Inferential Reservoir Modelling and History Matching Optimization using Different Data-Driven Techniques
Keywords:
Proxy Models; ANN; RSM; Genetic Algorithm; History matching; Optimization
Abstract
One of the major problems associated with history matching is the non-uniqueness of the solutions. A major flaw in this traditional history matching is that it lacks robustness as it shows a bias to the production data being matched while neglecting the mechanics governing other production data and such solutions generated are erroneous and gives a poor representation of the reservoir being matched.
In this study, data driven and numerical modeling of a synthetic PUNQS3 reservoir were carried out. Single objective function, aggregated and multi-objective functions were adopted for the reservoir history matching. A proxy model was developed with data generated from a reservoir simulator using Artificial Neural Network (ANN) and the Response Surface Methodology (RSM). Firefly Optimization (FFO), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms were used for the history matching process.
The results showed that the history matching process was strongly influenced by porosity and permeability. The interaction between the two was also established. The ANN appeared to provide a better match of the simulated data compared with the RSM. Although aggregated method of optimization is less computational expensive, the multi-objective approach provided a superior history matching optimization. The observed misfit values were 0.074, 0.073, and 0.073 for GA, PSO and FFO algorithms respectively for cumulative oil production history matching. Better predictions were obtained using the FFO and PSO compared with GA for single and aggregated objective function optimization. This work can be extended to investigate the performance of FFO and other recent methods using multi-objective approach and the influence of objective function on history matching.
References
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Silva, P. C., Maschio, C. and Schiozer, D. J. (2008) ‘Application of Neural Network and Global Optimization in History Matching’, Journal of Canadian Petroleum Technology, 47(11). doi: 10.2118/08-11-22-TN.
Sun, X. and Mohanty, K. K. (2005) ‘Estimation of Flow Functions During Drainage Using Genetic Algorithm’, SPE Journal, (December), pp. 449–457.
Wantawin, M., Yu, W. and Sepehrnoori, K. (2017) ‘An Iterative Work Flow for History Matching by Use of Design of Experiment, Response-Surface Methodology, and Markov Chain Monte Carlo Algorithm Applied to Tight Oil Reservoirs’, SPE Reservoir Evaluation & Engineering, 20(03), pp. 613–626. doi: 10.2118/185181-PA.
Xavier, C. R. et al. (2013) ‘Genetic algorithm for the history matching problem’, Procedia Computer Science, 18, pp. 946–955. doi: 10.1016/j.procs.2013.05.260.
Yang, X.-S. (2009) ‘Firefly Algorithms for Multimodal Optimization’, in Stochastic Algorithms: Foundations and Applications, Lecture notes in Computer Sciences. SAGA, pp. 169–178. doi: 10.1007/978-3-642-04944-6_14.
Yarpiz (2018) Optimization Algorithms, Optimization Algorithms. Available at: http://yarpiz.com/ (Accessed: 2 December 2018).
Yeten, B. et al. (2005) ‘A comparison study on Experimental Design and Response Surface Methodologies’, in SPE Reservoir Simulation Symposium. Houston Texas, USA: Society of Petroleum Engineers. doi: 10.2118/93347-MS.
Zhang, X. S. et al. (2012) ‘An automatic history matching method of reservoir numerical simulation based on improved genetic algorithm’, Procedia Engineering, 29, pp. 3924–3928. doi: 10.1016/j.proeng.2012.01.595.
Awasthi, A. et al. (2007) ‘Closing the Gap Between Reservoir Simulation and Production Optimization’, in SPE Digital Engergy Conference. Texas: Society of Petroleum Engineers, p. Houston, Texas.
Bertolini, A. C. and Schiozer, D. J. (2011) ‘Influence of the objective function in the history matching process’, Journal of Petroleum Science and Engineering. Elsevier B.V., 78(1), pp. 32–41. doi: 10.1016/j.petrol.2011.04.012.
Ehinmowo, A. B., Bishop, S. A. and Jacob, N. M. (2017) ‘Prediction of Riser Base Pressure in a Multiphase Pipeline- Riser System Using Artificial Neural Networks’, Journal of Engineering Research, 22(2), pp. 23–33.
Fu, J. and Wen, X.-H. (2018) ‘A Regularized Production-Optimization Method for Improved Reservoir Management’, SPE Journal, 23(02), pp. 467–481. doi: 10.2118/189457-PA.
Hajizadeh, Y., Christie, M. and Demyanov, V. (2011) ‘Towards Multiobjective History Matching Faster Convergence and Uncertainty Quantification’, in SPE Reservoir Simulation Symposium. Woodlands Texas: Society of Petroleum Engineers. doi: 10.2118/141111-MS.
Hutahaean, J., Demyanov, V. and Christie, M. (2016) ‘Many-Objective Optimization Algorithm Applied to History Matching’, in IEEE Symposium Series on Computational Intelligence (SSCI). Athens,Greece: IEEE, pp. 1–8.
Kabir, C. S., Chien, M. C. H. and Landa, J. L. (2013) ‘Experiences With Automated History Matching’, in SPE Reservoir Simulation Symposium. Houston,Texas: Society of Petroleum Engineers. doi: 10.2118/79670-MS.
Kim, J. et al. (2017) ‘Multi-objective history matching with a proxy model for the characterization of production performances at the shale gas reservoir’, Energies, 10(4). doi: 10.3390/en10040579.
Kumar, R. and Rockett, P. (2002) ‘Improved sampling of the Pareto-front in multiobjective genetic optimizations by steady-state evolution: A Pareto converging genetic algorithm’, Evolutionary Computation, 10(3), pp. 283–314. doi: 10.1162/106365602760234117.
