The reservoir in the Shunbei field is characterized by ultra-deep, ultra-high temperature, and ultra-high pressure. During the drilling process, the circulating temperature at the bottom of the well is higher than the temperature resistance of downhole instruments, which leads to frequent problems of device burnout and no signal. Therefore, it is of great significance to accurately predict the wellbore temperature field of ultra-deep directional wells. In this paper, the influence of the drilling string assembly, the flow channel structure and the flow pattern on the convective heat exchange coefficient is considered. Based on the energy conservation equation, a numerical model of wellbore-formation transient heat transfer is developed. Then, the model was verified by the real data of two ultra-deep wells in Shunbei block, China, and the results showed that the prediction errors of bottom-hole temperature were all within 2%. Finally, the key factors and rules of the wellbore annulus temperature are analyzed. The results show that the bottom-hole temperature decreases with the decrease of inlet temperature, the thermal conductivity of drilling fluid, and the thermal conductivity of drill pipe, and increases with the decrease of flow rate, the density of drilling fluid, viscosity of drilling fluid, and specific heat capacity of drilling fluid. The inlet temperature has the greatest influence on the outlet temperature, and the specific heat of the pipe string has a minor influence on the wellbore annulus temperature. The research results of this paper provide an accurate wellbore temperature field prediction method for ultra-deep directional wells in the Shunbei block, China, which is of great significance for temperature-controlled drilling.

Inadequate drill cuttings removal can cause costly problems such as excessive drag, lower rate of penetration, and even mechanical pipe sticking. Cuttings bed height is usually used to evaluate hole-cleaning efficiency in horizontal wells. In this study, artificial intelligence models, including artificial neural network (ANN), support vector regression (SVR), recurrent neural network (RNN), and long short-term memory (LSTM), were employed to predict cuttings bed height in the well-bore. A total of 136 different tests were conducted, and cuttings bed height under different conditions were measured in our previous study. By training four different artificial intelligence models with the experiment data, it was found that the ANN model performed best among other artificial intelligence models. The ANN model outperformed the dimensionless cuttings bed height model proposed in our previous study. Due to the amount of data points, the memory ability of RNN and LSTM models has not been entirely played compared with the ANN model.

In petroleum engineering, accurately predicting particle settling velocity during various stages of a well’s life cycle is vital. This study focuses on settling velocities of both spherical and non-spherical particles in Newtonian and non-Newtonian fluids. Utilizing a dataset of 931 experimental observations, an artificial neural network (ANN) model with a 7-42-1 architecture is developed (one input layer, one hidden layer with 42 neurons, and one output layer). This model effectively incorporates particle settling orientation and the inclusion of the settling area ratio, enhancing its predictive accuracy. Achieving an average absolute relative error (AARE) of 8.51%, the ANN model surpasses traditional empirical correlations for settling velocities in both Newtonian and power-law fluids. Key influencing factors, such as the consistency index and particle equivalent diameter, were identified. This approach in ANN model construction and data analysis represents a significant advancement in understanding particle dynamics.

During the drilling of ultra-deep wells, gas kick often occurs, influenced by the complex void pressure profile. The accurate description of particle settling behavior in the gas–liquid mixture is of great significance to effectively deal with gas kicks and ensure drilling safety. In this study, the gas–liquid two-phase annulus flow with different gas volume fractions is created through the transparent annular pipe, constant pressure air pump, and gas flowmeter. High-speed photography is used to record and analyze the sedimentation of particles in gas–liquid mixtures. This study is based on 288 tests. The main parameters in this experiment include the particle Reynolds number, the gas fraction, and liquid viscosity. The effects of wall and gas fraction on the drag coefficient were analyzed. The correlation of particle terminal settling velocity was established. The results obtained show a correlation with average absolute errors (AAE) of 10.7%. This study reveals the settling characteristics of particles in the annular gas–liquid mixed flow, provides an accurate terminal settling velocity prediction explicit formula, and provides guidance for the calculation of bottom hole pressure under the condition of gas kick.

With the expansion of oil and gas exploration and development to complex oil and gas resource areas such as deep and ultra-deep formation onshore and offshore, the kick is one of the high drilling risks, and timely and accurate early kick detection is increasingly important. Based on the kick generation mechanism, kick characterization parameters are preliminarily selected. According to the characteristics of the data and previous research progress, Random Forest (RF), Support Vector Machine (SVM), Feedforward Neural Network (FNN), and Long Short-term Memory Neural Network (LSTM) are established using experimental data from Memorial University of Newfoundland. The test results show that the accuracy of the SVM-linear model was 0.968, and the missing alarm and the false alarm rate only was 0.06 and 0.11. Additionally, through the analysis of the kick response time, the lag time of the SVM-linear model was 1.3 s, and the comprehensive equivalent time was 23.13 s, which showed the best performance. The different effects of the model after data transformation are analyzed, the mechanism of the best effect of the SVM model is analyzed, and the changes in the effect of other models including RF are further revealed. The proposed early-warning model warns in advance in historical well logging data, which is expected to provide a fast, efficient, and accurate gas kick warning model for drilling sites.

Reliable characterization of the wellbore temperature field is essential for ensuring drilling safety and optimizing operational parameters in shale oil horizontal wells. To address the limitations of conventional models that assume constant thermophysical properties and neglect interactions among multiple heat sources, a transient heat transfer model featuring one-dimensional heat transfer in the wellbore and two-dimensional heat transfer in the formation is developed. The model uniquely accounts for variable thermophysical properties along with three internal heat sources: bit–rock interaction heat (BRIH), viscous dissipation heat (VDH), and drillpipe–formation friction heat (DFFH). The governing equations are implemented numerically using a fully implicit finite-difference approach and verified against field measurements from 10 wells in the Shengli Oilfield. The model demonstrates high predictive accuracy, with an average relative error of 1.58%. VDH contributes significantly to wellbore temperature elevation (≈3.33 °C), whereas BRIH and DFFH exert comparatively minor effects (≈0.34 °C). Sensitivity analysis shows that geothermal gradient is the dominant factor controlling BHCT (correlation coefficients: 0.74 for OBDF; 0.65 for WBDF), followed by drilling fluid density, with all parameters exhibiting weak intercorrelations. Furthermore, a PSO-RBF optimization framework is developed, reducing computation time from 48.34 min per evaluation to an average of 9.0 min per well (81.4% efficiency improvement) while maintaining high prediction accuracy. Overall, this study contributes theoretical understanding and practical value to temperature prediction and parameter optimization in shale oil horizontal well drilling.