The PetroChallenge – An Innovative E&P Learning Experience Using an Interactive Learning Simulation
Bibliographic record
Abstract
Abstract For decades, the university education has provided a base foundation and prepared students to join the workforce. Despite the technical foundation, students lack practical exposure. To bridge the practical knowledge gap, the industry has launched various short duration interns/externs programs. Even then these programs are not broad enough to provide a holistic understanding of the Oil and Gas Industry. Therefore, in 2015 to address this practical application NExT, a Schlumberger company, launched an interactive simulation based learning competition called PetroChallenge sponsored by Oil and Gas companies. The students are grouped into integrated teams of 3 or 4 participants; for example, an engineer, geoscientist and a business major student forms a team. Each team then acts as an operating company being fully exposed to the complete upstream cycle of the oil and gas industry using a web-based simulator called OilSim. Throughout the event, these teams make Exploration and Production (E&P) decisions and their actions and choices are then judged through the Net Present Value (NPV) of their company. The winning teams are declared based on the combined highest NPV and credibility points, earned by each team based on their challenge decisions and corporate social activities and engagements. As E&P companies are making a stride to reduce the "Time to Autonomy" for new recruits, an event like the PetroChallenge can be a good enabler for students to be better prepared when joining the workforce. In these unique events, the sponsoring companies have an opportunity to evaluate potential recruits in action, not only from a technical perspective, but also, their business acumen including negotiation, risk taking and decision making skills. In the same token, students get an opportunity to network with their potential employers. In 2015, three Universities (Rice, Penn State and University of Toronto) and ShawCor partnered with NExT and launched the inaugural PetroChallenge. These three events marked a phenomenal impact in students' learning and understanding of the oil and gas decision making process. Two winning teams from each of these events met at the North America finals, with Penn State becoming the first North American PetroChallenge university champions. This presentation is prepared to share the key learnings and benefits of this type of student engagement prior to them moving into the real world.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".