Title: Development of a Predictive Model for Wax Content in Crude Oil Using Experimental Data from the Niger Delta Basin
Authors: Anthony Chikwe, Aniyom Ebenezer, Chiamaka Maryann Abalum
Volume: 9
Issue: 10
Pages: 44-55
Publication Date: 2025/10/28
Abstract:
Waxy crude oils pose major flow assurance challenges during production and transportation due to wax crystallization, which increases viscosity, elevates the pour point, and can lead to pipeline blockages. This study presents a predictive regression model for estimating wax content in crude oils from the Niger Delta Basin using experimentally measured properties. Ten crude oil samples were analyzed based on resin, asphaltene, pour point, density, and viscosity. The Box-Behnken Design (BBD) approach in Design Expert software was employed to develop and optimize the model, with statistical validation conducted using Analysis of Variance (ANOVA). Results revealed a strong correlation between measured and predicted wax contents, with a high coefficient of determination (Rē = 0.985) and prediction errors below 1%. The model demonstrated excellent accuracy and robustness in forecasting wax content using easily obtainable crude oil parameters. This predictive framework reduces dependence on time-consuming and costly laboratory analyses such as gravimetric or chromatographic techniques. Consequently, it provides a rapid and reliable tool for flow assurance planning, enabling timely mitigation of wax deposition risks and improving production efficiency across Niger Delta oilfields.