May News

Satellite imagery of areas of well exposed rock pavement can give great insight into natural fracture networks, over a range of scales that are difficult to image in the sub-surface. But care is needed when interpreting these data for use as quantitative reservoir analogues

Part of our ongoing work in fractured reservoirs is to carry out sensitivity testing on the different ways that we derive various inputs typically used for fracture modelling. At AAPG’s Annual Convention & Exhibition 2019 in San Antonio we’ll be presenting more results that show how user bias can have a big effect on several key fracture modelling parameters (see here for a copy of our poster). Come along to Session 180, “Characterizing Brittle Deformation and Its Impact on Reservoirs”, chaired by Julia Gale and Ron Nelson.

It’s going to be a busy week – we’re also presenting an update on our work on Machine Learning Using Natural Language Processing to Access Geoscience Knowledge (in Session 410 “The Digital Transformation in the Geosciences”), and then returning to the theme of Characterizing Brittle Deformation in the final conference session with Fractured Reservoir Analogues: From Virtual Outcrops to Discrete Fracture Models.

Hope to see you there!

Robust, statistically meaningful fracture model, carefully constrained with outcrop data

Robust, statistically meaningful fracture model, carefully constrained with outcrop data