Abstract:This study correlates the results obtained from the resistivity and spontaneous potential well logs in six boreholes for water extraction, located in the multilayer siliciclastic basin in the Madrid region, in the center of the Iberian Peninsula. Given the small lateral continuity that the layers considered in isolation show in this type of multilayer aquifer, geophysical stretches, with their corresponding average lithological assignments, have been established to achieve this objective from the well logs. Th… Show more
“…This spatial heterogeneity complicates the ability to accurately estimate parameters such as hydraulic conductivity and the contaminants distribution 3 – 6 . Traditional geological and hydrogeological methods can provide valuable insights, but they often fall short of capturing the full lithological variability within the aquifers 7 – 9 . On the other hand, Geophysical well logging offers a unique opportunity to attain a more comprehensive understanding of the aquifer system as it gives a continuous estimation of the aquifer characteristics 10 – 13 .…”
This research presents an unsupervised learning approach for interpreting well-log data to characterize the hydrostratigraphical units within the Quaternary aquifer system in Debrecen area, Eastern Hungary. The study applied factor analysis (FA) to extract factor logs from spontaneous potential (SP), natural gamma ray (NGR), and resistivity (RS) logs and correlate it to the petrophysical and hydrogeological parameters of shale volume and hydraulic conductivity. This research indicated a significant exponential relationship between the shale volume and the scaled first factor derived through factor analysis. As a result, a universal FA-based equation for shale volume estimation is derived that shows a close agreement with the deterministic shale volume estimation. Furthermore, the first scaled factor is correlated to the decimal logarithm of hydraulic conductivity estimated with the Csókás method. Csókás method is modified from the Kozeny-Carman equation that continuously estimates the hydraulic conductivity. FA and Csókás method-based estimations showed high similarity with a correlation coefficient of 0.84. The use of factor analysis provided a new strategy for geophysical well-logs interpretation that bridges the gap between traditional and data-driven machine learning techniques. This approach is beneficial in characterizing heterogeneous aquifer systems for successful groundwater resource development.
“…This spatial heterogeneity complicates the ability to accurately estimate parameters such as hydraulic conductivity and the contaminants distribution 3 – 6 . Traditional geological and hydrogeological methods can provide valuable insights, but they often fall short of capturing the full lithological variability within the aquifers 7 – 9 . On the other hand, Geophysical well logging offers a unique opportunity to attain a more comprehensive understanding of the aquifer system as it gives a continuous estimation of the aquifer characteristics 10 – 13 .…”
This research presents an unsupervised learning approach for interpreting well-log data to characterize the hydrostratigraphical units within the Quaternary aquifer system in Debrecen area, Eastern Hungary. The study applied factor analysis (FA) to extract factor logs from spontaneous potential (SP), natural gamma ray (NGR), and resistivity (RS) logs and correlate it to the petrophysical and hydrogeological parameters of shale volume and hydraulic conductivity. This research indicated a significant exponential relationship between the shale volume and the scaled first factor derived through factor analysis. As a result, a universal FA-based equation for shale volume estimation is derived that shows a close agreement with the deterministic shale volume estimation. Furthermore, the first scaled factor is correlated to the decimal logarithm of hydraulic conductivity estimated with the Csókás method. Csókás method is modified from the Kozeny-Carman equation that continuously estimates the hydraulic conductivity. FA and Csókás method-based estimations showed high similarity with a correlation coefficient of 0.84. The use of factor analysis provided a new strategy for geophysical well-logs interpretation that bridges the gap between traditional and data-driven machine learning techniques. This approach is beneficial in characterizing heterogeneous aquifer systems for successful groundwater resource development.
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