Search Results (5,252)

Fungal pathogens, such as Fusarium spp. and Alternaria spp., cause substantial yield losses worldwide through root and fruit rot, wilting, and leaf and fruit spots, and increasing restrictions on chemical pesticides have intensified interest in sustainable alternatives such as essential oils, plant extracts, and microbial biocontrol agents. However, these three treatment categories are rarely compared directly under identical experimental conditions, which limits conclusions about their relative efficacy. This study directly compared, for the first time under the same in vitro conditions, the antifungal activity of peppermint and thyme essential oils, clove and rosemary plant extracts, and three commercial biological products (Mycostop, Asir Fruit, Aegis) against Fusarium avenaceum and Alternaria alternata using the agar incorporation method, with fungicidal versus fungistatic activity determined by reinoculation. Most treatments inhibited mycelial growth of both pathogens relative to the untreated control, although a few treatments (Asir Fruit at early DAI and lowest-concentration thyme EO) showed no inhibition or even slight stimulation of A. alternata growth. At 7 days after inoculation, thyme essential oil (1000 µL/L) and clove plant extract at all concentrations tested completely inhibited both pathogens, and reinoculation confirmed that clove extract alone was fungicidal, likely reflecting its high eugenol content and consequent irreversible membrane damage; all other treatments were fungistatic, consistent with reversible effects on membrane permeability. Peppermint essential oil (2000 µL/L) inhibited F. avenaceum and A. alternata by 96% and 73%, respectively, while rosemary extract was comparatively weaker (57% and 21%). Among the commercial products, Mycostop, which contains Streptomyces griseoviridis, was the most effective, plausibly reflecting sustained antagonistic activity beyond metabolite secretion alone. These findings indicate that clove extract and thyme oil match or exceed commercial biocontrol products in vitro and warrant evaluation under greenhouse and field conditions as candidates for integrated disease management. Full article

Heavy and extra-heavy oils represent a large and growing share of recoverable hydrocarbon resources, yet their extreme viscosity, high heteroatom content, and non-Newtonian behavior routinely defeat empirical correlations developed for conventional crude. Machine learning has emerged as a candidate response to this modeling gap, but existing reviews largely catalog applications without asking whether the technology is actually ready for industrial deployment. This critical review synthesizes machine learning applications across five thematic domains of the heavy-oil value chain: physicochemical property prediction, enhanced oil recovery, flow assurance, reactive recovery, and downstream upgrading. Studies are read through a three-phase historical lens, tracing the field’s progression from empirical-correlation replacement to methodological diversification to physics-informed and closed-loop integration, and evaluated against a Technology Readiness Level (TRL) scale adapted specifically for heavy-oil machine learning. The multilayer perceptron anchors more of the primary corpus than any other architecture, a pattern that, in our interpretation, reflects small-sample, low-dimensional regression needs rather than any demonstrated advantage over other architectures. Enhanced oil recovery is the only cluster to reach organizational-scale deployment, anchored by a single multi-decade operator program, Chevron’s San Joaquin Valley i-field; the remaining clusters are constrained less by modeling sophistication than by single-basin datasets and undisclosed uncertainty. Measured against three falsifiable deployability criteria, fidelity preservation below 10° API, operator-grade interpretability, and demonstrated laboratory-to-field transferability, no study in the reviewed corpus is documented to satisfy all three simultaneously; because industrial implementations are frequently proprietary, this is a statement about the published record identified by this search, not a claim that the capability does not exist. Federated learning, physics-informed architectures, and sequence-aware models emerge as the directions most likely to close this gap. Full article

As the primary carrier for oil and gas transportation, pipelines are critical for the entire industry. Pipelines are continuously subjected to corrosion and abrasion in the oil and gas delivery process, leading to gradual wall thickness reduction, shortened service life, and deteriorated operational safety. Ultrasonic testing has been widely adopted for monitoring pipeline wall thickness. Conventional ultrasonic transducers possess rigid configurations, which hinder large-area inspection and exhibit poor adaptability to complex curved components. In contrast, flexible ultrasonic sensors show prominent advantages, with their small size, light weight, and excellent conformal contact with curved surfaces. Flexible piezoelectric thin-film sensors have been used in a wide range of fields. As one of the most representative piezoelectric polymers, poly(vinylidene fluoride–trifluoroethylene) (P(VDF-TrFE)) combines favorable piezoelectric coefficients and intrinsic flexibility, making it popular. Some research groups have investigated the influences of modified filler particles, doping ratios, and fabrication process optimization on the performance of P(VDF-TrFE)-based piezoelectric composites, while others have concentrated on the practical applications of existing flexible piezoelectric sensors. This study emphasizes a rapid customized fabrication strategy for flexible sensors instead of single-specification standardized probes; hence, it does not share the same comparison benchmark as conventional fixed-dimension sensors. Systematic research on flexible piezoelectric thin-film sensors is presented, including piezoelectric material modification, substrate design, laminated structural design, fabrication workflows, establishment of the testing platform, and the development of matched circuit systems. The material preparation and manufacturing processes are optimized, and a scalable technical route for fabricating flexible piezoelectric sensors is proposed. Using this route, flexible piezoelectric thin-film sensors can be rapidly tailored for different application scenarios to satisfy diverse engineering demands. Multiple experiments were conducted on pipeline samples with varying wall thicknesses and curvatures. The results verify that the sensor reaches a measurement precision of 0.01 mm, meeting the demands of high-precision pipeline structural health monitoring. Full article

