An accurate model to predict drilling fluid density at wellbore conditions

• Main Authors: Mohammad Ali Ahmadi, Seyed Reza Shadizadeh, Kalpit Shah, Alireza Bahadori
• Format: Article
• Language: English
• Published: Elsevier 2018-03-01
• Series: Egyptian Journal of Petroleum
• Online Access: http://www.sciencedirect.com/science/article/pii/S1110062116301817

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 literature were used. Based on the statistical criteria the PSO-ANN model has reasonable performance in comparison with other intelligent methods used in this study. Therefore, the PSO-ANN model can be employed reliably to estimate the drilling fluid density (g/cm3) at HPHT condition. Keywords: Viscous fluid, Density, Rheology, Artificial neural network (ANN), Modeling