DSDE: Emerging Technology

The massive system brings advanced capabilities for simulation, AI, and data analysis to drive breakthroughs in cancer research, materials discovery, energy technologies, and many other fields.

This paper proposes a methodology for preventing drillstring fatigue and failure in deep wells with large shallow doglegs.

This paper presents a novel workflow for using electromagnetic resistivity-based reservoir mapping logging-while-drilling technologies for successful well placement and multilayer mapping in low-resistivity, low-contrast, thinly laminated clastic reservoirs.

In this paper, the authors propose a regression machine-learning model to predict stick/slip severity index using sequences of surface measurements.

A recent study highlights the major challenges the technology faces as operators consider the pros and cons of using additive manufactured parts in a corrosion-prone environment.

The objective of this study is to develop an explainable data-driven method using five different methods to create a model using a multidimensional data set with more than 700 rows of data for predicting minimum miscibility pressure.

In this study, artificial-intelligence techniques are used to estimate and predict well status in offshore areas using a combination of surface and subsurface parameters.

The authors of this paper describe how deployment of dual-casing cement-bond-logging technology has provided critical insights in real time for decision-making on remedial jobs.

Underwater robots that can predict waves in real time could reduce the cost of producing offshore renewable energy, a study suggests.

This paper presents the operator’s learnings in evaluating the routine of a floating production, storage, and offloading asset crew to identify scenarios for the application of robotics in day-to-day offshore activities.