Search Results (444)

Multi-arm caliper logging tools provide a high-precision technology for monitoring tubing and casing integrity. However, due to the wellbore structure of horizontal wells and the self-weight of the instrument, the multi-arm caliper logging data of horizontal wells typically exhibit obvious eccentricity, resulting in a high misjudgment probability in the quantitative evaluation of oil and casing pipe damage. In this manuscript, simulation experiments were conducted using the 5.5-inch casing under laboratory conditions. The experiments clarified the response characteristics of forty-arm caliper logging tools in centered and eccentric states within intact and damaged casing. An equivalent model combining the instrument and the horizontal pipe structure was established, and a new method for correcting instrument eccentricity based on the forty-arm caliper logging data in horizontal wells was developed through theoretical analysis. The proposed model effectively overcomes the limitations of existing correction methods under casing damage conditions, reducing the relative error by up to 0.53% and 1.16% in intact and damaged well sections, respectively, compared to the ellipse-fitting algorithm. This effectively improves the correction coincidence rate for eccentricity influence of multi-arm caliper logging tools under various conditions in horizontal well. Applying this established method to the processing and interpretation of forty-arm caliper logging data in horizontal wells provides robust technical support for the high-precision quantitative evaluation, damage location identification, and repair of horizontal well casing. Full article

Optimizing the shut-in and flowback processes is crucial for improving oil recovery and mitigating formation damage in shale oil development. However, the mechanisms governing fracturing fluid migration and its impact on permeability, particularly across different lithologies, remain poorly understood. This study investigates the spontaneous imbibition behavior of fracturing fluid and the resulting permeability damage in two predominant lithotypes (dolomitic siltstone and argillaceous siltstone) from the Jimsar shale oil reservoir. By integrating low-field nuclear magnetic resonance (NMR) monitoring with core flooding experiments, we dynamically characterize fluid migration and quantitatively evaluate damage rates. The results reveal that lithology exerts a fundamental control on these processes. Dolomitic siltstone, with its higher brittle mineral content and well-connected pore network, facilitates deeper fracturing fluid invasion (30.47 mm) and more efficient oil displacement. In contrast, argillaceous siltstone, which is rich in clay minerals, exhibits stronger capillary trapping and suffers more severe permeability damage (~70%) compared to dolomitic siltstone (~46%), primarily due to the synergistic effects of water blocking and clay swelling. Furthermore, the impact of shut-in time on permeability damage follows a non-monotonic trend, reflecting a dynamic competition between imbibition-driven oil recovery and fluid-induced damage. Flowback analysis on core plugs reveals an economic critical point, beyond which further permeability recovery becomes marginal. This core-scale finding underscores the importance of the initial flowback stage for efficient cleanup and provides a scientific basis for optimizing flowback strategies in the Jimsar shale and similar unconventional reservoirs. These findings offer guidance for designing lithology-specific fracturing fluid systems, optimizing shut-in durations, and tailoring flowback strategies in the Jimsar shale and analogous unconventional reservoirs. Full article

Monitoring oil spills on coastal beaches using satellite imagery has received limited attention, primarily due to the lack of characteristic spectral data as well as constraints in spatial or temporal resolution. In this study, we employ both reflectance spectroscopy and CMOS-sensing imagery to detect and characterize different species of oil contaminants on sandy beaches and investigate their behavior throughout the weathering process. Laboratory and field measurements were conducted on oil-contaminated and clean beach samples with a high-resolution portable spectrometer and a highly sensitive CMOS camera. Predictive modeling of the reflectance spectra using LW-PLS, SVR, and SVM yielded R² values of 0.86 for oil concentration and 0.89 for weathering time, and achieved an oil species classification accuracy of 0.86. Furthermore, beach oil spills in the image dataset were detected using a DeepLabV3+ segmentation model with a ResNet-50 backbone, achieving a mean prediction accuracy of 98.73%. Finally, the segmentation model was successfully applied to accurately detect oil spill pollution on the beaches of Goa, India, confirming its field effectiveness. These reflectance spectroscopy and CMOS-sensing imagery technologies can provide critical data for calibrating remote sensing satellites, thereby offering direct technical support for targeted oil spill cleanup operations on beaches. Full article

