, geological conditions, and technological levels, and constructs a mathematical model of the production-reserve conversion relationship. The study also analyzes in detail the patterns of key parameters in the target area, such as the production establishment cycle, peak oil production rate and stable production cycle, well-controlled reserves, natural decline rate, and rated recovery rate.

[0040] In this embodiment, a model of the conversion relationship between reserves and production in Bohai Area A is established. Based on historical data collected from Bohai Area A, time series analysis, grey relational analysis and other methods are used to study the dynamic conversion relationship between production and reserves under different mining stages, different geological conditions and different technical levels. The model calculates the patterns of key parameters such as the production construction cycle, peak oil production rate and stable production cycle, well-controlled reserves, natural decline rate and rated recovery rate in Bohai Area A, so as to guide the planning of production to be discovered.

[0041] The Bohai Sea A region is characterized by numerous oilfields, complex geological conditions, and diverse reservoir types. Due to its sparse well network and large well spacing development methods, it is difficult to directly apply onshore classification standards. Therefore, based on the main controlling factors affecting oilfield development, a layered classification and primary / secondary factor method was adopted to classify the Bohai Sea reservoirs. Level I classification is used for evaluating development indicators and formulating development technology policies; Level II classification, further subdivided according to sedimentary facies and fluids, is used for evaluating and predicting production planning indicators.

[0042] S7: Construct an evaluation system for key parameters of undiscovered reserves and production conversion in the target area; specifically including: Based on the composition of remaining resources in the target area and the conversion patterns of proven reserves, the preprocessed data is divided into training and testing sets. The selected model is trained using training set data, and the parameters are adjusted to optimize it with the goal of minimizing the prediction error. The trained model is validated using test set data to evaluate prediction accuracy and generalization ability, and a system evaluation framework for key parameters of the target area's undiscovered reserves and production conversion is constructed.

[0043] In this embodiment, an evaluation system for key parameters of the transformation of undiscovered reserves and production in the Bohai Sea A region is constructed. Referring to Table 3, based on the composition of the remaining resources in the target area and the transformation law of the proven reserves, the system constructs an evaluation system for key parameters of the transformation of undiscovered reserves and production in the target area, which includes 9 key parameters at 3 levels of transformation, so as to achieve a scientific and reasonable evaluation of the undiscovered production.

[0044] Table 3. Nine key parameters for Level 3 transformation in Bohai Sea Region A S8: Construct an intelligent prediction model for the expected yield in the target area to achieve yield prediction. This includes: based on the evaluation system of key parameters for the conversion of expected reserves and yields, using VBA programming to construct an intelligent prediction model for the expected yield, enabling rapid and accurate prediction of the expected yield.

[0045] In this embodiment, an intelligent prediction model for the undiscovered yield in the Bohai Sea A region is constructed to achieve rapid and accurate prediction of the undiscovered yield. Based on the evaluation system of key parameters for the conversion of undiscovered reserves and yields, VBA programming is used to construct the intelligent prediction model for the undiscovered yield, enabling rapid and accurate prediction of the undiscovered yield.

[0046] This method for predicting the production of discovered resources based on the law of reserve-production transformation proposes a dynamic prediction method for the three-level transformation of oil and gas resources: from resources to discovered reserves, from discovered reserves to exploitable reserves, and from exploitable reserves to production. It constructs a full-chain transformation model of resources, discovered reserves, exploitable reserves, and production, filling the gap in existing technologies for the systematic quantitative characterization of the step-by-step transformation law of oil and gas resources. Through reserve quality grading evaluation, dynamic coupling of development constraints, and intelligent learning of production transformation laws, it achieves synergistic optimization and dynamic prediction of the three-level transformation process. Compared with traditional methods, this technology overcomes the limitations of static estimation and segmented analysis, realizing dynamic linkage prediction of the three-level transformation, and providing full-cycle quantitative support for oil and gas field exploration and development decisions.

[0047] The results have been applied to the research on rolling planning schemes in the Bohai Oilfield, enabling refined prediction of production to be discovered, and allowing for the prediction of risks in production to be discovered, thereby improving the scientificity and rationality of production planning in the Bohai Oilfield.

