Abstract
Production from low permeability carbonate reservoirs is commonly supported by the waterflood (WF) process. Despite decades of research and field experience, oil recovery expectations in carbonates typically remain low due to factors such as high permeability streaks, poor microscopic sweep efficiency, and low mobility ratios, all of which can dramatically impair oil recoveries. Vast amounts of remaining oil-in-place have led operators to analyze opportunities to improve WF management and production.
This paper presents an approach to increase recovery and improve performance indicators in a low permeability carbonate reservoir. The main objective of this effort is to maximize short-term oil rates and long-term recovery while honoring target constraints on voidage-replacement ratio (VRR), reservoir pressure, sweep efficiencies, and production and injection rates. In essence, our approach seeks optimum WF solutions by coupling a full-field reservoir simulator with an adaptive, simulated annealing optimization engine.
Predefined scenarios impose hard constraints on production and injection rates, field conditions, and well operating constraints. Voidage replacement ratios (VRR) and nominal pressure (Pn), volumetric sweep efficiency (Evol) and displacement efficiency (Ed) are soft constraints used to calculate design feasibility. Reservoir recovery and displacement efficiency performance indicators are pursued at different levels in the optimization loop. The outcome is that the following month's operational decisions are suggested by the optimizer. This paper describes how this optimization methodology provides improvements in short-term production rates of about 10–20% while enhancing oil recovery between 1–8%. This paper also discusses additional strategies to further improve oil recovery expectations using this automated workflow as the foundation.
