Nabiye Cold Lake Expansion - Leveraging Technology to Create Success

Fawcett, Heather (ExxonMobil Development Co.) | Kueh, Sylvia Ruoh Mei (Imperial Oil Resources Ltd.) | Scott, George R. (ExxonMobil Upstream Research Co.) | Hsu, Sheng-Yuan (Exxon Mobil Corporation) | Liang, Yueming (Imperial Oil Ltd.) | Dittaro, Larry Mark

OnePetro 

Abstract
The Cold Lake development, located in Alberta, Canada, is the world's largest heavy oil in situ thermal development. At Cold Lake, operated by Imperial Oil Resources, an ExxonMobil affiliate, the Cyclic Steam Stimulation (CSS) process is used to produce 23,500 m3/d (150 kB/d) of heavy oil. In 2009, Cold Lake produced its one billionth barrel (160 million m3) of heavy oil.

The Nabiye project will be the fifth central steam generation and fluid processing hub added at Cold Lake. Nabiye (Dené for Otter) continues the historical Cold Lake development concept of maximizing value through the utilization of a phased development strategy. Relative to current operations, the key reservoir difference at Nabiye is reduced pay thickness. Averaging 12 meters (40 feet), Nabiye pay is about half as thick as the initial pads of the previous expansion (Mahkeses). While reservoir of similar thickness as Nabiye is currently being developed as Productivity Maintenance pads to sustain production in the existing operation, the risk profile for Nabiye is higher because new plant investment is required. As Cold Lake develops more challenging subsurface environments, more advanced reservoir engineering techniques must be employed to mitigate risk. This paper describes the extensive use of both thermal simulation and wellbore integrity modeling to complement analog performance prediction techniques.

This paper will demonstrate how the Nabiye project is effectively commercializing an unconventional resource by integrating analog performance data and advanced reservoir and geomechanical modeling. The application of (1) thermal simulation for performance prediction and (2) geomechanical modeling for steam strategy optimization will be presented.