DSDE: In Practice

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.

This paper describes the operator’s digital-twin end-to-end production system deployed for model-based surveillance and optimization.

This paper presents an immersive platform that enables multidisciplinary teams and management to make decisions, connecting professionals to demonstrate and share findings in a way that capitalizes on artificial intelligence and cognitive capabilities.

The paper showcases the digital journey of a brownfield where digital solutions are enhancing recoverable volume, production, and process efficiency while minimizing losses and maximizing the return of investment.

This paper presents a lost-circulation model used during design and job-evaluation phases to accurately predict top of cement and equivalent circulating densities.

This comprehensive review of stuck pipe prediction methods focuses on data frequency, approach to variable selection, types of predictive models, interpretability, and performance assessment with the aim of providing improved guidelines for prediction that can be extended to other drilling abnormalities, such as lost circulation and drilling dysfunctions.

This paper describes a deep-learning image-processing model that uses videos captured by a specialized optical gas-imaging camera to detect natural gas leaks.

In this work, a perturbed-chain statistical associating fluid theory equation of state has been developed to characterize heavy-oil-associated systems containing polar components and nonpolar components with respect to phase behavior and physical properties.

As part of a subnational climate coalition, the state is moving forward with a satellite data project to track methane emissions.

This paper discusses the concept, applications, and continual evolution of a new 3D temperature and spectral-acoustics modeling and logging approach.