Evaluation and Prediction of Hydraulic Fractured Well Performance in Montney Formations Using a Data-Driven Approach

[1]  R. Lefebvre,et al.  An overview of Canadian shale gas production and environmental concerns , 2014 .

[2]  D. Mackay,et al.  Bayesian methods for adaptive models , 1992 .

[3]  R. Marc Bustin,et al.  Geological evaluation of Halfway–Doig–Montney hybrid gas shale–tight gas reservoir, northeastern British Columbia , 2012 .

[4]  Robert F. Shelley,et al.  Data Analysis of Barnett Shale Completions , 2008 .

[5]  Yuping Zhang,et al.  Clustering by propagating probabilities between data points , 2016, Appl. Soft Comput..

[6]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[7]  Anil K. Jain.  Data clustering: 50 years beyond K-means , 2010, Pattern Recognit. Lett..

[8]  Robert H. Lane,et al.  Analysis of Data From the Barnett Shale Using Conventional Statistical and Virtual Intelligence Techniques , 2011 .

[9]  Juliana Y. Leung,et al.  Practical implementation of knowledge-based approaches for steam-assisted gravity drainage production analysis , 2015, Expert Syst. Appl..

[10]  Janez Brest,et al.  Self-Adaptive Differential Evolution Algorithm in Constrained Real-Parameter Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[11]  K. Kim,et al.  Face recognition using kernel principal component analysis , 2002, IEEE Signal Process. Lett..

[12]  Christopher R. Clarkson,et al.  Petrophysical and geomechanical characteristics of Canadian tight oil and liquid-rich gas reservoirs: I. Pore network and permeability characterization , 2015 .

[13]  Wilfrido Gómez-Flores,et al.  Automatic clustering using nature-inspired metaheuristics: A survey , 2016, Appl. Soft Comput..

[14]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

[15]  Sadaaki Miyamoto,et al.  Semi-supervised Agglomerative Hierarchical Clustering with Ward Method Using Clusterwise Tolerance , 2011, MDAI.

[16]  Jiyuan Zhang,et al.  Proximate analysis based prediction of gross calorific value of coals: A comparison of support vector machine, alternating conditional expectation and artificial neural network , 2015 .

[17]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

[18]  H. Kurt,et al.  Prediction of thermal conductivity of ethylene glycol-water solutions by using artificial neural networks , 2009 .

[19]  Murray Reynolds,et al.  The Full Montney - A Critical Review of Well Performance by Production Analysis of Over 2,000 Montney Multi-Stage Fractured Horizontal Gas Wells , 2015 .

[20]  Ehsan Amirian,et al.  Integrated cluster analysis and artificial neural network modeling for steam-assisted gravity drainage performance prediction in heterogeneous reservoirs , 2015, Expert Syst. Appl..

[21]  Mukul M. Sharma,et al.  Fluid Selection for Energized Hydraulic Fractures , 2009 .

[22]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[23]  Q. Feng,et al.  Estimation of CO2–brine interfacial tension using an artificial neural network , 2016 .

[24]  Martin T. Hagan,et al.  Gauss-Newton approximation to Bayesian learning , 1997, Proceedings of International Conference on Neural Networks (ICNN'97).

[25]  C. Yoo,et al.  Nonlinear process monitoring using kernel principal component analysis , 2004 .

[26]  Bernhard Schölkopf,et al.  Kernel Principal Component Analysis , 1997, ICANN.