Petrophysical workflows are primarily designed to process static data for traditional openhole logs (i.e., triple & quad combo) which can provide estimates of porosity, saturation, lithology and mineralogy. Dynamic data from core analysis can help to extend the log analysis for estimates of the rock’s dynamic properties such as permeability. However, there is normally a high degree of uncertainty in these estimates and formation testing and sampling (FTS) data is often required for reservoir condition calibration. The workflow for analyzing FTS data is highly specialized, and normally not performed by an operating petrophysicist, but by a specialist whose expertise covers FTS tools and applications, openhole logs, and reservoir dynamics. This paper bridges the gap between operational petrophysicists and FTS specialists by documenting standardized methods of FTS measurements and introducing an automated workflow for petrophysicists to conduct FTS jobs. This workflow begins with job planning, to data processing, decision making and recommendations. Unique characteristics and capabilities of this workflow are summarized below. The job planning methodologies are based on fundamental principles to determine how different tool technologies will perform in specific reservoir conditions. It will ensure that the most optimized selection of tools and modules are made for FTS operations. Measurement uncertainties and data qualification criteria are critical parts of the workflow. Expected uncertainties are compared to measured uncertainties to assess the results’ reliability. With test uncertainties, error bars are established for each measurement which are then incorporated into data processing such as gradient estimates. A quality grading algorithm is used to objectively provide a rating for each test based on the measurements. Exceptions for test anomalies are also handled and interpreted automatically. In real-time testing operations, automatic methods for objectively quantifying data quality and uncertainty are used for pressure, mobility, and fluid gradient analyses. Real-time decisions can then be made to either adjust pressure points or take fluid samples at critical locations to reduce uncertainty. Preliminary results of applying this workflow to automatically process test data and determine data quality more objectively, consistently, and efficiently are demonstrated using field examples.