Hybridized machine-learning for prompt prediction of rheology and filtration properties of water-based drilling fluids

Abstract

Careful design and preparation of drilling fluids with appropriate rheology and filtration properties, combined with operational monitoring, is essential for successful drilling operations. Field results reveal that most drilling-fluid problems encountered are avoidable based on prompt detection of unexpected changes in fluid rheology and filtration behavior. Drilling-fluid rheology and filtration properties are typically only checked once or twice a day, whereas other drilling-fluid properties, such as fluid density (FD), solid percentage (S%), and March funnel viscosity (MFV), tend to be monitored several times per hour. Machine learning is therefore applied to estimate rheology and filtration properties with FD, S%, and MFV as input variables. A 1160-record field dataset collected from 14 wells drilled in two oil and gas fields in southwest Iran with water-based drilling fluids is used to predict the drilling fluid’s rheological and filtration characteristics. Plastic viscosity (PV), yield point (YP), and filtrate volume (FV) are the targeted prediction objectives. Of six models tested, Multilayer extreme learning machine (MELM) hybridized with the cuckoo optimization algorithm (COA) provides the best PV, YP, and FV predictions. It achieves root mean squared error (RMSE) values of 0.6357 mL (FV), 0.6086 cP (PV), and 0.6796 lb/100 ft 2 (YP). MELM-COA generates rapid and accurate estimations of rheology and filtration properties with potential for real-time monitoring during drilling operations, without recourse to time-consuming laboratory filtration and rheological tests. This work delivers, in a novel way, accurate and reliable predictions of drilling fluid filtration properties using only the more readily available FD, MFV, and S% variables as input features.