Machine learning based deep carbonate reservoir characterization with physical constraints

Y. Chen, L. Zhao, J. Pan, C. Li, K. Li, F. Zhang, J. Geng
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Abstract

Summary Seismic characterization of deep carbonate reservoir is challenging due to the heterogeneous reservoir properties caused by the complex diagenesis and deep buried physical conditions. We propose a variety of physical constraints (including spatial constraint, continuity constraint, gradient constraint and category constraint) to guide the machine learning (Random Forest method) for reservoir quality prediction using multi-seismic attributes. Taking the carbonate reservoirs in the Tarim Basin, Western China as an example, we demonstrate that, various physical constraints are effective in enhancing the prediction performance based on the well test. The combination of the four proposed physical constraints gives the best prediction performance in terms of identifying reservoir and non-reservoir as well as inferring reservoir quality. We also show that a two-step strategy gives higher F1 score for reservoir quality evaluation. Machine learning based seismic prediction of deep carbonate reservoir with physical constraints suggests that this approach can effectively delineate the heterogeneous reservoir distribution, laying the foundation for geological model building and sweet spot detection.
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基于机器学习的深部碳酸盐岩储层物理约束表征
由于复杂的成岩作用和深埋物性条件导致储层物性不均匀,因此深部碳酸盐岩储层地震表征具有挑战性。我们提出了多种物理约束(包括空间约束、连续性约束、梯度约束和类别约束)来指导机器学习(随机森林方法)利用多地震属性进行储层质量预测。以塔里木盆地碳酸盐岩储层为例,在试井基础上论证了各种物理约束条件对提高预测效果的有效性。四种物理约束的组合在识别储层和非储层以及推断储层质量方面具有最佳的预测效果。两步策略对储层质量评价具有较高的F1分数。基于机器学习的深部物理约束碳酸盐岩储层地震预测表明,该方法能有效圈定非均质储层分布,为地质模型建立和甜点探测奠定基础。
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