A Real-Time Downhole Fluid Identification System Empowered by Efficient Quadratic Neural Network

In the petroleum industry, accurately identifying downhole fluid is crucial for understanding fluid composition and estimating crude oil contamination and other properties. Near-infrared (NIR) spectrum analysis technology has achieved successful fluid identification applications due to its non-destructive nature and high efficiency. However, for real-time downhole fluid analysis, the NIR spectrometer faces challenges such as miniaturization and cost effectiveness. To address these issues, we construct a real-time downhole fluid identification system in this work. First, we propose a lightweight and deployable fluid identification model by integrating the successive projections algorithm (SPA) and a quadratic neural network (QNN). The SPA allows for spectral feature selection, and the QNN acts as an efficient identification model. Consequently, we use only four specific wavelengths with a one-hidden-layer QNN to achieve high identification accuracy. Second, we devise a hardware deployment scheme for real-time identification. We use four laser diodes to replace conventional light sources, further saving space. The QNN is then deployed to the STM32 MCU to implement real-time identification. Computational and online experiments demonstrate that our system functions well in real-time fluid identification and can further estimate the oil contamination rate with acceptable error.