Genetic Programming in Geostatistical Reservoir Geophysics(Conference Paper)

Proceedings - 2016 International Conference on Computational Science and Computational Intelligence, CSCI 2016

Conference Paper

Hydrocarbon reservoir modelling and characterization is a critical step for the success of oil and/or gas exploration and production projects. Reservoir modelling is frequently based on the results provided by geostatistical seismic inversion techniques. These procedures are computationally heavy and expensive even for small-to-medium size fields due to the use of stochastic sequential simulation as the model perturbation technique. This work proposes the use of machine learning techniques, specifically symbolic regression, a category from the group of genetic programming methodologies, as a proxy to surpass the need of stochastic sequential simulation without compromising the advantage of using these simulation methodologies, for example uncertainty assessment of the property of interest. The proposed methodology is illustrated with an application example to a real case study and the results compared with the traditional geostatistical seismic inversion approach. © 2016 IEEE.


Year of publication: 2017


ISBN: 978-150905510-4


DOI: 10.1109/CSCI.2016.0228

Alternative Titles