Simulation of Ionic Liquid Flooding for Chemical Enhance Oil Recovery Using CMG STARS Software
Bibliographic record
Abstract
Abstract A significant portion of crude oil remains in the reservoir after the application of conventional recovery. To meet the growing demand for energy, enhanced oil recovery (EOR) methods should be used efficiently to recover the extra amount of trapped crude oil after water flooding. Surfactant flooding is one of chemical EOR methods that can be implemented to recover oil from the remaining oil-in-place. Ionic liquids (ILs) which are salts having a melting point below 100 °C, can be considered as a prospective alternative to surfactant because of their superiority on surfactant in different points. Ionic liquids have unique advantages such as low cost, low toxicity, recyclable and high ability to work in harsh environments. In this work, three simulation runs were conducted to simulate core flooding experiments with an ionic liquid solution at different scenarios. Surfactant flood model (SFM) which is available in CMG STARS software was used to match the simulation results of oil recovery, well bottom-hole pressure, and imbibition relative permeability curves with flooding experimental results. The purpose of this paper is to prove the validity of core flooding experiments with ionic solutions to be simulated with SFM model. In addition, transfer the experimental work to simulation in order to facilitate the performance of other scenarios and to predict the future results through flooding with an ionic liquid solution. All three scenarios have given a good matching between simulation and real data of oil recovery, well bottom-hole pressure and imbibition relative permeability curves. Both simulation and experimental results indicated that secondary continuous flooding with IL solution gives oil recovery (71% original oil in place, OOIP) greater than secondary slug size flooding (64% OOIP); whereas tertiary flooding with IL solution was the lowest one with 48% OOIP.
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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 | 0.000 | 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 | 0.000 |
| 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".