High-speed sand-laden fluids can cause severe erosion of the packer mandrel in downhole packer systems. However, predictive models for packer systems under coupled thermo-mechanical stresses remain underdeveloped, and no research has been conducted on the multiphase flow erosion of the packer mandrel with temperature considerations. This study applies a coupled CFD–DPM framework to systematically investigate multiphase flow–particle interactions and erosion behavior in a retrievable test-treat-squeeze (RTTS) packer mandrel. The computational framework employs Lagrangian particle tracking coupled with turbulent flow modeling, enabling precise quantification of erosion patterns influenced by five key operational parameters: fluid velocity (5-35m/s), temperature (160-220°C), silica particle size (0.2-4.0mm), solids loading (0.5-5.0kg/s), and diameter ratio (0.35-0.80). The results show that the maximum erosion rate increases by up to 25.5 -fold as flow velocity increases, and by 8.27-fold as solids loading increases. Particle size exhibited a non-monotonic effect on erosion. Temperature exhibits a limited influence on erosion, contributing less than 2% variation under the assumption of constant fluid properties. Flow field analysis reveals that vortex intensity and particle impact localization are highly sensitive to flow acceleration and geometric contraction. The research results can provide a reference for the structural optimization design and failure position prediction of packer, in order to improve the service life and reliability of packer.

Summary Deepwell drilling is strongly affected by drillstring vibrations, especially stick/slip and whirl, which threaten safety and efficiency. Despite the extensive development of downhole measurement tools, systematic quantitative characterization and mechanistic analysis of vibration features remain insufficient due to the unpredictability of complex downhole conditions. For this study, we utilized high-frequency near-bit measurements of engineering parameters and triaxial acceleration data, combined with surface logging data, to systematically analyze the dynamic characteristics of three typical operating conditions—normal drilling, pure stick/slip vibration, and coupled whirl/stick/slip vibration. Time-domain statistical analysis, fast Fourier transform (FFT), and short-time Fourier transform (STFT) methods are used to establish quantitative dynamic feature signatures for each condition. Results reveal that normal drilling exhibits a quasistatic equilibrium with a dominant lateral vibration frequency at the rotation frequency and an acceleration amplitude of 0.0145 g. In contrast, pure stick/slip conditions show extreme downhole rotational speed (revolutions per minute or RPM) oscillations [coefficient of variation (CV) up to 350%], with the lateral vibration frequency shifting to the stick/slip characteristic frequency and amplitude reaching 0.259 g. Coupled whirl/stick/slip conditions demonstrate anomalous downhole torque CV peaks (up to 100%) and a lateral vibration amplitude surging to 0.511 g, reflecting strong nonlinear torsional/lateral coupling instability. Based on these findings, three quantitative evaluation indices are proposed as follows: the stick/slip index (SSI) for torsional vibration severity, the whirl index (WI) based on the triaxial vibration energy ratio, and the whirl impact evaluation parameter (WIEP), which utilizes the kurtosis features of engineering parameters for low-cost whirl detection. Validation using continuous drilling data from two representative wells with distinct well trajectories, bottomhole assembly (BHA) configurations, and measurement-sub positions shows that the proposed indices respond rapidly to condition transitions. In particular, the WIEP preserves stable whirl-event contrast in both tested wells, providing initial field evidence for the engineering applicability of the index system. This research provides a theoretical and feature-engineering basis for real-time monitoring and intelligent recognition systems in deepwell drilling.

