Dynamic Prediction Model for the Maximum Slurry Temperature and Interface Temperature During Cementing Operations Based on a Temperature-Pressure-Coupled Rheological Model

Summary

Accurate prediction of the maximum slurry temperature and interface temperature is important for selecting thickening-time and compatibility-test conditions and for maintaining zonal isolation integrity. However, conventional prediction methods may produce substantial deviations when the coupled effects of sequential multifluid pumping and temperature-pressure (T-P)-dependent rheology are neglected. This study establishes a transient wellbore-formation heat-transfer model that incorporates the T-P-dependent rheological properties of drilling fluid, spacer fluid, and cement slurries. A fully implicit finite-difference method is used to solve the model, together with a dynamic density-temperature mapping algorithm for tracking fluid interfaces. The Herschel-Bulkley model provided the best fit among the evaluated rheological models under the tested T-P conditions. Incorporating T-P-dependent rheology reduced the average relative error of the calculated drilling-fluid outlet temperature from 4.71% to 1.44%. For the field case analyzed, the model predicted that the maximum slurry temperature occurred in the annulus above the bottomhole. The predicted maximum lead-slurry temperature was 103.9°C at 6,175 m, which was 3.8°C higher than the predicted maximum tail-slurry temperature of 100.1°C at 6,775 m and 1.4°C higher than the corresponding interface temperature. Because downhole temperature measurements were unavailable, these temperatures and corresponding depths are model predictions indirectly supported by the agreement between calculated and measured wellhead temperatures. The predicted maximum lead-slurry temperature may be used as a model-derived reference condition for thickening-time test design, whereas the interface temperature should be treated as one input for compatibility-test design together with the expected contamination-ratio range and an appropriate safety margin. The model provides a case-specific computational basis for selecting laboratory test conditions and assessing cementing risks under the studied operating conditions.