Abstract
Grain-size sorting of buried formation in openholes only comes from the particle size analysis of drilled cores. However, cores impossibly cover the whole formation, and the sorting of uncored interval is absent. With ultrahigh-resolution, borehole electrical image contains particular geological attributes, such as grain-sizes, sedimentary structures, and facies. In this paper, a sorting evaluation method is proposed based on conductivity spectrum of borehole electrical image from Baikouquan Formation in Well M152, Mahu Depression. The first stage is revising the depth of the cores by calibrating the cores to the borehole electrical images. The second stage is computing the two-dimensional conductivity spectrum of conductivity image. The third stage is calculating the sorting index from conductivity spectrum. The fourth stage is converting the sorting index to the evaluated sorting by a mathematical equation between sorting index and laboratorial grain-size sorting. In the cored interval of the Well M152, the evaluated sorting is compared with the grain-size of gravels; what can be concluded is that the evaluated sorting values from borehole electrical images gradually reduce with the decrease of the gravelly grain-sizes. The proposed method is demonstrated be beneficial to reflect the vertical diameters and sorting of gravelly grains in deeply buried conglomeratic formation continuously.
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References
Bourke LT (1992) Sedimentological borehole image analysis in clastic rocks: a systematic approach to interpretation. Geo Soc Spec Pub 65:31–42. https://doi.org/10.1144/GSL.SP.1992.065.01.04
Brekke H, MacEachern JA, Roenitz T, Dashtgard SE (2017) The use of microresistivity image logs for facies interpretations: an example in point-bar deposits of the McMurray Formation, Alberta, Canada. AAPG Bull 101(5):655–682. https://doi.org/10.1306/08241616014
Chakravorty RN, Gopala Rao V, Kumar R, Roy S (2009) Extended Range Micro-Imager (XRMI)™ applications in coal environment. 2nd SPWLA-India Symp, H.
Church M (1999) Sediment sorting in gravel-bed rivers. J Sediment Res 69(1):20. https://doi.org/10.1306/D4268950-2B26-11D7-8648000102C1865D
Cooper DM, Dixon AJ (1989) Characterization of grain size distributions in Thames floodplain gravels. Math Geol 21(7):673–681. https://doi.org/10.1007/BF00893315
Doeglas DJ (1946) Interpretation of the results of mechanical analyses. J Sediment Res 16(1):19–40. https://doi.org/10.1306/D426924C-2B26-11D7-8648000102C1865D
Donselaar ME, Schmidt JM (2005) Integration of outcrop and borehole image logs for high-resolution facies interpretation: example from a fluvial fan in the Ebro basin, Spain. Sedimentology 52:1021–1042. https://doi.org/10.1111/j.1365-3091.2005.00737.x
Folk RL, Ward WC (1957) Brazos River Bar: a study in the significance of grain size parameters. J Sediment Res 27(1):3–26. https://doi.org/10.1306/74D70646-2B21-11D7-8648000102C1865D
Folkestad A, Veselovsky Z, Roberts P (2012) Utilising borehole image logs to interpret delta to estuarine system: a case study of the subsurface Lower Jurassic Cook Formation in the Norwegian northern North Sea. Mar Pet Geol 29:255–275. https://doi.org/10.1016/j.marpetgeo.2011.07.008
Gan SQ, Scholz CA (2017) Skew normal distribution deconvolution of grain-size distribution and its application to 530 samples from Lake Bosumtwi, Ghana. J Sediment Res 87:1214–1225. https://doi.org/10.2110/jsr.2017.68
Kleinhans MG (2005) Grain-size sorting in grainflows at the lee side of deltas. Sedimentology 52:291–311. https://doi.org/10.1111/j.1365-3091.2005.00698.x
Krumbein WC (1934) Size frequency distributions of sediments. J Sediment Res 4(2):65–77. https://doi.org/10.1306/D4268EB9-2B26-11D7-8648000102C1865D
Newberry BM, Hansen SM, Perrett TT (2004) A method for analyzing textural changes within clastic environments utilizing electrical borehole images. Gulf Coast Assoc Geo Soc Trans 54:531–539
Nian T, Jiang ZX, Wang GW, Xiao CW, He WJ, Fei LY, He ZB (2018) Characterization of braided river-delta facies in the Tarim Basin Lower Cretaceous: application of borehole image logs with comparative outcrops and cores. Mar Pet Geol 97(6):1–23. https://doi.org/10.1016/j.marpetgeo.2018.06.024
Núñez-González F, Martín-Vide JP, Kleinhans MG (2016) Porosity and size gradation of saturated gravel with percolated fines. Sedimentology 63:1209–1232. https://doi.org/10.1111/sed.12257
Pan J, Zhang CM, Pang L, Li P, Zhu R (2019) Depositional evolution characteristics of the Triassic Baikouquan Formation in Xiazijie fan area of Mahu sag, Junggar Basin. J Palaeogeog (Chinese Edition) 21(6):913–924. https://doi.org/10.7605/gdlxb.2019.06.062
Rice SP, Church M (2010) Grain-size sorting within river bars in relation to downstream fining along a wandering channel. Sedimentology 57:232–251. https://doi.org/10.1111/j.1365-3091.2009.01108.x
Sovich JP, Newberry B (1993) Quantitative applications of borehole imaging. SPWLA 34th Ann Log Symp FFF.
