The Tazhong No. 1 condensate gas reservoir in Tarim Basin shows complex storage spaces and strong heterogeneity. To intuitively assess the production performance characteristics of the condensate gas reservoir and understand the occurrence mechanism of condensate oil, an experimental device and method for visualized simulation of depletion exploitation in fracture-vuggy condensate gas reservoirs were established. The condensate oil precipitation characteristics and dynamic parameters of different reservoir types during depletion were tested. The research indicates that the condensate oil occurrence characteristics in the single-fracture-vuggy model are similar to those in the multi-fracture-vuggy model. Condensate oil that precipitates on the pore walls slides down as droplets under the influence of gravity, eventually gathering at the corners of the lower areas of the pores. Unlike porous reservoirs, fracture-vuggy condensate gas reservoirs exhibit significant retrograde vaporization during the late stages of depletion production. As the depletion rate increases, the recovery degree of condensate oil also rises. For the single-fracture-vuggy model, recovery increases by 7.68%, and for the multi-fracture-vuggy model, by 9.85%, when the depletion rate is 6 MPa/h compared to 3 MPa/h. The results of this research provide crucial insights for developing technical measures to enhance condensate oil recovery in the middle and late stages of fracture-vuggy condensate gas reservoir development.

The global carbon emissions resulting from the combustion of fossil fuels have had a negative impact on the environment. This research presents an in-depth analysis of the economic and environmental impacts associated with carbon storage initiatives in Malaysia. It also explores the distributional effects of carbon pricing and the perception of its regressive nature as a significant challenge. Urgent and thorough measures are necessary to reduce carbon emissions from oil and gas, thereby mitigating the catastrophic consequences of climate change and facilitating a transition towards a more sustainable and environmentally responsible energy system. A comprehensive assessment of the potential impacts of Malaysia's carbon pricing policy on the Organization's economy and environment. Four cases were analyzed: (1) New Field Development, (2) New Field Development with Carbon Tax, (3) New Field Development with CCS and Carbon Tax, and (4) New Field Development with CCS, Carbon Tax, and Subsidy. The results show Internal Rates of Return (IRR) of approximately 16.5%, 14.5%, 12.8%, and 15.0-15.5% respectively. These findings highlight that a combined policy mix (carbon tax + CCS + subsidies) substantially improves economic viability, aligning with global best practices and offering a strong policy pathway for Malaysia.

With the growing challenges in conventional hydrocarbon exploration, shale oil has emerged as a critical target for future energy supply. The Qingshankou Formation (K₂qn) in the southern Songliao Basin holds significant shale oil potential. However, the complex laminae architecture and poorly constrained lithofacies have hindered a clear understanding of shale oil enrichment mechanisms, thereby limiting efficient exploration and development. This study focuses on the K₂qn shale in the Changling Sag, where a comprehensive characterization of lithofacies and pore structures is carried out using an integrated approach of core description, geochemical analysis, and multi-scale imaging techniques, including thin-section petrography, micro-XRF, MAPS, QEMSCAN, and fluid injection experiments. Three types of argillaceous laminae are identified to reveal significant textural and structural heterogeneity in the shale. Pore systems are dominated by interparticle pores and microfractures, with microfractures playing a crucial role in enhancing pore connectivity and fluid transport. The results also indicate that reservoir quality is jointly controlled by organic matter content, mineral composition, and laminae characteristics. Importantly, two enrichment models are proposed that the layered mixed shale with medium to high organic matter represents a migration-accumulated model influenced by external hydrocarbon input, and the laminated siliceous shale and the mixed shale, both with moderate to high organic content, follow in-situ accumulation patterns dominated by thermal transformation of indigenous organic matter. These findings offer critical geological insights to guide more accurate reservoir prediction and targeted exploration in the lacustrine shale systems.

