论文详情
流体因子是储层流体识别中的重要参数,传统的流体因子不能较为准确地识别储层流体,等效流体体积模量对储层流体的变化更加敏感。多波AVO反演是从地震道集中提取等效流体体积模量的重要手段之一。常规的多波AVO反演基于最小二乘或贝叶斯理论,反演精度强烈依赖于初始模型,但大多数实际工区很难建立高精度、高分辨率的初始模型。为了进一步提高储层流体识别的精度,并降低反演对初始模型的依赖程度,在AVO反演理论的指导下,构建基于无监督深度学习的纵波和转换波道集直接反演等效流体体积模量的方法,将该方法应用于X工区实际数据的反演并进行储层流体识别。X工区内井旁道地震道集的试算结果表明,该方法具有较高的精度,反演结果与测井数据的相对误差约为2.949%,绝对误差小于0.1GPa;X工区内反演得到的等效流体体积模量的剖面和时间切片与已知测井解释结果匹配度良好,说明该方法能够较为精确地识别储层流体,具有良好的实用价值。
Fluid factors are key parameters in reservoir fluid identification;however,traditional fluid factors are unable to precisely identify the reservoir fluid.Among these factors,the effective pore fluid modulus is the most sensitive to the fluid.Multi-wave amplitude-versus-offset (MW AVO) inversion is an effective tool for characterizing reservoir fluids.The inverted accuracy of conventional MW AVO inversion methods,which are based on the least squares theory or Bayesian theory,depend heavily on the models that define the initial conditions.However,building such models with high resolution and high precision is difficult in most field areas.To improve the accuracy of reservoir fluid identification and reduce the dependence of the inversion method on the initial models,an unsupervised deep-learning MW AVO inversion method was developed,which incorporates deep learning within a conventional pre-stack AVO.This allows the effective fluid bulk modulus to be inverted directly by using both P-and S-wave gathers.This method was used to perform field data inversion and fluid identification in the X work area and achieved high accuracy.The relative error between the inverted results and the logging data was 2.949%,and the absolute error was less than 0.1 GPa.The inverted profiles and time slices matched well with the log interpretation results,thereby demonstrating that the proposed method can identify reservoir fluid accurately and has a significant practical value.
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