Real-Time Steam Allocation Workflow Using Machine Learning for Digital Heavy Oil Reservoirs
- N. Sibaweihi (University of Alberta) | R. G. Patel (University of Alberta) | J. L. Guevara (University of Alberta) | I. D. Gates (University of Calgary) | J. J. Trivedi (University of Alberta)
- Document ID
- Society of Petroleum Engineers
- SPE Western Regional Meeting, 23-26 April, San Jose, California, USA
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- Real-Time Steam Allocation, Thermal Oil Recovery, Machine Learning, Heavy Oil Reservoir, Intelligent Well Control
- 10 in the last 30 days
- 206 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 5.00|
|SPE Non-Member Price:||USD 28.00|
Thermal oil recovery processes are widely used to extract bitumen and heavy oil. Traditionally, a predetermined amount of steam is allocated to various injector wells using reservoir model based open-loop optimization. This practice can face a number of constraints including interruptions in well operations and/or surface facilities. Given that steam supply costs are a significant contributor to the overall production cost of heavy oil, dynamic and intelligent allocation of steam to various wells in the oilfield deserves further attention.
In this study, we propose a proactive steam allocation workflow that can learn the effect of steam injection pattern on heavy oil recovery by using machine learning. We employ data analytic predictive models for the short-term forecast of the key performance indicators (KPIs). Model parameters are updated continuously by using a moving horizon approach that considers selected prior data including real-time measurements. An objective function containing predicted KPIs is maximized by manipulating the amount of steam allocated to various injectors in the oilfield. The workflow is repeated on a daily basis for continuous optimum steam allocation.
A case study is performed by using a 3D reservoir model that represents a segment of the steam-assisted gravity drainage (SAGD) operation. For each well, the polynomial model is identified in the time-domain to forecast KPIs. The effectiveness of the proposed method is evident from the results as NPV is increased by almost 25% – 50% compared to the base case with a constant steam injection pattern in all cases studied. Due to the efficient use of available steam, the steam-to-oil ratio is reduced significantly. An adaptive and flexible steam supply is also honored by the proposed workflow, ensuring maximum efficiency of the oil recovery process. Practical implications of the proposed intelligent steam allocation workflow will be consequential in improving the operational efficiency of the digital heavy oil assets, thereby increasing profits and reducing the carbon footprint.
|File Size||2 MB||Number of Pages||22|
A. Tafti, Tayeb Iraj Ershaghi, Amin Rezapour, and Antonio Ortega. 2013. "Injection Scheduling Design for Reduced Order Waterflood Modeling." In SPE Western Regional & AAPG Pacific Section Meeting 2013 Joint Technical Conference. Monterey, California, USA: Society of Petroleum Engineers. https://doi.org/10.2118/165355-MS.
Alsalama Ahmed M., Joseph P. Canlas, and Salem H. Gharbi. 2016. "An Integrated System for Drilling Real Time Data Analytics." In SPE Intelligent Energy International Conference and Exhibition. Society of Petroleum Engineers. https://doi.org/10.2118/181001-MS.
Dehdari Vahid, Charlie Dong, and Eamon Marron. 2017. "Calibrating a Semi-Analytic SAGD Forecasting Model to 3D Heterogeneous Reservoir Simulations." In SPE Canada Heavy Oil Technical Conference. Society of Petroleum Engineers. https://doi.org/10.2118/184980-MS.
Edmunds Neil, and Harbir Chhina. 2001. "Economic Optimum Operating Pressure for SAGD Projects in Alberta." Journal of Canadian Petroleum Technology 40 (12). https://doi.org/10.2118/01-12-DAS.
Elgsaeter Steinar, Olav Slupphaug, and Tor Arne Johansen. 2008. "Production Optimization; System Identification and Uncertainty Estimation." In Intelligent Energy Conference and Exhibition. Amsterdam, The Netherlands: Society of Petroleum Engineers. https://doi.org/10.2118/112186-MS.
