Bibliographic record
Abstract
A clear change in the last 2–3 years is that the energy transition is becoming the undisputable trend for almost all major oil and gas companies and oilfield services providers, as evident from the news of rebranding, the roll-out of companies’ new strategies, and announcements of commitments to net zero. Geothermal energy, especially within deep, hot, dry rock, is abundant around the globe. It can be a great opportunity for the majors to boost their energy-transition process and open new business opportunities. Previously, however, deep geothermal development activities were rather limited and participation from the majors was sporadic. One reason was that drilling into such extra-hot and extra-deep impermeable rock could render excessively high cost and risk, making the development of deep geothermal energy far from economical and scalable and discouraging many companies from entering this hot spot. The domain expertise and technologies in the oil and gas industry such as integrated project planning and operating, extended-reach drilling (ERD), high-pressure/high-temperature drilling tools, multilateral drilling and completions, geomechanics, flow and thermal dynamics modeling, and artificial intelligence have been fueling the ongoing march for new records on ERD, multilateral, and other complex wells. They have made it possible to drill faster, further, and with more production, which will be the keys to accelerating the learning curve, filling the technology gap, and breaking the economic barrier on deep geothermal drilling, which in turn will attract more players to this new arena. A bold move of the majors into deep geothermal drilling could accelerate the removal of the economic and technical hurdles in harnessing the enormous baseload renewable energy and meanwhile backfeed new drilling technologies developed in deep geothermal applications to the oil and gas industry for a more sustainable future. Recommended additional reading at OnePetro: www.onepetro.org. SPE 206616 Deep Geothermal Well-Construction Problems and Possible Solutions by Mikhail Yakovlevich Gelfgat, Gubkin University, et al. SPE 200792 Lessons Learned and Case Studies of Overcoming Sustained Casing Pressure in Extended-Reach Wells by Svetlana Nafikova, SLB, et al. IPTC 21439 Application of Multilateral Wells for Production and Enhanced Oil Recovery: Case Studies From Canada by Eric Delamaide, IFP-Technologies
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 | 0.002 | 0.001 |
| 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".