AAPG Workshop: Boosting Reserves and Recovery Using Machine Learning and Analytics
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This machine learning and analytics workshop is unique in that it focuses on applied analytics with specific, powerful outcomes. These analytics are coupled with real-world operational knowledge. This workshop is ideal for large, small, and medium-sized operators, along with innovative start-ups.
Boost recoverable reserves, reduce operating costs, evaluate acquisitions, use analytics to manage midstream operations and detect leaks.
List of Topics and Accepted Papers
Big Data, Machine Learning, Deep Learning in Exploration and Production
Where do we use analytics in upstream?
Industrial Internet of Things in the oil field: What does it mean for the engineers, geologists, data scientists?
Supervised Learning Applied to Rock Type Classification in Sandstone Based on Wireline Formation Pressure Data, Jose Victor Contreras Sandia, Baker Hughes, a GE Company
Challenges faced with processing petrophysical big data for assessing viable opportunities, CJ Ejimuda, Hybrid Data Solutions
How I Transitioned from a Geologist to Data Scientist: Data Science from a Geologist's Perspective, Fuge Zou, Marathon
Geological Facies Prediction Using Computed Tomography in a Machine Learning and Deep Learning Environment, Uchenna Odi, Devon
A deep dive into automated switch point detection in estimating EUR's, Akash Sharma, DrillingInfo
Analytics for Finding and Producing Hydrocarbons
Seismic imaging breakthroughs using deep learning, etc.
Attribute determination using deep learning
How can you make the new apps, data sources, platforms, etc. work for you?
Finding Hydrocarbons and estimating reserves using Neural Net Classification and Geobodies, Deborah Sacrey, Auburn Energy
Application of Data Science and Machine Learning for Well Completion Optimization, Piyush Pankaj, Schlumberger
Exploring for Wolfcamp reservoirs, Eastern Shelf of the Permian Basin, Texas, using a Machine Learning approach, Florian Basier, Emerson
Generate Structural Models in Real time using Artificial Intelligence (AI) while Drilling, Sunil Garg, DataVedik
Geologic - Reservoir 3D Characterization Modeling Used to Develop Analytic and Economic Outlook Models of Unconventional Resources, Onshore Continental United States, Bill Fairhurst, BEG
Predicting Potential Reservoirs in Shale Plays by DNA Fingerprinting and Machine Learning, Robert Chelak, Biodentify
Predicting Fluid Behaviors in Reservoirs
Predicting fluid behaviors in drilling
Predicting fluid behaviors in completion
Predicting fluid behaviors in production
Deep QI: A Machine Learning Approach to Quantitative Interpretation Ehsan Naeini, IkonScience
Stochastic Realization of Parameter Inversion in Physics Based Empirical Models Srinivasan Jagannathan, Srinath Madasu, Oluwatosin Ogundare, Keshava Rangarajan, Halliburton
Deep Learning to Expand Productive Limits of Fields and Improve Production, Sashi Gunturu, Petrabytes
The Sparse Data Problem in Data Science, Giewee Hammond, Aramco Services Company
Synthetic Well Log Generation Using Machine Learning Techniques, Oyewande Akinnikawe, Devon
Machine Learning and Deep Learning for Risk Assessment, Susan Nash, AAPG
Evaluations, Mergers, Acquisitions, Assessing Opportunities
Using new AI / machine learning for property evaluations
Machine Learning / Deep Learning and Enhanced Oil Recovery
Machine learning and solving water / emissions / disposal issues
Asset and Production Optimization using Dynamic Modeling
Field Assessment and Asset Management gets smarter with an AI Colleague, Sidd Gupta, Nesh
Robust Life-Cycle Production Optimization with a Support-Vector-Regression Proxy, Zhenyu Guo, Oxy
A comparison of multivariate models for predicting production, Patrick Rutty, Drilling Info
Drill down into the Risk and the Return, Patrick Ng, Real Core Energy
Domain Expertise in “Steroids” - O&G Experts enabled by AI-ML can break down barriers to enter and compete in The highly desire Delaware Basin, Leo Pirela, VPlusEnergy
Machine Learning in 4D Seismic Interpretation: Monitoring the Reservoir, Mike Brhlik, ConocoPhillips
Non-Member Fee $795
Member Fee $695
Student Fee $395
Speaker Fee $395
Affiliated Society/Displaced Worker Fee $395
Daily Continental breakfast
Morning coffee breaks
Afternoon coffee breaks
Networking reception Day 1
Access to presentations
List of registrants
Select trial software licenses and tutorials
Visit the AAPG website for more information!
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