GAN River-I: A process-based low NTG meandering reservoir model dataset for machine learning studies.

The GAN River-I data set is designed to provide a stern test for machine learning and geostatistical tools that wish to recreate the complex geometries of realistic facies distributions in subsurface reservoirs. It provides more complex, non-stationary facies distributions than previous open data sets, some of which have modelled channels but do not include the number and complex association of facies types of this data set. GAN River-I is a dataset of 2D layers of 3D facies models produced from a process-based simulator of a meandering fluvial system.

Estimating the asphaltene critical nanoaggregation concentration region using ultrasonic measurements and Bayesian inference.

Bayesian inference and ultrasonic velocity have been used to estimate the self-association concentration of the asphaltenes in toluene using a changepoint regression model. The estimated values agree with the literature information and indicate that a lower abundance of the longer side-chains can cause an earlier onset of asphaltene self-association. Asphaltenes constitute the heaviest and most complicated fraction of crude petroleum and include a surface-active sub-fraction.