Search Results (172)

The development of cold atom interferometry (CAI) provides new opportunities for next-generation satellite gravity gradiometry missions. Compared with the electrostatic gradiometer onboard the GOCE satellite, CAI gradiometers exhibit white noise characteristics within the effective measurement bandwidth, enabling improved performance in the low-frequency range (<5 $\mathrm{m}\mathrm{E}/\sqrt{\mathrm{H}\mathrm{z}}$). However, the measurement cycle, including atom preparation, cooling, and laser interferometry, leads to a relatively longer sampling rate, which may limit observation performance. In this study, the impact of sampling rate on the performance of a spaceborne CAI gradiometer is systematically investigated. Closed-loop simulations were performed under different observation configurations, noise levels, and sampling rates. The results are evaluated in terms of static gravity field recovery accuracy and compared with those from the GOCE mission. The results indicate that, for single-axis observations, the $V_{\mathit{zz}}$ component in nadir pointing mode achieves the highest accuracy at the 5 $\mathrm{m}\mathrm{E}/\sqrt{\mathrm{H}\mathrm{z}}$ noise level, while at 0.1 $\mathrm{m}\mathrm{E}/\sqrt{\mathrm{H}\mathrm{z}}$ and a 1 s sampling interval, the accuracy improves by one order of magnitude compared to GOCE. For dual-axis observations, the combinations $V_{\mathit{xx}}+V_{\mathit{zz}}$ and $V_{\mathit{yy}}+V_{\mathit{zz}}$ in nadir pointing mode provide the best performance at 5 $\mathrm{m}\mathrm{E}/\sqrt{\mathrm{H}\mathrm{z}}$, and an improvement of up to one order of magnitude is achieved at 0.1 $\mathrm{m}\mathrm{E}/\sqrt{\mathrm{H}\mathrm{z}}$ with a 1 s sampling interval. For tri-axis observations, both pointing modes outperform GOCE across the full frequency band only at a 1 s sampling interval under 5 $\mathrm{m}\mathrm{E}/\sqrt{\mathrm{H}\mathrm{z}}$ noise. At 0.1 $\mathrm{m}\mathrm{E}/\sqrt{\mathrm{H}\mathrm{z}}$, all sampling configurations yield better results than GOCE, with the highest accuracy achieved in nadir pointing mode. These findings demonstrate the critical role of sampling rate in CAI-based gravity field recovery and provide useful guidance for the design of future spaceborne quantum gravity missions. Full article

Dust accumulation on photovoltaic (PV) modules reduces power generation efficiency, and traditional water-based cleaning is impractical in arid regions. Inspired by the classical acoustic phenomenon of Chladni figures—specifically the mechanism where an acoustic standing wave field drives the regular migration and accumulation of particles—this study proposes a waterless dust removal method using low-frequency ultrasonic vibration via piezoelectric excitation. Impedance analysis identifies optimal electromechanical coupling at 28 kHz. Experiments demonstrate that higher driving voltages accelerate cleaning, with recovery rates saturating beyond 125 V. Notably, intense friction and collisions between particles within high-density dust layers consume substantial kinetic energy, significantly multiplying the required cleaning time. Macroscopic transport analysis reveals that dust removal relies on the synergy of vibration-induced adhesion decoupling and gravity-driven transport. Sufficient tangential gravity is crucial for macroscopic particle removal, and tilt angles above 30° provide the necessary downward driving force to ensure smooth particle sliding. Under optimal conditions, the system achieves an over 97% short-circuit current recovery at a low power consumption of ~10 W, providing a theoretical basis for waterless PV self-cleaning systems. Full article

