Data & Analytics
The deployment of a new heavy-duty robotic system aims to reduce workforce exposure to high-risk areas while improving detection of potential leaks.
Aramco says it has saved $770 million over the past 3 years from the $70 million it has invested over the same period in corrosion management technologies.
The deal adds physics-based reservoir modeling and real-time decision workflows to SLB’s digital portfolio.
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The world’s first offshore gas-hydrate production was carried out successfully in deepwater Japan at Nankai trough in 2013. -
This paper presents a new approach in monitoring the hydraulic system and in the recognition of well‑control events at an early stage such that proper counteractions can be initiated before any damage occurs.
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Saudi Aramco is on a mission to increase the amount of seismic data that it collects by fourfold, while reducing costs and acquisition time by half of what it spends today.
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It has been an impressive comeback for a technology that once stood on the brink of failure. The upstream oil and gas industry has largely resolved crippling technical challenges that shortened the life of fiber-optic cables in downhole applications and is now working on a big encore.
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The benefits of modern industrial control systems have never been greater. A baseline system security image, as a start, allows a vessel owner or operator to understand the security risks.
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A reservoir-monitoring system has been installed on a medium-heavy-oil onshore field in the context of redevelopment by gravity-assisted steamflood.
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This paper addresses the architecture, implementation, and benefits of complex-event processing (CEP) as a solution for the intelligent field.
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What is taking so long? Well, more than a decade into the intelligent-fields initiative, this is a question that remains prominent.
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This paper describes the design, testing, installation, and performance of the first fully completed well using an intelligent inner completion inside an uncemented liner with openhole packers for zonal isolation.
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This study develops a workflow to design a proactive workover-optimization workflow by use of genetic algorithms (GAs).
