Artificial Intelligence-Based Screening of Enhanced Oil Recovery Materials for Reservoir-Specific Applications
- Ronaldo Giro (IBM Research) | Silas Pereira Lima Filho (IBM Research) | Rodrigo Neumann Barros Ferreira (IBM Research) | Michael Engel (IBM Research) | Mathias Bernhard Steiner (IBM Research)
- Document ID
- Offshore Technology Conference
- Offshore Technology Conference Brasil, 29-31 October, Rio de Janeiro, Brazil
- Publication Date
- Document Type
- Conference Paper
- 2019. Offshore Technology Conference
- Artificial Intelligence, Enhanced Oil Recovery
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- 92 since 2007
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The global average Recovery Factor (RF) in oil fields is only about 20-40%. A possible reason for such a low RF might be that Enhanced Oil Recovery (EOR) techniques are not yet broadly applied. This could be for economic reasons, concerns regarding the effectiveness of EOR and potential damage to the reservoir, or the lack of reservoir-specific recommendations.
In this contribution, we introduce a methodology that selects EOR materials for specific reservoir conditions by using Artificial Intelligence (AI) methods. We investigate the consistency of the screening results with the results obtained by state-of-the-art techniques that are used to identify EOR methods only, i.e., without EOR material specificity.
Our method correlates physical and chemical representations of injection fluids, including EOR materials, with reservoir-specific information on lithology, porosity, permeability, as well as oil, water and salt conditions. We have used machine learning on the combined data set in order to provide recommendation for EOR cocktail for injection fluids. Reservoir specific data input on rock, oil, and water conditions available in well logs is transformed by the AI model into a reservoir-specific recommendation of EOR candidate materials for optimized EOR effectiveness. The screening criteria are ranked based on EOR effectiveness and the similarity of key reservoir parameters at pore scale.
Methodologically, a Naïve Bayes Classifier with 10-fold cross-validation over the full training data set classified all instances with an accuracy of up to 90%. In order to compare with the EOR method screening criteria typically used in the industry, we have created a test data set containing instances based on averaged parameter values for representing each EOR method. In this case, our method is capable of classifying the test data set with nearly 100% accuracy. Our methodology allows to produce recommendations for EOR cocktails, including concentrations of their chemical components, for specific reservoir conditions that are readily available through well logs.
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Alvestad, J., Gilje, E., Hove, A. O., Langeland, O., Maldal, T., and Schilling, B. E. R. 1992. Coreflood experiments with surfactant systems for IOR: Computer tomography studies and numerical modelling. Journal of Petroleum Science and Engineering 7 (1-2): 155–171. https://doi.org/10.1016/0920-4105(92)90016-t
Broseta, D., Medjahed, F., Lecourtier, J., and Robin, M. 1995. Polymer Adsorption/Retention in Porous Media: Effects of Core Wettability and Residual Oil. SPE Advanced Technology Series 3 (1): 103–112. https://doi.org/10.2118/24149-pa
Engel, M.Bryant, P. W., Neumann, R. F., Giro, R., Feger, C., Avouris, P., and Steiner, M. B. 2017. A Platform for Analysis of Nanoscale Liquids with an Array of Sensor Devices Based on Two-Dimensional Material. Nano Letters 17 (5): 2741–2746. https://doi.org/10.1021/acs.nanolett.6b03561
Engel, M., Wunsch, B. H., Neumann, R. F., Giro, R., Bryant, P. W., Smith, J. T., and Steiner, M. B. 2017. Nanoscale Flow Chip Platform for Laboratory Evaluation of Enhanced Oil Recovery Materials. SPE Annual Technical Conference and Exhibition. https://doi.org/10.2118/187032-MS
Giro, R., Bryant, P. W., Queiroz da Silva, G. C., Neumann, R. F., Engel, M., and Steiner, M. B. 2018. A Multiscale Approach to Simulation of Fluid Flow in Tight Porous Media. SPE Argentina Exploration and Production of Unconventional Resources Symposium. https://doi.org/10.2118/191852-MS
Giro, R.Bryant, P. W., Engel, M., Neumann, R. F., and Steiner, M. B. 2017. Adsorption energy as a metric for wettability at the nanoscale. Scientific Reports 7 (1): 46317. https://doi.org/10.1038/srep46317
Giro, R.Bryant, P. W., Del Grande, R. R., Engel, M., and Steiner, M. B. 2015. From Nanoscale Wetting Towards Enhanced Oil Recovery. OTC Brasil. https://doi.org/10.4043/26232-MS
Jung, J., Jang, J. and Ahn, J. 2016. Characterization of a Polyacrylamide Solution Used for Remediation of Petroleum Contaminated Soils. Materials 9 (1): 16. https://doi.org/10.3390/ma9010016
Kang, P-S., Lim, J-S., and Huh, C. 2016. Screening Criteria and Considerations of Offshore Enhanced Oil Recovery. Energy 9: 44. http://dx.doi.org/10.3390/en9010044.
Maron, M. E. (1961). Automatic Indexing: An Experimental Inquiry. Journal of the ACM. 8 (3): 404–417. http://dx.doi.org/10.1145/321075.321084
Moreno, J. E., Gurpinar, O. M., Liu, Y., Al-Kinani, A., Cakir, N. 2014. EOR Advisor System: A Comprehensive Approach to EOR Selection. International Petroleum Technology Conference. https://doi.org/10.2523/iptc-17798-ms
Muggeridge, A., Cockin, A., Webb, K., Frampton, H., Collins, I., Moulds, T., and Salino, P. 2014. Recovery rates, enhanced oil recovery and technological limits. Phil. Trans. R. Soc. A 372: 20120320. http://dx.doi.org/10.1098/rsta.2012.0320
Steiner, M. B., Engel, M., Bryant, P. W., Giro, R., Neumann, R. F., Avouris, P. and Feger, C. 2015. Nanowetting Microscopy Probes Liquid-Solid Interaction at the Nanoscale. SPE Asia Pacific Enhanced Oil Recovery Conference. https://doi.org/10.2118/174622-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 Reservoir Engineering 12 (3):189–198. https://doi.org/10.2118/35385-pa
Wang, D., Zhao, L., Cheng, J., and Wu, J. 2003. Actual Field Data Show that Production Costs of Polymer Flooding can be Lower than Water Flooding. SPE International Improved Oil Recovery Conference in Asia Pacific. https://doi.org/10.2118/84849-ms
Zhu, D., Wei L., Wang, B., and Feng, Y. 2014. Aqueous Hybrids of Silica Nanoparticles and Hydrophobically Associating Hydrolyzed Polyacrylamide Used for (EOR) in High-Temperature and High-Salinity Reservoirs. Energies 7 (6): 3858–3971. https://doi.org/10.3390/en7063858