Real time data is not a new concept to ESP lifted wells. Many systems are available to gather and transmit data, coupled with services and software to help analyses data. However, the potential value of real time surveillance systems is often not fully realized. This paper discusses case studies of how information from intelligent real time processes was used to optimize production, increase run life and manage ultimate production from the reservoir.

Each well was equipped with a panel running a real time nodal analysis well model which enabled the draw down on the reservoir to be controlled using the ESP. Target draw down or flow rate values where supplied to each processor which calculated the optimum mode of operation for the ESP. Techniques included virtual real time well testing, balancing of draw down on the reservoir in real time using the lift system as a control, and the importance of protecting the lifting equipment during the automation process.

Oil production gains ranging from 6 to 50% were achieved using the system, some of which occurring on wells thought to be previously optimized. ESP uptime was improved through real time monitoring of well status, and pump operation in difficult wells improved through use of the advanced ESP diagnostics information.

The novel architecture of the system showed that parallel processing power enabled each well to automatically adapt to changes in reservoir or flow line performances in real time as the target draw down values where achieved. Fine tuning of the lift system frequency (pump speed) and wellhead pressures allowed each well to be optimized in line with the reservoir engineers recommended bottom hole flowing pressure requests. The individual production and draw down targets could be adjusted up or down at the reservoir engineers request and the system automatically recalculate the optimum operating condition for the lift system.

The real time system showed how draw down targets could be adjusted to enhance overall production in addition to achieving the targets through real time automation of each ESPs operation

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