The Upper Paleozoic tight sandstone reservoirs on the eastern margin of the Ordos Basin exhibit strong heterogeneity and complex pore structures, leading to poor correlation between porosity and permeability and insufficient accuracy in permeability calculations to meet the requirements of reservoir fine evaluation. Therefore, a new method for high-precision permeability calculation based on flow zone index (FZI) reservoir classification is proposed. This method determines the number of reservoir classifications based on the characteristics of the FZI normal probability distribution plot and establishes FZI division criteria for reservoir types. Classified reservoirs exhibit similar flow characteristics, significantly improving the correlation between permeability and porosity. Based on nuclear magnetic resonance (NMR) combined with mercury injection capillary pressure (MICP) experiments, a modeling method for calculating the flow zone index based on the geometric mean of NMR T₂ is proposed. This method realizes continuous calculation of FZI based on NMR logging, reservoir classification, and permeability for the entire wellbore, thereby constructing a new permeability prediction method for tight sandstone reservoirs based on NMR logging and FZI classification. Actual application results demonstrate that the permeability calculated using NMR logging is in high agreement with the permeability analyzed from core data, with an average relative error of 45.8%, proving the effectiveness of the proposed method in this study.

In order to understand the sustainable and effective utilization of groundwater resources in Fuyang River Basin, the chemical characteristics and genetic evolution process of characteristic shallow fluoride groundwater in the region were carried out. The results show that the chemical characteristics of groundwater are zonal in horizontal direction. From the piedmont plain to the central plain, the hydrochemical types of the first and second aquifers are mainly HCO₃-Ca-Mg-type water, HCO₃-Cl-Ca-Mg-type water, Cl-SO₄-Na-Ca-type water, and Cl-SO₄-Na-Mg-type water. The main mineral sources of hydrochemical ions are salt rock, carbonate rock, sulfate rock, and silicate rock. F^- has a significant positive correlation with HCO₃^- while it is negatively correlated with K⁺. In the strong evaporation and alkaline environment created by climatic conditions in the study area, F^- mainly comes from the dissolution of minerals. Gypsum and fluorite are in a relatively stable dissolved state continuously providing F^- and Ca²⁺ for groundwater; the adsorption of cation was gradually strengthened from the piedmont plain to the central plain. The high HCO₃^- content is also conducive to F^- desorption in alkaline environments. The samples with F^- concentration greater than 1mgL^-1 accounted for 35.7 and mainly distributed in the south of the study area. In summary, the chemical characteristics of shallow groundwater in Fuyang River Basin are affected by the combined effects including leaching effect, evaporation concentration, ion exchange-desorption, and human factors.

With immense potential to enhance oil recovery, CO₂ has been extensively used in the exploitation of unconventional tight oil reservoirs. Significant variations are observed to occur in the oil’s composition as well as in its physical properties after interacting with CO₂. To explore the impacts of oil properties on CO₂ extraction efficiency, two different types of crude oil (light oil and heavy oil) are used in CO₂ huff-n-puff experiments. Moreover, numerical simulation is implemented to quantitatively inspect the impacts of different influencing factors including production time, reservoir pressure and reservoir temperature on physical properties as well as on the oil composition variation of the crude oil. The findings of the experiments demonstrate that, whether for the light oil sample or for the heavy oil sample, hydrocarbon distribution becomes lighter after interacting with CO₂ compared with the original state. In addition, it is also discovered that the hydrocarbon distribution variation is more significant for the light oil sample. The findings of the numerical simulation suggest that production time, reservoir pressure and reservoir temperature have significant impacts on the produced oil composition and properties. The hydrocarbon distribution of the oil becomes lighter with the increasing of production time and formation pressure, while it becomes heavier with the increasing of reservoir temperature. At the very beginning of the oil production, the properties of the produced oil are worsened. Compared with the original state, the oil density and viscosity are 25.7% and 200% higher, respectively. It is suggested that viscosity reducers are added into the well to improve the oil properties in this period. With the continuing of the oil production, the oil properties are continuously promoted. At the end of the simulation time, the oil density and viscosity are 3.5% and 15.1% lower compared with the original oil, respectively. This paper has great significance for the implementation of CO₂ huff-n-puff in tight oil reservoirs.

