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《工程(英文)》 >> 2022年 第18卷 第11期 doi: 10.1016/j.eng.2022.04.015

裂缝性储层数据驱动模型证伪与不确定性量化

a School of Earth Resources, China University of Geosciences, Wuhan 430074, China
b Key Laboratory of Tectonics and Petroleum Resources of the Ministry of Education, Wuhan 430074, China
c Department of Geological Sciences, Stanford University, Stanford 94305, USA

收稿日期: 2021-10-27 修回日期: 2022-02-21 录用日期: 2022-04-24 发布日期: 2022-06-11

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摘要

天然裂缝的许多特性是不确定的,如裂缝的空间分布、岩石物理特性和流体流动性能。贝叶斯定理提供了一个框架来量化地质建模和流动模拟的不确定性,从而支持储层物性预测。贝叶斯方法在裂缝性储层中的应用大多局限于合成案例。然而,在现场应用中,一个主要问题是贝叶斯先验是被证伪的,因为它不能预测油气藏的生产历史。在本文中,我们展示了如何利用全局敏感性分析(GSA)来确定先验被证伪的原因。然后,我们采用近似贝叶斯计算(ABC)方法,结合基于决策树的代理模型来拟合生产历史。我们将这两种方法应用于一个复杂的裂缝性油气藏,其中综合考虑了所有不确定因素,包括油层物理特性、岩石物理特性、流体特性、离散裂缝参数以及压力和渗透率的动态变化。我们成功地找出了证伪的几个原因。结果表明,我们提出的方法可以有效地量化裂缝性储层建模和流动模拟的不确定性。此外,关键参数的不确定性,如裂缝开度和断层传导率,得到了降低。

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参考文献

[ 1 ] Bourbiaux B. Fractured reservoir simulation: a challenging and rewarding issue. Oil Gas Sci Technol 2010;65(2):227‒38. 链接1

[ 2 ] Azizmohammadi S, Matthai SK. Is the permeability of naturally fractured rocks scale dependent? Water Resour Res 2017;53(9):8041‒63. 链接1

[ 3 ] Nelson RA. Geological analysis of naturally fractured reservoirs. 2nd ed. Woburn: Gulf Publishing Company; 1985.

[ 4 ] Turcott DL, Moores EM, Rundle JB. Super fracking. Phys Today 2014;67(8):34‒9. 链接1

[ 5 ] Bonnet E, Bour O, Odling NE, Davy P, Main I, Cowie P, et al. Scaling of fracture systems in geological media. Rev Geophys 2001;39(3):347‒83. 链接1

[ 6 ] Li Y, Shang Y, Yang P. Modeling fracture connectivity in naturally fractured reservoirs: a case study in the Yanchang formation. Fuel 2018;211:789‒96. 链接1

[ 7 ] Pieters DA, Graves RM. Fracture relative permeability: linear or non-linear function of saturation. In: Proceedings of International Petroleum Conference and Exhibition of Mexico; 1994 Oct 10‒13; Veracruz, Mexico. 1994. 链接1

[ 8 ] Schiozer D, Muñoz Mazo E. Modeling fracture relative permeability—what is the best option? In: Proceedings of the 75th EAGE Conference & Exhibition incorporating SPE EUROPEC; 2013 Jun 1013; London, UK. 2013. 链接1

[ 9 ] Cho Y, Ozkan E, Apaydin O. Pressure-dependent natural-fracture permeability in shale and its effect on shale-gas well production. SPE Reserv Eval Eng 2012;16(02):216‒28. 链接1

[10] Chen D, Pan Z, Ye Z. Dependence of gas shale fracture permeability on effective stress and reservoir: model match and insights. Fuel 2015;139:383‒92. 链接1

[11] Mukerji T, Jorstad A, Avseth P, Markov G, Granli JR. Mapping lithofacies and pore-fluid probabilities in a North Sea reservoir: seismic inversions and statistical rock physics. Geophysics 2001;66(4):988‒1001. 链接1

[12] Pedersen SI, Randen T, Sonneland L, Steen O. Automatic fault extraction using artificial ants. SEG Tech Prog Exp Abstr 2002:512‒5. 链接1

[13] Leite EP, Vidal AC. 3D porosity prediction from seismic inversion and neural networks. Comput Geosci 2011;37(8):1174‒80. 链接1

[14] Xu K, Sun Z. A new fracture detection method based on full-azimuth anisotropic migration and shaping regularization. J Geophys Eng 2018;15(4):1624‒37. 链接1

[15] Guerriero V, Iannace A, Mazzoli S, Parente M, Vitale S, Giorgioni M. Quantifying uncertainties in multi-scale studies of fractured reservoir analogues: implemented statistical analysis of scan line data from carbonate rocks. J Struct Geol 2010;32(9):1271‒8. 链接1

