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.

Managed pressure drilling (MPD) is the core technology for developing formations with high pressure and narrow density windows. It precisely maintains the bottomhole pressure (BHP) within the safe operating window defined by formation pore pressure and fracture pressure by actively regulating the wellbore pressure profile. If pressure control becomes unstable, it can easily trigger gas kicks or lost circulation, posing a severe threat to operational safety. However, existing model predictive control (MPC) schemes have significant limitations: pure data-driven models exhibit poor generalization under complex conditions, while control algorithms based on traditional mechanistic models struggle to meet the stringent real-time requirements of field control cycles due to high-complexity numerical iteration processes. To balance control precision and real-time performance, this paper proposes a physics-informed model predictive control framework (PINC-MPC). During the training phase, physical prior knowledge such as the law of mass conservation is embedded into the neural network as constraints to construct a physically consistent deep surrogate model, enabling it to characterize complex wellbore characteristics. In the control phase, this surrogate model replaces the time-consuming numerical solving process of the mechanistic model within the MPC loop, achieving near-real-time state prediction and rolling optimization while ensuring physical fidelity. Experimental results indicate that PINC-MPC demonstrates superior control performance. Its median single-step solving time is only 16.81 ms, achieving an 11.1-fold acceleration compared to the mechanistic model-based scheme (187.3 ms). In a 5000 s full-cycle closed-loop control experiment, the total time required for the former is only 1.68 s, while the latter reaches 18.73 s, representing an efficiency improvement of approximately 91%. In terms of control accuracy, the integrated absolute error (IAE), reflecting the total deviation of the control process, significantly decreased from 63.40 MPa·s for the industrial successive linearization MPC (SLMPC) to 12.90 MPa·s, an improvement of 79.7%. Especially in extreme dynamic conditions such as simulated pump shutdowns for pipe connections and sudden gas kicks, the framework demonstrates excellent predictive ability and response efficiency. It can proactively trigger compensation actions to keep BHP fluctuations within 0.30 MPa, significantly outperforming the traditional SLMPC method. The research results prove that PINC-MPC provides an efficient, precise, and robust nonlinear control strategy for MPD systems, offering important engineering reference value for enhancing the automation level of intelligent drilling systems.

Deep and ultra-deep oil and gas reservoirs are characterized by extreme temperature and pressure conditions. During drilling, bottomhole temperatures often exceed the tolerance of downhole tools, leading to signal loss and damage to key components. Accurate prediction of the wellbore temperature field is therefore critical for ultra-deep drilling operations. Cementing quality significantly affects heat transfer between the wellbore and the formation, yet its influence is often neglected in existing prediction models. This study incorporates cementing quality into wellbore–formation heat transfer analysis, develops a method to calculate the effective thermal conductivity of cement, and establishes a transient heat transfer model based on energy conservation. The model is discretized and solved using the finite difference method. The effectiveness of the proposed model is validated against the Keller models, with a resulting relative error of 2.3%. Field data from three ultra-deep wells are used to evaluate the performance of the wellbore heat transfer model, incorporating cementing quality. The results indicate that the mean relative error of bottomhole temperature prediction is 0.77%, while that of outlet temperature prediction is 3.06%. This work provides an accurate method for predicting wellbore temperature profiles in ultra-deep wells and offers technical support for temperature-controlled drilling.

With the exploitation of oil and gas resources, deep and ultra-deep oil and gas resources are gradually becoming the core areas for increasing oil and gas reserves and production in the future. Gas kick is easy to occur during drilling in deep and ultra-deep wells. In order to reduce the harm of gas kick, this paper uses a numerical simulation method to study the mechanism of multiphase flow in the wellbore after a gas kick. Using Ansys Fluent, selecting volume of fluid (VOF) multiphase flow model and RNG k-ε turbulence model, taking gas kick velocity and drilling fluid density as variables, three-dimensional numerical simulation of bubble formation and rise in single hole of gas kick wellbore is carried out, and the influence of gas kick velocity and drilling fluid density on bubble formation and rise velocity is studied. The results show that the radius length of the bubble major axis is positively correlated with gas kick rate, and negatively correlated with drilling fluid density. The bubble rising velocity increases with the increase of gas kick rate due to the influence of bubble generation diameter and other factors; the drilling fluid density increases and bubble rising velocity decreases due to the influence of bubble generation diameter, gas-liquid resistance and bubble breakage caused by turbulence. In addition, the bubble generation chart under different gas kick rates and drilling fluid densities is established, which is helpful to analyze the bubble generation and evolution process under different conditions.

Accurately predicting wellbore temperature is critical to safe and efficient drilling in ultra-deep wells. With their narrow safe density window, ultra-deep wells are prone to lost circulation, yet conventional temperature models often overlook their impact on wellbore heat transfer. A transient wellbore-formation heat transfer model was developed in this study that explicitly accounts for lost circulation effects. This model is discretized and solved by employing the finite difference method. Validation against field measurements and existing models demonstrates that the predicted bottomhole temperature exhibits an error of less than 2%. Furthermore, the model elucidates the critical influences of lost circulation location, lost circulation rate, and circulation time on the temperature distribution, thereby providing theoretical and methodological support for precise temperature prediction in high-risk drilling operations.

The deep and ultra-deep oil and gas resources often have the characteristics of high temperature and high pressure, with complex pressure systems and narrow safety density windows, so risks such as gas invasion and overflow are easy to occur during the drilling. In response to the problems of low management efficiency and large gas kick by traditional gas invasion treatment methods, this paper respectively established and compared three intelligent control models for bottom hole pressure (BHP) based on a PID controller, a fuzzy PID controller, and a fuzzy neural network PID controller based on the non-isothermal gas–liquid–solid three-phase transient flow heat transfer model in the annulus. The results show that compared with the PID controller and the fuzzy PID controller, the fuzzy neural network PID controller can adjust the control parameters adaptively and optimize the control rules in real-time; the efficiency of the fuzzy neural network PID controller to deal with a gas kick is improved by 45%, and the gas kick volume in the process of gas kick is reduced by 63.12%. The principal scientific novelty of this study lies in the integration of a fuzzy neural network PID controller with a non-isothermal three-phase flow model, enabling adaptive and robust bottom hole pressure regulation under complex gas invasion conditions, which is of great significance for reducing drilling risks and ensuring safe and efficient drilling.