Bibliographic record
Abstract
Technology Focus Since the last time I wrote for this feature, enhanced-oil-recovery (EOR) activities around the world have been steadily increasing, meaning that the unusual crises our industry has been going through did not kill EOR. Instead, activities have expanded. For sure, current conditions have had some effect on how we see EOR, and, amazingly, with few exceptions, it has been positive. Current conditions have made most companies decide to optimize performance of their existing assets, and EOR is a key part of that. After all, optimizing is an ordinary ingredient of cost control. It is also worth adding that unconventional oil, which, for many, was the reason for the current crisis, is a big reason for the expansion of EOR. The unconventional producers are very keen on increasing recovery from their assets. Therefore, while enhancements on drilling and hydraulic fracturing will continue, it is expected that the next big wave will start when EOR becomes an integral part of unconventional development. The EOR papers I have had the privilege to review this year truly support these observations regarding the state of the industry. Novel EOR schemes, advancements in reservoir characterization leading to better insights into the recovery processes, and new physics and modeling techniques all demonstrate the high level of interest in EOR among operators, academia, and research organizations. In closing, I have to remind myself that we have been waterflooding since the 1930s and the fundamental EOR schemes (i.e., chemicals and CO2) have been with us since the late 1960s. Low-salinity and hybrid schemes have been growing during the past 10 years, and we are getting better at establishing conformance controls such as foams and thermally activated polymers. If we also add the inclusion of completions and EOR-specific monitoring technologies to the enablers, it is easy to anticipate that more and more EOR will be considered a normal part of field optimization. Recommended additional reading at OnePetro: www.onepetro.org. SPE 181156 Viscosity vs. Accuracy—Flow-Control-Feasibility Work Flow in Polymer Flooding by Kousha Gohari, Baker Hughes, et al. SPE 184086 Simulation of Chemical EOR Processes for the Ratqa Lower Fars Heavy-Oil Field in Kuwait: Multiscenario Results and Discussions by M.T. Al-Murayri, Kuwait Oil Company, et al. SPE 180208 Effects of Multicomponent Adsorption and Enhanced Shale Reservoir Recovery by CO2 Injection Coupled With Reservoir Geomechanics by S. Yang, University of Calgary, et al. SPE 180875 Effectiveness of Low-Salinity- and CO2-Flooding Hybrid Approaches in Low-Permeability Sandstone Reservoirs by H.T. Kumar, Texas A&M University, et al.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.016 |
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".