The Gravity Recovery and Climate Experiment (GRACE) mascon data relies on minor gravitational field variations to map terrestrial water storage anomaly (TWSA). However, the coarse spatial resolution of three degrees by three degrees restricts their application for evaluating small-scale changes in water storage. To address this challenge, in this study, GRACE and GRACE Follow-On (GRACE-FO) data from 2003 to 2023 were downscaled to 800-m resolution across the Coastal Lowland Aquifer System (CLAS) in Texas, Louisiana, Mississippi, Alabama, and Florida. This downscaling used machine learning (ML) models, including Random Forest (RF), Artificial Neural Network (ANN), and Deep Neural Network (DNN). These models incorporated variables such as anomalies in total precipitation (APT), mean temperature (ATM), normalized difference vegetation index (ANDVI), evapotranspiration (AET) from 2003 to 2023, Shuttle Radar Topography Mission DEM, slope angle, soil type, and lithology to generate monthly 800-m TWSA maps. The ANN model showed strong predictive performance (R² = 0.869–0.989 with low RMSE), although the DNN achieved slightly better statistical accuracy and spatial evaluation metrics; however, ANN was selected for its more realistic and spatially consistent outputs regionally. Building on this improved spatial resolution, analysis of the downscaled TWSA data from 2003 to 2023 identified an overall declining trend in water storage. Trend analysis using linear regression shows that the western CLAS—particularly the Gulf Coast aquifer in Texas and western Louisiana—experiences the strongest depletion, with rates of −0.30 and −0.17 cm/year in Zones 1 and 2, respectively, with Zone 1 being statistically significant. In contrast, the eastern CLAS shows relatively stable conditions, with weak, non-significant increases (+0.05 to +0.18 cm/year), likely reflecting natural variability rather than sustained long-term gain. Therefore, ML-based downscaling of GRACE data enables high-resolution TWS assessment and provides a framework for future extraction of groundwater storage anomalies (GWSA), supporting improved groundwater management. Full article

In order to protect the high-sensitivity optical lens of the “magnetic field and velocity field imager” in extreme deep space environments, this paper proposes a new type of dual redundant planetary differential lens cover drive mechanism. In view of the critical vulnerability that traditional single-motor direct drive is prone to sudden mechanical jamming and catastrophic single-point failure (SPF) in severe tasks such as Jupiter exploration, this study constructs a “dual input single output (DISO)” rigid decoupling architecture from the perspective of physical topology. Through theoretical analysis and kinematic modeling, the adaptive decoupling mechanism of the two-degree-of-freedom (2-DOF) system under unilateral mechanical stalling is revealed. Dynamic analysis shows that in the nominal dual-motor synergy mode, the system shows a significant “kinematic load-sharing effect”, thus greatly reducing the sliding friction and gear wear rate. In addition, under the severe dynamic fault injection scenario (maximum gravity deviation and sudden jam superposition of a single motor), the cold standby motor is activated and the dynamic takeover is quickly performed. The high-fidelity transient simulation based on ADAMS verifies that although the fault will produce transient global torque spikes and pulsed internal gear contact forces at the moment, all extreme dynamic loads remain well within the structural safety margin. The output successfully achieved a smooth transition, which is characterized by a non-zero-crossing velocity recovery. This research provides an innovative theoretical basis and a practical engineering paradigm for the design of high-reliability fault-tolerant mechanisms in deep space exploration. Full article

Earthquakes induce significant mass redistribution, generating temporal gravity variations detectable by GRACE and GRACE-FO missions. However, the capability of different gravity field recovery strategies, particularly spherical harmonic (SH) and mass concentration (MASCON) solutions, to capture coseismic signals remains insufficiently quantified. This study investigates coseismic gravity changes associated with three Mw 9.0-class earthquakes, including the 2004 Sumatra–Andaman, 2010 Maule, and 2011 Tohoku events, using both SH and MASCON products and theoretical dislocation models. Spectral analysis indicates that recovered signals are dominated by long-wavelength components, while short-wavelength deformation is strongly attenuated. SH products exhibit higher sensitivity to large-scale mass redistribution but are more affected by striping noise and leakage, whereas MASCON products provide improved stability at the cost of signal attenuation. Overall, these findings highlight fundamental limitations of current GRACE-derived products in fully recovering coseismic deformation signals and emphasize the need for improved signal separation strategies. Full article

