Research advancement in polymer flooding for Enhanced Oil Recovery (EOR) has been growing over the last decade. This growth can be tied to increased funding towards the development of superior polymers such as hydrophobically associating polymers when oil prices were high and increasing concern that "easy oil" has been exploited with the focus now on "difficult to extract" oil. The use of hydrophobically associating polymers for EOR was discussed along with its limitations. In this context, the improved rheological properties of associating polymers cannot only be linked to the molecular structures arising from different synthesis methods. Equally, external parameters similar to conditions of oil reservoirs affect the rheological properties of these polymers. As such, this review placed critical emphasis on the molecular architecture of the polymer and the synthesis route and this was linked to the observed rheological properties. In addition, the influence of some key oilfield parameters such as temperature, salinity, pH, and reservoir heterogeneity on the rheological behaviour of hydrophobically associating polymers were reviewed. In this respect, the various findings garnered in understanding the correlation between polymer rheological properties and oilfield parameters were critically reviewed. For associating polymers, an understanding of the molecular architecture (and hence the synthesis method) is crucial for its successful design. However, this must be theoretically linked to the preferred EOR application requirements (based on oilfield parameters).

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Production from gas condensate reservoir poses the major challenge of condensate banking or blockage. This occurs near the wellbore, around which a decline in pressure is initially observed. A good sign of condensate banking is a rise in the gas–oil ratio (GOR) during production and/or a decline in the condensate yield of the well, which leads to considerable reductions in well deliverability and well rate for gas condensate reservoirs. Therefore, determining the well deliverability of a gas condensate reservoir and methods to optimize productivity is paramount in the industry.

Prediction of the timing and location of condensate build-up around the wellbore in gas condensate reservoirs is essential for the selection of appropriate methods for condensate recovery from these challenging reservoirs. The present work focuses on the use of a novel phase change tracking approach in monitoring the formation of condensate blockage in a gas condensate reservoir. The procedure entails the simulation of tight, low and high permeability reservoirs using global and local grid analysis in determining the size and timing of three common regions (Region 1, near wellbore; Region 2, condensate build-up; and Region 3, single-phase gas) associated with single and two-phase gas and immobile and mobile gas condensate. The results show that permeability has a significant influence on the occurrence of the three regions around the well, which in turn affects the productivity of the gas condensate reservoir studied. Predictions of the timing and location of condensate in reservoirs with different permeability levels of 1 mD to 100 mD indicate that local damage enhances condensate formation by 60% and shortens the duration of the immobile phase by 45%. Meanwhile, the global change in permeability increases condensate formation by 80% and reduces the presence of the immobile phase by 60%. Finally, this predictive approach can help in mitigating condensate blockage around the wellbore during production.

Environmentally sustainable methods of extracting hydrocarbons from the reservoir are increasingly becoming an important area of research. Several methods are being applied to mitigate condensate banking effect which occurs in gas condensate reservoirs; some of which have significant impact on the environment (subsurface and surface). Electrokinetic enhanced oil recovery (EEOR) increases oil displacement efficiency in conventional oil reservoirs while retaining beneficial properties to the environment. To successfully apply this technology on gas condensate reservoirs, the behavior of condensate droplets immersed in brine under the influence of electric current need to be understood. A laboratory experiment was designed to capture the effect of electrical current on interfacial tension and droplet movement. Pendant drop tensiometry was used to obtain the interfacial tension, while force analysis was used to analyze the effect of the electrical current on droplet trajectory. Salinity (0–23 ppt) and electric voltage (0–46.5 V) were the main variables during the entire experiment. Results from the experiment reveal an increase in IFT as the voltage is increased, while the droplet trajectory was significantly altered with an increase in voltage. This study concludes that the interfacial tension increases progressively with an increase in DC current, until its effect counteracts the benefit obtained from the preferential movement of condensate droplet.

Liquid dropout occurs in gas condensate reservoirs below the dew point pressure around near wellbore region as a result of depletion from production of such reservoirs. Forecasting production as well as optimizing future recoveries of gas condensate reservoirs are highly desirable. This is not possible to achieve without accurate determination of liquid dropout viscosity () below the dew point. The focus of research in past decades has been on the development of accurate viscosity prediction models below the dew point pressure to ensure accurate condensate production forecast. Gas condensate production forecast and optimisation around this region and condition are complicated due to unique gas condensate behaviour that violates thermodynamic laws. Current methods are based on correlation estimation, however the accuracy of these correlations are less than satisfactory, and root cause is due to the miscapturing of complex behaviour of gas condensate reservoir near the wellbore region. These motivated the consideration of modern numerical approaches such as the Least Square Support Vector Machine (LSSVM) and Artificial Neural Network (ANN) used in this paper. These methods are considered as more data behaviour oriented, with the capability of capturing the fluid complexity of gas condensate in such conditions. In this study viscosity of condensate phase near the wellbore region was modelled using machine learning techniques including ANN and LSSVM. For this purpose, over 300 viscosity data sets were collected from published literature and experimental studies worldwide. This databank includes API gravity, reservoir temperature, solution gas to oil ratio (Rs), specific gas gravity, fluid compositions and reservoir pressure. Six well known previously published viscosity correlations refined using least-square approach to match the experimental data. Qualitative and quantitative error analysis of developed LSSVM and ANN showed their performance superiority over refined literature correlations. The new proposed models can be embedded as an extra feature of commercial reservoir simulation packages for optimization and future recoveries of gas condensate reservoirs.

Hydrocarbons continue to play an important role in providing affordable energy to meet rising energy demand. Amidst growing concerns on the environmental impact of oil and gas production processes, many researchers are increasingly exploring environmentally sustainable methods of extracting hydrocarbons from the reservoir. The introduction of direct current into the pore space activates mechanisms that enhance fluid flow, reduces produced water, decreases associated hydrogen sulfide production, and leaves no material footprint on the environment. Previous laboratory studies and field applications have reported varying degrees of success of the EK-EOR mechanism. However, the mechanism and effectiveness of this technique remain unclear. This systematic literature review provides an opportunity to critically evaluate laboratory results, establish a basis for the effectiveness of the EK-EOR mechanism and identify possible future research directions. In this study, 52 articles were identified and reviewed in a selection process that adhered to the PRISMA protocol. Data extracted from these articles were fed into the EK-EOR model, and Monte Carlo simulation (10,000 iterations) was used to determine the success rate of the EK-EOR process. Insights obtained from the simulation indicate that EK-EOR alone is not effective (with a success rate of 45%). Insights from published laboratory experiments indicate that interstitial clay affects the electro-osmotic permeability of reservoir rocks which determines the effectiveness of the EK-EOR mechanism. Salt deposition on the cathode and generation of gases (oxygen and chlorine at the anode) are significant limitations of the EK-EOR. The review concludes by identifying future areas of application of EK-EOR.