Translator Disclaimer
Paper
22 March 1996 Parallel approach to identifying the well-test interpretation model using a neurocomputer
Author Affiliations +
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.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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
PROCEEDINGS
11 PAGES


SHARE
Advertisement
Advertisement
RELATED CONTENT

Embedded digital oilfield model
Proceedings of SPIE (October 05 2017)
Inference on time series based on change points
Proceedings of SPIE (November 14 2007)
Using neural networks for acoustophonetic analysis of speech
Proceedings of SPIE (December 07 1994)
Surface classification using artificial neural networks
Proceedings of SPIE (January 20 1997)
ReverbaProp: simultaneous learning of credit and weight
Proceedings of SPIE (April 06 1995)

Back to Top