To address the challenges in predicting leakage rate and location during drilling in deep and complex formations, this study proposes a quantitative interpretation method for leakage conditions based on Physics-Informed Neural Networks (PINN). By utilizing automatic differentiation techniques, the method employs neural networks to solve the wellbore hydraulics model under leakage conditions, enabling intelligent and accurate predictions of both leakage rate and location. The neural network takes time and well depth as inputs, while its outputs include annulus flow velocity, pressure, and leakage rate, with the leakage location treated as a trainable variable. The total loss function is composed of several components: the residuals of the mass and momentum conservation equations, prediction errors of dual-point pressure data obtained from a downhole dualmeasurement tool, prediction errors of wellhead pressure data, and errors derived from expert knowledge constraints. The Gradnorm algorithm is employed to assign weights to each loss term adaptively, and the neural network is trained by minimizing the total loss. Test results demonstrate that the neural network model trained with this approach can efficiently and reliably solve the wellbore hydraulics model under leakage conditions. Physical constraints are satisfied throughout the input-output process, achieving a mean relative error (MRE) of less than 10 % for leakage rate predictions and an absolute error (AE) within 20 m for leakage location predictions. Compared with methods such as the Unscented Kalman Filter (UKF) and Genetic Algorithm (GA), this approach, which leverages a global optimization strategy and intrinsic physical constraints, exhibits superior stability and accuracy across different noise levels. When integrated with the downhole dual-measurement tool, this approach provides critical guidance for leakage mitigation operations during drilling processes in deep and ultra-deep wells.

Well control is essential for safe offshore drilling. Traditional kill models design pre-operation parameters, lacking real-time data to correct deviations, reducing accuracy under sudden influxes or operational errors. To address this, this paper proposes a real-time wellbore flow state inversion framework for kill operations based on the H-infinity filter. Using a transient gas-liquid multiphase flow model within a game-theoretic structure, the framework performs closed-loop correction of three real-time observations: casing pressure, standpipe pressure, and pit gain. These corrected observations act as updated boundary conditions to reconstruct the wellbore pressure field and gas holdup distribution. Combined with safety constraints like bottomhole pressure balance and surface equipment limits, a real-time kill plan optimisation method was established. Validated with wait-and-weight method observation data from Well Y, the framework achieved mean absolute errors of 0.028 MPa, 0.118 MPa, and 0.029 m3 for inverted casing pressure, standpipe pressure, and pit gain, respectively. Hold-out, sensitivity, and variability analyses confirmed robust reproducibility. Applying the optimised plan reduced peak pit gain by up to 20.3% and improved gas discharge efficiency by up to 76.8% during critical stages. This framework supports dynamic decision-making in complex offshore well control operations, helping prevent uncontrolled blowouts and protect marine ecosystems.

The rotary steerable system (RSS) is the core equipment for precise wellbore trajectory control in deep oil and gas drilling, and its performance is directly determined by the coordination and adaptability of the tool’s offset actuator and control platform. To overcome the limitations of complex control architectures and low positioning accuracy of conventional offset actuators for rotary steering drilling tools, a novel three hydraulic cylinder synchronous steering offset actuator driven by a drilling fluid rotary valve distributor, along with its dedicated control strategy, is proposed. Laboratory experiments and numerical simulations are performed to analyze the piston displacement characteristics of the three hydraulic cylinder under different drilling fluid flow rates and rotary valve rotational speeds. The results demonstrate that the proposed actuator exhibits controllable piston displacement behavior. The simulated and experimental data show consistent variation tendencies with a relative error of less than 8%, thus validating the reliability of the proposed numerical model. Increasing the flow rate from 1 to 1.5 L/s increases the cycle-averaged peak-to-peak piston displacement by 14.5 mm, while raising the rotational speed from 60 rpm to 120 rpm reduces it by 25.3 mm, corresponding to a dogleg severity variation of approximately 1.9–3.1°/30 m. Piston displacement deviations are mainly attributed to valve port machining tolerance, drilling fluid compressibility, pipeline pressure loss, and internal leakage, and these discrepancies are exacerbated as the rotary valve speed or flow rate increases. Finally, optimization strategies for improving synchronization performance are proposed, thereby providing theoretical and technical support for the engineering implementation and parameter optimization of the proposed actuator.

