It is often difficult to quantify the redevelopment potential of marginal oil and gas fields due to a wide range of depositional environments, variability in reservoir properties, large numbers of wells, and limited reservoir information. With traditional simulation methods, evaluation of infill potential for these fields is time consuming, 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 due to disappointing early results.

In this study, we developed a simulation-based regression technique to assess infill drilling potential in stripper gas well 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 not only the permeability distribution, but also, the pore volume distribution from available flow rate measurements. Future production is forecast and infill drilling potential is determined using the estimated permeability and pore volume distributions. Because the method employs 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 stripper gas well fields. In the 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. Prediction of infill potential in this gas field, which has more than 700 wells, demonstrates the power and utility of the proposed technique.

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