Water cut/salt content forecasting in oil wells using a novel data-driven approach
Department of Petroleum Engineering, Amirkabir University of Technology, Tehran Polytechnique, No. 350, Hafez Ave, Valiasr Square, 1591634311 Tehran, Iran
2 Department of Industrial Engineering & Management Systems, Amirkabir University of Technology, Tehran Polytechnique, No. 350, Hafez Ave, Valiasr Square, 1591634311 Tehran, Iran
Accepted: 20 June 2019
Water cut is an important parameter in reservoir management and surveillance. Unlike traditional approaches, including numerical simulation and analytical techniques, which were developed for predicting water production in oil wells based on some assumptions and limitations, a new data-driven approach is proposed for forecasting water cut in two different types of oil wells in this article. First, a classification approach is presented for water cut prediction in sweet oil wells with discontinuous salt production patterns. Different classification algorithms including Support Vector Machine (SVM), Classification Tree (CT), Random Forest (RF), Multi-Layer Perceptron (MLP), Linear Discriminant Analysis (LDA) and Naïve Bayes (NB) are investigated in this regard. According to the results of a case study on a real Iranian sweet oil well, RF, CT, MLP and SVM can provide the best performance measures, respectively. Next, a Vector Autoregressive (VAR) model is proposed for forecasting water cut in salty oil wells with continuous water production during the life of the well. The proposed VAR model is verified using data of two real salty oil wells. The results confirm that the well-tuned proposed VAR model could provide reliable and acceptable results with very good accuracy in forecasting water production for the near future days.
Key words: data mining / classification / prediction / vector autoregressive / sweet oil well / salty oil well
© R. Ahmadi et al., published by IFP Energies nouvelles, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.