Cement plugging materials for offshore abandoned wells suffer severe cracking and poor hydro-mechanical sealing performance. Sn58Bi low-melting alloy is a promising alternative owing to its low melting point, slight solidification expansion and superior fluidity, but intrinsic brittleness and segregated brittle Bi-rich phases weaken its long-term downhole reliability. Herein, Cu (0.5–2.0 wt.%) was added to modify Sn58Bi alloy. Uniaxial tensile, static bearing and gas tightness tests were conducted at 30, 60 and 90 °C, coupled with optical microscopy and SEM to observe microstructures, fractures and interfaces. Based on SEM observations and previous reports on Sn-Bi-Cu systems, Cu addition was considered to promote the formation of Cu₆Sn₅ intermetallic compounds during solidification.Moderate Cu₆Sn₅ grains refine Bi-rich phases, break brittle phase continuity and block crack propagation. At 90 °C, Sn58Bi-1.5Cu achieves 15.11% elongation; at 30 °C, Sn58Bi-1.0Cu has 281.5% higher bearing capacity than pure Sn58Bi, while Sn58Bi-0.5Cu exhibits the best gas tightness with 47% higher breakthrough pressure. Excessive Cu causes agglomerated coarse Cu₆Sn₅-related particles, inducing interfacial stress concentration and deteriorating overall performance. This study verifies that proper Cu microalloying eliminates brittle failure of Sn58Bi and imsuggests its applicability for offshore abandoned well casing plugging.

Accurate prediction of drilling fluid rheological parameters under high-temperature and high-pressure (HTHP) conditions is critical for reliable drilling hydraulics and wellbore pressure control in deep and ultra-deep wells. However, most existing empirical and semi-empirical rheological models are developed for limited temperature-pressure ranges and specific fluid formulations, which restrict their applicability and accuracy under HTHP conditions. In this study, systematic rheological experiments were conducted on multiple drilling fluid systems over wide temperature-pressure ranges (20-200 degrees C and 0.1-200 MPa). Based on the experimental data, a unified predictive model for key rheological parameters was developed using a symbolic regression (SR) algorithm. The model performance was evaluated using standard statistical metrics and compared with commonly used conventional models. Compared with conventional models, the proposed model shows stronger applicability for predicting the rheological parameters of the investigated oil-based and water-based drilling fluids over a wider temperature-pressure range. It effectively overcomes the limitations of existing models under HTHP conditions (150-200 degrees C and 80-200 MPa) and demonstrates improved prediction accuracy and robustness for both high- and low-density drilling fluids. The overall prediction errors are generally within approximately 10%. The results indicate that the proposed unified model provides a reliable and computationally efficient tool for predicting drilling fluid rheological parameters under HTHP conditions, facilitating its integration into wellbore hydraulics, wellbore pressure, and equivalent circulating density calculations in deep and ultra-deep well applications.

This paper aims to solve the problem of Sn-xBi alloy plugging in perforations at different ambient temperatures associated with varying formation depths in oil and gas wells. A method is proposed to investigate the influence of ambient temperature on the properties of Sn-xBi alloys. A dedicated experimental device is developed to test the mechanical pressure-bearing capacity and liquid sealing performance of Sn-xBi alloys with different component ratios in rock perforations. Mechanical sealing tests and hydraulic sealing tests are performed on rock perforations plugged with Sn-xBi alloys. The effects of alloy composition on the sealing performance of rock perforations at different temperatures are studied, and the underlying microstructural mechanisms responsible for these effects are analyzed. Experimental results show that at ambient temperatures of 30 ℃, 60 ℃, and 90 ℃, both the mechanical bearing strength and hydraulic sealing strength decrease with increasing temperature. The plugging performance of Sn-xBi alloys in rock perforations is affected by both the Sn/Bi content ratio and the ambient temperature. Among all tested compositions, the Sn-58Bi alloy exhibits the optimal mechanical pressure-bearing performance and hydraulic sealing performance. At 30 ℃, the maximum hydraulic sealing pressure of pure Sn is 0.79 MPa, while that of pure Bi is 1.04MPa, representing a 25% improvement in hydraulic sealing over pure Sn. Microstructural analysis reveals that a higher Bi content in the Sn-xBi alloy enhances its expansion behavior, which exerts a positive effect on plugging performance. However, excessive micro-expansion also leads to surface roughness, which can adversely affect the final sealing quality.

