Optimization of Subcool in SAGD Bitumen Processes | SLB

Optimization of Subcool in SAGD Bitumen Processes

Published: 03/05/2014

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Schlumberger Oilfield Services

Uniform steam chamber growth (conformance) in a steam-assisted gravity drainage (SAGD) process promotes enhanced bitumen recovery, economics, and environmental benefits. Operators have implemented many strategies to improve such conformance through simultaneous injection in the inner tubing and annulus space with dual-tubing completions, to provide some degree of injection control at the heel and toe regions of the horizontal well pair. Such strategies do not, however, necessarily guarantee the desired uniformity and efficiencies sought. Recent work suggests that using Proportional-Integral-Derivative (PID) feedback to control steam injection can provide beneficial gains in steam chamber conformance, particularly in heterogeneous reservoirs. The feedback control is applied to each steam injection point in the horizontal well pair. Injection at these control points is regulated by a PID feedback controller monitoring temperature differences between injected and produced fluids in order to both enforce a specified subcool and to achieve uniform production along the entire length of the producer. While it is beneficial to achieve a subcool so that steam utilization, or SOR, is improved, it may transpire that such metrics do not furnish the best (optimal) overall economics of the asset (i.e., Net Present Value or NPV).

This paper examines detailed wellbore simulations of a SAGD well pair with both dual tubing string and ICD completions in the producer and PID-controlled steam injection with dual tubing strings. Two synthetic reservoir models, one based on an idealized heterogeneity pattern and the other based on logs from the Athabasca region of Alberta, are utilized, representing a highly heterogeneous formation and typical of many bitumen assets. These simulations are employed as part of an optimization process in order to determine the optimal PID controller parameters, and thereby optimize project NPV.

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