The advent of Internet of Things (IoT) technology in the oil and gas industry enables operators to harness, analyze and act on large data sets from the myriad physical assets employed in the extraction and transportation of fossil fuels. By unlocking and aggregating previously unavailable or disparate data, operators can leverage the information now available to identify patterns indicative of potential mechanical failures or safety hazards. Using predictive analytics, condition-based maintenance, and data-driven diagnostics, operators can minimize the likelihood of Tier 1 Process Safety Events or costly unplanned downtime before a negative situation ever occurs, much less escalates. In order to improve financial performance while also maintaining critical uptime, petroleum industry operators are rapidly turning to IoT technology.
Mechanical failure in the transportation and extraction of fossil fuels is one of the leading causes of unplanned downtime and Loss of Primary Containment (LOPC), presenting hazards of almost unrivaled scale in terms of cost and negative publicity annually for the petroleum industry. Reducing the occurrence of mechanical failures and prolonging equipment longevity becomes an increasingly critical factor in the efficient management of the modern data-driven oil field. Visibility into and optimization of equipment parameters are paramount to ensuring proper working conditions and healthy long-term asset utilization. The inability to analyze and act on data related to settings, environmental conditions and any other parameter that factors into mechanical failure exposes the operation to limitless vulnerabilities.
Unplanned equipment downtime represents a significant challenge for oil and gas operators, adversely impacting revenue and causing increased operational costs.
DataV can significantly reduce unplanned downtime through predictive analytics, providing better foresight and more detailed real-time analysis of extraction activities and transportation processes.
While predictive failure can dramatically reduce asset downtime, and even eliminate unplanned downtime, there will still be cases where equipment must be taken off line for repairs. Here, data-driven diagnostics can speed the repair process and get assets back on line quickly. By employing rich, real-time analytics, technicians can be given ranked repair procedures that dynamically re-sort themselves as diagnostic steps are completed.
The emergence of IoT technology in industrial applications is transforming operational efficiency, bottom line productivity and safety for companies around the world. Through the establishment of comprehensive, data-driven predictive insights, oil and gas operators can employ sophisticated rules and machine learning to constantly adapt and tune expensive assets. For large scale extraction industries like petroleum operations, IoT has been proven to provide tangible financial benefits while at the same time delivering superior products with greater uptime characteristics to their customers.
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