Probabilistic Geological Predictions and EM-Log Modelling using Deep Neural Networks (Sergey Alyaev and Kristian Fossum, NORCE)

Abstract:

The traditional geophysical modeling workflows provide high-quality predictions but are often computationally demanding. For real-time applications, such as real-time well-placement optimization (geosteering), it is important to get approximate modeling results instantaneously. In this communication we demonstrate how different deep learning models can be used to (a) create a subsurface model prediction and (b) electro-magnetic log approximation. These models are integrated into a Bayesian workflow which enables a probabilistic prediction of ahead-of-bit, which in turn can aid operational decision-making.

For (a) we propose to use Generative Adversarial Network (GAN) trained on a realistic geological configuration for the geomodel prediction. This method creates a mathematically-sound parametrization of subsurface which uses few implicitly defined geological parameters.

Our forward deep neural network (FDNN) model (b) approximates the responses of all 22 logs of extra-deep EM tool sent in real time. This gives a look-around capability of up to 30 meters. FDNN has 50 times faster performance compare to a commercial high-fidelity simulator.

Biography:

Sergey Alyaev is a senior researcher in applied mathematics at NORCE with interests including forward and inverse modeling, data-driven methods, and decision theory. Sergey has MSc and PhD degrees in applied mathematics from the University of Bergen. The main focus of his research are the real-time applications of the above methods and integrated workflows in the field of drilling and geosteering. From 2018 he took responsibility for leading the project “Geosteering for IOR” where the team is developing advanced quantitative and statistical methods and applying them for real-time decision making.

Kristian Fossum is a researcher in the data assimilation and optimization group at NORCE. He holds a MSc in petroleum technology and a PhD in applied mathematics, both from the University of Bergen. His research interests include ensemble-based data assimilation, inverse problems, computational statistics, data driven methods, optimization, and automated decision support.