Bibliographic record
Abstract
Technology Focus A seminal event last year was the Climate Change Conference in Paris, where participating countries agreed to reduce their carbon output “as soon as possible” and to do their best to keep global warming “to well below 2°C.” History will be the judge of whether 2015 turns out to be a turning point in the journey to reducing global warming. There is still a long way to go to turn good intention into substantive action if the world is to transition to a low-carbon economy and ultimately to one of net zero carbon emissions. This challenge is all the tougher given increasing demand for energy, with the International Energy Agency expecting growth by one-third between 2013 and 2040. In the US, there has been a gradual shift in the balance of enhanced-oil-recovery (EOR) production between thermal and gas-injection projects. Since 2006, production from gas injection has outstripped that from thermal, and it is continuing to grow. Worldwide, gas-injection EOR is established as a successful, robust, commercial technology deployed in a wide range of operating conditions from onshore to shallow offshore and, more recently, deep water. A key differentiator of gas injection, compared with other EOR techniques targeting light oils, is the ability to overcome some of the variability in reservoir geology by recycling back-produced injectant. The deployment of gas-injection EOR is limited by the availability of gas; where there is access to a gas market, the use of hydrocarbon gas is generally not attractive, and carbon dioxide (CO2) is not widely available at acceptable prices. Carbon capture and storage (CCS) is a mechanism that can facilitate the transition to a low-carbon economy, and so something of a virtuous circle might exist. The use of CO2 captured for greenhouse-gas-management reasons can enable more-widespread gasinjection EOR. CO2 EOR can provide secure CO2 storage and additional revenues, accelerating the implementation of carbon capture and ultimately the building of a commercial CCS industry that can help realize the aspiration of net zero carbon emission fossil fuels. Even though conditions in the industry remain very tough at present, EOR is expected to be increasingly important in the future, with the possibility of significant further uptake of gas-injection EOR linked to the climate-change agenda. As ever, SPE continues to have a key role in disseminating best practices and project learnings. JPT Recommended additional reading at OnePetro: www.onepetro.org. SPE 169513 Case Study: Steam-Injection Step-Rate Test Run in the Shallow Low- Permeability Diatomite Formation, Orcutt Oil Field, Careaga Lease, Santa Barbara County, California by Ramon Elias, Santa Maria Energy, et al. SPE 174700 On the Road to 60% Oil Recovery by Implementing Miscible Hydrocarbon WAG in a North African Field by I. Maffeis, Eni, et al. SPE 177697 Use of an Integrated Approach To Optimize a Congested Brownfield Facilities Development by C. Roberts, S2V Consulting, et al. SPE 174656 Nano Spherical Polymer Pilot in a Mature 18 °API Sandstone Reservoir Waterflood in Alberta, Canada, by Randy Irvine, Harvest Operations, et al.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.418 | 0.354 |
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".