Utilizing Distributed Temperature and Pressure Data To Evaluate The Production Distribution in Multilateral Wells

One of the issues with multilateral wells is determining the contribution of each lateral to the total production that is measured at the surface. Also, if water is detected at the surface or if the multilateral well performance declines, then it is difficult to identify which lateral or laterals are causing the production decline. One way to estimate the contribution from each lateral is to run production Logging Tools (PLT). Unfortunately, PLT jobs are expensive, time-consuming, labor-intensive and involve operational risks. An alternative way to measure the production from each lateral is to use Distributed Temperature Sensing (DTS) technology. Recent advances in DTS technology enable measuring the temperature profile in horizontal wells with high precision and resolution. The changes in the temperature profile are successfully used to calculate the production profile in horizontal wells. In this research, we develop a computer program that uses a multilateral well model to calculate the pressure and temperature profile in the motherbore. The results help understand the temperature and pressure behaviors in multilateral wells that are crucial in designing and optimizing DTS installations. Also, this model can be coupled with an inversion model that can use the measured temperature and pressure profile to calculate the production from each lateral. Our model shows that changing the permeability or the water cut produced from one lateral results in a clear signature in the motherbore temperature profile that can be measured with DTS technology. However, varying the length of one of the lateral did not seem to impact the temperature profile in the motherbore. For future work, this research recommends developing a numerical reservoir model that would enable studying the effect of lateral inference and reservoir heterogeneity. Also recommended is developing an inversion model that can be used to validate our model using field data