Unconventional Resources: Cracking the Hydrocarbon Molecules In Situ
Bibliographic record
Abstract
R&D Grand Challenges - This is the sixth in a series of articles on the great challenges facing the oil and gas industry as outlined by the SPE Research and Development (R&D) Committee.The R&D challenges comprise broad upstream business needs: increasing recovery factors, in-situ molecular manipulation, carbon capture and sequestration, produced water management, higher resolution subsurface imaging of hydrocarbons, and the environment. The articles in this series examine each of these challenges in depth. The R&D Grand Challenges Series, comprising articles published in JPT during 2011 and 2012, is available as a collection on OnePetro (SPE-163061-JPT). Discoveries of accumulations of light crude oil are dwindling, and known resources are increasingly concentrated in areas that are predominantly accessible by state-owned or state-affiliated energy companies. As a result, the quality of (conventional) crude oil—particularly oil sourced from non-OPEC reservoirs—has been declining. The trend is bound to accelerate as unconventional hydrocarbons such as bitumen are brought into production to satisfy the world’s energy demand, which is expected to increase by slightly less than 50% in the next 20 years. Because the recovery and surface processing of these heavier molecules is more difficult, it is appropriate to ask whether a portion of the surface processing can be performed downhole (in situ). In fact, in the case of deeper-lying oil shale, it is the only realistic recovery option. There are three approaches by which in-situ manipulation of molecules can be accomplished: biological, chemical, and thermal. Although this article focuses on the thermal route, it should be noted that hybrid recovery methods may be more effective in satisfying the requirement to maximize economic value while minimizing water consumption, emissions, and land usage. The oil sands of Alberta, Canada, and the oil shale deposits in Colorado contain volumes of hydrocarbons that are at least comparable to the conventional oil resources in the Middle East (Fig. 1). Extraction by open pit mining is the dominant recovery method for commercial exploitation of the oil sands and is the only commercially practiced recovery method for oil shale. However, 70% to 80% of the oil sands in Alberta are deposited at intervals that are too deep (deeper than 60 m) to mine economically, and the same holds true for the richest and thickest sections of oil shale (deeper than 1,000 ft). Ever-increasing concerns about the environmental impact of mining (soil removal, tailings ponds, etc.) provide additional incentives toward the use of in-situ recovery methods.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".