Supriya S (1975) Size-sorting during suspension transportation-lognormality and other characteristics. Sedimentology 22:257–273. https://doi.org/10.1111/j.1365-3091.1975.tb00293.x
Tang Y, Xu Y, Li YZ, Wang LB (2018) Sedimentation model and exploration significance of large-scaled shallow retrogradation fan delta in Mahu Sag. Xinjiang Pet Geo 39(1):16–22. https://doi.org/10.7657/XJPG20180103
Thompson L (2009) Atlas of borehole imagery, 2nd edition. AAPG Discovery Series 13.
Visher GS (1969) Grain size distributions and depositional processes. J Sediment Res 39(3):1074–1106. https://doi.org/10.1306/74D71D9D-2B21-11D7-8648000102C1865D
Weltje GJ, Prins MA (2007) Genetically meaningful decomposition of grain-size distributions. Sediment Geol 202:409–424. https://doi.org/10.1016/j.sedgeo.2007.03.007
Wu F, Xi YP, Fan QC, Yao C, Cong LL, Zhang FS, Kuang Y (2019) Influence of spatial distribution of pores on NMR transverse relaxation time in pebbly sandstone. J Magn 24(4):704–716. https://doi.org/10.4283/JMAG.2019.24.4.704
Xiao M, Yuan XJ, Wu ST, Cao ZL, Tang Y, Xie ZR, Wang RJ (2019) Conglomerate reservoir characteristics of and main controlling factors for the Baikouquan Formation, Mahu sag, Junggar Basin. Earth Sci Front 26(1):212–224. https://doi.org/10.13745/j.esf.sf.2018.12.7
Xu CM, Gehenn JM, Zhao DH, Xie GY, Teng MK (2015) The fluvial and lacustrine sedimentary systems and stratigraphic correlation in the Upper Triassic Xujiahe Formation in Sichuan Basin, China. AAPG Bull 99(11):2023–2041. https://doi.org/10.1306/07061514236
Yuan R, Zhang CM, Tang Y, Qu JH, Guo XD, Sun YQ, Zhu R, Zhou YQ (2017) Utilizing borehole electrical images to interpret lithofacies of fan-delta: a case study of Lower Triassic Baikouquan Formation in Mahu Depression, Junggar Basin, China. Open Geosci 9(1):539–553. https://doi.org/10.1515/geo-2017-0041
Yuan R, Zhang CM, Wang XL, Zhu R (2018a) Utilizing skew normal distribution to unmix grain-size distribution of swampy lakeshore: example from Lake Ulungur, China. Arab J Geosci 11:695. https://doi.org/10.1007/s12517-018-4038-9
Yuan R, Zhu R, Qu JH, You XC, Wu J, Huang YF (2018b) Abnormal open-hole natural gamma ray (GR) log in Baikouquan Formation of Xiazijie Fan-delta, Mahu Depression, Junggar Basin, China. Open Geosci 10(1):844–854. https://doi.org/10.1515/geo-2018-0066
Zhang CM, Wang XL, Zhu R, Qu JH, Pan J, An ZY (2016) Lithofacies classification of Baikouquan formation in Mahu sag, Junggar basin. Xinjiang Petrol Geo 37(5):606–614. https://doi.org/10.7657/XJPG20160521
Acknowledgments
We are grateful to anonymous reviewers for their constructive reviews on the manuscript, and the editors for carefully revising the manuscript.
Funding
This research is financially supported by the Hubei Provincial Natural Science Foundation of China (No. 2019CFB343); Scientific Research Project of Hubei Provincial Department of Education (No. Q20181310); Open Fund of Key Laboratory of Exploration Technologies for Oil and Gas Resources (Yangtze University), Ministry of Education (No. K2018-21); National Science and Technology Major Project (No. 2017ZX05008003-050); National Naturel Science Foundation of China (No. 11871118); Open Fund of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Chengdu University of Technology) (No. PLC2088605); and Scientific Research Project of Educational Commission of Sichuan Province of China (No. 18ZB0076).
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Yuan, R., Yang, B., Huang, L. et al. Utilizing borehole electrical images to evaluate sorting of conglomeratic formation: example from Lower Triassic Baikouquan Formation in Well M152, Mahu Depression, Junggar Basin, China. Arab J Geosci 13, 408 (2020). https://doi.org/10.1007/s12517-020-05374-y
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DOI: https://doi.org/10.1007/s12517-020-05374-y