Oilfield production equipment, methods, devices, and products

Abstract

The present disclosure relates to the technical field of oil exploitation, and particularly provides an oilfield exploitation equipment, method, device and product. The method comprises: determining a multi-objective optimization model and a constraint condition of a target oilfield based on an optimization target of the target oilfield; wherein the optimization target comprises at least one of the following: oil production, yield increase and resource consumption, and the constraint condition is used to indicate the range of the oil production, the yield increase and the resource consumption; determining the predicted oil production of the target oilfield under each exploitation measure under the constraint of the constraint condition in combination with the constraint condition and the multi-objective optimization model; determining the maximum oil production of the target oilfield based on the predicted oil production; determining an exploitation scheme of the target oilfield based on the maximum oil production; wherein the exploitation scheme is used to indicate the target exploitation measure corresponding to the maximum oil production and an exploitation device used by the target exploitation measure; and controlling the exploitation device to exploit the target oilfield through the target exploitation measure.

Description

Technical Field

[0001] This disclosure relates to the field of oil extraction technology, and in particular to an oilfield extraction equipment, method, apparatus and product. Background Technology

[0002] Oilfield development planning involves many aspects. For example, oilfield extraction equipment not only needs to consider the information collected from the oilfield (i.e., the amount of oil produced and the amount of oil stored), but also needs to minimize resource consumption while maximizing the amount of oil produced, so as to ensure the sustainable extraction of the oilfield.

[0003] In related technologies, to address the balancing of multiple factors in oilfield development, oilfield development equipment needs to establish target-oriented decision-making methods and comprehensive evaluation systems to derive development plans for the oilfield. However, due to the complex and variable development environment of oilfields, the comprehensive evaluation system involves a large amount of data, and the target-oriented decision-making methods are easily influenced by subjective factors. Therefore, there are problems with low time efficiency and low accuracy in determining and implementing oilfield development plans using oilfield development equipment. Summary of the Invention

[0004] This disclosure is made in view of the above-mentioned problems. This disclosure provides oilfield extraction equipment, methods, apparatus, and products.

[0005] According to one aspect of this disclosure, an oilfield extraction apparatus is provided, comprising: a data acquisition device, a processor, and an extraction device;

[0006] The data acquisition device is used to determine a multi-objective optimization model and constraints for the target oilfield based on the optimization objectives of the target oilfield; wherein the optimization objectives include at least one of the following: oil production, production increase, and resource consumption, and the constraints are used to indicate the range of the oil production, the production increase, and the resource consumption. The processor is configured to combine the constraints and the multi-objective optimization model to determine the predicted oil recovery of the target oilfield under each extraction measure under the constraints; based on the predicted oil recovery, determine the maximum oil recovery of the target oilfield; and based on the maximum oil recovery, determine the extraction scheme of the target oilfield; wherein the extraction scheme is used to indicate the target extraction measure corresponding to the maximum oil recovery and the extraction equipment used by the target extraction measure. The extraction apparatus is used to extract the target oil field through the target extraction measures.

[0007] Furthermore, according to another embodiment of one aspect of this disclosure, the processor determines a multi-objective optimization model for the target oilfield based on the optimization objectives of the target oilfield, including: Determine the oil production parameters, recoverable reserves parameters, and resource parameters of the target oil field; Based on the oil production parameters, recoverable reserves parameters, and resource parameters, multiple sub-optimization models are determined for the target oilfield; wherein, the multiple sub-optimization models are used to indicate oil production, increased production, and resource consumption, respectively. Based on the multiple sub-optimization models, the multi-objective optimization model is determined.

[0008] Furthermore, according to another embodiment of one aspect of this disclosure, the plurality of sub-optimization models includes: a first sub-optimization model, the first sub-optimization model being used to indicate oil production, the first sub-optimization model being determined by the following formula: , ; in, The oil enhancement effect of taking the i-th extraction measure in the j-th oil layer of the target oilfield in the k-th year is given. Let J be the oil recovery volume of the j-th oil reservoir when the i-th extraction method is employed in year k. Let i be the single-well oil production increase coefficient of the i-th extraction method put into production in year k, and t be the single-well oil production increase coefficient in year t. Let t be the initial production of the target oilfield's old wells that have not undergone any exploitation measures in year t. Let the natural decline rate of the old well in year k be . Let t be the water-drive aftereffect production of the target oilfield in year t after planning, where t is the number of years in the planning period.

[0009] Furthermore, according to another embodiment of one aspect of this disclosure, the plurality of sub-optimization models includes: a second sub-optimization model, the second sub-optimization model being used to indicate increased production, the second sub-optimization model being determined by the following formula: , ; in, To increase the annual recoverable reserves of a single well in the j-th oil layer using the i-th extraction method, which is put into production in the k-th year, Let represent the oil recovery volume of the j-th oil layer when the i-th extraction method is employed in the k-th year.

[0010] Furthermore, according to another embodiment of one aspect of this disclosure, the plurality of sub-optimization models includes: a third sub-optimization model, the third sub-optimization model being used to indicate resource consumption, the third sub-optimization model being determined by the following formula: , ; in, The oil enhancement effect of taking the i-th extraction measure in the j-th oil layer of the target oilfield in the k-th year is given. Let be the cost coefficient of a single well in the j-th oil layer of the i-th extraction method, which is put into production in year t. Let J be the oil recovery volume of the j-th oil reservoir when the i-th extraction method is employed in year k. The resource coefficient associated with the j-th oil layer and well for the i-th extraction method in the target oil field.