Lin, S. (2018) Ngpm codes, Mathlab code for MOGA. Available at: https://www.mathworks.com/matlabcentral/profile/authors/2837501-song-lin (Accessed: 2 October 2018).
Maschio, C. and Schiozer, D. J. (2005) ‘Development and Application of Methodology for Assisted History Matching’, in SPE Latin American and Caribbean Petroleum Engineering Conference. Rio de Janeiro,Brazil: Society of Petroleum Engineers.
Maunde, A. et al. (2013) ‘Comparison of the history matching and forecasting capabilities of single and multi-objective particle swarm optimisation using the punq-s3 reservoir as a case study’, Internal Research Journal of Geology and Mining, 3(6), pp. 224–234.
Mohamed, L. et al. (2010) ‘Application of Particle Swarms for History Matching in the Brugge Reservoir’, SPE Annual Technical Conference and Exhibition. Florence, Italy: Society of Petroleum Engineers. doi: 10.2118/135264-MS.
Mohamed, L., Christie, M. and Demyanov, V. (2011) ‘History matching and uncertainty quantification: multiobjective particle swarm optimisation approach’, in SPE EUROPEC/EAGE annual conference and exhibition. Vienna,Austria: Society of Petroleum Engineers.
Negash, B. M. et al. (2016) ‘History matching of the PUNQ-S3 reservoir model using proxy modeling and multi-objective optimizations’, in International Conference on Industrial Engineering Operations Management. Kuala Lumpur, Malaysia: IEOM Society International, pp. 1374–1386.
Ogaga, I. B. et al. (2017) ‘Optimization of biodiesel production from Thevetia peruviana seed oil by adaptive neuro-fuzzy inference system coupled with genetic algorithm and response surface methodology’, Energy Conversion and Management. Pergamon, 132, pp. 231–240. doi: 10.1016/j.enconman.2016.11.030.
Queipo, N. V et al. (2000) ‘Surrogate Modeling – Based Optimization for the Integration of Static and Dynamic Data Into a Reservoir Description’, in SPE Annual Technical Comferece and Exhibition. Dallas,Texas: Society of Petroleum Engineers.
Rammay, M. H. and Abdulraheem, A. (2014) ‘Automated History Matching Using Combination of Adaptive Neuro Fuzzy System (ANFIS) and Differential Evolution Algorithm’, SPE Large Scale Computing and Big Data Challenges in Reservoir Simulation Conference and Exhibition. doi: 10.2118/172992-MS.
Romero, C. E. et al. (2000) ‘A Modified Genetic Algorithm for Reservoir Characterisation’, International Oil and Gas Conference and Exhibition in China. Beijing, China: Society of Petroleum Engineers. doi: 10.2118/64765-MS.
Sarma, P. and Xie, J. (2011) ‘Efficient and Robust Uncertainty Quantification in Reservoir Simulation with Polynomial Chaos Expansions and Non-intrusive Spectral Projection’, in SPE Reservoir Sim. Woodlands Texas: Society of Petroleum Engineers.
Shams, M. (2017) ‘Firefly Optimization, A Novel Algorithm to the Arena of Assisted History Matching’, in Offshore Mediterranean Conference and Exhibition. Ravenna, Italy: OMC, pp. 1–12.
Silva, P. C., Maschio, C. and Schiozer, D. J. (2008) ‘Application of Neural Network and Global Optimization in History Matching’, Journal of Canadian Petroleum Technology, 47(11). doi: 10.2118/08-11-22-TN.
Sun, X. and Mohanty, K. K. (2005) ‘Estimation of Flow Functions During Drainage Using Genetic Algorithm’, SPE Journal, (December), pp. 449–457.
Wantawin, M., Yu, W. and Sepehrnoori, K. (2017) ‘An Iterative Work Flow for History Matching by Use of Design of Experiment, Response-Surface Methodology, and Markov Chain Monte Carlo Algorithm Applied to Tight Oil Reservoirs’, SPE Reservoir Evaluation & Engineering, 20(03), pp. 613–626. doi: 10.2118/185181-PA.
Xavier, C. R. et al. (2013) ‘Genetic algorithm for the history matching problem’, Procedia Computer Science, 18, pp. 946–955. doi: 10.1016/j.procs.2013.05.260.
Yang, X.-S. (2009) ‘Firefly Algorithms for Multimodal Optimization’, in Stochastic Algorithms: Foundations and Applications, Lecture notes in Computer Sciences. SAGA, pp. 169–178. doi: 10.1007/978-3-642-04944-6_14.
Yarpiz (2018) Optimization Algorithms, Optimization Algorithms. Available at: http://yarpiz.com/ (Accessed: 2 December 2018).
Yeten, B. et al. (2005) ‘A comparison study on Experimental Design and Response Surface Methodologies’, in SPE Reservoir Simulation Symposium. Houston Texas, USA: Society of Petroleum Engineers. doi: 10.2118/93347-MS.
Zhang, X. S. et al. (2012) ‘An automatic history matching method of reservoir numerical simulation based on improved genetic algorithm’, Procedia Engineering, 29, pp. 3924–3928. doi: 10.1016/j.proeng.2012.01.595.
Published
2020-02-09
How to Cite
Ehinmowo, A. B., Ohiro, O. A., Olamigoke, O., & Adeyanju, O. (2020). Inferential Reservoir Modelling and History Matching Optimization using Different Data-Driven Techniques. Journal of Engineering Research, 24(2), 91-110. Retrieved from http://jer.unilag.edu.ng/article/view/581
Section
Articles
