A Simple Methodology for Direct Estimation of Gas-in-Place and Reserves Using Rate-Time Data
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AbstractEstimation of contacted gas-in-place/reserves in unconventional (low/ultra-low permeability) gas reservoirs is a problematic issue, and the uncertainty associated with the estimate is relatively higher. In this work we provide a robust methodology for estimating gas reserves in tight gas/shale gas systems based (primarily) on the use of an integrated approach which makes use of the following models: Semi-analytical formulation "Quadratic" rate-cumulative relation type curve (Blasingame and Rushing [2005]).Empirical formulation "power-law exponential" rate decline model (Ilk et al [2008a] and [2008b]).A dimensionless quadratic rate-cumulative type curve is derived using the rate-cumulative relation proposed by Blasingame and Rushing (2005) and is given by: qD=1GpD+2GpD2(whereqD=qg/qgi,GpD=Gp/G,and=G/(qgi/Di)bydefinition)A "reverse solution" for the -parameter enables the analyst to establish the existence of boundary-dominated flow directly from the data. Our experience using synthetic and field data suggests that, for the Blasingame and Rushing model, the -parameter is equal to two (2) for full boundary-dominated flow.We have also written the "power-law exponential rate decline relation" in dimensionless rate-time form and we employ the type-curve procedures for the analysis of a given set of rate-time data. The dimensionless power-law exponential rate decline relation is given as: qDd=exp[DtDdtDdn]We have validated our analysis procedure for this model using a two numerical gas simulation cases and we have applied this procedure to several tight gas and shale gas cases. Our results show that when gas production data are analyzed using the integrated approach described in this work then the uncertainty in reserves estimates is reduced. Specifically, expressing the rate-cumulative production data functions in dimensionless forms provides a direct diagnostic of the boundary-dominated flow regime. Power law exponential rate decline type curves help the analyst to identify the features (character) in the rate data.
