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Foam flooding has been successfully implemented in solving problems that arise during gas flooding as it increases gas viscosity and reduces gas relative permeability resulting in favorable mobility ratios and better volumetric sweep efficiency. This research study aims to evaluate the effect of different flood patterns on field performance during foam enhanced oil recovery. A three-dimensional reservoir simulation model was developed with a reservoir simulator and each flood pattern including normal and inverted four, five, seven, and nine-spots were modeled and simulated separately under consistent conditions of field gas injection rate, bottom-hole pressure of production wells, and surfactant concentration. Field Oil Efficiency (FOE), Net Present Value (NPV), Rate of Return (ROR), and Payout Time (POT) were used in making comparisons. An Original Oil in Place of 139.43 MMSTB was found at the initialization. Simulation results showed that a normal and an inverted five-spot pattern were more effective in their respective categories as they resulted respectively in higher FOE, lower FGOR, higher NPV and ROR, and shorter POT. An inverted five-spot resulted in an FOE of 4.36 %, an NPV of $ 83.48 million, and an ROR of 13.64% higher than that obtained for a normal five spot, and a payout time of 1.54 years shorter than that obtained for the normal five spots. The findings highlight the superior efficiency of the normal and inverted five-spot patterns, with the inverted five-spot pattern demonstrating significantly better performance in terms of FOE, NPV, ROR, and POT. Based on the presented results, an inverted five-spot pattern was recommended in this paper as the best flood pattern during foam-enhanced oil recovery applications. [ABSTRACT FROM AUTHOR]

The petroleum industry is becoming increasingly concerned with sand production, which is common in the Niger Delta and presents technical, operational, and financial difficulties. Literature has devoted a lot of time to the creation of sanding prediction tools and efficient management techniques. However, the majority of the published theoretical models have been supported by data from other petroleum regions than the Niger Delta or evidence from laboratories. An easy-to-use machine learning model was created that takes into account the idea of dimensionless quantities related to sanding. The parameters taken into consideration included Reynold's Number, Loading factor, Gas-Liquid Ratio and water-cut. An equation for predicting sand production rate is the output from this work. Model validation was carried out on the developed model and the deviation was less than 5% demonstrating the validity of the proposed model. The developed model, when compared to existing models, forecasts superior outcomes, particularly when the boost factor and GLR are very high. The study has a wide range of practical implications, including reservoir management, general well completion design, and field development plans and economics. [ABSTRACT FROM AUTHOR]