Bibliographic record
Abstract
Technology Focus SPE hosted two enhanced-oil-recovery (EOR) conferences in 2010: One was the 2010 SPE Improved Oil Recovery Symposium in Tulsa and the other was the 2010 SPE EOR Conference at Oil & Gas West Asia, Muscat, Oman. Many other SPE conferences had sessions on EOR technologies. Therefore, it is not surprising that more than 350 papers were presented on the topic in the past year. These papers covered past field-application experiences and accomplishments, current strides, and future directions. As in previous years, when I selected papers to highlight EOR this year, I classified the papers into the categories of gas injection, chemical methods, thermal methods, conformance control, microbial EOR, carbonate-reservoir EOR, reservoir-problem-identification technologies, and novel EOR methods such as low-salinity waterflooding and nanoparticles for EOR. However, it was still quite difficult to select only four to highlight from so many outstanding papers. In the past few EOR features, I highlighted chemical methods, CO2 EOR and sequestration, low-salinity waterflooding, particle gel for conformance control, wettability alteration, and methods to identify reservoir channels. Even though those topics are still quite important and many new ideas and information were presented this year, I decided to give preference to topics not highlighted before and to papers that comprehensively summarize field experiences. Overall, the leading-edge EOR technologies presented in SPE meetings this past year will be applied increasingly on a global scale and will increase the oil recovery of mature oil fields significantly as the technologies mature. EOR Performance and Modeling additional reading available at OnePetro: www.onepetro.org SPE 133089 • “Rock/Fluid Characterization for Miscible-CO2 Injection: Residual-Oil Zone, Seminole Field, Permian Basin” by M.M. Honarpour, SPE, Hess Corporation, et al. SPE 129899 • “Potential for Polymer Flooding Reservoirs With Viscous Oils” by R.S. Seright, SPE, New Mexico Petroleum Recovery Research Center. See SPE Res Eval & Eng, August 2010, page 730. SPE 132487 • “Air Injection in Heavy-Oil Reservoirs—A Process Whose Time Has Come (Again)” by M.G. Ursenbach, SPE, University of Calgary, et al. See J Can Pet Technol, January 2010, page 48. SPE 129925 • “Nanoparticle-Stabilized Supercritical CO2 Foams for Potential Mobility-Control Applications” by David Espinosa, SPE, University of Texas at Austin, et al. SPE 129692 • “Demonstration of Low-Salinity EOR at Interwell Scale, Endicott Field, Alaska” by Jim Seccombe, SPE, BP plc, et al. SPE 139667 • “Tertiary Oil Recovery and CO2 Sequestration by Carbonated-Water Injection” by N.I. Kechut, SPE, Heriot-Watt University, et al. SPE 132359 • “Identifying Injector/Producer Relationships in Waterflood Using Hybrid Constrained Nonlinear Optimization” by H. Lee, University of Southern California, 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 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 | 0.000 |
| 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".