Monday 10th Oct 2016
On the 13th of October, Dr. Mike Ashton will open the Autumn 2016 Seminar Series for the University of Manchester AAPG Student Chapter. This is a regular series of seminars held by the chapter where visiting representatives from a range of companies provide insights into the petroleum geoscience industry.In participating in this series Mike will be joining a prestigious group of prior presenters including representatives from BP, Shell and ENI. The lecture will take place at the University of Manchester, Williamson Building, Room 2.22 at 17:00.The core plug – a humble or proud contributor to reservoir description?
When we think of reservoir description we inevitably envisage the perfect 3D realisation of the depositional environment whether that is a submarine fan or a carbonate barrier island-lagoon complex. Whilst ‘data’ clearly underpins some of the thinking behind such realisations, it is ‘interpretation’ that excites……but should this be the case? Does concept always outweigh fact?This talk briefly explores how ‘fact’ or ‘data’ play a central role in reservoir description in the oil industry, and how the ‘mundane’ issues of sampling and representativeness are as critical to the understanding of reservoir performance as the recognition of the proverbial ‘channel-fill’. Key is the appreciation of the value of the myriad datasets derived from the core plug, and how their value can be maximised (and upscaled) through the use of ‘nested reservoir descriptors’ that can be applied across an array of datasets to generate enhanced conditioning data for reservoir models. The application of such related reservoir descriptors through workflows also provides a mechanism for inter-well, across field and basin-to-basin comparisons and learning. Despite the robustness of many of these workflows, new technologies, which are becoming integral to reservoir description, are refining ‘conventional thinking’ and improving rock characterisation by re-defining sampling strategies and illuminating the representativeness of datasets. These advances are enabling us to ‘work smarter’, whatever the commercial climate, and improve the end-product, – oil out of the ground.
