Cairn Oil & Gas is a major oil and gas exploration and production company in India. It currently contributes 25% to India’s domestic crude production (about 28.4 MMT) and is aiming to account for 50% of the total output. The company plans to spend ₹3,16,09 crores (₹31.6 billion) over the next three years to boost its production.

The oil and gas industry currently confronts three major challenges: huge price fluctuation with volatile commodity prices, capital-intensive processes and long lead times, and managing production decline.

Sandeep Gupta, chief digital and information officer at Cairn Oil & Gas, is using state-of-the-art technologies to overcome these challenges and achieve business goals. “We have adopted a value-focused approach to deploying technological solutions. We partner with multiple OEMs and service integrators to deploy highly scalable projects across the value chain,” he says.

Reducing operational costs with drones, AI, and edge computing

Sandeep Gupta, chief digital and information officer, Cairn Oil & Gas

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The oil and gas industry is facing huge price fluctuation due to volatile commodity prices and geopolitical conditions. In such a scenario, it becomes crucial for the business to manage costs.

Sustained oil production depends on uninterrupted power supply. However, managing transmission lines is a high-cost, resource-intensive task. For Cairn, it meant managing 250km of power lines spread across 3,111 square kilometers. They supply power to the company’s Mangala, Bhagyam, and Aishwarya oil fields and its Rageshwari gas fields in Rajasthan.

To reduce operational costs, the company decided to use drones. The images captured by the drones are run through an AI image-recognition system. The system analyses potential damage to power lines, predicts possible failure points, and suggests preventive measures, thereby driving data-driven decision-making instead of operator-based judgment.

“Algorithms such as convolutional neural networks were trained on images captured when the overhead powerlines are running in their ideal condition. The algorithm then compares the subsequent images that are taken at an interval of six months when any anomalies are captured. An observation is then put into portal for the maintenance team to take corrective and preventive action,” says Gupta.

This is a service-based contract between Cairn and the maintenance provider where the monitoring is carried out on biannual basis for 220kV power lines and annually for 500kV power lines.

“Since the implementation of drone-based inspection, the mean time between failure has increased from 92 to 182 days. This has reduced oil loss to 2,277 barrels per year, leading to cost savings worth approximately ₹12 crores [₹120 million]. As it enables employees to carry out maintenance activities in an effective manner, a small team can work more efficiently, and the manpower required reduces,” Gupta says.

The remote location of operations coupled with a massive volume of data (Cairn generates about 300GB data per day) that is generated make the oil and gas industry ideal for the use of edge-based devices for computing.

With smart edge devices, critical parameters are stored and processed at remote locations. The devices are installed in the field which send data via MQTT protocol where cellular network connectivity is available. They store data up to 250GB on the Microsoft Azure cloud and perform analytics using machine-learning algorithms, as well as provide intelligent alarms.

Without these devices, the data generated would be transported to faraway data centres, clogging the network bandwidth. “Edge computing helps reduce our IT infrastructure cost as lower bandwidth is sufficient to handle the large volume of data. These devices deployed are tracking critical operational parameters such as pressure, temperature, emissions, and flow rate. The opportunity cost of not having edge computing would result in requiring a higher bandwidth of network, which would amount to around 2X of the current network cost,” says Gupta. “This also has an implication on the health and safety risk of our personnel and equipment.”

Reducing lead times through a cloud-first strategy

The oil exploration process has a lead time of around three to five years and requires huge capital commitment. Out of these three to five years, a significant amount of time is taken up by petrotechnical experts (geologists, geophysicists, petroleum engineers, and reservoir engineers) in simulating models that require massive computational power.

Petrotechnical workflow entails evaluation of subsurface reservoir characteristics to identify the location for drilling the wells. These workflows are carried out by petrotechnical experts via multiple suites of software applications that can help identify the location and trajectory of wells to be drilled.

