This webinar covers an applied case of geological unit modeling done on the Queens Mary Reservoir, London, UK based on 266 drillings. The geological model was done in Python with the machine learning library Scikit Learn to create a geological model based on lithology from drillings. The code generates a point cloud of drilling lithologies that are transformed and scaled for a neural network classifier like the Multi-layer Perceptron classifier implemented on the Scikit Learn library as sklearn.neural_network.MLPClassifier. To validate the results of the geological model, an analysis of the confusion matrix from the neural network is performed. The webinar also includes a georeferenced 3D visualization and comparison from well lithology and interpolated geology as Vtk format in Paraview.
Intructor
Saul Montoya M.Sc
Hydrogeologist - Numerical Modeler
Mr. Montoya is a Civil Engineer graduated from the Catholic University in Lima with postgraduate studies in Management and Engineering of Water Resources (WAREM Program) from Stuttgart University – Germany with mention in Groundwater Engineering and Hydroinformatics. Mr Montoya has a strong analytical capacity for the interpretation, conceptualization and modeling of the surface and underground water cycle and their interaction. He is in charge of numerical modeling for contaminant transport and remediation systems of contaminated sites. Inside his hydrological and hydrogeological investigations Mr. Montoya has developed an holistic comprehension of the water cycle, understanding and quantifying the main hydrological dynamic process of precipitation, runoff, evaporation and recharge to the groundwater system.
Language
English
Event date
Monday, November 29, 2021 at 6:00 p.m. Amsterdam Time
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