Abstract：The classical wedge model experiment assumes that single sand bodies are developed under the background of mudstone, which contradicts the fact that the thin (interbed) layers are more extensively developed in real situations. Additiona-lly, the methods of employing functions to fit the relationship between seismic attributes and sand thickness for thin (interned) sand thickness have an accuracy limit. To this end, a new method for thin (interbed) sand thickness prediction is proposed based on well logging forward modeling template and Gaussian probability field. Firstly, seve-ral wedge models are built through reservoir logging curves of well logs in the W oilfield, and the relationship template of amplitude attribute vs thin (interbed) sand thickness suitable for the oilfield is obtained via convolution forward modeling. Semi-quantitative relationship between amplitude attri-butes and sand thickness is established. Secondly, based on data of thin (interbed) accumulated sand thickness and seismic amplitude attribute value of well points, Gaussian probability field of accumulated sand thickness vs amplitude attribute is constructed. Combined with the amplitude attribute vs thin (interbed) sand thickness relationship template, the most probable thin (interbed) sand thickness corresponding to seismic amplitude attributes is solved. Finally, attribute of thin (interbed) sand thickness and sand thickness confidence with maximum probability are obtained. The effectiveness and feasibility are verified through a 3D model containing several channels and actual oilfield data.
杜昕, 范廷恩, 范洪军, 董建华, 周建楠. 基于测井正演量板与高斯概率场的薄(互)层砂岩厚度预测[J]. 石油地球物理勘探, 2022, 57(5): 1174-1181.
DU Xin, FAN Ting'en, FAN Hongjun, DONG Jianhua, ZHOU Jiannan. Prediction of thin (interbed) sand thickness based on well logging forward modeling template and Gaussian probability field. Oil Geophysical Prospecting, 2022, 57(5): 1174-1181.
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