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March 5th DPS Meeting – Petrophysics, Digitalization & Machine Learning
March 5 @ 4:00 pm - 7:00 pm CETFree
We will be holding the DPS March meeting at KIVI on Thursday 5th March 2020. Doors open at 15:30 with the talks beginning at 16:00. Followed by drinks at one of our usual haunts.
The topic of the meeting is ‘Petrophysics, Digitalization & Machine Learning’ with two talks from Daria Lazareva from CGG GeoSoftware and Stefano Pruno from Stratum Reservoir. The abstracts are below.
To register, either register using the link below, or send a mail to firstname.lastname@example.org with your details. As always attendance is free for DPS members.
Hope to see you on 5th March
Machine Learning for Better Wells. Daria Lazareva, Technical Advisor – Petrophysicist at CGG GeoSoftware.
As data becomes more and more abundant, machine learning is rapidly becoming a
standard technology in the oil and gas industry, driving more effective methods and introducing tools and theories for discovering, modelling and extracting patterns and relationships embedded in large datasets. Reservoir properties can be determined faster and more accurately and using a new generation of analytics and prediction techniques from machine learning.
Today, machine learning can address complex petrophysical and reservoir engineering challenges by automating mundane routine tasks such as modelling missing log curves and use data clustering for facies classification essential for seismic reservoir characterization or automatically identifying and flagging poor-quality log curves in a project.
This presentation focuses on machine learning for petrophysical data. Its potential to improve understanding of wells, reservoir and producing fields is virtually unlimited, and to some extent, it all begins with well log data. We show some of the available workflows for Unsupervised Facies classification and Automated Log editing. For data clustering, we use environmentally corrected, normalized and depth-shifted data to ensure valid interpretation results. We also discuss leveraging machine learning for synthetic log generation using deep machine learning.
The Quest for Big Data – A New Frontier for Machine Learning Application: The Norwegian Cuttings Digitalization Project. Stefano Pruno, Stratum Reservoir Regional Technical Advisor for Core Analysis.
In the new digital era where the quest for Big Data has become paramount, the Norwegian oil industry and academic community has decided to spearhead the approach to Machine Learning challenges.
This DPS short talk will present the Norwegian Released Wells cuttings digitalization project and be mainly focused on the objectives, advantages and limitations related to this specific program.
This is a bold initiative in a collective research and development effort to create one unbiased database to train and develop specific artificial neural networks and algorithms.
Will the controversial cooperation between human and artificial intelligence be a success in the future scientific re-evolution…? I guess we will all see as the near future, for now, is still uncertain.