Global restrictions related to climate change and the increasing demand for electricity are accelerating the transition from conventional energy sources, such as oil, gas, and coal, to renewable options like wind, solar, and biomass. Among these, solar photovoltaic (PV) systems are highly promising, offering clean and reliable electricity generation. In support of the Maldives’ target to achieve net-zero emissions by 2030, the deployment of PV systems has significantly increased. However, there is still a lack of detailed operational performance assessment specific to the Maldives. This study aims to address this gap and fulfill three main objectives. Firstly, to evaluate the real performance of six selected rooftop grid-connected PV systems installed in the Greater Malé region, Maldives. Secondly, the ideal performance ignoring shading, soiling, and aging effects of the selected systems on the islands are simulated, and the optimal orientation angles are estimated. Finally, the real and predicted performances are compared, and a module-level analysis is conducted to pinpoint the area for improving the performance of the rooftop PV systems installed on the island. The well-known International Electro-Technical Commission (IEC) standard, IEC 61724, is used for operational performance assessment, in addition, the PVsyst simulation tool and the S-Miles microinverters monitoring system are implemented for simulation and module-level analysis, respectively. In 2023, the six studied sites recorded annual daily averages of 2.52–4.45 kWh/kWp/day for yield factor, 0.98–2.9 h/day for total loss, 45.19–82.13% for performance ratio (PR), 10.51–18.55% for capacity utilization factor (CUF), and 7.69–15.94% for system efficiency. The actual performance was found to be lower than the simulated ideal values. The main reasons for this reduction were near-shading and microinverter connection issues. The orientation study showed that a 5° tilt angle with an azimuth between −25° and 5° gives the best results for fixed PV installations. These findings can guide better PV system design and operation in the Maldives and other similar climates. Full article

Polycyclic aromatic hydrocarbons (PAHs) can enter the human body through the consumption of contaminated food, particularly seafood, which can bioaccumulate these toxic compounds. This study evaluated PAH contamination levels in fish, crabs, and shellfish from the Parnaiba River estuary following the 2019 oil spill that impacted over 3000 km of Brazil’s northeastern coastline with weathered, heavy crude. The results showed that PAH concentrations in 2019 were approximately 50% higher than those detected in 2021, indicating an acute contamination event linked to the spill. Among the sampled organisms, crabs had the lowest PAH levels, followed by shellfish with intermediate contamination levels, and fish with the highest concentrations. PAH profiles varied by species: shellfish were dominated by high-molecular-weight (HMW) compounds typical of pyrogenic sources; fish were primarily contaminated with low-molecular-weight (LMW) PAHs associated with crude oil; and crabs exhibited a balanced mix of both. Toxicity equivalency analysis revealed the presence of benzo[a]pyrene (BaP) only in 2019 shellfish samples, while BaP contamination was found in both fish and shellfish in 2021. Some samples exceeded regulatory limits for indeno[1,2,3-cd]pyrene. Mollusks collected during the 2021 dry season presented BaP and benzo[k]fluoranthene levels above the threshold of concern. These findings demonstrate the acute impact of the oil spill, characterized by a predominance of LMW PAHs, as well as a residual contamination pattern in 2021, likely associated with pyrogenic sources and driven by environmental degradation processes. This study also indicates that although overall carcinogenic PAH levels decreased, some carcinogenic PAHs continue to exceed legal limits in fish and shellfish samples, even 2 years after the oil spill. This work highlights the need for long-term monitoring and reinforces the importance of including food safety in environmental impact assessments, especially in vulnerable fishing communities. Full article