[16] Wilson CE, Aydin A, Karimi-Fard M, Durlofsky LJ, Sagy A, Brodsky EE, et al. From outcrop to flow simulation: constructing discrete fracture models from a LIDAR survey. AAPG Bull 2011;95(11):1883‒905. 链接1

[17] Agada S, Geiger S, Elsheikh A, Oladyshkin S. Data-driven surrogates for rapid simulation and optimization of WAG injection in fractured carbonate reservoirs. Petrol Geosci 2016;23(2):270‒83. 链接1

[18] Williams JH, Johnson CD. Acoustic and optical borehole-wall imaging for fractured-rock aquifer studies. Appl Geophys 2004;55(1‒2):151‒9. 链接1

[19] Kovac KM, Lutz SJ, Drakos PS, Byersdorfer J, Robertson-tait A. Borehole image analysis and geological interpretation of selected features in well DP 27-15 at desert peak, Nevada: pre-simulation evaluation of an enhanced geothermal system. In: Proceedings of Thirty-Fourth Workshop on Geothermal Reservoir Engineering; 2009 Feb 9‒11; California, USA. 2009.

[20] Caers J. History matching under training-image-based geological model constraints. SPE J 2003;8(3):218‒26. 链接1

[21] Oliver D, Chen Y. Recent progress on reservoir history matching: a review. Comput Geosci 2011;15(1):185‒221. 链接1

[22] Ghaedi M, Masihi M, Heinemann ZE, Ghazanfari MH. History matching of naturally fractured reservoirs based on the recovery curve method. J Petrol Sci Eng 2015;126:211‒21. 链接1

[23] Athens ND, Caers JK. A Monte Carlo-based framework for assessing the value of information and development risk in geothermal exploration. Appl Energy 2019;256:113932. 链接1

[24] Yin Z, Strebelle S, Caers J. Automated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0). Geosci Model Dev 2020;13(2):651‒72. 链接1

[25] Aydin O, Caers J. Quantifying structural uncertainty on fault networks using a marked point process within a Bayesian framework. Tectonophysics 2017;712‒713:101‒24.

[26] Bonet-Cunha L, Olive DS, Rednar RA, Reynolds AC. A hybrid Markov chain Monte Carlo method for generating permeability fields conditioned to multiwell pressure data and prior information. SPE J 1998;3(3):261‒71. 链接1

[27] Tjelmeland H, Eidsvik J. On the use of local optimizations within Metropolis-Hastings updates. J R Stat Soc B 2004;66(2):411‒27. 链接1

[28] Romary T. Integrating production data under uncertainty by parallel interacting Markov chains on a reduced dimensional space. Comput Geosci 2009;13(1):103‒22. 链接1

[29] Wen X, Chen W. Real-time reservoir model updating using ensemble Kalman filter with confirming option. SPE J 2006;11(4):431‒42. 链接1

[30] Haugen V, Naevdal G, Natvik LJ, Evensen G, Berg AM, Flornes KM. History matching using the Ensemble Kalman Filter on a North Sea Field case. SPE J 2008;13(4):382‒91. 链接1

[31] Nejadi S, Trivedi JJ, Leung J. History matching and uncertainty quantification of discrete fracture network models in fractured reservoirs. J Petrol Sci Eng 2017;152:21‒32. 链接1

[32] Evensen G, van Leeuwen PJ. An ensemble Kalman smoother for nonlinear dynamics. Mon Weather Rev 2000;128(6):1852‒67. 链接1

[33] Chen Y, Oliver DS. Ensemble randomized maximum likelihood method as an iterative ensemble smoother. Math Geosci 2012;44(1):1‒26. 链接1

[34] Gu Y, Oliver DS. An iterative ensemble Kalman filter for multiphase fluid flow data assimilation. SPE J 2007;12(4):438‒46. 链接1

[35] Emerick AA, Reynolds AC. Ensemble smoother with multiple data assimilation. Comput Geosci 2013;55:3‒15. 链接1

[36] Satija A, Caers J. Direct forecasting of subsurface flow response from nonlinear dynamic data by linear least-squares in canonical functional principal component space. Adv Water Resour 2015;77:69‒81. 链接1

[37] Satija A, Scheidt C, Li L, Caers J. Direct forecasting of reservoir performance using production data without history matching. Comput Geosci 2017;21(2):315‒33. 链接1

[38] Sun W, Hui MH, Durlofsky LJ. Production forecasting and uncertainty quantification for naturally fractured reservoirs using a new data-space inversion procedure. Computat Geosci 2017;21(5‒6):1443‒58. 链接1

[39] Sun W, Durlofsky LJ. A new data-space inversion procedure for efficient uncertainty quantification in subsurface flow problems. Math Geosci 2017;49(6):679‒715. 链接1

[40] Caers J. Bayesianism in the geosciences. In: Daya Sagar BS, Cheng Q, Agterberg F, editors. Handbook of mathematical geosciences. Chem: Springer; 2018.p. 527‒66. 链接1

