Comparison of Wellbore Stability of UBD in the Case of Laser Drilling and Conventional Operation

Abstract

In recent years, growing interest in underbalanced drilling has resulted in the rapid development of its associated equipment technology, practices, and procedures. Underbalanced drilling is used to avoid lost circulation, formation damage, Invasion of drilling fluid, which occurs in almost all the permeable formations during drilling and decreasing weight on bit. However, the risk of wellbore collapse due to lake of hydrostatic mud pressure is high. Therefore, using good geo-mechanical model may avoid wellbore instability problem. In order to evaluate the potential for wellbore instability, it is necessary to use elastoplastic model to compute the stresses and strains around the bore hole. Furthermore it should be mentioned that the laser drilling process extremely depends on rock's geo-mechanical properties. This paper describes a numerical investigation of wellbore instability in a fractured carbonate reservoir and verified with reservoir core samples. Some necessary conditions should be mentioned to gain the best method of comparison conventional UBD and laser UBD operations. It is vital to evaluate the sensitivity of the wellbore stability by focusing on the effect of a laser irradiation absorption. Finally, this paper fills the gap by paying attention to the sensitivity analysis and focuses on assessing the influence of various drilling methods, rock properties and optical fluid parameters.

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