Abstract
Selecting the optimum combination of technologies is a critical and challenging activity while conducting the opportunity assessment under high levels of uncertainty in a deep (~9000 feet) extra heavy oil green field transitioning between appraisal and development phases. Low mobility requires enhanced oil recovery to be addressed early in the life of the field, so selected wells can be drilled and completed in selected locations to reduce uncertainty about producibility and flow assurance.
This paper presents a practical approach to opportunity assessment based on Front End Loading (FEL) methodology, with three major steps: 1. Evaluation of known data, determination of complexities, uncertainties and risks by benchmarking with selected field analogs, 2. Identification of all potential technology options and 3. Definition of feasible appraisal and development scenarios and a high-level road map including estimates of life cycle cost opportunities for optimization.
We found reservoir static complexity medium, well complexity low, and reservoir dynamic complexity high. FEL definition indices for reservoir and well indicated low reservoir definition and acceptable index for wells. These complexity and definition indices were used for conducting benchmarking with three analog fields providing references for risks and ranges of production, recovery and total cost.
After multidisciplinary analysis with participation of 35 specialists organized into three clusters (subsurface, well and surface), 100 challenges (72 risks and 28 uncertainties) were identified, analyzed and ranked. Assessment of 36 parameters used for Enhanced Oil Recovery (EOR) screening were assessed from uncertainty perspective with preliminary selection of 7 potential EOR methods. Final integration was achieved with identification of 110 technology options for 30 key decisions, finally selecting best suitable options for 4 potential development chronological scenarios.
Results are presented in a cost breakdown structure reflecting the most critical cost drivers, where high percentage corresponds to OPEX affected by identified risks and causal maps describes effects on total costs for subsurface, well and surface. We modeled all significant risks by visualizing its impact on total cost and we defined the mitigation actions ranked by risk adjusted stochastic economics performed as input for decision-making.
This paper demonstrates that understanding the root causes of high cost per barrel and their relationship with uncertainties and risks during early stages of a heavy oil field life cycle, provides a common language for multidisciplinary cost optimization, and facilitates communication and involvement of all disciplines.
