Abstract How to obtain reliable permeability data is universally considered as one of the critical work that guides geologists to explore oil-gas accumulation zones underground. Many significant researches related to permeability prediction have revealed that permeability can be directly calculated from logging data under usage of some complex non-linear equations. In this way, the key of permeability prediction is how to establish relational expression between permeability and logging data. Support vector regression is one of the best mathematical models using to explain complex mapping relationship between independent and dependent variables, and thus it can be viewed as an ideal approach to predict permeability. However, such model cannot be effective when different kinds of input data have high correlation or network parameters are not evaluated well. Then other two mathematical models, continuous restricted Boltzmann machine and particle swarm optimization, are referred to use to support the application of SVR. CRBM is functional to make a new data separation from the raw data, and network parameters can be optimized after PSO process. Therefore a new data-driven permeability prediction model CRBM-PSO-SVR is provided in this article. Data source used for method validation derives from five coring wells of the IARA oilfield, Santos Basin, Brazil. In two self-designed experiments, the accuracy rates of new method are respectively 67.34% and 76.67%, both of which are higher than those of other comparison methods. Experiment results well demonstrate the effectiveness of new method in permeability prediction when only logging data is available.

中文翻译：

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使用连续受限玻尔兹曼机、粒子群优化和支持向量回归的混合技术进行渗透率预测

摘要 如何获得可靠的渗透率数据被普遍认为是指导地质学家探索地下油气聚集带的关键工作之一。许多与渗透率预测相关的重要研究表明，在使用一些复杂的非线性方程的情况下，可以直接从测井数据中计算渗透率。这样，渗透率预测的关键是如何建立渗透率与测井资料的关系式。支持向量回归是用于解释自变量和因变量之间复杂映射关系的最佳数学模型之一，因此它可以被视为预测渗透率的理想方法。然而，当不同类型的输入数据具有高相关性或网络参数没有得到很好的评估时，这种模型无法有效。然后参考另外两个数学模型，连续受限玻尔兹曼机和粒子群优化，支持SVR的应用。CRBM 的功能是从原始数据中分离出新的数据，在 PSO 处理后可以优化网络参数。因此，本文提供了一种新的数据驱动的渗透率预测模型 CRBM-PSO-SVR。用于方法验证的数据来源来自巴西桑托斯盆地 IARA 油田的 5 口取心井。在两个自行设计的实验中，新方法的准确率分别为67.34%和76.67%，均高于其他对比方法。