| 基于Adam-神经网络的致密砂岩脆性评价方法:以南堡凹陷高北边坡为例 |
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| 引用本文： | 万里明,吴均,卢军凯,刘彝,陈勉.基于Adam-神经网络的致密砂岩脆性评价方法:以南堡凹陷高北边坡为例[J].地质科技通报,2020,39(2):94-103. |
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| 作者姓名： | 万里明 吴均 卢军凯 刘彝 陈勉 |
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| 作者单位： | 中国石油大学（北京）油气资源与探测国家重点实验室;中国石油冀东油田公司钻采工艺研究院 |
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| 基金项目： | 国家自然科学基金联合基金项目“超深井井筒安全构建工程基础理论与方法”U1762215国家自然科学基金联合基金项目“高温高压油气安全高效钻完井工程基础理论与方法U19B6003-05国家十三五自然科学基金项目“丛式井缝网构建理论与控制技术”2017ZX05009 |
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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。
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| 关 键 词： | 致密砂岩 脆性评价 Adam-神经网络 矿物含量 测井曲线 南堡凹陷 |
| 收稿时间： | 2019-01-13 |
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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| Wan Liming,Wu Jun,Lu Junkai,Liu Yi,Chen Mian.Brittleness evaluation method of tight sandstone based on Adam-neural network: A case study of a block in Gaobei slope,Nanpu Sag[J].Bulletin of Geological Science and Technology,2020,39(2):94-103. |
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| Authors: | Wan Liming Wu Jun Lu Junkai Liu Yi Chen Mian |
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| Affiliation: | (State Key Laboratory of Petroleum Resources and Prospecting,China University of Petroleum(Beijing),Beijing 102200,China;Drilling&Production Technology Research Institute of PetroChina Jidong Oilfield,Tangshan Hebei 063004,China) |
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| 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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| Keywords: | tight sandstone brittleness evaluation Adam-neural network mineral content well log curve Nanpu Sag |
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