In the last few decades, deep learning (DL) has afforded solutions to macroscopic problems
in petroleum engineering, but mechanistic problems at the microscale have not benefited …

In recent years, Machine Learning (ML) has become a buzzword in the petroleum industry,
with numerous applications which guide engineers in better decision making. The most …

Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO 2
sequestration and wastewater injection. Managing the pressures by controlling …

Accurate prediction of shale gas well production and estimated ultimate recovery (EUR) is
always a difficult and hot spot in shale gas development. In particular, the production and …

Shale gas, as an important unconventional natural gas resource, is the main force to
increase natural gas reserves and production in the future. For shale gas with huge …

With the energy demand arising globally, geothermal recovery by Enhanced Geothermal
Systems (EGS) becomes a promising option to bring a sustainable energy supply and …

To reduce environmental risks and impacts from orphaned wells (abandoned oil and gas
wells), it is essential to first locate and then plug these wells. Manual reading and digitizing …

Reservoir history matching (HM) is an effective means for unconventional prospect studies,
which enables the calibration of reservoir models and lays a solid foundation for production …

Deep learning (DL) models are extensively used as surrogate models for high-fidelity
simulations of multiphase fluid flow in porous media at large scales, enabling fast forecasts …

This study assesses empirical correlations and time series models for forecasting production
in unconventional oil wells, focusing on their effectiveness in complex reservoir dynamics …