Successful in the Lab, Not as Effective in the Field? Uncertainties in the Field Observations of Low Salinity Water Flooding in Sandstone and Carbonate Reservoirs-A Critical Analysis
Bibliographic record
Abstract
Abstract Low salinity waterflooding has been an area of great interest for researchers for almost over three decades for its perceived "simplicity," cost-effectiveness, and the potential benefits it offers over the other enhanced oil recovery (EOR) techniques. There have been numerous laboratory studies to study the effect of injection water salinity on oil recovery, but there are only a few cases reported worldwide where low salinity water flooding (LSW) has been implemented on a field scale. In this paper, we have summarized the results of our analyses for some of those successful field cases for both sandstone and carbonate reservoirs. Most field cases of LSW worldwide are in sandstone reservoirs. Although there have been a lot of experimental studies on the effect of water salinity on recovery in carbonate reservoirs, only a few cases of field-scale implementation have been reported for the LSW in carbonate reservoirs. The incremental improvement expected from the LSW depends on various factors like the brine composition (injection and formation water), oil composition, pressure, temperature, and rock mineralogy. Therefore, all these factors should be considered, together with some specially designed fit-for-purpose experimental studies need to be performed before implementing the LSW on a field scale. The evidence of the positive effect of LSW at the field scale has mostly been observed from near well-bore well tests and inter-well tests. However, there are a few cases such Powder River Basin in the USA and Bastrykskoye field in Russia, where the operators had unintentionally injected less saline water in the past and were pleasantly surprised when the analyses of the historical data seemed to attribute the enhanced oil recovery due to the lower salinity of the injected water. We have critically analyzed all the major field cases of LSW. Our paper highlights some of the key factors that worked well in the field, which showed a positive impact of LSW and a comparative assessment of the incremental recovery realized from the reservoir visa-a-vis the expectations generated from the laboratory-based experimental studies. It is envisaged that such a comparison could be more meaningful and reliable. Also, it identifies the likely uncertainties (and their sources) associated during the field implementation of LSW.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".