A new integrated workflow for improving permeability estimation in a highly heterogeneous reservoir of Sawan Gas Field from well logs data
A new integrated workflow for improving permeability estimation in a highly heterogeneous reservoir of Sawan Gas Field from well logs data
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一种新的集成工作流程，用于根据测井数据改进 Sawan 气田高度非均质储层的渗透率估算
DOI：
10.1007/s40948-018-0101-y
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发表时间：
2019
影响因子：
5
通讯作者：
Azizullah
中科院分区：
文献类型：
--
作者：
Qamar Yasin;Qizhen Du;Atif Ismail;Azizullah
Sawan气田是巴基斯坦最有潜力的气田之一，累计产量为850 BCF。产层中粗砂岩、中砂岩、砂泥岩夹层的重复出现，造成了极强的非均质性。因此，渗透率变化很大（从0.01 mD到超过1000 mD）。然而，在没有先前实验室数据的情况下，生产区渗透率的可验证和准确估计被认为是一项具有挑战性的任务。在这项研究中，我们探讨了一种方法，用于改善渗透率估计的基础上结合神经网络（NN），多变量回归和分类的数据挖掘，使用传统的测井（GR，LLD，RHOB，DT和NPHI）。该方法分两步进行。首先，我们使用经验，统计和虚拟技术计算渗透率在一个完整的取芯井，以选择专门的回归模型，将负责建立数据划分和分类的数据挖掘任务。为了提高分类器模型的效率，我们将联合收割机神经网络与多元回归相结合，用于预测准确的渗透率值。在步骤2中，采用所提出的回归模型来确定来自数据挖掘的数据划分和分类的最终渗透率值。本研究的最终结果表明，所提出的方法相结合的神经网络，多元回归，分类数据挖掘提供了更统一，更准确，更定性的渗透率估计相比，独立的通用或全球回归模型。还进行了电相（EFs）分类的模型，以验证所提出的方法。
The Sawan Gas Field is one of the most promising gas fields in Pakistan with a cumulative production of 850 BCF. The repetition of coarse sandstone, medium sandstone, and sandstone-shale intercalation in the production zone cause extreme heterogeneity. Consequently permeability varies enormously (from 0.01 mD to more than 1000 mD). Nevertheless, verifiable and accurate estimation of permeability in the production zone with no previous laboratory-derived data is considered a challenging task. In this study, we explore a methodology for improving permeability estimation based on the combination of neural network (NN), multiple variable regression, and classification of data mining using conventional well logs (GR, LLD, RHOB, DT, and NPHI). The approach works in two-steps. First, we compute permeability using empirical, statistical, and virtual techniques on a fully cored well in order to select the specialized regression model that will be responsible for building data partitioning and classification of data mining task. To improve the efficiency of the classifier model, we combine the NN with multiple variable regression for predicting accurate permeability values. In step-2, the proposed regression model was employed to determine the final permeability values from data partitioning and classification of data mining. The final result of this study revealed that the proposed approach which combines NN, multiple variable regression, and classification of data mining provide more uniform, accurate, and qualitative estimation of permeability compared with stand-alone generic or global regression model. Also electrofacies (EFs) classification was conducted over the model to validate the proposed approach.