Motaz El-Saban - Academia.edu (original) (raw)
Papers by Motaz El-Saban
arXiv (Cornell University), Mar 13, 2018
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ITISE 2022
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IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
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Communications of the ACM, 2021
54 COMMUNICATIONS OF THE ACM | APRIL 2021 | VOL. 64 | NO. 4 P H O T O B Y D U L A A Z /S H U T T ... more 54 COMMUNICATIONS OF THE ACM | APRIL 2021 | VOL. 64 | NO. 4 P H O T O B Y D U L A A Z /S H U T T E R S T O C K .C O M demics and practitioners to tackle challenges within O&G using data science technologies. The Arab region is well suited for building data science teams serving a global market specially for the O&G industry: ˲ There is recent interest from governments in the region to offer data science-related programs and degrees. ˲ The region can supply a talented, well-trained workforce at a relatively lower cost. ˲ The O&G industry is key in the region; hence data is readily available in lenge is twofold: create opportunities for juniors to grow technically by working on challenging problems of global nature, and complement juniors by experienced returning expats to the region. We next detail some of the technical challenges that Raisa Energy faces and the novel approaches it uses in solving them that resulted in several academic publications and U.S. patents. Well production forecasting is a time series forecasting problem of an O&G well production. Well features include geological large quantity. Such massive data is the key behind any modern artificial intelligence system. For example, Raisa Energy, a U.S.-based O&G inves tment company, has its entire software and data science teams in Egypt building capacity in the important energy domain offering a unique edge for the region. Though junior talent is generally available, there remains a challenge in easily finding senior talent as professionals typically move early into managerial roles for career growth. Our answer to this chalO IL AND GAS (O&G) sources will still supply around 50% of the global energy demand by 2040. In this article, we make the case for why the Arab region is well positioned for building world-class data science teams to fill the supply shortage of data professionals, especially in the O&G field critical to region’s economy. This article presents challenges facing O&G industry players, such as governments, regulatory bodies, operators, and investors, and shows how Raisa Energy (with its Egyptbased data science team) is efficiently and effectively solving these challenges. Such challenges aim at assessing the economic viability of an O&G asset that depends on several factors (as shown in the accompanying figure) such as estimating well production, O&G prices, and risks associated with inputs uncertainty. It is worth emphasizing that the challenges presented here are global in nature and yet are tackled with a team fully formed from the region working at a worldclass research and development level. We hope this article will motivate aca-
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Day 1 Mon, September 24, 2018, 2018
This paper proposes a set of data driven models that use state of the art machine learning techni... more This paper proposes a set of data driven models that use state of the art machine learning techniques and algorithms to predict monthly production of unconventional horizontal wells. The developed models are intended to forecast both producing locations (PLs) and non-producing well locations (NPLs). Furthermore, results of extensive experiments are presented that were conducted using different methodologies and features combinations. Results are measured against conventional Arps's decline curve analysis showing significant boost in prediction accuracy for both NPLs and PLs. The most accurate model outperforms Arps's-based estimates by almost 23% for NPLs and 36% for PLs. Results also show that using data from multiple basins in training models for another basin yields gains in accuracy, especially for basins with relatively small data.
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Mobile Cloud Visual Media Computing, 2015
Constructing a panoramic video out of multiple incoming live mobile video streams is a challengin... more Constructing a panoramic video out of multiple incoming live mobile video streams is a challenging problem with many applications in consumer, education, and security domains. This problem involves multiple users live streaming the same scene from different points of view, using their mobile phones, with the objective of constructing a panoramic video of the scene. The main challenge in this problem is the lack of coordination between the streaming users, resulting in too much, too little, or no overlap between incoming streams. To add to the challenge, the streaming users are generally free to move, which means that the amounts of overlap between the different streams are dynamically changing. In this chapter, we propose a method for automatically coordinating between streaming users, such that the quality of the resulting panoramic video is enhanced. The method works by analyzing incoming video streams, and automatically providing active feedback to the streaming users. We investigate different methods for generating and presenting the active feedback to the streaming users resulting in an improved panoramic video output compared to the case where no feedback is utilized.
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2011 IEEE International Conference on Multimedia and Expo, 2011
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2012 IEEE International Conference on Multimedia and Expo Workshops, 2012
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2011 18th IEEE International Conference on Image Processing, 2011
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2010 IEEE International Conference on Image Processing, 2010
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2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)
Time-series forecasting, the process of predicting values in the future given the present and pre... more Time-series forecasting, the process of predicting values in the future given the present and previous history, is a challenging problem to tackle. Deterministic forecasting methods were thoroughly investigated but had limitations regarding reliability. Recent research efforts are exploring the advantages that come with probabilistic forecasting. The need to have large datasets for time-series to build more generalized models and thus being less dependent on data augmentation is also driving efforts to collect comprehensive data. This paper proposes a machine learning model to estimate prediction intervals on a large oil production dataset. Prediction intervals are estimated at different percentiles. Prediction Interval Coverage Probability (PICP) and Prediction Interval Normalized Average Width (PINAW) metrics are used for performance evaluation. The best results are obtained by removing trend and using differencing.