[41] Scheidt C, Li L, Caers J. Quantifying uncertainty in subsurface systems. Hoboken: Wiley; 2018. 链接1

[42] Hermans T, Nguyen F, Klepikova M, Dassargues A, Caers J. Uncertainty quantification of medium-term heat storage from short-term geophysical experiments using Bayesian evidential learning. Water Resour Res 2018;54(4):2931‒48. 链接1

[43] Slotte PA, Smorgrav E. Response surface methodology approach for history matching and uncertainty assessment of reservoir simulation models. In:Proceedings of the 70th EAGE Conference & Exhibition; 2008 Jun 9‒12; Rome, Italy. 2008. 链接1

[44] Castellini A, Gross H, Zhou Y, He J, Chen W. An iterative scheme to construct robust proxy models. In: Proceedings of the 12th European Conference on the Mathematics of Oil Recovery; 2010 Sep 6‒9; Oxford, UK. 2010. 链接1

[45] Friedmann F, Chawathe A, Larue DK. Assessing uncertainty in channelized reservoirs using experimental designs. SPE Reservoir Eval Eng 2003;6(4):264‒74. 链接1

[46] Aulia A, Jeong D, Saaid IM, Kania D, Shuker MT, El-Khatib NA. A random forests-based sensitivity analysis framework for assisted history matching. J Petrol Sci Eng 2019;181:106237. 链接1

[47] Brantson ET, Ju B, Omisore BO, Wu D, Selase AE, Liu N. Development of machine learning predictive models for history matching tight gas carbonate reservoir production profiles. J Geophys Eng 2018;15(5):2235‒51. 链接1

[48] Alfonzo M, Oliver DS. Evaluating prior predictions of production and seismic data. Computat Geosci 2019;23(6):1331‒47. 链接1

[49] Pradhan A, Mukerji T. Seismic Bayesian evidential learning: estimation and uncertainty quantification of sub-resolution reservoir properties. Comput Geosci 2019;24(3):1121‒40. 链接1

[50] Oda M. Permeability tensor for discontinuous rock masses. Geotechnique 1985;35(4):483‒95. 链接1

[51] Akin S. Estimation of fracture relative permeabilities from unsteady state core floods. J Petrol Sci Eng 2001;30(1):1‒14. 链接1

[52] Scheidt C, Jeong C, Mukerji T, Caers J. Probabilistic falsification of prior geologic uncertainty with seismic amplitude data: application to a turbidite reservoir case. Geophysics 2015;80(5):M89‒M. 链接1

[53] Noumir Z, Honeine P, Richard C. On simple one-class classification methods. In: Proceedings of the IEEE International Symposium on Information Theory; 2012 Jul 1‒6; Cambridge, MA, USA. 2012. 链接1

[54] Saltelli A, Ratto M, Andrews T, Campolongo F, Cariboni J, Gatelli D, et al. Global Sensitivity Analysis. New York: John Wiley & Sons, Ltd; 2008. 链接1

[55] Fenwick D, Scheidt C, Caers J. Quantifying asymmetric parameter interactions in sensitivity analysis: application to reservoir modeling. Math Geosci 2014;46(4):493‒511. 链接1

[56] Park J, Yang G, Satija A, Scheidt C, Caers J. DGSA: a Matlab toolbox for distancebased generalized sensitivity analysis of geoscientific computer experiments. Comput Geosci 2016;97:15‒29. 链接1

[57] Spear RC, Hornberger GM. Eutrophication in peel inlet-II. Identification of critical uncertainties via generalized sensitivity analysis. Water Res 1980;14(1):43‒9. 链接1

[58] Beaumont MA, Zhang W, Balding DJ. Approximate Bayesian computation in population genetics. Genetics 2002;162(4):2025‒35. 链接1

[59] Sadegh M, Vrugt JA. Approximate Bayesian Computation using Markov Chain Monte Carlo simulation: DREAM (ABC). Water Resour Res 2014;50(8):6767‒87. 链接1

[60] Barthelme S, Chopin N. ABC-EP: expectation propagation for likelihoodfree Bayesian computation. In: Proceedings of the 28th International Conference on Machine Learning. 2011 Jun 28‒Jul 2; Washington, USA. 2011.

[61] Trehan S, Carlberg KT, Durlofsky LJ. Error modeling for surrogates of dynamical systems using machine learning. Int J Numer Methods Eng 2017;112(12):1801‒27. 链接1

[62] Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. 2nd Edition. Springer Series in Statistics; 2009. 链接1

[63] Schapire RE. The strength of weak learnability. Mach Learn 1990;5(2):197‒227. 链接1

[64] Breiman L. Bagging predictors. Mach Learn 1996;24(2):123‒40. 链接1

[65] Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat 2000;29(5):1189‒232. 链接1

[66] Friedman JH. Stochastic gradient boosting. Comput Stat Data Anal 2002;38(4):367‒78. 链接1

[67] Breiman L. Random forests. Mach Learn 2001;45(1):5‒32. 链接1

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