How Cairn Oil and Gas Is Using IT To Overcome One Business Challenge After Another
Chief digital and information officer Sandeep Gupta's innovative use of technology has enabled the company to cut costs, reduce time to first oil, and manage decline in production.
Cairn Oil and Gas is a major oil and gas exploration and production company in India. It currently contributes 25% to India’s domestic crude production and is aiming to account for 50% of the total output. The company plans to spend ₹31.6 billion over the next 3 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, 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," he said. "We partner with multiple OEMs and service integrators to deploy highly scalable projects across the value chain.”
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 250 km of power lines spread across 3,111 km2. It supplies 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 artificial intelligence (AI) image-recognition system. The system analyzes 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 6 months when any anomalies are captured. An observation is then put into portal for the maintenance team to take corrective and preventive action,” Gupta said.