The Application of Artificial Neural Networks for the Prediction of Water Quality of Polluted Aquifer

Authors: Gümrah F.¹; Öz B.²; Güler B.³; Evin S.⁴

Source: Water, Air, and Soil Pollution, Volume 119, Numbers 1-4, April 2000 , pp. 275-294(20)

Publisher: Springer

Abstract:

From hydrocarbon reservoirs, beside of oil and natural gas, the brine is also produced as a waste material, which may be discharged at the surface or re-injected into the ground. When the wastewater is injected into the ground, it may be mixed with fresh water source due to to several reasons. Forecasting the pollutant concentrations by knowing the historical data at several locations on a field has a great importance to take the necessary precautions before the undesired situations are happened. The aim of this study is to describe Artificial Neural Network (ANN) approach that can be used to forecast the future pollutant concentrations and hydraulic heads of a groundwater source. In order to check the validity of the approach, a hypothetical field data as a case study were produced by using groundwater simulator (MOC). Hydraulic heads and chlorine concentrations were obtained from groundwater simulations. ANN was trained by using the historical data of last two years. The future chlorine concentrations and hydraulic heads were estimated by applying both the long-term and the short-term ANN predictions. An approach to overcome the effects of using the data of a single well was proposed by favouring the use of data set for a neighbour well. The higher errors for the long-term ANN predictions were obtained at the observation wells, which were away from an injection well. In order to minimise the difference between the results of long-term ANN approach and flow simulation runs; the short-term prediction was applied. The use of short-term prediction for the wells away from an injection well was found to give highly acceptable results when the long-term prediction fails. The average absolute error obtained from the shortterm forecasting study was 3.5% when compared to 18.5% for the long-term forecasting.

Keywords: Artificial Neural Network; chlorine concentration; groundwater simulation

Language: English

Document Type: Regular paper

Affiliations: 1: Petroleum and Natural Gas Engineering Department, Middle East Technical University, Ankara 06531, Turkey (author for correspondence, e-mail: fevzi@metu.edu.tr) 2: Petroleum Engineering Department, University of Alberta, Edmonton, Alberta, T6G 2G6, Canada 3: Petroleum and Natural Gas Engineering Department, Pennsylvania State University, U.S.A. 4: Turkish Petroleum Company, TPAO, Ankara, Turkey

Publication date: 2000-04-01

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• In this Subject: Hydraulic & Environmental Engineering
• By this author: Gümrah F. ;  Öz B. ;  Güler B. ;  Evin S.

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