A comparison of deep machine learning and Monte Carlo methods for facies classification from seismic data

蒙特卡罗方法 马尔科夫蒙特卡洛 计算机科学 人工神经网络 算法 机器学习 人工智能 数据集 贝叶斯概率 数据挖掘 统计 数学 地质学 古生物学 构造盆地
作者
Darío Graña,Leonardo Azevedo,Mingliang Liu
出处
期刊:Geophysics [Society of Exploration Geophysicists]
卷期号:85 (4): WA41-WA52 被引量:71
标识
DOI:10.1190/geo2019-0405.1
摘要

Among the large variety of mathematical and computational methods for estimating reservoir properties such as facies and petrophysical variables from geophysical data, deep machine-learning algorithms have gained significant popularity for their ability to obtain accurate solutions for geophysical inverse problems in which the physical models are partially unknown. Solutions of classification and inversion problems are generally not unique, and uncertainty quantification studies are required to quantify the uncertainty in the model predictions and determine the precision of the results. Probabilistic methods, such as Monte Carlo approaches, provide a reliable approach for capturing the variability of the set of possible models that match the measured data. Here, we focused on the classification of facies from seismic data and benchmarked the performance of three different algorithms: recurrent neural network, Monte Carlo acceptance/rejection sampling, and Markov chain Monte Carlo. We tested and validated these approaches at the well locations by comparing classification predictions to the reference facies profile. The accuracy of the classification results is defined as the mismatch between the predictions and the log facies profile. Our study found that when the training data set of the neural network is large enough and the prior information about the transition probabilities of the facies in the Monte Carlo approach is not informative, machine-learning methods lead to more accurate solutions; however, the uncertainty of the solution might be underestimated. When some prior knowledge of the facies model is available, for example, from nearby wells, Monte Carlo methods provide solutions with similar accuracy to the neural network and allow a more robust quantification of the uncertainty, of the solution.
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