“Capital allocation and planning for future exploration has become riskier due to long lead times. To achieve our goals, increasing computing capabilities are essential. For this, we have adopted and executed a cloud-first strategy,” says Gupta. Thus, Cairn has completely migrated the workloads for petrotechnical workflows to the cloud. “This migration has removed the constraints of on-premises computational capabilities. As a result, there is almost 30% reduction in time to first oil,” he says.

Managing decline in production through predictive analytics

Cairn has considerable volume, variety, and velocity of data coming from different sources across production, exploration, and administration. “Using this data, we have deployed multiple large-scale projects, including predictive analytics, model predictive control, and reservoir management, which have been scaled across multiple sites,” says Gupta. Model predictive control (MPC) is a technology where the equipment is monitored for various operating parameters and is then operated in a particular range to get maximum efficiency, while maintaining the constraints in the system.

At the heart of this lies Disha, a business intelligence initiative that uses dashboards driving critical actionable insights. “The philosophy for developing Disha was to make the right data available to the right people at the right time. We wanted to remove file-based data sharing and reporting as significant time goes in creating these reports. We connected data from various sources such as SAP HANA, Historian, Microsoft SharePoint, Petrel, LIMS, and Microsoft Azure cloud onto a single Microsoft PowerBI ecosystem where customized reports can be created,” says Gupta.

Disha was developed in a hybrid mode with an in-house team and an analytics provider over the course of three years. It offers more than 200 customized dashboards, including a well-monitoring dashboard, a production-optimisation dashboard, a CEO and CCO dashboard, and a rig-scheduling dashboard.

“With data now easily and quickly accessible in an interactive format across the organisation, which was earlier restricted to a select few, the corrective actions for resource allocation are now based on the data,” Gupta says. “For instance, we leverage Disha to monitor the parameter and output of the electronic submersible pump, which handles oil and water. It helps us in tracking the gains achieved through MPC implementation. All this enables better decision-making and has helped to allocate resources in optimized manner, thus managing the decline in productivity.” Going forward, Cairn plans to partner with a few big analytics providers and build a single platform to help contextualize its data and deploy micro solutions, according to business needs. “This will be a low-code platform that will enable individual teams to build solutions on their own,” Gupta says. “The initiatives are oriented towards sustaining the production levels, while reducing time to first oil. Some of the initiatives include artificial lift system monitoring, well monitoring, and well-test validation,” says Gupta.

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Fog computing extends the concept of cloud computing to the network edge, making it ideal for internet of things (IoT) and other applications that require real-time interactions.

Fog computing is the concept of a network fabric that stretches from the outer edges of where data is created to where it will eventually be stored, whether that’s in the cloud or in a customer’s data center.

Fog is another layer of a distributed network environment and is closely associated with cloud computing and the internet of things (IoT). Public infrastructure as a service (IaaS) cloud vendors can be thought of as a high-level, global endpoint for data; the edge of the network is where data from IoT devices is created.

Fog computing is the idea of a distributed network that connects these two environments. “Fog provides the missing link for what data needs to be pushed to the cloud, and what can be analyzed locally, at the edge,” explains Mung Chiang, dean of Purdue University’s College of Engineering and one of the nation’s top researchers on fog and edge computing.

According to the OpenFog Consortium, a group of vendors and research organizations advocating for the advancement of standards in this technology, fog computing is “a system-level horizontal architecture that distributes resources and services of computing, storage, control and networking anywhere along the continuum from Cloud to Things.”

Benefits of fog computing

Fundamentally, the development of fog computing frameworks gives organizations more choices for processing data wherever it is most appropriate to do so. For some applications, data may need to be processed as quickly as possible – for example, in a manufacturing use case where connected machines need to be able to respond to an incident as soon as possible.

Fog computing can create low-latency network connections between devices and analytics endpoints. This architecture in turn reduces the amount of bandwidth needed compared to if that data had to be sent all the way back to a data center or cloud for processing. It can also be used in scenarios where there is no bandwidth connection to send data, so it must be processed close to where it is created. As an added benefit, users can place security features in a fog network, from segmented network traffic to virtual firewalls to protect it.