[0011] Furthermore, according to another embodiment of one aspect of this disclosure, the constraints of the multi-objective optimization model include: ; ; ; in, Let J be the oil recovery volume of the j-th oil reservoir when the i-th extraction method is employed in year k. The maximum oil recovery rate for the i-th extraction method is... This represents the lower limit of oil production under the i-th extraction method in year t. This represents the upper limit of oil production under the i-th extraction method in year t. The oil enhancement effect of taking the i-th extraction measure in the j-th oil layer of the target oilfield in the k-th year is given. This represents the lower limit of oil production from wells developed before year t. This represents the upper limit of oil production from wells developed before year t.

[0012] Furthermore, according to another embodiment of one aspect of this disclosure, the processor determines the maximum oil recovery of the target oil field based on the predicted oil recovery, including: Determine the target range population for the predicted oil recovery; Based on the target interval population, determine the offspring population, non-dominated solutions, and total population of the predicted oil recovery; Based on the non-dominated solution and the total population, a regional leading center is determined, and a dynamic local search is performed based on the regional leading center to determine a specified population; wherein, the regional leading center is used to indicate the strong dominating point corresponding to the non-dominated solution and the sparse solution of the total population; The target interval population, the offspring population, and the designated population are merged to obtain a merged population, and the target interval population is redefined based on the merged population. The process involves performing steps to determine the offspring population, non-dominated solutions, and total population of the target interval population based on the target interval population, until the target interval population satisfies the iteration conditions, and then determining the maximum oil recovery based on the last determined target interval population.

[0013] According to another aspect of this disclosure, an oil field extraction method is provided, comprising: Based on the optimization objectives of the target oilfield, a multi-objective optimization model and constraints for the target oilfield are determined; wherein, the optimization objectives include at least one of the following: oil production, production increase, and resource consumption, and the constraints are used to indicate the range of the oil production, the production increase, and the resource consumption; Combining the constraints and the multi-objective optimization model, the predicted oil recovery of the target oilfield under each extraction measure is determined under the constraints. Based on the predicted oil production, the maximum oil production of the target oilfield is determined; Based on the maximum oil recovery, an exploitation plan for the target oil field is determined; wherein, the exploitation plan is used to indicate the target exploitation measures corresponding to the maximum oil recovery and the exploitation equipment used for the target exploitation measures; The extraction device is controlled to extract the target oil field through the target extraction measures.

[0014] According to another aspect of this disclosure, an oilfield extraction apparatus is provided, comprising: The first determining module is used to determine the multi-objective optimization model and constraints of the target oilfield based on the optimization objectives of the target oilfield; wherein the optimization objectives include at least one of the following: oil production, production increase, and resource consumption, and the constraints are used to indicate the range of the oil production, the production increase, and the resource consumption; The second determining module is used to combine the constraints and the multi-objective optimization model to determine the predicted oil production of the target oilfield under each extraction measure under the constraints. The third determining module is used to determine the maximum oil production of the target oil field based on the predicted oil production. The fourth determining module is used to determine the exploitation plan for the target oilfield based on the maximum oil recovery; wherein the exploitation plan is used to indicate the target exploitation measures corresponding to the maximum oil recovery and the exploitation equipment used by the target exploitation measures; The control module is used to control the extraction device to extract the target oil field through the target extraction measures.

[0015] According to another aspect of this disclosure, a computer program product is provided, including a computer program / instructions that, when executed by a processor, implement the steps of an oilfield extraction device.

[0016] As will be described in detail below, oilfield exploitation equipment, methods, apparatus, and products according to embodiments of this disclosure. An oilfield exploitation plan is determined by establishing a multi-objective optimization model and solving the model through constraints. This method comprehensively considers multiple factors influencing the oilfield exploitation plan. Through modeling, oilfield data can be systematically analyzed without needing to establish a comprehensive evaluation system based on the oilfield data. In the prior art, target decision-making methods rely on the subjective judgment of technicians. Therefore, this method improves time efficiency and the accuracy of the exploitation plan, thereby making the exploitation of the target oilfield more scientific.

[0017] It should be understood that both the foregoing general description and the following detailed description are exemplary and intended to provide further illustration of the claimed technology. Attached Figure Description

[0018] The above and other objects, features, and advantages of this disclosure will become more apparent from the more detailed description of the embodiments thereof in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the embodiments of this disclosure to explain the disclosure and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0019] Figure 1 A flowchart of an oilfield extraction device provided for an embodiment of this disclosure.

[0020] Figure 2 A detailed flowchart of an oilfield extraction device provided in this embodiment of the disclosure.

[0021] Figure 3 This is a schematic diagram of an oilfield extraction apparatus provided in an embodiment of the present disclosure.

[0022] Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of the present disclosure. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this disclosure more apparent, exemplary embodiments according to this disclosure will now be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this disclosure, and not all embodiments of this disclosure. It should be understood that this disclosure is not limited to the exemplary embodiments described herein.

[0024] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0025] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0026] Research has shown that oilfield development planning involves many aspects. For example, it is necessary to consider the oilfield’s information collection (i.e., oil production and storage), while maximizing oil production information and minimizing resource consumption to ensure the sustainable development of the oilfield.

[0027] In related technologies, balancing various factors in oilfield development requires establishing target-oriented decision-making methods and comprehensive evaluation systems to derive specific development plans. However, the complex and variable development environment of oilfields leads to a large data volume in the comprehensive evaluation system, and target-oriented decision-making methods are easily influenced by subjective factors. Therefore, these methods suffer from low time efficiency and low accuracy in determining oilfield development plans.

[0028] Based on the above research, this disclosure provides an oilfield exploitation device. By establishing a multi-objective optimization model and solving it under constraints, the exploitation plan for the oilfield can be determined. This method comprehensively considers multiple factors influencing the oilfield exploitation plan. Through modeling, oilfield data can be systematically analyzed without needing to establish a comprehensive evaluation system based on the oilfield data. In existing technologies, target decision-making methods rely on the subjective judgment of technicians. Therefore, this method improves time efficiency and the accuracy of the exploitation plan, making the exploitation of the target oilfield more scientific.

