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생산 영향인자를 고려한 셰일가스 저류층의 이력검증 및 생산성 평가 연구

A Study on the History Matching and Assessment of Production Performance in a Shale Gas Reservoir Considering Influenced Parameter for Productivity

  • 박경식 (전남대학교 에너지자원공학과) ;
  • 이정환 (전남대학교 에너지자원공학과)
  • Park, Kyung-Sick (Dept. of Energy and Resources Engineering, Chonnam National University) ;
  • Lee, Jeong-Hwan (Dept. of Energy and Resources Engineering, Chonnam National University)
  • 투고 : 2020.07.21
  • 심사 : 2020.08.17
  • 발행 : 2020.08.31

초록

본 연구에서는 캐나다 혼리버(Horn-River) 분지를 대상으로 셰일가스 저류층의 신뢰성 있는 생산성 평가와 미래 생산량 예측을 위한 효율적인 이력검증(history matching) 방법을 제안하였다. 이를 위해 셰일가스 저류층의 물성인자가 생산성에 미치는 영향을 분석하기 위한 민감도 분석을 수행하였으며, 그 결과를 바탕으로 저류층 물성인자를 4가지 case로 분류하여 이력검증의 목적함수로 활용하였다. 이력검증 이후 추가 취득된 약 3년간의 생산 자료를 포함하여 맹검시험(blind test)을 수행한 결과, Case 1(모든 물성인자)은 7.67%, Case 2(생산 영향인자)는 7.13%, Case 3(제어 가능 물성인자)는 17.54%, Case 4(제어 불가능 물성인자)는 10.04%의 생산량 오차율이 나타났다. 이는 이력검증을 수행한 초기 4년간의 생산 자료의 경우에는 모든 물성인자를 고려한 생산예측이 효과적이나, 향후 생산량 예측을 함에 있어 Case 2와 같이 생산성에 대해 민감도가 높은 물성인자를 고려할 때 가장 높은 신뢰도가 나타남을 의미한다. 가장 높은 신뢰도를 갖는 Case 2 모델을 이용해서 예측한 셰일가스 저류층 생산정의 긍극가채매장량은 2030년 12월 기준 약 17.24 Bcf이며, 원시부존량 대비 회수율은 약 32.2%이다.

This study presents a methodology of history matching to evaluate the productivity of shale gas reservoir with high reliability and predict future production rate in the Horn-River basin, Canada. Sensitivity analysis was performed to analyze the effect of physical properties of shale gas reservoir on productivity. Based on the results, reservoir properties were classified into 4 cases and history matching were performed considering the classified 4 cases as objective function. The blind test was conducted using additional field production data for 3 years after the history matching period. The error of gas production rate in Case 1(all reservoir parameters), Case 2(influenced parameters for productivity), Case 3(controllable parameters), and Case 4(uncontrollable parameters) were 7.67%, 7.13%, 17.54%, and 10.04%, respectively. This means that it seems to be effective to consider all reservoir parameters in early period for 4 years but Case 2 which considered influenced parameters for productivity shows the highest reliability in predicting future production. The estimated ultimate recovery (EUR) of production well predicted using the Case 2 model was estimated to be 17.24 Bcf by December 2030 and the recovery factor compared to original gas in place (OGIP) was 32.2%.

키워드

참고문헌

  1. Jarvie, D.M., Hill, R.J., Ruble, T.E., and Pollastro, R.M., "Unconventional shale-gas system: The Mississippian Barnett Shale of North-Central Texas as one model for thermogenic shale-gas assessment", AAPG Bulletin, 91(4), 475-499, (2007) https://doi.org/10.1306/12190606068
  2. Kundert, D., Mullen, M., "Proper evaluation of shale gas reservoirs leads to a more effective hydraulic-fracture stimulation", SPE 123586. Proceedings of the SPE Rocky Mountain Petroleum Technology Conference, Denver, Colorado, USA, (2009)
  3. Diaz, H.G., Lewis, R., Miller, R., and Fuentes, C.C., "Evaluating the impact of mineralogy on reservoir quality and completion quality of organic shale plays", AAPG Rocky Mountain Section Meeting, Salt Lake City, Utah, USA, (2013)
  4. Ko, K.N., Jeong, T.J., Kim, K.S., Park, K.S., and Woo, I.S., "A study of shale gas field sweet spot determination process", Journal of the Geological Society of Korea, 52(6), 799-814, (2016) https://doi.org/10.14770/jgsk.2016.52.6.799
  5. Kim, T.H., Park, K. and Lee, K.S., "Application of type curves for pressure transient analysis of multiple fractured horizontal wells in shale gas reservoirs", International Journal Oil. Gas and Coal Technology, 12(4), 359-378, (2016) https://doi.org/10.1504/IJOGCT.2016.077314
  6. Zhang, H., Wang, J., and Zhang H., "Investigation of the main factors during shale-gas production using Grey relation analysis", The Open Petroleum Engineering Journal, 9, 207-215, (2016) https://doi.org/10.2174/1874834101609160207
  7. Wang, H.Y., "What factors control shale-gas production and production-decline trend in fractured systems: A comprehensive analysis and investigation", SPE/IAEE Hydrocarbon Economics and Evaluation Symposium, Houston, Texas, USA, SPE-179967-MS, (2017)
  8. Reynolds, M.M., Munn, D.L., "Development update for an emerging shale gas giant field - Horn River basin, British Columbia, Canada", SPE Unconventional Gas Conference, Pittsburgh, Pennsylvania, USA, (2010)
  9. Petrel Roberston Consulting Ltd., "Horn River Basin aquifer characterization geological report", prepared for Horn River Basin Producers Group Geoscience BC, (2010)
  10. Warpinski, N.R., and Teufel, L.W., "Influence of geologic discontinuities on hydraulic fracture propagation", J. Pet. Technol., 39(2), SPE-13224-PA, (1987)
  11. Cipolla, C.L., Warpinski, N.R., Mayerhofer, M.J., Lolon, E.P. and Vincent, M.C., "The relationship between fracture complexity, reservoir properties, and fracture treatment design", SPE Production & Operations, 25(4), 438-452, SPE 115769, (2008)
  12. Novlesky, A., Kumar, A., Merkle, S., "Shale gas modeling workflow: From microseismic to simulation-A Horn River case study", Canadian Unconventional Resources Conference, Calgary, Alberta, Canada, (2011)
  13. Computer Modeling Group, User's Guide CMOST computer assisted history matching, optimization and uncertainty assessment tool, Computer Modeling Group Ltd., Calgary, Alberta, Canada, (2012).
  14. Lei, G., Dong, P.C., Yang, S., Li, Y.S., Mo, S.Y., Gai, S.H., and Wu, Z.S., "A new analytical equation to predict gas-water two-phase relative permeability curves in fractures", International Petroleum Technology Conference, Kuala Lumpur, Malaysia, SPE 17966, ( 2014)
  15. Jang, H., Lee, J., "Effect of fracture design parameters on the well performance in a hydraulically fractured shale gas reservoir," Energy Exploration & Exploitation, 33(2), 157-168, (2015) https://doi.org/10.1260/0144-5987.33.2.157
  16. Kim, J.G., Kang, I.O., Shin, C.H., Lee, S.M., and Lee, J.H., "A study on the effect of flow properties in shale gas reservoirs", Journal of the Korean Institute of Gas, 21(2), 50-57, (2017) https://doi.org/10.7842/kigas.2017.21.2.50