Lithofacies determination from wire-line log data using a distributed neural network

M. Smith, N. Carmichael, I. Reid, C. Bruce
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引用次数: 5

Abstract

A distributed neural network, running on a large transputer-based parallel computer, was trained to identify the presence of the main lithographical facies types in a particular oil well, using only the readings obtained by a log probe. The resulting trained network was then used to analyse a variety of other wells, and showed only a small decrease in accuracy of identification. Geologists classify well structures using rock and fossil samples in addition to the log data that was given to the network. Results are given here for the accuracy with which the learned network agreed with analyses performed by geologists. The study was then extended into two more areas, firstly to investigate the network's success in predicting physical attributes of the rocks, e.g. porosity and permeability, and secondly to investigate the ability of similar networks to isolate particular geological features.<>
利用分布式神经网络从电缆测井数据确定岩相
一个分布式神经网络,运行在一个大型的基于传输器的并行计算机上,经过训练,仅使用测井探头获得的读数,就能识别特定油井中主要岩性相类型的存在。然后,将得到的训练网络用于分析其他各种井,结果表明识别的准确性仅略有下降。除了提供给网络的测井数据外,地质学家还使用岩石和化石样本对井结构进行分类。这里给出的结果是,学习网络的准确性与地质学家所做的分析一致。该研究随后扩展到另外两个领域,首先是研究网络在预测岩石物理属性(如孔隙度和渗透率)方面的成功,其次是研究类似网络分离特定地质特征的能力。
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