[0029] To facilitate understanding of this embodiment, a detailed description of an oilfield extraction device disclosed in this disclosure will be provided first. The execution entity of the oilfield extraction device provided in this disclosure is generally an electronic device with a certain computing capability. In some possible implementations, the oilfield extraction device can be implemented by a processor calling computer-readable instructions stored in memory.

[0030] This disclosure provides an oilfield extraction device, including: a data acquisition device, a processor, and an extraction device; The data acquisition device is used to determine the multi-objective optimization model and constraints of the target oilfield based on the optimization objectives of the target oilfield; wherein the optimization objectives include at least one of the following: oil production, production increase, and resource consumption, and the constraints are used to indicate the range of the oil production, the production increase, and the resource consumption.

[0031] In the embodiments of this disclosure, the optimization objective of the target oil field can be understood as maximizing the oil production and increased production of the target oil field, and minimizing the resource consumption of the target oil field.

[0032] Here, after determining the optimization objectives of the target oilfield, a multi-objective optimization model can be established based on the oilfield parameters of the target oilfield, with the optimization objectives being to maximize the oil production and increase in production of the target oilfield and to minimize the resource consumption of the target oilfield.

[0033] Here, the oil production, increased production, and resource consumption of a target oilfield under various exploitation measures can be determined based on a multi-objective optimization model.

[0034] Among them, consumed resources are used to indicate the resources (i.e., costs) consumed in the exploitation of the target oil field.

[0035] The processor is configured to combine the constraints and the multi-objective optimization model to determine the predicted oil recovery of the target oilfield under each extraction measure under the constraints; based on the predicted oil recovery, determine the maximum oil recovery of the target oilfield; and based on the maximum oil recovery, determine the extraction scheme of the target oilfield; wherein the extraction scheme is used to indicate the target extraction measure corresponding to the maximum oil recovery and the extraction equipment used by the target extraction measure.

[0036] In the embodiments of this disclosure, solutions to multi-objective optimization models for different mining measures can be determined.

[0037] Here, the solution that meets the constraints in the above solutions can be identified as the predicted oil recovery. For example, with a 3-year oil recovery cycle and 5 extraction methods, there are 5... 3 There are 5 solutions. 3 The solution that meets the constraints is used to predict the oil production.

[0038] In the embodiments of this disclosure, the predicted oil production can be processed based on a multi-objective optimization algorithm to obtain the maximum oil production of the target oil field.

[0039] In the embodiments of this disclosure, the target extraction measure corresponding to the maximum oil recovery and the extraction apparatus used for the target extraction measure can be determined.

[0040] Here, while determining the target extraction measures, the corresponding resource consumption can also be determined. Then, the target extraction measures, the corresponding extraction equipment, and the corresponding resource consumption can be defined as the extraction plan for the target oil field.

[0041] For example, with a 3-year oil production cycle, the first extraction measure corresponding to the first year, the second extraction measure corresponding to the second year, and the third extraction measure corresponding to the third year can be identified as the target extraction measures. The extraction equipment corresponding to the first extraction measure, the second extraction measure, and the third extraction measure can be identified as the extraction equipment of the target extraction measures. The resources consumed in the first year, the second year, and the third year can be identified as the resources consumed by the target extraction measures.

[0042] The extraction apparatus is used to extract the target oil field through the target extraction measures.

[0043] In the embodiments of this disclosure, after the mining plan is determined, the mining equipment in the mining plan can be used to mine the target oil field in accordance with the target mining measures.

[0044] In the embodiments of this disclosure, firstly, based on the optimization objectives of the target oilfield, a multi-objective optimization model and constraints for the target oilfield are determined; wherein, the optimization objectives include at least one of the following: oil production, production increase, and resource consumption, and the constraints are used to indicate the range of oil production, production increase, and resource consumption; secondly, combining the constraints and the multi-objective optimization model, the predicted oil production of the target oilfield under each extraction measure is determined under the constraints; secondly, based on the predicted oil production, the maximum oil production of the target oilfield is determined; thirdly, based on the maximum oil production, an extraction scheme for the target oilfield is determined; wherein, the extraction scheme is used to indicate the target extraction measure corresponding to the maximum oil production and the extraction equipment used by the target extraction measure; finally, the extraction equipment is controlled to extract oil from the target oilfield through the target extraction measure.

[0045] In the above embodiments, a multi-objective optimization model is established and solved using constraints to determine the oilfield's exploitation plan. This method comprehensively considers multiple factors influencing the oilfield's exploitation plan. Through modeling, oilfield data can be systematically analyzed without needing to establish a comprehensive evaluation system based on the oilfield data. In existing technologies, target decision-making methods rely on the subjective judgment of technical personnel. Therefore, this approach improves time efficiency and the accuracy of the exploitation plan, making the exploitation of the target oilfield more scientific.

[0046] In an optional embodiment, the processor determines a multi-objective optimization model for the target oil field based on the optimization objectives of the target oil field, specifically including the following steps: First, determine the oil production parameters, recoverable reserves parameters, and resource parameters of the target oil field; Secondly, based on oil production parameters, recoverable reserves parameters, and resource parameters, multiple sub-optimization models for the target oilfield are determined; among them, the multiple sub-optimization models are used to indicate oil production, production increase, and resource consumption, respectively. Finally, based on multiple sub-optimization models, a multi-objective optimization model is determined.

[0047] In the embodiments of this disclosure, the oil production parameters include: the oil enhancement effect of each extraction measure, the single-well oil enhancement coefficient of each extraction measure, the natural decline rate of old wells, the oil production of old wells without extraction measures, and the water drive aftereffect production when extraction measures are taken.

[0048] Here, the recoverable reserves parameter includes: annual increase in recoverable reserves.

[0049] Here, resource parameters include: single-well cost resource coefficients (i.e., cost coefficients, oil, fluid, and water related) under each mining measure and well-related resource coefficients (i.e., cost coefficients) under each mining measure.

