Testing page for app

As can be common in many technical fields, the landscape of specialized roles is evolving quickly. With more people learning at least a little machine learning, this could eventually become a common skill set for every software engineer.

To keep pace with the digital age, the critical infrastructure and automation industries are looking beyond today’s control systems for new, common technologies to help balance requirements for uptime with digital technologies. Open standards can help.

The future of intelligent operations in our industry is being driven by advances from other sectors that have been embraced for petroleum applications. Foundational changes already taking place include advances in the type and volume of data being acquired and how the data are used.

The complete paper presents a discussion of the use of intelligent well completion in Santos Basin Presalt Cluster wells.

This paper presents a fatigue-prediction methodology designed to extend the life of unbonded flexible risers and improve the accuracy of floating production, storage, and offloading vessel response analysis.

Although multilateral and long-reach wells are common, the technological development is not complete. Effective implementation of digital technology requires complementary development of the technology itself.

A real-time deep-learning model is proposed to classify the volume of cuttings from a shale shaker on an offshore drilling rig by analyzing the real-time monitoring video stream.

The single independent riser (SIR) is a hybrid riser configuration optimized for ultradeepwater field development. The SIR is composed of a flexible jumper and a rigid part vertically tensioned by means of distributed or continuous buoyancy.

The complete paper describes the overall project execution of the Aasta Hansteen field development on the Norwegian Continental Shelf (NCS) north of the Arctic Circle. It is the deepest field yet developed on the NCS in 1300 m of water.

This paper describes an automated work flow that uses sensor data and machine-learning (ML) algorithms to predict and identify root causes of impending and unplanned shutdown events and provide actionable insights.