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.

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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.

Many petroleum operations have already embedded sensor technology enabling the collection of operating data from a range of critical points within their operating environments. To date, the primary purpose of this data collection system has been to feed real-time and historic data to the equipment operator so they can make more informed decisions concerning maintenance requirements.

The next step in the implementation of robust, fully automated IoT systems is to add real-time data analytics along with event-driven orchestration of remediation actions. Together, these additions can remove most manual, operator-driven steps thereby dramatically reducing costs while improving uptime and reliability.

The primary function of IoT technology is to collect data from a broad variety of physical assets over an extended period of time and deliver that data to cloud-based (public or private) databases so that rules and analytics can be applied to the data. The end goal is to improve business outcomes but a variety of complementary technologies are required in order to properly implement complete IoT systems.

Upstream, midstream, and downstream oil and gas operators can apply IoT technology toward specific use cases that generate tangibly improved business outcomes.

Predictive Failure

Unplanned equipment downtime represents a significant challenge for oil and gas operators, adversely impacting revenue and causing increased operational costs. IoT systems can significantly reduce unplanned downtime through predictive analytics, providing better foresight and more detailed real-time analysis of extraction activities and transportation processes. Indirect and less tangible benefits also accrue through heightened safety and enhanced oversight to ensure regulatory compliance.

Benefit: increased asset uptime, reduced service and support costs.

Adaptive Diagnostics

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.

Benefit: reduced mean-time-to-repair, increased asset uptime, reduced servicing costs.

Condition-based Maintenance (CBM)

Routine servicing of equipment at production sites is often accomplished according to an interval schedule prescribed by the manufacturer. Unfortunately, this virtually guarantees that equipment will be either over-serviced (which unnecessarily increases service costs) or under-serviced (adversely impacting equipment longevity). Condition-based servicing provides operators with the optimal timing in which to conduct equipment servicing based on real-time conditions and historical data analytics.

Benefit: reduced service costs, increased equipment longevity.

Device Optimization

Petroleum industry field operators strive to maximize the performance of their products, whether to improve output or increase efficiency. IoT systems can aggregate information from the entire fleet of equipment, identifying those assets that are under-performing and providing prescriptive, corrective actions. This information can be used to modify calibration or configuration settings immediately, and can serve as input to the R&D process.

Benefit: increased production efficiency, reduced service costs.

 

 

 

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