Exxonmobil Nov 2008 - Dec 2012
Section Supervisor - Fixed Equipment Reliability
Exxonmobil Nov 2008 - Dec 2012
Senior Materials Engineer
Exxonmobil Jan 2002 - Oct 2008
Materials Scientist
Education:
Clemson University 1995 - 2001
Master of Science, Masters, Materials Science, Engineering
Skills:
Refinery Corrosion Inspection Materials Materials Science Refining Petrochemical Petroleum Metallurgy Engineering Refineries Piping Pressure Vessels Project Engineering Reliability Engineering Pipelines Energy Gas Ndt Oil/Gas Strategic Planning Asme Troubleshooting Risk Assessment Characterization Welding Reliability Commissioning Root Cause Analysis Process Engineering Upstream Factory Chemical Engineering Oil
Noble Drilling Houston, TX Aug 2012 to Feb 2015 Senior Rig ElectricianAustal USA Mobile, AL Dec 2011 to Aug 2012 Test & Evaluation EngineerNoble Drilling Houston, TX Jul 2011 to Jan 2012 Senior Rig ElectricianNorthrop Grumman/HII Pascagoula, MS Sep 2007 to Jul 2011 Electrical Commissioning ForemanFluor - PFD
2006 to 2007 Electrical SuperintendentFluor - TRS Lake Charles, LA 2005 to 2006 Senior Site - QA InspectorA.I.E Mont Belvieu, TX 2005 to 2005 Electrical SupervisorHorizon Shipbuilding Bayou La Batre, AL 2001 to 2002 Electrical SuperintendentFreide Goldman Offshore Pascagoula, MS 1998 to 2001 Electrician/Electrical Foreman/General Foreman
- Annandale NJ, US Thomas S. COPELAND - League City TX, US
Assignee:
ExxonMobil Research and Engineering Company - Annandale NJ
International Classification:
E21B 47/00
Abstract:
A hybrid model for predicting corrosion in a system integrates a physics-based model developed using laboratory data and a machine-learning model developed using in-field data. Said hybrid model may be used, for example, in methods by: determining a physics-based measurement of corrosion using a physics-based model for a fluid's corrosion of a substrate based, at least in part on, lab-based measurements; determining a machine learning-based measurement of corrosion using a machine learning-based model for the fluid's corrosion of the substrate based, at least in part on, in-field-based measurements; and applying an ensemble method to the physics-based measurement of corrosion and the machine learning-based measurement of corrosion to yield an estimated measure of corrosion of the substrate. The hybrid model may be applied to corrosion mechanisms that occur in, for example, hydrocarbon transportation systems, hydrocarbon production systems, hydrocarbon refining systems, and alkylation systems.