Bailey, Jeffrey R. (ExxonMobil Upstream Research Co.) | Smith, Richard James (Imperial Oil Resources Ltd.) | Keith, Colum M. (Imperial Oil Resources Ltd.) | Searles, Kevin Howard (ExxonMobil Upstream Research Co.) | Wang, Lei (ExxonMobil Upstream Research Co.)
Cyclic Steam Stimulation (CSS) is a cost-effective means to produce heavy oil at the Cold Lake field in Alberta, Canada. The high viscosity of bitumen is the main obstacle to economic production, but the bitumen viscosity decreases significantly with temperature. Steam is injected at fracturing conditions, resulting in complex interactions of reservoir expansion (dilation) and contraction (recompaction) that propagate stress and strain fields in the overburden.
The mechanical loads on wells resulting from this production process are an important design consideration. To enhance operational integrity, a dedicated passive seismic monitoring well is installed on new development pads to provide early detection of casing failures and possible fracturing of the formation overburden. There is now an installed base of almost 90 such acoustic monitoring wells in the operator's field. With data acquisition of 15 to 30 geophones per system, recording continuously at 2000 or 3000 samples per second, the data management issues for this monitoring network are challenging.
Several classes of acoustic events have been identified, including those due to casing failure, formation heave, near-wellbore cement cracking, and production rod pump background noise, in addition to "Continuous Microseismic Radiation?? (CMR) that resembles harmonic tremors. Most casing failures are detected by observation of singular events. The detection of fracturing of the overburden, which may include the presence of bitumen and/or produced water that has migrated out of zone, is a more complex process that requires distinguishing shear events and CMR events from normal formation heave and other environmental noise.
The operator has stewarded the development of a cost-effective system that includes local pad data acquisition, uploading of selected data to a server with data archiving facilities, and downloading data to dedicated analysts. This paper will present a summary of the data management and processing technologies developed to address the challenge of managing this data-intensive problem.