基于测井曲线包络面积预测油气藏产能
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TE155

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陕西省重点研发计划项目(2022GY-130);西安石油大学研究生创新与实践能力培养计划(YCS22213073)


Reservoir productivity prediction based on envelope area of logging curve
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    摘要:

    为了准确预测油气藏产能,解决复杂孔隙结构致使测井响应特征弱化,常规测井手段预测产能精度低的问题,通过采用交会图手段研究了东海盆地西湖凹陷碎屑岩储层测井曲线包络面积与产能间的关系,并优选了产能敏感性包络面积作为产能预测指标,借助波叠加性原理构建了包络面积产能预测指数,利用熵权法确定了各预测指标的权值,进而分储层类型建立了产能预测模型。结果表明:补偿中子-渗透率、电阻率-密度、电阻率-视波阻抗和视波阻抗-声波时差四组包络面积对产能的敏感性较强;本文所构建的产能预测模型精度较高,平均误差为19.4%。可见基于包络面积的产能预测模型可用于未开展动态测试的油气藏产能预测,为油气藏产能预测方法提供了新思路。

    Abstract:

    In order to predict the reservoir productivity, solve the problem of weak log response characteristics caused by complex pore structure and low productivity prediction accuracy by conventional logging methods. The method of crossplots was used to investigate the relationship between the enveloped area of well log curves in the clastic rock reservoir of the Xihu Sag in the East China Sea Basin and productivity. The sensitivity envelope area of productivity was selected as the productivity prediction index. Based on the principle of wave superposition, the envelope area productivity prediction index is constructed, the entropy weight method is used to determine the weight of each prediction index, and then the productivity prediction model is established by reservoir type. The results show that the four envelope areas of compensation neutron permeability, resistivity density, resistivity apparent wave impedance and apparent wave impedance acoustic wave lag have strong sensitivity to productivity. The accuracy of the productivity prediction model established in this paper is high, and the average error is 19.4%. It is concluded that the productivity prediction model based on envelope area can be used to predict the productivity of oil and gas reservoirs without dynamic test, which provides a new idea for the oil and gas reservoir productivity prediction method.

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韩鸿来,刘之的,张志强,等. 基于测井曲线包络面积预测油气藏产能[J]. 科学技术与工程, 2024, 24(15): 6254-6262.
Han Honglai, Liu Zhidi, Zhang Zhiqiang, et al. Reservoir productivity prediction based on envelope area of logging curve[J]. Science Technology and Engineering,2024,24(15):6254-6262.

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  • 收稿日期:2023-08-11
  • 最后修改日期:2024-03-12
  • 录用日期:2023-12-02
  • 在线发布日期: 2024-06-04
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