Parallel approach to identifying the well-test interpretation model using a neurocomputer

Edward A. May Jr.; Cihan H. Dagli

doi:10.1117/12.235968

22 March 1996 Parallel approach to identifying the well-test interpretation model using a neurocomputer

Edward A. May Jr., Cihan H. Dagli

Proceedings Volume 2760, Applications and Science of Artificial Neural Networks II; (1996) https://doi.org/10.1117/12.235968
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

The well test is one of the primary diagnostic and predictive tools used in the analysis of oil and gas wells. In these tests, a pressure recording device is placed in the well and the pressure response is recorded over time under controlled flow conditions. The interpreted results are indicators of the well's ability to flow and the damage done to the formation surrounding the wellbore during drilling and completion. The results are used for many purposes, including reservoir modeling (simulation) and economic forecasting. The first step in the analysis is the identification of the Well-Test Interpretation (WTI) model, which determines the appropriate solution method. Mis-identification of the WTI model occurs due to noise and non-ideal reservoir conditions. Previous studies have shown that a feed-forward neural network using the backpropagation algorithm can be used to identify the WTI model. One of the drawbacks to this approach is, however, training time, which can run into days of CPU time on personal computers. In this paper a similar neural network is applied using both a personal computer and a neurocomputer. Input data processing, network design, and performance are discussed and compared. The results show that the neurocomputer greatly eases the burden of training and allows the network to outperform a similar network running on a personal computer.

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Edward A. May Jr. and Cihan H. Dagli "Parallel approach to identifying the well-test interpretation model using a neurocomputer", Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); https://doi.org/10.1117/12.235968

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KEYWORDS

Computing systems

Neural networks

Data modeling

Evolutionary algorithms

Binary data

Data conversion

Data processing

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