Bridging Laboratory Insights to Field Applications: Advancing Geochemical Modelling of Hybrid Low-Salinity Surfactant Flooding in Carbonates
Bibliographic record
Abstract
Abstract Low-salinity waterflooding (LSWF) is emerging as a promising enhanced oil recovery (EOR) technique. LSWF is being proposed in a hybrid form, where LSWF can be combined with other EOR processes to enhance the interactions between the reservoir fluids and the rock. The hybrid application of LSWF with surfactants could potentially offer a higher oil recovery efficiency as compared to surfactant flooding or LSWF individually. This has also been corroborated by our preliminary results from the hybrid low-salinity surfactant flooding studies to date. However, studies pertinent to the geochemical reactions in the hybrid low-salinity surfactant flooding is rather limited, especially in carbonates. For reservoir-condition simulation of hybrid low-salinity surfactant flooding, a multiphase multicomponent geochemical model is proposed to adequately examine the complex interactions among multicomponent reactions, wettability alteration, and oil recovery. In this paper, a comprehensive laboratory and modelling study is presented to capture rock-brine, brine-oil, and brine-surfactant interactions in the presence of potential determining ions (PDIs), specifically Ca2+, Mg2+, and SO42−. The laboratory experiments involve interfacial tension (IFT) measurements, static single-phase adsorption tests, coreflooding tests and effluent analysis. These tests provide critical reservoir-condition data for scaling up to field-scale simulations. As for the modelling part, a multiphase multicomponent geochemical model is proposed which includes intra-aqueous, mineral dissolution/precipitation, ion-exchange reactions, IFT and adsorption. The IFT tests demonstrated that the addition of surfactant to 1% diluted seawater (1%dSW) enhanced the oil-water interactions and significantly reduced the IFT to a much lower value compared to the LSW solution alone. The static adsorption isotherm of the A-2 surfactant in low-salinity water on crushed carbonate samples at 86°C reveals that surfactant adsorption increased rapidly at low equilibrium concentrations indicating a strong initial affinity between the surfactant and the carbonate surface. As the surfactant concentration increased, the adsorption rate slowed due to the decreasing availability of favorable sites, eventually reaching a plateau as the surface becomes saturated. The adsorption experimental data was fitted to different adsorption models: Langmuir, Freundlich, Temkin, and Sips. The findings indicated that most models align well with the experimental data, except for the Langmuir model, which poorly fits across the entire concentration range, suggesting limitations in monolayer adsorption. The effluent analysis during LSW and LSS flooding in carbonate cores initially saturated with seawater reveals significant ion exchange and dilution effects. The simulation models closely matched the experimental data, accurately capturing the ion transport and exchange mechanisms, with minor deviations possibly due to the carbonate core's complex pore structure. Experimental and modeled oil displacement efficiency, along with differential pressure profiles for 1%dSW flood showed a total recovery of 60.02%. For the 1%dSW+A-2 flood, the oil displacement reached 67.2% which was significantly higher than the 1%dSW flood case. Field-scale simulations showed a total oil recovery of 40% for the 1%dSW case, while the 1%dSW+A-2 case achieved a higher oil recovery of 51%. The experimental and modelling procedures employed in this research will enhance the relevance and applicability of the results to real-world field conditions. The insights presented in this paper will interest researchers involved in water-based EOR for their forthcoming investigations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".