data mining

Data mining techniques are unlocking new insights from the performance of thousands of Bakken wells. This article explores how those insights are helping refine completion optimization.

Over decades of exploration and production, the oil and gas sector has accumulated vast amounts of legacy data in various formats. Artificial intelligence and machine learning present an opportunity to transform how this unstructured data is processed and used, enabling significant improvements in operational efficiency and decision-making.

A self-updating and customizable data-driven strategy for real-time monitoring and management of screenout, integrated with proppant filling index and safest fracturing pump rate, is proposed to improve operational safety and efficiency at field scale.

In this paper, the authors present data analyses to comprehensively evaluate the performance of a steady-state multiphase-flow point model in predicting high-pressure, near-horizontal data from independent experiments.

This paper presents a physics-assisted deep-learning model to facilitate transfer learning in unconventional reservoirs by integrating the complementary strengths of physics-based and data-driven predictive models.

This paper focuses on characterization of fracture hits in the Eagle Ford, methods to predict their effects on production, and mitigation techniques.

The seismic and well data investigation tool integrates work flows to provide a consistent analysis of reservoirs.

This paper presents agile technologies that integrate data management, data-quality assessment, and predictive machine learning to maximize asset value using underused legacy core data.

Data mining and modern data analysis techniques allow us to do a much better job in finding good field analogies where new approaches to well remediation and restimulation have proven to have a high probability of commercial success.