Kick is one of the most common and highly hazardous downhole complications during drilling operations. Accurate early warning of kick events is of great significance for ensuring drilling safety and reducing drilling time. However, existing approaches are generally constrained by the scarcity of kick data and the subjectivity in setting threshold values for characteristic parameters. This constitutes a key bottleneck that limits the reliability and generalization capability of current methods. To address these issues, this study proposes an intelligent kick detection method that integrates anomaly detection with parameter trends. The proposed method adopts a two-stage framework. In the first stage, a Transformer encoder is used to reconstruct mud-logging parameters under normal operating conditions, and rotary drilling anomalies are identified based on reconstruction errors. This thereby transforms direct kick recognition into an anomaly-detection-first strategy and avoids direct dependence on scarce kick samples. In the second stage, once an anomaly signal is triggered, the fluctuation trends of characteristic parameters are extracted, and a kick risk index (KRI) is constructed by incorporating the differences in parameter responses across different stages of kick development. Meanwhile, the parameter trend thresholds are dynamically updated under the constraint that no kick risk exists prior to the anomaly alarm. Based on tests and comparative analyses on four case wells, the results show that the proposed method achieves an average early warning lead time of 4.25 min (minutes) and produces no false alarms. In contrast, under the small-sample setting, supervised learning methods such as random forest (RF), fully connected neural network (FCN), and long short-term memory network (LSTM) yield 2.25, 3.25, and 2.50 instances of false or missed alarms, respectively. Their average lead times are also shorter than that of the proposed method. The results indicate that the two-stage "anomaly detection-kick identification" framework exhibits superior generalization capability and practical value under conditions of data scarcity and uncertain thresholds, offering important engineering guidance for enhancing drilling safety and reducing drilling duration.

This study aims to reveal the controlling mechanism of cooling rate on the Sealing integrity of tin-bismuth alloy plugs used for casing sealing. Cooling rate plays a key role in the formation of interfacial microannuli and plugging defects by regulating the wetting, spreading, and interfacial filling of the alloy. Laboratory simulation experiments, scanning electron microscopy (SEM), and heat transfer simulations were employed to investigate the effects of cooling rate, casting temperature, and downhole ambient temperature on interfacial wetting, filling behavior, and plug integrity. Results indicate that an excessively high cooling rate severely restricts the wetting and spreading of molten tin-bismuth alloy, leading to gas entrapment, insufficient micro-gap filling, and poor interfacial bonding. At 200 degrees C (cooling rate > 66 degrees C/s), rapid solidification leads to inadequate wetting conditions and abundant interfacial defects. Increasing the casting temperature to 300 degrees C significantly prolongs the solidification time and greatly enhances wetting and filling performance, yielding highly intact alloy plugs. The novelty of this work lies in clarifying the coupling mechanism among cooling rate, casting temperature, and interfacial defect formation. This study determines the optimal casting temperature for Sn58Bi alloy plugging, which provides theoretical guidance and technical support for the development of wellbore plugging technologies.

Severe drill string vibrations, particularly stick–slip, significantly compromise drilling efficiency and tool longevity in deep hard formations. Compound percussive drilling (CPD) has emerged as a promising technique to mitigate these vibrations and enhance the rate of penetration (ROP). However, the complex coupling mechanisms between impact loads and bit dynamics remain insufficiently understood. This study aims to elucidate the axial–torsional vibration characteristics of the drill bit and the underlying vibration reduction mechanisms under CPD conditions. A multi-degree-of-freedom (MDOF) dynamic model was first established, integrating both the dynamics of the CPD tool and the regenerative cutting effects inherent in bit–rock interactions. The governing equations were then solved numerically using the fourth-order Runge–Kutta method, followed by a systematic parametric sensitivity analysis to quantify the influence of impact parameters on vibration mitigation. The results show that while CPD induces detrimental axial–torsional vibrations in soft rock formations, it effectively suppresses stick–slip and enhances ROP in hard rock formations. Notably, coupled axial–torsional impact loading exhibits superior vibration suppression capabilities compared to singular axial or torsional impacts. A critical proportional relationship for parameter optimization was identified; specifically, maximizing vibration mitigation requires scaling the axial impact load proportionally with the torsional impact load. For example, when the axial impact load amplitudes are 5 kN and 10 kN, the corresponding optimal torsional impact load amplitudes are approximately 500 N·m and 1000 N·m, respectively. Furthermore, maintaining the impact frequency within the range of 10–30 Hz yields optimal vibration reduction effects. The benefits of CPD become increasingly pronounced with higher rock strength and longer drill strings. These findings confirm the suitability of CPD technology for deep hard rock environments and provide theoretical guidelines for the optimal selection of impact parameters in engineering applications.

