QUANTITATIVE INTERPRETATION OF OIL-BASE MUD MICRORESISTIVITY IMAGER VIA ARTIFICIAL NEURAL NETWORKS
SPEAKER: Zikri Bayraktar - Schlumberger Doll-Research Center
Speaker Bio: Zikri Bayraktar is a senior machine learning research scientist at the Schlumberger-Doll Research Center in Cambridge, MA focusing on artificial intelligence and machine learning based solutions to engineering problems at various scales ranging from borehole to seismic. He received his PhD in electrical engineering and a PhD minor in computational science in 2011 from Pennsylvania State University. Dr. Bayraktar is also an alumnus of the Schreyer Honors College Integrated Undergraduates/Graduated program at Penn State with MSc and BSc degrees in electrical engineering. After graduation, he completed a year-long post-doctoral assignment at Penn State and then joined IBM Semiconductor R&D Center in 2012 as an advisory engineer focusing on production of IBM server microchips. Since 2014, Dr. Bayraktar is with Schlumberger initially in computational electromagnetic group and currently in automated geology department. Dr. Bayraktar has published more than 45 journal and conference papers with multiple patent submissions focusing on machine learning applications in oil and gas industry. He is a member of Society of Petroleum Engineers (SPE), Society of Petrophysicists and Wirelog Analysts (SPWLA) and IEEE. Dr. Bayraktar is also serving as one of the 2020 Distinguished Speaker of the SPWLA and the lead-guest editor of the IEEE Antennas and Propagation Wireless Letters Special Cluster Issue on “Machine Learning Applications in Electromagnetics, Antennas and Propagation.
Authors: Zikri Bayraktar, Dzevat Omeragic, and Yong-Hua Chen, Schlumberger-Doll Research Center
Abstract: The new-generation
oil-base mud (OBM) microresistivity imagers provide photorealistic
high-resolution quantified formation imaging. One of the existing
interpretation methods is based on composite processing providing an apparent
resistivity image largely free of the standoff effect. Another one is the
inversion-based workflow, which is an alternative quantitative interpretation,
providing a higher quality resistivity image, button standoff, and formation
permittivities at two frequencies. In this work, a workflow based on artificial
neural networks (NNs) is developed for quantitative interpretation of OBM
imager data as an alternative to inversion-based workflow.
The machine learning
approach aims to achieve at least the inversion-level quality in formation
resistivity, permittivity, and standoff images an order of magnitude faster,
making it suitable for implementation on automated interpretation services as
well as integration with other machine learning based algorithms. The major
challenge is the underdetermined problem since OBM imager provides only four
measurements per button, and eight model parameters related to formation, mud
properties, and standoff need to be predicted. The corresponding nonlinear
regression problem was extensively studied to determine tool sensitivities and
the combination of inputs required to predict each unknown parameter most
accurately and robustly. This study led to the design of cascaded feed-forward
neural networks, where one or more model parameters are predicted at each stage
and then passed on to following steps in the workflow as inputs until all
unknowns are accurately obtained.
Both inverted field data
sets and synthetic data from finite-element electromagnetic modeling were used
in multiple training scenarios. In the first strategy, field data from few
buttons and existing inversion results were used to train a single NN to
reproduce standoff and resistivity images for all other buttons. Although the
generated images are comparable to images coming from inversion, the method is
dependent on the availability of field data for variable mud properties, which
at the moment limits the generalization of the NNs to diverse mud and formation
In the second strategy,
we utilized the synthetic responses from a finite element model (FEM) simulator
for a wide range of standoffs, formation, and mud properties to develop a
cascaded workflow, where each stage predicts one or more model parameters.
Early stages of the workflow predict the mud properties from low formation
resistivity data sections. NNs then feed the estimated mud angle and
permittivities at two frequencies into next stages of the workflow to finally
predict standoff, formation resistivity, and formation permittivities.
Knowledge of measurement sensitivities was critical to design the efficient
parameterization and robust cascaded neural networks not only due
mathematically underdetermined nature of the problem but also the wide dynamic
range of mud and formation properties variation and the measurements. Results
for processed resistivity, standoff, and permittivity images are presented,
demonstrating very good agreement and consistency with inversion-generated
images. The combination of two strategies, training on both synthetic and field
data, can lead to further improvement of robustness allowing customization of
interpretation applications for specific formations, muds, or applications.
There are two identical sessions:
Morning - Tuesday, Jan 14th, 8am – 9am US Central Time.
Evening - Wednesday, Jan 15th, 8pm – 9pm US Central Time.
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