Patent ID: 9117169
Filing Date: 2015-08-25
Classification: G06N

Abstract:
1. A method for training an artificial neural network to model shale characteristics, comprising: determining a first characteristic of a source formation; determining a second characteristic of a source drilling fluid; determining an experimental formation/fluid interaction of the source formation and the source drilling fluid, wherein determining the experimental formation/fluid interaction comprises determining an experimental value for a swelling response of the source formation at least in part by fitting the following equation to the swelling response: wherein % S(t) represents the swelling of the source formation at a time t, A represents the maximum swelling of the sample formation, B represents a first-order rate of swelling, and C represents a filtrate loss parameter; inputting the first characteristic and the second characteristic into the artificial neural network; receiving from the artificial neural network a calculated formation/fluid interaction of the source formation and the source drilling fluid; comparing the calculated formation/fluid interaction to the experimental formation/fluid interaction; and altering an internal weight of the artificial neural network based, at least in part, on the comparison between the calculated formation/fluid interaction and the experimental formation/fluid interaction.