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    基于地震属性的致密碳酸盐岩储层裂缝分布的人工智能预测方法

    鲍明阳 董少群 曾联波 何娟 孙福亭 韩高松

    鲍明阳, 董少群, 曾联波, 何娟, 孙福亭, 韩高松, 2023. 基于地震属性的致密碳酸盐岩储层裂缝分布的人工智能预测方法. 地球科学, 48(7): 2462-2474. doi: 10.3799/dqkx.2022.290
    引用本文: 鲍明阳, 董少群, 曾联波, 何娟, 孙福亭, 韩高松, 2023. 基于地震属性的致密碳酸盐岩储层裂缝分布的人工智能预测方法. 地球科学, 48(7): 2462-2474. doi: 10.3799/dqkx.2022.290
    Bao Mingyang, Dong Shaoqun, Zeng Lianbo, He Juan, Sun Futing, Han Gaosong, 2023. Artificial Intelligence Prediction Method for Tight Carbonate Reservoir Fracture Distribution Based on Seismic Attributes. Earth Science, 48(7): 2462-2474. doi: 10.3799/dqkx.2022.290
    Citation: Bao Mingyang, Dong Shaoqun, Zeng Lianbo, He Juan, Sun Futing, Han Gaosong, 2023. Artificial Intelligence Prediction Method for Tight Carbonate Reservoir Fracture Distribution Based on Seismic Attributes. Earth Science, 48(7): 2462-2474. doi: 10.3799/dqkx.2022.290

    基于地震属性的致密碳酸盐岩储层裂缝分布的人工智能预测方法

    doi: 10.3799/dqkx.2022.290
    基金项目: 

    国家自然科学基金青年项目 42002134

    中国博士后科学基金第14批特别资助项目 2021T140735

    详细信息
      作者简介:

      鲍明阳(1995-),男,硕士研究生,主要从事储层裂缝识别预测、裂缝网络建模、机器学习等研究.ORCID:0000-0002-2681-7946. E-mail:baomingyang2020@163.com

    • 中图分类号: P631.4

    Artificial Intelligence Prediction Method for Tight Carbonate Reservoir Fracture Distribution Based on Seismic Attributes

    • 摘要: 裂缝是致密碳酸盐岩储层的重要渗流通道,影响油藏开发效果.由于裂缝的地球物理响应弱且复杂,使得裂缝预测困难.在深度挖掘地震属性中裂缝特征信息的基础上,建立了基于人工智能的裂缝分布预测方法.该方法通过支持向量机算法优选裂缝敏感属性,利用梯度提升决策树(GBDT)算法深度挖掘单井裂缝发育情况与地震属性之间的非线性关系,梯度提升决策树算法对于异常值有较强的鲁棒性,可以较好地解决裂缝地震响应弱且复杂的问题.该方法在中东扎格罗斯盆地某油田古近系渐新统‒新近系中新统Asmari组主力产油层位的致密碳酸盐岩储层中进行了实例应用,优选出方差、曲率、倾角偏差、倾角、方位角5种裂缝敏感地震属性,利用梯度提升决策树集成不同地震属性中的裂缝特征,建立裂缝分布预测模型,对研究区碳酸盐岩储层裂缝分布进行了预测.与常用裂缝预测方法的对比实验表明,本方法的裂缝预测结果与单井裂缝解释更为符合.预测结果表明,研究区北部裂缝更为发育,构造高部位附近裂缝更为发育,与生产动态认识相符合.

       

    • 图  1  研究区位置(据刘小兵等,2019修改)

      Fig.  1.  Location of the study area (modified from Liu et al., 2019)

      图  2  A层顶面构造图

      Fig.  2.  Structure diagram of the top surface of layer A

      图  3  裂缝类型及特征

      a.剪切裂缝;b.张裂缝;c.构造裂缝成像

      Fig.  3.  Fracture types and characteristics

      图  4  地震属性优选流程图

      Fig.  4.  Flow chart of seismic attribute selection

      图  5  梯度提升决策树的裂缝预测流程示意图(a)及梯度提升决策树原理示意图(b)

      Fig.  5.  Schematic diagram of fracture prediction flow of gradient boosting decision tree (a) and schematic diagram of gradient boosting decision tree (b)

      图  6  地震属性敏感性分析结果

      Fig.  6.  Sensitivity analysis of seismic attributes

      图  7  梯度提升决策树模型参数取值与测试样本预测结果的相关系数之间的关系

      Fig.  7.  Relationship between the parameters of gradient boosting decision tree model and the correlation coefficient of prediction results of test data

      图  8  不同人工智能方法裂缝预测结果对比

      Fig.  8.  Comparison of fracture prediction results of different artificial intelligence methods

      图  9  裂缝预测结果与单井裂缝发育强度交会图

      Fig.  9.  Crossplot of fracture prediction results and fracture development intensity of single well

      图  10  目的层A段地震裂缝预测结果分析

      a.地震预测结果;b.产液指数与预测结果交会图;c.井震对比

      Fig.  10.  Analysis of seismic fracture prediction results of A section of target layer

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    • 收稿日期:  2021-12-19
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