[0050] Here, a sub-optimization model can be established based on the oil production parameter to maximize the oil production of the target oil field (i.e., the first sub-optimization model below). A sub-optimization model can be established based on the recoverable reserves parameter to maximize the increased production of the target oil field (i.e., the second sub-optimization model below). A sub-optimization model can be established based on the resource parameters to minimize the resource consumption of the target oil field (i.e., the third sub-optimization model below).

[0051] Subsequently, the sub-optimization models that maximize the oil production of the target oil field, maximize the increased production of the target oil field, and minimize the resource consumption of the target oil field can be identified as multi-objective optimization models.

[0052] In an optional embodiment, the plurality of sub-optimization models include: a first sub-optimization model, which is used to indicate the oil recovery rate, and is determined by the following formula: , .

[0053] in, To determine the oil enhancement effect of the i-th extraction method when the j-th oil layer in the target oilfield is adopted in the k-th year, Let J be the oil recovery volume of the j-th oil reservoir when the i-th extraction method is employed in year k. Let i be the single-well oil production increase coefficient of the i-th extraction method put into production in year k, and t be the single-well oil production increase coefficient in year t. The initial production of the target oilfield's old wells that have not undergone exploitation measures in year t. Let the natural decline rate of the old well in year k be . Let t be the water-driven aftereffect production in year t after the target oilfield is planned, where t is the number of years in the planning period.

[0054] In the embodiments of this disclosure, the first sub-optimization model can be divided into three parts, namely: , and .

[0055] in, Increased production after implementing exploitation measures on old wells in the target oil field The initial production of old wells in the target oilfield that have not undergone any measures. The post-water drive yield after the target oilfield is planned.

[0056] Here, we discuss the oil enhancement effect of the i-th extraction method applied to the j-th oil layer in the target oilfield in year k. Let i be a deterministic variable. The single-well oil production increase coefficient of the i-th extraction method put into production in year k, in year t. This can be understood as the impact of any oil well adopting the i-th extraction method in year k on the increase in production of that oil well in year t.

[0057] Among them, the single-well oil production coefficient of the i-th extraction method put into production in year k is [missing information] in year t. It can be determined based on historical experience.

[0058] Here, the water-drive aftereffect production in year t after the target oilfield is planned. This can be understood as the water-drive post-production of the target oilfield in year t after adopting any extraction measures.

[0059] In an optional embodiment, the plurality of sub-optimization models includes: a second sub-optimization model, which is used to indicate the increase in production, and the second sub-optimization model is determined by the following formula: , ; in, To increase the annual recoverable reserves of a single well in the j-th oil layer using the i-th extraction method, which is put into production in the k-th year, Let represent the oil recovery volume of the j-th oil layer when the i-th extraction method is employed in the k-th year.

[0060] In the embodiments disclosed herein, the annual increase in recoverable reserves per well in the j-th oil layer using the i-th extraction method can be determined based on historical experience and the actual conditions of the target oil field, starting production in the k-th year. .

[0061] In an optional embodiment, the plurality of sub-optimization models includes: a third sub-optimization model, which is used to indicate resource consumption, and is determined by the following formula: , ; in, To determine the oil enhancement effect of the i-th extraction method when the j-th oil layer in the target oilfield is adopted in the k-th year, Let be the cost coefficient of a single well in the j-th oil layer of the i-th extraction method, which is put into production in year t. Let J be the oil recovery volume of the j-th oil reservoir when the i-th extraction method is employed in year k. The resource coefficient of the j-th oil layer and well associated with the i-th extraction method for the target oil field.

[0062] In the embodiments of this disclosure, the single-well cost coefficient of the j-th oil layer for the i-th extraction method put into production in year t is... This can be understood as the costs related to measures involving oil, fluids, and water injection in the development plan for the target oil field, as well as the costs related to the number of wells planted in the target oil field.

[0063] In an optional embodiment, the constraints of the multi-objective optimization model include: ; ; ; in, Let J be the oil recovery volume of the j-th oil reservoir when the i-th extraction method is employed in year k. The maximum oil recovery rate for the i-th extraction method is... This represents the lower limit of oil production under the i-th extraction method in year t. This represents the upper limit of oil production under the i-th extraction method in year t. The oil enhancement effect of taking the i-th extraction measure in the j-th oil layer of the target oilfield in the k-th year is given. This represents the lower limit of oil production from wells developed before year t. This represents the upper limit of oil production from wells developed before year t.

[0064] In embodiments of this disclosure, the total workload limits for various production enhancement measures during the planning period are defined. Within these limits, the workload resources are economically efficient, thus defining the range of oil production. It is determined as a constraint condition.

[0065] Here, to ensure relatively stable annual oil production during the planning period, the workload (i.e., production increase) limits for production enhancement measures in each year are specified, constraining the balance of annual oil production: In order to pursue sustainable benefits while ensuring relatively stable annual oil production during the planning period, the range of production increases is therefore... It is determined as a constraint condition.

[0066] Here, the oil production volume (i.e., the range of the aforementioned oil production volume) for different oilfields (areas) and different extraction methods is actually subject to certain limitations. The planning and deployment of extraction methods must consider both the resource status of the extraction methods and the relatively balanced construction of these methods to ensure the smooth completion of the workload. Simultaneously, a balanced drilling and construction schedule ensures that the oilfield drilling and construction and capital investment are coordinated and developed in a relatively regular manner across different years, avoiding an imbalance where the workload is excessively low or high in any given year. Therefore, the scope of resource consumption... It is determined as a constraint condition.

