سامانه پشتیبانی خدمات اطلاعات

  اخبار و اعلانات

 آمار

تعداد نشریات161
تعداد شماره‌ها6,532
تعداد مقالات70,501
تعداد مشاهده مقاله124,097,102
تعداد دریافت فایل اصل مقاله97,204,691
Bahonar, Ehsan, Ghalenoei, Yaser, Salmani, Sadegh. (1401). New Relative Permeability Correlations for Carbonate Reservoirs through Data-Driven Modeling. , 56(1), 15-35. doi: 10.22059/jchpe.2021.332924.1369
Ehsan Bahonar; Yaser Ghalenoei; Sadegh Salmani. "New Relative Permeability Correlations for Carbonate Reservoirs through Data-Driven Modeling". , 56, 1, 1401, 15-35. doi: 10.22059/jchpe.2021.332924.1369
Bahonar, Ehsan, Ghalenoei, Yaser, Salmani, Sadegh. (1401). 'New Relative Permeability Correlations for Carbonate Reservoirs through Data-Driven Modeling', , 56(1), pp. 15-35. doi: 10.22059/jchpe.2021.332924.1369
Bahonar, Ehsan, Ghalenoei, Yaser, Salmani, Sadegh. New Relative Permeability Correlations for Carbonate Reservoirs through Data-Driven Modeling. , 1401; 56(1): 15-35. doi: 10.22059/jchpe.2021.332924.1369

New Relative Permeability Correlations for Carbonate Reservoirs through Data-Driven Modeling

