Modeling Technique Simplifies Optimization Of Complex Waterfloods By Jeremy Viscomi and Carlos Calad Optimizing waterfloods can be complex and time-consuming. Ideally, optimization will begin at the design stage before the well spuds. However, most waterfloods in the Lower 48 have been in operation for many years. Because it is so difficult to model these fields, optimization efforts generally rely on consultants or the long-term experience of staff engineers and geologists. They also rarely consider reservoir conditions across the entire field. Big data, machine learning and artificial intelligence can make more comprehensive optimization efficient and affordable, but these digital buzzwords have been applied so widely that they mean little by themselves. Potential users must distinguish between machine learning programs that merely provide access to data and solutions that analyze data to guide decisions. The most effective solutions use an approach called " data physics, " which involves applying machine learning algorithms to historical data but constraining their inputs to ensure they honor the laws of physics. Data physics has proven so effective in waterfloods and other applications that it is getting attention from operators and service companies large and small. They recognize that it offers a fast but accurate way to optimize production. SEPTEMBER 2021 73