College of Engineering and Design, Hunan Normal University, Changsha, Hunan, 410083, PR China.
College of Physics and Opto-electronic Engineering, Ocean University of China, Qingdao, Shandong, 266100, PR China.
Anal Chim Acta. 2023 Jun 8;1259:341200. doi: 10.1016/j.aca.2023.341200. Epub 2023 Apr 7.
The qualitative and quantitative analysis of gas components extracted from drilling fluids during mud logging is essential for identifying drilling anomalies, reservoir characteristics, and hydrocarbon properties during oilfield recovery. Gas chromatography (GC) and gas mass spectrometers (GMS) are currently used for the online analysis of gases throughout the mud logging process. Nevertheless, these methods have limitations, including expensive equipment, high maintenance costs, and lengthy detection periods. Raman spectroscopy can be applied to the online quantification of gases at mud logging sites due to its in-situ analysis, high resolution, and rapid detection. However, laser power fluctuations, field vibrations, and the overlapping of characteristic peaks of different gases in the existing online detection system of Raman spectroscopy can affect the quantitative accuracy of the model. For these reasons, a gas Raman spectroscopy system with a high reliability, low detection limits, and increased sensitivity has been designed and applied to the online quantification of gases in the mud logging process. The near-concentric cavity structure is used to improve the signal acquisition module in the gas Raman spectroscopic system, thus enhancing the Raman spectral signal of the gases. One-dimensional convolutional neural networks (1D-CNN) combined with long- and short-term memory networks (LSTM) are applied to construct quantitative models based on the continuous acquisition of Raman spectra of gas mixtures. In addition, the attention mechanism is used to futher improve the quantitative model performance. The results indicated that our proposed method has the capability to continuously on-line detect 10 hydrocarbon and non-hydrocarbon gases in the mud logging process. The limitation of detection (LOD) for different gas components based on the proposed method are in the range of 0.0035%-0.0223%. Based on the proposed CNN-LSTM-AM model, the average detection errors of different gas components range from 0.899% to 3.521%, and their maximum detection errors range from 2.532% to 11.922%. These results demonstrate that our proposed method has a high accuracy, low deviation, and good stability and can be applied to the on-line gas analysis process in the mud logging field.
从钻井液中提取的气体成分的定性和定量分析对于在油田回收过程中识别钻井异常、储层特征和烃类特性至关重要。气相色谱（GC）和气相质谱仪（GMS）目前用于整个泥浆录井过程中的气体在线分析。然而，这些方法存在一些局限性，包括昂贵的设备、高维护成本和较长的检测周期。由于拉曼光谱可以进行原位分析、具有高分辨率和快速检测，因此可以应用于泥浆录井现场的气体在线定量。然而，激光功率波动、现场振动以及拉曼光谱在线检测系统中不同气体特征峰的重叠，会影响模型的定量准确性。出于这些原因，设计并应用了一种具有高可靠性、低检测限和更高灵敏度的气体拉曼光谱系统，用于在线定量泥浆录井过程中的气体。该系统采用近共心腔结构，对气体拉曼光谱系统的信号采集模块进行改进，从而增强了气体的拉曼光谱信号。一维卷积神经网络（1D-CNN）与长短时记忆网络（LSTM）相结合，用于构建基于气体混合物连续拉曼光谱采集的定量模型。此外，还应用了注意力机制进一步提高定量模型的性能。结果表明，我们提出的方法能够连续在线检测泥浆录井过程中的 10 种碳氢化合物和非碳氢化合物气体。基于所提出的方法，不同气体成分的检测限（LOD）范围在 0.0035%-0.0223%之间。基于所提出的 CNN-LSTM-AM 模型，不同气体成分的平均检测误差在 0.899%至 3.521%之间，最大检测误差在 2.532%至 11.922%之间。这些结果表明，所提出的方法具有高精度、低偏差和良好的稳定性，可应用于泥浆录井领域的在线气体分析过程。
