Long-Term Thermal Stability of Ionic Surfactants for Improving Oil Production at High-Salinity High-Temperature Conditions

Surfactants with stable chemical structures and robust ability are required to lower interfacial tension and stabilize emulsions for successful chemical injection applications. This work selected six surfactants, dodecyl carboxylic sodium (LAS), dodecyl sulfonate dodecyl sodium (SLS), dodecyl sulfate sodium (SDS), dodecyltrimethylammonium bromide (DTAB), 3-(N,N-dimethylmyristylammonio) propanesulfonate (SB3–14) and a sulfobetaine formulation (PCT-10), and systematically investigated the ionic-type effects on thermal stability at 95 °C for 150 days in high-salinity water (total dissolved solids (TDS) = 57,600 ppm). With characterizations of aged samples performed through a spinning drop tensiometer, high-performance liquid chromatography, and infrared spectroscopy, it can be seen that the long-term stability sequence of ionic surfactants in solutions is sulfobetaine ≈ quaternary ammonium > sulfonate > sulfate > carboxylate. The carboxylate possibly precipitates out from the solution in the acid form, and the sulfonate and sulfate decompositions are due to the hydrolysis of the anionic head, forming alcohol and NaHSO3/NaHSO4. Obvious decomposition of sulfobetaine and quaternary ammonium was not observed, but these molecules might suffer the elimination of the ionic head, forming the corresponding alkene and amine. The results also show that the dissolved oxygen in the solution preparation significantly sped up the degradation of sulfonates. At last, the emulsion stability tests of crude oil in surfactant solutions showed that sulfobetaine surfactants retained the highest emulsifying ability after thermal aging and thus are promising candidates for long-term chemical injection in high-temperature high-salinity reservoirs

Faster R-CNN network.

Faster R-CNN network.</p

The hybrid multi-head self-attention mechanism.

The hybrid multi-head self-attention mechanism.</p

Performance of all the tasks on TVQA dataset by question type.

M1-M5 represent Two-stream, PAMN, Multi-task, STAGE, and MAF-HMS, respectively.</p

Analysis by required modality of MAF-HMS.

Analysis by required modality of MAF-HMS.</p

Performance comparison on MSVD-QA and MSRVTT-QA dataset.

M1-M5 represent Two-stream, PAMN, Multi-task, STAGE, and MAF-HMS, respectively.</p

Ablation study on model variants of MAF-HMS on the validation set of TVQA.

Ablation study on model variants of MAF-HMS on the validation set of TVQA.</p

S1 Dataset -

(ZIP)</p

Evaluation results on the TVQA dataset by TV show.

Evaluation results on the TVQA dataset by TV show.</p

The network architecture of MAF-HMS.

The network architecture of MAF-HMS.</p