A New Approach for Optimization and Uncertainty Assessment of Surfactant-Polymer Flooding
Bibliographic record
Abstract
Abstract Surfactant-Polymer (SP) flooding has become an attractive Enhanced Oil Recovery (EOR) method. Defining chemical concentrations, chemical types and an injection schedule, according to geological features of a reservoir and well pattern, is key to making decisions for reservoir management. In this paper, we introduce an innovative approach for EOR optimization under geological uncertainty by integrating a reservoir geological property modelling and a robust optimizer. Multiple reservoir realizations are generated automatically by geology-driven modeling software and sent directly to an optimizer to analyze the effect of single or multi-parameters on objective functions such as cumulative oil production and net present value (NPV). Clay minerals play an important role in chemical flooding, but it is rarely included in the reservoir simulation. In this study, the distribution and proportion of clay are investigated in terms of facies and its relationship with porosity and permeability for a sandstone reservoir. Different facies and petrophysical properties are geostatiscally generated in a geologic manner that significantly improves the quality of history matching and optimization processes. It is found that SP flooding has the highest oil recovery factor in comparison with waterflooding, polymer flooding and surfactant flooding, and it demonstrates good performances even in high clay content reservoirs. The optimal formulation of SP and polymer slugs and injection schedule were proposed. The effect of clay content in cumulative oil and NPV were addressed, in which the more clay content is the lower NPVs obtain. A comprehensive geological uncertainty analysis has been performed for: (1) facies distribution only; (2) facies distribution and proportion. The results indicated that NPV uncertainty is less than 2.25% for (1) and about 4.18% to 5.68% for (2). The proposed optimization approach could be effectively applied to tertiary EOR techniques in various reservoir conditions under geological uncertainty. By integrating geological software, reservoir simulator and robust optimizer, it serves as a powerful tool for design and optimization of these processes. SP flooding is definitely a complicated process, therefore, an innovative modeling and optimization approach for SP flooding described in this paper is needed to improve the prediction of process performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".