[0067] In an optional embodiment, the processor determines the maximum oil recovery of the target oil field based on the predicted oil recovery, specifically including the following steps: First, determine the target population range for the predicted oil recovery; Secondly, based on the target interval population, the offspring population, non-dominated solutions, and total population for predicting oil recovery are determined; Secondly, regional leading centers are determined based on non-dominated solutions and the total population, and a dynamic local search is performed based on the regional leading centers to determine the specified population; wherein, the regional leading centers are used to indicate the strong dominating points corresponding to non-dominated solutions and the sparse solutions of the total population. Secondly, the target interval population, the offspring population, and the specified population are merged to obtain a merged population, and the target interval population is redefined based on the merged population. Finally, the steps of determining the offspring population, non-dominated solutions, and total population of the target interval population are performed until the target interval population satisfies the iteration conditions, and the maximum oil recovery is determined based on the last determined target interval population.

[0068] In embodiments of this disclosure, the interval population for predicting oil production can be randomly initialized to obtain the target interval population P. I .

[0069] Then, the target interval population P can be analyzed. I Non-dominated sorting was performed to obtain Pareto layers of different strata, and the population crowding distance of each layer was calculated to obtain the total population P. C Non-dominated solutions (i.e., Pareto first-level non-dominated solutions) P T .

[0070] Then, the tournament selection principle can be used to select the target interval population P. I Individual selection is performed, followed by crossover and mutation operations, to obtain a new offspring population P. M .

[0071] Then, within the defined range space, the non-dominated solution P can be computed by traversing the space. T The dominance strength of all non-dominated solutions is used to select strong dominance points based on the magnitude of the dominance strength.

[0072] Then, the total population P can be calculated by iterating through the population. C The sparsity of an individual solution is determined by selecting the solution with the lowest sparsity.

[0073] Then, the strongly dominated and sparse solutions can be set as regional leading centers, and a dynamic local search strategy can be used to iteratively generate a specified population P. N .

[0074] Then, the target interval population P can be... I Offspring population P M and the specified population P N Merge the populations, calculate and sort the crowding of the new populations, retain the elite solutions, select the top N solutions with the highest merit, and replace the target interval population P with the top N solutions with the highest merit. I From the data, a new target interval population P is obtained. I .

[0075] Finally, the steps of determining the offspring population, non-dominated solutions, and total population of the target interval population are performed until the target interval population satisfies the iteration condition (i.e., the number of iterations reaches the preset number of iterations). The final determined target interval population P is then used as the basis for this process. I Determine the maximum oil recovery rate.

[0076] Here, based on the last determined target interval population P I Determining the maximum oil recovery rate can be understood as setting the target population P within the target range. I The optimal solution for the multi-objective optimization model is determined, and the population P in the objective interval is set as follows. I The corresponding oil recovery rate is determined as the maximum oil recovery rate.

[0077] In embodiments of this disclosure, the sparsity described above can be determined in the following manner: Assuming the total population is N, the i-th solution x i The target vector is set to The normalization method is as follows: ; in, and Let $\mathbf{k}$ and $\mathbf{k}$ represent the maximum and minimum values ​​of the $k$-th objective function, respectively, where $k$ ranges from 1 to $m$, and $m$ is the dimension of the objective vector.

[0078] The dilution of the solution after normalization, SP(x) i The calculation is as follows: ; Where n i In the objective function space, with the objective vector The number of other target vectors whose Euclidean distance is less than the search radius r (0 < r < 1).

[0079] In an embodiment of the present disclosure, the above strong domination degree meets the following conditions: ; where the individual represents the solution of the first - layer non - dominated set in the Pareto solution set (i.e., the above - mentioned target vector), and respectively represent the maximum and minimum values of the k - th objective function.

[0080] In the above - mentioned embodiment, the domination strength is selected to compare the advantages and disadvantages of different individuals in the non - dominated solutions. The smaller the domination strength, the better the overall optimization effect. By performing dynamic local search on the strong domination points, the solution speed of the algorithm for high - dimensional data models is further improved.

[0081] In an embodiment of the present disclosure, the strong domination solutions and sparse solutions are set as the regional leading centers, and a dynamic local search strategy is used to iteratively generate a specified population PN, which specifically includes the following steps: Select three strategies of extreme optimization, random optimization, and dynamic optimization to perform dynamic local search on the regional leading center points to obtain the specified population PN.

[0082] For example, assuming that the population size is N, the decision - variable dimension is n, and the regional center solution is , n local solutions are generated in each mutation iteration during the population evolution process.

[0083] The mutation iteration principle of the extreme optimization strategy is as follows: ; where m is a random number in the interval (0, 1), and p is the shape parameter; ; where represents the maximum value of the variation interval of the decision variable, and respectively represent the upper and lower bounds of the corresponding variable.

[0084] The mutation iteration principle of the random search strategy is as follows: ; where , , and g represents the maximum percentage of the random solutions generated by the search strategy in the total population (0.2) ; where , are random numbers and , and These represent the upper and lower bounds of the corresponding variables, respectively.

[0085] The dynamic step size adjustment strategy can dynamically adjust the search step size according to the progress of the iteration, thereby exploring the search space more efficiently and improving search efficiency and convergence speed.

[0086] The formula for the step size in the first half of the iteration is: ; The formula for the step size in the second half of the iteration is: ; in This indicates the initial step size set at the start of the search. is the maximum allowed step size, k is the current iteration number, and K is the total maximum number of iterations.

[0087] In the above embodiments, the strongly dominated solutions and sparsely dominated solutions in the population are selected as region centers, and their leading regions are dynamically searched locally. The local method for obtaining the leading solutions of the region centers is as follows: the Pareto solution set is processed by fast non-dominated sorting. According to the principle of non-dominated solution sorting, the leading solution closest to the origin is the optimal solution set in the population evolution process. By calculating the dominance strength, the dominant solution with the smaller dominance strength is defined as the strongly dominated solution. By calculating the sparsity, the point with the smallest sparsity in the non-dominated solution set is defined as the sparse point. The strongly dominated point and the sparse point serve as two different central regions of the corresponding population.