Tight sandstone gas reservoirs exhibit low gas abundance and extensive gas-bearing lithology, presenting promising prospects for future exploration and development. However, their exploitation in the Sichuan Basin is severely constrained by low recovery efficiency, high water saturation, and pronounced retrograde condensation phenomena. In this study, we aim to analyze the complex fluid flow behavior within these reservoirs to overcome these challenges. To achieve this, a comprehensive experimental program is utilized, encompassing nuclear magnetic resonance, micro-computed tomography, core flooding, and retrograde condensation tests. The analysis reveals that tight sandstone reservoirs feature low porosity, low permeability, and poor pore connectivity, with pore structures predominantly composed of nanometer-to 10-μm-size micropores. Pore size distributions in both Type I and Type II reservoirs exhibit multimodal characteristics, rendering them highly sensitive to stress changes and condensate-induced formation damage. Permeability demonstrates significant hysteresis effects during cyclic changes in confining pressure. Precisely, after two complete cycles of increasing and decreasing confining pressure, average permeability damage rates reach 52.08% and 46.1%, respectively. This indicates damage levels ranging from moderate-weak to moderate-strong. In contrast, variations in pore pressure exhibit minimal impact on permeability. Under irreducible water saturation conditions, the average gas phase damage rate is 64%. Furthermore, initiating fluid flow within the reservoir requires a specific threshold pressure. The average damage rate caused by retrograde condensation is 24.7%. This experimental investigation elucidates the reservoir's pore-throat characteristics, the dynamics governing gas-water and gas-oil flow, and the damage patterns associated with retrograde condensation. These insights provide critical foundational data for optimizing development strategies and designing effective stimulation measures to achieve enhanced recovery.

We investigate full-field petroleum production forecasting using machine learning. A data-driven proxy model that predicts field-scale production under various operational scenarios is proposed. It offers an alternative to time-consuming numerical simulators for assisting operational oilfield management decisions like designing waterflooding schedules and quantifying production loss caused by schedule delays. Its accuracy is validated with real-world datasets. Beyond its practical application, we leverage the proposed workflow to conduct a systematic comparison of several machine learning and deep learning algorithms using four datasets and three forecast horizons. Such critical and systematic analysis is long overdue and seems to be extremely rare in related works, which consistently favor increasingly intricate neural networks, despite the presence of relatively limited and sparse datasets, low-dimensional input variables and the existence of well-defined physical laws to guide input selection. Our comparative study suggests deep learning is an overkill in the context of full-field production forecasts, without any tangible benefits in terms of accuracy. Kernel-based models consistently outperform neural-based architectures in this multi-dimensional analysis. In addition, we find evidence that algorithmic generalization in the full-field context seems to be more influenced by the spatial density of production data than by dataset size. The data-driven proxy model and the investigative findings of this work can help engineers apply machine learning to petroleum production forecasting. On a broader scale, our results endorse the Green AI paradigm, which advocates for the use of compact architectures that deliver good accuracy with a low computational cost.

The FM fault-fractured-vuggy volatile oil reservoir exhibits significant heterogeneity and rapid production decline. Furthermore, there are notable differences in the effects of depletion development, and the impact of volatile oil degassing on water injection parameters remains unclear. This paper established artificial cores that replicate both single-fractured-vuggy and multi-fractured-vuggy full-diameter systems, reflecting a typical reserves ratio of pores, fractures, and caves in the FM oilfield. Concurrently, water injection experiments were conducted for both single-well and multi-well units after natural energy depletion had occurred. The results indicated favorable development when single-well units opened the lower part of the fracture-vuggy zone, allowing for depletion below bubble pressure at an early stage. Due to dissolved gas flooding at the top of reservoirs, crude oil depletion recovery reached over 67%. The filling of fractures and caves led to delayed degassing: single-phase seepage before degassing was influenced by reservoir physical properties. In contrast, multiphase seepage after degassing was affected by fluid viscosity. The single-well units employed large-volume water injection huff-n-puff techniques, resulting in a pronounced gravity oil displacement effect. This method improved cumulative crude oil recovery by more than 5%. Additionally, it was recommended that multi-well units adopt a deep injection and shallow production development strategy, with continuous water flooding enhancing oil recovery by over 40%. However, during the asynchronous injection-production stage, the recovery improvement was limited to only 1%.

