Bibliographic record
Abstract
Technology Focus Enhanced-oil-recovery (EOR) technologies have drawn increased interested because of the decreasing number of new-field discoveries, increasing number of maturing fields worldwide, and higher oil price, with the oil price peaking above USD 145/bbl in July 2008. It is more important than ever to understand lessons learned from past EOR applications and to develop new technologies and methods. More than 200 papers on EOR were presented at various SPE conferences in 2008. The EOR feature is split this year: EOR Performance and Modeling herein and EOR Operations in June. I categorized EOR papers for this feature into the following seven technologies.Wettability-alteration methods: surfactant imbibition for carbonate reservoirs, low-salinity waterflooding for sandstone reservoirs, and "smart water" for carbonate reservoirs.Chemical-flooding methods for conventional light oil: polymer flooding, polymer/surfactant flooding, alkaline/surfactant/polymer flooding, and surfactant-micelle flooding.EOR methods for heavy oil: steamflooding, in-situ combustion, steam-assisted gravity drainage, surfactant-alternating-gas, gas-assisted gravity drainage, thermal-assisted/gas-assisted gravity drainage, and alkaline/surfactant/polymer and alkali/surfactant flooding.Gas injection and CO2 sequestration: CO2 miscible and immiscible flooding, water-alternating-gas injection, and CO2 EOR and sequestration.Conformance control: gel treatments and foam flooding.Diagnosis and evaluation of EOR applications: seismic method, tracer injection, and mathematical modeling for reservoir heterogeneity and for induced fractures caused by water injection.Other EOR methods: in-situ seismic stimulation, microbial EOR, electrical EOR for heavy oil, and others. The papers that I selected for this feature fall into the first four categories. EOR Performance and Modeling additional reading available at the SPE eLibrary: www.spe.org SPE 113304 • "Analysis of the Wettability-Alteration Process During Seawater Imbibition Into Preferentially Oil-Wet Chalk Cores" by L. Yu, University of Stavanger, et al. SPE 113417 • "Residual-Oil Saturation from Polymer Floods: Laboratory Measurements and Theoretical Interpretation" by Chun Huh, SPE, University of Texas at Austin, et al. SPE 113985 • "Evaluation of Manson Lease Oil Field for Improved-Oil-Recovery Process" by J.S. Tsau, University of Kansas, et al. SPE 117607 • "Experimental Analysis of CO2-Sequestration Efficiency During Oil Recovery in Naturally Fractured Reservoirs" by J.J. Trivedi, University of Alberta, et al. SPE 111403 • "EOR Methods To Enhance Gas/Oil Gravity Drainage" by P.M. Boerrigter, Shell, et al. SPE 115204 • "Dynamic Induced Fractures in Waterfloods and EOR" by P.J. van den Hoek, Shell, et al.
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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 | 0.000 | 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".