Search Results (243)

Cyberattacks on pipeline operational technology systems pose growing risks to energy infrastructure. This study develops a physics-informed simulation and optimization framework for analyzing cyber–physical threats in petroleum pipeline networks. The model integrates networked hydraulic dynamics, SCADA-based state estimation, model predictive control (MPC), and a bilevel formulation for stealthy false-data injection (FDI) attacks. Pipeline flow and pressure dynamics are modeled on a directed graph using nodal pressure evolution and edge-based Weymouth-type relations, including control-aware equipment such as valves and compressors. An extended Kalman filter estimates the full network state from partial SCADA telemetry. The controller computes pressure-safe control inputs via MPC under actuator constraints and forecasted demands. Adversarial manipulation is formalized as a bilevel optimization problem where an attacker perturbs sensor data to degrade throughput while remaining undetected by bad-data detectors. This attack–control interaction is solved via Karush–Kuhn–Tucker (KKT) reformulation, which results in a tractable mixed-integer quadratic program. Test gas pipeline case studies demonstrate the covert reduction in service delivery under attack. Results show that undetectable attacks can cause sustained throughput loss with minimal instantaneous deviation. This reveals the need for integrated detection and control strategies in cyber–physical infrastructure. Full article

The Qiongdongnan Basin constitutes a sedimentary basin characterized by elevated temperatures, significant overpressures, and abundant hydrocarbons. Investigations within this basin have identified hydrothermal fluid movements linked to overpressure conditions, comprising two vertically separated overpressured intervals. The shallow overpressure compartment is principally caused by a combination of undercompaction and clay diagenesis. In contrast, the deeper high-pressure compartment results from hydrocarbon gas generation. Numerical pressure modeling indicates late-stage (post-5 Ma) development of significant overpressure within the deep compartment. It is proposed that accelerated subsidence in the Pliocene-Quaternary initiated substantial gas generation, thereby promoting the formation of the deep overpressured system. Multiple organic maturation parameters, combined with fluid inclusion microthermometry, reveal a thermal anomaly adjacent to the upper boundary of the deep overpressured zone. This anomaly indicates vertical transport of hydrothermal fluids ascending from the underlying high-pressure zone. Laser Raman spectroscopy confirms the presence of both hydrocarbons and carbon dioxide within these migrating fluids. Integration of fluid inclusion thermometry with burial history modeling constrains the timing of hydrocarbon-carrying fluid charge to the interval from 4.2 Ma onward, synchronous with modeled peak gas generation and a phase of pronounced overpressure buildup. We propose that upon exceeding the fracture gradient threshold, fluid pressure triggered upward migration of deeply sourced, hydrocarbon-enriched fluids through hydrofracturing pathways. This process led to localized dissolution and fracturing near the top of the deep overpressured system, while simultaneously facilitating significant hydrocarbon accumulation and forming preferential accumulation zones. These findings provide critical insights into petroleum exploration in overpressured sedimentary basins. Full article

The Port of Veracruz, the main port in the Gulf of Mexico, has experienced a significant increase in its import and export operations, such as petroleum coke (Petcoke), a solid waste, mainly used in the steel industry. During the period of 2010–2023, approximately 7,401,594 tons of coke were stored outdoors, generating PM₁₀ particulate emissions due to wind erosion. These particles were dispersed to urban areas, reaching an estimated total emission of 5077 tons. The study used geospatial analysis and environmental modeling tools (ALOHA^®) to evaluate the dispersion and concentration of PM₁₀ in the atmosphere, comparing them with the limits established by the Mexican Official Standard NOM-025-SSA1-2021. The results indicate that in years with high port activity, such as 2014, PM₁₀ concentrations exceeded the normative values, representing a potential risk to public health and urban infrastructure. This study provides critical evidence on the environmental impacts of coke handling in ports and suggests mitigation strategies, including processes for the confinement of materials and the implementation of advanced emissions monitoring systems. Full article