The formation of inorganic scales such as calcium sulfate and barium sulfate during water injection in oilfields can severely impair productivity by clogging pores and reducing rock permeability. This study evaluates the performance of two industrial scale inhibitors—diethylenetriaminepenta (methylene phosphonic acid) (DTPMP) and polyphosphinocarboxylic acid (PPCA)—and their hybrid blends in mitigating scale formation under laboratory and simulated reservoir conditions. Laboratory assessments included scale prediction modeling, static bottle tests, dynamic tube blocking, turbidity, and core-flooding experiments to determine inhibition efficiency, retention behavior, and formation damage potential. Thermodynamic modeling using OLI Studio predicted the maximum scaling tendency at a mixture of 70% injection water and 30% formation water, which was used as the baseline brine for testing. The 50:50 PPCA:DTPMP blend achieved the highest inhibition efficiency (>90%) for both calcium and barium sulfate scales at only 50 ppm, outperforming individual inhibitors. Under dynamic tube blocking conditions, the hybrid system maintained stable pressure profiles, effectively delaying scale nucleation and reducing pressure buildup. Turbidity analyses showed lower light transmittance values (5.6%-6.1%) for the blend, confirming its superior resistance to particle formation and growth. Core-flooding experiments further revealed that the hybrid inhibitor minimized permeability loss to only 5%, compared with 72% in the blank and 9-13% for the single-inhibitor cases. Adsorption/desorption experiments confirmed rapid adsorption (equilibrium in <2 pore volumes) and sustained desorption over 20 pore volumes, indicating strong and prolonged retention. These results demonstrate a positive synergistic effect between the nucleation inhibition mechanism of PPCA and the crystal growth suppression mechanism of DTPMP. The 50:50 hybrid formulation offers a practical, efficient, and economically viable solution for long-term scale management under reservoir conditions.

This paper presents a highly selective water control system designed to address declining oil production in offshore wells caused by rising water cuts while overcoming the poor selectivity of current chemical water control materials. The system was developed by copolymerizing octadecyl methacrylate and butyl methacrylate, followed by emulsion polymerization to prepare oil-soluble polymers. This resulted in a water-dispersible, high-selectivity water control system in the form of an emulsion dispersion. The feasibility of its field application was assessed via median particle size and reservoir stability tests, and the system's water control performance, visual performance, and underlying mechanism were evaluated. The test results showed that the median droplet diameter of the emulsion in the system was 0.431 μm, and it remained stable for at least 8 h under reservoir conditions. The system demonstrated excellent selective water-control capabilities, with a water-phase permeability blocking rate of 82.42% and an oil-phase permeability blocking rate of 5.02%. The mechanism of action was further explored via visualized flat plate tests and low-field nuclear magnetic resonance scanning displacement experiments on rock cores. The findings suggest that oil-soluble particles exhibit effective selective water-control performance and can be applied in water control operations in offshore oil fields.

Surfactant-Alternating-Gas (SAG) utilizes foam to reduce gas mobility and improve reservoir sweep efficiency in gas-injection enhanced oil recovery and carbon capture and storage. Although SAG is known to have many benefits that increase hydrocarbon recovery, liquid injectivity is typically very poor during a SAG process. Conventional foam simulators use the Peaceman model to estimate injection-well pressure. However, because they assume uniform saturation in the grid block, they do not account for near-wellbore saturation distribution that improves gas and liquid injectivities during a SAG process. We use a conventional foam simulator to test the impact of grid refinement on injectivity. We compare simulation results to those with fractional-flow theory (FFT) using the a model very widely used in simulations. In addition, we test the effect of complete foam collapse at irreducible water saturation (S_wr), of neglecting the Peaceman model, and of disallowing foam in the injector grid block. We compare the transient behaviors and the average injectivities, calculated during one pore volume of injected fluid. Our results show that in a conventional simulation (including the Peaceman model) grid refinement increases gas and liquid injectivities. However, the results do not match FFT predictions. The assumption of complete foam collapse at S_wr increases gas injectivity (still without matching FFT results); however, it has a smaller impact on liquid injectivity and grid refinement results in an increase in gas and liquid injectivities. Excluding the Peaceman model reduces the error in simulated injectivities at coarse grid refinements but still without matching the FFT prediction. Finally, the procedure of excluding foam in the injection-well grid block gives average injectivities like our FFT calculations without requiring extraordinary grid refinement.

