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A new empirical model for enhancing well log permeability prediction, using nonlinear regression method: Case study from Hassi-Berkine oil field reservoir – Algeria

机译:利用非线性回归法提高井数渗透性预测的新实证模型:Hassi-Berkine油田水库案例研究 - 阿尔及利亚

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The reservoir permeability (K) factor is the key parameter for reservoir characterization. This parameter is considered as a determinant reservoir quality index. Depending on the data required and procedure availability, permeability can be defined from several methods such as; well test interpretation, wireline formation tester, and core data. These approaches can also be in assumption with permeability prediction targeting the non-cored sections. According to a similar status, well logs records can be an interesting support tool in use to reach the planned objectives. Thus, this investigation consists of finding out a model able to estimate the well log permeability and adjusting the outcome to the core permeability results.In this led research, the applied approach to the core data, to start with, was aimed to determine the reservoir rock types (RRT) using the flow zone indicator (FZI) method. The obtained classification allows stating a permeability model for each rock type.In order to calculate permeability from well logs, FZI has been founded out. A multi-regression technique was used to analyze the relationship of FZI with respect to specific logs such as Gamma-ray (GR), Density Log (RHOB), and Sonic log (DT). An objective function has been designated to minimize the quadratic error between the observed normalized FZI coming from core data, and the normalized FZI calculated from well logs. This process is carried out to identify a mathematical correlation allowing the estimation of FZI from porosity logs, leading to permeability determination. As results, permeability from logs was supporting relatively permeability defined from cores. The final results can be an accurate and real test for associating the exactitude performance of logging data records in boreholes with respect to the overall reservoir characterization sections. Thus, the applied investigation can be a genuine and quick method for essentially a specific deduction regarding the non-cored reservoir sections, with reference to rock typing, permeability and probably further reservoir factors.
机译:储层渗透率(k)因子是储层特征的关键参数。该参数被认为是决定因素储层质量指标。根据所需的数据和程序可用性,可以从多种方法中定义渗透率,例如;井测试解释,有线形成测试仪和核心数据。这些方法也可以符合靶向非核心部分的渗透性预测。根据类似的状态,井日志记录可以是用于达到计划目标的有趣支持工具。因此,该调查包括找出能够估计井数渗透率并将结果调整到核心渗透率结果的模型。在这种LED研究中,核心数据的应用方法旨在确定储层岩石类型(RRT)使用流量区指示器(FZI)方法。所获得的分类允许为每个岩石型阐述渗透性模型。为了计算从井日志来计算渗透性,Fzi已经开始。多元回归技术用于分析FZI关于特定日志的关系,例如伽马射线(GR),密度日志(Rhob)和声波日志(DT)。已经指定了一个客观函数,以最小化来自核心数据的观察到的归一化Fzi之间的二次误差,以及从井日志计算的归一化FZI。进行该过程以识别允许从孔隙程估计FZI的数学相关性,从而导致渗透性确定。结果,原木的渗透性是支持核心定义的相对渗透性。最终结果可以是一个准确和实际的测试,用于将测井数据记录的精确性能与整体储层表征部分相关联。因此,应用的研究可以是基本上是关于非粘合储层部分的特定扣除的真正和快速的方法,参考岩石打字,渗透率和可能进一步的储层因子。

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