Journal of Chemical and Petroleum Engineering
دوره 56، شماره 1، شهریور 2022، صفحه 15-35    اصل مقاله (1.48 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jchpe.2021.332924.1369
نویسندگان
Ehsan Bahonar* 1؛ Yaser Ghalenoei2؛ Sadegh Salmani3
1Faculty of Petroleum and Natural Gas Engineering, Sahand University of Technology, Tabriz, Iran.
2Faculty of Petroleum and Natural Gas Engineering, Sahand University of Technology, Tabriz, Iran
3Ahwaz Faculty of Petroleum Engineering, Petroleum University of Technology, Ahvaz, Iran.
چکیده
Relative permeability is a crucial input to reservoir simulators for modeling reservoir performance. Conventional methods of measuring relative permeability rely on either laboratory core-flooding experiments or fine-scale computer simulations. The former method is expensive and time-consuming, and the latter often does not represent the complex characteristics of existing systems. Data mining algorithms can be implemented to estimate relative permeability with reasonable accuracy for real applications without running laboratory or computer simulation experiments. This paper aims at presenting predictive correlations for relative permeability for carbonate rocks using data-driven approaches. To achieve this aim, a scatter plot matrix was applied to analyze 225 experimental datasets, including almost 3800 relative permeability data points (observations), for predicting relative permeability. Since relative permeability measures are often unavailable exactly at residual oil saturation and connate water saturation (known as endpoints); consequently, cubic equations were fitted and solved to precisely determine these points. Next, a symbolic regression algorithm was developed to predict relative permeability in different situations: when endpoints are available or unavailable and when the rock wettability is clear or not. For this purpose, all 225 datasets were divided into training and testing groups. The correlations were tested to predict testing data, which the symbolic regression algorithm has never seen before. Finally, the most accurate correlations were presented, and a detailed analysis was carried out. The results showed a good agreement between the real and the predicted data. The developed correlations proved to be very efficient in predicting the relative permeability accurately.
کلیدواژه‌ها
Correlation؛ Data Mining؛ End Point؛ Relative Permeability؛ Wettability
مراجع
  1. Honarpour MM, Koederitz L, Harvey AH. Relative permeability of petroleum reservoirs. Relative Permeability of Petroleum Reservoirs. CRC press; 2018. 1–143.
  2. Heaviside J, Black CJJJ, Berry JF. Fundamentals of relative permeability: experimental and theoretical considerations. In: SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers; 1983.
  3. Benjamin WJ, Ahmed T. Reservoir engineering handbook. Vol. 27, Cardiovascular Imaging. Gulf professional publishing; 2018. 11–13, 11f, 537, 992–1001 p. Available from: http://www.lavoisier.fr/notice/fr283071.
  4. Purswani P, Johns RT, Karpyn ZT, Blunt MJ. Predictive Modeling of Relative Permeability Using a Generalized Equation of State. SPE J. 2021;26(01):191–205.
  5. Khaz’ali AR, Emamjomeh A, Andayesh M. An accuracy comparison between artificial neural network and some conventional empirical relationships in estimation of relative permeability. Pet Sci Technol. 2011;29(15):1603–14.
  6. Miller MA, Ramey HJ, Ramey Jr HJ. Effect of temperature on oil/water relative permeabilities of unconsolidated and consolidated sands. Soc Pet Eng J. 1985;25(06):945–53.
  7. Sedaee Sola B, Rashidi F, Babadagli T. Temperature effects on the heavy oil/water relative permeabilities of carbonate rocks. J Pet Sci Eng. 2007;59(1–2):27–42.
  8. Esmaeili S, Modaresghazani J, Sarma H, Harding T, Maini B. Effect of temperature on relative permeability – Role of viscosity ratio. Fuel. 2020;278(June):118318. Available from: https://doi.org/10.1016/j.fuel.2020.
  9. Jahanbakhsh A, Sohrabi M, Fatemi SM, Shahverdi H. A comparative study of the effect of gas/oil IFT variation on two- and three-phase relative permeability and the performance of WAG injection at laboratory scale. Proc - SPE Symp Improv Oil Recover. 2016;2016-Janua.
  10. Mai A, Kantzas A. Heavy oil waterflooding: effects of flow rate and oil viscosity. J Can Pet Technol. 2009;48(03):42–51.
  11. Morgan JT, Gordon DT. Influence of pore geometry on water- oil relative permeability. JPT, J Pet Technol. 1970;22(10):1199–208.
  12. Zhao B, Ratnakar R, Dindoruk B, Mohanty K. A Hybrid Approach for the Prediction of Relative Permeability Using Machine Learning of Experimental and Numerical SCAL Data. In: SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers; 2019.
  13. Gates I, Gates JI, Lietz WT. Relative permeabilities of California cores by the capillary-pressure method. In: Drilling and production practice. American Petroleum Institute; 1950.
  14. Chwang AT, Corey AT, Rathjens CH. Effect of stratification on relative permeability. J Pet Technol. 1956;8(12):69–71.
  15. Torcaso MA, Wyllie MRJRJ. A comparison of Calculated krg/kro Ratios with a Correlation of Field Data. J Pet Technol. 1958;10(12):57–8.
  16. Pirson SJ. Petrophysics. Oil Reserv Eng ed) New York, USA McGraw-Hill B Co, Inc. 1958.
  17. Naar J, Henderson JHH. An imbibition model-its application to flow behavior and the prediction of oil recovery. Soc Pet Eng J. 1961;1(02):61–70.
