Development of Intelligent Decision and Prediction System using Cyber-enabled NESS Technology for Oil Availability and Yield Prediction

As flexibility and agility become key success factors of competitive oil business industries, the ability to support the short-term decision and prediction making of oil exploration, scheduling, oil availability and yield prediction becomes a critical issue. This research paper presents a rule-based expert knowledge system, i.e. web-based hybrid expert system, called intelligent hybrid decision and prediction system (IHDPS) run on the cyber-enabled .NET Expert System Shell (NESS) technology platform to addresses how engineering knowledge can be dynamically represented and efficiently utilised in oil business industries. The rule-based knowledge system, called web-based IHDPS, is designed and implemented using the rule-based inference, reasoning and decision-making approach. The distinctive technical contributions of IHDPS focus on three critically integrated elements: (1) a spreadsheet software for interpreting and evaluating performance data, (2) a knowledge rule for time-series pattern recognition, and (3) an embedded application component.