The phase control modeling of porous carbonate reservoir by machine learning for the Cretaceous Mishrif Formation reservoir of H Oilfield in the Middle East
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摘要:
孔隙型碳酸盐岩储层的化学及机械沉积作用使得各沉积微相空间上不具备碎屑岩储层沉积微相明确的几何形态和外部结构，且不同成因储层的物性差异明显。依据常规的沉积微相建模方法难以如实地再现不同微相复杂的空间展布规律，进而也降低了相控属性建模的精度。本文以中东H油田白垩系Mishrif组生物碎屑灰岩为研究对象，通过开展波阻抗、孔隙度和渗透率的反演，利用机器学习的方法建立研究区的三维沉积微相模型。在此基础上，通过不同微相的变差函数分析，开展相控属性建模。结果表明，利用机器学习方法建立的沉积微相模型符合海相碳酸盐岩台地相序变化规律，充分体现了微相的空间形态和各微相间的接触关系，以沉积微相为约束条件建立的储层属性模型不仅满足了模拟结果与已知数据的概率一致性问题，又能分相带反映储层的空间变化特征。
Abstract:The chemical and mechanical sedimentation of porous carbonate reservoir blurs geometric shape and external structure of clastic reservoir sedimentary microfacies in space, and complicated the physical properties of reservoirs of different origins. Conventional sedimentary microfacies modeling methods are difficult to reproduce objectively the complex spatial distribution of different microfacies, thereby the accuracy of facies control attribute modeling could be reduced. Therefore, taking the Cretaceous Mishrif Formation bioclastic limestone in the Middle East H Oilfield as the research object, we established a three-dimensional sedimentary microfacies model in the study area by carrying out wave impedance inversion, porosity inversion, and permeability inversion, using machine learning methods. The phase-controlled attribute modeling was performed through the variogram analysis of different microfacies. The sedimentary microfacies model established using machine learning methods conforms to the changes in facies sequence of marine carbonate platforms, fully reflecting the spatial morphology of microfacies and the contact relationship among microfacies. The reservoir attribute model that established with the sedimentary microfacies as a constraint not only meets the requirement of probability consistency between simulation results and known data, but also reflects the spatial variation characteristics of the reservoir by phase zones.
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表 1 基于机器学习沉积微相建模训练数据
Table 1. Training data of sedimentary microfacies modeling based on machine learning
I方向 J方向 K方向 沉积微相
类型孔隙度/% 渗透率/10−3 μm2 波阻抗/(g/cm3)•(m/s) 井网格 191 89 330 潮道 0.312 4 110.265 8 764.254 13 171 93 330 滩翼 0.182 6 12.456 1 1234.265 9 94 95 330 滩 0.281 4 46.325 9 165.4990 18 114 98 330 潮道 0.294 7 98.567 9 123.3808 2 134 101 330 潮道 0.334 5 89.365 7 057.1318 43 158 107 330 滩翼 0.212 4 8.362 10 476.5800 46 216 107 330 潟湖 0.144 2 5.324 10 084.6972 44 53 126 330 滩翼 0.128 2 10.362 9 208.2089 23 161 127 330 潟湖 0.180 7 7.486 9 904.0917 19 215 131 330 滩 0.286 1 45.368 8 664.0966 7 241 132 330 滩 0.262 5 14.256 8 959.1630 49 188 136 330 滩翼 0.171 9 25.321 9 057.4794 26 128 137 330 滩翼 0.226 4 13.425 9 213.25 8 207 155 330 滩 0.275 4 35.467 9 417.1972 27 137 161 330 潟湖 0.175 0 8.456 9 435.1845 20 116 166 330 潟湖 0.118 6 5.346 8 504.6259 10 68 170 330 潟湖 0.147 1 10.12 9 982.4414 34 100 170 330 滩翼 0.242 8 12.365 8 389.2607 25 44 173 330 潟湖 0.134 0 3.251 9 711.0238 33 35 137 330 潮道 0.323 9 230.536 7 435.6258 25 
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