Effective proppant placement is essential for increasing the production of unconventional oil/gas reservoirs. It can substantially affect the fracture roughness. This paper presents a laboratory fracture model to quantify the effect of fracture roughness under well-controlled conditions. This model simulates fracture roughness by embedding particles into the walls. Via this model, the roughness can be well controlled and defined. The effect of fracture roughness location is quantified as an example to demonstrate the usefulness of the controllable fracture model. Using a fracture model with two-sided roughness, the relationships between roughness location, pump rate, and inlet location were established. The experimental results show that the rough surface in the middle resulted in a large void space in the fracture, which became more evident at a higher pump rate. This phenomenon has unexpected consequences for oil and gas production. Based on these experimental results, different sand injection modes were tested to reduce the void space caused by the surface roughness in the fractured middle. An injection mode that involves alternatively pumping proppants of different sizes was proposed to solve this problem effectively.

Waterflooding persists as the primary secondary oil recovery method, although its efficiency evaluation is often hindered by limitations from reservoir heterogeneities alongside non-uniform sweep efficiency and uncertainty in predicting well-to-well connectivity. To improve the evaluation of waterflooding, this research develops and validates nine advanced machine learning models, covering key types such as neural networks, linear models, tree-based models, ensemble learning, and support vector machines. This research utilized a dataset of 6592 observations collected across four wells over 2000 days to train and evaluate these machine learning models for fluid production rate forecasting and injector-producer connectivity assessments. The most successful model was AdaBoost since it delivered both a minimum root mean squared error (RMSE) of 0.004 and a coefficient of determination (R²) of 0.99, surpassing traditional methods. Furthermore, SHapley Additive Explanations (SHAP) analysis provided interpretability by quantifying injection wells' relative influence on production rates, facilitating the construction of a connectivity-driven contour map. Traditional evaluation methods of empirical and analytical approaches, as well as capacitance-resistance models and numerical simulators, function under restrictive assumptions, extensive computational requirements, and significant data demands. This study is novel in uniting multiple ML algorithms with SHAP interpretability and a connectivity-driven contour map for injector-producer dynamics, providing a practical, real-time workflow for field-scale waterflood optimization. The results of this study demonstrate that ML models surpass traditional methods in terms of predictive accuracy, computer processing speed, and scalability when applied for real-time waterflood optimization. This study illustrates the transformative potential of ML in reservoir engineering by enabling data-driven decision-making and improving the injection optimization process.

CO₂ flooding is an efficient technique for enhancing oil recovery and carbon sequestration, where accurate estimation of the minimum miscibility pressure (MMP) is crucial. Conventional experiment and numerical simulations are time-cost and often inaccurate. Therefore, it is essential to develop an efficient and accurate model for MMP prediction. In this work, we introduced a genetic algorithm-optimized random forest (GA-RF) model using 260 samples with eight input features, including reservoir temperature (T_R), molecular weight of C₅₊ pseudo-components (MWC₅₊), ratio of the mole percent of volatile to intermediate fractions (vol./int.), and mole fractions of injected gas (CO₂, H₂S, CH₄, C₂-C₄, and N₂). Four algorithms-random forest (RF), multilayer perceptron (MLP), support vector regression (SVR), and gradient boosting decision tree (GBDT) are compared. RF performs best and is further optimized by GA. The resulting GA-RF model outperforms traditional methods, with prediction accuracy of 0.99 and a root mean square error of 0.74. The model was applied to CO₂-hydrocarbons system from different low-permeability reservoirs in China. Results show that the GA-RF model demonstrated an average relative error of 3.43%. These findings highlight that the GA-RF model has great potentials for predicting MMP during CO₂ flooding and improving CO₂ miscible flooding efficiency.