Complex narrow-channel reservoirs are strongly constrained by depositional architecture, resulting in highly nonuniform areal waterflood sweep, and pronounced water-cut variations among different channel types. These characteristics pose substantial challenges to stabilizing oil production and controlling water cut at high water-cut stages. To elucidate how narrow-channel planform architecture controls waterflood sweep, gel-assisted flow-field regulation by heterogeneous-phase composite flooding (HPCF), and remaining-oil mobilization, three representative configurations were reproduced in two-dimensional visual physical models. Sequential waterflood–HPCF–post-waterflood experiments were conducted, and time-lapse images and dynamic production data were integrated to characterize sweep evolution and remaining-oil mobilization across displacement stages. The results demonstrate that narrow-channel architecture exerts primary control on preferential flow-path development, gel migration and retention, spatial fluid redistribution, and displacement performance. During waterflooding, injected water preferentially migrated through high-permeability zones along channel centerlines, leaving channel margins, branch termini, and poorly connected regions insufficiently swept. After HPCF injection, the gel-containing composite system preferentially entered the established dominant flow paths. Gel retention and accumulation selectively increased flow resistance in these pathways, while mobility control induced subsequent fluids to divert toward bypassed regions, thereby enlarging the macroscopic swept volume and improving local displacement efficiency. A low injection rate promoted sustained gel-assisted flow diversion within bifurcated channels, whereas a high injection rate facilitated gel-slug propagation against the geometric constraints of highly sinuous channels and expanded its spatial coverage. Compared with waterflooding alone, HPCF increased the ultimate oil recovery of the three channel models by 19.23–26.47 percentage points. These findings clarify the coupled effects of narrow-channel architecture, gel transport and injection parameters on the profile-control and oil-recovery performance of HPCF, providing a mechanistic basis for water control and development optimization in high-water-cut narrow-channel reservoirs. Full article

A fractional-order image enhancement and fractal complexity-guided fusion method was developed to improve weak fault representation and introduce complexity-aware priors into dissolved gas analysis (DGA)-based diagnosis of oil-immersed power transformers. Gas-ratio features derived from five characteristic gases were combined into an extended DGA feature sequence and converted into two-dimensional representations using the Markov transition field (MTF), recurrence plot (RP), and Gramian angular field (GAF). A fractional-order difference operator then strengthened texture details and local variations, while fractal complexity features quantified structural irregularities across fault conditions. Based on these features, a fractal complexity-guided multi-image attention fusion module was designed to adaptively integrate the three image representations. An improved RIME optimization algorithm was further employed to jointly optimize the fractional order, imaging parameters, and network hyperparameters. On the public DGA dataset, the proposed model achieved precision, recall, accuracy, and F1-score values of 97.68%, 97.51%, 97.82%, and 97.71%, respectively. External validation on a self-collected DGA dataset further demonstrated its robust cross-condition generalization capability. Full article

Raw Chinese lacquer, tapped from the sap of Toxicodendron vernicifluum, is a natural water-in-oil microemulsion containing urushiol, polysaccharides, proteins, and laccase. Because this reactive system continues to oxidize and polymerize after harvesting, handling conditions directly determine water content, viscosity, and later film-forming performance. This review analyzes potential in situ stabilization routes that couple purification, low-temperature vacuum dehydration, and quality conditioning at, or near, the collection site. Emphasis is placed on how laccase retention, oxygen exposure, and urushiol polymerization are controlled together to limit transport losses and premature crusting. Portable filtration devices, reported centrifugal filtration systems, and proposed vacuum dehydration strategies are compared in terms of throughput, field compatibility, and process control. Physical and bio-based conditioning strategies, including shear adjustment, oxygen management, and natural film-forming aids, are further considered for on-site regulation. Surface-enhanced Raman spectroscopy (SERS) and portable spectroscopic devices are examined as feedback tools for parameter adjustment under field temperatures, humidity, and storage variation; however, these signals are treated as decision-support indicators that still require lacquer-specific calibration after tapping. The central task is to define a field-compatible process window for water removal, laccase retention, viscosity control, drying behavior, and storage stability before downstream coating preparation. The remaining challenges involve miniaturized equipment, standardized evaluation, evidence-level classification, and dynamic control of coupled variables. Full article

