Ensemble Learning: A Robust Paradigm for Data-Driven Modeling in Unconventional Reservoirs

2019 ◽  
Author(s):  
Jared Schuetter ◽  
Srikanta Mishra ◽  
Luan Lin ◽  
Divya Chandramohan
Keyword(s):  
Ensemble Learning ◽  
Unconventional Reservoirs ◽  
Data Driven ◽  
Data Driven Modeling

scholarly journals Geomechanical Properties of Unconventional Shale Reservoirs

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Mohammad O. Eshkalak ◽  
Shahab D. Mohaghegh ◽  
Soodabeh Esmaili
Keyword(s):  
Energy Demand ◽  
Marcellus Shale ◽  
Well Logs ◽  
Unconventional Reservoirs ◽  
Data Driven ◽  
Rock Properties ◽  
Real Field ◽  
Full Field ◽  
Geomechanical Properties ◽  
Data Driven Modeling

Production from unconventional reservoirs has gained an increased attention among operators in North America during past years and is believed to secure the energy demand for next decades. Economic production from unconventional reservoirs is mainly attributed to realizing the complexities and key fundamentals of reservoir formation properties. Geomechanical well logs (including well logs such as total minimum horizontal stress, Poisson’s ratio, and Young, shear, and bulk modulus) are secured source to obtain these substantial shale rock properties. However, running these geomechanical well logs for the entire asset is not a common practice that is associated with the cost of obtaining these well logs. In this study, synthetic geomechanical well logs for a Marcellus shale asset located in southern Pennsylvania are generated using data-driven modeling. Full-field geomechanical distributions (map and volumes) of this asset for five geomechanical properties are also created using general geostatistical methods coupled with data-driven modeling. The results showed that synthetic geomechanical well logs and real field logs fall into each other when the input dataset has not seen the real field well logs. Geomechanical distributions of the Marcellus shale improved significantly when full-field data is incorporated in the geostatistical calculations.

scholarly journals Are college campuses superspreaders? A data-driven modeling study

2021 ◽  
pp. 1-11
Author(s):  
Hannah Lu ◽  
Cortney Weintz ◽  
Joseph Pace ◽  
Dhiraj Indana ◽  
Kevin Linka ◽  
...  
Keyword(s):  
College Campuses ◽  
Data Driven ◽  
Data Driven Modeling ◽  
Modeling Study

scholarly journals Gain-Preserving Data-Driven Approximation of the Koopman Operator and Its Application in Robust Controller Design

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 949
Author(s):  
Keita Hara ◽  
Masaki Inoue
Keyword(s):  
Controller Design ◽  
Internal Model Control ◽  
Feedback Controller ◽  
Data Driven ◽  
Koopman Operator ◽  
Plant System ◽  
Dimensional Approximation ◽  
Model Control ◽  
Data Driven Modeling ◽  
L2 Gain

In this paper, we address the data-driven modeling of a nonlinear dynamical system while incorporating a priori information. The nonlinear system is described using the Koopman operator, which is a linear operator defined on a lifted infinite-dimensional state-space. Assuming that the L2 gain of the system is known, the data-driven finite-dimensional approximation of the operator while preserving information about the gain, namely L2 gain-preserving data-driven modeling, is formulated. Then, its computationally efficient solution method is presented. An application of the modeling method to feedback controller design is also presented. Aiming for robust stabilization using data-driven control under a poor training dataset, we address the following two modeling problems: (1) Forward modeling: the data-driven modeling is applied to the operating data of a plant system to derive the plant model; (2) Backward modeling: L2 gain-preserving data-driven modeling is applied to the same data to derive an inverse model of the plant system. Then, a feedback controller composed of the plant and inverse models is created based on internal model control, and it robustly stabilizes the plant system. A design demonstration of the data-driven controller is provided using a numerical experiment.

Data-Driven Modeling Approach to Predict the Recovery Performance of Low-Salinity Waterfloods

2021 ◽  
Author(s):  
Shams Kalam ◽  
Rizwan Ahmed Khan ◽  
Shahnawaz Khan ◽  
Muhammad Faizan ◽  
Muhammad Amin ◽  
...  
Keyword(s):  
Data Driven ◽  
Low Salinity ◽  
Modeling Approach ◽  
Recovery Performance ◽  
Data Driven Modeling

scholarly journals From SIR to SEAIRD: A novel data-driven modeling approach based on the Grey-box System Theory to predict the dynamics of COVID-19

2021 ◽  
Author(s):  
K. Midzodzi Pekpe ◽  
Djamel Zitouni ◽  
Gilles Gasso ◽  
Wajdi Dhifli ◽  
Benjamin C. Guinhouya
Keyword(s):  
System Theory ◽  
Data Driven ◽  
Modeling Approach ◽  
Data Driven Modeling
Sign in / Sign up

Export Citation Format

Share Document