Highlights of examples shown at the AAPG ML Workshop, Wichita KS two weeks ago.
Hybrid model combines neural-network enabled decline curve analysis (DCA) and modern portfolio theory (MPT). We illustrate a powerful methodology to quantify risk and optimize portfolio allocation with granularity, integrity, transparency and science-based machine learning in mind.
Examples using the same AI engine
First quick check on Hart Energy Majors portfolio (six stocks, https://www.hartenergy.com/markets/data
). Determine the max risk-adjusted return (Sharpe ratio) is Chevron. Calibration - Barron’s “Lowest Risk and Best in Class”, August 21, 2019 https://www.barrons.com/articles/ch...1566402158
Next, instead of financial assets, focus on real assets - petroleum basins, Bakken, Eagle Ford, Marcelleus and Permian. Recall “Hybrid approach to Well Economics”, https://www.ogj.com/home/article/17...-economics
published in July 2016: a) min variance portfolio - overweight Eagle Ford and Marcellus (i.e., light oil and gas rich, respectively); b) max Sharpe portfolio - all in Eagle Ford. Calibration - “Eagle Ford is weathering volatility better than most oil fields”, September 26, 2019. https://www.expressnews.com/busines...470238.php
Conclusion - Hybrid model advances quantitative risk management and strategy development in energy transition.
Relevant reading - https://superiorinvest.com/investor...f-capital/