Performance forecasting for polymer flooding in heavy oil reservoirs

Artificial intelligence techniques and their applications in drilling fluid engineering: A review

Applicability of deep neural networks on production forecasting in Bakken shale reservoirs

Review and analysis of supervised machine learning algorithms for hazardous events in drilling operations


 Due to the decrease in commodity prices in a constantly dynamic environment, there has been a constant urge to maximize benefits and attain value from limited resources. Traditional empirical and numerical simulation techniques have failed to provide comprehensive optimized solutions in little time. Coupled with the immense volumes of data generated on a daily basis, a solution to tackle industry challenges became imminent. Various expert opinion fraught with bias has posed extra challenges to obtain timely cost-effective solutions. Data Analytics has provided substantial contributions in several sectors. However, its value has not been captured in the Oil and Gas industry.
 This paper presents a review of various Machine Learning applications in exploration, completions, production operations to date. An overview of data-driven workflows in the fields of electric submersible pump (ESP) failure and shutdown prediction, reservoir databases’ analysis, reduction of subsurface uncertainty, EOR decisions using scarce data, improved oil recovery estimation, production impact assessment, horizontal completion, fracturing techniques, production optimization in unconventional reservoirs, production management, and field surveillance, is presented.
 The review attempts to shed light on the benefits and applications of multiple challenges faced on a daily basis by scientists, field personnel, and engineers to help solve and optimize the industry's multi-faceted data-intense challenges.

Michael Nickolaou

Papers

State of the Art of Artificial Intelligence and Predictive Analytics in the E&P Industry: A Technology Survey