基于LSTM神经网络的随钻方位电磁波测井数据反演

Data Inversion of Azimuthal Electromagnetic Wave Logging While Drilling Based on LSTM Neural Network

康正明 秦浩杰 张意 李新 倪卫宁 李丰波

KANG Zhengming QIN Haojie ZHANG Yi LI Xin NI Weining LI Fengbo

西安石油大学电子工程学院, 陕西西安 710065 中煤科工西安研究院（集团）有限公司, 陕西西安 710077 页岩油气富集机理与有效开发国家重点实验室, 北京 102206 中石化石油工程技术研究院有限公司, 北京 102206

School of Electronic Engineering, Xi’an Shiyou University, Xi’an, Shaanxi, 710065, China Xi’an Research Institute Co. Ltd., China Coal Technology and Engineering Group Corp., Xi’an, Shaanxi, 710077, China State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, Beijing, 102206, China Sinopec Research Institute of Petroleum Engineering Co., Ltd, Beijing, 102206, China

随钻方位电磁波测井仪器在地质导向和储层评价等方面具有重要作用，但其测量响应不具有直观性，需要用反演方法获得地层信息，高斯–牛顿法、随机反演算法等传统反演方法计算速度较慢，难以满足实时反演的要求。为此，提出了一种基于长短期记忆人工神经网络（LSTM）的新反演方法，用于求取地层电阻率。首先，基于广义反射系数法建立正演算法，完成样本集的制作；然后，搭建LSTM神经网络模型，基于样本集进行训练和测试，通过遍历的方法优选出合适的网络参数；最后，在测试集上完成电阻率的反演，将反演电阻率与正演电阻率进行对比，对比反演所需时间和相对误差，并在测试集中加入白噪声验证了模型的抗噪能力。研究结果表明，模型能够准确快速地反演地层电阻率信息，能够满足对含有噪声数据的反演需要，具有较好的鲁棒性。此反演方法为测井资料处理提供了新的思路和方向。

Azimuthal electromagnetic wave logging while drilling (LWD) tool plays an important role in geosteering and reservoir evaluation, but its measurement response is not intuitive. So inversion method is needed to obtain formation information. Traditional inversion methods (i.e., Gauss-Newton method, random inversion method, etc.) are difficult to meet the requirements of real-time inversion due to the slow calculation speed. In this paper, a new inversion method based on a long and short-term memory (LSTM) artificial neural network was proposed to obtain formation resistivity. Firstly, the forward algorithm was established based on the method of generalized reflection coefficient to produce the sample set. Then, the LSTM neural network model was built, and it was trained and tested on the sample set. The appropriate network parameters were optimized by the traversal method. Finally, the resistivity inversion was completed on the test set. The inverted resistivity was compared with the forward resistivity, and the inversion time and relative error were compared as well. Meanwhile, the anti-noise property of the model is verified by adding white noise to the test set. The results show that the model can accurately and rapidly invert formation resistivity and can invert data containing noise, indicating that the model has good robustness. This inversion method can provide a new idea and direction for logging data processing.

国家自然科学基金企业创新发展联合基金项目“海相深层油气富集机理与关键工程技术基础研究”（编号：U19B6003）、中国博士后科学基金项目“煤岩层界面及低阻异常体随钻方位电磁波探测方法研究”（编号：2022M711442）、陕西省重点研发计划项目“煤矿井下方位电磁波探测技术与仪器研究”（编号：2023-YBGY-111）、陕西省教育厅重点科学研究计划项目“基于随钻电成像测井的页岩气储层裂缝参数计算模型研究”（编号：22JY053）和西安石油大学研究生创新与实践能力培养计划（编号：YCS22214245）联合资助

https://doi.org/10.11911/syztjs.2023047