Kick is one of the most important drilling problems, and because its occurrence makes drilling engineering extremely complex, it is essential to predict the possibility of kick as soon as possible. In this study, k-means clustering was combined with four artificial neural networks: regularized RBFNN, generalized RBFNN, GRNN, and PNN, to estimate the kick risk. To reduce data redundancy and normalize the drilling data, which contain kick conditions, k-means clustering was introduced. The output layer weights were then determined using a brute-force search with different Gaussian function widths, resulting in a series of artificial neural networks composed of different clustering samples and different Gaussian function widths. The results showed that the prediction accuracy of regularized RBFNN + k-means model was the highest, that of the GRNN + k-means model was the lowest. The kick prediction accuracy for regularized RBFNN, generalized RBFNN, GRNN, and PNN were 75.90%, 65.20%, 51.70%, and 70.16%, respectively. This method can be used to enhance the speed and accuracy of kick risk prediction in the field while facilitating the use of and advances in risk warning technology for deep and high-temperature and high-pressure wells.

The exploitations of deep-water wells often use directional well drilling to reach the target layer. Affected by special environments in deep water, the prediction of pressure loss of cement slurry is particularly important. This paper presents a prediction model of pressure loss suitable for deep-water directional wells. This model takes the complex interaction between the temperature, pressure and hydration kinetics of cement slurry into account. Based on the initial and boundary conditions, the finite difference method is used to discretize and calculate the model to ensure the stability and convergence of the result calculated by this model. Finally, the calculation equation of the model is used to predict the transient temperature and pressure loss of Wells X1 and X2, and a comparison is made between the predicted value and the monitoring data. The comparison results show that the maximum error between the temperature and pressure predicted by the model and the field measured value is within 6%. Thus, this model is of high accuracy and can meet the needs of site construction. It is concluded that this result can provide reliable theoretical guidance for temperature and pressure prediction, as well as the anti-channeling design of HTHP directional wells.

The accurate prediction and monitoring of rock drillability are essential for geomechanical modeling and optimizing drilling parameters. Traditional methods often rely on laboratory core experiments and well logging data to evaluate rock drillability. However, these methods can only obtain core samples and sonic logging data in drilled wells. To enable the real-time monitoring of bottom-hole rock drillability during drilling, we propose the following novel approach: data fusion and a CNN-GBDT framework for surface-based real-time monitoring. The specific process involves using 1D-CNN convolution to extract deep features from historical wells’ drilling data and sonic log data. These deep features are then fused with the original features and passed to the GBDT framework’s machine learning model for training. To validate the effectiveness of this method, this study conducted a case analysis on two wells in the Missan Oil Fields. CNN-GBDT models based on XGBoost, LightGBM, and CatBoost were established and compared with physical methods. The results indicate that the CNN-GBDT model centered on LightGBM achieved a mean square error (MSE) of 0.026, which was one-tenth of the MSE of 0.282 of the physical evaluation method. Furthermore, the effectiveness of the proposed CNN-GBDT framework for monitoring rock drillability suggests potential applications in monitoring other bottom-hole parameters.

The packer is one of the most important tools in deep-water perforation combined well testing, and its safety directly determines the success of perforation test operations. The study of dynamic perforating pressure on the packer is one of the key technical problems in the production of deep-water wells. However, there are few studies on the safety of packers with shock loads. In this article, the three-dimensional finite element models of downhole perforation have been established, and a series of numerical simulations are carried out by using orthogonal design. The relationship between the perforating peak pressure on the packer with the factors such as perforating charge quantity, wellbore pressure, perforating explosion volume, formation pressure, and elastic modulus is established. Meanwhile, the database is established based on the results of numerical simulation, and the calculation model of peak pressure on the packer during perforating is obtained by considering the reflection and transmission of shock waves on the packer. The results of this study have been applied in the field case of deep-water well, and the safety optimization program for deep-water downhole perforation safety has been put forward. This study provides important theoretical guidance for the safety of the packer during deep-water perforating.

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.