Erosion induced by high-velocity sand-laden flow poses a major threat to the integrity and service life of downhole packers in high-temperature and high-production wells. To clarify the coupled thermo-hydrodynamic-mechanical mechanisms governing this degradation, this study develops a coupled computational fluid dynamics-discrete phase model (CFD-DPM) capable of resolving particle-wall interactions, fluid-solid coupling, and turbulence-driven energy dissipation. Controlled erosion experiments were conducted to benchmark the numerical predictions, ensuring reliable representation of erosion behavior under liquid-solid two-phase flow. A comprehensive systematic parametric analysis was performed to quantify the effects of fluid velocity (5-35 m/s), temperature (160-220 degrees C), particle size (1-4 mm), solids loading (0.5-5.0 kg/s), and diameter ratio (0.35-0.80) on erosion severity in packer center tubes. The results show that the maximum erosion rate increases by up to 25.5 times with fluid velocity and by 8.3 times with higher solids loading, whereas larger particles and greater diameter ratios reduce erosion by 83% and nearly three orders of magnitude, respectively. Temperature contributes less than 2% to erosion magnitude but modifies morphology by altering viscosity and turbulent structures. Flow-field diagnostics further reveal that vortex enhancement and particleimpact clustering near contraction zones dominate localized severe wear. By integrating experimentally benchmarked modeling with multi-parameter mechanistic analysis, this study provides quantitative insight into erosion evolution in packer components and offers practical guidance for structural optimization and service-life extension in high-temperature well operations.

In geological carbon storage, cyclic casing loading can induce micro-annuli in the B-annulus cement sheath, risking CO2 leakage. Compared with conventional cement, the Sn58Bi low-melting-point alloy boasts excellent flowability and favorable elastoplastic behavior, emerging as a promising sealing alternative. This study focuses on enhancing wellbore integrity by using Sn58Bi alloy to seal the B-annulus cement sheath. An experimental system was established to simulate micro-annulus evolution, with gas migration tests conducted under cyclic internal pressure to systematically evaluate the effects of temperature and cyclic loading on the alloy’s sealing performance. Additionally, a three-layer casing–annulus–formation coupling model was constructed to investigate the radial displacement of the Sn58Bi alloy sheath and cement sheath at 30 °C and 20 MPa casing pressure, clarifying their distinct mechanical responses. Results show that the alloy’s sealing performance improves with temperature (30–90 °C), while elevated cyclic internal pressure accelerates gas breakthrough and reduces sustainable cycles. Under identical conditions (30 °C, 20 MPa), Sn58Bi alloy exhibits significantly superior CO2 sealing capacity to conventional cement. This study confirms the alloy’s potential for enhancing wellbore integrity and provides theoretical support for its application in B-annulus plugging during subsurface carbon storage.

As deepwater drilling advances into increasingly narrow pressure margins, the available response time to wellcontrol anomalies diminishes to just a few minutes. Conventional surface-only monitoring techniques often experience signal delays and attenuation along the drillstring, which hampers the early detection of disturbances near the drill bit. This study introduces a dual-point near-bit diagnostic framework designed to provide early anomaly warnings. Specifically, two downhole measurement locations are utilized to capture both synchronous responses and the transmission characteristics between points. A data-driven multi-indicator analytic hierarchy process (MI-AHP) is applied to derive interpretable feature weights by integrating multiple importance metrics obtained from an offline random forest model, facilitating efficient linear scoring. To address class imbalance, a two-stage cascade model is implemented: the first stage discriminates anomalies from seven normal conditions, while the second stage classifies four distinct anomaly types (kick, lost circulation, washout, and bit sticking). The proposed system was validated on data from fifteen deepwater wells, encompassing 1586.3 h and 22 anomaly events, achieving a Macro-F1 score of 93.4% for normal condition recognition, 91.2% for anomaly detection, and 89.8% for anomaly classification. Deployment on an ARM Cortex-M4 embedded platform requires only 78 ms and 125 KB of memory, supporting real-time downhole operation. In independent well testing, the system attained a Macro-F1 score of 90.5% with a false positive rate below 0.6%, successfully detecting a kick event 36 min prior to conventional surface-based identification methods.