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2015 IEEE International Conference on Image Processing (ICIP), 2015
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Mobile Cloud Visual Media Computing, 2015
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2015 IEEE Winter Conference on Applications of Computer Vision, 2015
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Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), 2003
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2011 18th IEEE International Conference on Image Processing, 2011
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arXiv (Cornell University), Mar 13, 2018
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ITISE 2022
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IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
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Communications of the ACM, 2021
54 COMMUNICATIONS OF THE ACM | APRIL 2021 | VOL. 64 | NO. 4 P H O T O B Y D U L A A Z /S H U T T ... more 54 COMMUNICATIONS OF THE ACM | APRIL 2021 | VOL. 64 | NO. 4 P H O T O B Y D U L A A Z /S H U T T E R S T O C K .C O M demics and practitioners to tackle challenges within O&G using data science technologies. The Arab region is well suited for building data science teams serving a global market specially for the O&G industry: ˲ There is recent interest from governments in the region to offer data science-related programs and degrees. ˲ The region can supply a talented, well-trained workforce at a relatively lower cost. ˲ The O&G industry is key in the region; hence data is readily available in lenge is twofold: create opportunities for juniors to grow technically by working on challenging problems of global nature, and complement juniors by experienced returning expats to the region. We next detail some of the technical challenges that Raisa Energy faces and the novel approaches it uses in solving them that resulted in several academic publications and U.S. patents. Well production forecasting is a time series forecasting problem of an O&G well production. Well features include geological large quantity. Such massive data is the key behind any modern artificial intelligence system. For example, Raisa Energy, a U.S.-based O&G inves tment company, has its entire software and data science teams in Egypt building capacity in the important energy domain offering a unique edge for the region. Though junior talent is generally available, there remains a challenge in easily finding senior talent as professionals typically move early into managerial roles for career growth. Our answer to this chalO IL AND GAS (O&G) sources will still supply around 50% of the global energy demand by 2040. In this article, we make the case for why the Arab region is well positioned for building world-class data science teams to fill the supply shortage of data professionals, especially in the O&G field critical to region’s economy. This article presents challenges facing O&G industry players, such as governments, regulatory bodies, operators, and investors, and shows how Raisa Energy (with its Egyptbased data science team) is efficiently and effectively solving these challenges. Such challenges aim at assessing the economic viability of an O&G asset that depends on several factors (as shown in the accompanying figure) such as estimating well production, O&G prices, and risks associated with inputs uncertainty. It is worth emphasizing that the challenges presented here are global in nature and yet are tackled with a team fully formed from the region working at a worldclass research and development level. We hope this article will motivate aca-
Bookmarks Related papers MentionsView impact
Day 1 Mon, September 24, 2018, 2018
This paper proposes a set of data driven models that use state of the art machine learning techni... more This paper proposes a set of data driven models that use state of the art machine learning techniques and algorithms to predict monthly production of unconventional horizontal wells. The developed models are intended to forecast both producing locations (PLs) and non-producing well locations (NPLs). Furthermore, results of extensive experiments are presented that were conducted using different methodologies and features combinations. Results are measured against conventional Arps's decline curve analysis showing significant boost in prediction accuracy for both NPLs and PLs. The most accurate model outperforms Arps's-based estimates by almost 23% for NPLs and 36% for PLs. Results also show that using data from multiple basins in training models for another basin yields gains in accuracy, especially for basins with relatively small data.
Bookmarks Related papers MentionsView impact
Mobile Cloud Visual Media Computing, 2015
Constructing a panoramic video out of multiple incoming live mobile video streams is a challengin... more Constructing a panoramic video out of multiple incoming live mobile video streams is a challenging problem with many applications in consumer, education, and security domains. This problem involves multiple users live streaming the same scene from different points of view, using their mobile phones, with the objective of constructing a panoramic video of the scene. The main challenge in this problem is the lack of coordination between the streaming users, resulting in too much, too little, or no overlap between incoming streams. To add to the challenge, the streaming users are generally free to move, which means that the amounts of overlap between the different streams are dynamically changing. In this chapter, we propose a method for automatically coordinating between streaming users, such that the quality of the resulting panoramic video is enhanced. The method works by analyzing incoming video streams, and automatically providing active feedback to the streaming users. We investigate different methods for generating and presenting the active feedback to the streaming users resulting in an improved panoramic video output compared to the case where no feedback is utilized.
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
2011 IEEE International Conference on Multimedia and Expo, 2011
Bookmarks Related papers MentionsView impact
2012 IEEE International Conference on Multimedia and Expo Workshops, 2012
Bookmarks Related papers MentionsView impact
2011 18th IEEE International Conference on Image Processing, 2011
Bookmarks Related papers MentionsView impact
2010 IEEE International Conference on Image Processing, 2010
Bookmarks Related papers MentionsView impact
2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)
Time-series forecasting, the process of predicting values in the future given the present and pre... more Time-series forecasting, the process of predicting values in the future given the present and previous history, is a challenging problem to tackle. Deterministic forecasting methods were thoroughly investigated but had limitations regarding reliability. Recent research efforts are exploring the advantages that come with probabilistic forecasting. The need to have large datasets for time-series to build more generalized models and thus being less dependent on data augmentation is also driving efforts to collect comprehensive data. This paper proposes a machine learning model to estimate prediction intervals on a large oil production dataset. Prediction intervals are estimated at different percentiles. Prediction Interval Coverage Probability (PICP) and Prediction Interval Normalized Average Width (PINAW) metrics are used for performance evaluation. The best results are obtained by removing trend and using differencing.
Bookmarks Related papers MentionsView impact
2015 IEEE International Conference on Image Processing (ICIP), 2015
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Mobile Cloud Visual Media Computing, 2015
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
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Bookmarks Related papers MentionsView impact
2015 IEEE Winter Conference on Applications of Computer Vision, 2015
Bookmarks Related papers MentionsView impact
Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429), 2003
Bookmarks Related papers MentionsView impact
2011 18th IEEE International Conference on Image Processing, 2011
Bookmarks Related papers MentionsView impact