Search Results (74)

The development of deep coalbed methane (buried depth > 2000 m) in the Yan’an block of Ordos Basin is limited by low permeability, the pore structure of the coal reservoir, and the gas–water occurrence relationship. It is urgent to clarify the key control mechanism of pore structure on gas migration. In this study, based on high-pressure mercury intrusion (pore size > 50 nm), low-temperature N₂/CO₂ adsorption (0.38–50 nm), low-field nuclear magnetic resonance technology, fractal theory and Pearson correlation coefficient analysis, quantitative characterization of multi-scale pore–fluid system was carried out. The results show that the multi-scale pore network in the study area jointly regulates the occurrence and migration process of deep coalbed methane in Yan’an through the ternary hierarchical gas control mechanism of ‘micropore adsorption dominant, mesopore diffusion connection and macroporous seepage bottleneck’. The fractal dimensions of micropores and seepage are between 2.17–2.29 and 2.46–2.58, respectively. The shape of micropores is relatively regular, the complexity of micropore structure is low, and the confined space is mainly slit-like or ink bottle-like. The pore-throat network structure is relatively homogeneous, the difference in pore throat size is reduced, and the seepage pore shape is simple. The bimodal structure of low-field nuclear magnetic resonance shows that the bound fluid is related to the development of micropores, and the fluid mobility mainly depends on the seepage pores. Pearson’s correlation coefficient showed that the specific surface area of micropores was strongly positively correlated with methane adsorption capacity, and the nanoscale pore-size dominated gas occurrence through van der Waals force physical adsorption. The specific surface area of mesopores is significantly positively correlated with the tortuosity. The roughness and branch structure of the inner surface of the channel lead to the extension of the migration path and the inhibition of methane diffusion efficiency. Seepage porosity is linearly correlated with gas permeability, and the scale of connected seepage pores dominates the seepage capacity of reservoirs. This study reveals the pore structure and ternary grading synergistic gas control mechanism of deep coal reservoirs in the Yan’an Block, which provides a theoretical basis for the development of deep coalbed methane. Full article

The pore-throat structure of the extremely low-permeability sandstone reservoir in the Huachi area of the Ordos Basin is complex and highly heterogeneous. Currently, there are issues such as unclear understanding of the micro-pore-throat structural characteristics, primary controlling factors of reservoir quality, and classification boundaries of the reservoir in the study area, which seriously restricts the exploration and development effectiveness of the reservoir in this region. It is necessary to use a combination of various analytical techniques to comprehensively characterize the pore-throat structure and establish reservoir classification evaluation standards in order to better understand the reservoir. This study employs a suite of analytical and testing techniques, including cast thin sections (CTS), scanning electron microscopy (SEM), cathodoluminescence (CL), X-ray diffraction (XRD), as well as high-pressure mercury injection (HPMI) and constant-rate mercury injection (CRMI), and applies fractal theory for analysis. The research findings indicate that the extremely low-permeability sandstone reservoir of the Chang 3 section primarily consists of arkose and a minor amount of lithic arkose. The types of pore-throat are diverse, with intergranular pores, feldspar dissolution pores, and clay interstitial pores and microcracks being the most prevalent. The throat types are predominantly sheet-type, followed by pore shrinkage-type and tubular throats. The pore-throat network of low-permeability sandstone is primarily composed of nanopores (pore-throat radius r < 0.01 μm), micropores (0.01 < r < 0.1 μm), mesopores (0.1 < r < 1.0 μm), and macropores (r > 1.0 μm). The complexity of the reservoir pore-throat structure was quantitatively characterized by fractal theory. Nanopores do not exhibit ideal fractal characteristics. By splicing high-pressure mercury injection and constant-rate mercury injection at a pore-throat radius of 0.12 μm, a more detailed characterization of the full pore-throat size distribution can be achieved. The average fractal dimensions for micropores (Dh2), mesopores (Dc3), and macropores (Dc4) are 2.43, 2.75, and 2.95, respectively. This indicates that the larger the pore-throat size, the rougher the surface, and the more complex the structure. The degree of development and surface roughness of large pores significantly influence the heterogeneity and permeability of the reservoir in the study area. Dh2, Dc3, and Dc4 are primarily controlled by a combination of pore-throat structural parameters, sedimentary processes, and diagenetic processes. Underwater diversion channels and dissolution are key factors in the formation of effective storage space. Based on sedimentary processes, reservoir space types, pore-throat structural parameters, and the characteristics of mercury injection curves, the study area is divided into three categories. This classification provides a theoretical basis for predicting sweet spots in oil and gas exploration within the study area. Full article

