Lithologic and geomechanical facies classification for sweet spot identification in gas shale reservoir

Abstract

Unconventional reservoirs require advanced technologies such as horizontal well placement and hydraulic fracturing to be successfully exploited at economic rates. In this context, static and dynamic reservoir quality (RQ) concepts are introduced. Static RQ or standard RQ comprises a set of petrophysical parameters that describe formation tendency for development. Dynamic RQ or completion quality is defined by a set of geomechanical parameters that estimate formation tendency to be fractured. The convergence of static and dynamic RQs allows for evaluating the production potential of a field; particularly, productive sweet spots are located in those intervals in which good static and dynamic RQs are detected. We have developed a workflow to identify producible intervals in unconventional reservoirs by means of lithologic and geomechanical facies classification. Starting from core data, a clustering technique is used to create a set of lithologic facies that are then extended to the logged interval and characterized in terms of static RQ. The same approach is used to classify the logged interval with a set of geomechanical facies in which dynamic RQ is estimated. The integration of lithologic and geomechanical facies leads to sweet spot identification. Workflow application to available data from the Barnett Shale Formation allows us to classify the logged interval with four log facies (LF) and five geomechanical facies (GF) and to identify productive sweet spots in the upper and middle Lower Barnett. Eventually, LF and GF are linked to seismic facies probability volumes and Young’s modulus from elastic inversion of surface seismic. Seismic-driven geostatistical realization of LF and GF provides static and dynamic RQs volumes that are combined into volumes of productive and nonproductive facies.