Hydrocarbon exploration is a complex, risk-based process based on uncertain scientific data. Decision makers are faced with different types of decisions during different stages of the exploration workflow. Relevant data reside in structured and non-structured repositories. Most data are spatially located and are connected through complex spatial relationships which make the data harder to model and visualize. This case study describes the features of a novel web-based decision support system used at Saudi Aramco for hydrocarbon exploration. This new software allows the regional geologists to outline the play and assess the risks and attributes. It guides explorationists through proper risk assessment and probabilistic uncertainty analysis of the proposed prospects and leads. All these assessments are compared against the play and regional trends. The risk and uncertainty of volumetric computations are handled using Monte Carlo simulation. Collaboration and knowledge sharing features help in reaching group consensus and reducing uncertainty. After drilling, the system captures post drill analysis which helps in identifying areas of poor predictive performances and possible remediation steps can be taken. This will in turn help in reducing risk and uncertainty of proposed prospects. Finally, the system provides an integrated view of the heterogeneous data and offers spatial analytical techniques such prospect historical analysis and prospect depth analysis. Explorationists can create ad-hoc queries across the entire dataset to uncover trends and anomalies and then drill down to the details. Using charting tools and GIS analytics the data can be further analysed to verify whether trends are real or anomalies explained.


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