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Model development for shear sonic velocity using geophysical log data: Sensitivity analysis and statistical assessment
Gas Science and Engineering Pub Date : 2020-12-29 , DOI: 10.1016/j.jngse.2020.103778
Mohammad Islam Miah , Salim Ahmed , Sohrab Zendehboudi

Reservoir geomechanical parameters play a vital role in evaluating sanding potential, wellbore stability, and drilling performance for making decisions on development strategies of oil and gas fields. Compared to expensive experimental/laboratory investigations, the petrophysical log data-driven shear sonic velocity models are proven to be cost effective and quick to estimate geomechanical formation properties. The objective of this study is to introduce a new deterministic model using the most contributing predictor variables and to rank the variables according to their relative importance while predicting dynamic shear sonic wave velocity for clastic sedimentary rocks. The least-squares support vector machine with a global optimization technique is used to construct a data-driven model for shear sonic velocity estimation. A new regressive correlation is also proposed. The model performance is assessed using statistical parameters to ensure the model accuracy and reliability. Based on the relative contribution of log variables, the influential variables are ranked (higher to lower order) as follows: acoustic compressional sonic velocity, porosity, and bulk density. The developed model is validated; the predictions are compared with real data and existing log-based correlations for clastic sedimentary rocks using data from two real fields, namely the North Sea and the Niger Delta basins. The developed shear wave velocity model will assist geomechanists and drilling engineers in obtaining accurate formation elastic properties and rock strength, which are required for wellbore failure analysis as well as assessment of sanding occurrence during exploration and drilling.



中文翻译:

利用地球物理测井数据开发剪切声速模型:灵敏度分析和统计评估

储层的地质力学参数在评估打磨潜力,井眼稳定性和钻井性能以决定油气田的开发策略方面起着至关重要的作用。与昂贵的实验/实验室研究相比,岩石物理测井数据驱动的剪切声速模型已被证明具有成本效益,并且能够快速估算地质力学地层性质。这项研究的目的是引入一个使用确定性最强的预测变量的新确定性模型,并在预测碎屑沉积岩的动态切变声波速度时,根据变量的相对重要性对变量进行排名。使用具有全局最优化技术的最小二乘支持向量机构建用于剪切声速估算的数据驱动模型。还提出了一种新的回归相关性。使用统计参数评估模型性能,以确保模型的准确性和可靠性。基于对数变量的相对贡献,将影响变量按如下顺序(从高到低排序):声压声速,孔隙率和堆积密度。开发的模型经过验证;使用来自两个实际油田(即北海和尼日尔三角洲盆地)的数据,将预测结果与实际数据和现有的基于碎屑沉积岩的基于对数的相关性进行比较。所开发的剪切波速度模型将帮助地质力学和钻井工程师获得准确的地层弹性和岩石强度,这是井眼破坏分析以及在勘探和钻探过程中评估打磨情况所必需的。

更新日期:2021-02-08
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