Methanol is a critical molecular marker for the early aging of oil-paper insulation, and its rapid detection is highly valuable for insulation condition assessment and the fault warning of power transformers. Widely used chromatographic methods require sophisticated pretreatment workflow and are not suitable for in situ monitoring. Non-destructive spectroscopic methods remain challenging due to the intrinsically small cross section of trace molecules in complex liquid environments. The rapid, direct detection of trace methanol in an oil mixture has yet to be demonstrated. In this study, a high-performance Raman-enhancing substrate was developed through hierarchical microstructure regulation, combining microscale light-trapping structures and nanoscale field-confinement sites to sense the weak Raman response of methanol in transformer oil. Direct detection of ppm-level methanol in the oil matrix was achieved, without additional adsorption enrichment or other complicated pretreatment procedures. The characteristic Raman band of methanol in transformer oil was identified, and a quantitative sensing method was established. Furthermore, the intrinsic temperature-dependent Raman response of methanol was investigated to evaluate the stability of its characteristic fingerprint bands over a broad temperature range. This work demonstrates a rapid, sensitive, and pretreatment-free spectroscopic strategy for trace methanol detection in complex oil matrices, and also sheds light on the high-sensitivity detection of small molecular markers in complex liquid environments. Full article

We present FDS and AERMOD simulations of the Alaska Clean Seas (ACS) oil burn experiments to improve dispersion modeling of large, open-air fires relevant to industrial settings. We propose a method in which FDS smoke fields with available ground measurements are used to empirically calibrate AERMOD configured using volume sources to represent the fire source. FDS is first run for the three ACS experiments at high resolutions (~10 m) and verified against ground monitoring to provide detailed three-dimensional smoke fields. The fractional allocation of total fire emissions (weights, w_i) is then empirically specified for each volume source i so AERMOD smoke predictions fit both the ground level measurements and FDS simulations to acceptable accuracy. Runs for volumes at the surface (i = 1), 100 m AGL (i = 2) and 300 AGL (i = 3) and w_i = [0.01, 0.09, 0.9]–[0.04, 0.36, 0.6] accurately represent these data, suggesting this range as suitable for fire heat fluxes (~800–3000 kW/m²), wind speeds (5–10 m/s) and PBL depths (300–500 m with and without capping temperature inversions) of the three ACS experimental burns. Further work exploring the applicability of this AERMOD setup to a broader range of conditions is ongoing. Full article

Seismic monitoring networks increasingly develop capabilities that function as geographic information infrastructure, transforming continuous geophysical observations into spatial information that supports research, decision-making, and public awareness. In Texas, seismicity linked to oil and gas operations has increased since 2009 across major producing regions, prompting the Texas Legislature to establish the Texas Seismological Network and Seismology Research program (TexNet) in 2015. This paper examines TexNet’s seismic monitoring network, field operations, data-processing pipeline, and the information products, data services, and decision-support tools that transform seismic observations into accessible earthquake information. Since routine earthquake reporting began in 2017, TexNet has grown from an inherited network of eighteen broadband stations to a system directly maintaining 207 stations and incorporating 421 active stations to locate earthquakes across Texas. TexNet provides a suite of information products, data services, and decision-support tools—including the TexNet Earthquake Catalog, near-real-time notification systems, and open data products—that connect geophysical observations with the needs of researchers, regulatory agencies, industry, and the public. TexNet remains fundamentally a seismic monitoring network, with additional capabilities that support scientific, regulatory, and public information needs in Texas and other regions with induced seismicity. Full article

