Matrix-acid stimulation is a key production enhancement technique for carbonate reservoirs. The ability to control acid placement by diversion along the reservoir section is often challenging, particularly for heterogeneous formations where low-permeability intervals may remain under-stimulated. In this paper, we demonstrate via subsequent inflow profiling that efficient stimulation of both low- and high-permeability segments can be achieved using a completion method called a Smart Liner and a new methodology of downhole flow monitoring without operational well intervention by utilizing inline tracer system across the lower completion. The Smart Liner is a completion technique, which relies on a number of small and unevenly spaced holes to divert acid along a horizontal drain, while incorporating swellable packers to isolate segments with different reservoir properties. The hole spacing design is unique for each well and requires a software algorithm to balance the outflow. The inflow monitoring is assessed through both inline chemical tracers and conventional PLT. Main components of this algorithm has been presented in an earlier publication. The inline tracer system can provide "wireless" continuous production profiling without the risk & cost associated with conventional PLT methods. The inline tracer samples were collected during both stages of pre-stimulation and post-stimulation activities on oil producer wells, which shows downhole contribution of all segments, acid arrival and enhancement of Mid section of the reservoir drain. Moreover, PLT was conducted to validate inline tracer results. Past deployments of Smart Liners have targeted homogeneous reservoir intervals, but in this paper, we show that also reservoirs with significant permeability variation can be effectively matrix-acid stimulated. It is to our knowledge the first time that inflow profiling of a long horizontal well has been probed with inline tracers as well as PLT. Furthermore, we quantify the incremental impact of a given acid volume on injectivity based on the high-frequency data from the stimulation job.