Deep learning for prediction of complex geology ahead of drilling

• Published in: arXiv.org
• Main Authors: Fossum, Kristian, Alyaev, Sergey, Tveranger, Jan, Elsheikh, Ahmed
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 06.04.2021
• Subjects: Artificial neural networks; Computer Science - Learning; Data acquisition; Decision support systems; Deep learning; Drill bits; Drilling; Earth models; Geology; Machine learning; Real time; Statistics - Machine Learning; Time measurement; Uncertainty
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2104.02550

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.