Applications of fog computing

Fog computing is the nascent stages of being rolled out in formal deployments, but there are a variety of use cases that have been identified as potential ideal scenarios for fog computing.

Connected Cars: The advent of semi-autonomous and self-driving cars will only increase the already large amount of data vehicles create. Having cars operate independently requires a capability to locally analyze certain data in real-time, such as surroundings, driving conditions and directions. Other data may need to be sent back to a manufacturer to help improve vehicle maintenance or track vehicle usage. A fog computing environment would enable communications for all of these data sources both at the edge (in the car), and to its end point (the manufacturer).

Smart cities and smart grids Like connected cars, utility systems are increasingly using real-time data to more efficiently run systems. Sometimes this data is in remote areas, so processing close to where its created is essential. Other times the data needs to be aggregated from a large number of sensors. Fog computing architectures could be devised to solve both of these issues.

Real-time analytics A host of use cases call for real-time analytics. From manufacturing systems that need to be able to react to events as they happen, to financial institutions that use real-time data to inform trading decisions or monitor for fraud. Fog computing deployments can help facilitate the transfer of data between where its created and a variety of places where it needs to go.

Fog computing and 5G mobile computing

Some experts believe the expected roll out of 5G mobile connections in 2018 and beyond could create more opportunity for fog computing. “5G technology in some cases requires very dense antenna deployments,” explains Andrew Duggan, senior vice president of technology planning and network architecture at CenturyLink. In some circumstances antennas need to be less than 20 kilometers from one another. In a use case like this, a fog computing architecture could be created among these stations that includes a centralized controller that manages applications running on this 5G network, and handles connections to back-end data centers or clouds.

How does fog computing work?

A fog computing fabric can have a variety of components and functions. It could include fog computing gateways that accept data IoT devices have collected. It could include a variety of wired and wireless granular collection endpoints, including ruggedized routers and switching equipment. Other aspects could include customer premise equipment (CPE) and gateways to access edge nodes. Higher up the stack fog computing architectures would also touch core networks and routers and eventually global cloud services and servers.

The OpenFog Consortium, the group developing reference architectures, has outlined three goals for developing a fog framework. Fog environments should be horizontally scalable, meaning it will support multiple industry vertical use cases; be able to work across the cloud to things continuum; and be a system-level technology, that extends from things, over network edges, through to the cloud and across various network protocols. (See video below for more on fog computing from the OpenFog Consortium.)

Are fog computing and edge computing the same thing?

Helder Antunes, senior director of corporate strategic innovation at Cisco and a member of the OpenFog Consortium, says that edge computing is a component, or a subset of fog computing. Think of fog computing as the way data is processed from where it is created to where it will be stored. Edge computing refers just to data being processed close to where it is created. Fog computing encapsulates not just that edge processing, but also the network connections needed to bring that data from the edge to its end point.

[ Related (NetworkWorld): What is edge computing and how it’s changing the network ]

Based on actual users’ experience with IoT platforms, here are the leading features and functionalities potential users should be looking for.

Article published on NetworkWorld by , Contributor, Jan 16, 2018

As an IoT platform and middleware analyst, I am asked constantly about the benefits of IoT platforms and “what makes a great IoT platform.” In response, I often ask these curious inquirers if they’ve ever used IoT platforms themselves. Walking on the edge is exhilarating, but having hands-on insights, data and expertise on how to survive the journey is even better.

What do users actually experience when they use IoT edge platforms?

IoT edge computing is a technology architecture that brings certain computational and analytics capabilities near the point of data generation. IoT edge platforms provide the management capabilities required to deliver data from IoT devices to applications while ensuring that devices are properly managed over their lifetimes. Enterprises use edge platforms for factory automation, warehousing/logistics, connected retail, connected mining and many other solutions. With IoT platform revenue slated to grow to USD63.4 billion by 2026, IoT edge is one of the most highly relied upon enterprise IoT platform approaches.

