MARISA BAIRACHI

Data analytics and big data have emerged as powerful tools that can revolutionise industries globally.

Data analytics can play a critical role in revitalising mature oil fields in TT. As many of the oil fields in the country have been in production for several decades, we are facing declining production rates, increased operational challenges, fluctuating oil prices, and environmental concerns.

AI is becoming a household name, mostly thanks to the newest generative AI platforms and apps such as ChatGPT, Bard, and DALL-E.

The pace of AI’s evolution calls for swift action to ensure that we are ready to leverage promptly its latest capabilities but also to correctly govern it to deliver trusted digital solutions at scale to the energy industry. The open subsurface data universe (OSDU) and data analytics together have the potential to revolutionise oil and gas exploration by providing a standardised platform for data sharing, integration, and advanced analytics. OSDU is an industry-wide initiative aimed at creating a common data platform for the oil and gas industry, enabling seamless collaboration and data-driven decision-making. The OSDU and data analytics can work together to improve oil and gas exploration and brown field revitalisation.

Data analytics for exploration and production

By analysing seismic data, well logs, and geological information, data analytics can pinpoint potential drilling locations with higher success rates.

Machine learning algorithms can process vast amounts of data to identify promising reservoirs, enabling more targeted and cost-effective exploration efforts.

The OSDU platform provides a standardised data model that allows various data types from different sources to be integrated seamlessly. It enables the consolidation of geophysical data, well logs, seismic surveys, production data, and reservoir models from multiple operators, service companies, and government agencies into a single, unified data repository.

Data analytics can then be applied to this integrated dataset to gain a holistic understanding of the subsurface. By leveraging machine learning algorithms and data visualisation tools, geoscientists and engineers can analyse and interpret the data more effectively, identifying geological structures, hydrocarbon prospects, and exploration opportunities with greater accuracy through predictive modelling, and real-time data analytics.

Furthermore, data analytics can optimise production operations by continuously monitoring well performance, reservoir behaviour, and flow rates.

Predictive maintenance can detect equipment anomalies early, leading to reduced downtime and increased asset reliability. Integrating data analytics into real-time production management enables operators to make well-informed decisions, optimise production rates, and adapt to changing market conditions swiftly.

Data analytics in brown field optimisation

Data analytics plays a crucial role in revitalising mature oil fields, which are characterised by declining producti