Impact of rock-physics depth trends and Markov random fields on hierarchical Bayesian lithology/fluid prediction
Impact of rock-physics depth trends and Markov random fields on hierarchical Bayesian lithology/fluid prediction
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DOI：
10.1190/1.3463475
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发表时间：
2010-09
期刊：
影响因子：
3.3
通讯作者：
K. Rimstad;H. Omre
中科院分区：
文献类型：
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作者：
K. Rimstad;H. Omre
石油储层的早期评估通常基于地震数据和少量井的观测。如果可以整合这些数据并将其转换为储层的岩性/流体图，则有关储层的决策将得到改善。我们根据叠前地震数据和井观测，在贝叶斯环境中分析岩性/流体预测。似然模型包含卷积线性 Zoeppritz 关系和具有由压实和胶结作用引起的深度趋势的岩石物理模型。假设井观察是准确的。似然模型包含深度趋势、小波、误差参数等多个全局参数；这些推论是研究的一个组成部分。先前的模型基于参数化的剖面马尔可夫随机场，以捕获岩性和流体的不同连续性方向。后验模型捕获预测和模型参数不确定性，并通过马尔可夫链蒙特卡罗模拟进行评估...
Early assessments of petroleum reservoirs are usually based on seismic data and observations in a small number of wells. Decision-making concerning the reservoir will be improved if these data can be integrated and converted into a lithology/fluid map of the reservoir. We analyze lithology/fluid prediction in a Bayesian setting, based on prestack seismic data and well observations. The likelihood model contains a convolved linearized Zoeppritz relation and rock-physics models with depth trends caused by compaction and cementation. Well observations are assumed to be exact. The likelihood model contains several global parameters such as depth trend, wavelets, and error parameters; the inference of these is an integral part of the study. The prior model is based on a profile Markov random field parameterized to capture different continuity directions for lithologies and fluids. The posterior model captures prediction and model-parameter uncertainty and is assessed by Markov-chain Monte Carlo simulation-base...