Flow Rate Prediction in Multilateral Wells Using Distributed Temperature Sensors

摘要

The new advancements in well monitoring tools have increased the amount of data that could be retrieved with great accuracy. The new challenge that we are facing today is to maximize the benefits of the large amount of data provided by these tools. One of these benefits is to utilize the continuous stream of data to determine the flow rate in real time of a multilateral well. Temperature and pressure changes are harder to predict in horizontal laterals compared with vertical wells because of the lack of variation in elevation and geothermal gradient. Thus the need of accurate and high precision gauges becomes critical. A theoretical model is developed to predict temperature and pressure in trilateral wells. The model is used as a forward engine in the study and an inversion procedure is then added to interpret the data to flow profiles. The forward model starts from a specified reservoir with a defined well structure. Pressure, temperature and flow rate in the well system are calculated in the motherbore (main hole) and in the laterals. Then we use the inverse model to interpret the flow rate profiles from the temperature and pressure data measured by the downhole sensors. A gradient-based inversion algorithm is used in this work, which is fast and applicable for real-time monitoring of production performance. In the inverse model, the flow profile is calculated until the one that matches the temperature and pressure in the well is identified. The production distribution from each lateral is determined based on this approach. Examples are presented in the paper. The value of the model approach for production optimization for trilateral wells is illustrated through parametric study.