A Comprehensive Evaluation of the Performances of Alkaline/Surfactant/Polymer in Conventional and Unconventional Reservoirs
Bibliographic record
Abstract
Abstract Chemical flooding, especially alkaline/polymer/surfactant (ASP) flooding is of significant increasing interest in mature reservoirs because of high oil prices and larger energy demands. However, it is a very complicated process, and the success of ASP has been mostly demonstrated in laboratory scale. Additionally, ASP is very sensitive to reservoir heterogeneity; therefore, it is urgent and necessary to have a comprehensive investigation of ASP flooding in different reservoir types for a wider and more successful implementation. This paper aims to solve the prior problems for achieving a new insight vision of ASP flooding. Firstly, a series of laboratory experiments were conducted to optimize the ASP solution. A new mixture of single and double tail anionic surfactant was proposed as a promising approach for high temperature and hardness reservoirs. These mixtures are also more compatible with polymer and increase the optimum salinity. All of the important factors such as alkaline/polymer/surfactant concentration, pH, temperature, salinity, ionic strength were taken in account. And, the experimental result was analyzed by experimental design techniques which allow for evaluating the interaction among those factors which were usually eliminated in literature. Based on these above achievements in laboratory, full fields scale simulations have been done to assess the performance of ASP in various real field conditions from conventional to unconventional reservoirs which are including traditional sandstone reservoir, tight sandstone oil reservoir, naturally fractured basement reservoir. As an observation result, ASP flooding brings a higher oil recovery (about 10 to 28%), and lower water cut than traditional waterflooding in all of four case studies. However, the incremental amount is not similar since geology characteristics have strong effects on ASP process. The simulation result proved that the fractured reservoirs with low matrix permeability are not suitable candidates for ASP. On the contrary, ASP has great potential for enhancing heavy oil recovery, especially in small and thin reservoirs. A sensitivity analysis was performed for determining the optimal ASP solution and injective scheme for each reservoir condition.
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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 | 0.000 |
| 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".