Halliburton has announced a strategic collaboration with Petronas Carigali to advance subsurface modelling and reservoir management.
Halliburton has announced a strategic collaboration with Petronas Carigali to advance subsurface modelling and reservoir management. Petronas Carigali will deploy Halliburton Landmark’s DecisionSpace 365 Geosciences Suite and Unified Ensemble Modelling solutions to unify exploration and development workflows and accelerate time to first oil.
‘Halliburton Landmark’s scalable earth modelling and ensemble workflows are a significant evolution from traditional grid-based modelling and deterministic reservoir forecasting,’ said Halliburton in a statement. ‘These technologies are intended to enable Petronas Carigali exploration and asset teams to collaborate in real time using a unified live earth model, with the aim of achieving more accurate reserve estimations through ensemble modelling.’
The Geosciences Suite’s scalable earth modelling maintains geological fidelity across all scales – from basin-wide views to individual fields – to ensure consistent data and models flow from exploration to development. The workflow supports faster project maturation through the front-end loading process, according to Halliburton. Using the Unified Ensemble Modelling solution, asset teams can automate the generation of multiple probabilistic geological scenarios while integrating real-time reservoir flow data, the company added. This approach is designed to enhance forecast precision, accelerate scenario analysis, and improve confidence in decision making.
‘A harmonised, AI-assisted workflow anchored on a single live-earth model across the exploration and development phases is central to our strategy in achieving our ambitious project delivery targets,’ said Hazli Sham Kassim, senior vice-president of Malaysia Asset and CEO of Petronas Carigali.
The collaboration follows a comprehensive benchmarking of Petronas’ greenfield and mature reservoir practices. The new approach will incorporate multi-scenario probabilistic modelling, supported by AI and machine learning to drive greater efficiency and insight.
