Knowledge about rheology of drilling fluid at wellbore conditions (High pressure and High temperature) is a need for avoiding drilling fluid losses through the formation. Unfortunately, lack of a universal model for prediction drilling fluid density at the addressed conditions impressed the performance of drilling fluid loss control. So, the main motivation of this paper is to suggest a rigorous predictive model for estimating drilling fluid density (g/cm3) at wellbore conditions. In this regard, a couple of particle swarm optimization (PSO) and artificial neural network (ANN) was utilized to suggest a high-performance model for predicting the drilling fluid density. Moreover, two competitive machine learning models including fuzzy inference system (FIS) model and a hybrid of genetic algorithm (GA) and FIS (called GA-FIS) method were employed. To construct and examine the predictive models the data samples of the open …
کلید واژگان :FIS; PSO; GA; ANN
ارزش ریالی : 600000 ریال
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