Predicting the incremental recovery factor with an enhanced oil recovery (EOR) technique is a very crucial task. It requires a significant investment and expert knowledge to evaluate the EOR incremental recovery factor, design a pilot, and upscale pilot result. Water-alternating-gas (WAG) injection is one of the proven EOR technologies, with an incremental recovery factor typically ranging from 5 to 10%. The current approach of evaluating the WAG process, using reservoir modeling, is a very time-consuming and costly task. The objective of this research is to develop a fast and cost-effective mathematical model for evaluating hydrocarbon-immiscible WAG (HC-IWAG) incremental recovery factor for medium-to-light oil in undersaturated reservoirs, designing WAG pilots, and upscaling pilot results. This integrated research involved WAG literature review, WAG modeling, and selected machine learning techniques. The selected machine learning techniques are stepwise regression and group method of data handling. First, the important parameters for the prediction of the WAG incremental recovery factor were selected. This includes reservoir properties, rock and fluid properties, and WAG injection scheme. Second, an extensive WAG and waterflood modeling was carried out involving more than a thousand reservoir models. Third, WAG incremental recovery factor mathematical predictive models were developed and tested, using the group method of data handling and stepwise regression techniques. HC-IWAG incremental recovery factor mathematical models were developed with a coefficient of determination of about 0.75, using 13 predictors. The developed WAG predictive models are interpretable and user-friendly mathematical formulas. These developed models will help the subsurface teams in a variety of ways. They can be used to identify the best candidates for WAG injection, evaluate and optimize the WAG process, help design successful WAG pilots, and facilitate the upscaling of WAG pilot results to full-field scale. All this can be accomplished in a short time at a low cost and with reasonable accuracy.
This study evaluates the efficacy of palm oil-based nanoemulsion insecticides in thermal fogging applications against adult Ae. aegypti. The nanoemulsion formulations contained a palm oil methyl ester solvent, water, a non-ionic surfactant, and active ingredient deltamethrin, with nanoemulsion droplet diameters ranging from 362 to 382 nm. Knockdown and mortality rates of caged mosquitoes were measured at various distances up to 18 m from the spray nozzle. After 15 min of insecticide exposure, nanoemulsion insecticides achieved a knockdown rate of >97% at a spraying distance of 4 m, and the knockdown effect increased substantially with exposure time. At an 18 m spraying distance, the best nanoemulsion formulation, NanoEW8, achieved a high mosquito mortality rate of more than 80%, whereas the non-nanoemulsion and the commercial product reached only 14 and 8 m distances, respectively, for comparable mortality. The artificial neural network (ANN) was used to predict the mosquito knockdown distribution over the spraying distances and time intervals. The models predicted that NanoEW8 can still cause knockdown at a maximum distance of 61.5 m from the discharge point 60 min after spraying. The results established that Ae. aegypti was susceptible to the newly developed palm oil-based nanoemulsion insecticide, indicating a high potential for mosquito control.