Publications
Purpose: To deliver, transfer, and share a diversified, valued, and experienced knowledge to the domestic and global scientific and professional communities, in the oil and gas industry.
A. Patent
Al-Fattah, S.M. (2013). Patent No. 8,510,242. USA: United States Patent and Trademark Office (USPTO).
B. Books and Thesis
Al-Fattah, S.M., Barghouty, M.F., Dabbousi, B.O., et al. (2011). Carbon capture and storage: Technologies, policies, economics, and implementation strategies. Leiden, Netherlands: CRC Press.Download
Mohaghegh, S.D., Al-Fattah, S.M., and Popa, A.S. (2011). Artificial intelligence and data mining applications in the E&P industry. Richardson, TX, USA: Society of Petroleum Engineers.Download
Al-Fattah, S.M. (2011). Innovative methods for analyzing and forecasting world gas supply. Germany: Lambert Academic Publishing.Download
Al-Fattah, S.M. (2000). New approaches for analyzing and predicting global natural gas production. PhD dissertation, Texas A&M University, Texas, US.
Al-Fattah, S.M. (1994). Development of empirical equations for water-oil relative permeability. MS thesis, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia.
Reviewed Books with Testimonial
Pettit, Justin. (2017). The Final Frontier: E&P’s Low-Cost Operating Model. Hoboken, New Jersey: Wiley.
C. Online References
White, K.J., Jojarth, C., and Al-Fattah, S.M. (2013). The effect of U.S. energy self-sufficiency on its commitment to secure shipping lanes in the Strait of Hormuz. Social Science Research Network. Available at: Download
Gucwa, M., Nouri, A., Huntington, H., and Al-Fattah, S.M. (2012). Critical improvements in oil market modeling. Social Science Research Network. Available at: Download
Huntington, H., Al-Fattah, S.M., Huang, Z., Gucwa, M., and Nouri, A. (2012). Oil price drivers and movements: The challenge for future research. Social Science Research Network. Available at: Download
Huntington, H., Huang, Z., Al-Fattah, S.M., Gucwa, M., and Nouri, A. (2012). Oil markets and price movements: A survey of determinants. Social Science Research Network. Available at: Download
D. Journal Articles (Peer-Reviewed)
Alkhammash, Eman H., Kamel, Abdelmonaim F., Al-Fattah, Saud M., and Elshewey, Ahmed M. (2022). Optimized multivariate adaptive regression splines for predicting crude oil demand in Saudi Arabia, Discrete Dynamics in Nature and Society 2022(2022). Download
Al-Fattah, Saud M. (2021). Application of the artificial intelligence GANNATS model in forecasting crude oil demand for Saudi Arabia and China, Journal of Petroleum Science and Engineering 200(2021). Download
Al-Fattah, Saud M. (2020). A new artificial intelligence GANNATS model predicts gasoline demand of Saudi Arabia, Journal of Petroleum Science and Engineering 194(2020). Download
Al-Fattah, Saud M. (2020). Non-OPEC conventional oil: Production decline, supply outlook and key implications, Journal of Petroleum Science and Engineering 189(2020). Download
Al-Fattah, Saud M. (2019). Artificial intelligence approach for modeling and forecasting oil-price volatility, SPE Reservoir Evaluation & Engineering 22(3), 817-826. Download
Huntington, H., Al-Fattah, S.M., Huang, Z., Gucwa, M., and Nouri, A. (2014). Oil price drivers and movements: The challenge for future research. Alternative Investment Analyst Review Journal, 2(4), 11-28.
Al-Fattah, Saud M. (2013). National oil companies: Business models, challenges, and emerging trends. Corporate Ownership and Control Journal 11(1), 727-735.
Matar, W., Al‐Fattah, S.M., Atallah, T., and Pierru, A. (2013). An introduction to oil market volatility analysis. OPEC Energy Review, 37(3), 247-269.
Al-Fattah, S.M., Dallag, M.M., and Smith, C. (2010). Intelligent surveillance tools improve field management efficiency. Oil Review Middle East, 13(4) 74-76.
Al-Fattah, S.M., and Al-Nuaim, H.A. (2009). Artificial-intelligence technology predicts relative permeability of giant carbonate reservoirs. SPE Reservoir Evaluation and Engineering Journal, 12(1), 96-103.
Al-Fattah, S.M., Dallag, M., Abdulmohsin, R.A., Al-Harbi, W.A., and Issaka, M.B. (2008). Intelligent integrated dynamic surveillance tool improves field management practices. Saudi Aramco Journal of Technology, (Summer issue), 12-21.
Al-Fattah, S.M. (2006). Time series modeling for U.S. natural gas forecasting. e-Journal of Petroleum Management and Economics, Petroleum Journals Online, (Apr. 28)17 pages, ISSN: 1718-7559
Al-Fattah, S.M. (2004). Equations for water/oil relative permeability in Saudi Arabian sandstone reservoirs. Saudi Aramco Journal of Technology, (Summer issue), 48-58.
Al-Fattah, S.M. and Startzman, R.A. (2003). Neural network approach predicts U.S. natural gas production, SPE Production and Facilities Journal, 18(2), 84-91.
*Al-Fattah, S.M. and Startzman, R.A. (2000). Forecasting world natural gas supply. J Pet Technol 52(5), 62-72. [*Featured management paper]
Al-Fattah, S.M. and Al-Marhoun, M.A. (1995). Evaluation of empirical correlations for bubblepoint oil formation volume factor. Saudi Aramco Journal of Technology, (Fall issue).
Al-Fattah, S.M. and Al-Marhoun, M.A. (1994). Evaluation of empirical correlations for bubblepoint oil formation volume factor. J. Petroleum Science and Engineering 11(4), 341-350.
