[HTML][HTML] Applications of machine learning methods for engineering risk assessment–A review
The purpose of this article is to present a structured review of publications utilizing machine
learning methods to aid in engineering risk assessment. A keyword search is performed to …
learning methods to aid in engineering risk assessment. A keyword search is performed to …
[HTML][HTML] Basic tenets of classification algorithms K-nearest-neighbor, support vector machine, random forest and neural network: A review
In this paper, sixty-eight research articles published between 2000 and 2017 as well as
textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN) …
textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN) …
Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review
Mathematical modeling and simulation methods are important tools in studying various
processes in science and engineering. In the current review, we focus on the applications of …
processes in science and engineering. In the current review, we focus on the applications of …
Machine learning-A novel approach of well logs similarity based on synchronization measures to predict shear sonic logs
This study proposes a novel approach to predict missing shear sonic log responses more
precisely and accurately using similarity patterns of various wells with similar geophysical …
precisely and accurately using similarity patterns of various wells with similar geophysical …
Landslide displacement prediction based on multivariate chaotic model and extreme learning machine
This paper proposes a multivariate chaotic Extreme Learning Machine (ELM) model for the
prediction of the displacement of reservoir landslides. The displacement time series of the …
prediction of the displacement of reservoir landslides. The displacement time series of the …
A comparison of classification techniques for seismic facies recognition
During the past decade, the size of 3D seismic data volumes and the number of seismic
attributes have increased to the extent that it is difficult, if not impossible, for interpreters to …
attributes have increased to the extent that it is difficult, if not impossible, for interpreters to …
Optimizing acidizing design and effectiveness assessment with machine learning for predicting post-acidizing permeability
Formation damage poses a widespread challenge in the oil and gas industry, leading to
diminished permeability, flow rates, and overall well productivity. Acidizing is a commonly …
diminished permeability, flow rates, and overall well productivity. Acidizing is a commonly …
Application of machine learning for lithofacies prediction and cluster analysis approach to identify rock type
Nowadays, there are significant issues in the classification of lithofacies and the
identification of rock types in particular. Zamzama gas field demonstrates the complex nature …
identification of rock types in particular. Zamzama gas field demonstrates the complex nature …
Permeability and porosity prediction using logging data in a heterogeneous dolomite reservoir: An integrated approach
Z Zhang, H Zhang, J Li, Z Cai - Journal of Natural Gas Science and …, 2021 - Elsevier
The accurate prediction of permeability and porosity is an important foundation for high-
quality reservoir identification and geological modelling. However, the strong heterogeneity …
quality reservoir identification and geological modelling. However, the strong heterogeneity …
Comparison of supervised and unsupervised approaches for mudstone lithofacies classification: Case studies from the Bakken and Mahantango-Marcellus Shale …
Quantitative lithofacies modeling is important to understand the depositional and diagenetic
history, and hydrocarbon potential of unconventional resources at a regional scale. The …
history, and hydrocarbon potential of unconventional resources at a regional scale. The …