الفهرس 
1. Chapter One IntroductionOnly 14 pages are availabe for public view
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Abstract
This study presents two novel machine learning implementations in
petroleum engineering fields. The primary objective was to diagnose water.
production mechanisms in oil wells using machine learning algorithms.
Unlike previous studies, which addressed the problem as a classification.
task, this study applied concepts of computer vision to achieve better.
performance and overcome limitations and constraints that previous work.
faced. Methodology, in detail, is demonstrated where mean average.
precision was 0.848 on testing dataset. Model performance also showed.
the inevitability to remove anomalies from production data accurately prior.
to diagnostics or forecasting. Hence, the secondary objective of this study
emerged. A detailed evaluation of existing unsupervised techniques that
detect novelty in oil production data ascertained the limitations of them in
large oil fields. Thereafter, a novel ensemble learning model, which.
combines different algorithms, was implemented, and proved to
outperform other algorithms. The proposed model achieved accuracy of
84.02% on simulated and real-field datasets. In addition, the approach of
statistics was employed to optimize cut-off threshold of outliers.
automatically without human interference. Overall, the introduced
approaches could be implemented successfully in large digital oilfields for
the sake of surveillance and reservoir monitoring.