Article · Journal of Petroleum Exploration and Production Technology · June 21, 2019
Optimization of coiled tubing nozzle for sand removal from wellbore
Javed Akbar Khan, Sonny Irawan, Eswaran Padmanabhan, Hussain Hamoud Al-Kayiem, Sahil Rai
Abstract
Abstract Production is hampered by sand and scale that impede flow and require intervention. Although coiled tubing jetting tools have been very successful in wellbore cleaning operations, the coiled tubing unit has certain limitations and constraints. The fluid pumping pressures of fluids at the wellhead are limited to the normal pressure of pump, whereas some sand types and scales require a very high shear force to break and disintegrate. The dimensions of the coiled tubing unit nozzle size are directly related to the diameter of the production tubing. The present work consists in designing the nozzle of coiled tubing jetting tool with different dimensional parameters and analysing the design of the nozzle for required high outlet pressure, flow rate and velocity at various inlet pressures. Numerical technique was used to analyse the flow of fluid through the nozzle. The different port diameters and cone angles between 0.09 and 0.125 inch and 15° and 45°, respectively, were simulated. The effects of these parameters on the outlet pressure, the mass flow at the outlet and the outlet velocities were predicted and analysed. Inner nozzle diameters smaller than 0.75 inch gave good results, and the 1 inch inner diameter gave lower performance. Therefore, the lower diameter nozzle provides better performance. Decreasing the number of ports from seven to five did not create a big difference in nozzle performance, as the seven ported nozzles provide slightly better performance.
More by Sonny Irawan
- An explainable physics-informed neural network prototype for synthetic proppant transport in a two-dimensional fracture domainPhysics of Fluids · 2026
- Physics‐Guided Neural Regression Modeling of Energy‐Balance Proxies for Hydraulic Fracture Propagation in 1D–3DInternational Journal for Numerical and Analytical Methods in Geomechanics · 2026
- Modeling and Prediction of Kappaphycus alvarezii Viscosity Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference SystemJournal of Earth Energy Science Engineering and Technology · 2025 · Citations: 1
- Physics-Informed Machine Learning for Predicting Drilling Fluid Losses in Naturally Fractured Caspian FormationsSPE Annual Caspian Technical Conference and Exhibition · 2025 · Citations: 4