The global depletion of conventional oil and gas resources has led to the exploration of alternative energy resources. Among these resources, oil shale is a fine-grained sedimentary rock with a large amount of kerogen that has attracted increasing attention from the hydrocarbon industry and researchers due to the enormous global reserves of oil shale, which are larger than those of conventional crude oil. However, in situ conversion technologies (ICTs) constitute a practical and environmentally friendly technology for extracting hydrocarbons from oil shale formations without traditional surface mining. This study provides a comprehensive analysis of the primary ICTs developed and evaluated in this field to date. Convection-based approaches, such as steam injections and modified thermal injection (MTI), conduction-based heating approaches, such as Shell's in situ conversion process (ICP) and ExxonMobil's Electrofrac™, and radiant heating techniques that utilize radio frequency and microwave energy, are critically evaluated in this study. Advanced innovations, such as near-critical water extraction, nitrogen-assisted in situ conversion process (NAICP), and terahertz spectroscopy (a diagnostic tool for reservoir evaluation), highlight the advanced direction of this field. Mechanistic insights into pore-fracture network formation, kerogen pyrolysis, and their function in improving permeability and hydrocarbon flow have also been analyzed. Challenges related to energy efficiency, operating costs, and environmental impacts still remain despite these technological advancements. Addressing these limitations by maximizing thermal delivery, catalytic enhancement, and integrated modeling will be crucial for realizing the large-scale viability of ICTs. Future research should focus on increasing the efficiency of energy recovery while maintaining environmental sustainability and economic competitiveness.

Injecting CO₂ into shale oil reservoirs has the dual purpose of enhancing oil recovery rate and geological storage of CO₂. The phase behavior and mass transfer laws of the CO₂-shale oil are crucial for achieving highly efficient production shale oil and gas. Based on an improved vapor-liquid equilibrium (VLE) model in nanopores, which achieves a relative error of less than 3.16%, this study investigates the phase behavior and CO₂-oil mass transfer laws of two representative shale oils under CO₂ injection. Research results show that after CO₂ injection, the saturation pressure tends first to increase and then reduce. The CO₂ dissolution results in a significant reduction in the molecular weight and viscosity of Wolfcamp shale oil. During the multi-contact shale oil with CO₂ injection, both density and the interfacial tension (IFT) reduce at the forward contact edge, indicating forward-contact miscibility. Furthermore, the nanoconfinement effect reduces the density difference between the liquid and gas phases, leading to a reduction in the IFT, which is beneficial for gas-oil miscibility. The study results can provide a theoretical basis for understanding compositional variations in shale oils under CO₂ injection, offering promising improvements in miscibility and recovery efficiency.

To enable the precise prediction and control of erosion in hydraulic fracturing pipelines, this study employs a CFD-DEM method coupled with a wear model to quantitatively analyze the influence of shear-thinning fracturing fluids on proppant transport and elbow erosion. The study reveals a critical, non-monotonic effect of fluid concentration on erosion. Compared to the water baseline, the low-concentration (0.2%) fluid, by enhancing particle impact kinetic energy, counter-intuitively caused the maximum elbow erosion rate to surge by approximately 40%. However, as the concentration was further increased to 0.4%, the fluid's effective viscosity became dominant, causing the maximum erosion rate to plummet relative to the 0.2% case, ultimately falling to a level ~8% below the water baseline. More importantly, the high-concentration fluid altered particle trajectories, leading to a significant shift of the peak erosion zone from the elbow's midpoint (~77°) to its outlet (90°). This finding quantifies the complex impact of fracturing fluid rheology on erosion and provides critical data and a new perspective for proactively managing pipeline integrity by optimizing the fluid's non-Newtonian properties.

The drilling of non-vertical wellbores requires effective real-time monitoring techniques to improve drilling efficiency. This study innovatively develops nine robust hybrid machine learning (HML) models to develop a real-time surrogate model for the drilling cuttings transport index (DCTI). The DCTI, a comprehensive indicator of hole-cleaning efficiency, is typically calculated with a 2-4 h delay due to its dependency on laboratory-measured fluid properties. Our hybrid ML models overcome this limitation by accurately emulating the DCTI value using only real-time, surface-recorded drilling data. A 6818-record dataset was collected from three oil and gas fields (Eastern Province of Saudi Arabia), comprising three offshore and two onshore wells. Following a meticulous dataset preprocessing, a nondominated sorting genetic algorithm (NSGA-II) was applied with a multi-layer perceptron neural network (MLPNN), identifying seven key features influencing DCTI - inclination angle (ANG), rate of penetration, flow in, depth, torque, stand-pipe pressure (SPP), and revolutions per minute. Extreme learning machine (ELM), MLPNN, and radial basis function neural networks (RBFNN) were hybridized with particle swarm optimization (PSO) and a growth optimizer in two stages: (1) to determine the optimal model structure, and (2) to fine-tune the model hyperparameters to precisely estimate DCTI based on the selected features. The HML models achieved better estimation performance and generalizability than the standalone models. Achieving the lowest root mean square error (RMSE = 0.00344), the RBFNN-PSO model exhibited the best DCTI estimation performance. Analytical scoring, robustness, and Monte Carlo simulation confirmed this model's superior performance, robustness, and high generalization. Shapley additive explanations (SHAP) feature influence analysis revealed that ANG exerted the most influence on the RBFNN-PSO estimations, while SPP had the least impact. During drilling operations, the proposed data-driven approach practically enables drilling crews to diagnose and monitor hole-cleaning performance in real time and improve the rate of penetration. Furthermore, SHAP analysis transforms the model into a prescriptive tool, identifying the primary real-time parameters affecting hole cleaning. This enables drillers to make targeted operational decisions, such as adjusting the rate of penetration or flow rate, to mitigate developing issues.

