Journal ArticleDOI
Multivariate Analysis and Data Mining of Well-Stimulation Data by Use of Classification-and-Regression Tree with Enhanced Interpretation and Prediction Capabilities
Marko Maucec,Ajay Singh,Srimoyee Bhattacharya,Jeffrey Marc Yarus,Dwight D. Fulton,Jon Matthew Orth +5 more
About:
This article is published in Spe Economics & Management.The article was published on 2015-04-01. It has received 21 citations till now. The article focuses on the topics: Decision tree learning & Decision tree.read more
Citations
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Journal ArticleDOI
A machine learning approach to predict drilling rate using petrophysical and mud logging data
Mohammad Sabah,Mohsen Talebkeikhah,David A. Wood,Rasool Khosravanian,Mohammad Anemangely,Alireza Younesi +5 more
TL;DR: The MLP-PSO model as a hybrid ANN demonstrated superior accuracy and effectiveness compared to the other ROP-prediction algorithms evaluated, but its performance is rivalled by the SVR model.
Journal ArticleDOI
Application of decision tree, artificial neural networks, and adaptive neuro-fuzzy inference system on predicting lost circulation: A case study from Marun oil field
TL;DR: Intelligent prediction models including decision tree (DT), adaptive neuro-fuzzy inference systems (ANFIS), artificial neural networks (ANN) and also hybrid artificial neural network namely genetic algorithm-multi-layer perception (GA-MLP) are developed to make a quantitative prediction on lost circulation.
Journal ArticleDOI
Digital petrography: Mineralogy and porosity identification using machine learning algorithms in petrographic thin section images
Rafael Andrello Rubo,Cleyton de Carvalho Carneiro,Mateus Fontana Michelon,Rafael dos Santos Gioria +3 more
TL;DR: In this paper, a number of configurations were tested, using different convolutional filters and classifier's parameters, and five models were created: 1. mineralogical model using artificial neural network; 2. natural mineralogy model using random forest; 3. natural porosity model using natural forest validated by chemical measurements; 4. biological model using Artificial Neural Networks; and 5. porosity modeling model using Random Forest.
References
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Book
Using multivariate statistics
TL;DR: In this Section: 1. Multivariate Statistics: Why? and 2. A Guide to Statistical Techniques: Using the Book Research Questions and Associated Techniques.
Journal ArticleDOI
Classification and Regression Trees.
Book
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
TL;DR: In this paper, the authors describe the important ideas in these areas in a common conceptual framework, and the emphasis is on concepts rather than mathematics, with a liberal use of color graphics.
Journal ArticleDOI
Nonlinear component analysis as a kernel eigenvalue problem
TL;DR: A new method for performing a nonlinear form of principal component analysis by the use of integral operator kernel functions is proposed and experimental results on polynomial feature extraction for pattern recognition are presented.
Journal ArticleDOI
Multivariate Adaptive Regression Splines
TL;DR: In this article, a new method is presented for flexible regression modeling of high dimensional data, which takes the form of an expansion in product spline basis functions, where the number of basis functions as well as the parameters associated with each one (product degree and knot locations) are automatically determined by the data.
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