Bibliographic record
Abstract
Technology Focus Enhanced oil recovery (EOR) targets the approximately two-thirds of oil remaining in reservoirs after conventional recovery methods have been exhausted. More than 350 EOR papers were published from October 2011 to September 2012 because two major EOR meetings were held in Tulsa and Oman and a few international meetings had EOR sections as a primary topic, such as the International Petroleum Technology Conference in Thailand and the SPE Heavy Oil Conference Canada in Calgary. I categorized these EOR papers into the topics of EOR screening methodologies, reservoir problem identification and evaluation technologies, chemical EOR methods for conventional oil, chemical methods for heavy oil, gasflooding, conformance control using in-situ and preformed particle gels and foams, thermal EOR methods, smart-water flooding, nanofluid for EOR, EOR for shale oil, and new EOR technologies. Five papers were published to provide methods to screen EOR technologies. More than 20 papers published were related to EOR by altering water salinity and compositions, such as low-salinity waterflooding, seawater flooding, and tuning water salinity and ionic content. More than 10 papers were published to address one recent interest: using chemical methods to enhance heavy-oil recovery. The development of high-molecular-weight polymer and new types of polymer such as associated polymer has made some chemical methods such as polymer flooding feasible for some heavy oils. However, these three types of papers were not featured this time, either because the category was featured in the last couple of years or because the papers are too long or too complex to be synopsized. But these topics, along with some others, such as gas-flooding for shale oil and nanoparticle EOR, are selected to form the list of papers suggested to be read. I have selected the following four topics for the EOR feature this time: experience of alkaline/surfactant/polymer flooding in China, polymer flooding pilot in the Middle East, a review of mobility and conformance control for CO2 EOR, and a new correlation to predict optimum surfactant structure. Recommended additional reading at OnePetro: www.onepetro.org. SPE 141283 Full Barrel Analysis: A Simulation Model Interrogation Tool To Assess Sweep Efficiencies and Identify Targets for Improved Oil Recovery by M.S. Beckman, ExxonMobil, et al. SPE 155546 PDO’s EOR Screening Methodology for Heavy-Oil Fractured Carbonate Fields—A Case Study by Georg Warrlich, Shell, et al. SPE 154218 Four-Phase Equilibrium Calculations of CO2/Hydrocarbon/Water Systems Using a Reduced Method by Saeedeh Mohebbinia, The University of Texas at Austin, et al. SPE 154675 Viscosifying Surfactant Technology for Chemical EOR: A Reservoir Case by G. Degré, Rhodia, et al. SPE/CSUG 148971 Sweep Efficiency Improvement by Alkaline Flooding for Pelican Lake Heavy Oil by Mingzhe Dong, University of Calgary, 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 | 0.001 |
| 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".