Journal of the Korean Institute of Gas (한국가스학회지)

The Korean Institute of GAS (한국가스학회)
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A Study on Production Well Placement for a Gas Field using Artificial Neural Network

인공신경망 시뮬레이터를 이용한 가스전 생산정 위치선정 연구

  • Han, Dong-Kwon (Dept. of Energy and Resource, Dong-A University) ;
  • Kang, Il-Oh (Korea Gas Research & Development Division, KOGAS) ;
  • Kwon, Sun-Il (Dept. of Energy and Resource, Dong-A University)
  • 한동권 (동아대학교 에너지자원공학과) ;
  • 강일오 (한국가스공사 연구개발원) ;
  • 권순일 (동아대학교 에너지자원공학과)
  • Received : 2013.03.18
  • Accepted : 2013.04.23
  • Published : 2013.04.30
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Abstract

This study presents development of the ANN simulator for well placement of infill drilling in gas fields. The input data of the ANN simulator includes the production time, well location, all inter well distances, boundary inter well distance, infill well position, productivity potential, functional links, reservoir pressure. The output data includes the bottomhole pressure in addition to the production rate. Thus, it is possible to calculate the productivity and bottomhole pressure during production period simultaneously, and it is expected that this model could replace conventional simulators. Training for the 20 well placement scenarios was conducted. As a result, it was found that accuracy of ANN simulator was high as the coefficient of correlation for production rate was 0.99 and the bottomhole pressure 0.98 respectively. From the resultes, the validity of the ANN simulator has been verified. The term, which could produce Maximum Daily Quantity (MDQ) at the gas field and the productivity according to the well location was analyzed. As a result, the MDQ could be maintained for a short time in scenario C-1, which has the three infill wells nearby aquifer boundary, and a long time in scenario A-1. In conclusion, it was found that scenario A maintained the MDQ up to 21% more than those of scenarios B and C which include parameters that might affect the productivity. Thus, the production rate can be maximized by selecting the location of production wells in comprehensive consideration of parameters that may affect the productivity. Also, because the developed ANN simulator could calculate both production rate and bottomhole pressure, respectively, it could be used as the forward simulator in a various inverse model.

본 연구에서는 가스전의 추가 생산정 위치선정을 위해 고속의 연산이 가능한 인공신경망을 이용하여 저류 전산시뮬레이터를 개발하였다. 입출력자료와 알고리즘을 설계하였으며, 개발한 시뮬레이터를 이용하여 가스전의 추가 생산정 위치선정을 위한 연구를 수행하였다. 입력값은 생산시간, 생산정간 상관관계, 추가 생산정 위치좌표, 생산성 잠재력, 함수적 연관관계, 저류층 압력으로 구성하였으며, 출력값은 생산량과 함께 공저압력을 동시에 사용하였다. 20가지의 생산정 위치 시나리오에 대해 학습을 수행한 결과, 생산량의 상관계수 값은 0.99, 공저압력은 0.98로 상관관계가 매우 높은 것으로 확인되어 인공신경망 시뮬레이터의 타당성이 검증되었다. 가스전에서 최대공급계약량 유지시점을 산출함으로써 생산정 위치에 따른 생산성을 분석하였다. 그 결과 시나리오 C-1이 최대공급계약량 유지기간이 가장 짧았으며, 시나리오 A-1이 가장 오랫동안 유지시킬 수 있는 것으로 산출되었다. 결론적으로, 시나리오 A가 생산성에 영향을 받는 인자를 포함한 시나리오 B, C보다 최대 21% 더 최대공급계약량을 유지시킬 수 있는 것으로 확인되었다. 따라서 생산성에 영향을 미치는 요소를 종합적으로 고려하여 생산정의 위치를 선정해야 생산량을 극대화 할 수 있다. 본 인공신경망 시뮬레이터를 이용 시 생산기간동안 생산량과 공저압력 변화를 동시에 비교 분석하는 것이 가능하여 다양한 최적화 모델에 전위모델로 사용하는 것이 가능하다.

Keywords

References

  1. H. Doraisamy, "Methods of Neuro-Simulation for Field Development", Paper SPE 39962, presented at the Rocky Moutain Regional/Low-Permeability Reservoirs symposium held in Denver, Colorado, April 5-8, (1998).
  2. A. Centilmen, T. Ertekin., and A. S. Grader., "Applications of Neural Networks in Multiwell Field Development", Paper SPE 56433, presented at the SPE Annual Technical Conference and Exhibition held in Houston, Texas, October 3-6, (1999).
  3. Z.. He., L. Yang., J. Yen., and C. Wu., "Neural- Network Approach To Predict Well Performance Using Available Field Data", Paper SPE 68801, presented at the Western Regional Meeting held in Bakersfield, California, March 26-30, (2001).
  4. B.H., Min, "Well Placement Optimization Using Artificial Neural Network" Thesis submitted Seoul National Univ., The degree of master, (2011)
  5. H. Doraisamy., T. Ertekin., and A.S. Grader., "Key Parameters Controlling the Performance of Neuro-Simulation Applications in Field Development", Paper SPE 51079, presented at the Eastern Regional Meeting held in Pittsburgh, PA, November 9-11, (1998).
  6. Cheng, Y., McVay, D.A., Wang, J., and Ayers, W.B., "Simulation-Based Technology for Rapid Assessment of Redevelopment Potential in Marginal Gas Fields-Technology Advances and Validation in Garden Plains Field, Western Canada Sedimentary Basin," SPE Reservoir Evaluation & Engineering, Vol. 11, No. 3, pp. 521-534, (2008). https://doi.org/10.2118/100583-PA
  7. B. Guyaguler, "Optimization of Well Placement in a Gulf of Mexico Waterflooding Project", Paper SPE 63221, presented at the Annual Technical Conference and Exhibition held in Dallas, Texas, October 1-4, (2000).
  8. I. Uraz, "Optimization of Well Placement in complex carbonate reservoirs using Artificial Intelligence", A thesis submitted Middle East Technical Univ, The degree of master of Scince in Petroleum and Natural Gas Engineering, December, (2004)
  9. I,O., Kang, "A Study on Numerical Simulation for Production Well Placement Optimization" Thesis submitted Dong-A Univ., The degree of master, (2011)
  10. W.S., Lee, S.J., Kim, and S.H., Nam, "Simulation Study on Water-Drive Gas Reservoir Performance" Journal of Korean Society for Geosystem Engineering, Vol. 43, No. 4, pp. 282-292, (2006)