Offshore extended-reach drilling is widely employed for the efficient development of deepwater hydrocarbon fields. However, the complex subsea formation environment and downhole frictional losses lead to significant inaccuracies and limitations in bottomhole weight on bit (WOB) calculations, severely impacting drilling operations. Therefore, this study proposes an improved bottomhole WOB prediction model that combines CNN-BiLSTM networks with Transformer encoders. By introducing a loss function (named DRE) based on the rate of penetration equation into the intelligent algorithm, four models were constructed: CNN-BiLSTM, CNN-Encoder-BiLSTM, CNN-BiLSTM (DRE), and CNN-Encoder-BiLSTM (DRE). The eight models were compared and evaluated, including both the established models and conventional models. Experimental results show that the newly developed CNN-Encoder-BiLSTM (DRE) model parameters' influence degree basically conforms to the DRE with high generalization capability. On the test well, it achieved MAE of 0.859, MRE of 0.1, MAPE of 10%, MSE of 1.201, and R² of 0.867, demonstrating good performance. The research results prove that the new model is suitable for bottomhole WOB prediction in offshore extended-reach wells, which is expected to improve drilling efficiency and enable subsequent complex situation analysis, providing new ideas for intelligent drilling research in the marine sector.

Degradation of biomaterials under high temperatures and saline conditions leads to deterioration and loss of performance in deep well drilling applications. This research focuses on examining the impact of colloidal and dually modified tapioca starch (CMITS) on filtration characteristics. Mud filtration properties under various conditions of temperatures were evaluated through API and HPHT filter presses. Different rock samples, including sandstone and limestone, each characterized by unique pore size distributions, porosity, and permeability, are utilized to simulate filtration processes. The thermal stability of drilling fluid additives and the interactions between rocks and filtrate are investigated in hot roll oven and aging cells. The findings evaluate the interaction between temperature and starch additives on mud filtration properties. Notably, in sandstone samples, at 200 ℉, Bandera Brown exhibits the lowest filtrate volume (6.2 mL) with DS 0.66 starch, while Boise sandstone produced a higher volume (38.6 mL) due to its inherent permeability. As the temperature rises to 250 ℉, the effects become more pronounced. At 250 ℉, Indiana limestone with DS 0.66 starch shows minimal filtrate loss (5.4 mL), while Boise sandstone faces pore plugging issues resulting in higher filtrate volumes. At 300 ℉, temperature continues to exert change, with Indiana limestone and Bandera Brown displaying minimal filtrate volumes.