The Dengying Formation gas reservoir in the Penglai gas field, located in the central Sichuan Basin, exhibits substantial resource potential and promising development prospects. This reservoir is characterized by well-developed fractures and dissolution cavities, strong heterogeneity, complex gas–water relationships, and widespread edge–bottom water. During production, edge–bottom water is prone to channeling and intrusion through high-permeability pathways, which severely constrains well productivity and overall gas recovery. To address these challenges, this study takes a fractured-vuggy carbonate edge–bottom water gas reservoir as an example. By integrating large-scale physical simulation with cross-scale numerical simulation, a rational production allocation method suitable for strongly heterogeneous gas reservoirs has been developed. The research results indicate that: (1) Large-scale physical simulation experiments demonstrate that for fractured-vuggy bottom water gas reservoirs, implementing rate reduction and pressure control after water breakthrough can effectively suppress water invasion and coning, extend the stable production period, and increase the recovery factor by approximately 16%; (2) Based on the dynamic characteristics of water invasion, key similarity criteria including the Bond number, capillary number, gravity–viscous force ratio, and geometric–temporal similarity ratio were selected to establish a scientific parameter design method for cross-scale numerical simulation; (3) By considering factors such as reservoir type and aquifer energy, single-well mechanistic models were used to determine appropriate production rates for individual wells, enabling rapid optimization of production allocation plans. This provides crucial guidance for efficient gas well development and surface facility planning. Full article

Extended-solvent steam-assisted gravity drainage (ES-SAGD) has emerged as a promising advancement over conventional SAGD for improving the efficiency and sustainability of in situ heavy oil and bitumen recovery. By co-injecting light hydrocarbon or alternative solvents with steam, ES-SAGD integrates thermal and compositional mechanisms to reduce viscosity, accelerate chamber development, and reduce steam–oil ratios. This review synthesizes the current state of knowledge on ES-SAGD, encompassing fundamental transport mechanisms, solvent selection and phase behavior, mass transfer dynamics, laboratory and physical modeling studies, numerical simulation approaches, and field-scale operational experiences. Experimental evidence consistently demonstrates substantial mobility enhancement through solvent-induced dilution, while compositional thermal simulations highlight an improved sweep efficiency and reduced energy intensity relative to steam-only processes. Field pilots further validate accelerated early-time production and significant steam savings, though challenges related to solvent retention, asphaltene stability, and reservoir heterogeneity persist. Key research gaps are identified in solvent transport prediction, formation damage risk, long-term solvent recovery, and integrated economic–environmental optimization. Overall, ES-SAGD offers a viable pathway toward lower-emission, higher-efficiency bitumen production, provided that solvent chemistry, reservoir complexity, and operational controls are carefully managed through continued research and targeted field deployment. Full article

In the field of oil reservoir engineering, the development of large-dip-angle reservoirs poses significant challenges due to their strong heterogeneity, pronounced gravity effects, and inefficient water flooding sweep, all contributing to suboptimal oil recovery rates. This study aims to address these challenges by focusing on the core issue of optimizing water injection development strategies for such reservoirs. A numerical simulation mechanism model is constructed based on actual large-dip-angle reservoir A, and the impact of key parameters—including reservoir dip angle, permeability, injection–production well spacing, water injection intensity, and crude oil viscosity—on oil recovery is systematically analyzed under the “water injection at structural lows and oil production at structural highs” high-pressure water injection development mode. The simulation results reveal that the oil recovery rate increases with higher dip angles, permeability, injection–production well spacing, and water injection intensity; however, excessive water injection intensity or crude oil viscosity can lead to premature water breakthrough, reducing efficiency. Using the analytic hierarchy process, the primary controlling factors are ranked as permeability > crude oil viscosity > reservoir dip angle > water injection intensity > injection–production well spacing. Furthermore, development theory charts are established to guide the selection of appropriate water injection intensities for different injection–production well distances and permeabilities. This study offers valuable theoretical insights for optimizing water injection development in large-dip-angle reservoirs, thereby enhancing oil recovery and economic benefits and laying a foundation for future research and practical applications in similar reservoir settings. Full article

