Importance of Economic and Risk Analysis on Today's Petroleum Engineering Education
Bibliographic record
Abstract
Abstract Graduate students in a master or PhD program tend to naturally concentrate their efforts on the understanding of theoretical issues related to their main research area of interest. This will frequently lead to a cultural shock when they, after concluding their program, enter or return to the job market. Commonly these professionals are asked to analyze, develop and/or implement projects based not only on technical premises but also on solid and very well supported economical feasibility studies. Frequently the common graduate academic background does not prepare students for those tasks. The importance of a thorough understanding of economic issues and associated risks is even more noticeable in the oil industry, where uncertainties related to oil and gas reserves, prices and government regulations make any long term project extremely risky. Based on the abovementioned factors, a graduate course was designed and implemented in order to prepare the students to deal with the main economic issues and challenges faced by the industry. Besides the basics on economic engineering, the course covers advanced material related to budgeting, scheduling and corporate planning. Monte Carlo simulation, economic decision tools, risk analysis for oil industry projects, investment risk and simulation, economic analysis of operations, production forecasts and its associated costs and expected profits are also studied. Furthermore, the students are also required to prepare a project where actual challenges from the industry are analyzed under the perspective of the risks and uncertainties involved. In the project, associated costs and economic results also have to be determined and scrutinized. All projects are presented in a seminar at the end of the course. In addition, a "Newsletter" analyzing current problems, challenges and industry development is produced weekly by the group during the duration of the course. These two tools, the project and the Newsletter, have raised in participants a great deal of interest for economic matters related to each one's individual area of expertise. The paper details the course experience providing examples of main projects developed as well as participants’ feedback.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".