Casing window sidetracking is a key technique for tapping remaining oil in small-scale enriched zones, such as minor anticlines and thin interbeds. While reaming-while-drilling (RWD) technology can mitigate issues like wellbore necking and stuck pipe caused by narrow sidetracked holes, conventional RWD tools are often excessively long, failing to meet the demands of high-curvature window side-tracking operations. To address this challenge, this study systematically investigates the structural principles and mechanical performance of hydraulic RWD tools. Through theoretical analysis, structural design, and numerical simulation, a novel micro hydraulic RWD tool activated by differential pressure was developed, followed by prototype fabrication and laboratory testing. The research results demonstrate that: Cutter stress analysis reveals that axial stress on arm is proportional to the cutting arc angle, while cutting resistance inversely correlates with it. An optimal 35° cutting arc angle minimizes resistance and prevents tool failure caused by locking ball bearing damage. The locking mechanism employs a line-line contact design between ball, housing, and sliding sleeve, effectively avoiding stress concentration and plastic strain. Based on hydrodynamic theory, the influence of the sliding sleeve's inner diameter on flow velocity, pressure differential, and total head loss was investigated, establishing a design methodology to ensure stable tool activation while minimizing energy dissipation. Applying thick-walled cylinder elastic theory, an interference-fit length calculation model was developed for the sleeve-housing interface. This methodology effectively prevents premature tool triggering while guaranteeing reliable arm retraction performance. Laboratory tests confirm successful tool activation/deactivation at theoretical flow rates, validating the design methodology. This study provides theoretical foundations for slimhole RWD tool design, enabling customized reaming applications. Its technical solutions hold significant economic and practical value for precisely exploiting residual reserves in mature wells and enhancing reservoir recovery efficiency.

Pipelines are an indispensable part of the global infrastructure for energy transportation, and their integrity poses a significant risk as the dynamic loads generated throughout their life cycle might result in catastrophic failures if not properly managed. A systematic review is presented, including the sources, effects, and mitigation measures of dynamic loads classified into four categories: environmental, mechanical, accidental, and human-induced. A thorough literature review highlights computational methods, particularly finite element method and computational fluid dynamics, contribute to estimating failure modes and developing preventive measures. Primary mitigation measures comprise flexible joints for the seismic response of pipelines, vortex-induced vibration suppressors for offshore applications, and protective barriers against impact loads. There are gaps in modeling coupled multiaxial loads and aging infrastructure under seismic loading conditions, with limitations in real-time monitoring. Adaptive computational frameworks integrating FSI with probabilistic risk assessments should be developed, with innovative technologies such as self-healing coatings and IoT-enabled monitoring requiring rigorous attention. This study aims to serve as a guide to enhancing pipeline resilience in extreme environments, thus directing future endeavors toward safe and sustainable transportation of energy through resilient infrastructures.

Corrosion remains a persistent challenge in drilling operations, particularly under high-pressure, high-temperature (HPHT) conditions, resulting in economic losses and safety risks. While conventional corrosion inhibitors have been effective to varying extents, their limitations, particularly in terms of toxicity, biodegradability, and compatibility with drilling fluid properties, necessitate exploring sustainable alternatives. Recent advances have focused on green inhibitors, notably plant-derived extracts, due to their biodegradability and low toxicity. However, a critical gap remains: few studies holistically evaluate both corrosion inhibition efficiency and the resultant impact on drilling fluid performance. To address this, the present review introduces an Integrated Screening and Corrosion Inhibition Modelling (I-SCIM) framework. This review comprehensively synthesizes recent research on corrosion inhibitors used in water-based, oil-based, and synthetic-based fluids. It categorizes inhibitors, evaluates their performance, and discusses their integration into complex mud systems. Key findings highlight that while conventional inhibitors achieve 70-85% efficiency, emerging green and hybrid formulations often exceed 90% inhibition. Furthermore, the paper explores the emerging class of smart and responsive inhibitors that adapt to environmental stimuli such as pH and temperature, highlighting their potential in dynamic wellbore conditions. By adopting the I-SCIM framework, this paper bridges the fragmented assessment approaches in current literature and proposes a unified strategy for evaluating corrosion inhibitors beyond inhibition efficiency alone. The review culminates in future research recommendations for inhibitors that are sustainable, efficient, and compatible with the evolving demands of HPHT drilling.

The pressure fluctuation caused by the axial movement of the string easily causes the bottomhole pressure to exceed the safe pressure window, which seriously restricts the horizontal open-hole extension capacity (OHEC) of the offshore extended-reach horizontal wells (ERHWs). In this work, in order to improve the horizontal OHEC for ERHWs, an optimal mud density model in extended-reach horizontal drilling (ERHD) was established based on the prediction and analysis of horizontal OHEC of ERHWs under string tripping operations. The results show that with the increase of mud density, the horizontal OHEC has a trend of first increasing and then decreasing in both tripping operations and casing tripping operations, and there exists an optimal mud density to maximize the horizontal OHEC. The optimal mud density of the two operations is obtained when the horizontal OHEC of the two operations is equal, and it is positively correlated with pull out of hole (POOH) speed, diameter of the wellbore and drill string, whereas it is negatively correlated with diameter of the casing and casing running speed. Controlling the tripping speed and optimizing the rheological properties are crucial to improve the horizontal OHEC. This model can provide scientific basis for optimization design of ERHD and the improvement of horizontal OHEC of ERHWs.

