Presented by Lalitha Venkataramanan, Scientific Advisor at Schlumberger.
Abstract: Deep learning techniques have been shown to be extremely effective for various classification and regression problems, but quantifying the uncertainty of their predictions and separating them into the epistemic and aleatoric fractions is still considered challenging. In subsurface characterization projects, tools consisting of seismic, sonic, magnetic resonance, resistivity, dielectric and/or nuclear sensors are sent downhole through boreholes to probe the earth’s rock and fluid properties. The measurements from these tools are used to build reservoir models that are subsequently used for estimation and optimization of hydrocarbon production. Machine learning algorithms are often used to estimate the rock and fluid properties from the measured downhole data. Quantifying uncertainties of these properties is crucial for rock and fluid evaluation and subsequent reservoir optimization and production decisions. These machine learning algorithms are often trained on a ‘ground-truth’ or core database.
During the inference phase which involves application of these algorithms to field data, it is critical that the machine learning algorithm flag data as ‘out of distribution’ from new geologies that the model was not trained upon. It is also highly important to be sensitive to heteroscedastic aleatoric noise in the feature space arising from the combination of tool and geological conditions. Understanding the source of the uncertainty and reducing them is key to designing intelligent tools and applications such as automated log interpretation answer products for exploration and field development. In this presentation, Dr. Lalitha Venkataramanan will discuss a few methods researchers have used in uncertainty quantification.
Speaker Bio: Lalitha Venkataramanan is a Scientific Advisor at Schlumberger Doll Research and Program Manager for Automated Log Interpretation which aims to integrate and automate algorithms from sub-surface measurements and contributes to end-to-end workflows in the exploration and development phase of an oilfield. Her research interests include machine learning, data science, forward modeling and inversion of nuclear magnetic resonance, dielectric and optical measurements obtained from downhole and laboratory data as well as optimization, optimal experimental design and probability and stochastic processes. Trained as an Electrical Engineer, she obtained her M.S and Ph.D. degrees from Yale University in 1998. She has over 20 granted patents and 15 pending patent applications, and over 25 refereed Journal papers. She is based in Cambridge, MA.
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