基于岩石物理模型的正交各向异性储层孔隙长径比和毛细管压力系数估算S波速度

Pub Date : 2022-09-02 DOI:10.1080/08123985.2022.2117602
Fan Wu, Jingye Li, Xiaohong Chen, W. Geng, Wei Tang
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引用次数: 0

摘要

S波速度的准确预测在反演、偏移、脆性指数计算等方面具有重要意义。在正常情况下,已知输入参数类型越多,岩石的具体情况就越准确,预测的S波速度也就越准确。然而,考虑到实际情况,通过测井曲线获得的参数类型相对有限。一些参数无法测量和计算,这限制了S波预测的准确性。因此,如果能够预测参数,则能够通过这些参数来描述更准确的地下情况。通过岩石物理分析,孔隙长径比和毛细管压力系数会影响速度。通过这种方式,提出了一种新的考虑孔隙长径比和毛细管压力系数的正交岩石物理建模方法。该模型由矿物压实或结构排列引起的VTI各向异性和地层压力引起的高角度裂缝引起的HTI各向异性组成,从而显示出ORT各向异性。模型的输入可以是多种矿物。并考虑了孔隙结构和混合流体在孔隙中的模量。我们使用逆理论(量子遗传算法)来获得孔隙纵横比和毛细管压力系数,并通过上述参数最终计算S波速度。页岩油藏的计算结果表明,预测的S波速度与实际测井资料吻合较好。这表明所提出的岩石物理建模过程和逆算法方法是有效的。
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Estimation of pore aspect ratio and capillary pressure coefficient to predict S-wave velocity based on rockphysics modeling in orthorhombic anisotropic reservoirs
Accurate prediction of S-wave velocity is of great significance in many aspects, such as inversion, migration, brittleness index calculation, etc. Under normal circumstances, the more types of known input parameters there are, the more accurate the rock’s specific situation, and the more accurate the predicted S-wave velocity. However, considering the actual situation, the types of parameters obtained through logging curves are relatively limited. Some parameters cannot be measured and calculated, which limits the accuracy of S-wave prediction. Therefore, if the parameters can be predicted, a more accurate underground situation is able to described by these parameters. Through rockphysical analysis, the pore aspect ratio and capillary pressure coefficient can affect the velocity. In this way, a new orthorhombic (ORT) rockphysical modeling process considering the pore aspect ratio and capillary pressure coefficient is proposed. The model consists of VTI anisotropy from compaction or textural alignment of minerals, and HTI anisotropy from high-angle fractures caused by stratum pressure, thus showing ORT anisotropy. The inputs of the model can be multiple minerals. And the pore structure and the modulus of the mixed fluids in the pores are considered. We use inverse theory (quantum genetic algorithms) to obtain the pore aspect ratio and capillary pressure coefficient and finally calculate the S-wave velocity through the above parameters. The calculation results in a shale reservoir show that the predicted S-wave velocity is in good agreement with the real logging data. This shows that the proposed rockphysical modeling process and inverse algorithm method are effective.
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