Reservoir

The COVID-19 pandemic naturally has affected SPE meetings, causing many to be rescheduled or postponed indefinitely, but SPE papers continue to be a crucial source of technical knowledge. The selected papers explore simple and complex innovative approaches toward reservoir characterization to work around the absence of certain data.

The authors describe an integrated multiscale data methodology involving machine-leaning tools applied to the Late Jurassic Upper Jubaila formation outcrop data.

The authors describe a logging-while-drilling nuclear-magnetic-resonance method applied in Bohai Bay, China, as an alternative to radioactive source-porosity measurements.

The authors develop a collocated finite-volume method to study induced seismicity as a result of pore-pressure fluctuations.

This paper describes the application of a synthetic seismic-catalog-generation method followed by application of a neural network on a seismic data set for an oil-producing field in the North Sea.

This paper describes an integrated work flow developed for 3D seismic reservoir characterization of deep and thin layers without sufficient well data in a South China Sea formation.

The author writes that careful calibration of a common, simple rock physics model can provide valuable insights into reservoir and seal elastic properties.

Two well-test logging operations have been carried out for the first time in a conventional carbonate reservoir in safe operating conditions and with repeatable results.

The past couple of years has been a rollercoaster for energy professionals, but it did not stop the incredible achievements in machine-learning (ML) techniques, particularly neural networks to improve seismic imaging.