According to the “Five Development Concepts” of the new national development plan, the study of inclusive green development in the petroleum and gas sector (IGDPG) is crucial for enhancing production efficiency and safeguarding the environment and resources. This study constructs the IGDPG indicator system from industrial development, social opportunity equity, poverty and income inequality reduction, and green ecology dimensions, and the CRITIC Portfolio empowerment-TOPSIS method was used to measure the level of IGDPG in the eastern, central, and western regions using panel data. The Dagum Gini coefficient method was applied to analyze regional disparities and their causes, while the obstacle degree model and the Tobit model were used to identify internal and external factors of IGDPG. We found that IGDPG levels in the three regions showed fluctuating growth, and the eastern region (0.394) had much higher IGDPG levels than the central (0.337) and western (0.355) regions. The overall Gini coefficient for IGDPG is small, while inter-regional disparities are the primary source of overall disparities, and the intra-regional disparities of the three main areas exhibit a declining tendency. In terms of internal factors, social opportunity equity has been identified as the primary obstacle constraining IGDPG. Externally, factors such as industrial cluster, industrial upgrading, urbanization rate, and digital economy exhibit a facilitative effect on IGDPG, whereas environmental burdens exert an inhibitory influence. Moreover, all of these internal and external drivers demonstrate significant regional variations. Therefore, breaking regional restrictions and promoting the coordinated development of IGDPG so as to improve China’s IGDPG level as a whole is the forecasted trend. Full article

This study investigates the formation mechanism and stress response characteristics of normal faults in coal-bearing strata through large-scale physical simulation experiments. A multi-layer heterogeneous model with a geometric similarity ratio of 1:300 was constructed using similar materials that were tailored to match the mechanical properties of real strata. Real-time monitoring techniques, including fiber Bragg grating strain sensors and a DH3816 static strain system, were employed to record the evolution of deformation, strain, and displacement fields during the fault development. The results show that the normal fault formation process includes five distinct stages: initial compaction, fault initiation, crack propagation, fault slip, and structural stabilization. Quantitatively, the vertical displacement of the hanging wall reached up to 5.6 cm, equivalent to a prototype value of 16.8 m, and peak horizontal stress increments near the fault exceeded 0.07 MPa. The experimental data reveal that stress concentration during the fault slip stage causes severe damage to the upper coal seam roof, with localized vertical stress fluctuations exceeding 35%. Structural planes were found to control crack nucleation and slip paths, conforming to the Mohr–Coulomb shear failure criterion. This research provides new insights into the dynamic coupling of tectonic stress and fault mechanics, offering novel experimental evidence for understanding fault-induced disasters. The findings contribute to the predictive modeling of stress redistribution in fault zones and support safer deep mining practices in structurally complex coalfields, which has potential implications for petroleum geomechanics and energy resource extraction in similar tectonic settings. Full article

This study investigates the uplift and exhumation history of the southern segment of the western margin of the Ordos Basin using low-temperature thermochronology, including zircon (U-Th)/He (ZHe), apatite fission-track (AFT), and apatite (U-Th)/He (AHe) data, combined with thermal history modeling. The study area exhibits a complex structural framework shaped by multiple deformation events, leading to the formation of extensively developed fault systems. Such faulting can adversely affect hydrocarbon preservation. To better constrain the timing of fault reactivation in this area, we carried out an integrated study involving low-temperature thermochronology and burial history modeling. The results reveal a complex, multi-phase thermal-tectonic evolution since the Late Paleozoic. The ZHe ages (291–410 Ma) indicate deep burial and heating related to Late Devonian–Early Permian tectonism and basin sedimentation, reflecting early orogenic activity along the western North China Craton. During the Late Jurassic to Early Cretaceous (165–120 Ma), the study area experienced widespread and differential uplift and cooling, controlled by the Yanshanian Orogeny. Samples on the western side of the fault show earlier and more rapid cooling than those on the eastern side, suggesting a fault-controlled, basinward-propagating exhumation pattern. The cooling period indicated by AHe data and thermal models reflects the Cenozoic uplift, likely induced by far-field compression from the rising northeastern Tibetan Plateau. These findings emphasize the critical role of inherited faults not only as thermal-tectonic boundaries during the Mesozoic but also as a pathway for hydrocarbon migration. Meanwhile, thermal history models based on borehole data further reveal that the study area underwent prolonged burial and heating during the Mesozoic, reaching peak temperatures for hydrocarbon generation in the Late Jurassic. The timing of major cooling events corresponds to the main stages of hydrocarbon expulsion and migration. In particular, the differential uplift since the Mesozoic created structural traps and migration pathways that likely facilitated hydrocarbon accumulation along the western fault zones. The spatial and temporal differences among the samples underscore the structural segmentation and dynamic response of the continental interior to both regional and far-field tectonic forces, while also providing crucial constraints on the petroleum system evolution in this tectonically complex region. Full article

