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Machine Learning from Core to Seismic; Porosity Prediction in the Norwegian North SeaNormal access

Authors: S. Purves, B. Alaei, J.B. Bonas and P. Aursand
Event name: 81st EAGE Conference and Exhibition 2019
Session: Deep Learning and Data Analytics - Seismic Applications I & Methods and Applications II
Publication date: 03 June 2019
DOI: 10.3997/2214-4609.201901619
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 1.07Mb )
Price: € 20

We present results from direct seismic scale porosity and lithology prediction work in the Norwegian North Sea, targeting the Paleocene, Cretaceous and Jurassic. Firstly machine learning models were built at well scale using measurements and descriptions from core as target labels and conditioned wireline logs across 44 wells in order to predict effective porosity and lithology logs across the entire well track from Paleocene to Jurassic. Well scale prediction used Random Forests and MLPs achieving blind test accuracies of 74%. Subsequently, a seismic survey with partial stack data was selected and a deep convolutional neural network was used to predict porosity and lithology based on upscaled version of the predicted logs. A subset of 11 wells were used in a blind cross validation training scheme allowing network hyperparameters to be tuned. We achieve overall R2 scores of 56%-62% for porosity at blind well locations. Over the extent of the survey we find that responses conform very well to the major interval boundaries. Maps generated using the predicted 3D data conform to known field boundaries and distribution of lithologies were consistent with depositional environment expected from traditional stratigraphic analysis and experience.

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