In this study, a new strategy based on integrating geostatistical seismic inversion and optimized support vector regression (OSVR) will be utilized to transform multi seismic attributes to sand fraction log. In first step, owing to compatibility relation between acoustic impedance (AI) and sand fraction, a high resolution value of this important attribute was extracted through a geostatistical seismic inversion (GSI). In second step, in addition to AI, several physical attributes are obtained from seismic data and then all of extracted attributes (AI and other seismic attributes) evaluated by step-wise regression for selecting best attributes that have highest effect on predicting sand fraction. In final step, selected attributes have been fed in the bat inspired optimized support vector regression as input and the sand fraction log is estimated. For the assessment of proposed strategy, the values of predicted sand fraction are compared with their real corresponding values in a blind well. It will be evident from the results that the proposed strategy is qualified for modeling the sand fraction as a function of seismic attributes.

中文翻译：

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使用优化的支持向量回归在油藏中根据地震属性预测砂粒含量

在这项研究中，将采用一种基于地统计地震反演和优化支持向量回归（OSVR）集成的新策略，将多种地震属性转换为砂级测井。第一步，由于声阻抗（AI）与砂分数之间的兼容性关系，通过地统计地震反演（GSI）提取了这一重要属性的高分辨率值。在第二步中，除了AI之外，还从地震数据中获得了几个物理属性，然后通过逐步回归评估所有提取的属性（AI和其他地震属性），以选择对预测砂粒含量影响最大的最佳属性。在最后一步中，将选定的属性输入到蝙蝠启发的优化支持向量回归中作为输入，并估算出砂分记录。为了评估所提出的策略，在盲井中将预测的砂分数与实际值进行比较。从结果中可以明显看出，所提出的策略适合于根据地震属性对沙分数进行建模。