A Noniterative Blind Deconvolution Approach to Unveil Early Time Behavior of Well Testings Contaminated by Wellbore Storage Effects

Abstract

Deconvolution method is generally used to eliminate wellbore storage dominant period of well testing. Common Deconvolution techniques require knowledge of both pressure and rate variations within test duration. Unfortunately, accurate rate data are not always available. In this case, blind deconvolution method is used. In this work, we present a new approach to improve the ability of blind deconvolution method in well testing. We examined the behavior of rate data by comparing it with a special class of images and employed their common properties to represent gross behavior of extracted rate data. Results of examinations show ability of our developed algorithm to remove the effect of wellbore storage from pressure data. Our Algorithm can deal with different cases where wellbore storage has made two different reservoirs behave identical in pressure response. Even if there is no wellbore effect or after wellbore storage period is passed, proposed algorithm can work routinely without any problem.