Petrophysical parameters estimation of a reservoir using integration of wells and seismic data: a sandstone case study

Leisi, Ahsan; Saberi, Mohammad Reza

doi:10.1007/s12145-022-00902-8

Cited by 13 publications

(1 citation statement)

References 22 publications

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“…Given this constraint, factor analysis emerged as a suitable method for the estimation of key petrophysical and hydrogeological parameters and facilitating the characterization of groundwater systems 38 . However, in the petroleum industry, where more comprehensive reservoir characterization is required, more sophisticated machine learning methods such as neural networks are commonly employed 73 , 74 . These methods offer high accuracy and flexibility in handling complex relationships between well-log data and target parameters 75 .…”

Section: Discussionmentioning

confidence: 99%

Assessing heterogeneous groundwater systems: Geostatistical interpretation of well logging data for estimating essential hydrogeological parameters

Mohammed,

Flores,

Szabó

et al. 2024

Sci Rep

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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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