Power Cost Optimization Using Data Analytics-Based Load Forecasting in Upstream O&G Operations

Abstract

Oilfield power demand is extremely dynamic in both time and space, and a lack of accurate forecasting causes increased cost for electric utilities to extend their grids to the field. It also causes increased demand charges for producers, which increases their lifting costs. As well production declines with time, electric utilities may end up with stranded assets which represent a large investment that is costly to maintain but is not being fully utilized. Therefore, both producers and utilities have a common interest in improved load forecasting to cut down on cost of power, especially during times of low oil prices. In this research, we show how data analytics can be used to predict load growth evolution in time and space. We incorporate production operational data, well location and geometry, and historical power consumption of neighboring wells into a data analytics engine to develop a platform for improved oilfield load forecasting. This data-driven approach is shown to decrease a utility's kilowatt prediction error for new well pads by 48 to 78% for annual average power and by 10 to 26% for peak power.