USING ARTIFICIAL NEURAL NETS TO IDENTIFY THE WELL-TEST INTERPRETATION MODEL Academic Article

abstract

• Summary This paper presents a new approach, based on artificial neural net technology, to identify a preliminary well-test interpretation model from derivative plot data. Artificial neural nets can identify patterns from incomplete and distorted data and also eliminate the need for elaborate data preparation, such as smoothing.

authors

• Lee, W. John

published proceedings

• SPE FORMATION EVALUATION

altmetric score

• 3

author list (cited authors)

• ALKAABI, A., & LEE, W. J.

citation count

• 59

complete list of authors

• ALKAABI, AAU||LEE, WJ

publication date

• September 1993

publisher

• Society of Petroleum Engineers (SPE)  Publisher

published in

• SPE Formation Evaluation  Journal

keywords

• Neurosciences

Digital Object Identifier (DOI)

• 10.2118/20332-PA

start page

• 233

end page

• 240

volume

• 8

issue

• 3

URL

• http://dx.doi.org/10.2118/20332-pa