DSDE: In Practice

This paper describes the development of a method of predicting drillstring-friction coefficient during tripping operations that can be used for early warning of stuck pipe.

This paper provides guidelines for thermal modeling for carbon capture and storage projects in a depleted gas field.

The authors of this paper write that autonomous outflow-control devices can positively affect matrix acidizing by providing the best possible conformance.

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

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.