Development of HT-BP nueral network system for the identification of well test interpretation model

Conference · Sun Dec 31 04:00:00 EST 1995

OSTI ID:182253

Sung, W; Hanyang, U; Yoo, I ^[1]

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1. and others

The neural network technique that is a field of artificial intelligence (AI) has proved to be a good model classifier in all areas of engineering and especially, it has gained a considerable acceptance in well test interpretation model (WTIM) identification of petroleum engineering. Conventionally, identification of the WTIM has been approached by graphical analysis method that requires an experienced expert. Recently, neural network technique equipped with back propagation (BP) learning algorithm was presented and it differs from the AI technique such as symbolic approach that must be accompanied with the data preparation procedures such as smoothing, segmenting, and symbolic transformation. In this paper, we developed BP neural network with Hough transform (HT) technique to overcome data selection problem and to use single neural network rather sequential nets. The Hough transform method was proved to be a powerful tool for the shape detection in image processing and computer vision technologies. Along these lines, a number of exercises were conducted with the actual well test data in two steps. First, the newly developed AI model, namely, ANNIS (Artificial intelligence Neural Network Identification System) was utilized to identify WTIM. Secondly, we obtained reservoir characteristics with the well test model equipped with modified Levenberg-Marquart method. The results show that ANNIS was proved to be quite reliable model for the data having noisy, missing, and extraneous points. They also demonstrate that reservoir parameters were successfully estimated.

• OSTI ID: 182253
• Report Number(s): CONF-950983--
• Country of Publication: United States
• Language: English

Related Subjects

02 PETROLEUM
99 GENERAL AND MISCELLANEOUS
A CODES
ARTIFICIAL INTELLIGENCE
COMPUTERIZED SIMULATION
DATA BASE MANAGEMENT
FIELD TESTS
GEOLOGIC MODELS
NEURAL NETWORKS
OIL WELLS
PETROLEUM INDUSTRY
RESERVOIR ROCK
USES