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Permeability variations in reservoirs and around boreholes are of great interest in petroleum engineering due to the fact that they can significantly affect reserve estimates, reservoir development, well production or injection rate, and the likely success of remedial actions of near-wellbore damage. A fully coupled transient thermo-poroelastic concept with and without rock mechanical damage models is employed to evaluate stress distribution and permeability variation around the boreholes and breakouts. The anisotropy concept is applied to permeability, rock modulus, and uniaxial compressive strength using Weibull distribution. The Mogi-Coulomb failure criterion is employed to model breakout initiation and propagation around the borehole. Strain-based stress-permeability equation and stress-permeability models based on the experimental results and correlations are used to evaluate permeability changes near the borehole. Two cases of overbalanced and underbalanced drilling conditions are employed. The simulations for medium with isotropic and anisotropic physical properties result in varied effective stress distribution, breakout geometries, and consequently different distributions of tangential and radial permeability. Generally, in isotropic medium, permeability reduces to less than 70 % of its original value in the overbalanced condition and 80 % in underbalanced operations; however, there are differences between permeability along x and y directions. For anisotropic medium, permeability increases to more than 150 % all over the borehole periphery in the overbalanced operations; moreover, permeability enhancement is essentially larger along maximum in situ springline. In the underbalanced condition, permeability decreases to less than 70 % along minimum in situ stress direction and increases to more than 200 % in a limited area near the borehole along maximum in situ stress springline.

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An automated drilling system requires a real-time evaluation of the drilling bit during drilling to optimize operation and determine when to stop drilling and switch bits. Furthermore, in the dynamic modeling of drill strings, it is necessary to take into account the interactions between drilling bits and rock. To address this challenge, a hybrid approach that combines physics-based models with data analytics has been developed to handle downhole drilling measurements in real time. First, experimental findings were used to formulate mathematical models of cutter–rock interaction in accordance with their geometrical characteristics, rock properties, and drilling parameters. Specifically, these models represent the normal and contact forces of polycrystalline diamond compact cutters (PDCs). Experimental data are analyzed utilizing deep learning, nonlinear regression, and genetic algorithms to fit nonlinear equations to data points. Following this, the recursive least square was implemented as a data analytic method to integrate real-time drilling data, drilling bit models, and mathematical models. Drilling data captured by the along-string measurement system (ASM) is implemented to estimate cutting and normal forces, torque, and specific energy at the bit. The unique aspect of this research is our approach in developing a detailed cutter–rock interaction model that takes all design and operation parameters into account. In addition, the applicability of the algorithm is demonstrated by real-time assessments of drilling dynamics, utilizing downhole digital data, that enable the prediction of drilling events and problems related to drilling bits.