Search

Copyright of Pearl River is the property of Pearl River Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Climate change poses significant threats to the water reservoir, with potential repercussions for Pakistan's economy. This study investigates the impact of climatic variations on seepage in Tarbela dam, Pakistan, by analyzing hydro-climatological factors. The research employs statistical multivariant time series (SARIMA and ARDL) and machine learning (ANN and Catboost) modeling techniques to assess the influence of climatic variability on seepage dynamics. The analysis focuses on annual and seasonal climatic variables from a data set spanning from 2003 to 2015, aiming to explore the relationship between climatic variability and dam seepage. The SARIMA model is utilized to estimate water inflow, temperature, rainfall, and reservoir level, validating the integration of multiple data sets. Machine learning models demonstrate superior performance, achieving high R2 scores during the training, validation, and testing. The findings derived from the ARDL analysis reveal a significant increase in seepage with rising water inflow, rainfall, and reservoir level. Among the machine learning models, CatBoost performs the best. The study recommends implementing appropriate adaptive approaches to mitigate any negative impact on Tarbela dam seepage. Moreover, this research serves as a guide for enhancing the dam's power generation capacity and improving irrigation water management in the face of existing crises in Pakistan. [ABSTRACT FROM AUTHOR]

The seepage of the dam is an important representation of the operation characteristics of the dam, and there are many factors affecting the seepage with a certain lag. It is still difficult to predict its change and sensitivity because of complex operating conditions. At present, the lag-sensitivity of influence factors of the dam seepage has not been studied. The time series influence factors of seepage are determined by HTRT (hydrostatic-thermal-rainfall-time) model in this paper. To avoid the pseudo fitting of conventional methods, HTRT model nested random forest algorithm is used to establish the predicting model of the dam seepage. And MIC algorithm is used to achieve the dual purposes of time lag and sensitivity analysis. Firstly, the time lag of relationship between seepage and its influencing factors is characterized, and the most appropriate lag time of the HTRT model factors is determined. Secondly, independent correlation analysis on all influencing factors is carried out and the sensitivity of each factor is analyzed by MIC. Meanwhile, the sensitivity of the factors to seepage is quantitatively analyzed by the two parameters of %IncMSE and IncNodePurity of RF algorithm. The sensitivity of influencing factors is analyzed by comparing MIC results with RF results. Combined with the case, taking the error of fitting prediction as the evaluation index of seepage prediction, the prediction accuracy of MIC-RF model, RF model and MIC-BPNN (Back Propagation neural network) model is calculated and compared. Case study showed that MIC- RF monitoring model has high prediction accuracy, strong adaptability and high robustness in dam seepage, and the sensitivity and time lag of influencing factors can be quantitatively analyzed. The water pressure and rainfall of the lag time are 14 days and 16 days respectively. The sensitivity study of the time series influencing factors of seepage showed that the water pressure component is the main controlling factor of seepage, and rainfall component is more sensitive to later stage. The MIC-RF model can be used as a new dam seepage safety monitoring model. [ABSTRACT FROM AUTHOR]

Diffusion ability is an important indicator of shale gas reservoir quality. In this paper, the diffusion coefficient of the Longmaxi Formation is measured via the free hydrocarbon concentration method, and the diffusion ability, influencing factors, and seepage flow are discussed. Results show that the diffusion coefficient of the Longmaxi Formation is between 1.23 × 10−5 and 2.98 × 10−5 cm2 s−1 with an average value of 2.19 × 10−5 cm2 s−1 (confining pressure 3.0 MPa). The diffusion coefficient is calculated for various pressures using an empirical formula (D = 0.339K0.67/M0.5) and experimentally measured data. The estimated, temperature-corrected diffusion coefficient of the Longmaxi Formation is 3.94 × 10−6–7.24 × 10−6 cm2 s−1 with an average value of 5.28 × 10−6 cm2 s−1 for depths from 1000 to 3000 m (confining pressure 16.7–39.7 MPa). The diffusion coefficient increases with increasing depth of the reservoir due to the changes in pressure and temperature. Fitting parameters show that the porosity of the reservoir and clay minerals is positively correlated with the diffusion coefficient, and the diffusion coefficient is also related to factors such as total organic carbon and the maximum reflectance of vitrinite (Ro). The diffusion flow rate is 0.177–0.204 m3 d−1 with an average of 0.182 m3 d−1. Linear seepage flow is 4.95 × 10−4–14.29 × 10−4 m3 d−1 with an average of 8.87 × 10−4 m3 d−1, calculated from the diffusion coefficient and permeability per unit flow. These results indicate that the migration of shale gas in the deep region of the reservoir is mainly by diffusion. Therefore, diffusion is an important shale gas flow mechanism. [ABSTRACT FROM AUTHOR]