Evaluation and development of a predictive model for geophysical well log data analysis and reservoir characterization: Machine learning applications to lithology …
This work critically evaluated the applicability of machine learning methodology applied to
automated well log creation towards reliable lithology prediction and subsequent reservoir …
automated well log creation towards reliable lithology prediction and subsequent reservoir …
Research on lithology identification method based on mechanical specific energy principle and machine learning theory
H Liang, H Chen, J Guo, J Bai, Y Jiang - Expert Systems with Applications, 2022 - Elsevier
Lithology identification is an important part of petroleum drilling engineering. Accurate
identification of lithology is the foundation to ensure the smooth operation of drilling …
identification of lithology is the foundation to ensure the smooth operation of drilling …
Automated well-log processing and lithology classification by identifying optimal features through unsupervised and supervised machine-learning algorithms
The application of specialized machine learning (ML) in petroleum engineering and
geoscience is increasingly gaining attention in the development of rapid and efficient …
geoscience is increasingly gaining attention in the development of rapid and efficient …
Well-logging-based lithology classification using machine learning methods for high-quality reservoir identification: A case study of Baikouquan formation in Mahu …
J Zhang, Y He, Y Zhang, W Li, J Zhang - Energies, 2022 - mdpi.com
The identification of underground formation lithology is fundamental in reservoir
characterization during petroleum exploration. With the increasing availability and diversity …
characterization during petroleum exploration. With the increasing availability and diversity …
A coarse-to-fine approach for intelligent logging lithology identification with extremely randomized trees
Y Xie, C Zhu, R Hu, Z Zhu - Mathematical Geosciences, 2021 - Springer
Lithology identification is vital for reservoir exploration and petroleum engineering. Recently,
there has been growing interest in using an intelligent logging approach for lithology …
there has been growing interest in using an intelligent logging approach for lithology …
A semi-supervised coarse-to-fine approach with bayesian optimization for lithology identification
Y Xie, L Jin, C Zhu, S Wu - Earth Science Informatics, 2023 - Springer
Lithology identification is critical in the interpretation of well-logging data for petroleum
exploration and development. However, the limited availability of labeled well-logging data …
exploration and development. However, the limited availability of labeled well-logging data …
Safety evaluation method for operational shield tunnels based on semi-supervised learning and a stacking algorithm
The safety assessment of structural defects in operational shield tunnels is crucial for
ensuring their serviceability and safe operation. This study developed a novel …
ensuring their serviceability and safe operation. This study developed a novel …
[HTML][HTML] Enhanced machine learning tree classifiers for lithology identification using Bayesian optimization
S Asante-Okyere, C Shen, H Osei - Applied Computing and Geosciences, 2022 - Elsevier
Lithology identification is a fundamental activity in oil and gas exploration. The application of
artificial intelligence (AI) is currently being adopted as a state-of-the-art means of automating …
artificial intelligence (AI) is currently being adopted as a state-of-the-art means of automating …
[HTML][HTML] Enhancing lithofacies machine learning predictions with gamma-ray attributes for boreholes with limited diversity of recorded well logs
DA Wood - Artificial Intelligence in Geosciences, 2021 - Elsevier
Derivative and volatility attributes can be usefully calculated from recorded gamma ray (GR)
data to enhance lithofacies classification in wellbores penetrating multiple lithologies. Such …
data to enhance lithofacies classification in wellbores penetrating multiple lithologies. Such …
Carbonate/siliciclastic lithofacies classification aided by well-log derivative, volatility and sequence boundary attributes combined with machine learning
DA Wood - Earth Science Informatics, 2022 - Springer
Derivative and volatility attributes calculated for well-log versus depth sequences extract
characteristics that can be usefully exploited by automated machine-learning (ML) …
characteristics that can be usefully exploited by automated machine-learning (ML) …