It is often difficult to quantify the redevelopment potential of marginal oil and gas fields because of a wide range of depositional environments, variability in reservoir properties, a large number of wells, and limited reservoir information. Evaluation of infill potential in these fields with traditional simulation methods is timeconsuming, labor-intensive, and frequently cost-prohibitive. Without adequate assessment technology, some unprofitable infill campaigns may be initiated, while other promising infill campaigns may be terminated prematurely because of disappointing early results.
In this paper, we present a simulation-based regression technique to assess infill-drilling potential in marginal gas fields. With limited, basic reservoir information, this technique first estimates the spatial distribution of subsurface reservoir properties by rapid history matching of well production data. We implemented a sequential regression algorithm to estimate, from available flowrate measurements, not only the permeability distribution, but also the pore-volume distribution. Future production is forecasted and infill-drilling potential is determined using the estimated permeability and pore-volume distributions. Because the method uses an approximate reservoir description, it identifies regions of the field with promising infill potential rather than individual-infill-well locations.
The proposed technique provides rapid, reliable, and cost-effective assessment of redevelopment potential in marginal gas fields. In this paper, we first validate our approach using synthetic reservoir data. We then apply the approach to the Second White Specks formation, Garden Plains field, Western Canada Sedimentary Basin. The prediction of infill potential in this gas field, which has more than 700 wells, demonstrates the power and utility of the proposed technique.