Permeability prediction with artificial neural network modeling in the Venture gas field, offshore eastern Canada
Permeability prediction with artificial neural network modeling in the Venture gas field, offshore eastern Canada
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DOI：
10.1190/1.1443970
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发表时间：
1996-03-01
期刊：
影响因子：
3.3
通讯作者：
Katsube, J
中科院分区：
文献类型：
--
作者：
Huang, ZH;Shimeld, J;Katsube, J
利用测井资料估算未取芯井段渗透率是许多地球科学学科面临的一项重要而艰巨的任务。最常见的是利用经验关系或某种形式的多元线性回归(MLR)从各种测井曲线中估计渗透率。更复杂的多元非线性回归(MNLR)技术并不常见，因为在选择适当的数学模型和分析所选模型对各种输入变量的敏感度方面存在困难。然而，最近发展起来的一类被称为人工神经网络(ANN)的非线性优化技术在很大程度上克服了这些困难。我们使用反向传播神经网络(BP-ANN)来建模空间位置、六种不同的测井曲线和渗透率之间的相互关系。来自Venture气田(加拿大东部近海)的四口井的数据被组织成训练和监督数据集，用于BP-ANN建模。来自同一油田的第五口井的数据将作为独立的数据集保留以供测试。当应用于这一测试数据时，训练好的BP-ANN产生的渗透率值与取心段的实测值很好地比较。用训练好的BP-ANN计算的渗透率剖面显示出许多低渗透层，这些低渗透层在Venture的井之间是可对比的。这些层位可能代表了流体运移的重要储集层内障碍，这对Venture未来的储集层生产计划具有重要意义。为了便于讨论，我们还使用传统的统计方法(即MLR和MNLR)推导出预测方程，并使用用于BP-ANN建模的相同数据集。这些例子突出了BP-ANN作为一种获得诸如渗透率估计等困难问题的多变量、非线性模型的方法的有效性。
Estimating permeability from well log information in uncored borehole intervals is an important yet difficult task encountered in many earth science disciplines. Most commonly, permeability is estimated fr om various well log curves using either empirical relationships or some form of multiple linear regression (MLR). More sophisticated, multiple nonlinear regression (MNLR) techniques are not as common because of difficulties associated with choosing an appropriate mathematical model and with analyzing the sensitivity of the chosen model to the various input variables. However, the recent development of a class of nonlinear optimization techniques known as artificial neural networks (ANNs) does much to overcome these difficulties.We use a back-propagation ANN (BP-ANN) to model the interrelationships between spatial position, six different well logs, and permeability. Data from four wells in the Venture gas field (offshore eastern Canada) are organized into training and supervising data sets for BP-ANN modeling. Data from a fifth well in the same field are retained as an independent data set for testing. When applied to this test data, the trained BP-ANN produces permeability values that compare well with measured values in the cored intervals. Permeability profiles calculated with the trained BP-ANN exhibit numerous low permeability horizons that are correlatable between the wells at Venture. These horizons likely represent important, intra-reservoir barriers to fluid migration that are significant for future reservoir production plans at Venture.For discussion, we also derive predictive equations using conventional statistical methods (i.e., MLR, and MNLR) with the same data set used for BP-ANN modeling. These examples highlight the efficacy of BP-ANNs as a means of obtaining multivariate, nonlinear models fur difficult problems such as permeability estimation.