Processes

Clouds are a key factor affecting solar radiation, and their dynamic variations directly cause uncertainty and fluctuations in photovoltaic (PV) power output. To improve PV power prediction accuracy, this paper proposes an enhanced short-term photovoltaic power forecasting approach based on a hybrid neural network architecture using features extracted from satellite cloud images. First, a dual-layer image fusion method is developed for satellite cloud images from different wavelengths and spectral bands, effectively improving fusion accuracy. Second, texture descriptors derived from the Gray-Level Co-occurrence Matrix and multiscale information obtained via the wavelet transform are employed for feature extraction from fused images. Combined with a residual network (ResNet), an optical flow method, as well as an LSTM-based temporal modeling module, multidimensional features of the predicted cloud images are obtained. An improved Bayesian optimization (IBO) algorithm is then employed to derive the optimal fused features, thereby improving the matching between cloud image features and PV power. Third, an enhanced hybrid architecture integrating a convolutional neural network and long short-term memory units with a multi-head self-attention mechanism is developed. Numerical weather prediction (NWP) meteorological features are incorporated, and a tilted irradiance model is introduced to calculate the solar irradiance received by PV modules for use in near-term photovoltaic power forecasting. Finally, measurements collected at a photovoltaic power plant located in Hebei Province are used to validate the proposed method. The results show that, relative to the SA-CNN-MSA-LSTM and BO-CNN-LSTM models, the developed approach lowers the RMSE to an extent of 22.56% and 4.32%, while decreasing the MAE by 24.84% and 5.91%, respectively. Overall, the proposed model accurately captures the characteristics of predicted cloud images and effectively improves PV power prediction accuracy.

Despite extensive pore-scale studies on oil–water displacement, quantitative understanding of the dynamic evolution of residual oil morphology and waterflooding efficiency in geologically heterogeneous sandstones remains limited, particularly under large water-injection multiples. To better understand pore-scale oil–water distribution and its influence on enhanced oil recovery, this study utilized Micro-CT combined with SEM-EDS to examine the 3D pore structure and oil–water phase evolution in a heterogeneous sandstone sample from the Xiayang Formation, Wushi Sag, Zhanjiang. Mineralogical analyses reveal that dolomite cementation and vermicular kaolinite infilling introduce strong pore-scale heterogeneity by selectively reducing pore connectivity and permeability, posing challenges for uniform fluid displacement. A 30% KI solution was used to enhance X-ray attenuation of the aqueous phase, enabling clear discrimination between oil and water. Micro-CT reconstructions reveal a relatively uniform pore network dominated by medium-to-large intergranular pores. As the water-injection multiple increases, water progressively invades larger pores, while residual oil is immobilized by capillary forces within micro-throats, forming isolated clusters. The oil-droplet size distribution broadens from a narrow range (50–100 µm) to a wider one (200–300 µm), indicating interfacial destabilization and droplet coalescence. Quantitative analysis indicates that oil saturation decreases from approximately 90% to 36%, while waterflooding efficiency increases rapidly to ~45% at 1 PV and gradually approaches a plateau of ~60% beyond 500–1000 PV. This waterflooding plateau is attributed to capillary trapping and pore-scale connectivity limitations imposed by mineral-induced heterogeneity, which prevent further mobilization of residual oil despite continued water injection. This study advances pore-scale waterflooding research by combining mineralogical heterogeneity with long-term micro-CT imaging, revealing the pore-scale mechanisms controlling residual oil evolution and ultimate waterflooding limits in realistic sandstone.

Coalbed methane (CBM) hosted by multiple (>20) thin (<2 m) seams in South China represents an important unconventional gas supplement. In the Yanjiao composite syncline, high-frequency sea-level fluctuations produced widely distributed thin seams (20–60 layers), with tidal-flat coal groups I (No. 2–No. 9) and III (No. 20–No. 35) as primary targets. Variable magma intrusion drives the present coal-rank partitioning (1.8–4.3% R_o) and pronounced reservoir heterogeneity. A basalt floor (>200 m) and the Lower Triassic Feixianguan caprock (~100 m) confine the Longtan strata into an independent hydrodynamic unit. Groundwater migrates from syncline wings to the axial domain and seals CBM in stagnant zones, resulting in higher gas contents toward the axis. Deep CBM is constrained by high in situ stress and low permeability (typically <0.1 mD below 600 m) and a relatively uniform and low-abnormal pressure system. The syncline is divided into four parts: Part I is the most favorable, where staged fracturing of closely spaced (<60 m) coal group III achieved a maximum production rate of 2400 m³/d and a stabilized rate of 2100 m³/d, whereas Part IV (depth > 1000 m) records a peak daily gas rate of 512–654 m³/d and shows no stabilized-production stage.