Integrating Geomechanics in SAGD Reservoir Surveillance Programs

Abstract

Reservoir and geomechanical monitoring programs are critical to ensuring operational safety in thermal projects. Although many technologies have been applied to monitor thermal operations, most of them aim to measure just a few parameters, such as pressure, temperature, and surface deformation. While monitoring may aid in understanding phenomena in the subsurface, the data obtained from these observations is significantly divergent from what current models predict. This discrepancy is mainly attributable to the inherent uncertainty in the modeling assumptions and in the input parameters. While reservoir and geomechanical monitoring are not sufficient to inform our understanding of the subsurface's behaviour, they are valuable tools for gaining understanding of the actual behaviour. Monitoring is a widely used technique in geotechnical projects, where various methodologies and approaches have been proposed to optimize the program's design. These approaches seek to answer fundamental questions such as where to place the instruments, which devices to select, and how the model and the design of the project may be improved through the use of the monitoring results. The present research incorporated knowledge from geotechnical engineering into reservoir engineering to identify an appropriate methodology rooted in engineering principles, which can be followed in thermal operations monitoring planning and deployment. The proposed methodology is logic-based and helps maximize the value of monitoring programs, thereby safely increasing bitumen production in thermal projects. Coupled geomechanical and flow simulations were performed using a 3D high-resolution geomechanical model to evaluate the response of subsurface to SAGD. Typical monitored parameters were analyzed from different simulation cases to predict monitored results helping design optimal monitoring programs for SAGD. Finally, a case study was used to demonstrate that designing monitoring programs based on prediction results in cost-effective monitoring programs.