Suhaib Umer Ilyas
Verified @gmail.com
Assistant Professor, Chemical Engineering Department
University of Gujrat
Scopus Publications
Scopus Publications
Nik Eirdhina Binti Nik Salimi, Suhaib Umer Ilyas, Syed Ali Ammar Taqvi, Nawal Noshad, Rashid Shamsuddin, Serene Sow Mun Lock, and Aymn Abdulrahman
Elsevier BV
Mehtab Ali Darban, Serene Sow Mun Lock, Suhaib Umer Ilyas, Dun-Yen Kang, Mohd Hafiz Dzarfan Othman, Chung Loong Yiin, Sharjeel Waqas, and Zunara Bashir
Royal Society of Chemistry (RSC)
A computational molecular simulation approach to design hybrid membrane having [P8883][Tf2N] ionic liquid decorated silica as filler and 6FDA-ODA as the polymer for enhanced carbon dioxide separation from methane based on solution-diffusion mechanism.
Zunara Bashir, Serene Sow Mun Lock, Noor e Hira, Suhaib Umer Ilyas, Lam Ghai Lim, Irene Sow Mei Lock, Chung Loong Yiin, and Mehtab Ali Darban
Royal Society of Chemistry (RSC)
This review thoroughly investigates the wide-ranging applications of cellulose-based materials, with a particular focus on their utility in gas separation processes.
Abulhassan Ali, Haris Naseer, Suhaib Umer Ilyas, Patrick E. Phelan, Rizwan Nasir, Mustafa Alsaady, and Yuying Yan
Springer Science and Business Media LLC
Shwetank Krishna, Sayed Ameenuddin Irfan, Sahar Keshavarz, Gerhard Thonhauser, and Suhaib Umer Ilyas
Springer Science and Business Media LLC
AbstractPredicting pore pressure in the formation is crucial for assessing reservoir geomechanical characteristics, designing drilling schemes/mud programs, and strategies to enhance oil recovery. Accurate predictions are vital for safe and cost-effective exploration and development. Recent research has seen the emergence of intelligent models utilizing machine learning (ML) and deep learning (DL) algorithms, offering promising outcomes. However, there remains a need to identify the most accurate and dependable model among these. This study aims to address this gap by comparing the performance of various ML and DL models, as reported in existing literature, to determine the optimal approach for pore pressure prediction. The sorted machine learning (ML) and deep learning (DL) regression algorithms used for the comparative analysis are decision tree (DT), extreme gradient boosting (XGBoost), random forest (RF), recurrent neural network (RNN), and convolutional neural network (CNN). A total dataset of 22,539 is gathered from five wells (15/9-F-1 A, 15/9-F-1 B, 15/9-F-11 A, 15/9-F-11 T2, and 15/9-F-14) drilled at North-sea Volve oil field, Norway. The first four wells are used to train and test the ML and DL algorithm, and the remaining well (15/9-F-14) is used to evaluate the best-performing algorithm’s universality in predicting pore pressure at the field of study. Seven different petrophysical parameters are used as input parameters to develop the predictive models. Statistical performance metrics are carried out to analyze the applied ML and DL performance. Based on performance indicators, the RF algorithm showed superior results compared to other predictive models with R2 and RMSE values of 0.97 and 2.70 MPa, respectively. Furthermore, the best-performing predictive model with low prediction error RMSE value is applied to the other well dataset from the field of study to access the universality of the RF algorithm to predict pore pressure in the field of study. The results of the universality analysis show a satisfactory prediction accuracy with R2 and RMSE values of 0.905 and 6.48 MPa, respectively.
