Two aspects are always considered in the design and development of new surfactants for oilfield application. One of them is that surfactant must be sufficiently stable at reservoir temperature and the other is the solubility of the surfactant in the injection water (usually seawater) and the formation brine. Most industrially applied surfactants undergo hydrolysis at elevated temperature and the presence of reservoir ions causes surfactant precipitation. In relevance to this, a novel series of quaternary ammonium gemini surfactants with different length of spacer group (C8, C10, and C12) was synthesized and characterized using FT-IR, 13C NMR, 1H NMR, and MALDI-TOF MS. The gemini surfactants were prepared by solvent-free amidation of glycolic acid ethoxylate lauryl ether with 3-(dimethylamino)-1-propylamine followed by reaction with dibromoalkane to obtain quaternary ammonium gemini surfactants. The gemini surfactants were examined by means of surface properties and thermal stabilities. The synthesized gemini surfactants showed excellent solubility in the formation brine, seawater, and deionized water without any precipitation for up to three months at 90 °C. Thermal gravimetric data revealed that all the gemini surfactants were decomposed above 227 °C, which is higher than the oilfield temperature (≥90 °C). The decrease in critical micelle concentration (CMC) and surface tension at CMC (γcmc) was detected by enhancing spacer length in the order C8 ˃ C10 ˃ C12 which suggested that the larger the spacer, the better the surface properties. Moreover, a further decrease in CMC and γcmc was noticed by enhancing temperature (30 °C ˃ 60 °C) and salinity (deionized water ˃ seawater). The current study provides a comprehensive investigation of quaternary ammonium gemini surfactants that can be further extended potentially to use as a suitable material for oilfield application.

This study offered a detailed review of data sciences and machine learning (ML) roles in different petroleum engineering and geosciences segments such as petroleum exploration, reservoir characterization, oil well drilling, production, and well stimulation, emphasizing the newly emerging field of unconventional reservoirs. The future of data science and ML in the oil and gas industry, highlighting what is required from ML for better prediction, is also discussed. This study also provides a comprehensive comparison of different ML techniques used in the oil and gas industry. With the arrival of powerful computers, advanced ML algorithms, and extensive data generation from different industry tools, we see a bright future in developing solutions to the complex problems in the oil and gas industry that were previously beyond the grip of analytical solutions or numerical simulation. ML tools can incorporate every detail in the log data and every information connected to the target data. Despite their limitations, they are not constrained by limiting assumptions of analytical solutions or by particular data and/or power processing requirements of numerical simulators. This detailed and comprehensive study can serve as an exclusive reference for ML applications in the industry. Based on the review conducted, it was found that ML techniques offer a great potential in solving problems in almost all areas of the oil and gas industry involving prediction, classification, and clustering. With the generation of huge data in everyday oil and gas industry activates, machine learning and big data handling techniques are becoming a necessity toward a more efficient industry.

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