[0088] Because high-dimensional data input leads to lower superiority of the new population generated during crossover and mutation, the solution is more difficult, increasing the algorithm's convergence difficulty and making it prone to getting trapped in local optima. Therefore, performing a local search on the parent population before elite retention, thereby generating better local solutions to participate in the competition of offspring, can effectively improve convergence speed and global search capability.

[0089] Reference Figure 1 The diagram shows a flowchart of an oilfield extraction device provided in an embodiment of this disclosure. The method includes steps S101-S105, wherein: S101. Based on the optimization objectives of the target oilfield, determine the multi-objective optimization model and constraints of the target oilfield; wherein, the optimization objectives include at least one of the following: oil production, production increase, and resource consumption, and the constraints are used to indicate the range of the oil production, the production increase, and the resource consumption; S102. Combining the constraints and the multi-objective optimization model, determine the predicted oil production of the target oilfield under each extraction measure under the constraints. S103. Based on the predicted oil production, determine the maximum oil production of the target oil field; S104. Based on the maximum oil recovery, determine the exploitation plan for the target oil field; wherein, the exploitation plan is used to indicate the target exploitation measures corresponding to the maximum oil recovery and the exploitation equipment used by the target exploitation measures; S105. Control the extraction device to extract the target oil field through the target extraction measures.

[0090] In the embodiments of this disclosure, the optimization objective of the target oil field can be understood as maximizing the oil production and increased production of the target oil field, and minimizing the resource consumption of the target oil field.

[0091] Here, after determining the optimization objectives of the target oilfield, a multi-objective optimization model can be established based on the oilfield parameters of the target oilfield, with the optimization objectives being to maximize the oil production and increase in production of the target oilfield and to minimize the resource consumption of the target oilfield.

[0092] Here, the oil production, increased production, and resource consumption of a target oilfield under various exploitation measures can be determined based on a multi-objective optimization model.

[0093] Among them, consumed resources are used to indicate the resources (i.e., costs) consumed in the exploitation of the target oil field.

[0094] In the embodiments of this disclosure, solutions to multi-objective optimization models for different mining measures can be determined.

[0095] Here, the solution that meets the constraints in the above solutions can be identified as the predicted oil recovery. For example, with a 3-year oil recovery cycle and 5 extraction methods, there are 5... 3 There are 5 solutions. 3 The solution that meets the constraints is used to predict the oil production.

[0096] In the embodiments of this disclosure, the predicted oil production can be processed based on a multi-objective optimization algorithm to obtain the maximum oil production of the target oil field.

[0097] In the embodiments of this disclosure, the target extraction measure corresponding to the maximum oil recovery and the extraction apparatus used for the target extraction measure can be determined.

[0098] Here, while determining the target extraction measures, the corresponding resource consumption can also be determined. Then, the target extraction measures, the corresponding extraction equipment, and the corresponding resource consumption can be defined as the extraction plan for the target oil field.

[0099] For example, with a 3-year oil production cycle, the first extraction measure corresponding to the first year, the second extraction measure corresponding to the second year, and the third extraction measure corresponding to the third year can be identified as the target extraction measures. The extraction equipment corresponding to the first extraction measure, the second extraction measure, and the third extraction measure can be identified as the extraction equipment of the target extraction measures. The resources consumed in the first year, the second year, and the third year can be identified as the resources consumed by the target extraction measures.

[0100] In the embodiments of this disclosure, after the mining plan is determined, the mining equipment in the mining plan can be used to mine the target oil field in accordance with the target mining measures.

[0101] In the above embodiments, a multi-objective optimization model is established and solved using constraints to determine the oilfield's exploitation plan. This method comprehensively considers multiple factors influencing the oilfield's exploitation plan. Through modeling, oilfield data can be systematically analyzed without needing to establish a comprehensive evaluation system based on the oilfield data. In existing technologies, target decision-making methods rely on the subjective judgment of technical personnel. Therefore, this approach improves time efficiency and the accuracy of the exploitation plan, making the exploitation of the target oilfield more scientific.

[0102] Reference Figure 2 The diagram shown is a detailed flowchart of an oilfield extraction equipment provided in an embodiment of this disclosure, wherein: First, based on the optimization objectives of the target oilfield, the multi-objective optimization model and constraints for the target oilfield are determined.

[0103] The optimization objectives include at least one of the following: oil production, production increase, and resource consumption, and the constraints are used to indicate the range of oil production, production increase, and resource consumption.

[0104] Secondly, by combining the constraints and the multi-objective optimization model, the predicted oil production of the target oilfield under each extraction measure is determined under the constraints.

[0105] Secondly, the target range population for the predicted oil recovery is determined.

[0106] Secondly, based on the target interval population, the offspring population, non-dominated solutions, and total population of the predicted oil recovery are determined.

[0107] Secondly, based on the non-dominated solution and the total population, a regional leading center is determined, and a dynamic local search is performed based on the regional leading center to determine the specified population.

[0108] The regional leading center is used to indicate the strong dominating point corresponding to the non-dominated solution and the sparse solution of the total population.

[0109] Next, the target interval population, the offspring population, and the designated population are merged to obtain a merged population, and the target interval population is redefined based on the merged population.

[0110] Next, the steps of determining the offspring population, non-dominated solutions, and total population of the target interval population are performed until the target interval population satisfies the iteration conditions, and the maximum oil recovery is determined based on the last determined target interval population.

[0111] Secondly, based on the maximum oil recovery, the exploitation plan for the target oil field is determined.

[0112] Finally, the extraction device is controlled to extract the target oil field through the target extraction measures.