  18. Brooks RH, Corey AT, Brentrup G, Moawad HMM, Jain H. Hydraulic properties of porous media. Phys Chem Glas Eur J Glas Sci Technol Part B. 1964;49(6):293–6.
  19. Land CS. Comparison of calculated with experimental imbibition relative permeability. Soc Pet Eng J. 1971;11(04):419–25.
  20. Owens WW, Archer Dl. The effect of rock wettability on oil-water relative permeability relationships. J Pet Technol. 1971;23(07):873–8.
  21. Carlson FM. Simulation of relative permeability hysteresis to the nonwetting phase. In: SPE annual technical conference and exhibition. Society of Petroleum Engineers; 1981.
  22. Honarpour M, Koederitz LF, Harvey AH. Empirical equations for estimating two-phase relative permeability in consolidated rock. J Pet Technol. 1982;34(12):2–905.
  23. Timmerman EH. Practical Reservoir Engineering, Part II, Methods for Analyzing Output from Equations and Computers. Penn Well Books, Penn Well Publishing Company, Tulsa, OK, USA; 1982.
  24. Fulcher RA, Ertekin T, Stahl CD, Fulcher Jr RA, Ertekin T, Stahl CD. Effect of capillary number and its constituents on two-phase relative permeability curves. J Pet Technol. 1985;37(02):249–60.
  25. Alpak FO, Lake LW, Embid SM. Validation of a modified Carman-Kozeny equation to model two-phase relative permeabilities. In: SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers; 1999. p. 1–11.
  26. Mohamad Ibrahim MN, Koederitz LF, Ibrahim MN, Koederitz LF. Two-phase relative permeability prediction using a linear regression model. In: SPE eastern regional meeting. Society of Petroleum Engineers; 2000. p. 111–21.
  27. Behrenbruch P, Goda HM. Two-Phase Relative Permeability Prediction: A comparison of the modified Brooks-Corey methodology with a new Carman-Kozeny based flow formulation. In: SPE Asia Pacific Oil & Gas Conference and Exhibition. Society of Petroleum Engineers; 2006. p. 810–27.
  28. Ghanbarian B, Ioannidis MA, Hunt AG, Perry LK. Theoretical insight into the empirical tortuosity‐connectivity factor in the Burdine‐Brooks‐Corey water relative permeability model. Water Resour Res. 2017;53(12):10395–410.
  29. Andrew M. Permeability prediction using multivariant structural regression. In: E3S Web of Conferences. EDP Sciences; 2020. p. 4001.
  30. Jung S, Sabharwal M, Jarauta A, Wei F, Gingras M, Gostick J, et al. Estimation of Relative Transport Properties in Porous Transport Layers Using Pore-Scale and Pore-Network Simulations. J Electrochem Soc. 2021;168(6):64501.
  31. Mehmani Y, Tchelepi H. Pore-network modeling vs. direct numerical simulation: A comparative study. In: Agu fall meeting abstracts. 2016. p. H51D-1519.
  32. Sorbie KS, Skauge A. Can network modeling predict two-phase flow functions? Petrophysics. 2012;53(06):401–9.
  33. Raoof A, Nick HM, Hassanizadeh SM, Spiers CJ. PoreFlow: A complex pore-network model for simulation of reactive transport in variably saturated porous media. Comput Geosci. 2013;61:160–74.
  34. Zhao B, Ratnakar R, Dindoruk B, Mohanty K. A Hybrid Approach for the Prediction of Relative Permeability Using Machine Learning of Experimental and Numerical SCAL Data. SPE Annu Tech Conf Exhib. 2019;25(5):2749–64.
  35. Luo G, Tian Y, Bychina M, Ehlig-Economides C. Production optimization using machine learning in Bakken shale. In: Unconventional Resources Technology Conference, Houston, Texas, 23-25 July 2018. Society of Exploration Geophysicists, American Association of Petroleum …; 2018. p. 2174–97.
  36. Bestagini P, Lipari V, Tubaro S. A machine learning approach to facies classification using well logs. In: Seg technical program expanded abstracts 2017. Society of Exploration Geophysicists; 2017. p. 2137–42.
  37. Bahonar E, Ghalenoei Y, Chahardowli M, Simjoo M. New correlations to predict oil viscosity using data mining techniques. J Pet Sci Eng. 2021;109736.
  38. Ramirez AM, Valle GA, Romero F, Jaimes M. Prediction of PVT properties in crude oil using machine learning techniques MLT. In: SPE Latin America and Caribbean Petroleum Engineering Conference. Society of Petroleum Engineers; 2017.
  39. Ahmadi MA, Chen Z. Analysis of gas production data via an intelligent model: Application to natural gas production. First Break. 2018;36(12):91–8.
  40. Al-Fattah SM, Al-Naim HA. Artificial-intelligence technology predicts relative permeability of giant carbonate reservoirs. SPE Reserv Eval Eng. 2009;12(1):96–103.
  41. Graczyk KM, Matyka M. Predicting porosity, permeability, and tortuosity of porous media from images by deep learning. Sci Rep. 2020;10(1):1–11.
  42. Kalam S, Khan M, Khan RA, Alam MMM, Sadeed A, Mahmoud M, et al. Advancing Relative Permeability Estimation Through Data-Driven Modeling. In: Abu Dhabi International Petroleum Exhibition & Conference. OnePetro; 2020.
  43. Adibifard M, Talebkeikhah M, Sharifi M, Hemmati-Sarapardeh A. Iterative Ensemble Kalman Filter and genetic algorithm for automatic reconstruction of relative permeability curves in the subsurface multi-phase flow. J Pet Sci Eng. 2020;192(March 2019):107264.
  44. Mirzaei-Paiaman A. New methods for qualitative and quantitative determination of wettability from relative permeability curves: Revisiting Craig’s rules of thumb and introducing Lak wettability index. Fuel. 2021;288(November 2020):119623.
آمار
تعداد مشاهده مقاله: 512
تعداد دریافت فایل اصل مقاله: 1,011


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب