The digital transformation in the oil and gas sector began because of two economic downturns. Literature review suggests an increasing work on outlier identification in geophysical well logs with some Exploration and Production companies have started working internally on this aspect as it consumes a significant amount of time for a geoscientist. While performing 1D geomechanical modelling, data preparation is the most time-consuming step, often demanding close to 4 to 5 hours. The data preparation steps include removing the outliers present in the well log data caused by the casing shoe, bad hole intervals, tool noise, and many other factors. Geoscientists invest hours to clean these outliers and handle the resulting missing values. Identification of outliers is mostly manual and missing data are replaced with simple methods based on linear, power, or exponential algorithms derived in a laboratory setup. In this work, seven different anomaly detection techniques were evaluated on well logs. The proposed workflow includes a combination of Z-score and Isolation Forest algorithms to clean and condition the data and handle missing values. The workflow then constructed the cleaned composite log data in quick time, which was then directly consumed by 1D geomechanical workflows. This workflow is a handy tool that adds value by saving time in data preparation. This study will redound to the benefit of geoscientists by saving significant amount of the time spent in the identification of outliers and hence reducing the overall turnaround time of modelling.