The Role of Big Data Analytics in Exploration and Production: A Review of Benefits and Applications

Abstract

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.

Machine Learning Model of Oilfield Productivity Prediction and Performance Evaluation

“…The productivity prediction method based on the numerical model relies on the formation data, rock fluid, fractures and other parameters with high accuracy requirements, and the geological model has great limitations [2]. With the development and application of machine learning methods, the data-driven productivity prediction method can better solve the multi factor and non-linear prediction optimization problems, and has attracted the attention and research of many scholars [3].…”

“…3 /d, the LR model prediction error obviously increases, and the error gradually increases as the productivity increases. The performance index evaluation results of the model are shown in Table1.…”

Use of UIoT for Offshore Surveys Through Autonomous Vehicles

“…Big Data was defined by IBM in 2012 [36] by the properties of the volume, velocity and variety of the dataset. Big Data analysis requires the development of deep knowledge of their installations and operations together with robust and complex algorithms [37,38]. The design of a unified data architecture and online applications is a challenge [39].…”