Rizwan Safdar

In the oil and gas industry, it is very important to recover the maximum amount of oil extracted from the reservoirs. Many methods are employed in the extraction process in Enhanced Oil Recovery (EOR) to obtain maximum oil recovery, but unfortunately, only up to 70% can be recovered from the reservoirs. One of the most important and new techniques in EOR is the use of nanotechnology during the recovery process. Without mathematical analysis, it is difficult to perform experiments to recover oil, as the experimental setup is very costly. Hence, it is essential to study the physical properties of the reservoir and its flow behaviour to get maximum output. In this paper, we studied the impact of the physical behaviour of the oil reservoir and its characteristics in terms of oil flow by considering a hypothetical cubical oil reservoir. Our aim is to predict the maximum oil recovery under the influence of temperature (i.e., 20oC, 90oC, and 130oC) by using nanofluid flow and to investigate the impact of geometry on oil recovery. The ANSYS Fluent-based finite volume method is used for the simulation process. The results obtained from the mathematical analysis show that the impact of geometrical behaviour is very significant in improving oil recovery. It is also found that when the temperature of the reservoir increases, the oil recovery rate suddenly decreases, and maximum oil recovery is obtained at 90 degrees Celsius in all cases of the nanoparticles at different flow rates. In the future, we will extend this work to more complex geometries to predict maximum oil recovery under the influence of high temperature and pressure.

The complexity and fusion dynamism of the modern industrial and chemical sectors have been increasing with the rapid progress of IR 4.0–5.0. The transformative characteristics of Industry 4.0–5.0 have not been fully explored in terms of the fundamental importance of explainability. Traditional monitoring techniques for automatic anomaly detection, identifying the potential variables, and root cause analysis for fault information are not intelligent enough to tackle the intricate problems of real-time practices in the industrial and chemical sectors. This study presents a novel dynamic machine learning based explainable fusion approach to address the issues of process monitoring in industrial and chemical process systems. The methodology aims to detect faults, identify their key causes and feature variables, and analyze the root path of fault propagation with the time and magnitude of one cause variable to another impact. This study proposed using a time domain multivariate granger-entropy-aided dynamic independent component analysis (DICA)—distributed canonical correlation analysis approach, incorporating the dynamics time wrapping supported time delay-signed directed graph. The proposed methodology utilized the application to industrial and chemical processes and verified using the continuous stirred tank reactor and Tennessee Eastman process as practical application benchmarks. The framework’s validations and efficiency are evaluated using established techniques such as classic computed ICA and DICA as standard model scenarios. The outcomes and results showed that the newly developed strategy is preferable to previous approaches regarding explainability and robust detection and identification of the actual root causes with high FDRs and low FARs.

The global demand for oil and petroleum products is experiencing a significant increase, while the number of newly discovered oil reservoirs is relatively low. This emphasises the critical need for innovative techniques to enhance the rate of oil recovery in order to meet global demand. The implementation of nanotechnology in enhanced oil recovery (EOR) yields numerous advantages, including cost savings in production and improved oil recovery. Additionally, it is critical to investigate the geometrical behaviour of the reservoir, as this information is vital for determining the maximum oil recovery rate. This review highlights the significance of nanotechnology through the utilisation of various geometries to determine the rate of hydrocarbon recovery. Furthermore, this paper also addresses the obstacles associated with achieving maximum oil recovery and provides suggestions for future research in this field. The review concludes with its key finding, which indicates that an examination of reservoir geometry utilising nanofluid in the presence of electromagnetic waves can significantly improve the rate of hydrocarbon recovery.