E. Conference Papers and Proceedings
Al-Fattah, S.M. (2018a). Artificial intelligence in global energy markets and economics. Invited paper for presentation at the Oil and Gas Council’s NOC’s Assembly, The Netherlands.
Al-Fattah, S.M. (2018b). Intelligent gasoline demand analytics: A case study of Saudi Arabia. Proceeding paper accepted at the IAEE/USAEE North American Conference, Washington, D.C., USA.
Al-Fattah, S.M. (2018c). Intelligent oil demand outlook analytics. Proceeding paper accepted at the IAEE Asian Conference, Wuhan, China.
Al-Fattah, S.M., Barghouty, M.F., Le Thiez, P., Le Gallo, Y., Rambourg, D, Brugeron, A., and Quinquis, H. (2011). A new carbon capture and storage initiative in Saudi Arabia: Development of an innovative GIS-based system for managing source-sink matching scenarios. Paper presented at the 10th Annual Conference on Carbon Capture and Sequestration, Pittsburgh, PA, USA.
Al-Bishara, M., Al-Fattah, S.M., Nashawi, I.S., and Malallah, A.H. (2009). Forecasting OPEC crude oil supply. Paper SPE-120350 presented at the 2009 SPE Middle East Oil and Gas Show and Conference, Manama, Bahrain.
Al-Fattah, S.M. (2007a). Artificial intelligence technology predicts relative permeability of giant carbonate reservoirs. Paper SPE-109018 presented at the 2007 SPE Offshore Europe, Aberdeen, United Kingdom.
Al-Fattah, S.M. (2007b). Artificial neural networks determine relative permeability of carbonate reservoirs. Paper SPE-105120 presented at the 2007 SPE Middle East Oil Show and Conference, Manama, Bahrain.
Al-Fattah, S.M., Dallag, M.M., Al-Abdalmohsen, R.A., Al-Harbi, W.A., and Issaka, M.B. (2006a). Intelligent integrated dynamic surveillance tool improves field-management practices. Paper presented at the 2006 SPE-Saudi Arabia Annual Technical Symposium, Dhahran, Saudi Arabia.
Al-Fattah, S.M., Dallag, M.M., Al-Abdalmohsen, R.A., Al-Harbi, W.A., and Issaka, M.B. (2006b). Intelligent integrated dynamic surveillance tool improves field-management practices. Paper SPE-99555 presented at the 2006 SPE Intelligent Energy Conference, Amsterdam, Netherlands.
Al-Fattah, S.M. (2005a). Time series modeling for U.S. natural gas forecasting. Paper IPTC-10592 presented at the 2005 International Petroleum Technology Conference, Doha, Qatar.
Al-Fattah, S.M. (2005b). Artificial neural network models predict two-phase relative permeability of carbonate reservoirs. Poster presented at the 2005 SPE/EAGE Reservoir Characterization and Simulation Symposium, Dubai, United Arab Emirates.
Al-Fattah, S.M. (2003). Empirical equations for water/oil relative permeability in Saudi sandstone reservoirs. Paper SPE-85652 presented at the 2003 SPE Annual International Conference and Exhibition, Abuja, Nigeria.
Al-Fattah, S.M. and Startzman, R.A. (2001a). Neural network approach predicts U.S. natural gas production. Paper SPE-67260 presented at the 2001 SPE Production and Operations Symposium, Oklahoma, OK, USA.
Al-Fattah, S.M. and Startzman, R.A. (2001b). Predicting natural gas production using artificial neural network. Paper SPE-68593 presented at the 2001 SPE Hydrocarbon Economics and Evaluation Symposium, Dallas, TX, USA.
Al-Fattah, S.M. and Startzman, R.A. (2000). Forecasting world natural gas supply. Paper SPE-59798 presented at the 2000 SPE/CERI Gas Technology Symposium, Calgary, Canada.
Al-Fattah, S.M. and Startzman, R.A. (1999). Analysis of worldwide natural gas production. Paper SPE-57463 presented at the 1999 SPE Eastern Regional Meeting, Charleston, WV, USA.
F. Working Papers
Al-Fattah, Saud M. (2019a). Non-OPEC Conventional Production: Decline Analysis, Implications and Outlook, (USAEE Working Paper No. 19-076). Ohio, USA: United States Association for Energy Economics. Download
Al-Fattah, Saud M. (2019b). Reasonable Predictions of Oil Prices: Why is it So Difficult? Social Science Research Network. Download
Al-Fattah, Saud M. (2019c). A Hybrid Artificial-Intelligence Predictive Model for Crude Oil Demand: A Case Study for a High Producer and a High Consumer, Social Science Research Network.Download
Al-Fattah, S. M. (2013). Artificial Neural Network Models for Forecasting Global Oil Market Volatility, (USAEE Working Paper No. 13-112). Ohio, USA: United States Association for Energy Economics. Download
Al-Fattah, S. M. (2013). National Oil Companies: Business Models, Challenges, and Emerging Trends, (USAEE Working No. 13-138). Ohio, USA: United States Association for Energy Economics. Download
Huntington, H, Al-Fattah, S. M., Huang, Z., Gucwa, M., and Nouri, A. (2013). Oil Markets and Price Movements: A Survey of Models, (USAEE Working Paper No. 13-129). Ohio, USA: United States Association for Energy Economics. Download
Al-Fattah, S. M. (2012). The Role of National and International Oil Companies in the Petroleum Industry, (USAEE Working Paper No. 13-137). Ohio, USA: United States Association for Energy Economics. Download
Matar, W., Al-Fattah, S. M., Atallah, T.N., and Pierru, A. (2012). An Introduction to Oil Market Volatility Analysis, (USAEE Working Paper No. 12-152). Ohio, USA: United States Association for Energy Economics. Download