Dr Christopher Dyt1, Dr Irina Emeylanova1, Dr Ben Clennell1
1CSIRO Energy, Kensington, Australia
To perform robust seismic inversions, accurate velocity and impedance profiles need to be defined for each of the different rock types present in the subsurface, both reservoir lithologies (e.g. clean sandstone, porous limestone) and the overburden rocks (shales, marls). In order to generate these profiles rapidly, objectively and in a repeatable manner we have developed software to automatically determine lithology from well logs.
To classify the rock sequence into types relevant to seismic inversion we must first capture an expert’s methodology of interpreting facies from the well logs, and convert these into mathematical algorithms. These algorithms first of all help us identify rapidly any non siliciclastic rocks (such as limestone, coal and pyrite-rich sediments) due to their very distinctive signals. The remainder of the well log section is then grouped using a k-mean clustering technique using the basic petrophysical well log properties such as gamma ray, density, neutron porosity and photoelectric factor that a geologist uses to classify the section. These clusters are then classified, according to the expert’s rules, into broad textural categories: sand, coarse to medium grain, coarse to fine grain, and shale.
The technique was applied to four wells in the Carnarvon Basin, North West Shelf, Western Australia. Despite the wells being hundreds of kilometres apart, a remarkably consistent depth v velocity and depth v impedance profile was obtained for the different rock types. These common trends should help to establish reliable prior probabilities for velocity and impedance that can be used to improve seismic inversion workflows.
Chris Dyt is an applied mathematician with over 20 years experience in developing numerical solutions in the oil and gas sector. Chris has had the good fortune to work across a broad array of projects, including sequence stratigraphy, pipe line flow, well injection prediction and fracture modelling. In the last two years, Chris has joined the Data Analytics Group based at ARRC WA.