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Improvement of petrophysical workflow for shear wave velocity prediction based on machine learning methods for complex carbonate reservoirs
Journal of Petroleum Science and Engineering ( IF 5.168 ) Pub Date : 2020-04-02 , DOI: 10.1016/j.petrol.2020.107234
Yan Zhang , Hong-Ru Zhong , Zhong-Yuan Wu , Heng Zhou , Qiao-Yu Ma

Shear wave velocity (S-wave velocity or Vs) is one of the most critical issues for carbonate reservoirs characterization, because of its complexity of rock compositions and pore structures. The Xu-Payne petrophysical model is a commonly used method to predict S-wave velocity. However, the model excessively depends on the interpretation accuracy of rock compositions, pore structures, fluid properties and fluid saturation, which makes the prediction result potentially uncertain. With the development of intelligent algorithms, a machine learning method (Long-short Term Memory Neural Network, LSTM) is proposed to improve the traditional petrophysical workflow. With the capabilities of deep learning and data mining, the LSTM model can deeply mine the rich information in the wireline logs and then establish the relationships between S-wave velocity and wireline logs. In order to illustrate the prediction effect, the carbonate reservoir in the Majiagou Formation Member 5(Ma 5 Member) which belongs to the Ordovician system in the D area of Sulige gas field is taken as an example. Six kinds of sensitive wireline logs, including compensated acoustic log (AC), natural gamma ray log(GR), photoelectric absorption cross-section log (PE), density log (DEN), deep lateral resistivity log(RLLD), neutron log(CNL), are selected as the input data of the LSTM model. The results reveal that the accuracy of LSTM method is up to 98.9%, whereas the Xu-Payne model is only 73%. With the predicted result of all wells in the areas using LSTM, the sensitive elastic properties, including P-and S-wave velocity, P- and S-wave impendences, ratio of P- and S-wave velocity, Poisson's ratio, lame coefficient and fluid sensitivity factor, are also concluded. Compared with the Xu-Payne model, the newly proposed workflow is proved to be convenient and suitable for the prediction of S-wave velocity in carbonate reservoirs.



中文翻译:

基于机器学习方法的复杂碳酸盐岩储层剪切波速度预测的岩石物理工作流程的改进

剪切波速度(S波速度或Vs碳酸盐岩储层表征的复杂性是岩石成分和孔隙结构的复杂性,因此它是碳酸盐岩储层表征最关键的问题之一。Xu-Payne岩石物理模型是预测S波速度的常用方法。但是,该模型过分依赖于岩石成分,孔隙结构,流体性质和流体饱和度的解释精度,这使得预测结果可能不确定。随着智能算法的发展,提出了一种机器学习方法(长期记忆神经网络,LSTM)来改善传统的岩石物理工作流程。借助深度学习和数据挖掘功能,LSTM模型可以深度挖掘有线测井中的丰富信息,然后建立S波速度与有线测井之间的关系。为了说明预测效果,以苏里格气田D区奥陶系的马家沟组5(Ma 5)段碳酸盐岩储层为例。六种敏感的电缆测井曲线,包括补偿声波测井曲线(AC),自然伽马射线测井曲线(GR),光电吸收截面测井曲线(PE),密度测井曲线(DEN),深侧电阻率测井曲线(RLLD),中子测井曲线( CNL)作为LSTM模型的输入数据。结果表明,LSTM方法的准确性高达98.9%,而Xu-Payne模型的准确性仅为73%。根据使用LSTM对该区域中所有井的预测结果,敏感的弹性特性包括P和S波速度,P和S波势,P和S波速比,泊松比,me腿系数和流体敏感性因子,也得出结论。与Xu-Payne模型相比,新提出的工作流程被证明是方便的并且适合于碳酸盐岩储层中的S波速度的预测。

更新日期:2020-04-02
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