To address the challenges associated with predicting wellbore fluid flow behavior and gas kick rates in deep, complex formations following gas kick events, this study develops a quantitative interpretation method of gas kick driven by physics-informed neural network (PINN). The proposed method integrates a physical model of gas-liquid two-phase flow in the wellbore into the neural network by formulating it as a loss function, leveraging annulus temperature and pressure data obtained from downhole dual measurement tools. The feasibility and effectiveness of this method are evaluated through comparative analysis. The result indicates that during gas kick occurrences, this method achieves mean relative errors of 8.49% and 9.07% for the predicted gas volume fraction and apparent gas phase velocity between the dual measurement points, respectively, and 3.76% for the bottomhole gas kick rate, without the need for mesh discretization or predefined initial conditions, demonstrating strong applicability in field scenarios. Compared to the unscented Kalman filter (UKF) and genetic algorithm (GA), this method exhibits higher prediction accuracy and stability due to its global optimization capability, overcoming the divergence issues encountered by UKF and GA during point-wise recursive predictions under noisy pressure data conditions. Integrating this method with downhole dual measurement tools can provide valuable guidance for blowout risk assessment, well-control method selection, and well-killing parameter design after a gas kick. (c) 2026 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-ncnd/4.0/).

Horizontal well drilling is the mainstream technology for developing deep oil and gas resources. Engineering practice has demonstrated that hydraulic oscillators can solve the problem of the backing pressure of pipe strings and improve drilling efficiency. However, the design of excitation parameters for hydraulic oscillators is currently largely based on idealized friction models and does not fully consider the nonlinear characteristics of friction between the drill string and the formation, resulting in a lack of quantitative basis for parameter selection under different operating conditions. A series of laboratory friction tests was conducted to systematically characterize the dependence of interfacial friction behavior on sliding velocity across different combinations of drill string materials, drilling fluid systems, and rock lithologies. Based on the experimentally determined velocity-friction relationships, a drill string dynamic model incorporating a hydraulic oscillator was developed in which nonlinear frictional effects at the interface were explicitly represented. Using this modeling framework, parametric simulations were carried out to examine how variations in excitation amplitude and excitation frequency influence drag reduction performance under diverse operating conditions. The simulation results indicate that the contribution of drill string material to overall drag reduction effectiveness is comparatively limited, whereas drilling fluid type plays a dominant regulatory role. Oil-based drilling fluids significantly enhance drag reduction performance relative to water-based systems and exhibit greater responsiveness to adjustments in excitation parameters. Rock lithology exerts a pronounced influence on the effectiveness of drag reduction. When water-based drilling fluids are used, the overall performance ranks from highest to lowest as limestone, shale, and sandstone. In contrast, under oil-based drilling fluid conditions, the relative ordering shifts to shale, followed by sandstone, and then limestone. Excitation amplitude is the dominant parameter in enhancing drag reduction capability, and in most cases, its incremental effect exceeds that of excitation frequency; however, under certain specific operating conditions, increasing the excitation frequency can provide additional drag reduction benefits. Based on the above findings, a hydraulic oscillator excitation parameter design method was proposed that matches drilling conditions and formation characteristics by distinguishing between different drilling fluid environments and lithologies, with amplitude as the primary control parameter and frequency as a supplementary parameter. This method provides a theoretical foundation for the design of output parameters of hydraulic oscillators operating under diverse working conditions.