Sedimentary facies identification constitutes a cornerstone of reservoir engineering. Traditional facies interpretation methods, reliant on manual log-response parameter analysis, are constrained by interpreter subjectivity, reservoir heterogeneity, and inefficiencies in resolving thin interbedded sequences and concealed fluvial sand bodies—issues marked by high interpretive ambiguity, prolonged cycles, and elevated costs. This study focuses on the Lower Cretaceous Yaojia Formation Member 1 (K2y1) in the satellite oilfield of the Songliao Basin, integrating sequence stratigraphy into a machine learning framework to propose an innovative convolutional neural network (CNN)-based facies recognition method using log-curve image groups by graphically transforming five log curves and establishing a CNN model that correlates log responses with microfacies. Results demonstrate the model’s capability to identify six microfacies types (e.g., subaqueous distributary channels, estuary bars, sheet sands) with 83% accuracy, significantly surpassing conventional log facies analysis. This breakthrough in interpreting complex heterogeneous reservoir lithofacies establishes a novel technical avenue for intelligent exploration of subtle hydrocarbon reservoirs. Full article

Carbonate geothermal reservoirs, characterized by widespread distribution, a high discharge capacity, and favorable reinjection conditions, have become a key target for geothermal resource development. However, the karst geothermal reservoir system in the Juancheng geothermal field exhibits significant heterogeneity, leading to substantial disparities in productivity among multiple geothermal wells and severely restricting efficient regional exploitation. This study systematically investigates the hydraulic characteristics and development potential of the karst geothermal reservoir in the Juancheng geothermal field using sodium fluorescein tracing experiment technology. The results reveal that the reservoir system contains multiple flow channels with distinct permeability differences. The dominant flow pathways, controlled by fault structures, exhibit an apparent velocity of up to 10.98 m/h, significantly higher than other regions in the study area. In contrast, low-permeability zones, influenced by the burial depth of the Ordovician strata, show poor connectivity due to limited karst development, with the lowest apparent velocity of only 1.03 m/h. By integrating pumping test data and tracer response characteristics, the dominant flow direction (northeast) demonstrates a stronger recharge capacity and water abundance, offering a higher development value. Conversely, the southeast low-permeability zone has weaker water production and constrained recharge conditions, resulting in a relatively limited development potential. Additionally, it is recommended that the direction of future geothermal well placement in the Juancheng geothermal field should avoid being parallel to the fault strike to prolong the thermal breakthrough arrival time. In regions with deeper Ordovician strata burial, denser well network deployment is suggested to enhance the reservoir utilization efficiency. Full article