Abulhassan Ali, Nawal Noshad, Abhishek Kumar, Suhaib Umer Ilyas, Patrick E. Phelan, Mustafa Alsaady, Rizwan Nasir, and Yuying Yan
MDPI AG
The use of nanofluids in heat transfer applications has significantly increased in recent times due to their enhanced thermal properties. It is therefore important to investigate the flow behavior and, thus, the rheology of different nanosuspensions to improve heat transfer performance. In this study, the viscosity of a BN-diamond/thermal oil hybrid nanofluid is predicted using four machine learning (ML) algorithms, i.e., random forest (RF), gradient boosting regression (GBR), Gaussian regression (GR) and artificial neural network (ANN), as a function of temperature (25–65 °C), particle concentration (0.2–0.6 wt.%), and shear rate (1–2000 s−1). Six different error matrices were employed to evaluate the performance of these models by providing a comparative analysis. The data were randomly divided into training and testing data. The algorithms were optimized for better prediction of 700 experimental data points. While all ML algorithms produced R2 values greater than 0.99, the most accurate predictions, with minimum error, were obtained by GBR. This study indicates that ML algorithms are highly accurate and reliable for the rheological predictions of nanofluids.
Mustafa Alsaady, Suhaib Umer Ilyas, Abulhassan Ali, Khuram Maqsood, Yuying Yan, and Pau Loke Show
Springer Science and Business Media LLC
Suhaib Umer Ilyas, Rashid Shamsuddin, Tan Kai Xiang, Patrice Estellé, and Rajashekhar Pendyala
Elsevier BV
Yee Cai Ning, Syahrir Ridha, Suhaib Umer Ilyas, Shwetank Krishna, Iskandar Dzulkarnain, and Muslim Abdurrahman
Springer Science and Business Media LLC
AbstractA detailed understanding of the drilling fluid rheology and filtration properties is essential to assuring reduced fluid loss during the transport process. As per literature review, silica nanoparticle is an exceptional additive to enhance drilling fluid rheology and filtration properties enhancement. However, a correlation based on nano-SiO2-water-based drilling fluid that can quantify the rheology and filtration properties of nanofluids is not available. Thus, two data-driven machine learning approaches are proposed for prediction, i.e. artificial-neural-network and least-square-support-vector-machine (LSSVM). Parameters involved for the prediction of shear stress are SiO2 concentration, temperature, and shear rate, whereas SiO2 nanoparticle concentration, temperature, and time are the inputs to simulate filtration volume. A feed-forward multilayer perceptron is constructed and optimised using the Levenberg–Marquardt learning algorithm. The parameters for the LSSVM are optimised using Couple Simulated Annealing. The performance of each model is evaluated based on several statistical parameters. The predicted results achieved R2 (coefficient of determination) value higher than 0.99 and MAE (mean absolute error) and MAPE (mean absolute percentage error) value below 7% for both the models. The developed models are further validated with experimental data that reveals an excellent agreement between predicted and experimental data.
Aftab Hussain Arain, Syahrir Ridha, Raja Rajeswary Suppiah, Sonny Irawan, and Suhaib Umer Ilyas
Elsevier BV
Abhishek Kumar, Syahrir Ridha, Suhaib Umer Ilyas, Iskandar Dzulkarnain, and D. N. Astra Agus Pramana
Springer Science and Business Media LLC
Aftab Hussain Arain, Syahrir Ridha, Suhaib Umer Ilyas, Mysara Eissa Mohyaldinn, and Raja Rajeswary Suppiah
Springer Science and Business Media LLC
AbstractThe oil-based mud is preferred to drill highly technical and challenging formations due to its superior performance. However, the inadequate chemical and thermal stability of conventional additives have greatly influenced the performance of oil-based mud at high-temperature conditions. Therefore, it is critical to design an oil-based mud with additives that withstand and improve its performance at high-temperature conditions. The nanoparticles have emerged as an alternative to the conventional additives that can significantly enhance the rheological and filtration characteristics of oil-based mud at high-temperature conditions. In this research study, a novel