[0048] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for predicting undiscovered yield based on the law of storage-production conversion, characterized in that, Includes the following steps: S1: Determine the resource conversion rate; S2: Predict the remaining undiscovered reserves; S3: Analyze the quality of reserves in the target area; S4: Statistical analysis of the newly proven geological reserves in the target area over the years, and analysis of the changing patterns; S5: Analyze the utilization rate of reserves in the target area; S6: Establish a model for the conversion of reserves and production, and analyze the patterns of key parameters in the target area; S7: Construct an evaluation system for key parameters of undiscovered reserves and production conversion in the target area; S8: Construct an intelligent prediction model for the output to be discovered in the target area to achieve output prediction.

2. The method for predicting undiscovered production based on the law of storage-production conversion according to claim 1, characterized in that, In step S1, the resource conversion rate is determined, including: Based on the total petroleum geological resources in the target area, the resource conversion rate is determined by combining the life cycle method and the analogy method of key oil and gas basins.

3. The method for predicting undiscovered yield based on the storage-production conversion law according to claim 1, characterized in that, In step S2, the remaining undiscovered reserves are predicted, including: The total target for proven oil geological reserves in the target area is calculated, and the remaining undiscovered reserves are predicted based on the currently proven reserves.

4. The method for predicting undiscovered production based on the law of storage-production conversion according to claim 1, characterized in that, In step S3, the quality of reserves in the target area is analyzed, including: identifying the distribution of reserves in restricted areas and low-grade reserves, wherein: Reserve quality is classified into conventional, heavy oil, low-permeability, buried hill, low oil column, and thin and poor layer; the distribution of newly proven reserves in the target period is analyzed to clarify the proportion and variation pattern of low-grade reserves.

5. The method for predicting undiscovered production based on the law of storage-production conversion according to claim 1, characterized in that: In step S4, the newly discovered geological reserves in the target area over the years are statistically analyzed to analyze the changing patterns. The statistical unit is the year, and the statistical period covers at least the past 10 years. The annual average, maximum and minimum values ​​of newly discovered reserves are calculated.

6. The method for predicting undiscovered production based on the law of storage-production conversion according to claim 1, characterized in that, In step S5, the utilization rate of reserves in the target area is analyzed, including: distinguishing the utilization characteristics of conventional reserves and low-grade / restricted area reserves, clarifying the total geological reserves proven in production and the reserves currently utilized in the target area, and calculating the utilization rate 5 and 10 years after the discovery of reserves on a 5-year cycle; and developing low-grade reserves and restricted area reserves in stages.

7. The method for predicting undiscovered production based on the law of storage-production conversion according to claim 1, characterized in that, In step S6, a model for the conversion of reserves and production is established, and the patterns of key parameters in the target area are analyzed, including: Based on collected historical data, a mathematical model of the relationship between reserves and production was constructed using time series analysis and grey relational analysis. The analysis of the target area includes the patterns of key parameters such as production cycle, peak production rate, stable production cycle, well-controlled reserves, natural decline rate, and rated recovery rate.

8. The method for predicting undiscovered production based on the law of storage-production conversion according to claim 7, characterized in that: The historical data includes the target area's reserves, production, and development technical parameters over the years.

9. The method for predicting undiscovered production based on the law of storage-production conversion according to claim 1, characterized in that, In step S7, an evaluation system for key parameters of the transformation of undiscovered reserves and resources in the target area is constructed, specifically including: Based on the composition of remaining resources in the target area and the conversion patterns of proven reserves, the preprocessed data is divided into training and testing sets. The selected model is trained using training set data, and the parameters are adjusted to optimize it with the goal of minimizing the prediction error. The trained model is validated using test set data to evaluate prediction accuracy and generalization ability, and a system for evaluating key parameters for the transformation of undiscovered reserves and resources in the target area is constructed.

10. The method for predicting undiscovered production based on the law of storage-production conversion according to claim 1, characterized in that, In step S8, an intelligent prediction model for the yield to be discovered in the target area is constructed to achieve the prediction of the yield to be discovered, including: Based on the evaluation system of key parameters for the conversion of undiscovered reserves and production, VBA programming is used to construct an intelligent prediction model for undiscovered production, enabling rapid and accurate prediction of undiscovered production.

Images