In this paper, we determine the 2 -rank of the class group of certain classes of real cyclic quartic number fields. Precisely, we consider the case in which the quadratic subfield is Q( √ ) with = 2 or a prime congruent to 1 (mod 8) .
The visual interpretation of geological thin section is a meticulous endeavor carried out by geoscientific specialists in order to ground truth log interpretation as well as guide the spatial distribution of properties required by reservoir simulation models. At the same time, the shortage of qualified personnel, the abundance of dormant core data and the requirements for increased reservoir model accuracy have created operational needs that human interpreters alone can hardly fulfill. In this context, a method for AI-assisted thin section interpretation was developed, leveraging the latest advances in the field of deep learning to provide geologists with a comprehensive set of reservoir properties derived from rock images. While a significant part of the solution relies on the training of supervised convolutional neural networks, establishing consistent labeling procedure, enforcing geological rules, removing input and output image artifacts and close communication with subject matter experts were equally critical ingredients to a geologically-realistic prediction as well as supplementing a scarce amount of input training data. The main outcome of this multi-step domain-knowledge and data science work not only led to an increase in the mean intersection-of-union metric but also to the assurance that fundamental geological principles were honored. In practice, the algorithm ensured that petrographic object detection was constrained by biostatistical population criteria as well as prohibit the occurrence of non-natural combination of nested framework grain. The aforementioned enhancements were subsequentially implemented and deployed at company scale for ADNOC's specialists to carry out their geological interpretation through conventional web-browser applications.
Simulation Engineers and Geomodelers rely on reservoir rock geological descriptions to help identify baffles, barriers and pathways to fluid flow critical to accurate reservoir performance predictions. Part of the reservoir modelling process involves Petrographers laboriously describing rock thin sections to interpret the depositional environment and diagenetic processes controlling rock quality, which along with pressure differences, controls fluid movement and influences ultimate oil recovery. Supervised Machine Learning and a rock fabric labelled data set was used to train a neural net to recognize Modified Durham classification reservoir rock thin section images and their individual components (fossils and pore types) plus predict rock quality. The image recognition program's accuracy was tested on an unseen thin section image database.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.