There is no deep understanding of the application of nanoparticles in water-based muds (WBM). Therefore, such study that helps to enhance the knowledge in the field of well stability using modern methods in an unforgiving industry is very much needed. The nanoparticles accumulate on the wellbore wall and due to their very small sizes, they seal the pores in the mud cake, which plasters the wellbore. This paper focuses on empirical aspects of using nano-bentonite for filter loss control. The current work was applied on a nano-drilling fluid to improve filtration characteristics of drilling fluid in the wellbore. Therefore, nano-bentonite WBM (size between 90 and 100 nm) was introduced as the smart drilling fluid with abilities to overcome the tight spot problem in wellbores, which has been investigated in the paper. Three different drilling fluids were prepared using nano-bentonite clay with the main focus of enhancing those rheological features of the fluid expected to improve the mud characteristics, especially the plastering properties. Low pressure low temperature (LPLT) filter press test has been utilized to calculate the filter loss volume and the viscosity, yield point and gel strength of the understudy samples and the results have been compared. It was found that the filtration loss during the LPLT test was reduced by an overall average of 34% for all of the three samples, resulting in better filtration characteristics.

A set of slimtube experiments is designed and presented to study the effect of cold temperature CO2 on recovery factor in reservoirs with high temperature. The comparison of the results indicates the positive effect of temperature on recovery trend in early stage as well as ultimate recovery in different injection pressures. The approach is based on a long slimtube to show the effect of temperature on the recovery. The study considers different temperatures and pressures of injection and reservoir allowing both miscible and immiscible flooding of CO2. Using non-isothermal conditions, the results show that, lowering temperature of injection can yield in higher recovery in early stage significantly. Also, considering ultimate recovery, it is observed that low temperature CO2 injection into high temperature reservoir can result in slightly higher recovery factor than isothermal injection. The reason for recovery increase is mainly due to elimination of the interfacial tension between CO2 and reservoir fluids especially near the injection point. Another finding is that the minimum miscibility pressures is lowered by means of lowering the temperature of injection which is again caused by elimination of interfacial tension between CO2 and oil. This is important because forming a single phase can increase the ability of CO2 to extract different components of the crude oil as well as lowering viscosity of the mixture, resulting in a better sweep efficiency. It appears that using liquid CO2 in high temperature reservoirs can be a promising method for better oil recovery in high temperature reservoirs.

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In CO2 gas injection, the oil recovery rate of the miscible flooding is significantly higher than that of the non-miscible flooding. The miscibility of oil and CO2 can only be achieved when pressure is above the minimum miscible pressure (MMP), hence MMP is an important parameter for the optimal design of the CO2 injection in the reservoir. The MMP can be determined by traditional methods such as experimental and empirical correlation approaches. The experimental method is accurate but time-consuming and expensive, while the correlation methods are efficient but have limited application range due to its theoretical assumption's limitation. Thus, a more efficient and accurate method for MMP measurement is in need. Artificial neuro network (ANN) is an effective tool for engineering estimation, hence some MMP prediction model based on ANN was proposed by researchers, but the accuracy of models still can be improved. In this study, four factors (heavy hydrocarbon molecular weight, reservoir temperature, volatile and intermediate ratio oil) of MMP were designed as input, MMP as output, and ANFIS model is constructed for MMP prediction, the accuracy is estimated by the root mean square error (RMSE). The simulation results indicate that the ANFIS model has higher accuracy (average RMSE=1.846) and wider application range than those traditional correlation approaches (best-performed correlation RMSE=4.25). ANFIS runs faster and cheaper than the experimental approach. Among all the ANFIS models, the hybrid algorithm optimized ANFIS with Gaussian member function is most accurate with RMSE=1.44. BP algorithm optimized ANFIS with PSigmoid membership function produces the largest error (RMSE=2.83). Therefore, the ANFIS model developed in this study is more accurate and time-saving than traditional methods in predicting MMP and is more accurate than the previous neural network MMP prediction models.

In recent years, chemical enhanced oil recovery (EOR) application for heavy oil fields has been limited by low oil prices. The use of chemicals, though proven to be effective, can be toxic, expensive, and/or non-biodegradable. Deep Eutectic Solvents (DES) are potentially a much cheaper, greener alternative to conventional surfactants. However, there are very limited studies on the DES application. This study focused on numerical modeling of the effectiveness of a specific DES (Choline Chloride: Glycerol), in improving oil recovery and evaluating the parameters affecting the DES behavior. Data was gathered based on a literature review of the various but limited studies available and used to construct a black oil numerical model representing a heavy core-flooding experiment. The model was calibrated to some of the available published data. The study then used this model to examine different scenarios by comparing the performance of conventional water flooding using formation brine, with the performance of DES injection (after an initial period of brine flooding). The simulations were repeated at multiple temperatures and concentrations. Simulation results tally with the hypothesis and published data which is in favor of oil recovery enhancement with DES injection. In brine flooding cases, the oil recovery factor was in the range of 36-39%, with the high end occurring at a higher temperature. Increasing the DES concentration reduces the interfacial tension (IFT) which further improves oil recovery with the assistance of wettability alteration. Injected DES alters the rock wettability from oil-wet to water-wet thus improving the oil mobility. It was also observed that the oil recovery factor increases significantly with the increase of temperature as this reduces the oil viscosity and in turn, the mobility ratio, given the same formation properties. The highest recovery factor (60-62%) was achieved at the maximum injected concentration although this may not be practical in field application. Based on this, DES performance can be successfully applied as a substitute for current surfactant flooding methods in light of its low-cost, ‘greener’ nature as well as performance as a chemical EOR agent.