Dec. 14, 2021


Description

Learn about mathematical applications for petrophysics and the use of deep learning for analyses of complex geological features. Event will include lunch and mutiple networking sessions.

 

 Dr. Maksym Pryporov's Talk:-

Title: Mathematical Applications in Petrophysics 

Abstract: In this presentation, we review the major applications of data science, machine learning modeling, and applied mathematics into the petrophysics workflow. We start with machine learning techniques in model training for creating synthetic PEF curves using four standard logs: GR, NPHIL, RESD, and RHOB. We continue with the new Facies Classification model that ties core data with the log data, providing facies predictions and facies probabilities. The output of the model is used in petrophysical parameter estimation and water saturation calculation. Furthermore, the results are applied to the calculation of relative permeability parameters and Corey Exponents. Another successful example of using core data is a machine learning model for absolute permeability. All results from the applications are used as an input for the fractional flow parameter optimization and tie to the production application. The applications are deployed to IP software and are part of the daily work of petrophysicists.  

Julian Chenin's Talk:-

Title: Integrating Interactive Deep Learning for Complex Geological Features

Abstract: Detailed interpretation of stratigraphic and structural features within high-resolution, 3D seismic data is a tedious and time-consuming process. However, recent deep learning methodologies utilizing neural networks are revolutionizing traditional seismic interpretation tasks by accelerating the speed at which geologic features can be mapped. We introduce a novel, interactive deep learning methodology which enables the interpreter to exert more control over network predictions in real-time.

Results using this interactive approach can be obtained in a fraction of the time compared to traditional interpretation workflows while also enabling geoscientists to better characterize complex petroleum systems. Interactive deep learning, as an interpretation tool, has the potential to optimize day-to-day exploration and production operations and improve interpretations while helping to reduce human error. We will explore this interactive workflow and the results within the complex Santos Basin, offshore Brazil.


Featured Speakers

Speaker: Dr. Maksym Pryporov
Speaker Dr. Maksym Pryporov

Dr. Maksym Pryporov, selected bio:  
2008 – 2013 – Ph.D. in Applied Mathematics at Iowa State University, Ames, IA 
2013 – 2014 – Postdoctoral Associate at Iowa State University, Ames, IA 
2015 – 2017 – Visiting Assistant Professor at University of Denver, Denver, CO 
2018 – 2021 – Mathematical Scientist at Occidental Petroleum Corporation, …

Dr. Maksym Pryporov, selected bio:  


2008 – 2013 – Ph.D. in Applied Mathematics at Iowa State University, Ames, IA 


2013 – 2014 – Postdoctoral Associate at Iowa State University, Ames, IA 


2015 – 2017 – Visiting Assistant Professor at University of Denver, Denver, CO 


2018 – 2021 – Mathematical Scientist at Occidental Petroleum Corporation, Houston, TX  


2021 – present – Staff Analytics Engineer at Occidental Petroleum Corporation, Houston, TX 


Research interests: applied mathematics, machine learning, statistical modeling, geoscience, image processing, compressive sensing.  

Full Description

Speaker: Julian Chenin
Speaker Julian Chenin

Julian Chenin, Geophysical Data Scientist at Bluware:
Julian Chenin is a passionate leader and geoscientist who is now quantitatively optimizing machine learning algorithms for various geoscience applications at Bluware. He recently completed his MSc in Geophysics at the University of Oklahoma where his thesis work used unsupervised machine learning techniques to …

Julian Chenin, Geophysical Data Scientist at Bluware:


Julian Chenin is a passionate leader and geoscientist who is now quantitatively optimizing machine learning algorithms for various geoscience applications at Bluware. He recently completed his MSc in Geophysics at the University of Oklahoma where his thesis work used unsupervised machine learning techniques to better image gas in the subsurface. Julian serves on the AAPG Sustainable Development Committee, the GEO2021 Young Professionals Committee (YPC), and as the incoming alternate USA member of the World Petroleum Council’s YPC for the 2021 – 2023 cycle.

Full Description

View all speakers

Organizer

SPE GCS Data Analytics Study Group


Date and Time

Tue, Dec. 14, 2021

11:30 a.m. - 1:30 p.m.
(GMT-0600) US/Central

Registration has ended

Location

Norris Conference Centers - Houston/Westchase

9990 Richmond Ave.
Houston, TX 77042

Parking is free. Please see attached. Starting June, norris Center building management will be STRICTLY enforcing parking. ALL Norris attendees must park on the roof of the garage. Those who need handicapped or oversized vehicle parking will be the exception and there are designated spots for those folks. All others will …

Parking is free. Please see attached. Starting June, norris Center building management will be STRICTLY enforcing parking. ALL Norris attendees must park on the roof of the garage. Those who need handicapped or oversized vehicle parking will be the exception and there are designated spots for those folks. All others will be required to park on the roof. If unloading items, that is fine to do so in a nearby parking spot and then move the vehicle once finished. Attached is the parking map. 


Full Description


Group(s): Data Analytics