TUESDAY, 24-JUN-25 11:07
TUESDAY, 24-JUN-25 11:07
"The Challenges and Rewards of Image-Based Cuttings Analysis"
There are constant commercial pressures on the oil & gas and mineral resource exploration sectors to develop more efficient working practices. One example is the decision whether to core a well or not. Coring is an expensive and time-consuming enterprise. Sometimes a cheaper alternative is sidewall sampling, usually by rotary tools, but this is still costly.
Cuttings (“ditch cuttings” or “drill cuttings”) are an often overlooked alternative to coring and sidewall samples. As a drilling by-product, cuttings are free, potentially making them a very attractive option. Despite many challenges, it may be possible to gain valuable information about the formations being drilled.
Data collected from cuttings can be used to determine porosity, permeability and mineralogy, providing information on diagenesis, stratigraphy and sedimentology. Advances in analytical technologies, many of which depend on advanced rock imaging, have enhanced the data extraction from cuttings to provide key subsurface insights.
This webinar by the Rock Imaging Special Interest Group (RISIG), the organisers of the highly regarded inaugural International Rock Imaging Summit held in November 2020, will bring firsthand accounts of the difficulties and benefits of image-based cuttings analysis from some of the world’s leading practitioners.
Members who registered for the webinar can view the presentations by logging in and clicking on the title of the talk opposite. Others may request a code by to view individual presentations.
Agenda - Wednesday 7th April 2021 - all times are GMT.
Talks will be 20 minutes long followed by 10 minutes of Q&A.
BIO: Malgorzata graduated in 2004 with Master's degree in environmental geology at AGH, University of Science and Technology in Krakow, Poland. Like many, she began her Oil and Gas carrier as a mudlogging geologist and Wellsite Geologist. She worked on Norwegian offshore installations where she had her share of collecting and analyzing drilling cuttings samples. After moving to Norway in 2011, she became an Operation Geologist in Equinor working both with Exploration and Field Development. In 2019 she was seconded from Equinor to be a project leader for Released Wells Initiative, digital cuttings project in Norwegian Oil and Gas association.
SUMMARY: The Released Well Initiative aims to revitalize old data (cutting samples) from all released wells on Norwegian Continental Shelf by making them digital accessible. The Released Wells Initiative is owned and organized by Norwegian Oil and Gas Association and funded by almost all licence owners on the Norwegian Continental Shelf. The project is digitising 750K cuttings samples from 1,500 wells over three years in order to improve geological stratigraphic control and evaluate drilling issues based on geological information.
BIO: Dr. Eirik Larsen is cofounder and CEO of Earth Science Analytics, a company focusing on the commercial application of AI in petroleum geoscience. He has more than 20 years experience from the E&P industry. He has held various technical and managerial roles in oil companies including Statoil, and 4 years as Exploration Manager in Rocksource. He has experience from exploration, field development, and production on the NCS as well as internationally. He holds a PhD in Petroleum Geology from the University of Bergen, and is now laser focused on implementation of AI in petroleum geoscience.
SUMMARY: We trained machine-learning models, using well data from 126 wells, to predict lithology, porosity, saturation, and pay intervals. We have developed methods to deal with both the sparsity and quality of feature data (wireline logs) and labelled data (ground truth information on lithology, porosity, and saturation). One of these methods involves using information from cuttings samples available from the Released Wells Initiative. We have developed supervised, unsupervised, and semi-supervised machine-learning methods for classification and segmentation of the cuttings images. This enables us to use this valuable data resource for quantitative reservoir characterization in a data-sparse part of the stratigraphy, where core samples are very rare.
BIO: Gwenolé TALLEC has had his master degree from the University of Bordeaux, France in 2002, in computer science, image processing and 3D graphics programming. He focused in Digital Rock Analysis in 2010, and is one of the PerGeos Expert at ThermoFisher, delivering training courses, defining workflows at customer sites, and linking users and software’s R&D.
SUMMARY: In this presentation we will show an innovative workflow that starts with image based cuttings analysis. Extracted properties will be used to derive 3D models and generate porosity and permeability values. Using automation tools, this extraction can be easily replicated hundreds of time. Locating the cuttings in the well will then allow to generate logs that can be used as additional input into a whole well ecosystem, where existing logs, Bore Hole Images and CT scans are used to produce meaningful information to the asset teams, and predict rock facies for geologists.
BIO: After completing his Geology degree in 1977 Richard worked as a seismologist, subsequently moving into ground and airborne geophysics in the mining industry, in field and management roles. In 2000 he joined forces with Dr Gavin Hunt to found Spectra-Map Ltd, and develop a portable laboratory grade imaging spectrometer, the SpecCam system, for use in both oil & gas and mining exploration. Richard is Commercial Director at Spectra-Map, which now provides mineral analysis services using a range of spectroscopy techniques.
SUMMARY: Hyperspectral Imaging is a Big Data petrographic technique that has been used extensively in the mining industry for several decades, and since 2008 has become well-established in the oil sector. The method is non-contact and non-destructive, so samples can subsequently be used in other analyses. Cuttings, chips, cores, plugs and hand samples can all be measured, generally the only sample preparation required is to wash and dry the cuttings from oil wells. The method measures the absorption and reflection of visible/short wave infrared (SWIR) light at narrow and discreet wavelengths off a sample surface. Many amorphous and crystalline minerals have their diagnostic molecular (overtone) vibrations in this region of the electromagnetic spectrum. Its main strength is in its unique ability to accurately discriminate and quantify the swelling and non-swelling clays, carbonates, sulphates, along with hydrocarbon information. The ability to image and map subtle compositional and crystallinity changes in important reservoir-influencing minerals in a continuous format, can help identify unconformities and aid well to well correlations.
BIO: Jenny is a reservoir quality geologist previously with BP, CASP and HM Research Associates. She has expertise in petroleum geology, sedimentology, diagenesis and sediment provenance analysis. At Rocktype Jenny heads the geological work programme, delivering a wide range of QEMSCAN based studies.
SUMMARY: Last year at Rocktype we delivered QEMSCAN data from 25K cuttings samples to the Norwegian Oil & Gas Released Well Initiative. At Rocktype we are committed to techniques improvement and in 2021 we plan to scan 60K+ samples across a range of projects, including full well cuttings analysis and continuous core scanning with QEMSCAN. Full well cuttings and core QEMSCAN data has the potential to underpin next generation subsurface workflows, bringing large scale mineralogy data to the heart of subsurface workflows, including 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 standardised and open with public hosting and change management of both data and analysis routines on platforms like the Open Subsurface Data Universe and Github.