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Texas A&M University at Qatar hosts neural networking conference

Published Nov 12, 2012

Texas A&M University at Qatar launched the 19th International Conference on Neural Information Processing (ICONIP) Monday at the Renaissance Doha City Center Hotel. The event’s opening ceremony was hosted by HE Dr. Mohammed bin Saleh Al Sada, minister of energy and industry, and attended by guests from local industry and around the world. The University is the conference organizer of the event that seeks to advance Qatar’s culture of research and development by providing information about new analytical processes to address energy-related engineering challenges.

HE Al Sada’s opening remarks noted that Qatar’s emergence as a world leader in promoting education and scientific research works toward building the State’s knowledge-based economy and ensuring Qatar’s post-hydrocarbon prosperity. Opportunities for scientific collaboration and exchange such as ICONIP, he stressed, are a part of Qatar’s exciting future.

Platinum sponsor for the event is United Development Company (UDC), Gold sponsors are Qatar Petroleum (QP), Qatar Petrochemical Company (QAPCO) and ExxonMobil.

Dr. Mark H. Weichold, dean and CEO and honorary conference chair, said, "Texas A&M at Qatar is honored to help bring this important conference to Doha. The support of the event's sponsors illustrates the significant importance this state-of-the-art neural network technology has in solving real-world challenges in the energy and engineering industries. As Qatar moves toward becoming a knowledge-based economy, dialogues such as ICONIP become even more important to growth, as they help academics and industry practitioners come together to solve tomorrow’s problems with these new techniques."

Artificial neural networks, a biologically inspired computing methodology, have the ability to learn by imitating the learning method used in the human brain. These networks are an interconnected assembly of simple processing elements, units, or neurons, whose functionality is loosely based on the brain neuron. Neural networks are well suited to complex problems, generally have large degrees of freedom and have the ability to determine the underlying relationship between model inputs and outputs resulting in good generalization ability. Neural networks have been applied to financial markets and to engineering in disciplines such as petroleum and chemical engineering.

Uses in the energy industry include interpreting logs, diagnosing and prescribing remedies for stuck drill pipes, locating mineral deposits, configuring seismic processing runs, selecting optimal drilling mud, problem diagnoses, identifying the cause of chemical spills and recommending action, selection and design of Enhanced Oil Recovery (EOR) processes, well stimulation, testing and logging and prediction of fluid properties. Artificial neural network technology is suggested in order to determine reservoir properties from well logs and used as a nonlinear regression method to develop transformation between the selected well logs and core analysis data. The neural network model is able to predict the fracture gradient as a function of pore pressure, depth and rock density.

"Texas A&M at Qatar is very proud to be the organizer of ICONIP 2012, as this series of conferences is one of the premier international conferences in the area of neural networks," said Dr. Tingwen Huang, conference chair and associate professor of mathematics at Texas A&M at Qatar. "Scholars from more than 60 countries submitted nearly 700 papers for presentation at this event and about 400 of them were selected for publication. In addition to nearly 320 oral presentations and 100 poster presentations, this year's technical program includes about 30 keynote, plenary and invited speeches and two panels. The possibilities for applications of neural networks are endless, and I hope the discussions and discovery at this year's event will lead to dynamic collaborations and advance the theoretical development and applications of neural networks."

ICONIP is an annual conference of the Asia Pacific Neural Network Assembly.