Utilizing Distributed Temperature Sensors in Predicting Flow Rates in Multilateral Wells
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The new advancement in well monitoring tools have increased the amount of data that could be retrieved with great accuracy. Downhole pressure and temperature could be precisely determined now by using modern instruments. The new challenge that we are facing today is to maximize the benefits of the large amount of data that is being provided by these tools and thus justify the investment of more capital in such gadgets. One of these benefits is to utilize the continuous stream of temperature and pressure data to determine the flow rate in real time out 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. The trade-off of high resolution sensors is the related cost and resulting complication in modeling. Interpreting measured data at real-time to a downhole flow profile in multilateral and horizontal wells for production optimization is another challenge. In this study, a theoretical model is developed to predict temperature and pressure in trilateral wells based on given flow conditions. The model is used as a forward engine in the study and inversion procedure is then added to interpret the data to flow profiles. The forward model starts from an assumed well flow pressure in a specified reservoir with a defined well structure. Pressure, temperature and flow rate in the well system are calculated in the motherbore and in the laterals. These predicted temperature and pressure profiles provide the connection between the flow conditions and the temperature and pressure behavior. Then we use an 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 generates the matching temperature and pressure profiles in the well is identified. The production distribution from each lateral is determined based on this approach. At the end of the study, the results showed that we were able to successfully predict flow rates in the field within 10% of the actual rate. We then used the model to optimize completion design in the field. In conclusion, we were able to build a dependable model capable of predicting flow rates in trilateral wells using pressure and temperature data provided by downhole sensors.
Al Mulla, Jassim Mohammed A. (2012). Utilizing Distributed Temperature Sensors in Predicting Flow Rates in Multilateral Wells. Doctoral dissertation, Texas A&M University. Available electronically from