Atomic interferometric gravity gradient measurement enables atomic interference by manipulating atoms with lasers of specific frequencies. Thus, the frequency and phase−locking performance of the laser system exerts a significant impact on key experimental parameters, including the loading rate and ultimate cooling temperature of atomic clouds, the state selection efficiency of Raman transitions, the contrast of atomic interference fringes, and the level of detection noise. As atomic interferometric gravity gradient measurement transitions from static laboratory measurements to mobile field operations, conventional laser frequency and phase−locking methods struggle to meet the demand for rapid re−locking after device movement and cannot achieve timely system recovery in the event of laser unlocks. This work proposes an automatic laser frequency and phase−locking system that can detect real−time deviations in laser frequency and phase and implement rapid and precise corrections. Meanwhile, by utilizing the reference signal source in the optical phase−locked loop, the system realizes laser frequency hopping to satisfy the diverse laser frequency requirements across all stages of atomic interferometric gravity gradient measurement. Full article

The dynamic approach integrates Global Positioning System and K-band range-rate (KRR) observations to enable precise orbit determination (POD) and gravity field recovery. However, background model uncertainties and temporal aliasing introduce frequency-dependent noise into the post-fit KRR residuals, thereby degrading overall solution accuracy. To mitigate these effects, empirical signals are typically modeled using either dynamic (DYN) or kinematic (KIN) parameterization strategies. Nevertheless, the combined use of DYN and KIN parameterizations remains largely unassessed, and their potential synergistic impact on POD and gravity field recovery merits systematic evaluation. This study evaluates the individual and joint impacts of DYN and KIN (DYN+KIN) on The Gravity Recovery and Climate Experiment (GRACE) Follow-On orbit accuracy and monthly gravity field recovery using nearly one year of 2019 data (excluding February due to severe data gaps). The refined solutions act as empirical temporal filters, effectively suppressing low-frequency components in KRR residuals, particularly below 1-cycle-per-revolution. Relative to nominal ambiguity-fixed reduced-dynamic orbits, the refined solutions mainly enhance the cross-track component, with DYN+KIN showing the largest improvement, while along-track precision experiences only minor (sub-millimeter) degradation. Overall three-dimensional orbit accuracy improves from 3.8 cm to 3.0 cm (DYN), 2.8 cm (KIN), and 2.8 cm (DYN+KIN). In terms of gravity field recovery, the DYN+KIN solution begins to exhibit more pronounced deviations from the other solutions beyond degree and order 30. Over oceanic regions, residual mass anomaly analysis shows that the DYN+KIN solution is associated with an approximately 16% higher noise level compared to the individual DYN and KIN strategies, which exhibit modest noise reductions relative to the nominal solution. The DYN+KIN also exhibits a dampened ~160-day periodicity in the temporal evolution of low-degree coefficients (e.g., C_2,0), likely due to spectral overlap between empirical parameter frequencies and low-degree gravity signal components. These results indicate that over-parameterization introduces spectral redundancy and absorbs geophysical signals, underscoring the need to balance parameter flexibility and signal fidelity in gravity recovery strategies. Full article

