In this work, we provide a comparative study of recently proposed deconvolution algorithms which were designed to function in the presence of reasonable levels of noise in both the rate and pressure input data. The algorithms considered for comparison are those presented by von Schroeter et al.1,2Levitan,3,4 and Ilk et al.5,6 These works offer robust solution algorithms to the long-standing deconvolution problem and make deconvolution a viable tool to well-test and production data analysis. However, there exists no comparative study revealing and discussing specific features associated with the use of each algorithm in a unified manner. We have independently reproduced the von Schroeter et al. and Levitan algorithms to assess the specific advantages and limitations of each method (as well as the Ilk et al. method), and we provide a comparative study of these algorithms using synthetic and field case examples.
Our results identify the key issues regarding the successful and practical application of each algorithm. In addition, we show that with proper care and attention in applying these methods, deconvolution can be used as an important tool for the analysis and interpretation of variable rate/pressure reservoir performance data.