DSDE: In Practice

This paper presents the development of a robust, physics-based, and data-driven workflow for modeling mud loss in fractured formations and predicting terminal mud loss volume and time, as well as equivalent hydraulic fracture aperture.

The use of real-time wireless downhole pressure gauges proved a valuable alternative to workover operations in two onshore fields in Iraq.

This study aims to use machine-learning techniques to predict well logs by analyzing mud-log and logging-while-drilling data.

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 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.