Analysis of Production Data from Fractured Shale Gas Wells

Abstract

Abstract Long-term shale gas well performance characteristics are generally not well understood. The ultra-low permeability of shale ensures the continuing presence of pressure transient effects during well production. This makes production forecasting a difficult and non-unique exercise. Conventional methods have proven to be too pessimistic, in many cases, because they assume a depletion-dominated system. Recently, more suitable forecasting methods have been developed that account for long-term transient effects. These methods incorporate a transient model (usually linear flow) which transitions into a conventional boundary-dominated flow model after a prescribed time or upon achieving a certain region of investigation. The underlying concept assumes that once a transition to boundary-dominated flow is observed, depletion will dominate the production going forward. Although this methodology has been successfully applied for a variety of tight gas reservoirs, it may not be the right model for fractured shale gas (and some conventional tight gas) reservoirs. Fractured shale gas reservoirs get their productivity from the stimulated reservoir volume (SRV), which may be quite limited in areal extent but is surrounded by a low-permeability reservoir (matrix). Thus, the mechanism for long-term production includes a late-time transition from depletion of the SRV, back to infinite acting (linear or pseudo-radial) flow. This "return" to infinite acting flow may or may not provide contribution to recoverable reserves within a practical time-frame, but it should be considered nonetheless. In this paper we present a straight forward methodology for determining the major well performance characteristics of fractured horizontal shale gas wells, considering the impact of uncertainty and non-uniqueness. The focus will be on determining the dominant flow regimes and bulk properties from the data, and then defining a suitable, simple reservoir model for production forecasting, using practical experience and all available information. Field examples from the Barnett, Marcellus, and Haynesville shales are included.