[0113] In the above embodiments, a multi-objective optimization model is established and solved using constraints to determine the oilfield's exploitation plan. This method comprehensively considers multiple factors influencing the oilfield's exploitation plan. Through modeling, oilfield data can be systematically analyzed without needing to establish a comprehensive evaluation system based on the oilfield data. In existing technologies, target decision-making methods rely on the subjective judgment of technical personnel. Therefore, this approach improves time efficiency and the accuracy of the exploitation plan, making the exploitation of the target oilfield more scientific.

[0114] Based on the same inventive concept, this disclosure also provides an oilfield extraction scheme determination device corresponding to oilfield extraction equipment. Since the principle of the device in this disclosure for solving the problem is similar to that of the oilfield extraction equipment described above in this disclosure, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0115] Reference Figure 3 The diagram shown is a schematic of an oilfield exploitation scheme determination device provided in an embodiment of this disclosure. The device includes: a first determination module 11, a second determination module 12, a third determination module 13, a fourth determination module 14, and a control module 15; wherein: Determine the target range population for the predicted oil recovery; Based on the target interval population, determine the offspring population, non-dominated solutions, and total population of the predicted oil recovery; Based on the non-dominated solution and the total population, a regional leading center is determined, and a dynamic local search is performed based on the regional leading center to determine a specified population; wherein, the regional leading center is used to indicate the strong dominating point corresponding to the non-dominated solution and the sparse solution of the total population; The target interval population, the offspring population, and the designated population are merged to obtain a merged population, and the target interval population is redefined based on the merged population. The process involves performing steps to determine the offspring population, non-dominated solutions, and total population of the target interval population based on the target interval population, until the target interval population satisfies the iteration conditions, and then determining the maximum oil recovery based on the last determined target interval population.

[0116] This embodiment establishes a multi-objective optimization model and solves it using constraints to determine the oilfield's exploitation plan. This method comprehensively considers multiple factors influencing the oilfield's exploitation plan. Through modeling, oilfield data can be systematically analyzed without needing to establish a comprehensive evaluation system based on the data. In existing technologies, target decision-making methods rely on the subjective judgment of technical personnel. Therefore, this approach improves time efficiency and the accuracy of the exploitation plan, making the exploitation of the target oilfield more scientific.

[0117] The processing flow of each module in the device and the interaction flow between each module can be referred to the relevant descriptions in the above method embodiments, and will not be detailed here.

[0118] Corresponding to Figure 1 In addition to oilfield extraction equipment, this disclosure also provides an electronic device 400, such as... Figure 4 The diagram shown is a structural schematic of an electronic device 400 provided in an embodiment of this disclosure, including: The system includes a processor 41, a memory 42, and a bus 43. The memory 42 stores execution instructions and includes main memory 421 and external memory 422. The main memory 421, also called internal memory, temporarily stores the computational data in the processor 41, as well as data exchanged with external memory such as a hard disk. The processor 41 exchanges data with the external memory 422 through the main memory 421. When the electronic device 400 is running, the processor 41 communicates with the memory 42 through the bus 43, causing the processor 41 to execute the following instructions: Based on the optimization objectives of the target oilfield, a multi-objective optimization model and constraints for the target oilfield are determined; wherein, the optimization objectives include at least one of the following: oil production, production increase, and resource consumption, and the constraints are used to indicate the range of the oil production, the production increase, and the resource consumption; Combining the constraints and the multi-objective optimization model, the predicted oil recovery of the target oilfield under each extraction measure is determined under the constraints. Based on the predicted oil production, the maximum oil production of the target oilfield is determined; Based on the maximum oil recovery, an exploitation plan for the target oil field is determined; wherein, the exploitation plan is used to indicate the target exploitation measures corresponding to the maximum oil recovery and the exploitation equipment used for the target exploitation measures; The extraction device is controlled to extract the target oil field through the target extraction measures.

[0119] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0120] The block diagrams of devices, apparatuses, devices, and systems disclosed herein are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0121] Additionally, as used herein, the "or" used in a list of items beginning with "at least one" indicates a separate list, such that a list of, for example, "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not imply that the described example is preferred or better than other examples.

[0122] It should also be noted that in the systems and methods of this disclosure, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions to this disclosure.

[0123] Various changes, substitutions, and modifications can be made to the technology described herein without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, events, means, methods, and actions described above. Currently existing or later-developed processes, machines, manufactures, events, means, methods, or actions that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein can be utilized. Therefore, the appended claims include such processes, machines, manufactures, events, means, methods, or actions within their scope.

[0124] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0125] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. An oilfield extraction device, characterized in that, include: Data acquisition device, processor, and mining device; The data acquisition device is used to determine a multi-objective optimization model and constraints for the target oilfield based on the optimization objectives of the target oilfield; wherein the optimization objectives include at least one of the following: oil production, production increase, and resource consumption, and the constraints are used to indicate the range of the oil production, the production increase, and the resource consumption. The processor is configured to determine the oil production parameters, recoverable reserve parameters, and resource parameters of the target oilfield; determine multiple sub-optimization models for the target oilfield based on the oil production parameters, recoverable reserve parameters, and resource parameters; determine a multi-objective optimization model based on the multiple sub-optimization models; determine the predicted oil recovery of the target oilfield under various extraction measures under the constraints of the constraints by combining the constraints and the multi-objective optimization model; determine the maximum oil recovery of the target oilfield based on the predicted oil recovery; and determine the extraction scheme for the target oilfield based on the maximum oil recovery; wherein the extraction scheme is used to indicate the target extraction measures corresponding to the maximum oil recovery and the extraction equipment used by the target extraction measures. The extraction device is used to extract the target oil field through the target extraction measures; The processor determines a multi-objective optimization model for the target oilfield based on its optimization objectives, including: Determine the oil production parameters, recoverable reserves parameters, and resource parameters of the target oil field; Based on the oil production parameters, recoverable reserves parameters, and resource parameters, multiple sub-optimization models are determined for the target oilfield; wherein, the multiple sub-optimization models are used to indicate oil production, increased production, and resource consumption, respectively. Based on the multiple sub-optimization models, the multi-objective optimization model is determined; The plurality of sub-optimization models include: a first sub-optimization model, which is used to indicate the oil production rate, and is determined by the following formula: , ; in, The oil enhancement effect of taking the i-th extraction measure in the j-th oil layer of the target oilfield in the k-th year is given. Let J be the oil recovery volume of the j-th oil reservoir when the i-th extraction method is employed in year k. Let i be the single-well oil production increase coefficient of the i-th extraction method put into production in year k, and t be the single-well oil production increase coefficient in year t. Let t be the initial production of the target oilfield's old wells that have not undergone any exploitation measures in year t. Let the natural decline rate of the old well in year k be . Let t be the water-drive aftereffect production of the target oilfield in year t after planning, where t is the number of years in the planning period.

