DocumentCode
2209166
Title
A hybrid system for well test analysis
Author
May, Edward A. ; Dagli, Cihan H.
Author_Institution
Smart Eng. Syst. Lab., Missouri Univ., Rolla, MO, USA
Volume
1
fYear
1998
fDate
4-8 May 1998
Firstpage
295
Abstract
Petroleum well test analysis is a tool for estimating the average properties of the reservoir rock. It is a classic example of an inverse problem. Visual examination of the pressure response of the reservoir to an induced flow rate change at a well allows the experienced analyst to determine the most appropriate model from a library of generalized analytical solutions. Rock properties are determined by finding the model parameters that best fit the observed data. This paper describes a framework for hybrid network to assist the analyst in selecting the appropriate model and determining the solution. The hybrid network design offers significant advantages by reducing training time and allowing incorporation of both symbolic and numeric data. The network structure is described and the advantages and disadvantages compared to previous approaches are discussed
Keywords
geology; geophysical prospecting; geophysical techniques; geophysics computing; inverse problems; neural nets; parameter estimation; petroleum industry; rocks; generalized analytical solution library; geology; hybrid neural network; hybrid system; induced flow rate change; inverse problem; measurement technique; numeric data; oil well; petroleum well test analysis; pressure response; reservoir rock; symbolic data; visual examination; well test analysis; Boundary conditions; Inverse problems; Laboratories; Logic; Neural networks; Petroleum; Predictive models; Reservoirs; System testing; Systems engineering and theory;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location
Anchorage, AK
ISSN
1098-7576
Print_ISBN
0-7803-4859-1
Type
conf
DOI
10.1109/IJCNN.1998.682280
Filename
682280
Link To Document