Probabilistic forecasting for geosteering in fluvial successions using a generative adversarial network

• URL: http://arxiv.org/abs/2207.01374v1
• Date: Mon, 4 Jul 2022 12:52:38 GMT
• Title: Probabilistic forecasting for geosteering in fluvial successions using a generative adversarial network
• Authors: Sergey Alyaev, Jan Tveranger, Kristian Fossum, Ahmed H. Elsheikh
• Abstract summary: Fast updates based on real-time data are essential when drilling in complex reservoirs with high uncertainties in pre-drill models. We propose a generative adversarial deep neural network (GAN) trained to reproduce geologically consistent 2D sections of fluvial successions. In our example, the method reduces uncertainty and correctly predicts most major geological features up to 500 meters ahead of drill-bit.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantitative workflows utilizing real-time data to constrain ahead-of-bit uncertainty have the potential to improve geosteering significantly. Fast updates based on real-time data are essential when drilling in complex reservoirs with high uncertainties in pre-drill models. However, practical assimilation of real-time data requires effective geological modeling and mathematically robust parameterization. We propose a generative adversarial deep neural network (GAN), trained to reproduce geologically consistent 2D sections of fluvial successions. Offline training produces a fast GAN-based approximation of complex geology parameterized as a 60-dimensional model vector with standard Gaussian distribution of each component. Probabilistic forecasts are generated using an ensemble of equiprobable model vector realizations. A forward-modeling sequence, including a GAN, converts the initial (prior) ensemble of realizations into EM log predictions. An ensemble smoother minimizes statistical misfits between predictions and real-time data, yielding an update of model vectors and reduced uncertainty around the well. Updates can be then translated to probabilistic predictions of facies and resistivities. The present paper demonstrates a workflow for geosteering in an outcrop-based, synthetic fluvial succession. In our example, the method reduces uncertainty and correctly predicts most major geological features up to 500 meters ahead of drill-bit.

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