Optimization of well placement and control to maximize CO2 trapping during geologic sequestration

• Autorzy: Stopa J. , Janiga D. , Wojnarowski P. , Czarnota R.

Identyfikatory

DOI

10.7494/drill.2016.33.1.93

• Języki publikacji: EN

Abstrakty

EN

The CO2 injection into geological formations such as saline aquifers can be effective method of sequestration enabling efficient immobilization of gas by surface and capillary forces. The main objective of presented method of optimization was to determine optimal well position and injection rates that maximize residual trapping. This would mitigate the risk of the CO2 leakage outside of storage zone. A genetic algorithm and particle swarm optimization have been developed and coupled with reservoir simulator to optimally examine various placement and injection control strategies for vertical as well as horizontal well. Optimization was carried out in 3D heterogeneous real field model with water-CO2 -formation rock relationship. Numerical examples confirm that developed algorithm can find a promising optimal solution effectively within a partial number of simulation runs.

Słowa kluczowe

EN

CO2 sequestration; optimization; numerical simulation

• Wydawca: AGH University of Science and Technology Press
• Czasopismo: AGH Drilling, Oil, Gas
• Rocznik: 2016
• Tom: Vol. 33, no. 1
• Strony: 93--104
• Opis fizyczny: Bibliogr. 18 poz., rys., tab., wykr., zdj.

Twórcy

autor

Stopa J.

• AGH University of Science and Technology, Faculty of Drilling, Oil and Gas, Krakow, Poland

autor

Janiga D.

• AGH University of Science and Technology, Faculty of Drilling, Oil and Gas, Krakow, Poland

autor

Wojnarowski P.

• AGH University of Science and Technology, Faculty of Drilling, Oil and Gas, Krakow, Poland

autor

Czarnota R.

• AGH University of Science and Technology, Faculty of Drilling, Oil and Gas, Krakow, Poland

Bibliografia

• [1] Gale J.: Overview of Sources, Potential, Transportation and Geological Distribution of Storage Possibilities. Presentation at IPCC Workshop on Carbon Capture and Storage, Regina, Canada, 2002.
• [2] Song J., Zhang D.: Comprehensive review of caprock-sealing mechanisms for geologic carbon sequestration. EnvSci& Tech 47, 2012, pp. 2–22.
• [3] Juanes R., Spiteri E.J., Orr F.M. Jr., Blunt M.J.: Impact of Relative Permeability Hysteresis on Geological CO2 Storage. Water Resources Research, vol. 42, 2006, pp. 1–13.
• [4] Kumar A., Noh M., Pope G.A., Sepehrnoori K., Bryant S., Lake L.W.: Reservoir Simulation of CO2 Storage in Deep Saline Aquifers. SPE Journal, September 2005, pp. 336–348.
• [5] Mo S., Akervoll I.: Modeling Long-Term CO2 Storage in Aquifer with a Black-Oil Reservoir Simulator. Paper SPE 93951 presented at SPE/EPA/DOE Exploration and Production Environmental Conference, 2005, pp. 1–9.
• [6] Bachu S.: CO2 storage in geological media: Role, means, status and barriers to deployment. Progress in Energy and Combustion Science, 34, 2008, pp. 254–273.
• [7] Adamczyk K., Premont-Schwarz M., Pines D., Pines E., Nibbering E.T.J.: Real-time observation of carbonic acid formation in aqueous solution. Science, 2009, pp. 1690–1694.
• [8] Black J.R., Carroll S.A., Haese R.R.: Rates of mineral dissolution under CO2 storage conditions. Chemical Geology, 399, 2015, pp. 134–144.
• [9] Sarma P., Chen W.: Efficient well placement optimization with gradient-based algorithms and adjoint models. SPE 112257. In: SPE Intelligent Energy Conference and Exhibition, 2008, pp. 1–18.
• [10] Zandvliet M.H.M., van Essen G., Brouwer R., Jansen J.D.: Adjoint-based well-placement optimization under production constraints. SPE Journal, 13(4), 2008, pp. 392–399.
• [11] Yeten B., Durlofsky L.J., Aziz K.: Optimization of nonconventional well type, location, and trajectory. SPE Journal, 8, 2003, pp. 200–210.
• [12] Onwunalu J., Durlofsky L.J.: Application of a particle swarm optimization algorithm for determining optimum well location and type. Computational Geosciences, 14(1), 2010, pp. 183–198.
• [13] Jansen J.D., Brouwer D., Naevdal G., Van Kruijsdijk C.: Closed-loop reservoir management. First Break, 23(1), 2005, pp. 43–48.
• [14] Kennedy J., Eberhardt R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Joint Conference on Neural Networks, 1995, pp. 1942–1947.
• [15] Eberhardt R.C., Kennedy J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micromachine and Human Science, 1995, pp. 39–43.
• [16] Eberhardt R.C., Shi Y.: A modified particle swarm optimizer. In: Proceedings of the IEEE International Conference on Evolutionary Computation, 1998, pp. 69–73.
• [17] Engelbrecht A.P.: Fundamentals of Computational Swarm Intelligence. Wiley, 2005.
• [18] Killough J.E.: Reservoir simulation with history-dependent saturation functions. Soc. Pet. Eng. J., 16(1), 1976, pp. 37 – 48.

Uwagi

The research leading to these results has received funding from the Polish-Norwegian Research Programme operated by the National Centre for Research and Development under the Norwegian Financial Mechanism 2009–2014 in the frame of Project Contract No. Pol-Nor/235294/99/2014.

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.