Lagrangian Decomposition of Oil-Production Optimization Applied to the Troll West Oil Rim
- Bjarne Foss (NTNU) | Vidar Gunnerud (NTNU) | Marta Dueñas Díez (StatoilHydro ASA)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- December 2009
- Document Type
- Journal Paper
- 646 - 652
- 2009. Society of Petroleum Engineers
- 4.6.2 Liquified Natural Gas (LNG), 4.6 Natural Gas, 3.1 Artificial Lift Systems, 4.2 Pipelines, Flowlines and Risers, 4.1.2 Separation and Treating, 5.8.8 Gas-condensate reservoirs, 5.2 Reservoir Fluid Dynamics, 3.1.2 Electric Submersible Pumps, 5.3.2 Multiphase Flow, 5.2.1 Phase Behavior and PVT Measurements, 4.1.4 Gas Processing, 4.1.9 Tanks and storage systems, 1.6 Drilling Operations, 5.5 Reservoir Simulation, 4.1.5 Processing Equipment
- 2 in the last 30 days
- 678 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 12.00|
|SPE Non-Member Price:||USD 35.00|
This paper proposes a method based on Lagrangian decomposition for real-time production optimization. It argues the method is well suited for the well-allocation and routing problem in the upstream industries. A semirealistic field case assuming steady-state conditions is used to compare the merits of the method with current industrial practice. The method compares favorably and is therefore an interesting option in real-time production optimization. There are several reasons for this. An error bound on the solution of the production optimization problem can be computed, which is clearly information of interest to any user. Furthermore, the algorithm is efficient and can be parallized for an even higher efficiency. Finally, the Lagrangian decomposition method provides a tool for understanding how a limited resource, such as gas capacity, is used in production optimization.
Development of a field asset requires planning on multiple horizons. On a long-term horizon, strategic decisions are made on field development (e.g., choice of technology and export options, investment strategies, and recovery strategies despite uncertainties) (Saputelli et al. 2007). For offshore assets, the choice of technology may include subsea solutions and the issue of processing the reservoir fluid either offshore or onshore. Export choices include pipelines or liquified natural gas tankers for gas, and pipelines or oil tankers for heavier components. Furthermore, option management may point to a flexible, and thereby more costly development to allow for future development, such as tie-ins from possible neighboring assets. The analyses and subsequent development plan seek to maximize the net present value of the asset or maximizing oil recovery.
On a medium time horizon, typically 3 months to 2 years, production rates and, if applicable, injection rates are decided. Depending on the life cycle of an asset, decisions may also involve a drilling program. During the green field stage, it is important to plan, drill, and commission new wells to reach some predefined plateau rate as soon as possible. During plateau production, there may be an in-field drilling program for production and/or injection wells. This program involves decisions about the location and completion of wells. During the decline phase of a field, lift technology may be an issue involving decisions (e.g., the distribution of limited amounts of lift gas between wells). A reservoir simulator is usually an important planning tool on the medium time horizon. A reservoir simulator is quite complex if the geology is complex, because of heterogeneities, like faults and shale layers, to represent flow patterns accurately. Furthermore, complexity increases if phase behavior is complicated. Gas-condensate reservoirs often necessitate the use of a compositional simulator instead of a black-oil simulator to get accurate prediction of phase behavior, which increases computational load significantly because of thermodynamics.
On a short-time horizon, typically days to weeks, production optimization in which both the subsurface part (i.e., reservoir and wells) and the surface part (i.e., collection and downstream production equipment) of the system are taken into account is important. We denote this optimization to be the real-time production optimization (RTPO) problem and return to a more precise definition later. Production may be constrained by reservoir conditions, such as coning effects and/or the production equipment like pipeline capacity or downstream water-handling capacity. Constraints may move from one part of the system to another part over time. Water production may be low early and increase dramatically during the decline phase of a reservoir, thereby making water-handling capacity an issue. Decision variables in RTPO include production and possibly injection rates, artificial lift inputs like lift-gas rates and electric-submersible pump (ESP) rates, and routing of well streams.
There are also faster time scales, including supervisory and regulatory control, such as those presented in several references [e.g., the operational hierarchy in Fig. 1 in Saputelli et al. (2007)].
