MICHAEL J. PYRCZ, Ph.D., P.Eng., Associate Professor
H.B. Harkings, Jr. Professor of Petroleum Engineering
Hildebrand Department of Petroleum and Geosystems Engineering and Bureau of Economic Geology, Jackson School of Geoscience
The University of Texas at Austin
The current stochastic realization of my look (January, 2018).
The previous stochastic realization of my look (January, 2017).
About Michael Pyrcz
Here's some more about my background, specifically my path and then some of my random musings.
Introduction to Data Analytics, Geostatistics and Machine Learning, undergraduate class for the Spring, 2018 term.
Visited the University Co-op for some Texas Longhorn Apparel after a couple of guest lectures during the spring term, 2016.
Dar Es Salaam, Tanzania (1994), I have to jump in ever time I see a new ocean!
Lecture to Student Chapter of AAPG and SEG at Texas A&M, Winter Term, 2016.
Introduction to Data Analytics, Geostatistics and Machine Learning, undergraduate class for the Fall, 2018 term.
Mount Grotto scramble, near Canmore, Canada during the Ph.D. at University of Alberta (summer, 2003).
I grew up in rural Alberta, Canada. My early work experience included was working on a dairy farm. The days were long and exhausting, but I loved the outdoors, machinery and hard work. I spent a couple of years in Tanzania as a volunteer before University (I still speak a little Kiswahili). I really enjoyed the new perspectives on culture and the opportunity to learn a new language.
Immediately after returning home I started into a Mining Engineering B.Sc. This aligned my love of the outdoors, and interests in engineering and geology. I benefited from the opportunity to participate as a student engineer (mine planning, drilling and blasting and survey) and equipment operator (mining haul trucks).
In 2000, I graduated rank #1 within my engineering class (B.Sc.) and immediately started a M.Sc. (skipped later to Ph.D.) working with Prof. Clayton Deutsch in Geostatistics. During my Ph.D. I worked on a variety of research topics related to geostatistical resource modeling. I was fortunate to received various awards including the prestigious National Science and Engineering Council of Canada (NSERC) Post Graduate Scholarship Award (PGS A and B) and Alberta Innovation iCore Scholarships (A and B).
After my Ph.D.?
After completing my Ph.D. in 2004, I took the opportunity to work in industrial research with Chevron's Energy Technology Company. This allowed me to continue my focus on R&D with a strong connection to challenging business problems and the opportunity work with a variety of distinguished researchers and world class data sets. During this time I continued to publish (about 2-3 peer review papers per year and a textbook on geostatistics coauthored with Clayton Deutsch for Oxford University Press) and to work directly with academia to partner in research. One of his most enjoyable roles has been teaching and mentoring. This includes about 2 – 4 weeks per year of formal classroom teaching and various formal and informal roles mentoring students and technical experts (engineering, geologists and geophysicists) within Chevron and participating on Ph.D. committees at various universities. In addition, I have collaborated on research and lectured at various academic institutions (e.g. University of Alberta, Stanford, Colorado School and Mines, University of Minnesota, Rice University, Texas A&M, University of Texas at Austin etc.).
For the first 9 years I expanded my business impact and sphere of influence inside of Chevron, becoming one of Chevron’s leading experts on geostatistics, numerical modeling, uncertainty characterization. I have used my unique knowledge to develop new methodologies, and change scientific and engineering practice. This has resulted in major business and technology impact (including coauthorship on several patents).
More recently I spend a couple of years in a more formal leadership role as the technical team leader of Reservoir Modeling R&D. In this position my responsibilities focused on research leadership, including developing research proposals for securing funding, innovation and directing and delivering several million dollars in research per year to international business units. I have been successful at relating my research to business needs and generating the interest and funding to support this work and adding value by matching scientific approaches to challenging business problems.
Musings / Lessons Learned
Here's some reflections from my career up to now that I hope may be helpful to those in grad school or early career. This is in addition to the usual axioms of work hard, find your niche and add business value etc. This worked for me, but results are not guaranteed.
Live Life as a Random Function
Try new things, learn new things, change things and gain new perspectives. It's very easy to take small steps. e.g. change were you sit to gain a new view of a classroom, talk to strangers every [safe] chance you have, and make friends with people you disagree with. Make this part of your life. Try out a totally new type of music. Change your look. The bigger steps are a lot of fun. Take up photography and shoot folks in a home studio, learn a martial art and spar, or learn a new instrument. Go back to school. Always learn new technical skills.
No Such Thing as Independence, We Are All Correlated
Build constructive positive professional relationships. Patiently assist others and don't hesitate to ask for help. Avoid useless conflicts. Some of the folks that rubbed me the wrong way initially eventually became great friends and assets for getting work done. Think about the long term. Help those outside your team or even organization. Prioritize assistance to students, they are unknowns and may be your boss one day! Everyone regardless of experience can be part of your network. Diversity of background and opinion is powerful.
Raise those around you. Is there altruism in this? - certainly, but this is all tenable by cost-benefit analysis alone. As a retiring Engineer told me on their last day, "It's all about the people, Michael".
Quantification is Powerful
It has been possible to assign a metric to almost every feature, pattern, trend, or even artifact that I have encountered in modeling spatial phenomenon. Often this requires abstraction. Quantification allows for comparison over space and time. What is an outlier and interesting vs. what is more of the same? It is often possible to bootstrap this metric and assess uncertainty distributions for deductive and inductive workflows. We can start asking powerful questions about what is known and what remains unknown given available data.