New Insights into the Critical Role of Geology in Modeling and Prediction of Low Salinity Waterflooding
Bibliographic record
Abstract
Summary Although the advantages of Low Salinity Waterflooding (LSW) have been widely reported, studies of LSW in the past two decades have mainly focused on the underlying mechanisms through core flooding experiments. For more successful and broader applications of LSW in the field scale, it is required to have a comprehensive understanding of the LSW performance with complex geological features on a large scale that has never been addressed in the past. This paper presents insights on field scale modeling and prediction of LSW to address the current challenges with: (1) an equation-of-state compositional simulator fully coupled to multiple ion exchanges, geochemical reactions, and wettability alteration; (2) incorporation of critical geological properties important in LSW; (3) effective closed-loop reservoir management for design and prediction of the LSW process; (4) LSW evaluation in a full field scale. A mechanistic LSW model and a closed-loop modeling approach are introduced in this paper that can efficiently capture the critical effects of geology on the LSW process by integrating the use of geological software, a reservoir simulator and a robust optimizer. First, eighty geostatiscal realizations with different facies and lithology properties and distributions are generated to evaluate the effect of reservoir geology, in particular the critical effects of clay, on LSW. A wide range of recovery factors from 19% to 40% indicate that the effectiveness of LSW strongly depends on geological factors such as facies properties, clay distribution and clay proportion. In consistency with the laboratory and field-scale observations, wettability alteration has been identified as the dominant effect that contributes approximately 58% to 73% to the incremental oil recovery from these realizations. Detailed analyses of the key factors were addressed to allow the design of optimal injection strategies to maximize the oil recovery by LSW. LSW is then evaluated in a closed-loop reservoir management for a sandstone reservoir in both secondary and tertiary modes. It is found that secondary and tertiary LSW give about 6% and 4.1% incremental OOIP over high salinity waterflooding, respectively. The simulation results also indicate that the sooner the LSW process is started, the better the benefit is.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".