Shu LIU, Gang LIN, Ming ZENG, Yu-lin CHENG, Long FANG, Jia-ming ZENG, Meng WANG

Diagenesis and diagenetic facies distribution prediction of Chang 8 tight oil reservoir in Maling area, Ordos Basin, NW China

In this study, characteristics of Chang 8 tight sandstone pore structure and diagenesis are analyzed. A classification standardfor sandstone diagenetic facies is established. Combined with the probabilistic neural network method, logging curves are used topredict the distribution of diagenetic facies. The following results are found: the Chang 8 reservoir has low porosity-low permeabilitycharacteristics, and the pore-throat structures have strong microscopic heterogeneity; both compaction and siliceous and carbonatecementation promote reservoir densification; chlorite cement lining, hydrocarbon emplacement, and the dissolution of feldspar androck fragments have constructive effects on reservoir development; and two favorable diagenetic facies, a weakly compacted andchlorite-authigenic quartz cementation facies and an intensely compacted-dissolution facies, are developed in the middle of the thicksandstone and are a good match in their horizontal distribution with distributary channel sands. The Chang 81 member has a muchlarger area that is more favorable for the development of reservoir diagenetic facies than the Chang 82 member.

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Ahmadi MA (2011). Prediction of asphaltene precipitation using artificial neural network optimized by imperialist competitive algorithm. J Petrol Explor Prod Technol 1: 99-106.
Aminzadeh F, Barhen J, Glover CW, Toomarian NB (2000). Reservoir parameter estimation using a hybrid neural network. Comput Geosci 26: 869-875.
Cheng JG, Zhou GW, Wang XX (2007). The probability neural networks for lithology identification. Contr Automat 23: 288- 289 (in Chinese with English abstract).
Dou W, Liu L, Wu K, Xu Z, Feng X, Ruffell A (2017). Diagenesis of tight oil sand reservoirs: upper Triassic tight sandstones of Yanchang formation in ordos basin, china. Geol J 53: 707-724.
Fan Y, Li F, Deng SR, Chen Z, Li G, Li J (2018). Characteristics analysis of diagenetic facies in tight sandstone reservoir and its logging identification. Well Logging technol 42: 307-314 (in Chinese with English abstract).
Fu J, Wu SH, Fu JH, Hu LQ, Zhang HF, Liu X (2013). Research on quantitative diagenetic facies of the Yanchang formation in Longdong area, Ordos basin. Earth Sci Frontiers 20: 86-97.
Ghosh S, Chatterjee R, Shanker P (2016). Estimation of ash, moisture content and detection of coal lithofacies from well logs using regression and artificial neural network modelling. Fuel 177: 279-287.
Guo H, He R, Jia W, Peng P, Lei Y, Luo X (2018). Pore characteristics of lacustrine shale within the oil window in the upper Triassic Yanchang formation, southeastern Ordos basin, China. Mar Petrol Geol 91: 279-296.
Huang SJ, Xie LW, Zhang M, Wu WH, Shen LC, Liu J (2004). Formation mechanism of authigenic chlorite and relation to the preservation of porosity in nonmarine Triassic reservoir sandstones, Ordos basin and Sichuan basin, China. J Chengdu Univ Technol 31: 273-281 (in Chinese with English abstract).
Jia CZ, Zou CN, Li JZ, Li DH, Deng M (2012). Assessment criteria, main types, basic features and resource prospects of the tight oil in China. Acta Petrol Sin 33: 343-350 (in Chinese).
Kakouei A, Masihi M, Sola BS, Biniaz E (2014). Lithological facies identification in iranian largest gas field: a comparative study of neural network methods. J Geol Soc India 84: 326-334.
Lai J, Wang GW, Huang LX, Guan B, Jiang C, Ran Y (2015). Quantitative classification and logging identification method for diagenctic facies of tight sandstones. Bull Mineral Petrol Geochem 34: 128-138 (in Chinese with English abstract).
Li H, Liu YQ, Liu LY (2006). Diagenesis of Chang 81 reservoir with low permeability in Xifeng oilfield, Ordos basin. Oil Gas Geol 27: 209-217 (in Chinese with English abstract).
Li SH, Fang GQ, Yang JL, Liao JB, Fan JM (2012). Origin of ultralow permeability reservoirs in ordos basin and its significance. Lithol Reserv 24: 32-37 (in Chinese with English abstract).
Lippmann RP (1989). Pattern classification using neural networks. IEEE Commun Mag 27: 47-50.
