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Norwegian AI Directory

AI Augmented Analysis in digital biostratigraphy - palynology


Description:

Biostratigraphy, using fossils to date rocks, is obligatory for all exploration wells drilled in the offshore Norwegian Sector. It is used to help understand the sub-surface geology and to correlate sections in wells both within fields and on a larger regional scale. Although biostratigraphic data was part of the digital revolution in the industry of the 1980’s, the actual study of the microfossils with a microscope and prepared glass slides has changed very little in the past decades. By using image digitalisation and Artificial Intelligence a technique can be developed to scan microscope slides into a digital high-resolution image and an AI software specifically developed for this usage will find, identify and quantify the fossil content. With this new analysis method, we believe that we can not only decrease the time used for analyses but also obtaining data at a more statistically accurate level and mitigate human inconsistencies and biases. With 3-dimentional microfossils that need to be identified in a 2-dimentional view, based on several morphological parameters, the image recognition software needs to identify fossils from different angles, poorly preserved and fragmented fossils, as well as partially hidden or folded fossils. A species can also have a morphological range and evolution. This morphological and evolutionary complexity is the base for the research needed to develop the AI software. Within this project we intend to establish if the digital scans of the biostratigraphical samples have the resolution needed to be used for quantitative analyses, we will also develop a sample preparation process to reliably produce the best digital samples possible. We will then design and develop an Artificial intelligence software for clustering and classification of microfossils based on deep-learning based algorithms for detection and segmentation of microfossils and algorithms based on autoencoders for extracting features predictive of different fossil types.


Project leader: Thomas Løkken Rustad

Started: 2021

Ends: 2023

Category: Næringsliv

Sector: Næringsliv

Budget: 5522000

Institution: APPLIED PETROLEUM TECHNOLOGY AS

Address: Oslo