An automation system for gas-lifted oil wells Model indentification, control, and optimization
Camponogara, Eduardo Plucenio, Agustinho Teixeira, Alex F.
An automation system for gas-lifted oil wells Model indentification, control, and optimization - feb. 2010 - 11 p. ; 157-167
Transcripción del resumen del autor. 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.
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An automation system for gas-lifted oil wells Model indentification, control, and optimization - feb. 2010 - 11 p. ; 157-167
Transcripción del resumen del autor. 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.
3-4
