Omma, J., Eidesgaard, Ó R., Benedictus, A., Szewczyk, A
External pressures on the oil & gas industry require a shift to more efficient working practices, with shorter project time scales. In response, the industry is becoming more collaborative, digital and data-centric and there is an increased call to create Big Data to analyse in algorithmic, next generation subsurface workflows. Big Data studies require standardised, digital data, which in turn requires data collection to be standardised and automated.
To date the impact of mineral data on many subsurface workflows has been limited due to the time-consuming and expensive nature of collecting mineralogical data. High-volume data collection is made possible by key automation decisions, strategic technological innovation and continuous process improvement. At Rocktype this year we are supplying 25K QEMSCAN data from cuttings samples to the Digital Cuttings Project, a Norwegian industrywide collaboration digitising 600K samples from 1500 wells over three years. In 2021 we plan to scan 60K+ samples, across a range of projects, increasing our capacity month by month.
In this talk we present data from 5000 cuttings samples from eight Faroe Islands wells, sampled from first returns to TD. Data includes cuttings photographs, modal mineralogy, per lithotype mineralogy, cuttings size, grain size, calculated log properties and digital mineral maps.
The dataset is currently being tested as input priors for rock physics models, for next generation seismic inversion workflows and as input data for reservoir simulations powered by machine learning. To encourage the collaborative development of these analysis tools and long lasting value from these mineralogical data we support making datasets open and standardised with public hosting and change management of both data and analysis routines on platforms like the Open Subsurface Data Universe and Github.
