18FORCE Brhlik Machine Learning in 4D Seismic Interpretation Monitoring the Reservoir
Channel: Force Seminars
Date: 2018-10-02

Machine Learning in 4D Seismic Interpretation: Monitoring the Reservoir

Quantitative time-lapse seismic interpretation is notoriously difficult given the fact that reservoir pressure, saturation, compaction and/or temperature changes seismic responses constitute a second order effect on the background of 3D lithology 3D seismic signal. Traditional methods relying on pre-stack seismic inversion and subsequent rock physics transforms are hampered by paucity of dynamic well data, seismic processing challenges and severe non-linearity of the problem. Data analytics and supervised machine learning methods can provide significant accuracy and efficiency uplift in solving this problem by streamlining seismic attribute analysis and avoiding the need to pass through the elastic property domain. Resulting time-lapse changes in reservoir parameters can then be more readily translated into reservoir management decisions and integrated into dynamic reservoir modeling workflows. We present applications of machine learning regression algorithms to specific 4D seismic projects across several assets and highlight the advantages, challenges and systematic biases encountered in this type of analysis, and discuss potential extensions of the data analytics approach using deep learning methods.

Mike Brhlik 
ConocoPhillips