Geostatistical Methods for Unconventional Reservoir Uncertainty Assessments

Abstract

New methods are required to support unconventional reservoir uncertainty modeling. Unconventional plays add additional complexity with greater uncertainty in direct reservoir measures (e.g., unreliable permeability measures in low-permeability rock) and weakened relationships between currently measurable reservoir properties and production results (production mechanisms may not be well understood). As a result, unconventional plays are often referred to as “statistical plays,” suggesting the reliance on statistical characterization of production distributions as a function of well counts. The application of the techniques described herein can be utilized to integrate all available information to determine appropriate levels of drilling activity to reduce uncertainty to an acceptable level.

Geostatistical approaches provide opportunities to improve the rigor in the dealing with statistical plays. Rigor is introduced through integration of methods that account for representative statistics, spatial continuity, volume-variance relations, and parameter uncertainty.

Analog production data from US shale gas plays are utilized for demonstration. These datasets, after debiasing, are sources for analog production rate distributions and spatial continuity. Given these statistics along with a decision of stationarity, geostatistical workflows provide repeatable uncertainty models that may be summarized over a spectrum of model parameters, drilling strategy, and well counts.

These geostatistical methods do not replace the need for expert judgment, but they improve the rigor of statistical-based approaches that are essential in statistical plays.