[HTML][HTML] Application of machine learning and artificial intelligence in oil and gas industry
Oil and gas industries are facing several challenges and issues in data processing and
handling. Large amount of data bank is generated with various techniques and processes …
handling. Large amount of data bank is generated with various techniques and processes …
Laboratory earthquake forecasting: A machine learning competition
PA Johnson, B Rouet-Leduc… - Proceedings of the …, 2021 - National Acad Sciences
Earthquake prediction, the long-sought holy grail of earthquake science, continues to
confound Earth scientists. Could we make advances by crowdsourcing, drawing from the …
confound Earth scientists. Could we make advances by crowdsourcing, drawing from the …
Machine Learning in Oil and Gas Exploration-A Review
A comprehensive assessment of machine learning applications is conducted to identify the
developing trends for Artificial Intelligence (AI) applications in the oil and gas sector …
developing trends for Artificial Intelligence (AI) applications in the oil and gas sector …
A semi-supervised two-stage approach to learning from noisy labels
The recent success of deep neural networks is powered in part by large-scale well-labeled
training data. However, it is a daunting task to laboriously annotate an ImageNet-like …
training data. However, it is a daunting task to laboriously annotate an ImageNet-like …
Seismic features and automatic discrimination of deep and shallow induced-microearthquakes using neural network and logistic regression
SM Mousavi, SP Horton, CA Langston… - Geophysical Journal …, 2016 - academic.oup.com
We develop an automated strategy for discriminating deep microseismic events from
shallow ones on the basis of the waveforms recorded on a limited number of surface …
shallow ones on the basis of the waveforms recorded on a limited number of surface …
A deep neural networks approach to automatic recognition systems for volcano-seismic events
M Titos, A Bueno, L Garcia… - IEEE Journal of Selected …, 2018 - ieeexplore.ieee.org
Deep neural networks (DNNs) could help to identify the internal sources of volcano-seismic
events. However, direct applications of DNNs are challenging, given the multiple seismic …
events. However, direct applications of DNNs are challenging, given the multiple seismic …
Detection and classification of continuous volcano-seismic signals with recurrent neural networks
M Titos, A Bueno, L García… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
This paper introduces recurrent neural networks (RNN), long short-term memory (LSTM),
and gated recurrent unit (GRU) to detect and classify continuous sequences of volcano …
and gated recurrent unit (GRU) to detect and classify continuous sequences of volcano …
Log-based abnormal task detection and root cause analysis for spark
Application delays caused by abnormal tasks arecommon problems in big data computing
frameworks. Anabnormal task in Spark, which may run slowly withouterror or warning logs …
frameworks. Anabnormal task in Spark, which may run slowly withouterror or warning logs …
Lithofacies prediction in non-cored wells from the Sif Fatima oil field (Berkine basin, southern Algeria): a comparative study of multilayer perceptron neural network …
The purpose of this study is to investigate the possibility of applying multilayer perceptron
neural network (MLPNN) and cluster analysis approaches for rebuilding non-cored …
neural network (MLPNN) and cluster analysis approaches for rebuilding non-cored …
Data space reduction, quality assessment and searching of seismograms: autoencoder networks for waveform data
AP Valentine, J Trampert - Geophysical Journal International, 2012 - academic.oup.com
What makes a seismogram look like a seismogram? Seismic data sets generally contain
waveforms sharing some set of visual characteristics and features—indeed, seismologists …
waveforms sharing some set of visual characteristics and features—indeed, seismologists …