DSDE: Emerging Technology

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.

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

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.