Abstract
Abstract Type curve analysis of pressure transient data influenced by wellbore storage effects can yield non-unique answers due to similarity in the shapes of the curves. In order to alleviate this problem, several researchers have proposed methods for unbiased parameter estimation using nonlinear regression analysis techniques. Typically, these methods are sensitive to initial guesses for the parameter values; thus, with poor initial estimates, it is possible that the schemes may diverge or converge to wrong answers. The probability of convergence to correct parameter values is further decreased if the data contains outliers; i.e., measured data whose behavior is significantly different from the "average" behavior of the data set. In this work, we focus on practical methods of applying the nonlinear regression to the analysis of pressure transient data. Our aim is to provide procedures that enhance the probability of obtaining a unique match, and which detect and properly account for the presence of outliers. A new two-step procedure which utilizes the pressure-pressure derivative ratio in the first step, is demonstrated to increase the chances of obtaining a unique fit. Also, we present a new method of applying "robust" parameter estimation (which accounts for data outliers) that uses commonly available algorithms based on least squares (LS) regression. We demonstrate applicability of the proposed methods by analyzing several sets of field data.
Published Version
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