Intelligent control of CO2-EOR process

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

Identyfikatory

DOI

10.7494/drill.2018.35.1.235

• Języki publikacji: EN

Abstrakty

EN

One of the enhanced oil recovery methods, which enables to recover an additional 15–20% of oil resources is the CO2-EOR method based on carbon dioxide injection into partially depleted reservoirs. Determination of the optimal process control facilitates effective use of natural resources. The idea of this paper is to develop an algorithm that optimizes the CO2-EOR process. This algorithm is based on the combination of artificial intelligence, control theory and computer simulation of hydrocarbon reservoirs. The effect of the proposed solution is the CO2-EOR process control, which is optimal in the case of the adopted objective function expressing the economic value of the project. The obtained results suggest that the use of artificial intelligence methods in the hydrocarbon production allows to improve the process efficiency by an additional 31% compared to the project carried out with the use of engineering knowledge.

Słowa kluczowe

EN

CO2-EOR; production optimization; intelligent control; artificial intelligence

• Wydawca: AGH University of Science and Technology Press
• Czasopismo: AGH Drilling, Oil, Gas
• Rocznik: 2018
• Tom: Vol. 35, no. 1
• Strony: 235--243
• Opis fizyczny: Bibliogr. 19 poz., wykr.

Twórcy

autor

Mikołajczak E.

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

autor

Stopa J.

• 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

Janiga D.

• 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] Bello O., Holzmann J., Yaqoob T., Teodoriu C.: Application of artificial intelligence methods in drilling system design and operations: a review of the state of the art. Journal of Artificial Intelligence and Soft Computing Research, vol. 5, no. 2, 2015, pp. 121–139.
• [2] Chathurangani L.B.J., Halvorsen B.M.: Near well simulation of CO2 injection for Enhanced Oil Recovery (EOR). Conference on Simulation and Modelling, Linkőping University, Sweden, Oct. 7–9 2015.
• [3] Garrouch A.A., Lababidi H.M.S., Ebrahim A.S.: A web-based expert system for the planning and completion of multilateral wells. Journal of Petroleum Science and Engineering, vol. 49, iss. 3–4, 2005, pp. 162–181.
• [4] Gaspar A.T.F.S., Baretto C.E.A.G., Schiozer D.J.: Assisted process for design optimization of oil exploitation strategy. Journal of Petroleum Science and Engineering, vol. 149, 2016, pp. 473–488.
• [5] Guerillot D.R.: EOR Screening with an Expert System. SPE-17791-MS, 1988.
• [6] Hutter F., Hoos H.H., Leyton-Brown K.: An evaluation of sequential model-based optimization for expensive blackbox functions. Annual Conference Companion on Genetic and Evolutionary Computation, 2013.
• [7] Janiga D., Stopa J., Mikołajczak E., Wojnarowski P., Czarnota R.: Smart control of CO2 Huff and Puff process in dual porosity reservoir. 17th International Multidisciplinary Scientific GeoConference, Vienna, Austria, Nov. 27–29 2017.
• [8] Jeong M.S., Lee K.S.: Maximizing Oil Recovery for CO2 Huff and Puff Process in Pilot Scale Reservoir. World Congress on ACEM15, Icheon, Korea, 2015.
• [9] Mohaghegh S., Balan B., Ameri S., McVey D.S.: A Hybrid, Neuro-Genetic Approach to Hydraulic Fracture Treatment Design and Optimization. SPE-36602-MS, 1996.
• [10] Popa A.S.: Identification of Horizontal Well Placement Using Fuzzy Logic. SPE-166313-MS, 2013.
• [11] Romero C.E., Carter J.N., Gringarten A.C., Zimmerman R.W.: A Modified Genetic Algorithm for Reservoir Characterisation. SPE-64765-MS, 2000.
• [12] Różanowski K.: Sztuczna inteligencja: rozwój, szanse i zagrożenia, Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki, 2007, pp. 109–136.
• [13] Rychlicki S., Stopa J., Uliasz-Misiak B., Zawisza L.: Kryteria typowania złóż do zastosowania zaawansowanej metody wydobycia ropy naftowej poprzez zatłaczanie CO2. Gospodarka Surowcami Naturalnymi, t. 27, nr 3, 2011, pp. 125–139.
• [14] Salam D.D., Gunari I., Yasutra A.: Production Optimization Strategy Using Hybrid Genetic Algorithm. SPE-177442-MS, 2015.
• [15] Stopa J., Janiga D., Wojnarowski P, Czarnota R.: Optimization of well placement and control to maximize CO2 trapping during geologic sequestration. AGH Drilling, Oil, Gas, vol. 33, no. 1, 2016, pp. 93–104.
• [16] Tadeusiewicz R., Paliwoda-Pękosz G., Lula P.: Metody sztucznej inteligencji i ich zastosowania w ekonomii i zarządzaniu. Wydawnictwa Akademii Ekonomicznej w Krakowie, Kraków 2007.
• [17] Uliasz-Misiak B., Kosowski P., Lewandowska-Śmierzchalska J.: Analysis of reservoir properties and parameters of oil fields suitable for the application of CO2-EOR method. AGH Drilling, Oil, Gas, vol. 32, no. 1, 2015, pp. 33–48.
• [18] White A.C., Molnar D., Aminian K., Mohaghegh S., Ameri S., Esposito P.: Application of ANNs for zone identification on a complex reservoir. SPE-30977-MS, 1995.
• [19] Wu F.H., Chiu T.J., Dairymple D., Dahl J., Rahimi A.B.: Development of an Expert System for Water Control Applications. SPE-27552-MS, 1994.

Uwagi

EN

Paper prepared within the statutory research program of the Faculty of Drilling, Oil and Gas, AGH University of Science and Technology no. 11.11.190.555

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).