High-resolution marine gravity fields are critical for interpreting seafloor structure, investigating marine geodynamics, and enabling gravity-aided navigation. However, sparse shipborne observations, heterogeneous multi-source geodetic datasets, and the inability of conventional methods to handle nonlinear inversion limit accurate gravity recovery. To overcome these limitations, we propose a spectral physics-informed constraint deep-learning framework based on a multi-channel Swin Transformer to reconstruct high-resolution marine gravity anomaly fields. The model ingests multi-source geodetic inputs organized as 64 × 64 grid patches centered near each computation point and fuses them to predict the target gravity anomaly. We adopt a remove–compute–restore (RCR) strategy that isolates residual gravity signals, which improves numerical stability and accelerates training. Inputs include satellite-altimetry-derived vertical gravity gradients, vertical deflections, mean sea surface height, and topography; the model is trained on over 430,000 shipborne gravity samples from the South China Sea (0–30° N, 105–125° E). To enforce physical consistency, we embed a spectral-domain physics constraint derived from potential-field theory into the loss function; this constraint helps recover short-wavelength gravity signals. We also introduce an adaptive multi-domain multi-scale feature fusion module (AMAMFF) to improve the integration of heterogeneous inputs, and we demonstrate its benefits in experiments across complex terrain. Validation against independent shipborne gravity checkpoints yields an RMS error of 3.09 mGal, indicating a substantial performance advantage over existing deep-learning approaches and conventional gravity-field models. Full article

Carbon dioxide injection is one of the most advanced and commercially proven methods of enhanced hydrocarbon recovery, and CO₂ injection has been shown to be very effective in conventional oil reservoirs and is gaining attention in gas, unconventional, and coalbed methane reservoirs. The advantages of CO₂ injection lie in the favorable phase properties and interactions with reservoir fluids, such as swelling, reduction in oil viscosity, reduction in interfacial tension, and miscible displacement in favorable cases. But the low viscosity and density of CO₂ compared to the reservoir fluids result in unfavorable mobility ratios and gravity override, resulting in sweep efficiency limitations. This review offers a broad and EOR-centric evaluation of the various CO₂ injection methods for a broad array of reservoir types, such as depleted oil reservoirs, gas reservoirs for the purpose of gas recovery, tight gas/sands, as well as coalbed methane reservoirs. Particular attention will be given to the use of mobility control/sweep enhancement techniques such as water alternating gas (CO₂-WAG), foam-assisted CO₂ injection, polymer-assisted WAG processes, as well as hybrid processes that combine the use of CO₂ injection with low salinity or engineered waterflood. Further, recent developments in compositional simulation, fracture-resolving simulation, hysteresis modeling, and data-driven optimization techniques have been highlighted. Operational challenges such as injectivity reduction, asphaltene precipitation, corrosion, and conformance problems have been reviewed, along with the existing methods to mitigate such issues. Finally, key gaps in the current studies have been identified, with an emphasis on the development of EHR processes using CO₂ in complex and low-permeability reservoirs, enhancing the resistance of chemical and foam methods in realistic conditions, and the development of reliable methods for optimizing the process on the field scale. This review article will act as an aid in the technical development process for the implementation of CO₂ injection projects for the recovery of hydrocarbons. Full article

The Three-Product Dense Medium Cyclone (TPDMC) has been widely applied in the coal preparation industry, yet the adaptive optimization of its parameters based on feed characteristics remains under-researched. This study utilizes a semi-industrial experimental platform with a JX300/240 TPDMC to investigate the influence of pump frequency (PF) and four second-stage structural parameters—cylindrical section length (L_2cy), overflow pipe insertion depth (Dep_2o), overflow pipe diameter (D_2o), and conical section length (L_2co)—on the separation performance of three feed materials with distinct washability characteristics. Experiments conducted with density tracer particles revealed a distinct hydrodynamic coupling effect: PF and D_2o were the only factors modulating inlet pressure (varying from 0.12 to 0.45 bar), which directly altered the clean coal yield. In contrast, L_2cy, Dep_2o, and L_2co primarily influenced the second-stage internal flow field and concentration effect, thereby affecting the yield and ash content of middling coal (gangue). To quantify feed-specific sensitivities, a new index, Near-Gravity-Range Material (NGRM), was proposed. Results demonstrated that Sample-3 exhibited the highest sensitivity to parameter variations, with its middling coal yield variation reaching 41.25% due to its high NGRM of 71%. Furthermore, statistical analyses were conducted to quantify the influence of each parameter on the heavy product partition ratio across different density fractions. Based on these findings, the following targeted optimization strategies are proposed: (1) for feeds rich in the 1.40–1.50 RD range, increasing PF or decreasing D_2o is recommended to enhance clean coal yield; (2) for materials dominated by the 1.7 ± 0.10 RD fraction, increasing D_2o, PF, or L_2cy maximizes middling coal recovery; and (3) for feeds high in the 1.90 ± 0.10 RD fraction, reducing Dep_2o, PF, L_2cy, or L_2co effectively minimizes middling coal contamination by high-density particles. Full article

