Smart Water Flooding: An Economic Evaluation and Optimization
Bibliographic record
Abstract
Abstract Smart water (or low salinity water) flooding has been an emerging technology in the petroleum industry since last two decades. Low capital cost and operating expenses of this flooding make it attractive for the petroleum industry. This paper examines the economic feasibility of the injection of smart water and compares with other conventional water flooding techniques. Optimization has also been done with different dilution schemes through particle swarm optimization. This study analyzes the effect of smart water and sequential dilution of injected sea water through reservoir modeling. A three-dimensional black oil reservoir model is developed by using ECLIPSE 100. In addition, this study presents the economic feasibility of the injection of smart water and compares with other conventional water flooding techniques. The study is divided into four cases: i) oil is produced without water flooding, ii) formation water is injected in the reservoir, iii) sea water is injected in the reservoir, and iv) water injection is taken place by sequential dilution of high salinity water. In each case, economic evaluation is completed by calculating the costs and revenues generated by water injection, and oil prod uction. The results show that sea water injection did not give additional oil recovery compared to formation water injection for our case. However, additional results show that sequential dilution flood recovers more oil than sea water and formation water injection. Moreover, five main parameters are optimized such as number of cycles of different salinities, duration of various cycles, salinity values for different cycles, injection rate and production rate. Optimization results show even better results than sequential dilution. The optimization also shows that the additional oil recovery is achieved when the dilution sequence is altered. This outcome illustrates that increased oil recovery is not only dependent on step wise reduction of sea water salinity but also with the variation of dilution pattern. This paper presents a novel technique for the reservoir engineers to study smart water flooding with different perspective. Sequential dilution has been an acceptable technique for increasing oil recovery. However, change of the dilution pattern could be a good alternative and thus provides a cost-effective technique as compared to sequential dilution.
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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 | 0.002 | 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".