Transparent open-box learning network and artificial neural network predictions of bubble-point pressure compared

by David A. Wood, Abouzar Choubineh
Published 2020-12-01

Reliable predictions of oil formation volume factor based on transparent and auditable machine learning approaches

by David A.Wood, Abouzar Choubineh
Published 2019-09-01

Transparent machine learning provides insightful estimates of natural gas density based on pressure, temperature and compositional variables

by David A. Wood, Abouzar Choubineh
Published 2020-02-01

Estimation of minimum miscibility pressure of varied gas compositions and reservoir crude oil over a wide range of conditions using an artificial neural network model

by Abouzar Choubineh, Abbas Helalizadeh, David A.Wood
Published 2019-03-01

The impacts of gas impurities on the minimum miscibility pressure of injected CO2-rich gas–crude oil systems and enhanced oil recovery potential

by Abouzar Choubineh, Abbas Helalizadeh, David A. Wood
Published 2018-09-01

Predicting liquid flow-rate performance through wellhead chokes with genetic and solver optimizers: an oil field case study

by Hamzeh Ghorbani, David A. Wood, Jamshid Moghadasi, Abouzar Choubineh, Peyman Abdizadeh, Nima Mohamadian
Published 2018-08-01

Prediction of oil flow rate through an orifice flow meter: Artificial intelligence alternatives compared

by Hamzeh Ghorbani, David A. Wood, Abouzar Choubineh, Afshin Tatar, Pejman Ghazaeipour Abarghoyi, Mohammad Madani, Nima Mohamadian
Published 2020-12-01