Design and Development of Data-Driven Screening Tools for Enhanced Oil Recovery Processes
- G. Yalgin (Middle East Technical University) | N. Zarepakzad (Middle East Technical University) | E. Artun (Middle East Technical University) | I. Durgut (Middle East Technical University) | M. V. Kok (Middle East Technical University)
- Document ID
- Society of Petroleum Engineers
- SPE Western Regional Meeting, 22-26 April, Garden Grove, California, USA
- Publication Date
- Document Type
- Conference Paper
- 2018. Society of Petroleum Engineers
- 7.6.7 Neural Networks, 7.6 Information Management and Systems, 6.1.5 Human Resources, Competence and Training, 5.3.6 Chemical Flooding Methods (e.g., Polymer, Solvent, Nitrogen, Immiscible CO2, Surfactant, Vapex), 5.4.6 Thermal Methods, 7 Management and Information, 6.1 HSSE & Social Responsibility Management, 5.4 Improved and Enhanced Recovery, 5.4 Improved and Enhanced Recovery, 6 Health, Safety, Security, Environment and Social Responsibility
- polymer flooding, data-driven modeling, enhanced oil recovery, cyclic steam injection, screening
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Computationally efficient screening and forecasting tools can offer faster decision-making and value- creation opportunities for enhanced oil recovery (EOR) operations without requiring a high-fidelity reservoir model. In this paper, we present a data-driven modeling approach utilizing numerical models and neural networks (ANN) to screen EOR methods in a rapid way. Numerical modeling is employed to generate the data for the training of the neural-network based data-driven model. It is aimed to develop comprehensive and globally applicable screening tools that can be used to identify reservoirs where the EOR method would be applicable through estimation of performance indicators, such as utilization efficiency. Important process variables related to the EOR method are identified and grouped, while also defining their limits and statistical distributions. A large number of scenarios are generated and run using the numerical model that represents the EOR method under consideration. A simple yet representative performance indicator is defined and calculated for each scenario, which takes into account both additional income due to incremental oil recovery and the cost of the injected agent. Considering annual volumes in the calculation and evaluating the time-dependency of the performance indicator allow to incorporate time-value of the money. Finally, the knowledge base is used to train a neural network that can capture the signatures within the dataset through an iterative training process. This methodology is explained by highlighting important components of the workflow as well as best practices of each step. Results of two example applications are summarized: 1) Cyclic steam injection, 2) Polymer flooding. The results indicated that presented methodology can be successfully followed for different EOR methods. In both cases, the data-driven screening model was able to predict the efficiency indicator within acceptable accuracy levels and identified the degree of success of each method under different reservoir and operational conditions. Comparison of sensitivities between numerical and data-driven models showed that the data-driven model captured the physics of both problems as reflected by the numerical model. Predicting the indicator at 2-year intervals allowed to estimate the feasibility as a function of time.
|File Size||2 MB||Number of Pages||22|
Al-Dousari, M. M. and Garrouch, A. A. 2013. An Artificial Neural Network Model for Predicting The Recovery Performance of Surfactant Polymer Floods, J Pet Sci Eng, 109: 51–62. doi: 10.1016/j.petrol.2013.08.012.
Al Adasani, A. and Bai, B. 2011. Analysis of EOR Projects and Updated Screening Criteria, J Pet Sci Eng, 79 (1-2): 1024. doi: 10.1016/j.petrol.2011.07.005.
Alfarge, D., Wei, M. and Bai, B. 2017. Feasibility of CO2-EOR in Shale-Oil Reservoirs: Numerical Simulation Study and Pilot Tests. CMTC-485111-MS. Presented at the Carbon Management Technology Conference, 17-20 July, Houston, Texas, USA. doi: 10.7122/485111-MS.
Andrade, E.N. da C. 1930. The Viscosity of Liquids. Nature, 125: 309–310. doi: 10.1038/125309b0.
Anifowose, F. A. 2011. Artificial Intelligence Application in Reservoir Characterization and Modeling: Whitening the Black Box. SPE-155413-MS. Presented at the SPE Saudi Arabia section Young Professionals Technical Symposium, 14-16 March, Dhahran, Saudi Arabia. doi: 10.2118/155413-MS.
Artun, E., Ertekin, T., Watson, R.. 2010. Development and Testing of Proxy Models for Screening Cyclic Pressure Pulsing Process in a Depleted, Naturally Fractured Reservoir. J Pet Sci Eng, 73 (1): 73–85. doi: 10.1016/j.petrol.2010.05.009.
Bourdarot, G. and Ghedan, S. G. 2011. Modified EOR Screening Criteria as Applied to a Group of Offshore Carbonate Oil Reservoirs. SPE-148323-MS. Presented at the SPE Reservoir Characterisation and Simulation Conference and Exhibition, 9-11 October, Abu Dhabi, UAE. doi: 10.2118/148323-MS.
Bravo, C.E.,Saputelli, L., Rivas, F.. 2014. State of the Art of Artificial Intelligence and Predictive Analytics in the E&P Industry: A Technology Survey. SPE J. 19 (4): 547–563. SPE-150314-PA. doi: 10.2523/150314-PA.
Ebaga-Ololo, J. and Chon, B.H. 2017. Prediction of Polymer Flooding Performance with an Artificial Neural Network: A Two-Polymer-Slug Case. Energies, 10 (7), 844. doi: 10.3390/en10070844.
El-M Shokir, E.M.,Goda H.,Sayyouh M.. 2002. Selection and Evaluation EOR Method Using Artificial Intelligence. SPE-79163-MS. Presented at the SPE 26th Annual International Technical Conference and Exhibition, 5-7 August, Abuja, Nigeria. doi: 10.2523/79163-MS.