To address the absence of fire resistance evaluation methods for drilling wellhead structures under gas well blowout fires, this study firstly conducted experiments to investigate the mechanical properties of wellhead materials under high-temperature conditions. Subsequently, a temperature model and a mechanical response model of the wellhead structure under fire were established and validated respectively based on thermo-mechanical coupling methods. Furthermore, a fire resistance limit evaluation method of wellhead structures in well blowout fires was developed. The results indicated that with clearance operations at the well site, a thermal radiation threshold existed; Only when the thermal radiation exceeded this threshold, the wellhead reached its fire resistance limit and failed. Without clearance operations at the well site, the wellhead structure was certain to reach its fire resistance limit and failed. The upper surface of the wellhead had the largest displacement and reached its fire resistance limit first. The convective heat transfer coefficient, pressure load on the upper surface of the metal sealing ring, and casing gravity load had minimal influence on the fire resistance limit. Without clearance operations at the well site, the wellhead surface temperature approached the flame temperature, and the rubber seal reaches its decomposition point, leading to seal failure. The research findings could provide valuable references for the safety assessment of wellheads during gas well blowout fires and contribute to the development of effective response strategies for blowout fire control.

The friction factor plays a critical role in drag and torque calculations. In the drag and torque model, the friction factor is typically treated as an average value, or the wellbore is roughly divided into casedhole and openhole segments with different values assigned. Such simplified model parameters fail to account for variations in the friction factor across different well segments, impacting the accuracy of the computations. Precise drag and torque calculations are significant, and the prerequisite for accurate drag prediction is the inverse analysis of realistic friction factor parameters. This study proposes a friction factor inverse analysis method based on morphological characteristics of measured data. The Fisher optimal segmentation method is employed to divide the field-measured data into multiple reasonable subsegments based on curve morphology features, indirectly determining the segmentation of the friction factor. The 3D stiff-string drag and torque model is used to establish friction factors inverse model, with corresponding friction factors introduced separately for the segmented well sections. The sparrow search algorithm, enhanced by opposition-based learning strategy, is utilized to optimize the segmented friction factors, with the optimization evaluation criteria simultaneously assessing the deviation and similarity between simulated and actual curves. Case studies demonstrate that the friction factor inverse analysis method proposed in this study, leveraging the curve morphology features of realtime drilling data, can efficiently obtain high-precision segmented friction coefficients. This method significantly reduces the error between simulated and field-measured data, improving calculation accuracy by about 50%, thus providing robust data support for accurate drag and torque prediction. Furthermore, the division approach can be applied during a round trip to optimize drilling parameters, enhancing the efficiency of drilling parameter control.

Soil contamination by total petroleum hydrocarbons (TPH) poses a major risk to soil health and crop productivity. This study aims to investigate the effects of TPH on soil properties and rice growth factors, as well as the potential of sodium persulfate (SP) to mitigate negative impacts. The study includes four treatments: control (no SP or TPH), SP (0.0, 1.50 and 3.0%), TPH (TPH-contaminated soil, 4.0%, 6.0%, and 8.0%), and SP + TPH (SP and TPH levels). Results show that TPH contamination led to reductions in pH, organic matter, nutrient availability, root and shoot growth, chlorophyll content, plant height, grain and biomass yield. SP facilitates the restoration of soil physicochemical properties, mitigates toxicity, and enhances plant growth conditions by promoting both chemical oxidation and microbial degradation of TPH contaminants. Higher doses of SP are required to counteract higher TPH levels. The findings suggest that SP can be an effective amendment for remediating TPH-contaminated soils, but dosage and application should be tailored to site-specific conditions. This study highlights the potential of SP for sustainable management of TPH-contaminated agricultural land, but further work is needed to fully understand its impact on different plant species and to optimize remediation strategies for specific site conditions.