Against the backdrop of China’s “carbon peaking” and “carbon neutrality” goals, as well as the advancement of new industrialization, the oil and gas industry is undergoing a critical transformation from resource-dependent growth toward innovation-driven, low-carbon, and high-quality development. The integrated advancement of high-end, intelligent, and green transformation—collectively referred to as the “Three Modernizations”—has become a vital pathway for promoting industrial upgrading and sustainable growth. Based on panel data from 30 Chinese provinces from 2009 to 2023, this study constructs a comprehensive evaluation index system covering 19 secondary indicators across three dimensions: high-end, intelligent, and green development. Using the entropy-weighted TOPSIS method, kernel density estimation, Dagum Gini coefficient decomposition, and σ–β convergence models, the study examines the spatiotemporal evolution, regional disparities, and convergence characteristics of HIG integration, and further explores its driving mechanisms through a two-way fixed effects model and mediation effect analysis. The results show that (1) the overall HIG integration index rose from 0.34 in 2009 to 0.46 in 2023, forming a spatial pattern of “high in the east, low in the west, stable in the center, and fluctuating in the northeast”; (2) regional disparities narrowed significantly, with the Gini coefficient declining from 0.093 to 0.058 and σ decreasing from 7.114 to 6.350; and (3) oil and gas resource endowment, policy support, technological innovation, and carbon emission constraints all positively promote integration, with regression coefficients of 0.152, 0.349, 0.263, and 0.118, respectively. Heterogeneity analysis reveals an increasing integration level from upstream to downstream, with eastern regions leading in innovation-driven development. Based on these findings, the study recommends strengthening policy and institutional support, accelerating technological innovation, improving intelligent infrastructure, deepening green and low-carbon transformation, promoting regional coordination, and establishing a long-term monitoring mechanism to advance the integrated high-quality development of China’s oil and gas industry. Overall, this study deepens the understanding of the internal logic and spatial dynamics of the “Three Modernizations” integration in China’s oil and gas industry, providing empirical evidence and policy insights for accelerating the construction of a low-carbon, secure, and efficient modern energy system. Full article

This study presents a novel top-down approach to quantify diffuse methane (CH₄) emissions at oil and gas well sites. It uses an unmanned aerial vehicle (UAV) equipped with a scanning–sampling tunable diode laser absorption spectroscopy (TDLAS) CH₄ measurement instrument. By integrating the top-down emission rate retrieval algorithm (TERRA) and adopting concentric circular sampling, the method aims to overcome the limitations of traditional ground-based measurements. The UAV system was deployed at 11 oil and gas sites in the Changqing Oilfield. The results show that the average CH₄ emission rate detected by the UAV is 1.425 kg/h (excluding non-detected samples), which is larger than the 1.061 kg/h obtained from ground-based onsite direct measurement. This discrepancy may be because the UAV’s scanning–sampling capability can cover a larger area, capturing scattered or hidden diffuse emission sources that might be missed by ground-based onsite direct measurement. The study demonstrates that the UAV-based approach with a scanning–sampling TDLAS CH₄ measurement instrument, integrated with the TERRA and concentric circular sampling, is effective in capturing diffuse CH₄ emissions at oil and gas well sites, providing a viable method for large-scale and efficient monitoring of such emissions. This approach could provide an effective pathway for large-scale, efficient, and cost-effective monitoring of methane emissions. Full article

Marine oil spill incidents are one of the major global marine pollution issues, which pose significant threats to ocean ecosystems. However, traditional monitoring methods often suffer from time delays, high costs, and limited real-time capability, making them inadequate for timely and large-scale oil spill detection. With the development of remote sensing (RS) technology and artificial intelligence (AI) methods, as well as the increasing frequency of marine oil spill accidents, plenty of AI-based methods using RS imagery have been proposed for more efficient and accurate oil spill monitoring. This review presents a comprehensive and systematic overview of recent progress in marine oil spill analysis using RS imagery, emphasizing the integration of AI methods across three key tasks: detection, classification, and thickness estimation. Specifically, we first introduce the main types of RS data and discuss the significance of publicly available datasets, which can facilitate method validation and model comparison. Second, we briefly review the application of RS imagery from different sensors in oil spill detection, highlighting the strengths of various spectral and polarimetric methods. Third, we summarize advances in oil spill classification, including AI-based methods that enable differentiation between mineral oil, biogenic films, and various emulsified oils. Fourth, we discuss emerging techniques for oil spill thickness estimation. Finally, we analyze the challenges of existing methods and future directions, including the need for real-time monitoring, the integration of multi-source RS data, and the development of robust models that can generalize across different environmental conditions. This review adopts a comprehensive perspective from both AI methods and RS technology, provides a systematic overview of recent advancements, identifies critical gaps in current methodologies, and serves as a valuable reference for researchers and practitioners working on oil spill monitoring. Full article