A comprehensive study on the distribution characteristics and exploitation strategies of remaining oil was carried out in the Ordovician ultra-deep fault-controlled fractured–vuggy carbonate reservoir within the Shunbei No. 1 strike–slip fault zone. This research addresses challenges such as severe watered-out and gas channeling encountered during multi-stage development, marking a shift toward a development phase focused on residual oil recovery. By integrating seismic attributes, drilling, logging, and production performance data—and building upon previous methodologies of “hierarchical constraint and genetic modeling”—a three-dimensional geological model was constructed with a five-tiered architecture: strike–slip fault affected zone, fault-controlled unit, cave-like structure, cluster fillings, and fracture zone. Numerical simulations were subsequently performed based on this model. The results demonstrate that the distribution of remaining oil is dominantly controlled by the coupling between key geological factors—including fault kinematics, reservoir architecture formed by karst evolution, and fracture–vug connectivity—and the injection–production well pattern. Three major categories with five sub-types of residual oil distribution patterns were identified: (1) local low permeability, weak hydrodynamics; (2) shielded connectivity pathways; and (3) Well Pattern-Dependent. Accordingly, two types of potential-tapping measures are proposed: improve well control through optimized well placement and sidetrack drilling and reservoir flow field modification via adjusted injection–production parameters and sealing of high-permeability channels. Techniques such as gas (nitrogen) huff-and-puff, gravity-assisted segregation, and injection–production pattern restructuring are recommended to improve reserve control and sweep efficiency, thereby increasing ultimate recovery. This study provides valuable guidance for the efficient development of similar ultra-deep fractured–vuggy carbonate reservoirs. Full article

Traditional fusion methods for integrating multi-source gravity data rely on predefined mathematical models that inadequately capture complex nonlinear relationships, particularly at wavelengths shorter than 10 km. We developed a convolutional neural network incorporating differential marine geodetic data (DMGD-CNN) to enhance marine gravity anomaly recovery from HY-2A satellite altimetry. The DMGD-CNN framework encodes spatial gradient information by computing differences between target points and their surrounding neighborhoods, enabling the model to explicitly capture local gravity field variations. This approach transforms absolute parameter values into spatial gradient representations, functioning as a spatial high-pass filter that enhances local gradient information critical for short-wavelength gravity signal recovery while reducing the influence of long-wavelength components. Through systematic ablation studies with eight parameter configurations, we demonstrate that incorporating first- and second-order seabed topography derivatives significantly enhances model performance, reducing the root mean square error (RMSE) from 2.26 mGal to 0.93 mGal, with further reduction to 0.85 mGal achieved by the differential learning strategy. Comprehensive benchmarking against international gravity models (SIO V32.1, DTU17, and SDUST2022) demonstrates that DMGD-CNN achieves 2–10% accuracy improvement over direct CNN predictions in complex topographic regions. Power spectral density analysis reveals enhanced predictive capabilities at wavelengths below 10 km for the direct CNN approach, with DMGD-CNN achieving further precision enhancement at wavelengths below 5 km. Cross-validation with independent shipborne surveys confirms the method’s robustness, showing 47–63% RMSE reduction in shallow water regions (<2000 m depth) compared to HY-2A altimeter-derived results. These findings demonstrate that deep learning with differential marine geodetic features substantially improves marine gravity field modeling accuracy, particularly for capturing fine-scale gravitational features in challenging environments. Full article