The traditional exploration approach in oil and gas sector is expensive, risky, and prone to mistakes, as it heavily relies on human fieldwork. Even drilling dry holes hit oil and gas companies where it hurts – their wallets. The investment poured into geological assessment, drilling and testing goes up in smoke when the well doesn’t deliver. Artificial intelligence (AI) reduces the likelihood of such costly surprises and Oil and Natural Gas Corporation (ONGC) is showing the way forward. This state-owned enterprise, which produces nearly 68% of indigenous crude oil and 54% of country’s gas production, has made judicious use of AI technologies, such as machine learning and data analytics, thereby improving the accuracy of exploration predictions and resource estimation.
ONGC has deployed AI/ML techniques at its Gandhar oil field, Cambay basin, near Bharuch town in Gujarat, to enhance its explorative and operational efficiency. “The project is top-down reservoir modelling in the GS-3A sand of the Gandhar oil field,” said Arun Kumar Singh, chairman & CEO, ONGC. This field has been in production for over 35 years and has a complex history involving oil, gas, water, and water injection. The project leveraged spatio-temporal learning, a machine-learning algorithm designed for fluid flow through porous media.
“Using the neural networks techniques and model training, 98% accuracy in history matching and production forecasting could be achieved. The AI-driven workflow also identified a new infill development well; the location was drilled to extract remaining oil and enhance recovery from the field,” Singh said. Similar top-down reservoir modelling is planned for Heera field in western offshore of ONGC, indicating the potential for broader application of this AI-driven approach.
The ONGC chairman said the integration of AI in the oil and gas upstream industry has emerged as a big enabler. AI technologies, such as machine learning and data analytics, have enabled companies to extract valuable insights from vast datasets, optimise processes, and enhance decision-making. During the exploratory phase, AI may help facilitate advanced seismic interpretation and reservoir modelling, improving the accuracy of exploration predictions and resource estimation. Smart drilling systems powered by AI may enhance drilling efficiency, reduce downtime, and optimise well performance through real-time data analysis during drilling operations. AI algorithms can analyse production data to identify patterns, predict equipment failures, and optimise production processes, ultimately maximising output during the production phase.
ONGC has embarked on an ambitious digital transformation journey, aiming to transform its processes and workforce skillset while inducting the latest technology to create an integrated and intelligent digital oil field ecosystem. In alignment with the recently launched Project DOT (Digitalisation for Organisational Transformation) initiatives, the PSU behemoth is working to enhance transparency by integrating data-driven insights into business functions. The focus areas include optimising resource recovery and field performance, improving the evaluation and approval of development schemes, accelerating asset monetisation through AI-based analytics, enhancing asset performance and operational efficiency, streamlining offshore logistics operations for better resource utilisation, and facilitating the transition to renewable energy sources.
Project DOT is a bold initiative impacting every activity of an exploration & production (E&P) company, underscoring ONGC’s commitment to leveraging digital technologies for enhanced efficiency, productivity, transparency, and decision-making capabilities, while embracing sustainability. Moreover, the next wave of AI will unlock significant opportunities, from predictive maintenance to autonomous drilling operations through these initiatives. “Through these initiatives, ONGC aims to achieve ambitious goals such as higher exploration success, reduced discovery-to-development timelines, and lower operating costs,” Singh stressed.
Additionally, ONGC has identified several AI and analytics use cases across various domains. While many solutions, such as the enterprise-wide dashboard DARPAN, HR, and healthcare applications, have been delivered, others including AI-based automatic seismic trace editing for exploration, AI-driven chatbots for processes, intelligent production dashboards, predictive maintenance of critical equipment, and competency mapping of employees are in development.
Looking ahead, ONGC is set to accelerate AI and ML investments over the next 3-4 years. As Singh summarised: “ONGC’s vision is clear – to lead the energy sector’s AI revolution, creating a data & AI-driven enterprise that is smarter, faster, and more agile than ever before.”