Data mining: A novel strategy for production forecast in tight hydrocarbon resource in Canada by random forest analysis
L Liao, Y Zeng, Y Liang, H Zhang - International petroleum technology …, 2020 - onepetro.org
Unconventional hydrocarbon resources, including shale hydrocarbon, tight hydrocarbon
and coalbed methane, have become an increasingly essential part of global oil and gas …
and coalbed methane, have become an increasingly essential part of global oil and gas …
A machine learning analysis based on big data for eagle ford shale formation
Hydrocarbon production from shale formation has become an essential part of the global
energy supply in the past decade. The life of a project in an unconventional play significantly …
energy supply in the past decade. The life of a project in an unconventional play significantly …
A novel adaptive non-linear regression method to predict shale oil well performance based on well completions and fracturing data
A Bakshi, E Uniacke, M Korjani… - SPE Western Regional …, 2017 - onepetro.org
This paper presents the results of applying a novel nonlinear regression method, Variable
Structure Regression (VSR), to forecasting well performance given the well completion and …
Structure Regression (VSR), to forecasting well performance given the well completion and …
A big data study: correlations between EUR and petrophysics/engineering/production parameters in shale formations by data regression and interpolation analysis
Y Liang, L Liao, Y Guo - SPE Hydraulic Fracturing Technology …, 2019 - onepetro.org
Shale hydrocarbon production has become an increasingly important part of global oil and
gas supply during the past decade. The life of projects in unconventional plays, such as …
gas supply during the past decade. The life of projects in unconventional plays, such as …
Well completion optimization in Canada tight gas fields using ensemble machine learning
L Liao, G Li, H Zhang, J Feng, Y Zeng, K Ke… - Abu Dhabi International …, 2020 - onepetro.org
With the coming of increasingly large databases, the growing amount of computational
resources and latest algorithmic advancements, data driven and machine learning …
resources and latest algorithmic advancements, data driven and machine learning …
On establishing nonlinear combinations of variables from small to big data for use in later processing
JM Mendel, MM Korjani - Information Sciences, 2014 - Elsevier
This paper presents a very efficient method for establishing nonlinear combinations of
variables from small to big data for use in later processing (eg, regression, classification …
variables from small to big data for use in later processing (eg, regression, classification …
Using the Adaptive Variable Structure Regression Approach in Data Selection and Data Preparation for Improving Machine Learning-Based Performance Prediction …
The application of data-driven techniques for unconventional oil and gas resources is an
active research area due to the poor understanding of physics-based simulation models that …
active research area due to the poor understanding of physics-based simulation models that …
[HTML][HTML] On a novel way of processing data that uses fuzzy sets for later use in rule-based regression and pattern classification
JM Mendel - International Journal of Fuzzy Logic and Intelligent …, 2014 - ijfis.org
This paper presents a novel method for simultaneously and automatically choosing the
nonlinear structures of regressors or discriminant functions, as well as the number of terms …
nonlinear structures of regressors or discriminant functions, as well as the number of terms …
A Predictive Model for Improving the Efficiency of Frac Jobs
This paper presents a new method for forecasting post-fracturing responses in a tight oil
reservoir using historical hydraulic fracturing data. The methodology is based on a nonlinear …
reservoir using historical hydraulic fracturing data. The methodology is based on a nonlinear …