Fedutenko Eugene, Chaodong Yang, Colin Card, and Long X. Nghiem. 2014. "Time-Dependent Neural Network Based Proxy Modeling of SAGD Process." In SPE Heavy Oil Conference-Canada. Society of Petroleum Engineers. https://doi.org/10.2118/170085-MS.
Gates Ian Donald, Joseph Kenny, Ivan Lazaro Hernandez-Hdez, and Gary L. Bunio. 2007. "Steam Injection Strategy and Energetics of Steam-Assisted Gravity Drainage." SPE Reservoir Evaluation & Engineering 10 (01): 19–34. https://doi.org/10.2118/97742-PA.
Gonzalez Luis E., Peter Ficocelli, and Tad X. Bostick. 2012. "Real Time Optimization of SAGD Wells." In. Society of Petroleum Engineers. https://doi.org/10.2118/157923-MS.
Guevara, J. L., Rajan G. Patel, and Japan J. Trivedi. 2018. "Optimization of Steam Injection for Heavy Oil Reservoirs Using Reinforcement Learning." In. Society of Petroleum Engineers. https://doi.org/10.2118/193769-MS.
Hao Minshen, and Andrei Popa. 2015. "Steam Generators Optimization Using A Modified Quantum-Behaved Particle Swarm Optimization (QPSO) Algorithm." In SPE Digital Energy Conference and Exhibition. Society of Petroleum Engineers. https://doi.org/10.2118/173391-MS.
Holanda Rafael Wanderley de, Eduardo Gildin, and Jerry L. Jensen. 2015. "Improved Waterflood Analysis Using the Capacitance-Resistance Model Within a Control Systems Framework." In SPE Latin American and Caribbean Petroleum Engineering Conference. Society of Petroleum Engineers. https://doi.org/10.2118/177106-MS.
Hourfar Farzad, Behzad Moshiri, Karim Salahshoor, Mojtaba Zaare-Mehrjerdi, and Peyman Pourafshary. 2016. "Adaptive Modeling of Waterflooding Process in Oil Reservoirs." Journal of Petroleum Science and Engineering 146 (October): 702–13. https://doi.org/10.1016/j.petrol.2016.06.038.
Jones, J. A., and P. Dwivedi. 2018. "An Integrated Workflow Approach to Manage Steamflood Operations." In SPE Western Regional Meeting. Society of Petroleum Engineers. https://doi.org/10.2118/190064-MS.
Kim Min, and Hyundon Shin. 2017. "Development and Field Application of Proxy Models for Predicting the Shale Barrier Size Using SAGD Production DATA." In SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition. Society of Petroleum Engineers. https://doi.org/10.2118/186925-MS.
Kumar Anjani, Alex Novlesky, Erykah Bityutsky, Paul Koci, and Jeff Wightman. 2018. "Field Surveillance and AI Based Steam Allocation Optimization Workflow for Mature Brownfield Steam Floods." In SPE International Heavy Oil Conference and Exhibition. Society of Petroleum Engineers. https://doi.org/10.2118/193700-MS.
Manchuk Johnathan G., and Clayton V. Deutsch. 2013. "Optimization of Drainage-Area Configurations To Maximize Recovery From SAGD Operations." Journal of Canadian Petroleum Technology 52 (03): 233–42. https://doi.org/10.2118/165573-PA.
Mohajer Mahyar Matt, Carlos Emilio Perez Damas, Alexander Jose Berbin Silva, and Andreas Al-Kinani. 2010. "An Integrated Framework for SAGD Real-Time Optimization." In SPE Intelligent Energy Conference and Exhibition. Society of Petroleum Engineers. https://doi.org/10.2118/128426-MS.
Nourozieh Hossein, Ehsan Ranjbar, Anjani Kumar, and Colin C. Card. 2017. "Impact, Mitigation and Optimization Strategies When Low Oil Prices Alter Long Term SAGD Project Implementation." In SPE Canada Heavy Oil Technical Conference. Calgary, Alberta, Canada: Society of Petroleum Engineers. https://doi.org/10.2118/185007-MS.
