The Metrics and Value of Real Time Data: Mitigating Hazards

| June 2, 2015

2015-AADE_RTD-Metrics-and-Value_Final-030315

by D.M. Pritchard, Successful Energy Practices Intl; P. York, Weatherford; J. Roye, Digital Oilfield

Abstract:

Historically, the drilling industry has utilized real time data (RTD) for improving drilling efficiencies, documenting occurred events, and reactive decision-making. RTD should promote situational awareness with respect to wellbore instability and hazards. Drilling trends identifiable in RTD change over time as abnormal trends leading to hazards and rarely occur instantaneously. These destabilizing events such as cuttings loading, barrier compromise, kicks, pack-offs, wellbore collapse, or other mechanical and pressure-related causes, whether in rotating or flat-time operations, can be avoided or mitigated. Accepting these hazards as necessary and expected consequences can be a dangerous proposition and denies risk accountability.

Enhancing the systemic approach to process safety represents a paradigm shift in the industry. This requires changing behaviors through proactively managing and controlling the pace of the drilling process. A new paradigm suggests the use of a deterministic, physics-based model of real time force-balances in the wellbore during the well construction process. Trends of these forces become predictors of hazards and are routinely identified in many process safety failures and consistently result in non-productive time (NPT) and removable lost time (RLT), notwithstanding the large associated costs. The drilling trend behaviors that predict hazards can facilitate leading as opposed to lagging Key Performance Indicators (KPIs). The new paradigm suggested will enable hazards avoidance and thus significantly reduce the costs of drilling. This paper describes and explains these predictive techniques as well as offers suggestions for industry training with respect to surveillance and hazards avoidance using RTD simulators, much as airline pilots would use for continual trainin

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