Eppelbaum, L., Kutasov I. and Pilchin A. 2014. Thermal Properties of Rocks and Density of Fluids, In Applied Geothermics, Lecture Notes in Earth System Sciences, 1st edition, ed. Eppelbaum, L., Kutasov I. and Pilchin A, Chap. 2, 99–149. Springer-Verlag Berlin Heidelberg. doi: 10.1007/978-3-642-34023-92.
Guerillot, D. R. 1988. EOR Screening with an Expert System. SPE-17791-MS. Presented at the SPE Petroleum Computer Conference, 27-29 June, San Jose, California. doi: 10.2118/17791-MS.
Guler, B., Ertekin, T. and Grader, A. 2003. An Artificial Neural Network Based Relative Permeability Predictor. J Can Pet Technol, 42 : 49–57, PETSOC-03-04-02. doi: 10.2118/03-04-02.
Huang, G. B. 2003. Learning Capability and Storage Capacity of Two-Hidden-Layer Feedforward Networks. IEEE Trans Neural Netw, 14 (2): 274–281. doi: 10.1109/TNN.2003.809401.
Jinchuan, K. and Xinzhe, L. 2008. Empirical Analysis of Optimal Hidden Neurons in Neural Network Modeling for Stock Prediction. Presented at the IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, 2: 828-832, 19-20 December, Wuhan China. doi: 10.1109/PACIIA.2008.363.
Karambeigi, M., Zabihi, R. and Hekmat, Z. 2011. Neuro-Simulation Modeling of Chemical Flooding. J Pet Sci Eng, 78 (2): 208–219. doi: 10.1016/j.petrol.2011.07.012.
Lee, A. L.,Gonzalez, M. H. and Eakin, B. E. 1966. The Viscosity of Natural Gases. SPE-1340-PA. J Pet Technol, 18 (8): 997–1000. doi: 10.2118/1340-PA.
Mohaghegh, S. D. 2005. Recent Developments in Application of Artificial Intelligence in Petroleum Engineering, SPE- 89033-JPT. J Pet Technol. 57 (4): 86–91. doi: 10.2118/89033-JPT.
Parada, C. and Ertekin, T. 2012. A New Screening Tool for Improved Oil Recovery Methods Using Artificial Neural Networks. SPE-153321-MS. Presented at the SPE Western Regional Meeting, 21-23 March, Bakersfield, California, USA. doi: 10.2118/153321-MS.
Ramgulam, A., Ertekin, T. and Flemings, P. B., 2007. An Artificial Neural Network Utility for the Optimization of History-Matching Process. SPE-107468-MS. Presented at the SPE Latin American & Caribbean Petroleum Engineering. 15-18 April. Buenos Aires, Argentina. doi: 10.2118/107468-MS.
Ren, W., Cunha, L. B. and Bentsen, R. 2003. Numerical Simulation and Screening of Oil Reservoirs for Gravity Assisted Tertiary Gas-Injection Processes. SPE-81006-MS. Presented at the SPE Latin American and Caribbean Petroleum Engineering Conference, 27-30 April, Port-of-Spain, Trinidad and Tobago. doi: 10.2118/81006-MS.
Solomatine, D., See, L. and Abrahart, R. 2008. Data-Driven Modeling: Concepts, Approaches and Experiences, In Practical Hydroinformatics, 1st edition, ed. Solomatine, D., See, L. and Abrahart, R., Chap. 2, 17–30. Springer-Verlag Berlin Heidelberg. doi: 10.1007/978-3-540-79881-12.
Sun, Q. and Ertekin, T. 2015. The Development of Artificial-Neural-Network-Based Universal Proxies to Study Steam Assisted Gravity Drainage (SAGD) and Cyclic Steam Stimulation (CSS) Processes. SPE-174074-MS. SPE Western Regional Meeting, 27-30 April, Garden Grove, California, USA. doi: 10.2118/174074-MS.
Sun, Q. and Ertekin, T. 2017. Structuring an Artificial Intelligence Based Decision Making Tool for Cyclic Steam Stimulation Processes. J Pet Sci Eng, 154 : 564–575. doi: 10.1016/i.petrol.2016.10.042.
Surguchev, L. M.,Korbol, R., Haugen, S.. 1992. Screening of WAG Injection Strategies for Heterogeneous Reservoirs. SPE-25075-MS. Presented at the SPE European Petroleum Conference, 16-18 November, Cannes, France. doi: 10.2118/25075-MS.
Taber, J. J.,Martin, F. D. and Seright, R. S. 1997. EOR Screening Criteria Revisited - Part 1: Introduction to Screening Criteria and Enhanced Recovery Field Projects. SPE Res Eng, 12 (3): 189–198. SPE-35385-PA. doi: 10.2118/35385-PA.
Trenn, S. 2008. Multilayer Perceptrons: Approximation Order and Necessary Number of Hidden Units. IEEE Trans Neural Netw, 19 (5): 836–844. doi: 10.1109/TNN.2007.912306.
Vazquez, M. and Beggs, H. D. 1977. Correlations for Fluid Physical Property Prediction. SPE-6719-MS. Presented at the SPE Annual Fall Technical Conference and Exhibition, 9-12 October, Denver, Colorado. doi: 10.2118/6719-MS.
Wright, W. 2014. Simple Equations to Approximate Changes to the Properties of Crude Oil with Changing Temperature, http://www.petroskills.com/blog/entry/crude-oil-and-changing-temperature (accessed 6 Feb 2018).