This study quantifies morphological features of global braided river deltas using Google Earth imagery, analyzing eight systems (e.g., Yukon–Kuskokwim, Poyang Lake, Lena River deltas). Methods include listwise deletion for missing data (retaining 87% of Poyang Lake delta samples) and sensitivity analysis (threshold changes ≤2.4%). Nonparametric tests (Kruskal–Wallis, H = 12.73, p = 0.005) show significant differences in bifurcation angles across deltas, with the wave-dominated Po River (59.2°) having an 18% higher 80% threshold the than tide-dominated Poyang Lake (50.1°, p = 0.003). Key quantitative results include the following: 1.65% of bifurcation angles cluster at 30–60°, differing from fan deltas (p < 0.01); wavelength–amplitude relationships are nonlinear (R² = 0.537–0.913), with positive slopes indicating a high sediment supply (e.g., Yukon–Kuskokwim) and negative slope channel avulsion (e.g., Poyang Lake); bifurcation spacing correlates with the sediment supply—54% of Poyang Lake spacings < 2000 m (dense networks) vs. 80% of Lena River spacings < 15,000 m (stable channels). The resulting dataset enables global, remote-sensing-based comparisons, providing thresholds for sedimentary modeling and reservoir prediction. Moderate missing data (≤13%) minimally affect results, though high-missingness cases need further analysis. This study replaces empirical rules with statistical validation, showing that morphometric differences reflect depositional dynamics, which are critical for reservoir heterogeneity assessments. Full article

Deep coalbed methane (CBM) resources represent a significant opportunity for future exploration and development. The combination of horizontal well drilling and hydraulic fracturing technology has emerged as the most efficient method for extracting deep CBM. By optimizing the fracturing parameters for horizontal wells, we can improve the effectiveness of reservoir stimulation even further. In this paper, taking the deep coalbed methane in the Daniudi gas field in the Ordos Basin as the research object, using Numerical simulation software such as Petrel, comprehensively considering the field logging, logging data and laboratory experimental data of rock mechanical parameters, the three-dimensional geomechanical and stress field model of deep coalbed methane is established, and on this basis, the numerical simulation research on the fracture network expansion and construction parameter optimization of single well and well group is carried out. Through the qualitative evaluation of fracture network morphology, the change of in situ stress field, the quantitative evaluation of post-pressure conductivity and fracture volume, the section spacing, construction fluid volume, and construction displacement under the conditions of single well and well group were optimized. The results show that under the condition of a certain well spacing, the fracture propagation of the well group is affected by stress shadowing and channeling, and the fracture pattern is more complex, and the construction scale is smaller than that of a single well. These findings provide critical insights for improving the efficiency of deep CBM recovery. Full article

Electrical imaging logging technology precisely characterizes the features of the formation on the borehole wall through high-resolution resistivity images. However, the problem of blank strips caused by the mismatch between the instrument pads and the borehole diameter seriously affects the accuracy of fracture identification and formation continuity interpretation in marine oil and gas reservoirs. Existing inpainting methods struggle to reconstruct complex geological textures while maintaining structural continuity, particularly in balancing low-frequency formation morphology with high-frequency fracture details. To address this issue, this paper proposes an inpainting method using a dual-stream network based on the collaborative optimization of wavelet and spatial-channel convolution. By designing a texture-aware data prior algorithm, a high-quality training dataset with geological rationality is generated. A dual-stream encoder–decoder network architecture is adopted, and the wavelet transform convolution (WTConv) module is utilized to enhance the multi-scale perception ability of the generator, achieving a collaborative analysis of the low-frequency formation structure and high-frequency fracture details. Combined with the spatial channel convolution (SCConv) to enhance the feature fusion module, the cross-modal interaction between texture and structural features is optimized through a dynamic gating mechanism. Furthermore, a multi-objective loss function is introduced to constrain the semantic coherence and visual authenticity of image reconstruction. Experiments show that, in the inpainting indexes for Block X in the South China Sea, the mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) of this method are 6.893, 0.779, and 19.087, respectively, which are significantly better than the improved filtersim, U-Net, and AOT-GAN methods. The correlation degree of the pixel distribution between the inpainted area and the original image reaches 0.921~0.997, verifying the precise matching of the low-frequency morphology and high-frequency details. In the inpainting of electrical imaging logging images across blocks, the applicability of the method is confirmed, effectively solving the interference of blank strips on the interpretation accuracy of marine oil and gas reservoirs. It provides an intelligent inpainting tool with geological interpretability for the electrical imaging logging interpretation of complex reservoirs, and has important engineering value for improving the efficiency of oil and gas exploration and development. Full article

