Did I Make the Right Career Choice?
Bibliographic record
Abstract
Special Section: The Value and Future of Petroleum Engineering JPT asked several active young professionals about their career path thus far and what they liked about petroleum engineering. Here are some of their answers. Feeling Lucky Carter Clemens, BP I lucked into the petroleum industry; I did not know much about it before choosing it as a major at the University of Texas. It has allowed me to live and travel to distant countries I never thought I would visit—whether it is Abu Dhabi, Port of Spain, Cairo, or Aberdeen, the oil industry has an incredible reach to some interesting locations. It has also enabled me to pursue engineering while spending a lot of my time outside instead of in front of a computer screen. When I was riding around with well operators in Wyoming and Colorado, I thought of how lucky I was to not be in a cubicle. There is something special about being on a well-site surrounded by snow in Wyoming or watching a sunrise from a rig in the middle of the ocean—you can’t get that with most industries. Personal Satisfaction Bruno S. Rivas, Mexico National Hydrocarbons Commission Petroleum engineering is more than get-ting oil out of the ground; it means delivering the energy that the world needs to fight poverty, increase human wellness, and accelerate growth in a sustainable way. The oil and gas industry has given me the opportunity to interact with professionals from all over the world, to exchange different experiences, to solve problems in a responsible and efficient manner, and to inspire future generations. With no doubt, if I had to decide again what to study, my choice would be oil and gas; it is certainly not an easy path, but realizing that I’m generating a positive impact on others’ lives is a personal satisfaction. Let’s Talk Climate Change Angela Dang Atkinson, Encana Corp. I love saying, “I’m a petroleum engineer and I believe in anthropogenic climate change.” It catches people off guard and begins a nuanced conversation about energy. It is an opportunity for me to talk about the importance of incremental change and that there is no silver bullet in solving the world’s energy challenges. As Harvard economics professor Ed Glaeser states, “Once we start thinking that there’s a silver bullet…we lose the fact that we need to be working day by day, over decades, to effect change.” We, the oil industry, are among those working day by day to effect change—whether we are increasing the use of recycled fracture water or finding creative ways to reduce emissions, these are the types of incremental gains on the way to better energy solutions. This nuanced conversation should not primarily exist in 150-character tidbits online. It is up to us to have that conversation in a grassroots manner, face to face, with our community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".