In this work, we present our unique technology to develop and apply digital twins for drilling fluidsand associated wellbore phenomena during drilling operations. A drilling fluid digital twin is a series ofinterconnected models that incorporate the learning from geological uncertainty, complexity of drillingfluids and operational parameters to support well planning and real-time operations. We transform datacollected throughout the years from all aspects of drilling fluids to value-added applications and includereal-world conditions such as operation at extended temperatures and pressures, consideration for sensitivezones with tight control of the equivalent circulating density (ECD), and hole cleaning in deviated wellbores.With the advances presented in this work, cuttings bed height along the wellbore can be determined moreaccurately, and hence the ‘fluid plan’ together with operational parameters (such as mud flow rate andrate of penetration (ROP)) can be optimized to a higher level. In addition to the cuttings bed, an accurateworkflow for monitoring of downhole fluid rheological properties at the rigsite is developed. Through theuse of a digital twin, accurate high-pressure high-temperature (HPHT) properties can be determined atthe rigsite, which can more easily enable efficient and safe operations, as opposed to lab experiments thatare often conducted remotely. We demonstrate the application of automated machine learning (autoML)to represent computational simulations and lab experiments. We also use test datasets and rotating cross-validation methods to ensure accurate and robust results. In both cases, very accurate models were obtained,and point the way for the inclusion of more aspects of the drilling operation including ROP optimization,fluid-loss control, drilling fluid properties management, and power transmission.
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