Analytics Support for Decision Making in Crude Oil Trading
Publication Date: 2012-Mar-31
The IP.com Prior Art Database
Crude oil prices are very volatile, and they vary very dynamically based on many factors. Entering into a transaction in crude oil trade is often very tricky due to the volatility of the prices and the complex considerations involved; such considerations include the high storage cost and transporation cost of crude oil. For example, when an oil major is faced with the question of whether to enter into a trade deal to commit to buy A barrels of crude oil at a price of B$/barrel C months from now at location D, it has to consider a variety of factors such as the projected input requirement of its refinery at location D, C months from now. This is complicated by other extrinsic factors such as the possibility of availability of crude oil at a lower rate at location D, C months from now. Apart from more deterministic estimates such as the forecasted input requirement of the refinery at D, statistical models that can predict the price of oil at the appropriate location and time using historical transaction logs and patterns, can aid crude oil decision making to a large extent. In this publication, we disclose such methods of usage of analytics (that harness several data sources that are available to a crude oil company) to aid crude oil trade decision making.