Rockwash Geodata has agreed with analytics and machine learning company Earth Science Analytics (ESA) to combine technical expertise and software to ‘transform vast quantities of dormant oil and gas data into high-value digital assets’.
Rockwash Geodata has agreed with analytics and machine learning company Earth Science Analytics (ESA) to combine technical expertise and software to ‘transform vast quantities of dormant oil and gas data into high-value digital assets’.
Having long been overlooked and stored in geological core repositories, many drill cutting samples remain accessible only through physical visitation with many often unwashed and coated in drilling fluids. As a pioneer in the field of national cuttings digitalisation, Rockwash Geodata has brought these geological resources into the digital environment. With an emphasis on the collection of repeatable data from every sample in every well, many thousands of data points are gathered throughout the entire stratigraphic section, generating an excellent candidate dataset for computer-driven deep learning techniques.
The collaboration will combine ESA’s web-based cloud-native geoscience software and Rockwash’s geological expertise. In the first project under this partnership, Rockwash Geodata experts have taken a proprietary database of cuttings photographs and, using ML workflows from ESA, categorized the photos in terms of bulk lithology to create a fully labelled dataset of cuttings sample photographs.
By combining these newly created digital inputs with a quality assured set of traditional log suite curves prepared by ESA, they have generated a set of high-quality rock property predictions which can be used to build larger, reservoir-scale interpretations.
Jack Cawthorne, co-director of Rockwash Geodata, said: ‘We have spent several years fighting the corner for cuttings as a crucial geological resource that is undervalued and have refined our procedures to ensure our data is produced as consistently as possible.’
Tatiana Moguchaya, CEO at Earth Science Analytics, said: ‘This collaboration paves the way for the practical application of cuttings data in daily subsurface workflows by demonstrating how the value of these, quite literally, tiny fragments of data, can be unlocked when augmented with machine learning techniques.
‘The activity will support a well cuttings scale interpretation workflow that is essential for good business decision making, not only in oil and gas exploration, but also for sub-surface CO2 storage and mining industries. We have already identified a broad scale of application areas, where this technology will enable and accelerate the industrial digital transformation, as an opening up for new business opportunities.’
