A novel directional-oriented method for predicting shear wave velocity through empirical rock physics relationship using geostatistics analysis.

This study attempts to design a novel direction-oriented approach for estimating shear wave velocity (VS) through geostatistical methods (GM) using density employing geophysical log data. The research area involves three hydrocarbon wells drilled in carbonate reservoirs that are comprised of oil and water. Firstly, VS was estimated using the four selected empirical rock physics relationships (ERR) in well A (target well), and then all results were evaluated by ten statistical benchmarks. All results show that the best ERR is Greenberg and Castagna, with R2 = 0.8104 and Correlation = 0.90, while Gardner's equation obtained the poorest results with R2 = 0.6766 and correlation = 0.82. Next, Gardner's method was improved through GM by employing Ordinary Kriging (OKr) in two directions in well A, and then Cross-Validation and Jack-knife methods (JKm and CVm, respectively) were used to assess OKr's performance and efficiency. Initially, CVm and JKm were employed to estimate Vs using the available density and its relationship with shear wave velocity, where the performance of CVm was better with R2 = 0.8865 and correlation = 0.94. In this step, some points from the original VS were used to train the data. Finally, Vs was estimated through JKm and using the relationship between the shear wave velocity of two wells near the target well, including wells B and C; however, in this step, the original shear wave velocity of the target well was completely ignored. Reading the results, JKm could show excellent performance with R2 = 0.8503 and Corr = 0.922. In contrast to previous studies that used only Correlation and R-squared (R2), this study further provides accurate results by employing a wide range of statistical benchmarks to investigate all results. In contrast to traditional empirical rock physics relationships, the developed direction-oriented technique demonstrated improved predicted accuracy and robustness in the investigated carbonate field. This work demonstrates that GM can effectively estimate Vs and has a significant potential to enhance VS estimation using density.

A comprehensive study of the parameters affecting the stable isotopes in the precipitation of the Bangkok metropolitan area using model-based statistical approaches.

This study determined the main local and regional parameters affecting the stable isotopes (18O, 2H) in the Bangkok precipitation and developed the Bangkok meteoric water line (BMWL) (δ2H = (7.68 ± 0.07) δ18O + (7.25 ± 0.48)). First, Pearson correlation coefficients were used to determine the correlation between local and regional parameters. Six different regression methods were used based on Pearson correlation coefficients. The stepwise regression had the most accurate performance among them according to the R2 values. Second, three different methods were used to develop the BMWL, and their performances were also studied. Third, the stepwise regression method was used to study the effects of local and regional parameters on the stable isotope content of precipitation. The results showed that the local parameters had a greater effect on the stable isotope content than the regional ones. The stepwise models developed based on the northeast and southwest monsoons showed that moisture sources also affected the stable isotope content of precipitation. Finally, the developed stepwise models were validated by calculating the root mean square error (RMSE) and R2. This study demonstrated that the local parameters mainly controlled the stable isotopes in the Bangkok precipitation, while the regional parameters had a slight effect on them.

The deterioration of groundwater quality by seawater intrusion in the Chao Phraya River Basin, Thailand.

The Chao Phraya River Deltaic Plain is the largest basin in Thailand and the second largest one in Southeast Asia after the Mekong River Delta. In recent decades, the groundwater quality in the Lower Chao Phraya River Basin in Thailand has deteriorated due to salinization caused by seawater intrusion. In the present study, hydrogeochemical and statistical methods were employed to determine the hydrochemical characteristics of the groundwater and to investigate the possible sources of salinity in the study region for the years 2008 and 2020. In addition, samples were taken from precipitation, sea water, and river water to analyze their hydrochemical properties. Then, they were used as input in the "Simmr" code in the R programming language to model the hydrochemical conditions of the study area and their evolution over time. The results indicated that in the non-coastal regions, water-rock interaction (mineral weathering and ion exchange), and brine/connate water infiltration affected the quality of the groundwater. However, the seawater intrusion was limited only to the coastal regions. Furthermore, the groundwater quality deteriorated from 2008 to 2020. Finally, using stepwise regression in the R language, the salinity of the groundwater was simulated and compared with the measured salinity data. The results obtained by the stepwise model were in close agreement with those obtained from the hydrochemical studies. This study confirmed seawater intrusion in the coastal aquifer as well as the deterioration of groundwater quality over time. To slow down this process and to achieve sustainable conditions, groundwater extraction should be reduced in the study region.