Luo ZT, Wang YC, editors (1986). Pore Structure of Oil and Gas Reservoir. Beijing, China: Science Press (in Chinese).
Ma CL, Wang RJ, Luo BL, Duan WB, Feng CH (2012). Characteristics of Chang-8 oil reservoir and distribution of oil reservoirs in Maling oilfield, Ordos basin. Nat Gas Geosci 23: 514-519 (in Chinese with English abstract).
Maast TE, Jahren J, Bjørlykke K (2011). Diagenetic controls on reservoir quality in Middle to Upper Jurassic sandstones in the South Viking Graben, North Sea. AAPG Bull 95: 1937-1958.
Maurya SP, Singh NP (2018). Application of LP and ML sparse spike inversion with probabilistic neural network to classify reservoir facies distribution - a case study from the Blackfoot Field, Canada. J Appl Geophys 159: 511-521.
McCulloch WS, Pitts W (1943). A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5: 115-133.
Morad S, Al-Ramadan K, Ketzer JM, DeRos LF (2010). The impact of diagenesis on the heterogeneity of sandstone reservoirs: a review of the role of depositional facies and sequence stratigraphy. AAPG Bull 94: 1267-1309.
Ozkan A, Cumella SP, Milliken KL, Laubach SE (2011). Prediction of lithofacies and reservoir quality using well logs, Late Cretaceous Williams Fork Formation, Mamm Creek field, Piceance Basin, Colorado. AAPG Bull 95: 1699-1723.
Rafik B, Kamel B (2016). Prediction of permeability and porosity from well log data using the nonparametric regression with multivariate analysis and neural network, Hassi R’mel Field, Algeria. Egyptian J Petrol 26: 763-778.
Railsback LB (1984). Carbonate diagenetic facies in the Upper Pennsylvanian Dennis formation in Iowa, Missouri and Kansas. J Sediment Petrol 54: 986-999.
Reza M, Mohammad AR, Mona A (2018). Detection of the gas-bearing zone in a carbonate reservoir using multiclass relevance vector machines (RVM): comparison of its performance with SVM and PNN. Carbonate Evaporite 33: 347-357.
Rosenblatt F (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65: 386-408.
Wang GC, Carr TR (2012). Marcellus shale lithofacies prediction by multiclass neural network classification in the Appalachian basin. Math Geosci 44: 975-1004.
Wang GC, Carr TR, Ju Y, Li C (2014). Identifying organic-rich Marcellus shale lithofacies by support vector machine classifier in the Appalachian basin. Comput Geosci 64: 52-60.
Wang GC, Ju Y, Carr TR, Tan F (2015). The hierarchical decomposition method and its application in recognizing Marcellus shale lithofacies through combining with neural network. J Petrol Sci Eng 127: 469-481.
Wang GW, Chang XC, Yin W, Li Y, Song TT (2017). Impact of diagenesis on reservoir quality and heterogeneity of the upper Triassic Chang 8 tight oil sandstones in the Zhenjing area, Ordos basin, China. Mar Petrol Geol 83: 84-96.
Ye B, Song J, Zhang J, Cao R, Liang X, Wang J (2018). Major control factors of oil accumulation of Chang 8 reservoir in Maling area, Ordos Basin, China. J Chengdu Univ Technol 45: 529-538 (in Chinese with English abstract).
Zeng HL, Zhu XM, Zhu RK, Zhang QS (2013). Seismic prediction of sandstone diagenetic facies: applied to cretaceous Qingshankou formation in Qijia Depression, Songliao Basin, East China. Petrol Explor Dev 40: 287-295.
Zhang J, Li X (2014). Application of reservoir property prediction based on probabilistic neural network (PNN) in Y3 block. Xinjiang Petrol Geol 35: 582-586 (in Chinese with English abstract).
Zhou Y, Ji Y, Xu L, Che S, Niu X, Wan L, Zhou Y, Li Z, You Y (2016). Controls on reservoir heterogeneity of tight sand oil reservoirs in upper Triassic Yanchang formation in Longdong area, southwest Ordos Basin, China: implications for reservoir quality prediction and oil accumulation. Mar Petrol Geol 78: 110-135.
Zou CN, Tao SZ, Zhou H, Zhang XX, He DB, Zhou CM, Wang L, Wang XS, Li FH, Zhu RK et al. (2008). Genesis, classification, and evaluation method of diagenetic facies. Petrol Explor Dev 35: 526–540.
Zou CN, Wang L, Li Y, Tao SZ, Hou LH (2012). Deep-lacustrine transformation of sandy debrites into turbidites, Upper Triassic, central China. Sediment Geol 265: 143–155 (in Chinese with English abstract).
Zou CN, Zhu RK, Wu ST, Yang Z, Tao SZ, Yuan X, Hou L, Yan H, Xu C, Li D et al. (2012). Types, characteristics, genesis and prospects of conventional and unconventional hydrocarbon accumulations: taking tight oil and tight gas in china as an instance. Acta Petrol Sin 33: 173-187.