A comprehensive study on wormholing has been conducted to improve the understanding of matrix acidizing in carbonate reservoirs. This work is a continuation of the previous work by 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 have been conducted to characterize wormholing in outcrop-chalk samples. In addition, field data including acid-pumping data and post-stimulation pressure-falloff data have been collected and analyzed to evaluate stimulation effectiveness. Pressure-buildup data from stimulated wells have also been analyzed to evaluate the sustainability of the acid-induced skin benefits. Production-logging data have 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 were also compared to 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 wormholepenetration model is proved to successfully simulate matrix-acidizing processes in both laboratory-scale and field-scale work; (4) the small- and large-block laboratory tests reconfirmed wormholing efficiency, which was discussed as a scale effect in the previous studies; and (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-coreflooding 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.
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