Deep learning for prediction of complex geology ahead of drilling

• URL: http://arxiv.org/abs/2104.02550v1
• Date: Tue, 6 Apr 2021 14:42:33 GMT
• Title: Deep learning for prediction of complex geology ahead of drilling
• Authors: Kristian Fossum, Sergey Alyaev, Jan Tveranger, Ahmed Elsheikh
• Abstract summary: Decision support systems can help cope with high volumes of data and interpretation complexities. They can assimilate the real-time measurements into a probabilistic earth model and use the updated model for decision recommendations. In this paper, we introduce two ML techniques into the geosteering decision support framework.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: During a geosteering operation the well path is intentionally adjusted in response to the new data acquired while drilling. To achieve consistent high-quality decisions, especially when drilling in complex environments, decision support systems can help cope with high volumes of data and interpretation complexities. They can assimilate the real-time measurements into a probabilistic earth model and use the updated model for decision recommendations. Recently, machine learning (ML) techniques have enabled a wide range of methods that redistribute computational cost from on-line to off-line calculations. In this paper, we introduce two ML techniques into the geosteering decision support framework. Firstly, a complex earth model representation is generated using a Generative Adversarial Network (GAN). Secondly, a commercial extra-deep electromagnetic simulator is represented using a Forward Deep Neural Network (FDNN). The numerical experiments demonstrate that the combination of the GAN and the FDNN in an ensemble randomized maximum likelihood data assimilation scheme provides real-time estimates of complex geological uncertainty. This yields reduction of geological uncertainty ahead of the drill-bit from the measurements gathered behind and around the well bore.

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