During the development of oil and gas fields, the plugging problem in the wellbore and near-wellbore area is a key factor affecting oil recovery efficiency and economic benefits. This paper systematically reviews the research progress on the formation mechanisms, influencing factors, and plugging removal technologies of four common types of plugging, namely wax plugging, scaling, sand plugging, and hydrate plugging. Studies show that plugging is the result of the coupling of multiple physicochemical processes, and is jointly affected by multiple factors such as fluid properties, temperature and pressure conditions, flow rate, and surface properties. Currently, the plugging removal technology has formed a synergistic system of multiple methods including chemical, physical, mechanical, and thermodynamic approaches; however, it still faces challenges such as limited treatment depth, high cost, and risk of secondary damage. In the future, efforts should be made to strengthen research on multi-scale plugging mechanisms and develop environmentally friendly and high efficiency plugging removal agents as well as intelligent monitoring technologies, so as to improve the reliability and economy of complex oil and gas resource development. This paper aims to provide theoretical support and technical directions for researchers and engineers, and promote the innovation and development of efficient oil and gas field development and flow assurance technologies. Full article

Rotary Steerable System (RSS) enhances directional drilling efficiency by over 300% via dynamic bit adjustment during string rotation. This study aims to systematically address these bottlenecks, quantify technical boundaries, and propose actionable breakthrough paths for China’s RSS technology in shale gas development. To address China’s shale gas RSS bottlenecks, this study proposes a “Material-Algorithm-System” tri-level strategy centered on an innovative “Tri-loop System.” Key innovations include (1) silicon nitride–tungsten carbide composite coatings to enhance thermal resilience, tested to withstand 220 °C, reducing thermal failure risk by 40% compared to conventional materials; (2) downhole reinforcement learning optimization; (3) a “Tri-loop System” integrating downhole intelligent control, wellbore-surface bidirectional communication, and cloud monitoring, shortening downhole command response latency from over 5 s to less than 1 s. In practical shale gas development scenarios—such as the Sichuan Basin’s deep coalbed methane wells and Shengli Oilfield’s tight reservoirs—this tri-level strategy has proven effective: the high-frequency electromagnetic wave radar increased thin coal seam drilling encounter rate by 23%, while the piezoelectric ceramic micro-actuators reduced tool failure rate by 35% in 175–200 °C environments. This approach targets raising China’s shale gas RSS application rate to 60%, supporting sustainable oil and gas exploration. Full article

Conductivity sensors play a vital role in monitoring production data in oil wells to ensure efficient oilfield operations, and their service performance depends on the durability of Invar alloy electrodes. The alloy electrodes are susceptible to damage from abrasive solid particles, corrosive media, and oil fluids in downhole environments. The degradation of the alloy electrodes directly compromises the signal stability of conductivity sensors, resulting in inaccurate monitoring data. Inspired by the intrinsic oleophobic properties of fish scales, we developed a fluorinated boron-doped diamond (FBDD) film with biomimetic micro–nano structures to enhance the wear resistance, corrosion resistance, and amphiphobicity of Invar alloy electrodes. The fish scale architecture was fabricated through argon-rich hot-filament chemical vapor deposition (90% Ar, 8 h) followed by fluorination. FBDD-coated electrodes surpass industrial benchmarks, exhibiting a friction coefficient of 0.08, wear rate of 5.1 × 10⁻⁷ mm³/(N·mm), corrosion rate of 3.581 × 10⁻³ mm/a, and oil/water contact angles of 95.32°/106.47°. The following underlying improvement mechanisms of FBDD films are proposed: (i) the wear-resistant matrix preserves the oleophobic nanostructures during abrasive contact; (ii) the corrosion barrier maintains electrical conductivity by preventing surface oxidation; (iii) the oil-repellent surface minimizes fouling that could mask corrosion or wear damage. Full article

Residual oil (RO) in terrigenous reservoirs formed after waterflooding can exceed 60% of the original oil in place; approximately 70% is trapped at the macro-scale in barriers and lenses, whereas about 30% remains at the micro-scale as film and capillary-held oil. This review aims to synthesize current knowledge of RO formation mechanisms, localization methods and chemical recovery technologies. It analyzes laboratory, numerical and field studies published from 1970 to 2025. The physical and technological factors governing RO distribution are systematized, and the effects of heterogeneities of various types, imperfections in pressure-maintenance (waterflood) systems and contrasts in oil–water properties are demonstrated. Instrumental monitoring techniques—vertical seismic profiling (VSP), well logging (WL), hydrodynamic well testing (WT) and geochemical well testing (GWT)—are discussed alongside indirect analytical approaches such as retrospective production-data analysis and neural-network forecasting. Industrial experience from more than 30,000 selective permeability-reduction operations, which have yielded over 50 Mt of additional oil, is consolidated. The advantages of gel systems of different chemistries are evaluated, and the prospects of employing waste products from agro-industrial, metallurgical and petroleum sectors as reagents are considered. The findings indicate that integrating multi-level neural-network techniques with instrumental monitoring and adaptive selection of chemical formulations is crucial for maximizing RO recovery. Full article