In the development of carbonate reservoirs via water flooding huff-n-puff, the osmotic pressure effect is frequently overlooked, and existing models inadequately quantify the matrix imbibition and oil expulsion driven by salinity gradients. To address this issue, this study establishes a coupled oil–water two-phase huff-n-puff flow model for carbonate reservoirs that incorporates the interplay between salt concentration and osmotic pressure, which, for the first time, fully couples the van ’t Hoff osmotic pressure equation with solute transport equations for fractured-vuggy carbonate huff-n-puff, filling the gap that prior tight/shale reservoir low-salinity flow models fail to adapt to cyclic injection-soaking production regimes of carbonates. Based on the IMPES (implicit pressure–explicit saturation) numerical simulation method, an equivalent single-nucleus model is adopted to characterize the fractured-vuggy reservoir architecture. The model integrates the osmotic pressure formula, solute transport equation, and two-phase seepage governing equations, enabling a systematic analysis of the mechanisms by which osmotic pressure affects the multi-stage seepage process and the influence of key parameters on development performance. Quantitative simulation reveals three core laws controlled by salinity-induced osmosis: first, osmotic pressure drives water molecules to spontaneously migrate from the high-permeability fracture inner core toward the tight matrix pores, thereby modifying the water saturation distribution, expanding the water sweep region, and smoothing the saturation gradient between the inner and outer cores, which effectively mitigates water channeling in fractured reservoirs. Under the base case (injected water salinity = 1000 mg/L, inner-core permeability = 1000 mD, shut-in time = 80 d), the oil recovery factor with osmotic pressure considered reaches 13.46%, representing a 3.50% increment over the case without osmotic pressure. The recovery factor decreases monotonically with increasing injected water salinity, while it increases with longer shut-in time and higher inner-core permeability, both exhibiting pronounced diminishing marginal returns; the optimal shut-in time is approximately 80 d under the simulated conditions. This work delivers a fully coupled numerical tool and quantitative evaluation standard for osmotic imbibition mechanisms in fractured-vuggy carbonates. The quantified recovery increment and optimal soaking window established herein can directly guide field parameter optimization of injection water salinity, shut-in cycle and fracture reconstruction scale, balancing oil increment revenue and water treatment/well shutdown operation costs for on-site low-salinity huff-n-puff design. Full article

To address the challenge of efficiently blending low-mutual-solubility gas–liquid two-phase systems, a composite structure comprising a Venturi and a static mixer was designed, and its flow field characteristics were analyzed using computational fluid dynamics (CFD) simulations. The results indicate that positioning the static mixer at the exit of the Venturi diffusion section yields optimal performance. This configuration prevents disruption of the jet premix flow field and facilitates the uniform dispersion of gas–liquid mixtures throughout the entire domain via six sets of SK-type single-spiral static mixer (SK) units following the initial blending. The composite structure exhibits a three-tier synergistic mechanism characterized by “suction–premix–mixing intensification”: the negative pressure zone within the throat tube induces suction of the gas phase, the diffusion section converts pressure energy to enhance shearing and crushing, and the static mixing section disrupts the axial jet through cutting and swirling effects, thereby generating secondary vortices. This process ultimately achieves uniform dispersion of gas and liquid across the entire domain. The structure’s lack of moving parts addresses the issues of low efficiency and unstable flow fields associated with traditional devices. This design facilitates enhanced crude oil recovery and low-pressure reservoir gas injection drilling. Full article

Existing piston–cylinder lubrication studies often simplify the pressure boundary as a constant load or a single field, making it difficult to capture pump-level pressure excitation, local oil-film response, and non-concentric posture under variable loading. This paper establishes a thermo–fluid–solid coupling framework integrating an AMESim full-pump model, a Fluent transient oil-film model, and a Transient Structural model; UDF transfer of periodic pressure, dynamic meshes, and a calibrated Roelands law were used to analyze parallel-offset and center-tilted postures. As the load pressure increased from 10 to 30 MPa, the maximum discharge–half-cycle temperature rose from 28.39 to 36.95 °C, and the average positive leakage during the third-cycle high-pressure stage increased from 0.0201 to 0.1026 L/min; increasing speed from 1000 to 3000 r/min reduced cycle-averaged leakage by 8.93%. At 500 r/min and 30 MPa, the parallel-offset case reached 46.34 °C, 41 kPa, and 0.0990 L/min in maximum temperature, maximum shear stress, and average leakage, whereas the center-tilted case produced a peak resultant force of 3537.12 N, a cylinder inner-wall high-stress band of 76.96 MPa, and a maximum piston deformation and equivalent stress of 4.31 μm and 83.16 MPa. These results clarify the distinct lubrication behavior and potential uneven-wear risk associated with the two representative non-concentric postures, and provide a basis for clearance design and posture-sensitive condition assessment of axial piston pumps. Full article

