About this paper
For energy companies, generating an accurate system design forecast for pipeline operations is no small task. Forecasts must be based on siloed big data and measure and evaluate economic assumptions such as energy market shifts, commodity price fluctuations, supply and demand shifts, contractual obligations and field price estimates. As explored in this paper, pipeline firms can vastly improve their forecasting process by adopting an integrated data management and analytics framework that enables seamless integration and analysis of data. Results can surface new production trends, generate probabilistic curves for supply forecasting, support “what-if” simulations for agile strategies and tactics, gauge demand-side forecasts that integrate economic data and reduce decision-making cycles.
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