Innovation is among the top priorities for the BRU21 program. We have established an innovation culture by organizing inspirational presentations showcasing examples of successful innovation, supported by workshops and training in skills necessary for efficient innovation and research. Most BRU21 research results are transferred directly to the sponsoring partner companies. Novel ideas with a wider potential impact are turned into innovation projects at NTNU’s Technology Transfer AS, which provides support in IP protection, securing investments for further development and commercialization,  establishing startup companies and licensing agreements. The extensive industrial network of the BRU21 program is used for finding relevant industrial partners.   

ComputerWell: Drillstring digital twin

ProDecs: Better investment decisions

The BRU21 research project (financed by OKEA) on valuation of marginal fields has resulted in methods that can be extended and used throughout the whole industry. After identifying the commercial potential of this method developed by Dr. Semyon Fedorov and Professor Verena Hagspiel, this innovation project was transferred to NTNU Technology Transfer. It received funding for further development into a commercial product from OKEA and secured in-kind support from the Norwegian Petroleum Directorate. ProDecs is a part of the startup accelerator program KongsbergHOW.

PERMEAN: Rapid downhole testing of permeability anisotropy

In his BRU21 project (financed by Lundin Energy Norway), BRU21 PhD candidate Guowen Lei developed a novel method for reducing the time needed to conduct downhole testing of permeability anisotropy, as well as increasing the testing accuracy. Permeability anisotropy is a key parameter for reservoir characterization. It is measured through testing in most discovery wells and many production wells. After validating the finding with extensive simulations, a patent application was filed with the support of NTNU Technology Transfer Office. This innovation project has also received an innovation scholarship from the Engineering Faculty of NTNU and is currently under further development.

MAC: Acoustic look-ahead technology based on machine learning

Drilling in karstified carbonates involves high risks, as karsts can lead to serious drilling incidents. In his research project financed by Lundin Energy Norway, Dr. Danil Maksimov developed a new method for detecting small, but dangerous, karstification objects ahead of the bit. The method is based on a new way of conducting acoustic surveys (Method of Acoustic Comparisons) and machine learning for processing the test data. After validating the concept with extensive simulations, the project got support from NTNU Technology Transfer Office, which helped to file a patent application. Currently the project heads towards further development.

ADF: Drilling Data Analytics tool

Drilling data is usually very noisy and require filtering (de-noising) before applying advanced data analytics methods. Conventional filters often remove, in addition to the noise, valuable information from the signal. This hides important information on small events and onsetting drilling problems. Professor Alexey Pavlov and his team developed a method, called Adaptive Differentiating Filter, which solves this problem by automatically tuning filter parameters to the signal properties in real time. In addition to efficient noise filtering, the method automatically calculates trends in drilling data and highlights periods of suspicious changes in the measurements.  This enables early detection of onsetting drilling problems and identification of small drilling events.