Data Science Symposium
1325 Glenarm Pl
Denver, CO 80204
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Event has been postponed (new date pending)
"Digital Workflows in Oil and Gas"
View Poster Session
RMAG's Data Science Symposium focuses on data science and software development in the oil and gas industry. Data analytics is leading companies to new ways of thinking and faster insights.
Students! We have a limited number of discounted student registrations at $100. You must be an RMAG student member to register at the student price.
- Members: $225 (remember to login to get member pricing)
- Non-Members: $250
- Student members (limited number, first come, first served): $100
- Food: Registration includes a light breakfast and catered lunch. A reception will follow the talks.
- Online registration closes March 31, 2020
- Refunds for the Data Science Symposium are available only until March 24, 2020.
If you are unable to attend, your registration is transferable. RMAG members may transfer their registration to another RMAG member, and non-members can transfer their registrations to whomever they wish. Should an RMAG member wish to transfer their registration to a non-member, the non-member would need to pay the balance between the member and non-member price.
Sponsorship Opportunities Available!
Seeking meal sponsors, student registration sponsors and advertising partners! Get your brand and name in front of this unique group of geologists, data scientists, IT professionals, and engineers. Click the button below to download the details and registration form.
|Laura Elliott (Crossroads Geoscience Consulting); Ajith Patnaik (Kabbage, Inc.)||Synthesis of DJ Basin Horizontal Play Performance - A Data Science Approach|
|Carrie Harrington, Jill Thompson, Chris Buscemi (QEP) Resources; Cory Kalicki, Nick Franciose (Jonah Energy)||Leveraging seismic and reservoir property variables to improve prediction of horizontal well performance: a case study of machine learning in the Midland Basin, Texas|
|Pengfei Hou, Zane R. Jobe, Lesli J. Wood (Chevron Center of Research Excellence & Sedimentary Analogs Database, Colorado School of Mines)||Statistical characterization of structurally-complex turbidite system: the Pennsylvanian lower Atoka formation, Ouachita Mountains, USA|
|Allie Jackson (Anthropocene Analytics), Dana Stright (Skye Analytics), David M. Advocate||Using Python and JupyterLab in the oilfield: how to better understand input parameters for geologic calculations and efficiently assess data relationships|
|Ayman Kaheel (Raisa Energy)||How software and data science changed Raisa business for the better and why you should care|
|Heather Leahey, Nick Volkmer, Jimmy McNamara, Brad Johnston, Kevin Runciman (RS Energy Group)||Eagle Ford: Diagnosing Degradation|
|Terri Olson (Digital Rock Petrophysics), Ridvan Akkurt (Schlumberger)||Improving Well Log Data with Machine Learning: An Application from the Powder River Basin|
|Julie Sebby (SM Energy)||99 Problems and Data Quality Is All of Them|
|David Thul, Kali K. Blevins (PetroLuminary)||Image-based AI Classification System Using Porosity and Permeability Cross Plots|
|Brian Towell, Xiaoou "Ochi" Deng (Lario Oil & Gas)||Back end automation and data centralizing to support front end, multi-disciplinary analysis at a small private E&P|
|Aleksandr Voishchev, Holly Lindsey (PetroDE)||Using Self Organizing Maps to Create Operator Specific Completion Fingerprints: Optimize Completion Practices for Greater ROI|
|Carly Wolfbrandt, Jessica Iriarte (Well Data Labs)||Harnessing the Interpretability of Decision Trees for Completions Data Channel Classification|
|Jennifer Markham (BPX Energy)||Leveraging "No Code Task Automation" in a development ramp-up to identify, extract and store data files for loading|
|Joel Mazza, Jason Edwards (Fracture ID)||From Algorithm to Application: The development of a geomechanics informed web-based limited entry application for rapid analysis and optimization|
|Beth Postlewait, Ralph Merry, Danielle Robinson, Harold Hansen, Tyler Dilling, Amy Richardson, Anne Grau, Aro Terrell (WPX)||Data Analytics to Optimize Core Data Integration (Showcase Analytical Tool)|
|Samantha Siegal (Enverus)||Quantifying the Impact of Well Spacing on Bakken Production: A Multivariate Study|
|Anne Steptoe, Holly Lindsey, Aleksandr Voishchev (PetroDE)||Visualization and Collaboration throughout the Data Science Workflow: Maximize Domain Knowledge Impact|
|Noah Vento, Lisa Stright (Colorado State University), Stephen M. Hubbard (University of Calgary), Brian W. Romans (Virginia Tech)||Can Machine Learning Help Predict Channel Stacking Patterns in Deep-Water Systems?|