Seismic Facies Recognition and Stratigraphic Trap Characterization Based on Neural Networks

Si-Hai Zhang, Yin Xu, M. Abu-Ali, M. Teng
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引用次数: 2

Abstract

Reservoirs and the lateral seal of stratigraphic traps are controlled by the depositional environment or diagenesis. The recognition of facies and lithology from seismic attributes is an effective approach for identifying stratigraphic traps related to the depositional environment. In this paper, the occurrence of stratigraphic traps related to depositional environment in Permian aeolian clastics and Jurassic carbonate-evaporites was studied. To identify these stratigraphic traps, multiple seismic attributes were classified using supervised and unsupervised artificial neural networks (ANNs), which allowed the recognition of seismic facies and lithology. Neural networks are a powerful classification technique, which incorporates multiple attributes into a number of classes to identify sedimentary facies. Two algorithms comprising supervised and unsupervised neural networks are commonly implemented. With a supervised learning algorithm, prior information such as typical facies at the control wells are required to train the multilayer perceptron (MLP) network. With an unsupervised algorithm, only seismic data is input to the neural network, and competitive-learning techniques are employed to classify or self-organize the data based on its internal characteristics. Without prior information, the output classes are not labeled with lithofacies. According to the availability of prior information, supervised and unsupervised learning were applied to recognize dune-playa and carbonate-evaporite combinations, respectively. To characterize the depositional environments, joint interpretation with a geological model is necessary for both supervised and unsupervised classification. Two major findings have been derived from this work. First, the learning technology based on ANNs is effective to recognize sedimentary facies. The microfacies and lithologies identified by both supervised and unsupervised ANNs are very consistent with the drilled wells. Second, the recognition of depositional facies and lithology can characterize the stratigraphic traps in the study areas. Lateral seal plays a key role in stratigraphic traps. Playa siltstone and tight lagoonal limestone constitute the lateral seal in dune-playa and carbonate-evaporite combinations, respectively.
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基于神经网络的地震相识别与地层圈闭表征
储层和地层圈闭的侧向封闭受沉积环境或成岩作用的控制。通过地震属性识别相岩性是识别与沉积环境有关的地层圈闭的有效方法。本文研究了二叠系风成碎屑和侏罗系碳酸盐岩-蒸发岩中与沉积环境有关的地层圈闭的产状。为了识别这些地层圈闭,使用有监督和无监督人工神经网络(ann)对多个地震属性进行分类,从而识别地震相和岩性。神经网络是一种强大的分类技术,它将多个属性合并到多个类别中来识别沉积相。有监督神经网络和无监督神经网络是常用的两种算法。使用监督学习算法,需要先验信息(如控制井的典型相)来训练多层感知器(MLP)网络。采用无监督算法,只将地震数据输入神经网络,并采用竞争学习技术根据其内部特征对数据进行分类或自组织。在没有先验信息的情况下,输出的分类不能用岩相标记。根据先验信息的可用性,分别采用监督学习和无监督学习对沙丘-playa和碳酸盐-蒸发岩组合进行识别。为了刻画沉积环境,需要结合地质模型进行监督分类和非监督分类的联合解释。从这项工作中得出了两个主要发现。首先,基于人工神经网络的学习技术能够有效地识别沉积相。有监督人工神经网络和无监督人工神经网络识别的微相和岩性与实测井非常吻合。其次,通过沉积相和岩性的识别,可以对研究区地层圈闭进行表征。侧向封闭在地层圈闭中起着关键作用。盐湖粉砂岩和致密泻湖灰岩分别构成沙丘-盐湖组合和碳酸盐岩-蒸发岩组合的侧向封闭。
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