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PESA WA: July Technical Lunch – Data-Driven Analytics for Petroleum Well Logs Interpretation
Thursday, 18 July, 2019 @ 12:00 pm - 2:00 pmFree
Hosted with thanks to our Platinum sponsor:
And with thanks to our Gold sponsors:
PESA WA invites you to our July technical luncheon with guest presenter Dr. Irina Emelyanova from CSIRO Energy.
“Data-Driven Analytics for Petroleum Well Logs Interpretation”
Presented by Dr. Irina Emelyanova
The modern world is experiencing a paradigm shift how we store, process and analyse data sets due to the boom driven by Big Data. Data-intensive science is considered to be the fourth paradigm of science these days. It is seen as a data-driven style of science, where Artificial Intelligence (AI) tools including Machine Learning (ML) are heavily used to help specialists to manage, analyze, and share data.
This talk will overview the key AI and ML mathematical concepts by clarifying the meaning of the buzz words; explaining the key algorithms in simple terms and highlighting their advantages and limitations. Several examples of AI and ML applications for well log data interpretation developed by the Geoscience Data Analytics team form CSIRO Energy will be presented to demonstrate the value of data-driven solutions for subsurface characterization.
1. Big data and Data-driven analytics:
– Big data revolution (what and why; new algorithms needed)
– Paradigm shift (four science paradigms; data intensive science emerged)
– Data-driven analytics (terminology, methods and main players)
2. Artificial Intelligence methods overview (theory explained in simple terms, advantages and limitations):
– Supervised (Artificial Neural Networks, Support Vector Machines, Boosted Decision Trees, Random Forest)
– Unsupervised (k-means, Self-Organising Maps, Spectral Clustering)
– Ensemble and deep learning (definitions and methods)
3. Examples of data-driven solutions for oil and gas applications:
– Supervised prediction of total organic carbon from core and log data
– Unsupervised identification of electrofacies from well logs
– Semi-supervised hierarchical clustering of well logs for characterization of continental sandstones
– Seismic – well log data integration for prediction of geological facies from seismic data
Member (Early Bird): $69.00
Concession Member [Retired, Graduate or Hardship] (Early Bird): $59.00
Student Member (Early Bird): $39.00
Early bird pricing ends Monday 15th July at 5pm (AWST).
Member (Non-Early Bird): $79.00.
All ticket sales close at 5pm (AWST) Tuesday 16th July.