Cross-domain lithology identification using active learning and source reweighting [PDF]
, 2022 Cross-domain lithology identification (CDLI) is a common case in lithology identification, which aims to train a machine learning model using the logging data of an interpreted well to predict the lithology of another uninterpreted well.
Kang, Yu +5 more
core +1 more source
A Hierarchical Multi-Feature Point Cloud Lithology Identification Method Based on Feature-Preserved Compressive Sampling (FPCS). [PDF]
Sensors (Basel)Lithology identification is a critical technology for geological resource exploration and engineering safety assessment. However, traditional methods suffer from insufficient feature representation and low classification accuracy due to challenges such ...
Duan X +6 more
europepmc +2 more sources
Lithology Identification of Uranium-Bearing Sand Bodies Using Logging Data Based on a BP Neural Network [PDF]
, 2022 Lithology identification is an essential fact for delineating uranium-bearing sandstone bodies. A new method is provided to delineate sandstone bodies by a lithological automatic classification model using machine learning techniques, which could also ...
Xinyu Jin +5 more
core +1 more source
Lithology identification based on interpretability integration learning
Earth Science Informatics, 2023 Abstract A lithology intelligent identification interpretability model is proposed based on Ensemble Learning Stacking, Permutation Importance (PI) and Local Interpretable Model-agnostic Explanations (LIME). The method aiming to provide more accurate geological information and more scientific theoretical support for oil and gas resource ...
Xiaochun Lin, Shitao Yin
openaire +1 more source
Cross-well Lithology Identification [PDF]
, 2018 this dataset is used to evaluate the performance of lithology identification in cross-well manner. There are 12 wells with same logs and 5 lithologies: mudstone, siltstone, muddy siltstone, silty mudstone, and oil shale.
Zhimin Cao (5445206)
core +1 more source
Shale lithology identification using stacking model combined with SMOTE from well logs
Unconventional Resources, 2022 Shale lithology identification is the basis of geological research and reservoir characterization, and is an essential task for oil exploration. Recently, several machine learning algorithms have been applied to improve the accuracy of lithology ...
Jinlu Yang +8 more
doaj +1 more source
Application of BiLSTM in Lithology Identification of Beach-Bar Sand Reservoir [PDF]
, 2023 The tight beach-bar sand reservoir in the study area has rich petroleum reserves and high exploration and development potential. However, it is characterized by deep burial, thin single-layer thickness, ultra-low permeability, complex pore structure, and
CHEN Ganghua +4 more
core +1 more source
Semi-supervised learning for lithology identification using Laplacian support vector machine [PDF]
, 2020 Lithology identification is a fundamental task in well log interpretation. Considering the presence of substantial unlabeled data in the field of petroleum exploration, this paper investigates the semi-supervised learning method for lithology ...
Wang, Xingmou +6 more
core +1 more source
Intelligent identification of logging cuttings based on deep learning
Energy Reports, 2022 In oil and gas exploration, rock sample identification is a basic and important work. At present, the methods of rock sample identification mainly include gravity and magnetism, well logging, earthquake, remote sensing, electromagnetism, geochemistry ...
Huijia Wang
doaj +1 more source
Unsupervised domain adaptation using maximum mean discrepancy optimization for lithology identification [PDF]
, 2021 Lithology identification plays an essential role in geologic exploration and reservoir evaluation. In recent years, machine-learning-based logging lithology identification has received considerable attention due to its ability to fit complex models ...
Kang, Yu +8 more
core +1 more source