How to Avoid the Ditch When Trying to Find and Develop Oil and Gas Internationally
Bibliographic record
Abstract
The Allure and the Unfamiliar Risks The allure for Canadian-based oil companies to expand their exploration and development activities into the international arena is easy to understand-the Western Canadian basin is a fiercely competitive environment, where the conventional oil and gas pools being discovered continue to get smaller. Other hydrocarbon regions of the world are typically much less explored, and offer the potential for new and larger conventional opportunities. One of the trade-offs in "going international," however, is that the management of some unfamiliar risks suddenly becomes key to project success. Important decisions can be made by default by the investor or manager when the risks are not adequately understood. It has been my observation that international ventures often fail for reasons that were not anticipated by the participants, but could have been. I interviewed some experts on this topic, and the most cited risks were in the category of cultural differences, and relations with the host government and/or national oil company (NOC). In the Canadian oil and gas industry, our success depends largely on how efficiently and effectively we can find, develop, and produce oil and gas. The quality of our relationship with government and regulatory bodies is a secondary focus in Canada, and we also largely take for granted the tremendous access to data that we have. Some Ideas as a Starting Point I have some references towards the end of the article for further reading, but here are some risks that are accentuated when working internationally, along with some mitigating advice. Risk Category A: Project Selection and Data PhaseAlthough you're doing a great job of managing the risks in the project you've got, you didn't pick the right project.Understand your company's niche (considering skills, knowledge, and financial strength), and find a project where that niche is applicable. Do your homework, get the data, and involve the right people in the evaluation. This is when you have the most leverage. In later stages of the project, your ability to make major changes decreases.You have only limited access to data when selecting the project, and get nasty subsequent surprises. Once you get full permission to access data, it still takes years for it to be in your hands, and you're still making decisions with partial data sets.Recognize that data will be much harder to come by than in Canada, allow enough time and resources to gather the necessary data, and build the necessary relationships with the data custodians.You get into a new area and don't know your way around, don't know the rules of the game, don't know the key players, and spend a lot of money learning things the hard way.Knowledge implies focus. If you're new to an area, either:partner with someone more experienced,get low cost entry and build slowly, orspend time and money to build knowledge (including hiring experienced personnel).There are multiple sources of data-use them all (competitors, government, service companies, legal and accounting firms, etc.). Do your homework.
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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 | 0.022 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.022 |
| Scholarly communication | 0.021 | 0.034 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".