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首页> 外文期刊>Computers & geosciences >Intelligent approaches for prediction of compressional, shear and Stoneley wave velocities from conventional well log data: A case study from the Sarvak carbonate reservoir in the Abadan Plain (Southwestern Iran)
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Intelligent approaches for prediction of compressional, shear and Stoneley wave velocities from conventional well log data: A case study from the Sarvak carbonate reservoir in the Abadan Plain (Southwestern Iran)

机译：根据常规测井数据预测压，切变和斯通利波速度的智能方法：以阿巴丹平原（伊朗西南部）萨尔瓦克碳酸盐岩储层为例

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摘要

Compressional, shear and Stoneley wave velocities (V_p, V_s and V_(st), respectively) are important reservoir characteristics that have many applications in petrophysical, geophysical and geomechanical studies. In this study V_p, V_s and V_(st) were predicted from well log data using genetic algorithms, fuzzy logic and neuro-fuzzy techniques in an Iranian carbonate reservoir (Sarvak Formation). A total of 3030 data points from the Sarvak carbonate reservoir which have V_p, V_s, V_(st) and conventional well log data were used. These data were divided into two groups; one group included 2047 data points used for constructing intelligent models, and the other included 983 data points used for models testing. The measured mean squared errors (MSEs) of predicted V_p in the test data, using genetic algorithms, fuzzy logic and neuro-fuzzy techniques, were 0.0296, 0.0148 and 0.029, respectively, for V_s these errors were 0.0153, 0.0084 and 0.0184, respectively, and for V_(st) they were 0.00035, 0.00020 and 0.00062, respectively. Despite different concepts in these intelligent techniques, the results (especially those from fuzzy logic) seem to be reliable.
机译：压力波，剪切波和斯通利波速度（分别为V_p，V_s和V_（st））是重要的储层特征，在岩石物理，地球物理和地球力学研究中具有许多应用。在这项研究中，使用遗传算法，模糊逻辑和神经模糊技术在伊朗碳酸盐岩储层（萨尔瓦克组）中根据测井数据预测了V_p，V_s和V_（st）。总共使用了来自Sarvak碳酸盐储层的3030个数据点，这些数据点具有V_p，V_s，V_（st）和常规测井数据。这些数据分为两组。一组包含用于构建智能模型的2047个数据点，另一组包含用于模型测试的983个数据点。使用遗传算法，模糊逻辑和神经模糊技术在测试数据中测得的预测V_p的均方误差（MSE）分别为0.0296、0.0148和0.029，对于V_s，这些误差分别为0.0153、0.0084和0.0184，对于V_（st）分别为0.00035、0.00020和0.00062。尽管这些智能技术的概念不同，但结果（尤其是来自模糊逻辑的结果）似乎是可靠的。

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