Abstract
The scrapping of old waterflooding wells and the increase in new waterflooding wells results in mixed flooding of high–low pressure wells in various oil layers in waterflooding systems. In order to meet production operation requirements, the whole system is in a state of high pressure, which leads to an increase in energy consumption and complicates the operation of waterflooding networks. According to the pressure distribution of wells, proceeding with regional accurate waterflooding can reduce operation costs and improve development efficiency. Considering the technical constraints of waterflooding networks, a method was proposed, which can quantitatively optimize classification and zoning for waterflooding of high–low pressure wells according to the pressure of networks and wells. At the same time, the ant colony algorithm and genetic algorithm were fused to form a new adaptive ant colony genetic hybrid algorithm, which can effectively determine the best pumping scheme of the waterflooding station, the pumping flow and optimize the low-pressure area. The K-means algorithm was used to optimize the topology of the pipe network in the high-pressure area to reduce the overall waterflooding pressure. Finally, the method was successfully applied to the large-scale waterflooding system including 2200 wells and 10 waterflooding stations in sites in China. The results show that the method is effective for the operation and reconstruction of waterflooding pipe networks with large-scale and serious mixed high–low pressure.
Highlights
An oilfield waterflooding system is a large, complex hydrodynamic system that consumes a lot of energy
In order to meet the pressure requirements of high-pressure wells, when the system is in a high-pressure state it leads to an increase in energy consumption and complicates the optimal operation of waterflooding pipe networks
In this case it is necessary to analyze the characteristics and rules of the pressure distribution of wells, to analyze the feasibility of different zones and pressure waterflooding according to production requirements, and to study the application of new intelligent optimization methods and machine learning methods to solve the problems of topology optimization and pumping scheme optimization in high–low pressure areas [1,2,3]
Summary
An oilfield waterflooding system is a large, complex hydrodynamic system that consumes a lot of energy. In order to meet the pressure requirements of high-pressure wells, when the system is in a high-pressure state it leads to an increase in energy consumption and complicates the optimal operation of waterflooding pipe networks In this case it is necessary to analyze the characteristics and rules of the pressure distribution of wells, to analyze the feasibility of different zones and pressure waterflooding according to production requirements, and to study the application of new intelligent optimization methods and machine learning methods to solve the problems of topology optimization and pumping scheme optimization in high–low pressure areas [1,2,3]. Aiming to address the problem of high–low pressure mixed flooding in oil fields, a new adaptive ant colony genetic hybrid algorithm was proposed for low-pressure area optimization in this paper It is the first time it has been considered from the perspective of zone and pressure division in order to improve system operation efficiency. If this is in the isolated low-pressure area, it needs to be merged into the surrounding high-pressure area
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