Ockree Matthew, Kenneth G. Brown, Joseph Frantz, Michael Deasy, and Ramey John. 2018. "Integrating Big Data Analytics Into Development Planning Optimization." In SPE/AAPG Eastern Regional Meeting. Society of Petroleum Engineers. https://doi.org/10.2118/191796-18ERM-MS.
Patel Rajan G., Vinay Prasad, and Japan J. Trivedi. 2018. "Real-Time Production Optimization of Steam-Assisted-Gravity-Drainage Reservoirs Using Adaptive and Gain-Scheduled Model-Predictive Control: An Application to a Field Model." SPE Production & Operations, September. https://doi.org/10.2118/185688-PA.
Patel Rajan G., and Japan J. Trivedi. 2017. "SAGD Real-Time Production Optimization Using Adaptive and Gain-Scheduled Model-Predictive-Control: A Field Case Study." In SPE Western Regional Meeting. Bakersfield, California: Society of Petroleum Engineers. https://doi.org/10.2118/185688-MS.
Popa Andrei S., Eli Grijalva, Steve Cassidy, Juan Medel, and Andrew Cover. 2015. "Intelligent Use of Big Data for Heavy Oil Reservoir Management." In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers. https://doi.org/10.2118/174912-MS.
Renard, G., D. Dembele, J. Lessi, and J.L. Mari. 1998. "System Identification Approach to Watercut Analysis in Waterflooded Layered." In SPE/DOE Improved Oil Recovery Symposium. Tulsa, Oklahoma: Society of Petroleum Engineers. https://doi.org/10.2118/39606-MS.
Salehinia Saeed, Yaser Salehinia, Fatemeh Alimadadi, and Seyed Hossein Sadati. 2016. "Forecasting Density, Oil Formation Volume Factor and Bubble Point Pressure of Crude Oil Systems Based on Nonlinear System Identification Approach." Journal of Petroleum Science and Engineering 147 (November): 47–55. https://doi.org/10.1016/j.petrol.2016.05.008.
Saputelli, L., M. Nikolaou, and M. J. Economides. 2006. "Real-Time Reservoir Management: A Multiscale Adaptive Optimization and Control Approach." Computational Geosciences 10 (1): 61–96. https://doi.org/10.1007/s10596-005-9011-5.
Sun Qian, and Turgay Ertekin. 2015. "The Development of Artificial-Neural-Network-Based Universal Proxies to Study Steam Assisted Gravity Drainage (SAGD) and Cyclic Steam Stimulation (CSS) Processes." In SPE Western Regional Meeting. Society of Petroleum Engineers. https://doi.org/10.2118/174074-MS.
Temizel Cenk, Sinem Aktas, Harnn Kirmaci, Onur Susuz, Ying Zhu, Karthik Balaji, Rahul Ranjith, Sofiane Tahir, Fred Aminzadeh, and Cengiz Yegin. 2016. "Turning Data into Knowledge: Data-Driven Surveillance and Optimization in Mature Fields." In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers. https://doi.org/10.2118/181881-MS.
Vanegas Jose Walter Prada, Clayton Vernon Deutsch, and Luciane Bonet Cunha. 2008. "Uncertainty Assessment of SAGD Performance Using a Proxy Model Based on Butler's Theory." In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers. https://doi.org/10.2118/115662-MS.
Yang, C., C. Card, and L. Nghiem. 2009. "Economic Optimization and Uncertainty Assessment of Commercial SAGD Operations." Journal of Canadian Petroleum Technology 48 (09): 33–40. https://doi.org/10.2118/09-09-33.
Yao Song, Japan J. Trivedi, and Vinay Prasad. 2015. "Proxy Modeling of the Production Profiles of SAGD Reservoirs Based on System Identification." Industrial & Engineering Chemistry Research 54 (33): 8356–67. https://doi.org/10.1021/ie502258z.
Zheng Jingwen, Juliana Y. Leung, Ronald P. Sawatzky, and Jose M. Alvarez. 2016. "A Proxy Model for Predicting SAGD Production from Reservoirs Containing Shale Barriers." In SPE Canada Heavy Oil Technical Conference. Society of Petroleum Engineers. https://doi.org/10.2118/180715-MS.