Conceptual Models for Fast Tracking Decision Making in the Reservoir Management

Abstract

Abstract Conceptual models are used to solve specific problems in selected sectors of reservoirs; study production mechanisms; understand behavior of a particular process in a reservoir system, and assess impacts of changing input parameters during reservoir modeling. They are tools of choice for assessing risks, evaluating "worst-case" scenarios, validating analyst's intuition, and to support informed decision making. Our objective is to demonstrate via two case studies how conceptual numerical models were used to shorten the time required to make reservoir management decisions. The first case study involves making a decision, either to develop or sell an oil property. Target formation is sandstone saturated with heavy oil (12°API gravity) which is overlain by a gas cap. Conceptual numerical simulation models provided answers to two questions: What is the impact of gas production from the gas cap on the underlying heavy oil zone? Can gas production from up-structure wells meet field deliverability requirements? Second case study uses conceptual models to optimize well placement and support infill drilling. Infill well placement posed a challenge because thickness of target formation is not well known, and oil zone is bounded on top by a massive impermeable shale boundary, and by oil-water contact (OWC) located about 20-40 feet below. Conceptual models answered the following questions: What type of well to drill–vertical or horizontal? What is the impact of horizontal well's vertical placement (offset distance from OWC) on oil recovery and water breakthrough times? What is the optimum horizontal well lateral length and its impact on oil recovery? This paper describes modeling methodology, major observations and conclusions. We discuss the benefits and lessons learned from the case studies and demonstrate that successful application of conceptual models requires identifying key well/reservoir performance drivers and assessing their impacts on the reservoir management decisions.