This research introduces an innovative fractal–fractional synergy framework for multiscale analysis of stress field dynamics in geo-energy systems. By integrating fractional calculus with multiscale fractal dimension analysis, we develop a coupled approach examining stress redistribution patterns across different geological scales. The methodology combines fractal characterization of rock mechanical parameters with fractional-order stress gradient modeling, validated through integrated analysis of core testing, well logging, and seismic inversion data. Our fractal–fractional operators enable simultaneous characterization of stress memory effects and scale-invariant fracture propagation patterns. Key insights reveal the following: (1) Non-monotonic variations in rock mechanical properties (fractal dimension D = 2.31–2.67) correlate with oil–water ratio changes, exhibiting fractional-order transitional behavior. (2) Critical stress thresholds (12.19–25 MPa) for fracture activation follow fractional power-law relationships with fracture orientation deviations. (3) Fracture network evolution demonstrates dual-scale dynamics—microscale tip propagation governed by fractional stress singularities (order α = 0.63–0.78) and macroscale expansion obeying fractal growth patterns (Hurst exponent H = 0.71 ± 0.05). (4) Multiscale modeling reveals anisotropic development with fractal dimension increasing by 18–22% during multi-well fracturing operations. The fractal–fractional formalism successfully resolves the stress-shadow paradox while quantifying water channeling risks through fractional connectivity metrics. This work establishes a novel paradigm for coupled geomechanical–fluid dynamics analysis in complex reservoir systems. Full article

The extraction of small water bodies in the Yellow River Basin has always been a key issue of concern in the fields of remote sensing technology application, water resource management, environmental science, and geographic information systems. Due to factors such as water bodies, human activities, and cloud cover, water body extraction becomes difficult. In addition, convolutional neural networks are prone to losing small water body feature information during the process of extracting local features, which can cause more imbalance between positive and negative samples of water bodies and non-water bodies. In response to these issues, this study focused on a specific research area—the middle and lower reaches of the Yellow River. We processed and analyzed high-resolution optical satellite images collected from the Yellow River Basin and other areas, with a particular emphasis on precise identification of small water bodies, and proposed a network structure, the SE-Attention-Residual-Unet (SE-ResUnet), for water extraction tasks.The main contributions of this article are threefold: (1) Introducing a channel attention mechanism with residual structure in the down-sampling process, and learning Unet’s skipping structure for multi-scale feature extraction and compensation, thereby enhancing the feature extraction ability of small water bodies, including rivers, lakes, and reservoirs. (2) Introducing a weighted-Dice (W-Dice) loss function to balance positive and negative samples and enhance the generalization of the model. (3) In comparative experiments on improving the Unet model with semantic segmentation networks such as Unet, PSPNet, Deeplabv3+ on a self-built dataset and remote sensing interpretation public dataset, excellent performance and results were achieved on the mIoU, OA, and F1-score metrics. On the self-built dataset, compared with Unet, the mIoU, OA, and F1-score improved by 0.38%, 0.12%, and 0.08%, respectively. On the publicly available dataset, for remote sensing interpretation of water extraction, the mIoU, OA, and F1-score improved by 0.63%, 0.26%, and 0.25%, respectively. The experimental results demonstrate that a strategy combining an attention mechanism and a weighted loss function has a significant effect on the effectiveness of the collaborative improvement of neural network models in water extraction tasks. Full article