2. The device as described in claim 1, characterized in that, The plurality of sub-optimization models includes: a second sub-optimization model, which is used to indicate the increase in production, and is determined by the following formula: , ; in, To increase the annual recoverable reserves of a single well in the j-th oil layer using the i-th extraction method, which is put into production in the k-th year, Let represent the oil recovery volume of the j-th oil layer when the i-th extraction method is employed in the k-th year.

3. The device as described in claim 1, characterized in that, The plurality of sub-optimization models includes: a third sub-optimization model, which is used to indicate resource consumption, and is determined by the following formula: , ; in, The oil enhancement effect of taking the i-th extraction measure in the j-th oil layer of the target oilfield in the k-th year is given. Let be the cost coefficient of a single well in the j-th oil layer of the i-th extraction method, which is put into production in year t. Let J be the oil recovery volume of the j-th oil reservoir when the i-th extraction method is employed in year k. The resource coefficient associated with the j-th oil layer and well for the i-th extraction method in the target oil field.

4. The device as described in claim 1, characterized in that, The constraints of the multi-objective optimization model include: ； ； ； in, Let J be the oil recovery volume of the j-th oil reservoir when the i-th extraction method is employed in year k. The maximum oil recovery rate for the i-th extraction method is... This represents the lower limit of oil production under the i-th extraction method in year t. This represents the upper limit of oil production under the i-th extraction method in year t. The oil enhancement effect of taking the i-th extraction measure in the j-th oil layer of the target oilfield in the k-th year is given. This represents the lower limit of oil production from wells developed before year t. This represents the upper limit of oil production from wells developed before year t.

5. The device as described in claim 1, characterized in that, The processor determines the maximum oil recovery of the target oilfield based on the predicted oil recovery, including: Determine the target range population for the predicted oil recovery; Based on the target interval population, determine the offspring population, non-dominated solutions, and total population of the predicted oil recovery; Based on the non-dominated solution and the total population, a regional leading center is determined, and a dynamic local search is performed based on the regional leading center to determine a specified population; wherein, the regional leading center is used to indicate the strong dominating point corresponding to the non-dominated solution and the sparse solution of the total population; The target interval population, the offspring population, and the designated population are merged to obtain a merged population, and the target interval population is redefined based on the merged population. The process involves performing steps to determine the offspring population, non-dominated solutions, and total population of the target interval population based on the target interval population, until the target interval population satisfies the iteration conditions, and then determining the maximum oil recovery based on the last determined target interval population.

6. An oilfield exploitation method, characterized in that, Applied to the oilfield extraction equipment as described in claim 1, comprising: Based on the optimization objectives of the target oilfield, a multi-objective optimization model and constraints for the target oilfield are determined; wherein, the optimization objectives include at least one of the following: oil production, production increase, and resource consumption, and the constraints are used to indicate the range of the oil production, the production increase, and the resource consumption; Combining the constraints and the multi-objective optimization model, the predicted oil recovery of the target oilfield under each extraction measure is determined under the constraints. Determine the target interval population for the predicted oil recovery; based on the target interval population, determine the offspring population, non-dominated solutions, and total population for the predicted oil recovery; Based on the non-dominated solution and the total population, the regional leading center is determined, and a dynamic local search is performed based on the regional leading center to determine the specified population; The target interval population, the offspring population, and the designated population are merged to obtain a merged population, and the target interval population is redefined based on the merged population. The process involves performing steps to determine the offspring population, non-dominated solutions, and total population of the target interval population, based on the target interval population, until the target interval population satisfies the iteration conditions, and then determining the maximum oil recovery of the target oilfield based on the last determined target interval population. Based on the maximum oil recovery, an exploitation plan for the target oil field is determined; wherein, the exploitation plan is used to indicate the target exploitation measures corresponding to the maximum oil recovery and the exploitation equipment used for the target exploitation measures; The extraction device is controlled to extract the target oil field through the target extraction measures.

7. An apparatus for determining an oilfield exploitation scheme, characterized in that, Applied to the oilfield extraction equipment as described in claim 1, comprising: The first determining module is used to determine the multi-objective optimization model and constraints of the target oilfield based on the optimization objectives of the target oilfield; wherein the optimization objectives include at least one of the following: oil production, production increase, and resource consumption, and the constraints are used to indicate the range of the oil production, the production increase, and the resource consumption; The second determining module is used to combine the constraints and the multi-objective optimization model to determine the predicted oil production of the target oilfield under each extraction measure under the constraints. The third determining module is used to determine the maximum oil production of the target oil field based on the predicted oil production. The fourth determining module is used to determine the exploitation plan for the target oilfield based on the maximum oil recovery; wherein the exploitation plan is used to indicate the target exploitation measures corresponding to the maximum oil recovery and the exploitation equipment used by the target exploitation measures; The control module is used to control the extraction device to extract the target oil field through the target extraction measures.

8. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the oilfield exploitation method of claim 6.

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