data mining

Data-driven methods offer significant advantages in the industry, under certain conditions, over conventional methods. But reservations still exist about their use. The paper serves to bridge the gap between unclear understanding of these methods and their successful acceptance and implementation.

A recently launched joint industry project (JIP) is working to improve petrophysical analysis methods to reduce the time and expense of characterizing tight sandstone gas reservoirs for exploration, appraisal, and production.

Data-driven, or top-down, modeling uses machine learning and data mining to develop reservoir models based on measurements, rather than solutions of governing equations.

A data-driven approach to successfully analyze and evaluate production-fluid impact during facility system divert events is presented. The work flow effectively identifies opportunities for prompt event mitigation and system optimization.

Failure to prioritize objectives and improper selection of candidate wells can have significant implications for both derived value and potential risk.

Data mining for production optimization in unconventional reservoirs brings together data from multiple sources with varying levels of aggregation, detail, and quality.

The challenges facing the oil and gas industry require crossover technologies from other industries such as aerospace, automotive, and medicine to help drive efficiency, boost productivity, and optimize performance.