Knowledge of the mechanical properties of clathrate hydrates is central for studying the mechanical properties of hydrate-bearing sediments, their associated applications in wellbore stability, exploitation in stratum deformation, geological disaster prevention, and risk assessment of CO₂ buried in oceans. However, because of the limited understanding of hydrate formation conditions and limited methods to investigate these, the understanding of the mechanical properties of hydrates is still poor and even controversial to some extent. This paper reviews current experimental and theoretical results on mechanical properties of hydrates, and discusses the typical difficulties faced in this area. On the experimental side, the most important problem is obtaining pure hydrate samples. Theoretically, the essential origin of the mechanical properties has not been explained in terms of molecular interactions. The hope is to resolve these issues by combining novel macroscopic experiments and microscopic methods. In order to avoid difficulties caused by impurities, it is proposed to use molecular dynamics simulations. This technique can be used to reveal the nature of the mechanical characteristics of hydrates at the molecular and nanometre scale. The goals of this paper are to establish a bridge between the micromechanical nature and the macromechanical properties of hydrates, and to lay a solid theoretical basis for the study of the mechanical properties of hydrate-bearing sediments. These goals are important for the future safe and efficient exploitation of natural hydrates, hydrate-induced seabed geological disaster prevention, the safety of CO₂ geological burial, and the deployment of a reliable long-term seabed-borehole coupled hydrate observation system in the integrated Ocean Drilling Program.

Shale inhibition, low-temperature performance, the ability to prevent calcium and magnesium-ion pollution, and hydrate inhibition of polyethylene glycol drilling fluid were each tested with conventional drilling-fluid test equipment and an experimental gas-hydrate integrated simulation system developed by our laboratory. The results of these tests show that drilling fluid with a formulation of artificial seawater, 3% bentonite, 0.3% Na₂CO₃, 10% polyethylene glycol, 20% NaCl, 4% SMP-2, 1% LV-PAC, 0.5% NaOH and 1% PVP K-90 performs well in shale swelling and gas hydrate inhibition. It also shows satisfactory rheological properties and lubrication at temperature ranges from −8 °C to 15 °C. The PVP K-90, a kinetic hydrate inhibitor, can effectively inhibit gas hydrate aggregations at a dose of 1 wt%. This finding demonstrates that a drilling fluid with a high addition of NaCl and a low addition of PVP K-90 is suitable for drilling in natural marine gas-hydrate-bearing sediments.

A method is proposed that uses renewable solar energy to supply energy for the exploitation of marine gas hydrates using thermal stimulation. The system includes solar cells, which are installed on the platform and a distributor with electric heaters. The solar module is connected with electric heaters via an insulated cable, and provides power to the heaters. Simplified equations are given for the calculation of the power of the electric heaters and the solar battery array. Also, a case study for the Shenhu area is provided to illustrate the calculation of the capacity of electric power and the solar cell system under ideal conditions. It is shown that the exploitation of marine gas hydrates by solar energy is technically and economically feasible in typical marine areas and hydrate reservoirs such as the Shenhu area. This method may also be used as a good assistance for depressurization exploitation of marine gas hydrates in the future.

Current methods for designing three-dimensional trajectories rarely account for complex formation constraints, focusing primarily on geometric relationships. However, trajectory adjustments are often necessary during drilling operations. These field adjustments typically lack systematic optimization, resulting in suboptimal trajectories. This study introduces a novel trajectory optimization framework that integrates formation fitness for curve construction and proactive anti-collision trajectory adjustment (PACTA). The framework begins by incorporating PACTA and optimizing the initial trajectory to minimize total measured depth (TMD) using a genetic algorithm. Subsequently, a second optimization phase identifies curve sections passing through formations with low build-up fitness, automatically splitting them into combinations of curves and straight lines. Dynamic trajectory equations are then constructed based on these adjustments, and the final trajectory is optimized accordingly. Case studies demonstrate that the proposed method effectively adjusts curve positions in the presence of multiple formations with low build-up fitness while avoiding wellbore collisions. The approach achieves an average 10% reduction in total drilling time when minimizing TMD and an average 19.7% reduction in drillstring torque when torque minimization is prioritized. This new trajectory design method is expected to significantly reduce well construction costs.

Currently, most oil and gas wells are drilled by continuously transmitting downhole measured information (directional and geological information) in real-time to the surface to monitor and steer the well along a pre-defined path. The intelligent drill pipe method can transmit data over longer distances and at a higher rate than other methods, such as mud pulse telemetry, acoustic telemetry, and electromagnetic telemetry. Nevertheless, it is expensive and requires boosters along the drill string. In the available literature, academic research rarely addresses the data transmission process in boreholes using intelligent drill pipes. Furthermore, there is a need for an effective and validated model to study various controllable parameters to enhance the efficiency of the intelligent drill pipe telemetry without the need to develop several physical lab or field prototypes. This paper presents the development of a model based on MATLAB Simulink to simulate the process of data transmission in boreholes utilizing intelligent drill pipes. Laboratory experimental prototype measurements have been used to test the model’s effectiveness. A good correlation is found between the measured lab data and the model’s predictions for the signals transmitted contactless through intelligent drill pipes with a correlation coefficient (R²) above 0.9. This model can enhance data transmission efficiency via intelligent drill pipes, study different concepts, and eliminate the need to develop several unnecessarily expensive and time-consuming physical lab prototypes.

To address the challenges posed by class imbalance and temporal dependency in drilling condition data and enhance the accuracy of condition identification, this study proposes an integrated method combining feature engineering, data resampling, and deep learning model optimization. Firstly, a feature selection strategy based on weighted symmetrical uncertainty is employed, assigning higher weights to critical features that distinguish minority classes, thereby enhancing class contrast and improving the classification capability of the model. Secondly, a sliding-window-based Synthetic Minority Oversampling Technique (SMOTE) algorithm is developed, which generates new minority-class samples while preserving temporal dependencies, achieving balanced data distribution among classes. Finally, a coupled model integrating bidirectional long short-term memory (BiLSTM) networks and gated recurrent units (GRUs) is constructed. The BiLSTM component captures global contextual information, while the GRU efficiently learns features from complex sequential data. The proposed approach was validated using logging data from 14 wells and compared against existing models, including RNN, CNN, FCN, and LSTM. The experimental results demonstrated that the proposed method achieved classification F1 score improvements of 8.95%, 9.58%, 10.25%, and 8.59%, respectively, over these traditional models. Additionally, classification loss values were reduced by 0.32, 0.3315, 0.2893, and 0.2246, respectively. These findings underscore the significant improvements in both accuracy and balance achieved by the proposed method for drilling condition identification. The results indicate that the proposed approach effectively addresses class imbalance and temporal dependency issues in drilling condition data, substantially enhancing classification performance for complex sequential data. This work provides a practical and efficient solution for drilling condition recognition.