The exploration phase in the oil and gas (O&G) industry is extremely expensive and yet surprisingly inefficient. Even the most advanced exploration techniques can at most detect the probable presence of hydrocarbons, but not quantify it. O&G companies spend hundreds of million of euros to drill wells that turn out not to have a real commercial value while the ecosystem in O&G field is permanently damaged by the intrusive methods in use nowadays. Due to environmental concerns, the policy makers and the citizens call for 'greener' solutions, to the best possible extent. We offer an innovation that avoids unnecessary exploration drilling: our patented and tested technology predicts with high accuracy whether an O&G field will be productive by only analysing the DNA of one sample of soil of 1 mm3 at about 50 cm below the surface.
The 'trick' is to correlate the microbes in the shallow soil with the presence of hydrocarbons in the deeper subsurface by means of machine learning techniques. We have proved in numerous field experiments that our method is up to three times more accurate and 100 times cheaper to perform than the mainstream ones. We have already a few paying customers but we want to reach a global commercial success. This Phase 2 project has one main goal: open up the markets the European North Sea (for offshore) and of USA and Argentina (for shale), which we have been identified as the most interesting markets for us in the short and medium term. Our project will benefit the EU in many respects: Biodentify is a Dutch company, and as a result of the activities of this project we will reach a cumulated turnover of about €50M by the end of 2022 and increase our staff to 39 FTEs in the same period. Additionally, offshore O&G in the North Sea is by far the largest source of fossil fuel of the EU and we need to preserve the North Sea ecosystem while using the resources it offers to advance our industry and society.
Field of science
- /natural sciences/chemical sciences/organic chemistry/hydrocarbons
- /natural sciences/computer and information sciences/artificial intelligence/machine learning
Call for proposal
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