formulation of OBM enhanced with GNP is formulated, and its performance at high-temperature conditions is investigated. An extensive experimental study has been performed to study the effect of graphene nanoplatelets on the rheological and filtration properties along with flow behaviour, viscoelastic properties, electrical stability and barite sagging of oil-based mud at high temperatures. The graphene nanoplatelets are characterised to ascertain their purity and morphology. The result shows that the graphene nanoplatelets exhibited efficient performance and improved the rheological and filtration properties of oil-based mud. The plastic viscosity and yield point are improved by 11% and 42%, with a concentration of 0.3 ppb. Similarly, the gel strength and barite sagging tendency are enhanced by 14% and 2%, respectively. The filtration loss is also significantly decreased by up to 62% and 46%, with 0.5 ppb concentration at 100 and 120 °C. The addition of GNP results in the formation of a thin mud cake compared to the base mud sample. The rheological modelling recommends the shear-thinning behaviour of oil-based mud (n < 1), which is correlated with the Herschel–Bulkley model. An Artificial Neural Network model is developed to predict the viscosity of OBM based on the four input parameters (concentration of nanoparticles, temperature, shear rate and shear stress). The results demonstrate that graphene nanoplatelets have a favourable impact on the performance of oil-based mud. The addition of graphene nanoplatelets, even at small concatenation, has significantly improved the properties of oil-based mud at high-temperature. Graphical abstract
Kai Xiang Tan, Suhaib Umer Ilyas, Rajashekhar Pendyala, and Muhammad Rashid Shamsuddin
AIP Publishing
S. Ridha, I. Dzulkarnain, M. Abdurrahman, S. U. Ilyas, and M. Bataee
Springer Science and Business Media LLC
Abhishek Kumar, Syahrir Ridha, Suhaib Umer Ilyas, Iskandar Dzulkarnain, and Agus Pratama
Springer Science and Business Media LLC
Mohammad Galang Merdeka, Syahrir Ridha, Berihun Mamo Negash, and Suhaib Umer Ilyas
MDPI AG
Steam huff and puff injection is one of the thermal EOR methods in which steam is injected in a cyclical manner alternating with oil production. The cost and time inefficiency problem of reservoir simulation persists in the design of a steam huff and puff injection scheme. Building predictive proxy models is a suitable solution to deal with this issue. In this study, predictive models of the steam huff and puff injection method were developed using two machine learning algorithms, comprising conventional polynomial regression and an artificial neural network algorithm. Based on a one-well cylindrical synthetic reservoir model, 6043 experiment cases with 28 input parameter values were generated and simulated. Outputs from the results such as cumulative oil production, maximum oil production rate and oil rate at cycle end were extracted from each simulation case to build the predictive model. Reservoir properties that could change after an injection cycle were also modeled. The developed models were evaluated based on the fitting performance from the R-square value, the mean absolute error (MAE) value and the root mean square error (RMSE) value. Then, Sobol analysis was conducted to determine the significance of each parameter in the model. The results show that neural network models have better performance compared to the polynomial regression models. Neural network models have an average R-square value of over 0.9 and lower MAE and RMSE values than the polynomial regression model. The result of applying the Sobol analysis also indicates that initial reservoir water saturation and oil viscosity are the most important parameters for predicting reservoir production performance.
Bilal kazmi, Syed Ali Ammar Taqvi, Muhammad Naqvi, Suhaib Umer Ilyas, Ali Moshin, Farah Inamullah, and Salman R. Naqvi
Springer Science and Business Media LLC
Mohammad Galang Merdeka, Syahrir Ridha, Berihun Mamo Negash, and Suhaib Umer Ilyas
Springer Nature Singapore
Khuram Maqsood, Abulhassan Ali, Suhaib Umer Ilyas, Sahil Garg, Mohd Danish, Aymn Abdulrahman, Saeed Rubaiee, Mustafa Alsaady, Abdulkader S. Hanbazazah, Abdullah Bin Mahfouz,et al.
