A few take-away messages from the conference Petroleum Geostatistics 2019
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On September 2-6, 2019, I attended on behalf of Estimages (the company funding my PhD) the conference Petroleum Geostatistics 2019 in Florence, Italy. Here is my take on the messages that were delivered during this exciting conference.
For the first time, a whole session was dedicated to Machine Learning (ML), clearly showing the growing interest of the industry for the subject. It was kicked off by a keynote lecture which focused on how ML could be applied to make better reservoir predictions. In his lecture, Pr. Demyanov insisted on the fact that these algorithms should not be used as black boxes on the data. Instead they should be carefully designed with the help of a domain expert in order to ensure the quality and the interpretability of their outputs. Geologists, geophysicians and geostatisticians, let’s rejoice! It seems like we will not be replaced by an artificial intelligence (that) soon…
Several talks presented applications of some “trendy” ML algorithms to seismic and geological data, among which Convolutional Neural Networks and Generative Adversarial Networks (GANs). In particular, the latter proved to be a very serious contender to the use of multi-point statistics for facies inversion and geological image synthesis.
As for Geostatistics, one can note the return in an industrial context of neglected geostatistical methods, such as simulations using the turning bands method or the use of truncated Gaussian fields for facies inversion. As for the new methods, we retain the use of the stochastic partial derivative (SPDE) approach as a new paradigm allowing to easily integrate local geometric information and work with non-stationary fields. Estimages (represented by yours truly) and MINES ParisTech (represented by researchers Nicolas Desassis and Didier Renard) presented successful applications of this approach to, respectively, seismic image filtering and facies inversion.
Finally, a need for practical solutions to inversion problems transpired from the talks and from a very interesting discussion that concluded the conference. Geostatistics are most useful when integrated in fully-operational workflows, and some of which were presented during the conference. Also, communication is key: designing complex algorithms, even if they work well, will not ensure that they will be subsequently used by practitioners…
Big thanks to EAGE for organizing a great conference. We all look forward to the next one!