The impact of well data quality on machine learning performance

PhD Candidate Veronica Torres Caceres

Main Supervisor Kenneth Duffaut

Sponsor: Aker BP

The project focuses on two topics:
1) Prototyping the “future” well database that integrates ‘’all” measurements acquired in wells together with their corresponding metadata;
2) applying and training machine learning algorithms to automatically access data quality, depth shifting, rock typing, similarity recognition, as well as estimate petrophysical and geophysical parameters

Project result: Automatic depth matching of well log data

Structured well log data base and algorithm for fast and accurate depth-matching of well log data