Brittleness evaluation method of tight sandstone based on Adam-neural network: A case study of a block in Gaobei slope, Nanpu Sag
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摘要: 致密砂岩储层脆性评价对于“甜点”区预测和压裂改造都有重要作用。针对目前脆性评价力学机理不足、脆性矿物组分分析准确性不高的问题，提出了一种考虑岩石力学性质、脆性矿物组分和岩石成熟度的Adam-神经网络脆性综合评价方法。根据南堡凹陷高北边坡27块岩样的三轴力学实验结果，分析了岩石应力-应变曲线和破坏形态得出Rickman脆性指数，根据全岩矿物X-衍射实验分析得到反映成熟度的黏土矿物和反映脆性组分的非黏土矿物的含量，然后以反映力学性质的Rickman脆性指数为目标函数，以黏土矿物和非黏土矿物含量为训练参数，通过改进的Adam算法建立神经网络脆性评价模型，最后用测井曲线验证模型的准确性。研究表明，该地区脆性矿物以石英、长石为主，中等脆性程度，岩石区域各向异性较强，测井动态力学参数计算的脆性指数与模型相吻合。该Adam-神经网络算法结合力学、地质和矿物学因素, 可以快速得到更加准确的区域脆性指数，对指导选井选层，压裂施工都有很好的指导意义。Abstract: The brittleness evaluation of tight sandstone is of great importance in sweet point prediction and fracturing stimulation. To clarify the mechanics mechanism of brittleness and better the accuracy of the brittle mineral analysis, we propose an Adam-neural network brittleness evaluation method, which takes the mechanical property, mineral component and rock maturity into account. Firstly, we conducted triaxial mechanical experiments on 27 samples from the northern Nanpu Sag, and analyzed the stress-strain curve so as to obtain the brittleness index based on Rickman method. Secondly, according to the X-ray diffraction, we obtained the content of clay and non-clay, which respectively reflect the rock maturity and brittle component. Then we used an advanced Adam algorithm to form a neural network evaluation model, setting the Rickman brittleness index as objection function and mineral content as training parameters. Finally, we validated the model accuracy with the logging curve. The result shows that the brittle minerals of the region are mainly quartz and feldspar. The rock shows medium brittleness but strong anisotropy. This result is consistent with the brittleness index calculated by logging data. With all the mechanical, geological and mineralogical factors combined, this Adam-neural network model can help obtain more accurate brittleness index in a broader area, which provides a good basis for fracturing parameter optimization and target layer selection.
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Key words:
- tight sandstone /
- brittleness evaluation /
- Adam-neural network /
- mineral content /
- well log curve /
- Nanpu Sag
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图 1 南堡凹陷北部区域构造单元划分图[21]
Figure 1. Tectonic unit zoning of the north region of Nanpu Sag
图 4 南堡凹陷致密砂岩典型破坏形式
Figure 4. Sketchs of typical failure mode of tight sandstone in Nanpu Sag
图 6 Adam-神经网络脆性评价示意图
Figure 6. Schematic diagram of Adam-neural network brittleness evaluation
图 7 岩石矿物组分相关性分析(横坐标为相关系数)
Figure 7. The correlation analysis of the rock mineral composition
图 8 脆性评价的Adam-神经网络结构示意图(w、b分别为权重和阈值)
Figure 8. The schematic diagram of Adam-neural network for brittleness evaluation
图 9 神经网络不同学习率收敛图
Figure 9. Convergence curves of different learning rates in neural network
图 10 神经网络不同算法收敛图
Figure 10. Convergence curves of different algorithms in neural network
图 11 脆性指数神经网络测试集结果误差分析
Figure 11. Comparison diagram of brittleness index between prodection value and true value
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