A comprehensive study on wormholing has been conducted to improve the understanding of matrix acidizing in carbonate reservoirs. This work is a continuation of previous work (Furui et al. 2012a, 2012b). An analysis of additional experimental results as well as field measurements is provided to reinforce and extend the wormhole penetration model and productivity benefits provided by Furui et al. (2012b). A series of small block tests and one large block test under geomechanical stresses has been conducted to characterize wormholing in outcrop chalk samples. In addition, field data including acid pumping data as well as post-stimulation pressure falloff data has been collected and analyzed to evaluate stimulation effectiveness. Pressure buildup data from stimulated wells has also been analyzed to evaluate the sustainability of the acid induced skin benefits. Production logging data has been used to investigate whether created wormhole networks have remained stable or have collapsed under production stresses. To statistically analyze the data more comprehensively, the new data was also compared to the data from other field data available in the literature. The following conclusions are drawn from an analysis of the laboratory data and field data: 1) a skin value of −4 is achievable in carbonate reservoirs by matrix acidizing, 2) the negative acid skin is relatively stable under production stresses, 3) the wormhole penetration model is proven to successfully simulate matrix acidizing processes in both laboratory scale and field scale work, 4) the small and large block laboratory tests re-confirmed wormholing efficiency which was discussed as a scale effect in previous studies, 5) an understanding of the possible range of wormhole penetration has allowed us to improve field acid treatments and reduce the risk of connecting to water. This comprehensive study includes acid linear core flooding tests, small block tests, large block tests and field measurements to thoroughly analyze acid wormholing in carbonate rock. The database can be very useful information for understanding, benchmarking and optimizing future completion/stimulation design.