DSDE: Emerging Technology

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.

There is no shortage of quantum technology applications in the upstream industry, from supply chain to well placement, making now the time to take it seriously.

Analytics company GlobalData has released a report, “Robotics in Oil and Gas,” that aims to provide an overview of robotics technology and its applications in the oil and gas industry, highlighting the role of major oil and gas companies in the development and adoption of robotics to enhance safety and productivity.

This paper aims to emphasize the importance of decision-making based on quantitative monitoring outputs, from both a business perspective and an ecosystem-service perspective, in future offshore projects.