Despite recognition by the drilling industry that historic data can be used to inform the efficiency of drilling operations, published research into methods to systematically exploit historic data for this purpose are relatively scarce. In the present paper, we describe a novel method and automated solution that does just this it was developed for land-based wells drilled on the same pad or in similar geologic formations (i.e., offset wells), and it uses machine learning to search the offset data for epochs of highly efficient drilling. Once these epochs are identified, drilling parameter settings including weight on bit (WOB), drill string or drill bit revolutions per minute (RPM), differential pressure (ΔP), and pump rate (total pump output; TPO) settings linked to the highly efficient drilling epochs are extracted from the offset data. These settings then are formation- and depth-aligned to the new well to be drilled, and they are smoothed and displayed on the electronic drilling recorder (EDR) in real-time as "drilling parameter roadmaps." Such roadmaps were created for surface, intermediate, build and lateral sections for two trial wells. Trial results indicated that the average rates of penetration for the trial wells exceeded the average rates of penetration for the offsets by 20.1% and 47.8%, and time to drill the trial wells (i.e., spud to target depth) took 4.3 and 4.1 fewer days compared to the average number of days for the offsets. Per-day cost to operate these rigs was approximately $80, 000 suggesting our solution yielded substantial cost savings.