Optimization of Horizontal Wellbore Trajectory and Placement of Hydraulic Fracturing Stages in Tight Heterogeneous Gas Condensate Reservoirs Using Derivative Free Algorithms
- B. Olusola (University of Calgary) | R. Aguilera (University of Calgary)
- 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
- 5.8.8 Gas-condensate reservoirs, 1.6 Drilling Operations, 7.1 Asset and Portfolio Management, 2 Well completion, 5.8 Unconventional and Complex Reservoirs, 3 Production and Well Operations, 7 Management and Information, 7.1.6 Field Development Optimization and Planning, 2.4 Hydraulic Fracturing, 5 Reservoir Desciption & Dynamics, 1.6.6 Directional Drilling
- derivative free algorithm, field development optimization, hydraulic fracture, condensate reservoir, horizontal well
- 2 in the last 30 days
- 127 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 9.50|
|SPE Non-Member Price:||USD 28.00|
We present hybrid derivative free algorithm methods that maximize NPV while solving the problem of placement of hydraulic fracturing stages and horizontal wellbore trajectory in a tight heterogeneous gas condensate reservoir. These parameters are important in reservoir management and field development optimization as they determine the interaction of the wellbore with the reservoir. Thus, they have a large impact on production performance of the field and the cost of field development.
We couple a tight heterogeneous gas condensate reservoir model to an optimization algorithm that determines the parameters that maximize the economic value of the field. The optimization algorithm proposed in this paper exploits the advantage of stochastic global search (particle swarm optimization) and local pattern search (hill climber) techniques to find the optimized parameters. The optimization process starts with the particle swarm optimization (PSO) algorithm, which is executed with an initial guess based on engineering experience until the objective function (in this study NPV) fails to improve in the next couple of iterations. This is followed by the hill climber (HC) algorithm that improves the objective function.
Observations from our investigation show that an optimal field development plan (FDP) is essential to optimize the placement of hydraulic fracturing stages along a horizontal wellbore and to optimize the wellbore trajectory inside the reservoir. However, these parameters would have to be optimized simultaneously in a non-systematic manner. We integrate reservoir engineering experience and economics knowledge into the optimization algorithm by embedding practical constraints into the problem formulation.
The algorithm is executed in a reasonable amount of computation time, considering the complexity of the problem. The optimization process evaluates various possible field development plans involving different hydraulic fracturing stages and well placement trajectories. The investigation demonstrates how these parameters impact the economic value of the field, how to optimize the placement of hydraulic fracture stages along a horizontal wellbore, and how to optimize the wellbore trajectory inside the reservoir.
The methodology presented in this work should allow industry professionals working with unconventional reservoirs to improve the economic value of a field in a shorter timeframe while considering all possible field development plans, a task that would be time consuming and tedious if carried out manually.
|File Size||1 MB||Number of Pages||18|
Aguilera, R. (2014). Flow Units: From Conventional to Tight-Gas to Shale-Gas to Tight-Oil to Shale-Oil Reservoirs. Society of Petroleum Engineers. doi:10.2118/165360-PA.
Dong, Y., Tang, J., Xu, B. and Wang, D. 2005. An application of swarm optimization to nonlinear programming, In Computers & Mathematics with Applications, Vol. 49, Issues 11-12, 2005, pp. 1655–1668, ISSN 0898-1221, https://doi.org/10.1016Zj.camwa.2005.02.006.
Fan, L., Harris, B.W.,Jamaludin, A., Kamath, J., Mott, R., Pope, G.A, Shandrygin, A., and Whitson, C.H. 2005/2006. Understanding Gas-Condensate Reservoirs. Oilfield Review. https://www.slb.com/~/media/Files/resources/oilfield_review/ors05/win05/02_understanding_gas_condensate.pdf
Hooke, R. and Jeeves, T.A. 1960. Direct Search- Solution of Numerical and Statistical Problems. Journal of the. A.C.M., 212–229, vol.8, no.2, https://dl.acm.org/citation.cfm?doid=321062.321069
Isebor, O. J., Echeverria Ciaurri, D., & Durlofsky, L. J. 2014. Generalized Field-Development Optimization with Derivative-Free Procedures. Society of Petroleum Engineers. doi:10.2118/163631-PA.
Investopedia. 2017. https://www.investopedia.com/terms/n/npv.asp
Jesmani, M., Bellout, M.C.,Hanea,Hanea, R. and Foss, B. 2016. Well Placement Optimization Subject to Realistic Field Development Constraints. Comput. Geoscience, Springer International Publishing. https://doi.org/10.1007/s10596-016-9584-1
Kennedy, J. and Eberhart, R.C. 1995. Particle Swarm Optimization. Proceeding of the 1995 IEEE International conference on Neural Networks, IEEE Service Center, USA. pp. 1942–1948 vol.4. doi:10.1109/ICNN.1995.488968
Kennedy, J. and Eberhart, R.C,. 1995. A New Optimizer Using Particle Swarm Theory. Micro Machine and Human Science, MHS ‘95., Proceedings of the Sixth International Symposium on, Nagoya, pp. 39–43. doi:10.1109/MHS.1995.494215
Minton, J.J. and Archer, R. 2014. A Comparison of Common Methods for Optimal Well Placement. SIAM Journal https://www.siam.org/students/siuro/vol7/S01251.pdf
Shi, Y. and Eberhart, R. C. 1999. Empirical Study of Particle Swarm Optimization. Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), Washington, DC, 1999, pp. 1950 Vol. 3. doi:10.1109/CEC.1999.785511
Yeten, B., Durlofsky, L. J., & Aziz, K. (2003, September 1). Optimization of Nonconventional Well Type, Location, and Trajectory. Society of Petroleum Engineers. doi:10.2118/86880-PA.
Zhang, W.J.,Xie, X.F. and Bi, D.C., 2004. Handling Boundary Constraints for Numerical Optimization by Particle Swarm Flying in Periodic Search Space. Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753), pp. 2307–2311 Vol.2.doi:10.1109/CEC.2004.1331185