Andy McDonald

Andy is the IP Product Manager for AI, Machine Learning and Python at Geoactive Limited in Aberdeen. He has over 17 years of industry experience, which includes petrophysics, geoscience and machine learning. He currently provides petrophysical expertise to software development projects and specialises in Python development, artificial intelligence and applications of machine learning to petrophysics. Andy holds an MSc in Earth Science from the Open University, and a BSc (Hons) in Geology & Petroleum Geology from the University of Aberdeen. He has also co-authored several technical conference papers for the SPWLA and SPE on topics covering machine learning, heavy oil, geomechanics and low salinity waterflooding. In 2021, Andy was selected as a Distinguished Speaker by the SPWLA for his paper on the importance of data quality for machine learning models in petrophysics.

Machine Learning, Petrophysics, AI, Artificial Intelligence, Data Analysis, Data Quality, Data Exploration

How to Identify Problematic Well Log Data During QC with Interactive Petrophysics (IP)

Well log data is a key data source for petrophysical analysis and machine learning models, however, it can be affected by a range of issues including sensor issues, borehole environmental conditions, missing data and even human error (Data Quality...

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Data Quality in Petrophysical Machine Learning Workflows: Why it matters

When developing petrophysical machine learning models, it is essential to ensure that input data is of high quality, trustworthy and reliable. Data...

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Discover the role of feature selection in machine learning for petrophysics and how optimising input data can enhance predictive capabilities.

Webinar: Introducing Experienced Eye - An Automated Feature Selection Tool for Machine Learning

Webinar Details Join Andy McDonald, IP Product Manager for AI, Machine Learning and Python at Geoactive Limited in Aberdeen, as he introduces...

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Machine Learning in Petrophysics, Feature Selection for Machine Learning

The Importance of Feature Selection for Machine Learning in Petrophysics

When planning and building machine learning models, a question often asked is, “What features should I use as input to my model?”.

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