The monitoring and prediction of air quality (AQ) is key to minimizing the negative impact of air pollution, as it enables the implementation of corrective measures. An IoT-based multi-purpose monitoring system has therefore been designed. To develop a reliable remote system, this study addresses three challenges: (1) design of a low-cost compact, robust, multi-sensor system, (2) model validation over several months to ensure accurate detection, and (3) the application of machine learning (ML) techniques to classify and predict AQ. The developed system demonstrates a significant cost reduction for regular monitoring, including effective data management under harsh environmental conditions. The prototype integrates pollutant sensors, as well as the detection of liquified petroleum gas, humidity, and temperature. A dataset with more than 30,000 entries per month (data recorded approximately every minute) was saved on the platform. Results identified the three highest pollution categories, highlighting the urgency of addressing AQ in densely populated regions. The ML algorithms allowed us to predict AQ trends with 99.97% accuracy. To summarize, by reducing monitoring costs and enabling large-scale data management, this system offers an effective solution for real-time environmental monitoring. It also highlights the potential of artificial intelligence-based AQ predictions in supporting public health initiatives. This is particularly interesting for developing countries, where pollution control is limited. Future research will develop the models to include data from different environments and seasons, exploring its integration into mobile apps and cloud platforms for real-time monitoring. Full article

As information and sensor technologies advance swiftly, data-driven approaches have emerged as a dominant paradigm in scientific research. In the petroleum industry, precise forecasting of patterns of two-phase flow involving oil and water is essential for enhancing production efficiency and ensuring safety. This study investigates the application of Kolmogorov–Arnold Networks (KAN) for predicting patterns of two-phase flow involving oil and water and compares it with the conventional Multi-Layer Perceptron (MLP) neural network. To obtain real physical data, we conducted the experimental section to simulate the patterns of two-phase flow involving oil and water under various well angles, flow rates, and water cuts at the Key Laboratory of Oil and Gas Resources Exploration Technology of the Ministry of Education, Yangtze University. These data were standardized and used to train both KAN and MLP models. The findings indicate that KAN outperforms the MLP network, achieving 50% faster convergence and 22.2% higher accuracy in prediction. Moreover, the KAN model features a more streamlined structure and requires fewer neurons to attain comparable or superior performance to MLP. This research offers a highly effective and dependable method for predicting patterns of two-phase flow involving oil and water in the dynamic monitoring of production wells. It highlights the potential of KAN to boost the performance of energy systems, particularly in the context of intelligent transformation. Full article

Background: Managing multiproduct pipeline systems is a complex task of critical importance in the petroleum industry. Experts frequently rely on simulation tools to design and validate pumping operation schedules. However, existing tools are often problem-specific and too slow to be effectively used for optimization purposes. Methods: In this paper, a new scheduling model is introduced, which inherently eliminates all conflicts except for tank overflows and underflows. A Discrete-Event Simulation algorithm was developed, capable of handling mesh-like pipeline topologies, reverse flows, and interface tracking. The computational performance of the new method is demonstrated using three local search-based optimization variants, including a simulated annealing metaheuristic. Results: A case study was made involving four problems, with 4–6 sites and 5–7 products in mesh-like and straight topologies, respectively, and a large-scale instance. Scheduling horizons of 2–28 days were used. The proposed simulation algorithm significantly outperforms a prior approach in speed, and the optimization algorithms effectively converged to feasible, high-quality schedules for most instances. Conclusions: This paper proposes a novel simulation technique for multiproduct pipeline scheduling along with three local search algorithm variants that demonstrate optimization capabilities. Full article

