Lamination Type Recognition with Artificial Intelligence Based on Optical Thin Section
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摘要:
纹层类型的准确识别是光学薄片技术在油田勘探开发过程中的一个重要应用。在页岩储层改造过程中,由于页岩特有的薄层理构造与非均质性,准确识别地层中纹层类型,对选取储层改造位置和优化改造方案具有重要意义。光学大薄片相较于传统测井数据具有更加精确的岩性划分,相较于普通薄片具有更大尺度的纵向连续岩性变化规律特征,可提供厘米级别的储层信息,从而能够准确划分纹层类型,优选工程甜点。基于卷积神经网络(CNN)构建了纹层分类模型(简称CNN模型),利用纵向上连续的光学大薄片数据,CNN模型可以准确识别细砂质纹层、粉砂质纹层和泥质纹层,分类精度最高达73%,且分类准确率优于YOLOv5模型。研究结果表明,CNN模型能够有效实现纹层类型智能识别,且能够应对复杂背景和精细纹层特征,为页岩油气储层的精细化表征和开发提供了一种高效、精准的解决方案。
Abstract:The accurate recognition of lamination types is an important application field of optical thin section technology in the process of oilfield exploration and development. In the process of shale reservoir stimulation, due to the unique thin bedding structure and heterogeneity of shale, it is of great significance to accurately identify the lamination types in the formation for selecting the reservoir stimulation location and optimizing the stimulation plan. Compared with the well logging data, the large optical thin sections can achieve more accurate lithology division, and they have more obvious longitudinally continuous lithology variation characteristics than the ordinary thin sections, which can provide reservoir information at the centimeter level, so as to accurately classify the lamination types and optimize the engineering sweet spots. A lamination classification model (referred to as the CNN model) was constructed based on a convolutional neural network (CNN), and three types of lamination were classified and recognized by longitudinally continuous large optical thin section data. The results show that the CNN model can accurately identify fine sand lamination, silty sand lamination, and argillaceous lamination, and the classification accuracy can reach 73% and it is better than that of the YOLOv5 model. The results show that the CNN model can effectively realize intelligent lamination recognition and can deal with complex background and fine lamination features, which provides an efficient and accurate solution for the fine characterization and development of shale oil and gas.
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Keywords:
- shale /
- lamination type /
- optical thin section /
- image classification /
- neural network /
- intelligent recognition
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表 1 单井段CNN模型纹层分类效果
Table 1 Single-well segment lamination classification effects based on CNN model
纹层类型 精度 召回率 F1值 泥质纹层 0.45 0.42 0.44 细砂质纹层 0.71 0.69 0.70 粉砂质纹层 0.62 0.69 0.65 表 2 4 669.94~4 670.37 m井段连续纹层识别结果
Table 2 Identification results of continuous lamination in well section of 4 669.94~4 670.37 m
序号 井段/m 纹层类型 准确率 1 4 669.94~4 669.96 细砂质 0.75 2 4 669.96~4 670.00 粉砂质 0.60 3 4 670.03~4 670.07 粉砂质 0.67 4 4 670.07~4 670.09 泥质 0.42 5 4 670.10~4 670.13 细砂质 0.73 6 4 670.13~4 670.18 粉砂质 0.61 7 4 670.21~4 670.27 粉砂质 0.68 8 4 670.31~4 670.34 粉砂质 0.66 9 4 670.34~4 670.37 泥质 0.47 表 3 多井段CNN模型纹层分类效果
Table 3 Multi-well segment lamination classification effects
纹层类型 精度 召回率 F1值 泥质纹层 0.54 0.53 0.54 细砂质纹层 0.73 0.77 0.75 粉砂质纹层 0.66 0.64 0.65 表 4 4 711.14~4 712.40 m井段连续纹层识别结果
Table 4 Identification results of continuous lamination in well section of 4 711.14~4 712.40 m
序号 井段/m 纹层类型 准确率 1 4 711.14~4 711.16 粉砂质 0.68 2 4 711.16~4 711.20 细砂质 0.72 3 4 711.31~4 711.35 泥质 0.55 4 4 711.35~4 711.37 细砂质 0.77 5 4 711.68~4 711.73 粉砂质 0.73 6 4 711.90~4 711.91 粉砂质 0.71 7 4 711.91~4 711.93 粉砂质 0.68 8 4 711.93~4 711.95 泥质 0.66 9 4 711.95~4 711.96 粉砂质 0.76 10 4 712.02~4 712.09 泥质 0.68 11 4 712.21~4 712.27 粉砂质 0.72 12 4 712.33~4 712.40 泥质 0.62 -
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