[HTML][HTML] Advancing reservoir evaluation: machine learning approaches for predicting porosity curves
Porosity assessment is a vital component for reservoir evaluation in the oil and gas sector,
and with technological advancement, reliance on conventional methods has decreased. In …
and with technological advancement, reliance on conventional methods has decreased. In …
Identifying payable cluster distributions for improved reservoir characterization: a robust unsupervised ML strategy for rock ty** of depositional facies in …
The oil and gas industry relies on accurately predicting profitable clusters in subsurface
formations for geophysical reservoir analysis. It is challenging to predict payable clusters in …
formations for geophysical reservoir analysis. It is challenging to predict payable clusters in …
Application of Deep Learning for Reservoir Porosity Prediction and Self Organizing Map for Lithofacies Prediction
While there is a connection between petrophysical logs and reservoir porosity, finding
analytical solutions for this relationship is still difficult. This paper presents a novel approach …
analytical solutions for this relationship is still difficult. This paper presents a novel approach …
Recognition of drill string vibration state based on WGAN-div and CNN-IWPSO-SVM
FT Qu, HL Liao, M Lu, W Niu, F Shi - Geoenergy Science and Engineering, 2024 - Elsevier
During drilling operations, complex and variable dynamic nonlinear loads result in intricate
vibrations in the drill string, severely impacting drilling safety and efficiency. The vibration …
vibrations in the drill string, severely impacting drilling safety and efficiency. The vibration …
The role of stylolites as a fluid conductive, in the heterogeneous carbonate reservoirs
Stylolites possess a dual function in assessing the quality of the Lower Cretaceous
carbonate reservoir in the Abadan Plain, Zagros Basin. They can either operate as barriers …
carbonate reservoir in the Abadan Plain, Zagros Basin. They can either operate as barriers …
A Robust Strategy of Geophysical Logging for Predicting Payable Lithofacies to Forecast Sweet Spots Using Digital Intelligence Paradigms in a Heterogeneous Gas …
The most crucial elements in the oil and gas sector are predicting subsurface lithofacies
utilizing geophysical logs for reservoir characterization and sweet spot assessment …
utilizing geophysical logs for reservoir characterization and sweet spot assessment …
Characterization of lacustrine shale oil reservoirs based on a hybrid deep learning model: A data-driven approach to predict lithofacies, vitrinite reflectance, and TOC
The integration of deep learning technologies into geoscience domains enables the
evaluation of rock properties in unconventional shale reservoirs. In particular, combinations …
evaluation of rock properties in unconventional shale reservoirs. In particular, combinations …
An ensemble-based strategy for robust predictive volcanic rock ty** efficiency on a global-scale: A novel workflow driven by big data analytics
Laboratory measurements, paleontological data, and well-logs are often used to conduct
mineralogical and chemical analyses to classify rock samples. Employing digital intelligence …
mineralogical and chemical analyses to classify rock samples. Employing digital intelligence …
Enhanced Lithology Classification Using an Interpretable SHAP Model Integrating Semi-Supervised Contrastive Learning and Transformer with Well Logging Data
In petroleum and natural gas exploration, lithology identification—analyzing rock types
beneath the Earth's surface—is crucial for assessing hydrocarbon reservoirs and optimizing …
beneath the Earth's surface—is crucial for assessing hydrocarbon reservoirs and optimizing …
[HTML][HTML] Integrating petrophysical data into efficient iterative cluster analysis for electrofacies identification in clastic reservoirs
Efficient iterative unsupervised machine learning involving probabilistic clustering analysis
with the expectation-maximization (EM) clustering algorithm is applied to categorize …
with the expectation-maximization (EM) clustering algorithm is applied to categorize …