The U.S. Geological Survey estimates tight-gas sands and shales may contain up to 460 trillion cubic feet (Tcf) of gas in the U.S. alone - almost three times the amount of currently proven gas reserves - and that some 135 Tcf may be technically recoverable. Since natural fractures are the main source of permeability in gas shales, key to producing this vast resource is locating areas where natural fractures are abundant. By placing infill wells in such locations operators can significantly increase gas recovery.

In most cases, in fields that produce from gas shales, monthly production rate is the only data that is available. This paper introduces a new strategy for estimation of major and minor natural fracture using production data.

The framework includes the use of Geographic Information System as an environment to analyze the production data where the preliminary estimation of natural fracture trends is developed. Then by superimposing the results of the preliminary estimation on reservoir quality indices developed using a fuzzy pattern recognition technique, the uncertainty associated with the initial estimation is reduced. This technique is validated using a set of gas shale wells in Kentucky and West Virginia.

Keywords:
Pattern Recognition, US government, tier-three well, shale gas, cumulative production, machine learning, complex reservoir, GIS method, gas shale, minor natural fracture pattern
Subjects:
Unconventional and Complex Reservoirs, Shale gas, Naturally-fractured reservoirs
Copyright 2006, Society of Petroleum Engineers
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