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This article describes the Open Science Grid, a large distributed computational infrastructure in the United States which supports many different high-throughput scientific applications, and partners (federates) with other infrastructures nationally and internationally to form multi-domain integrated distributed systems for science. The Open Science Grid consortium not only provides services and software to an increasingly diverse set of scientific communities, but also fosters a collaborative team of practitioners and researchers who use, support and advance the state of the art M. Altunay · T. Levshina · R. Pordes (B) · C. Sehgal in large-scale distributed computing. The scale of the infrastructure can be expressed by the daily throughput of around seven hundred thousand jobs, just under a million hours of computing, a million file transfers, and half a petabyte of data movement. In this paper we introduce and reflect on some of the OSG capabilities, usage and activities.

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Early Kick Detection (EKD) is one of the most important areas for improvement in well control safety. The need for earlier, more accurate, more reliable kick detection across a wide range of drilling operations has become increasingly important as more operations are being conducted in deep water with increasingly tight pressure margins. In order to accomplish this, it is important to start measuring the indicators that have the greatest impact. This paper identifies and proposes two risk based Key Performance Indicators (KPIs) related to kicks: how long it takes to positively identify a kick, and how long it takes to respond to a kick once the identification is made. These KPIs are the Kick Detection Volume (KDV) and the Kick Response Time (KRT) respectively. They provide the ability to directly measure kick detection and management approaches. A third metric, the Drilling Mode Kick Frequency (DMKF), while not a performance indicator, is critical to help determine the point at which drilling operation kicks are most likely to occur and thereby to aid in the evaluation of kick detection methodologies. This paper discusses and compares technical approaches to early kick detection including how they relate to safety, efficiency, and reliability over a range of common deep water operations. By identifying "actionable" indications of a kick, a general approach is suggested to help focus on technologies leading to the most likely improvements for EKD. Irrespective of the EKD optimization path chosen however, the proposed KPIs can be used to quantitatively evaluate and compare the performance of different technologies and operational strategies.