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An automation system for gas-lifted oil wells: Model identification, control, and optimization

机译:气举油井自动化系统:模型识别,控制和优化

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摘要

Smart fields technology advocates the use of a suite of skills, workflows, and technologies to drive efficiency gains while maximizing oil recovery from reservoirs. This paper contributes to smart fields technology by developing an automation system for integrated operation of gas-lift platforms, thereby bridging the gap between downhole devices (sensors, valves, and controllers) and surface facilities (operating policies, constraints, and faults). The components of the system are: (1) a module for identification of well-performance curves from downhole pressure measurements; (2) a control strategy for the pressure of the gas-lift manifold and a software sensor to indirectly measure the gas-mass flow-rate available for artificial lifting; and (3) an algorithm for optimal allocation of limited resources, such as the lift-gas rate, fluid handling capacities, and water-treatment processing capacity. The paper reports results from simulations performed with a prototype platform as a proof of concept
机译:智能油田技术提倡使用一套技能,工作流程和技术来提高效率,同时最大程度地提高储层的采油率。本文通过开发用于气举平台集成运行的自动化系统,从而缩小井下设备(传感器,阀门和控制器)与地面设施(运行策略,约束和故障)之间的差距,为智能现场技术做出了贡献。该系统的组件为:(1)一个模块,用于根据井下压力测量结果识别井眼性能曲线; (2)气举歧管压力的控制策略和软件传感器,以间接测量可用于人为举升的气体质量流量; (3)一种优化分配有限资源的算法,例如提升气量,流体处理能力和水处理能力。该论文报告了使用原型平台执行的仿真结果作为概念验证。

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