At present, the ball-and-stick model is widely used to simulate the reservoir pore-throat network. However, due to the large span of reservoir pore-throats, there is still a lack of effective verification on whether the ball-stick model is fully applicable. The configuration analysis of a constant velocity mercury injection curve is carried out by using configuration theory and the hierarchical analysis method. The applicability of the ball-and-stick model in a reservoir pore-throat network simulation is discussed by interpreting the reservoir pore-throat structure information behind the configuration. The results show that there are two configuration regions, A and B, in the constant velocity mercury injection curve. In configuration region A, the mercury saturation of pores, the pore channel and the throats increase monotonically with the increase in mercury inlet pressure, indicating that pore channels and throats coexist in this configuration region, the pore-throat ratio is greater than one, and the ball-and-stick model is applicable. In the B configuration region, mercury saturation in pores and throats increases monotonically with the increase of mercury inlet pressure, while mercury saturation in the pore channel remains unchanged, indicating that this configuration region is basically throat with a porethroat ratio of one. The ball-and-stick model is no longer applicable, and the capillary model is more suitable. The ball-and-stick model combined with the capillary model can simulate the full-scale pore throat network of the reservoir. This study provides a method to calibrate the application zone of ball-stick model and capillary model by using constant velocity mercury injection curve configuration, which has important guiding significance for the simulation of the reservoir pore-throat network and the study of the seepage law. Full article

Channel prediction is an effective technology to support adaptive transmission in wireless communication. To solve the difficulty of accurately predicting channel state information (CSI) due to fast time-varying characteristics, a next-generation reservoir calculation network (NGRCN) is combined with CSI, and a channel prediction method for OFDM wireless communication systems based on an adaptive reinforced reservoir learning network (adaptive RRLN) is proposed. An adaptive elastic network (adaptive EN) is used to estimate the output weight matrix to avoid ill-conditioned solutions. Therefore, the adaptive RRLN has echo and oracle properties. In addition, an adaptive singular spectral analysis (adaptive SSA) method is proposed to improve the local predictability of CSI by decomposing and reconstructing CSI to improve the fitting accuracy of the channel prediction model. In the simulation section, the OFDM wireless communication systems are constructed using IEEE802.11ah and the one-step prediction, the multi-step prediction, and the robustness test are implemented and analyzed. The simulation results show that the prediction accuracy of the adaptive RRLN can reach 3 × 10⁻⁵ and 8.36 × 10⁻⁶, which offers satisfactory prediction performance and robustness. Full article

The demand for high-capacity communication systems has grown exponentially in recent decades, constituting a technological field in constant change. Data transmission at high rates, reaching tens of Gb/s, and over distances that can reach hundreds of kilometers, still faces barriers to improvement, such as distortions in the transmitted signals. Such distortions include chromatic dispersion, which causes a broadening of the transmitted pulse. Therefore, the development of solutions for the adequate recovery of such signals distorted by the complex dynamics of the transmission channel currently constitutes an open problem since, despite the existence of well-known and efficient equalization techniques, these have limitations in terms of processing time, hardware complexity, and especially energy consumption. In this scenario, this paper discusses the emergence of photonic neural networks as a promising alternative for equalizing optical communication signals. Thus, this review focuses on the applications, challenges, and opportunities of implementing integrated photonic neural networks for the scenario of optical signal equalization. The main work carried out, ongoing investigations, and possibilities for new research directions are also addressed. From this review, it can be concluded that perceptron photonic neural networks perform slightly better in equalizing signals transmitted over greater distances than reservoir computing photonic neural networks, but with signals at lower data rates. It is important to emphasize that photonics research has been growing exponentially in recent years, so it is beyond the scope of this review to address all existing applications of integrated photonic neural networks. Full article