Distributed temperature sensing (DTS) has long been employed in the oil and gas industry to characterize reservoirs, optimize production, and extend well life. More recently, its application has expanded to geothermal energy development, where DTS provides critical insights into transient wellbore temperature profiles and flow behavior. A comprehensive understanding of such field measurements can be achieved by systematically comparing and interpreting DTS data in conjunction with robust numerical models. However, many existing wellbore models rely on steady-state heat transfer assumptions that fail to capture transient dynamics, while fully coupled wellbore–reservoir simulations are often computationally demanding and mathematically complex. This study aims to address this gap by developing a transient wellbore heat transfer model validated with DTS data. The model was formulated using a thermal-analogy approach based on the theoretical framework of Eickmeier et al. and implemented with a finite-difference scheme. Validation was performed by comparing thermal slug velocities predicted by the model with those extracted from DTS measurements. The results demonstrated strong agreement between modeled and measured slug velocities, confirming the model’s reliability. In addition, the modeled thermal slug velocity was lower than the corresponding fluid velocity, indicating that thermal front propagates more slowly than the fluid front. Consequently, this computationally efficient approach enhances the interpretation of DTS data and offers a practical tool for improved monitoring and management of geothermal operations. Full article

Obtaining accurate information on stratigraphic conditions and drilling status is necessary to ensure the safety of the drilling process and to guarantee the production of oil and gas. Sensing while drilling and intelligent monitoring technology, which employ multiple sensors and involve the use of intelligent algorithms, can be used to collect downhole information in situ to ensure safe, reliable, and efficient drilling and mining operations. These approaches are characterized by effective sensing and comprehensive utilization of drilling information through the integration of multi-sensor signals and intelligent algorithms, a core component of machine learning. The article summarizes the current research status of domestic and international sensing while drilling and intelligent monitoring technology using systematically collected relevant information. Specifically, first, the drilling-sensing methods used for in situ acquisition of downhole information, including fiber-optic sensing, electronic-nose sensing, drilling engineering-parameter sensing, drilling mud-parameter sensing, drilling acoustic logging, drilling electromagnetic wave logging, and drilling seismic logging, are described. Next, the basic composition and development direction of each sensing technology are analyzed. Subsequently, the application of intelligent monitoring technology based on machine learning in various aspects of drilling- and mining-status identification, including bit wear monitoring, stuck drill real-time monitoring, well surge real-time monitoring, and real-time monitoring of oil and gas output, is introduced. Finally, the potential applications of sensing while drilling and intelligent monitoring technology in deep-earth, deep-sea, and deep-space contexts are discussed, and the challenges, constraints, and development trends are summarized. Full article

In oil well environments, pressure sensors are often challenged by electromagnetic interference, temperature drift, and corrosive fluids, which reduce their stability and service life. To improve long-term reliability under these conditions, we developed a fiber optic Fabry–Perot (FP) cavity pressure sensor that employs an Inconel 718 diaphragm to provide both high mechanical strength and corrosion resistance. An integrated fiber Bragg grating (FBG) was included to monitor temperature simultaneously, allowing temperature–pressure cross-sensitivity to be decoupled. The sensor was fabricated and tested over a temperature range of 20–100 °C and a pressure range of 0–60 MPa. Experimental characterization showed that the FP cavity length shifted linearly with pressure, with a sensitivity of 377 nm/MPa, while the FBG demonstrated a temperature sensitivity of 0.012 nm/°C. After temperature compensation, the overall pressure measurement accuracy reached 0.5% of the full operating pressure range (0–60 MPa). These results confirm that the combined FP–FBG sensing approach maintained stable performance in harsh downhole conditions, making it suitable for pressure monitoring in shallow and medium-depth reservoirs. The proposed design offers a practical route to extend the operational lifetime of optical sensors in oilfield applications. Full article