Enterprises spend a tremendous amount of time completing edge-related IoT platform activities. According to hands-on tests of IoT platforms in MachNation’s IoT Test Environment (MIT-E), the majority of an enterprise user’s edge-related time is spent creating visualizations to gain insight from IoT data. 35% of a user’s time is spent creating dashboards with filtered alerts. And a combined 16% of a user’s time is spent viewing sensor data for an individual device (8%) or a group of devices (8%). Data from an IoT platform are critically important, so the ability to assemble dashboard sensor data and alerts are key – expect to spend a lot of time doing it.

Since the edge is critical for enterprises deploying IoT solutions, we’ve identified the top five user requirements of IoT edge platforms, based on IoT platform users’ experiences with these platforms.

1. Pick a platform with extensive protocol support for data ingestion

To seamlessly bring data from devices into the edge platform, enterprises should choose leading IoT platforms that support an extensive mix of protocols for data ingestion. The list of protocols for industrial-minded edge platforms generally includes brownfield deployment staples such as OPC-UA, BACNET and MODBUS as well as more current ones such as ZeroMQ, Zigbee, BLE and Thread. Equally as important, the platform must be modular in its support for protocols, allowing customization of existing and development of new means of asset communications.

2. Ensure the platform has robust capability for offline functionality

To ensure that the edge platform works when connectivity is down or limited, enterprises should choose leading IoT edge platforms that provide capabilities in four functional areas. First, edge systems need to offer data normalization to successfully clean noisy sensor data. Second, these systems must offer storage to support intermittent, unreliable or limited connectivity between the edge and the cloud. Third, an edge system needs a flexible event processing engine at the edge making it possible to generate insight from machine data when connectivity is constrained. Fourth, an IoT edge-enabled platform should integrate with systems including ERP, MES, inventory management and supply chain management to help ensure business continuity and access to real-time machine data.

3. Make sure the platform provides cloud-based orchestration to support device lifecycle management

To make sure that the edge platform offers highly secure device management, enterprises should select IoT platforms that offer cloud-based orchestration for provisioning, monitoring and updating of connected assets. Leading IoT platforms provide factory provisioning capabilities for IoT devices. These API-based interactions allow a device to be preloaded with certificates, keys, edge applications and an initial configuration before it is shipped to the customer. In addition, platforms should monitor the device using a stream of machine and operational data that can be selectively synced with cloud instances. Finally, an IoT platform should push updates over-the-air to edge applications, the platform itself, gateway OSs, device drivers and devices connected to a gateway.

4. The platform needs a hardware-agnostic scalable architecture

Since there are tens of thousands of device types in the world, enterprises should select IoT platforms that are capable of running on a wide range of gateways and specialized devices. And these platforms should employ the same software stack at the edge and in the cloud allowing a seamless allocation of resources. Platforms should support IoT hardware powered by chips that use ARM-, x86-, and MIPS-based architectures. Using containerization technologies and native cross-compilation, the platforms offer a hardware-agnostic approach that makes it possible to deploy the same set of functionalities across a varied set of IoT hardware without modifications.

5. Comprehensive analytics and visualization tools make a big difference

As we’ve already discussed enterprises should choose IoT platforms that offer out-of-the-box capabilities to aggregate data, run common statistical analyses and visualize data. These platforms should make it easy to integrate leading analytics toolsets and use them to supplement or replace built-in functionality. Different IoT platform users will require different analyses and visualization capabilities. For example, a plant manager and a machine worker will want to access interactive dashboards that deliver useful information and relevant controls for each of their respective roles. Having flexibility in analytics and visualization capabilities will be essential for enterprises as they develop IoT solutions for their multiple business units and operations teams.

Enterprises worldwide are using IoT to increase security, improve productivity, provide higher levels of service and reduce maintenance costs. As they seek to adopt IoT solutions to improve their critical business processes, they should conduct hands-on usability tests to understand edge platform capabilities. Keep watching as more and more enterprises start walking on the edge.