For an array of applications of the high voltage pulse discharge technology in reservoir stimulations and to gain a deeper understanding of the fractures mechanism of deep well rock under cyclic electrohydraulic shock waves (EHSWs), the effect of confining pressure and hydrostatic pressure on the fracturing of rock under EHSWs are investigated in this paper. Firstly, a two-dimensional (2D) water-explosive numerical model is built to match the computed peak pressure of the EHSW with that obtained by the empirical formula by tuning the relevant parameters, based on the equivalent method of EHSWs. Then, a rock model is established to obtain the stress distribution under static loads. Subsequently, the water-explosive model is coupled with the rock model to obtain the stress distribution under static and dynamic loads. In addition, based on this coupling model, the influences of confining pressure and hydrostatic pressure on circumferential stress, radial stress in the rock and the fracturing of rock around the wellbore are discussed. Finally, two improvement measures (increasing discharge energy and changing loading mode) are proposed to acquire greater fracture density based on intensive numerical simulations. The results show that the increase in hydrostatic pressure is beneficial to the crack formation and development, whereas confining pressure is harmful. Moreover, the inhibitory effect of confining pressure on crack formation is greater than the promotion effect of hydrostatic pressure on crack formation. Increasing the discharge energy can effectively promote the development of the number and length of main cracks. Under four repetitive loading modes with the same total discharge energy (1.36 × 15 kJ), the greatest fracture density can be obtained by using repetitive loading mode with a gradually decreasing mode of discharge energy (first level: 2 times (1.36 × 5 kJ); second level: 5 times (1.36 × 1 kJ)).

Due to the limitations that the current interwell connectivity can not consider shut-in wells and the inversion solving method is not reliable, a novel model that can consider the compressibility and shut-in situations is established. In order to further decrease the ill-posedness of the solutions for the connectivity model, a constraint optimization algorithm by using Bayesian theories and projection gradient method are proposed. The tests for a synthetic reservoir model and a real oil field indicate that the proposed methodology can efficiently estimate the interwell connectivity with high precision and reliability for practical application.

Exploration and development of deep and extended-reach wells faces extreme challenges including an extremely narrow mud weight window and complex well trajectories, with frequent occurrences of wellbore instability during drilling and completion operations. Tubular strings in the wellbore are highly prone to wellbore wall contact. However, most current studies on wellbore instability neglect the contact interaction between tubular strings and the wellbore during drilling, wiper tripping, and casing running operations. Therefore, this paper quantitatively investigates how tubular-string contact pressures destabilize wellbore integrity, a novel model integrating soft-string model, beam-column model, and contact mechanics was developed to compute the contact pressure between tubular string and wellbore walls under point-contact and wrap-contact cases. An enhanced model coupling tubular string, wellbore trajectory, and rock mechanical properties was established to predict stress distribution around the wellbore. The wellbore instability coefficient was then determined using the Mohr–Coulomb criterion. Key findings demonstrate that tubular string contact induces stress perturbations—radial stress increases while circumferential stress decreases, creating asymmetric wellbore instability patterns. The instability coefficient increases with larger contact force, larger wellbore diameter, higher rock elastic modulus, higher Poisson’s ratio and lower wellbore curvature radius. The instability coefficient exhibits symmetrical variation with azimuth angle, peaking at 0° and 180° while maintaining stability between 75° and 105°, with low sensitivity to inclination angles. Notably, drilling fluid density effects create a narrowed safe window, as higher densities damage contact areas while lower densities collapse non-contact regions. This study’s novelty lies in its systematic incorporation of mechanical contact effects—a factor previously oversimplified in stability analyses. Practical solutions proposed include trajectory optimization and operational strategies. These findings advance wellbore stability theory and provide actionable guidelines for high-risk formations and extended-reach wells.

Wellbore instability is a frequent problem of shale drilling. Accurate calculation of surge-swab pressures in tripping processes is essential for wellbore pressure management to maintain wellbore stability. However, cutting plugs formed in shale horizontal wells have not been considered in previous surge-swab pressure models. In this paper, a surge-swab pressure model considering the effect of cutting plugs is established for both open pipe string and closed pipe string conditions; In this model, the osmotic pressure of a cutting plug is analyzed. The reduction of cutting plug porosity due to shale hydration expansion and dispersion is considered, ultimately resulting in an impermeable cutting plug. A case study is conducted to analyze swab pressures in a tripping out process. The results show that, in a closed pipe condition, the cutting plug significantly increases the swab pressures below it, which increase with the decrease of cutting plug porosity and the increase of cutting plug length. Under the give condition, the swab pressure at the bottom of the well increases from 3.60 MPa to 8.82 MPa due to the cutting plug, increasing by 244.9%. In an open pipe string condition, the cutting plug affects the flow rate in the pipes and the annulus, resulting in a higher swab pressure above the cutting plug compared to a no-cutting plug annulus. The difference increases with the decrease of the porosity and the increase of the length and the measured depth of the cutting plug. Consequently, the extra surge-swab pressures caused by cutting plugs could result in wellbore pressures out of safety mud density window, whereas are ignored by previous models. The model proposes a more accurate wellbore pressure prediction and guarantees the wellbore stability in shale drilling.

Early prediction of casing-running sticking is essential, as the mitigation of stuck-pipe incidents often incurs significant time and economic costs. Previous studies have largely relied on purely theoretical torque and drag models that are constrained by simplified assumptions, preventing them from fully leveraging available field data and often leading to insufficient prediction accuracy. To address this challenge, we developed a hybrid mechanistic-data-driven intelligent model for hook-load prediction and casing-sticking risk assessment. The model combines mechanical models with ensemble learning algorithms, incorporating both mechanically derived parameters (theoretical hook load, casing–borehole compatibility, casing-bottom deflection and tilt angle) as well as operational and casing structural features. To evaluate its cross-field generalizability, the proposed model was trained on 13,449 samples from 14 wells across three oilfields and tested on 3961 samples from an independent well in a separate Oilfield. Three ensemble algorithms (XGBoost, Random Forest, and LightGBM) were evaluated, among which XGBoost achieved the highest predictive accuracy (RMSE = 3.50, MAE = 2.51, R² = 0.97) and was selected for subsequent friction-factor-based casing sticking risk assessment. A genetic-algorithm-based optimization framework was further developed to minimize sticking risk by optimizing the centralizer configuration under a friction constraint. The proposed sticking-risk assessment and optimization strategy was validated through field implementation. This mechanistic-data-driven intelligent model outperforms traditional theoretical approaches in predictive accuracy, interpretability, and engineering applicability, providing a practical and explainable tool for casing-running risk mitigation and design optimization in complex three-dimensional wells.