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Predicting well markers from artificial neural-network-predicted lithostratigraphic facies

机译:从人工神经网络预测的岩石地层相​​预测井标记

摘要

This disclosure generally describes methods and systems, including computer-implemented methods, computer-program products, and computer systems, for predicting well markers. One computer-implemented method includes separating neural-network (NN)-predicted facies output associated with a plurality of wells into two sets, a first set of NN-predicted facies output of training wells and a second set of NN-predicted facies output of target wells, calculating, for each training well of the plurality of wells, a sameness score between zones of NN-predicted facies output and human-identified lithostratigraphic units (finer zones), calculating a mean sameness score for the finer zones for all training wells, identifying finer zones with a mean sameness score greater than a threshold value as dominant facies zones, and iterating over each target well to calculate a top and depth position of each dominant facies zone determined based upon the NN-predicted facies output of the target well.
机译:本公开总体上描述了用于预测井标记的方法和系统,包括计算机实现的方法,计算机程序产品和计算机系统。一种计算机实现的方法包括将与多个井关联的神经网络(NN)预测的相输出分成两组,第一组是训练井的NN预测相输出,第二组是NN预测的井输出。目标井,针对多个井中的每个训练井,计算NN预测相输出区域与人为识别的岩石地层学单位(精细地带)之间的相似度得分,计算所有训练井的较精细区域的平均相似度,将平均平均得分大于阈值的较细区域识别为优势相区,并在每个目标井上进行迭代,以计算基于目标井的NN预测相输出确定的每个优势相区的顶部和深度位置。

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