Moisture in coal seams significantly impacts methane adsorption/desorption, yet its microscopic mechanism in intact coal remains poorly characterized due to methodological limitations. This study introduces a novel approach that integrates low-field nuclear magnetic resonance (LF-NMR) with volumetric analysis to quantify, in real-time, the effect of moisture on methane dynamics in intact coal samples. The results quantitatively demonstrate that micropores (relative specific surface area > 700 m²/cm³) are the primary adsorption sites, accounting for over 95% of the stored gas. Moisture drastically reduces the adsorption capacity (by ~72% at 0.29 MPa and ~57% at 1.83 MPa) and inhibits the desorption process, evidenced by a strong linear decrease in desorption ratio (DR) (R² = 0.906) and a sharp exponential drop in the initial desorption rate (R² = 0.999) with increasing moisture content. The findings provide a mechanistic understanding that is crucial for optimizing coalbed methane (CBM) recovery and enhancing strategies for outburst prevention and methane emission mitigation. The results reveal distinct adsorption and desorption features of intact coal compared with coal powder, which can be useful in total methane utilization and mining safety enhancement.

Fracture diagnosis with water hammer pressure data is an important technology for petroleum exploration and development. Efficient filtering of acquired pressure data is a critical process to enhance diagnostic accuracy. The water hammer pressure monitored at the wellhead is observed data, whose generation process is influenced by multiple known or unknown factors such as fractures and the pipeline friction, and contains uncertain noise. Existing filtering algorithms mainly focus on the water hammer signal itself, ignore the uncertainty of its generation factors, and have poor filtering ability for the complex wellbore and formation environments. From the perspective of data generation, this study formulates the factors affecting water hammer generation as latent variables, and proposes a latent space convolutional filtering (LSCF) model. The model estimates the probability distribution of latent variables, samples to obtain latent variables, then uses a convolutional neural network to filter out noise factors, and finally infers the probability distribution of the clean water hammer signal. Filtering experiments were conducted on both water hammer simulated datasets and field datasets using the model, with correlation coefficient (CC), mean squared error (MSE) and signal-to-noise ratio (SNR) adopted as quantitative evaluation metrics, combined with qualitative analysis of spectrum and cepstrum. Compared with existing advanced filtering algorithms, the LSCF model achieves optimal performance across all filtering metrics, verifying the advancement of the filtering strategy, providing a new technical reference for noise reduction and filtering of fracturing pump shutdown water hammer data.

Active magnetic ranging technology is extensively employed in relief well operations for target wells with inaccessible wellheads. However, in the governance of complex oil and gas wells, achieving high-precision ranging results remains a significant challenge. To address this, a theoretical ranging model based on a semi-infinite formation was developed by accounting for the insulating properties of the ground surface and the potential distribution characteristics at the casing-formation interface. The proposed framework integrates a refined casing current analytical model with a magnetic flux density approximation model. The applicability of the integrated ranging model were subsequently evaluated through numerical simulations. The results demonstrate the following: (1) The proposed ranging model exhibits superior applicability when the electrode is positioned far from the target well bottom, extending the operational range of classical models which are constrained to scenarios where the electrode remains distant from both the wellhead and the well bottom.(2) The primary source of error in the ranging model is the casing current analytical model, whose accuracy is predominantly influenced by electrode vertical depth, casing length, and formation conductivity. Higher formation conductivity and greater casing length lead to a reduction in the overall ranging error and an expansion of the electrode depth range relative to the total casing length. (3) Operational nomograms were established to delineate the applicable electrode depth ranges for various conductivities and casing lengths. When the coordinates defined by the electrode depth and casing length fall within the region beneath the boundary curves, the relative deviation induced by the theoretical model is maintained below 10%. In conclusion, this study provides a more profound understanding of the error mechanisms across various influencing factors, offering guidance for active magnetic ranging operations in the field.