Bibliographic record
Abstract
Technology Focus Unconventional gas is poised to enter its Golden Age and to effect positive global change if it can meet three challenges. Consider the 7 billion people on Earth who use energy each day. Consider the World Health Organization statistic that 3% of all world health issues are related to indoor burning of fossil fuels. Consider the potential effects of global warming and the fact that gas emits less greenhouse gas than coal, currently the most prevalent energy source. As you consider this global perspective, you will recognize the biggest driver of energy demand—namely, the human desire to sustain and improve the well-being of ourselves, our families, and our communities. You will also notice the positive effect gas can have. Through 2040, we will see enormous population and economic growth that will increase energy demand by more than 40%, according to an ExxonMobil study, with gas projected to surpass coal demand in the next decade. Gas-driven changes have already begun. In fact, the use of gas instead of coal in the power-generation sector is already helping slow global carbon dioxide emissions, and, in the United States and other developed economies, emissions are actually falling. Again, unconventional gas will drive further transformation if we can overcome three critical challenges. The first is our ability to deliver the technologies necessary to unlock the vast global unconventional gas potential of more than 14,000 Tcf. For example, horizontal, hydraulically fractured wells have been a key to unlocking the shale potential in North America, but are they the ideal technology in other parts of the world, and are they ideal for coal and tight gas? The second is our ability to deliver a cost-effective, reliable, and timely energy option for the world. For upstream engineers and geoscientists, cost-effective, efficient delivery includes the development of smart, streamlined factory processes and technologies in the field and office throughout the appraisal and development phases. The third challenge is our demonstration of social responsibility, measured by the public. This will be based on our capability to leave the environment better than we found it, by providing jobs, supporting local investment, and maintaining the highest environmental standards. If we can deliver in these three areas, the Golden Age of unconventional gas will occur and make a better world. And I am confident that we will. Recommended additional reading at OnePetro: www.onepetro.org. IPTC 16608 Integrating an LNG Plant With an Unconventional Gas Supply by Chris Langley, Arrow Energy, et al. SPE 162777 What Don’t We Know About Self-Sourced Oil Reservoirs: Challenges and Potential Solutions by Steve Larter, University of Calgary, et al. SPE 163987 Optimizing Remote Unconventional Gas Exploration by Martin Rylance, BP SPE 149872 Stimulation Unlocks Coalbed Methane: Lessons Learned in India by Shahvir Pooniwala, Baker Hughes
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".