IoT in O&G Field Development – Moving ahead with Real Time Data Integration
The Internet of Things is transforming the oil and gas (O&G) industry at a very high pace by attaching assets, services and streamlining the data flow. It allows real-time decision making, improves asset performance, increases process and product quality and opens up new opportunities.
According to an article published in Houston Chronicle, IoT experts estimate that IoT-driven solutions can eliminate unexpected well outages and improve crude oil output by as much as 10% in a couple of years. They also forecast that the integration of IoT solutions will enhance profits by almost $1billion for huge O&G companies.
The O&G industry is specifically data-intensive and controlling huge amounts of data assembled at various points across the operation is a big challenge to resolve. Conventional modeling workflows are generally time-consuming and need systematic cross-disciplinary incorporation between geoscientists.
An essential premise of the digital oil field combines real-time data streams, process control networks and advanced analytics, that will provide constantly improved asset management results & containing important processes such as maintenance as well as supply chain optimization. Most oil companies have concentrated on the next-generation oil field and Internet-enabled interfaces to generate a real-time, remotely operated digital oil-field environment.
Components of Real-Time Data Integration
Real-time data is essential, but just obtaining data is not enough for a Digital Oil filed to become a successful project. It must depend on the following four key elements:
1. Instruments and actuators located suitably in tanks, surface facilities, pumps as well as valves.
2. Controllers utilized for collecting all data, execute process control and report accordingly.
3. Modern telecom as well as IT infrastructure to transfer and bring all field data to upper control levels and also to acquire control set-points back to the field actuators.
4. A real-time and feasible integration middleware to consistently transfer all data to the collective levels of the DOF (Digital Oil Field) structure using standard industry protocols.
The data connections make it possible to centrally analyze asset performance and health. Real-time data streaming can instantly provide the status of crucial production assets, enabling evaluation of operation health from desktops or mobile devices.
Whenever there is a huge amount of complex data gets generated then predictive analysis becomes a prime concern. Predictive analysis maintains the record of the real-time data and lets you know about the even minute changes in the behavior of the equipment. Based on the data generated and reference to analytics, failure prediction notification gets shared to the concern team for specific action to be take taken before a disaster happens.
One of the most basic use cases for IoT is the utilizing of real-time and historical data from large occupants of equipment spread over various sites in order to design (using machine learning) highly precise digital models of physical assets. IoT then holds data analytics so that potential equipment failures can be forecasted with a maximum probability. This enables equipment curative to be scheduled during planned maintenance windows thereby providing smoother operations and decreasing or even eliminating unplanned downtime.
Eventually, IoT is all about capturing data and utilizing it to get valuable insights. Equipped with sensors, O&G production sites, likewise pipelines, oil fields and refineries, are able to collect terabytes of data, all of which require being stored, categorized and monitored. Few of this data are time-sensitive: for instance, accident alerts must be on the high priority as compared to the other data.
Before the implementation of intelligent IoT Technology, all the inspection was done manually which has higher chances of error, not having the proper data which results in explosions and the company suffers the long terms losses. For example, the offshore oil rig accident happens with Piper Alpha in 1988 due to gas leakage which causes gas ignition and serial explosions on the entire platform. Even the smallest mechanical failure such as a faulty valve can have catastrophic consequences like Deepwater Horizon calamity of the 21st century(20 April 2010), which was caused due to mechanical failure in one of the blowout preventers.
IoT utilizes LPWANs (low power wide area networks) for data transmission and edge computing to process large data sets. This allows IoT solutions to instantly provide insightful information to end-users in thorough and readable formats. Implementation of IoT solutions helps O& G companies to enhance operational efficiency and reduce costs by connecting internal infrastructure. The traditional processes of analyzing oil levels, ordering and refilling were ineffective and unsuitable for the consumers.
Therefore, by utilizing smart sensors, delivery companies, as well as consumers, can get notifications when fuel level goes down which will save time as well as money and provide real-time data.
Applications in O&G Operations
Remote Services
Generally, oil isn’t found in easily available areas. With safety and regulation concerns, it enhances the price of equipment maintenance, whereas, IoT enables O&G companies data are available real-time to concern person, ranging from leaks to emergency shutdowns and remote field operations. The upstream industry where the oil extraction process is initialized, lose billions of dollars each year because of non-productive time (NPT). IoT could be utilized to minimize NPT by using real-time data to forecast breakdowns and schedule preventive maintenance.
Predictive and Preventive Maintenance
Predictive maintenance is executed based on the recent condition of the equipment. Predictive maintenance enables O&G companies to leverage the remote monitoring of equipment via sensors to make important decisions. Sensors that gather data will send companies an alert when machines require maintenance, preventing costly equipment failure, money as well as manpower and element human error.
Predictive maintenance is essential by using IoT and creating a maintenance plan, O&G operators have the capability to track parts & equipment failure with improved possibility of recognizing a problem remotely via preventive maintenance. The accumulation of real-time data is normally fed to the cloud, giving access to the most current data from the field. This data will help to complete operation from failing or shutting down.
Asset Tracking and Monitoring
Asset management is one of the key areas in the industry that can considerably influence operational performance. Operation productivity can be increased by optimizing production and creating production more predictable via enterprise asset management (EAM). IoT sensors can analyze key pipeline equipment precisely and cheaply.
It can enable companies to observe potential drilling sites and show the exact location for a pump and filter replacement improving the process and provide significant insight. In addition to that, the use of sensors allows oil companies to analyze a huge number of processes along with inventory and oil and gas shipments.
These tracking sensors will provide useful data so that the companies can recognize the number of supplies in inventories. If any item is missing, an IoT system can be utilized to track shipments with the accurate location of each oil and gas asset. Most important is the benefit of having real-time insight with the help of GPS, cellular and other connectivity solutions.
Asset management enables companies to enhance performance and locate infrastructures with a maximized output. With a large amount of information and data gathered, whoever is in charge of an oil field or gas plant will have insights into how operations are running generally from a mobile device and/or application.
Data Management
One of the biggest challenges for O&G companies is combining data as it correlates to data quality and the capability to take data and monitor it in real-time with operational benefits. Utility companies can process up to 1.1B data per day which is a challenge for companies of how to obtain insights from their data when their equipment is operating remotely with restricted network connectivity that restricts sending data to the cloud.
Data virtualization takes different sets of data sources and combines them into one centralized logical database for users. As data sources don’t have to restore locally. This integration gives instant access to data whenever someone requires it. Companies can recover and utilize data without needing to know where the data is physically situated or formatted. Data plays an important role in improvements, such as improving uptime, making better decisions and increasing refining ability.
IoT and RTC are strong technologies on their own merits, but together, they can allow companies to utilize their assets more effectively and productively with greater operational insights.