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A Neural-Network Approach for Modeling a Water-Distribution System

Fig. 1—Crossplot of results of the third neural-network-model run: predicted vs. actual flow-rate data. The outliers are highlighted by the yellow circle in the graph.

The authors present a new data-driven approach to estimate the injection rate in all noninstrumented wells in a large waterflooding operation accurately. The paper outlines the methodology and procedures used to analyze a branch of the water-network system and the modeling of accurate estimation of injection rates. The model performance is distinctive in its use of only field and wellhead measured data and considering the natural uncertainty inherited in these values.

Introduction

Lost Hills is a relatively large oil field located in western California. The field contains a significant amount of remaining producible reserves. There are six oil pools in five producing units of varying geologic age. The permeability of one of these units, the Monterey formation, is very low on the scale of millidarcies, which leads to lower production rates and even a very low recovery factor for the field. To address this challenge, a new expansion of the field was undertaken, with the introduction of waterflood development and close infill drilling.

Lost Hills operations follow a basic ­injection-system setup of a typical water­flood. Produced water is transported to the water strain. After the filtration steps are complete, the polished water is pumped into a 20-in. trunk line to the main tank battery. From the main trunk line, the line is reduced to 16 in. and is dedicated to the northernmost portions of the fields. It then is further reduced to an 8-in. line to the western portion, where Header 43 (the subject of this study) is located. This type of system with two separate strings, more formally known as a dual-string injection system, has the advantage of being able to inject water into specific depths of the reservoir. Basic terminology for this type of system classifies the short string as surface casing and the long string as production tubing. Such a system might have the ability to inject at shallow depths by use of the short string, while producing from a deeper interval by use of the long string.

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 185678, “A Neural-Network Approach for Modeling a Water-Distribution System,” by Andrei S. Popa, Chevron; Conor O’Toole, University of Southern California; Juan Munoz, Steve Cassidy, and Dallas Tubbs, Chevron; and Iraj Ershaghi, University of Southern California, prepared for the 2017 SPE Western Regional Meeting, Bakersfield, California, USA, 23–27 April. The paper has not been peer reviewed.
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A Neural-Network Approach for Modeling a Water-Distribution System

01 May 2018

Volume: 70 | Issue: 5

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