This paper focuses on the RTPO problem. The contribution is a method supported by optimization theory that enables us to decompose an RTPO problem into parts (i.e., applying a type of divide-and-conquer strategy). This approach has some intriguing properties highlighted through a simplified field case. To elaborate, the conquer-and-divide strategy is a commonly used engineering design principle that has survived since the complex system came into making. The earlier referenced operational hierarchy in Saputelli et al. (2007) is one example of the conquer-and-divide strategy.
|File Size||383 KB||Number of Pages||7|
Beasley, J. 1993. Lagrangean relaxation. In Modern Heuristics Techniquesfor Combinatorial Problems, ed. C.R. Reeves, 243-303. New York: HalsteadPress.
Bieker, H.P. 2007. Topics in Offshore Oil Production Optimization usingReal-Time Data. PhD dissertation, Norwegian University of Science andTechnology, Trondheim, Norway (June 2007).
Bieker, H.P., Slupphaug O., and Johansen, T.A. 2006. Real-Time Production Optimization ofOil and Gas Production Systems: A Technology Survey. SPE Prod &Oper 22 (4): 382-391. SPE-99446-PA. doi: 10.2118/99446-PA.
Dueñas Díez, M. 2007. How to optimize a petroleum field with 54 wells?Presented at the 5th International Conference on Integrated Operations in thePetroleum Industry (IO 09 Science and Practice), Trondheim, Norway, 29-30September.
Fisher, M.L. 1985. AnApplications Oriented Guide to Lagrangian Relaxation. Interfaces 15 (2): 10-21. doi: 10.1287/inte.15.2.10.
Foss, B. and Halvorsen, I. 2009. Dynamic optimization of the LNG valuechain. In Proceedings of the 1st Annual Gas Processing Symposium, 10-12January 2009, Doha, Qatar, ed. H. Alfadala, G.V.R. Reklaitis, and M.M.El-Halwagi. Amsterdam, The Netherlands: Advances in Gas Processing,Elsevier.
Güyaguler, B. and Byer, T. 2007. A New Production AllocationOptimization Framework. Paper SPE 105200 presented at the SPE ReservoirSimulation Symposium, Houston, 26-28 February. doi: 10.2118/105200-MS.
Hauge, J. and Horn, T. 2005. The Challenge of Operating andMaintaining 115 Subsea Wells on the Troll Field. Paper OTC 17111 presentedat the Offshore Technology Conference, Houston, 2-5 May.
Jackson, J.R. and Grossmann, I.E. 2003. A Temporal Decomposition Scheme forNonlinear Multisite Production Planning and Distribution Models. Industrialand Engineering Chemistry Research 42: 3045-3055.
Kosmidis, V.D., Perkins, J.D., and Pistikopoulos, E.N. 2005. A mixed integeroptimization formulation for the well scheduling problem on petroleumfields. Computers & Chemical Engineering 29 (7):1523-1541. doi:10.1016/j.compchemeng.2004.12.003.
Nocedal, J. and Wright, S.J. 1999. Numerical Optimization. New York:Springer.
Saputelli, L.A., Mijares, G., Rodriguez, J.A., Sankaran, S., Garibaldi, L.,and Revana, K. 2007. ManagingRisk and Uncertainty in the Visualization of Production Scenarios. PaperSPE 107562 presented at the Hydrocarbon Economics and Evaluation Symposium,Dallas, 1-3 April. doi: 10.2118/107562-MS.
Saputelli, L.A., Mochizuki, S., Hutchins, L., Cramer, R., Anderson, M.B.,Mueller, J.B., Escorcia, A. et al. 2003. Promoting Real-Time Optimization ofHydrocarbon Producing Systems. Paper SPE 83978 presented at OffshoreEurope, Aberdeen, 2-5 September. doi: 10.2118/83978-MS.
Selot, A., Kuok, L.K., Robinson, M., Mason, T.L., and Barton, P.I. 2008. A short-term operational planningmodel for natural gas production systems. AIChE Journal 54 (2): 495-515. doi: 10.1002/aic.11385.
Wang, P. 2003. Development and Application of Production Techniques forPetroleum Fields. PhD dissertation, Stanford University, Stanford,California.