How Cairn Oil & Gas is using IT to overcome one business challenge after another

Cairn Oil & Gas is a major oil and gas exploration and production company in India. It currently contributes 25% to Indian crude oil production (about 28.4 MMT) and aims to reach 50% of total production. The company plans to spend £3.16.09 million (£31.6 billion) over the next three years to ramp up its production.

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

Sandeep Gupta, Chief Digital and Information Officer at Cairn Oil & Gas, uses cutting-edge technology to meet these challenges and achieve business goals. “We have taken a value-based approach to deploying technological solutions. We work with multiple OEMs and service integrators to deliver highly scalable projects across the value chain,” he says.

Lowering operational costs with drones, AI and edge computing

The oil and gas industry faces huge price swings due to volatile commodity prices and geopolitical conditions. In such a scenario, it becomes crucial for the company to manage costs.

Sustainable oil production depends on an uninterruptible power supply. However, managing transmission lines is a costly and resource-intensive task. For Cairn, this meant managing 250 km of power lines over an area of ​​3,111 square kilometers. They power the company’s Mangala, Bhagyam and Aishwarya oil fields and the company’s Rageshwari gas fields in Rajasthan.

To reduce operating costs, the company decided to use drones. The images captured by the drones are passed through an AI image recognition system. The system analyzes potential damage to power lines, predicts potential failure points and suggests preventive actions, driving data-driven decision making rather than operator-based assessment.

“Algorithms like convolutional neural networks were trained on images captured when overhead lines were running in their ideal state. The algorithm then compares subsequent images taken six months apart if anomalies are detected. An observation is then posted to the portal for the maintenance team to take corrective and preventative action,” says Gupta.

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

“Since the introduction of drone-based inspection, the mean downtime has increased from 92 to 182 days. This has reduced oil loss to 2,277 barrels per year, resulting in cost savings of around £12m [₹120 million]. Because it allows employees to perform maintenance effectively, a small team can work more efficiently and the manpower required is reduced,” says Gupta.

The remote operational location coupled with the massive volume of data (Cairn generates approximately 300GB of data per day) that is generated makes the oil and gas industry ideal for deploying edge-based devices for data processing.

With smart edge devices, critical parameters are stored and processed at remote locations. The devices are installed in the field and send data via the MQTT protocol when a cellular network connection is available. They store data up to 250 GB in the Microsoft Azure cloud and perform analysis with machine learning algorithms and provide intelligent alarms.

Without these devices, the generated data would be transported to distant data centers and network bandwidth would be congested. “Edge computing helps reduce the cost of our IT infrastructure because less bandwidth is enough to handle the large volume of data. These deployed devices track critical operating parameters such as pressure, temperature, emissions and flow rate. The opportunity cost of not using edge computing would result in requiring more network bandwidth, which would be about twice the current network cost,” says Gupta. “This also has implications for the health and safety risk of our personnel and equipment.”

Reduce lead times with a cloud-first strategy

The oil exploration process has a lead time of around three to five years and requires a huge capital investment. Of those three to five years, petrotechnical professionals (geologists, geophysicists, petroleum engineers, and reservoir engineers) take a significant portion of the time to simulate models that require tremendous computational power.

The petrotechnical workflow involves evaluating the properties of the underground reservoir to identify the location for drilling the wells. These workflows are performed by petrotechnical experts through 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 more risky due to long lead times. In order to achieve our goals, increasing computing capacities are essential. To do this, we adopted and implemented a cloud-first strategy,” says Gupta. With this, Cairn has completely migrated the workloads for petrotechnical workflows to the cloud. “This migration has eliminated on-site computing capacity limitations. This reduces the time to first oil by almost 30%,” he says.

Coping with production decline through predictive analytics

Cairn has significant volume, variety and velocity of data coming from various sources in production, exploration and management. “Using this data, we ran several large-scale projects, including predictive analytics, model predictive control, and reservoir management, which were scaled to multiple sites,” says Gupta. Model Predictive Control (MPC) is a technology that monitors equipment for various operating parameters and then operates it within a specific range to achieve maximum efficiency while maintaining constraints in the system.

The focus is on Disha, a business intelligence initiative that uses dashboards to uncover key actionable insights. “The philosophy behind developing Disha was to get the right data to the right people at the right time. We wanted to remove file-based data sharing and reporting because these reports take a long time to generate. We have connected data from various sources such as SAP HANA, Historian, Microsoft SharePoint, Petrel, LIMS and Microsoft Azure Cloud into a single Microsoft PowerBI ecosystem where custom reports can be created,” says Gupta.

Disha was developed in a hybrid mode with an in-house team and an analytics provider over a three-year period. It offers 200+ customized dashboards, including a well monitoring dashboard, a production optimization dashboard, a CEO and CCO dashboard, and a facility planning dashboard.

“Because data is now easily and quickly accessible in an interactive format across the enterprise that used to be limited to a select few, corrective actions for resource allocation are now based on the data,” says Gupta. “For example, we use Disha to monitor the parameters and performance of the electronic submersible pump that pumps oil and water. It helps us track the gains made by the MPC implementation. All of this enables better decision-making and has helped to best allocate resources to deal with the productivity drop.” Going forward, Cairn plans to work with some major analytics vendors and build a single platform to contextualize its data and provide micro-solutions based on business needs . “This will be a low-code platform that will allow individual teams to build their own solutions,” says Gupta. “The initiatives are designed to maintain production levels while reducing time to first oil. Some of the initiatives include monitoring artificial lift systems, monitoring wells and validating wells,” says Gupta.