Ariffin Samsuri

University of Technology Malaysia

Professor Dr. Ariffin Samsuri is a Senior Professor of the Petroleum Engineering, Universiti Teknologi Malaysia (UTM) with vast experience in managing R&D projects, faculty and department. He had more than 36 years teaching and supervising experiences in petroleum engineering including academic program development, staffing, faculty, and facilities establishment. He also had been appointed as a visiting professor at Universiti Teknologi Petronas, peer reviewers, external examiners, expert and academic advisory panels. He has published 142 technical papers for conferences/seminars and journals, authored 6 books, 3 book chapters, translated 4 books, edited 11 books and 5 research monographs. He also involved in more than 30 research projects in production optimization, rock mechanics, wellbore stability, well stimulation, cement and cementing, drilling and drilling fluid, biofuel and nanotechnology application in oil & gas.

2books edited

3chapters authored

Latest work with IntechOpen by Ariffin Samsuri

Concerned with production decline, shortages of new oil reserves, and increasing world energy demand, the oil sector continues to search for economic and efficient techniques to enhance their oil recovery from the existing oil field using several enhanced oil recovery techniques (EOR)methods. Despite its highefficiency, widely acclaimed potentials, and limitations, the Low Salinity Water Flooding (LSWF), hybrid, and nanotechnology applications have gained vast interest with promising future to increase ultimate oil recovery, tackle operational challenges, reduce environmental damage, and allow the highest feasible recoveries with lower production costs. This synergistic combination has opened new routes for novel materials with fascinating properties. This book aims to provide an overview of EOR technology such as LSWF, hybrid, and nanotechnology applications in EOR processes.

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Enhanced Oil Recovery Processes New Technologies Edited by Ariffin Samsuri

By Mansoor Zoveidavianpoor, Ariffin Samsuri, Seyed Reza Shadizadeh and Samir Purtjazyeri

Part of the book: Greenhouse Gases

Overview of Environmental Management by Drill Cutting Re-Injection Through Hydraulic Fracturing in Upstream Oil and Gas Industry

By Mansoor Zoveidavianpoor, Ariffin Samsuri and Seyed Reza Shadizadeh

Part of the book: Sustainable Development

Rate of Penetration Prediction Utilizing Hydromechanical Specific Energy

By Omogbolahan Ahmed, Ahmed Adeniran and Ariffin Samsuri

The prediction and the optimization of the rate of penetration (ROP), an important measure of drilling performance, have increasingly generated great interest. Several empirical techniques have been explored in the literature for the prediction and the optimization of ROP. In this study, four commonly used artificial intelligence (AI) algorithms are explored for the prediction of ROP based on the hydromechanical specific energy (HMSE) ROP model parameters. The AIs explored are the artificial neural network (ANN), extreme learning machine (ELM), support vector regression (SVR), and least-square support vector regression (LS-SVR). All the algorithms provided results with accuracy within acceptable range. The utilization of HMSE in selecting drilling variables for the prediction models provided an improved and consistent methodology of predicting ROP with drilling efficiency optimization objectives. This is valuable from an operational point of view, because it provides a reference point for measuring drilling efficiency and performance of the drilling process in terms of energy input and corresponding output in terms of ROP. The real-time drilling data utilized are must-haves, easily acquired, accessible, and controllable during drilling operations.

Part of the book: Drilling

Ariffin Samsuri

Latest work with IntechOpen by Ariffin Samsuri

Enhanced Oil Recovery Processes

Books edited

Drilling

Chapters authored

Related collaborators

Maryam Takht Ravanchi

Regina Moreira

Tirzhá Dantas

Moses Adebajo

Mansoor Zoveidavianpoor

Ali Kargari

Alírio Rodrigues

Ray Frost

Seyed Reza Shadizadeh

Samir Purjazayeri