Pressure Rate Deconvolution Methods for Well Test Analysis

Abstract

The deconvolution method has received much attention recently, and is becoming one of the major tools for well test and production data analysis in oil and gas industry. Here, we present a new deconvolution approach, which we believe is relevant and can be an important addition to the existing efforts made in this field. We show that the solution of the deconvolution problem can be successfully represented as a linear combination of nonorthogonal exponential functions. Also, we present three deconvolution algorithms. The first two algorithms are based on regularization concepts borrowed from the well-known Tikhonov and Krylov methods, while the third algorithm is based on the stochastic Monte Carlo method.

New Regularization Algorithms for Solving the Deconvolution Problem in Well Test Data Interpretation

“…The method appears to be quite robust, although it does not take into account errors in flow rates and the initial reservoir pressure. Andrecut and Madni [26] and Andrecut [27] studied three methods: 1) Tikhonov regularization, 2) Krylov subspace projection method, and 3) stochastic Monte-Carlo. The authors stated that the stochastic Monte-Carlo method was more robust than the other two.…”