Elsevier BV
Humaira Gul Zaman, Lavania Baloo, Rajashekhar Pendyala, Pradeep Kumar Singa, Suhaib Umer Ilyas, and Shamsul Rahman Mohamed Kutty
MDPI AG
A large volume of produced water (PW) has been produced as a result of extensive industrialization and rising energy demands. PW comprises organic and inorganic pollutants, such as oil, heavy metals, aliphatic hydrocarbons, and radioactive materials. The increase in PW volume globally may result in irreversible environmental damage due to the pollutants’ complex nature. Several conventional treatment methods, including physical, chemical, and biological methods, are available for produced water treatment that can reduce the environmental damages. Studies have shown that adsorption is a useful technique for PW treatment and may be more effective than conventional techniques. However, the application of adsorption when treating PW is not well recorded. In the current review, the removal efficiencies of adsorbents in PW treatment are critically analyzed. An overview is provided on the merits and demerits of the adsorption techniques, focusing on overall water composition, regulatory discharge limits, and the hazardous effects of the pollutants. Moreover, this review highlights a potential alternative to conventional technologies, namely, porous adsorbent materials known as metal–organic frameworks (MOFs), demonstrating their significance and efficiency in removing contaminants. This study suggests ways to overcome the existing limitations of conventional adsorbents, which include low surface area and issues with reuse and regeneration. Moreover, it is concluded that there is a need to develop highly porous, efficient, eco-friendly, cost-effective, mechanically stable, and sustainable MOF hybrids for produced water treatment.
Abhishek Kumar, Syahrir Ridha, Marneni Narahari, and Suhaib Umer Ilyas
Elsevier BV
Shwetank Krishna, Syahrir Ridha, Scott Campbell, Suhaib Umer Ilyas, Iskandar Dzulkarnain, and Muslim Abdurrahman
Elsevier BV
Rajashekhar Pendyala, Suhaib Umer Ilyas, and Yean Sang Wong
EDP Sciences
The heat transfer process takes place in numerous applications through the natural convection of fluids. Investigations of the natural convection heat transfer in enclosures have gained vital importance in the last decade for the improvement in thermal performance and design of the heating/cooling systems. Aspect ratios (AR=height/length) of the enclosures are one of the crucial factors during the natural convection heat transfer process. The investigated fluids consisting of air, water, engine oil, mercury, and glycerine have numerous engineering applications. Heat transfer and fluid flow characteristics are studied in 3-dimensional rectangular enclosures with varying aspect ratios (0.125 to 150) using computational fluid dynamics (CFD) simulations. Studies are carried out using the five different fluids having Prandtl number range 0.01 to 4500 in rectangular enclosures with the hot and cold surface with varying temperature difference 20K to 100K. The Nusselt number and heat transfer coefficients are estimated at all conditions to understand the dependency of ARs on the heat transfer performance of selected fluids. Temperature and velocity profiles are compared to study the flow pattern of different fluids during natural convection. The Nusselt number correlations are developed in terms of aspect ratio and Rayleigh number to signify the natural convection heat transfer performance.
Bilal kazmi, Syed Ali Ammar Taqvi, Muhammad Naqvi, Suhaib Umer Ilyas, Ali Moshin, Farah Inamullah, and Salman R. Naqvi
Springer Science and Business Media LLC
AbstractHydrocarbon processing from extraction to the final product is an important aspect that needs an optimised technology for consumption-led market growth. This study investigated real data from the oil processing facility and analysed the simulation model for the entire crude oil processing unit based on the process system engineering aspect using Aspen HYSYS. The study mainly emphasises the process optimisation in processing the hydrocarbon for the maximum yield of the product with less energy consumption. The investigation also includes a thorough economic analysis of the processing facility. The datasets for oil properties are obtained from a modern petroleum refinery. The investigation comprises of varying transient conditions, such as well shutdowns using three oil reservoirs (low, intermediate, and heavy oil). The impact of various conditions, including process heating, well shutdown, oil combinations, presence of water on the production, is analysed. The results indicate that the factors involving crude oil processing are significantly affected by the process conditions, such as pressure, volume, and temperature. The vapour recovery unit is integrated with the oil processing model to recover the separator's gas. The optimisation analysis is performed to maximise the liquid recovery with Reid vapour pressure of 7 and minimum water content in oil around 0.5%. Economic analysis provided an overall capital cost of $ 9.7 × 106 and an operating cost of $2.1 × 106 for the process configuration. The model results further investigate the constraints that maximise the overall energy consumption of the process and reduce the operational cost.
Suhaib Umer Ilyas, Syahrir Ridha, Suneela Sardar, Patrice Estellé, Abhishek Kumar, and Rajashekhar Pendyala
Elsevier BV