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76th EAGE Conference and Exhibition - Workshops
- Conference date: 16 Jun 2014 - 19 Jun 2014
- Location: Amsterdam, Netherlands
- ISBN: 978-90-73834-90-3
- Published: 16 June 2014
141 - 142 of 142 results
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Bridging the Gap - From Data to Gridblock
By G. MassonnatWhen populating gridblocks in geo/reservoir models with petrophysical properties, it is clear that the first challenge is reproduce in the model the main trends that have been captured in the actual geology from data and knowledge. However, and while preserving these trends, distributing in the model correct values for properties is a key concern since no data can be acquired at the scale at which the data must be estimated or simulated.
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Machine Learning Methods for Reservoir Prediction Modelling Under Uncertainty - Tackling Multiples Scales
By V. DemyanovReservoir 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.
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