Neural networks predict well inflow performance /

Bibliographic Details
Main Author:	Alrumah, Muhammad K., 1976-
Other Authors:	Schechter, David S. (Thesis advisor), Startzman, Richard (Thesis advisor)
Format:	Thesis eBook
Language:	English
Published:	[College Station, Tex.] : [Texas A&M University], [2004]
Subjects:	Major petroleum engineering. neural network inflow performance
Online Access:	Link to OAK Trust copy

Description
Abstract:	Predicting well inflow performance relationship accurately is very important for production engineers. From these predictions, future plans for handling and improving well performance can be established. One method of predicting well inflow performance is to use artificial neural networks. Vogel's reference curve, which is produced from a series of simulation runs for a reservoir model proposed by Weller, is typically used to predict inflow performance relationship for solution-gas-drive reservoirs. In this study, I reproduced Vogel's work, but instead of producing one curve by conventional regression, I built three neural network models. Two models predict the IPR efficiently with higher overall accuracy than Vogel's reference curve.
Item Description:	"Major Subject: Petroleum Engineering" Title from author supplied metadata (automated record created on Apr. 30, 2004.) Vita. Abstract. Electronic resource.
Format:	Mode of access: World Wide Web. System requirements: World Wide Web access and Adobe Acrobat Reader.
Bibliography:	Includes bibliographical references.