Machine learning-assisted log quality control (QC) and reconstruction
Challenge
- Improving log data quality to ensure comprehensive analysis and confident decision making.
- Automating manual log conditioning to enable petrophysicists to focus on more valuable tasks.
- Implementing AI and ML solutions for automated wellbore quality control, reconstruction, and enhanced efficiency in managing uncertainty.
- Overlooked log data: 70% of log data is dismissed due to poor quality, hindering comprehensive analysis.
- Confidence and time drain: manual log conditioning can consume 50–70% of petrophysicists' time, leading to lower confidence in models and decisions.
Solution
Our ML-assisted log quality control (QC) and reconstruction solution is a fully automated and assisted conditioning workflow that makes more data available for all geoscience workflows, reducing uncertainty and rejuvenating legacy data.
“The full cycle of ML training, logs edition and results review has been reduced from 15 days to two days, bringing true efficiency gain for the team.”
Middle Eastern NOC
Results
Automated log conditioning.
Enhanced data quality with ML.
Integrated AI for uncertainty.
5–10 x acceleration in log conditioning.
70%↑ leverage.
2–3x resource efficiency.
