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A Machine Learning Approach to Optimize Shale Gas Supply Chain Networks
H. I. Asala
, J. Chebeir
, W. Zhu
, I. Gupta
,
A. Dahi Taleghani
, J. Romagnoli
Leone Family Department of Energy and Mineral Engineering (EME)
Research output
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Dive into the research topics of 'A Machine Learning Approach to Optimize Shale Gas Supply Chain Networks'. Together they form a unique fingerprint.
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Keyphrases
Neural Network Algorithm
100%
Machine Learning Approach
100%
Supply Chain Network
100%
Shale Gas
75%
Producing Well
50%
Reservoir Simulation
50%
Development Strategy
50%
Field Development
50%
North America
25%
Shale
25%
Compositional Reservoir Simulation
25%
Reservoir Model
25%
Production Data
25%
Embedding Method
25%
Planning Horizon
25%
Marcellus
25%
Python
25%
Pressure Data
25%
Supply Chain Optimization
25%
Water Management
25%
Error Function
25%
Neural Network Training
25%
Gas Rate
25%
Net Present Value
25%
Produced Water
25%
Oil Price
25%
Feedforward Neural Network
25%
Decision Variables
25%
History Matching
25%
Misclassification Probability
25%
Mixed Integer Nonlinear Programming
25%
High-dimensional Data
25%
Gas Network
25%
Real-time Decision Making
25%
Rate Decline
25%
Low-dimensional Manifolds
25%
Shale Play
25%
Well Pad
25%
Water Rates
25%
Management Structure
25%
Supervised Machine Learning
25%
T-distributed Stochastic Neighbor Embedding (t-SNE)
25%
Gas Prices
25%
Liquid-rich Shale
25%
Four-layer
25%
Local Variables
25%
Global Variables
25%
Water Recycling
25%
Refracture
25%
Low Oil Price
25%
Long Short-term Memory
25%
Long Short-term Memory Neural Network
25%
Fractured Well
25%
Redundant Parameters
25%
Long Short-term Memory Algorithm
25%
Engineering
Shale Gas
100%
Learning Approach
100%
Supply Chain Network
100%
Learning System
100%
Gas Supply
100%
Artificial Neural Network
66%
Field Development
50%
Long Short-Term Memory
50%
Lstm
50%
Natural Gas
33%
Raw Data
16%
Feedforward
16%
Hydraulic Fracturing
16%
Misclassification
16%
Dimensional Data
16%
Development Project
16%
Pressure Data
16%
Water Management
16%
Earliest Time
16%
Error Function
16%
Net Present Value
16%
Oil Price
16%
Decision Variable
16%
Produced Water
16%
Gas Network
16%
Planning Horizon
16%
Rich Shale
16%
Decline Rate
16%
Critical Input
16%
Gas Field
16%
Superlattice
16%
Recurrent Neural Network
16%
Local Variable
16%
Mixed-Integer Non-Linear Programming
16%
Global Variable
16%
Chemical Engineering
Learning System
100%
Neural Network
100%
Long Short-Term Memory
100%
Long Short-Term Memory Network
100%
Natural Gas Demand
66%
Recurrent Neural Network
33%
Feedforward Neural Network
33%