Mineral-scale deposition remains a persistent flow-assurance and asset-integrity constraint in oil and gas production. Calcium carbonate, calcium sulfate, barium sulfate, iron sulfide, and mixed inorganic scale deposits reduce tubing internal diameter. They also impair near-wellbore permeability, block safety-critical valves, reduce heat-transfer efficiency, and intensify under-deposit corrosion. Conventional management relies on prediction, chemical inhibition, squeeze treatments, acid dissolution, chelation, mechanical scraping, milling, jetting, and operational water management. These methods are indispensable, but each has a restricted operating envelope. Key limitations include mineral selectivity, corrosion risk, environmental discharge, intervention cost, debris generation, and poor effectiveness against chemically resistant sulfate scales, particularly BaSO₄. Ultrasound has therefore attracted interest as a non-chemical technology. Acoustic cavitation, microstreaming, pressure oscillation, mechanical vibration, and micro jetting may suppress nucleation, disturb boundary layers, weaken adhesion, and fragment brittle deposits. This review critically evaluates ultrasound-assisted scale prevention and removal, with emphasis on production tubing and oilfield relevance. Existing studies show credible mechanistic plausibility and promising laboratory performance for CaCO₃, CaSO₄/gypsum, KCl, NaCl, and membrane or heat-transfer fouling systems. It also compares performance metrics, field cases, and technology-readiness barriers. The evidence is less mature for long steel tubulars operating under high-pressure, high-temperature, multiphase production conditions. Current evidence positions ultrasound at technology-readiness level (TRL) 3–4 for CaCO₃ and CaSO₄ systems, where laboratory and bench-scale validation is established, and at TRL 2–3 for BaSO₄, where mechanistic plausibility exists but controlled experimental validation remains absent. The technology is not yet at the pilot–production transition for downhole tubing applications, but it is approaching that threshold for surface process equipment. Its most credible near-term role is as an intensifier paired with low-dose chemical inhibitors, where acoustic boundary-layer disruption can reduce the minimum inhibitory concentration threshold of inhibitors, and with mild chelating agents for early-stage BaSO₄ management, where ultrasound-enhanced mass transfer may accelerate chelant penetration into deposit microstructure. Advancing ultrasound from its current TRL toward field qualification requires targeted BaSO₄ scale validation in steel tubing systems, acoustic field mapping under HPHT multiphase conditions, mass-removal metrics, and a structured pilot programme. Full article

Rapid inversion of coarse-scale parameters is important for history matching in reduced-order reservoir models, yet conventional methods require repeated simulation and iterative updating. This study proposes a residual conditional generative adversarial network (Res-cGAN) to reconstruct structured control-pore-volume parameters from well liquid production rate (WLPR), well oil production rate (WOPR), and pressure responses. The methodological contribution is a response-conditioned inversion framework that directly maps multivariate production responses to the structured CPV representation, combining residual feature learning, cross-level feature fusion, conditional adversarial learning, and local matrix-continuity regularization. A dataset of 3000 samples was generated using the interwell numerical simulation model (INSIM), with training sets of 600, 1200, 1800, and 2400 samples and fixed validation and test sets of 300 samples each. Increasing the training size reduced mean squared error (MSE) from 0.0128 to 0.0059 and increased the coefficient of determination (R²) from 0.9120 to 0.9670. With 2400 training samples, Res-cGAN achieved an MSE of 0.0059, mean absolute error (MAE) of 0.0493, mean absolute percentage error (MAPE) of 5.18%, and R² of 0.9670, outperforming the baseline conditional GAN (Base-cGAN). These results suggest the potential of Res-cGAN for rapid post-training CPV initialization and candidate screening for reduced-order history matching workflows, while field validation and cross-reservoir testing remain necessary. Full article

Carbon dioxide water-alternating-gas (CO₂-WAG) flooding simultaneously enables carbon emission mitigation, improved oil displacement efficiency, and expanded gas sweep coverage. Nevertheless, the field performance of this technology remains significantly constrained by gravity override effects, especially in ultra-thick oil reservoirs. In this work, a synthetic heterogeneous dipping mechanistic reservoir model is constructed. Using a quantitative metric for gravity override index in WAG processes, the variation patterns of gravity override under various operational factors are systematically analyzed. Furthermore, the eXtreme Gradient Boosting (XGBoost) machine learning algorithm is employed to conduct feature importance analysis of the controlling factors, identifying parameters with the most substantial impacts. The results indicate that well spacing, oil production rate, WAG injection strategy, and WAG slug duration all exert pronounced effects on both gravity override index and oil recovery factor. Gravity override is confirmed as the dominant factor governing the production performance of WAG flooding in ultra-thick reservoirs. In addition, an optimal combination of operational parameters exists that counterbalances the adverse effects of gravitational and viscous forces, thereby maximizing gas sweep efficiency, delaying gas breakthrough, and enhancing oil recovery. This study provides valuable insights and technical guidance for gas channeling mitigation, vertical gas sweep improvement, and efficient development of analogous ultra-thick reservoirs. Full article