The lacustrine to deltaic depositional systems of the Upper Jurassic Qigu Formation in the Yongjin area constitute a significant petroleum reservoir in the central Junggar Basin, China. Based on core observations, petrology analyses, paleoenvironment indicators and modern sedimentary analyses, sequence stratigraphy, lithofacies associations, sedimentary environment, evolution, and models were investigated. The Qigu Formation can be divided into a third-order sequence consisting of a lowstand systems tract (LST) and a transgressive systems tract (TST), which is further subdivided into six fourth-order sequences. Thirteen lithofacies and five lithofacies associations were identified, corresponding to shallow-water delta-front deposits. The paleoenvironment of the Qigu Formation is generally characterized by an arid freshwater environment, with a dysoxic to oxic environment. During the LST depositional period (SQ1–SQ3), the water depth was relatively shallow with abundant sediment supply, resulting in a widespread distribution of channel and mouth bar deposits. During the TST depositional period (SQ4–SQ6), the rapid rise in base level, combined with reduced sediment supply, resulted in swift delta retrogradation and widespread lacustrine sedimentation. Combined with modern sedimentary analysis, the shallow-water delta in the study area primarily comprises a composite system of single main channels and distributary channel-mouth bar complexes. The channel-bar complex eventually forms radially distributed bar assemblages with lateral incision and stacking. The distributary channel could incise a mouth bar deeply or shallowly, typically forming architectural patterns of going over or in the mouth bar. Reservoir test data suggest that the mouth bar sandstones are favorable targets for lithological reservoir exploration in shallow-water deltas. Full article

Wax deposition, driven by the crystallization of long-chain n-alkanes, poses severe challenges across industries such as petroleum, oil and natural gas, food processing, and chemical manufacturing. This phenomenon compromises flow efficiency, increases energy demands, and necessitates costly maintenance interventions. Wax inhibitors, designed to mitigate these issues, operate by altering wax crystallization, aggregation, and adhesion over the pipelines. Classic wax inhibitors, comprising synthetic polymers and natural compounds, have been widely utilized due to their established efficiency and scalability. However, synthetic inhibitors face environmental concerns, while natural inhibitors exhibit reduced performance under extreme conditions. The advent of nano-based wax inhibitors has revolutionized wax management strategies. These advanced materials, including nanoparticles, nanoemulsions, and nanocomposites, leverage their high surface area and tunable interfacial properties to enhance efficiency, particularly in harsh environments. While offering superior performance, nano-based inhibitors are constrained by high production costs, scalability challenges, and potential environmental risks. In parallel, the development of “green” wax inhibitors derived from renewable resources such as vegetable oils addresses sustainability demands. These eco-friendly formulations introduce functionalities that reinforce inhibitory interactions with wax crystals, enabling effective deposition control while reducing reliance on synthetic components. This review provides a comprehensive analysis of the mechanisms, applications, and comparative performance of classic and nano-based wax inhibitors. It highlights the growing integration of sustainable and hybrid approaches that combine the reliability of classic inhibitors with the advanced capabilities of nano-based systems. Future directions emphasize the need for cost-effective, eco-friendly solutions through innovations in material science, computational modeling, and biotechnology. Full article

Petroleum oily sludge (OLS), a hazardous by-product of the petroleum industry, and high-alkali lignite (HAL), an underutilized low-rank coal, pose significant challenges to sustainable waste management and resource efficiency. This study systematically investigated the combustion behavior, reaction pathways, and gaseous-pollutant-release mechanisms across varying blend ratios, utilizing integrated thermogravimetric-mass spectrometry analysis (TG-MS), interaction analysis, and kinetic modeling. The key findings reveal that co-combustion significantly enhances the combustion performance compared to individual fuels. This is evidenced by reduced ignition and burnout temperatures, as well as an improved comprehensive combustion index. Notably, an interaction analysis revealed coexisting synergistic and antagonistic effects, with the synergistic effect peaking at a blending ratio of 50% OLS due to the complementary properties of the fuels. The activation energy was found to be at its minimum value of 32.5 kJ/mol at this ratio, indicating lower reaction barriers. Regarding gas emissions, co-combustion at a 50% OLS blending ratio reduces incomplete combustion products while increasing CO₂, indicating a more complete reaction. Crucially, sulfur-containing pollutants (SO₂, H₂S) are suppressed, whereas nitrogen-containing emissions (NH₃, NO₂) increase but remain controllable. This study provides novel insights into the synergistic mechanisms between OLS and HAL during co-combustion, offering foundational insights for the optimization of OLS-HAL combustion systems toward efficient energy recovery and sustainable industrial waste management. Full article