The Sn58Bi alloy plug, composed of 58% bismuth (Bi) and 42% tin (Sn), emerges as a promising alternative to conventional cement plugs. Investigating its mechanical behavior throughout the molten-tosolidified transition is crucial for predicting its performance in downhole oil and gas applications. Particular emphasis was placed on characterizing early expansion behavior during solidification, as the magnitude of expansion force directly correlates with sealing integrity. To analyze temperature and expansion force dynamics during plug formation, a specialized experimental apparatus was developed. Expansion and sealing integrity tests revealed three key findings: Applying overlying axial pressure (0-2 MPa) significantly enhanced plug sealing capacity, with a linear relationship observed between sealing performance and pressure magnitude. In addition, slower and more uniform cooling facilitated expansion both radially and at the axially constrained bottom. Increasing the length-to-diameter ratio (L/D) of 2-6 induced sequential solidification patterns, wherein final-stage solidification drove radial expansion of residual molten alloy, thereby improving gas sealing integrity. These findings establish a theoretical framework for the application of Sn58Bi alloy as downhole casing-plug material. (c) 2025 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-ncnd/4.0/).

Abstract This study investigates the temperature-dependent corrosion of bismuth-based low-melting alloys (Sn-58Bi and Sn-65.7Bi-2.3Sb) in simulated geological CO2 storage environments. Experiments were conducted in a high-pressure autoclave, with post-corrosion analysis including gas breakthrough tests, digital microscopy, and XPS. Results show that corrosion depth and rate increase with temperature. When temperature rose from 30 °C to 90 °C, the corrosion-related degradation extent of cement plugs increased from 15.2 mm to 31.6 mm, significantly higher than the increases for Sn-58Bi (0.35 to 0.65 mm) and Sn-65.7Bi-2.3Sb (0.11 to 0.29 mm). The degradation mechanisms of the two types of sealing materials were fundamentally different: class G cement plugs mainly experienced CO2-induced bulk degradation within the cement matrix, resulting in structural deterioration, whereas bismuth-based alloy plugs exhibited limited bulk corrosion and were primarily affected by localized degradation at the alloy–casing interface. XPS identified (BiO)2CO3 as the primary corrosion product. Corrosion impaired sealing integrity: after 90 °C exposure, the breakthrough pressure of cement dropped by 73%, compared to only 2.4% for Sn-58Bi and 0.6% for Sn-65.7Bi-2.3Sb. The study concludes that corrosion accelerates with temperature in CO2-rich environments, and Sn-65.7Bi-2.3Sb exhibits superior corrosion resistance to Sn-58Bi, providing experimental insights into the corrosion resistance and sealing performance evolution of these alloys under simulated CCUS conditions.

Accurate real-time estimation of pore pressure (Pp) is essential in high-temperature and high-pressure (HTHP) wells to prevent blowouts and lost circulation, given the complexity of their pressure regimes. However, conventional methods are inadequate: seismic-based and logging-based models are hindered by geological uncertainties, the dc-index principle is incompatible with PDC bits, and reliable while-drilling acoustic measurements remain prohibitively expensive. To overcome existing limitations, a surface-based and real-time Pp estimation framework is proposed, in which a direct Pp equation is derived by integrating an approximation using friction-corrected mechanical specific energy as the confined compressive strength (CCS) into the Mohr-Coulomb failure criterion. To ensure high-fidelity inputs for this equation, ridge regression is employed to invert rock strength parameters from drilling data, while a transient thermo-hydraulic model accurately calculates dynamic downhole pressure instead of relying on the static assumption. Validation on five HTHP wells in the Ying-Qiong Basin demonstrates that after accounting for thermo-pressure coupling, the method reduces the mean absolute error (MAE) in Pp equivalent density by 0.085 g/cm3 compared to the hydrostatic assumption. Furthermore, the proposed method achieves an MAE of 4.12%, outperforming the dc-index method, which achieves an MAE of 5.78%. Notably, the new method is more stable, with its prediction error envelope remaining within ±5%, whereas the dc-index’s error extends to ±10%. Given its theoretical compatibility with modern PDC bits and its demonstrated high accuracy, this surface-based and real-time scheme has the potential to overcome the conventional limitations of Pp estimation from surface data, providing a robust safeguard for well control in HTHP environments.