-
oa Machine Learning Methods for Reservoir Prediction Modelling Under Uncertainty - Tackling Multiples Scales
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, 76th EAGE Conference and Exhibition - Workshops, Jun 2014, cp-401-00142
- ISBN: 978-90-73834-90-3
- Previous article
- Table of Contents
- Next article
Abstract
Reservoir prediction modelling conventionally involves complex statistical models that aim to integrate feature on multiple scales. These features are sourced from various types of data and often have a significant impact on flow performance. Conventional geostatistical algorithms provide a framework to integrate data from different scales, such as: geological interpretation of depositional structure based on analogues (e.g. by using conceptual training images); spatial correlation of geological bodies, their variety and geometrical relations (e.g. with imbedded geometrical shapes or elicited relations from analogues); high resolution seismic can be a source of multi-scale model features that can be integrated into stochastic model by means of soft conditioning.