This article presents the development, integration, and experimental validation of a modular microgrid for sustainable hydrogen production, addressing global electricity demand and environmental challenges. The system was designed for initial validation in a thermoelectric power plant environment, with scalability to other applications. Centered on a six-compartment skid, it integrates photovoltaic generation, battery storage, and a liquefied petroleum gas generator to emulate typical cogeneration conditions, together with a high-purity proton exchange membrane electrolyzer. A supervisory control module ensures real-time monitoring and energy flow management, following international safety standards. The study also explores the incorporation of blockchain technology to certify the renewable origin of hydrogen, enhancing traceability and transparency in the green hydrogen market. The experimental results confirm the system’s technical feasibility, demonstrating stable hydrogen production, efficient energy management, and islanded-mode operation with preserved grid stability. These findings highlight the strategic role of hydrogen as an energy vector in the transition to a cleaner energy matrix and support the proposed architecture as a replicable model for industrial facilities seeking to combine hydrogen production with advanced microgrid technologies. Future work will address large-scale validation and performance optimization, including advanced energy management algorithms to ensure economic viability and sustainability in diverse industrial contexts. Full article

This paper presents a comprehensive study of torsional stick–slip vibrations in rotary drilling systems through a comparison between two lumped parameter models with differing complexity: a simple two-degree-of-freedom (2-DOF) model and a complex high-degree-of-freedom (high-DOF) model. The two models are developed under identical boundary conditions and consider an identical nonlinear friction torque dynamic involving the Stribeck effect and dry friction phenomena. The high-DOF model is calculated with the Finite Element Method (FEM) to enable accurate simulation of the dynamic behavior of the drill string and accurate representation of wave propagation, energy build-up, and torque response. Field data obtained from an Algerian oil well with Measurement While Drilling (MWD) equipment are used to guide modeling and determine simulations. According to the findings, the FEM-based high-DOF model demonstrates better performance in simulating basic stick–slip dynamics, such as drill bit velocity oscillation, nonlinear friction torque formation, and transient bit-to-surface contacts. On the other hand, the 2-DOF model is not able to represent these effects accurately and can lead to inappropriate control actions and mitigation of vibration severity. This study highlights the importance of robust model fidelity in building reliable real-time rotary drilling control systems. From the performance difference measurement between low-resolution and high-resolution models, the findings offer valuable insights to optimize drilling efficiency further, minimize non-productive time (NPT), and improve the rate of penetration (ROP). This contribution points to the need for using high-fidelity models, such as FEM-based models, in facilitating smart and adaptive well control strategies in modern petroleum drilling engineering. Full article

This study aims to provide a comprehensive overview of the application of artificial intelligence (AI) methods to solve real-world problems in the oil and gas sector. The methodology involved a two-step process for analyzing AI applications. In the first step, an initial exploration of scientific articles in the Scopus database was conducted using keywords related to AI and computational intelligence, resulting in a total of 11,296 articles. The bibliometric analysis conducted using VOS Viewer version 1.6.15 software revealed an average annual growth of approximately 15% in the number of publications related to AI in the sector between 2015 and 2024, indicating the growing importance of this technology. In the second step, the research focused on the OnePetro database, widely used by the oil industry, selecting articles with terms associated with production and drilling, such as “production system”, “hydrate formation”, “machine learning”, “real-time”, and “neural network”. The results highlight the transformative impact of AI on production operations, with key applications including optimizing operations through real-time data analysis, predictive maintenance to anticipate failures, advanced reservoir management through improved modeling, image and video analysis for continuous equipment monitoring, and enhanced safety through immediate risk detection. The bibliometric analysis identified a significant concentration of publications at Society of Petroleum Engineers (SPE) events, which accounted for approximately 40% of the selected articles. Overall, the integration of AI into production operations has driven significant improvements in efficiency and safety, and its continued evolution is expected to advance industry practices further and address emerging challenges. Full article