Application of Fiber Bragg Grating Sensor Networks in Oil Wells
Y. Pan, Z. Chen, L. Xiao, Y. Zhang, and J. FuLogin to View Article
As the fiber Bragg grating (FBG) sensors, with a high credibility, high temperature resistance, corrosion-resistant, and anti-electromagnetic interference, are suitable for working in a harsh environment of oil and gas wells, we develop a FBG wireless sensor network to monitor the temperature and pressure of the reservoir formation. A data acquisition module is set up at wellhead to demodulate the analog signals into digital signals. Another data transmission module installed at wellhead can send data from the data acquisiton module through a RS-232 interface to dadabase by a GPRS wireless mobile communication network. The user can browse the real-time published data through internet.
We build an experimental apparatus to simulate high temperature and high pressure of the downhole environment. We put a FBG sensor into the apparatus, increase the temperature and pressure gradually, and then reduce them back. The data acquisition module and data transmission module succeeded in their roles. In addition, we determined the extremes of the FBG sensor on temperature and pressure.
Through repeating the above operation a couple of times, we obtained a satisfactory match between the input values and measured values. Our system can measure the deferent depth temperature and pressure of the formation in real time. It has many properties: responsivity, accuracy, a high speed transmission rate, and a low bit error rate. In addition, it can work for 24 hours and 7 days a week in all weather.
To real-time monitor the temperature and pressure of the formation, the system can provide more reliable bases to engineers to predict and solve production problems. It has important practical significance particularly for outlying remote areas and offshore oil production. The application of this technology will effectively reduce the production of human errors and labor costs. Moreover, it will benefit the statistical analysis of massive data that require a unified management and sharing.