The hanging-wall ramps of rift basins are prone to the accumulation of large sedimentary bodies and are potential areas for the presence of large subsurface geological reservoir volumes. This paper comprehensively utilizes data from sedimentology, seismic reflection, geochemistry, and palynology to study the paleotopography, water conditions, paleoclimate, and sediment supply of the fourth member (Mbr 4) of the Shahejie Formation in the Raoyang Sag of the Bohai Bay Basin, China. The sedimentary characteristics, evolution, and preserved stratigraphic architectures of shallow-water deltaic successions are analyzed. Multiple indicators—such as sporopollen, ostracoda, fossil algae, major elements, and trace elements—suggest that when Mbr 4 was deposited, the climate became progressively more humid, and the lake underwent deepening followed by shallowing. During rift expansion, the lake level began to rise with supplied sediment progressively filling available accommodation; sand delivery to the inner delta front was higher than in other parts of the delta, and highly active distributary channels formed a reticular drainage network on the delta plain, which was conducive to the formation of sandstone up-dip pinch-out traps. In the post-rift period, the lake water level dropped, and the rate and volume of sediment supply decreased, leading to the formation of a stable dendritic network of distributary channels. At channel mouths, sediments were easily reworked into sandsheets. The distribution of sandstone and mudstone volumes is characterized by up-dip pinch-out traps and sandstone lens traps. The network of channel body elements of the shallow-water deltaic successions is expected to act as an effective carbon dioxide storage reservoir. This study reveals the influence of multiple factors on the sedimentary characteristics, evolution, and internal network of shallow-water deltas at different stages of rift basin evolution. This knowledge helps improve resource utilization and the sustainable development of comparable subsurface successions. Full article

Planning and managing water resource systems with multipurpose surface reservoirs demand the careful consideration of several critical factors, including system longevity, multiple uses, phased development, substantial financial investments, complex management requirements, uncertainties in predicting future hydrological inputs, and the estimation of economic parameters. Despite the importance of these factors, recent research and software development efforts for water resource system planning and management have predominantly focused on hydrological models. Economic models that describe the phased growth of such systems are often either absent or not fully integrated into decision-making software. Well-established network-based decision support tools, such as MODSIM, AcquaNet, and SIMYLD-II, provide robust hydrological modeling but lack explicit economic sub-models to address the evolving nature of water resource systems over time. This paper introduces a novel approach that combines optimization and simulation techniques to evaluate the multi-year performance of a growing water resource system. By conducting multiple model runs for each phase of system development—each characterized by unique topologies, demand patterns, hydrological conditions, and water delivery preferences—economic analyses are suggested to complete the global evaluation of the system. This comprehensive approach provides valuable insights into long-term planning horizons. Research in this direction continues to evolve, aiming to bridge the gap between hydrological and economic modeling in decision support frameworks. To illustrate the proposed methodology, a test example is presented, showcasing the implementation of the economic calculations and computer routines described in the paper. This example serves as a foundational step toward more comprehensive tools for sustainable and economically viable water resource system planning and management. Full article

A recrosslinkable CO₂-resistant branched preformed particle gel (CO₂-BRPPG) was developed for controlling CO₂ injection conformance, particularly in reservoirs with super-permeable channels. Previous work focused on a millimeter-sized CO₂-BRPPG in open fractures, but its performance in high-permeability channels with pore throat networks remained unexplored. This study used a sandpack model to evaluate a micro-sized CO₂-BRPPG under varying conditions of salinity, gel concentration, and pH. At ambient conditions, the equilibrium swelling ratio (ESR) of the gel reached 76 times its original size. This ratio decreased with increasing salinity but remained stable at low pH values, demonstrating the gel’s resilience in acidic environments. Rheological tests revealed shear-thinning behavior, with gel strength improving as salinity increased (the storage modulus rose from 113 Pa in 1% NaCl to 145 Pa in 10% NaCl). Injectivity tests showed that lower gel concentrations reduced the injection pressure, offering flexibility in deep injection treatments. Gels with higher swelling ratios had lower injection pressures due to increased strength and reduced deformability. The gel maintained stable plugging performance during two water-alternating-CO₂ cycles, but a decline was observed in the third cycle. It also demonstrated a high CO₂ breakthrough pressure of 177 psi in high salinity conditions (10% NaCl). The permeability reduction for water and CO₂ was influenced by gel concentration and salinity, with higher salinity increasing the permeability reduction and higher gel concentrations decreasing it. These findings underscore the effectiveness of the CO₂-BRPPG in improving CO₂ sweep efficiency and managing CO₂ sequestration in reservoirs with high permeability. Full article