PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Simplified Multivariate Linear Regression based Model to Predict Asphaltene Onset Pressure under Reservoir Pressure Depletion Conditions using Data Clustering Technique
Version 1
: Received: 27 September 2023 / Approved: 27 September 2023 / Online: 28 September 2023 (10:24:29 CEST)
How to cite:
Ali, M. Simplified Multivariate Linear Regression based Model to Predict Asphaltene Onset Pressure under Reservoir Pressure Depletion Conditions using Data Clustering Technique. Preprints2023, 2023091987. https://doi.org/10.20944/preprints202309.1987.v1
Ali, M. Simplified Multivariate Linear Regression based Model to Predict Asphaltene Onset Pressure under Reservoir Pressure Depletion Conditions using Data Clustering Technique. Preprints 2023, 2023091987. https://doi.org/10.20944/preprints202309.1987.v1
Ali, M. Simplified Multivariate Linear Regression based Model to Predict Asphaltene Onset Pressure under Reservoir Pressure Depletion Conditions using Data Clustering Technique. Preprints2023, 2023091987. https://doi.org/10.20944/preprints202309.1987.v1
APA Style
Ali, M. (2023). Simplified Multivariate Linear Regression based Model to Predict Asphaltene Onset Pressure under Reservoir Pressure Depletion Conditions using Data Clustering Technique. Preprints. https://doi.org/10.20944/preprints202309.1987.v1
Chicago/Turabian Style
Ali, M. 2023 "Simplified Multivariate Linear Regression based Model to Predict Asphaltene Onset Pressure under Reservoir Pressure Depletion Conditions using Data Clustering Technique" Preprints. https://doi.org/10.20944/preprints202309.1987.v1
Abstract
The precipitation, flocculation, and deposition of asphaltene cause severe formation damage within a reservoir and shorten a well’s productive life. Pressure depletion is one factor that contributes to asphaltene precipitation during production; therefore, the first step in managing asphaltene is to determine the onset pressure of the precipitation. While there are numerous equation of state models that can be used to predict the onset pressure, these models are complex and heavily reliant on tuning parameters. Using multivariate linear regression, this work attempts to develop a simple and accurate thermodynamic model for predicting the upper precipitation onset pressure under pressure depletion above the bubble point pressure (Pb) at various temperatures. A total of 94 experimental data points from 37 published crude oil data sets were compiled from the literature. To develop the model, 59 experimental data points were used as training data and 35 experimental data points as testing data. According to the results of the multicollinearity test, the bubble point pressure, temperature, resins, and saturate-to-aromatic ratio were chosen as predictors. The upper onset pressure data with comparable trends were clustered, and unsupervised recognition of three distinct cluster groups was performed. For each cluster identified, a multivariate linear regression model was developed. The model was chosen based on Mallow’s coefficient of determination (Cp), adjusted R2 (statistical measure of fit), and S (standard error of the regression slope). The developed model was tested using a data set, and the results showed an adjusted R2 of 96.25%, with a mean absolute error of 4.1%. The model was randomly applied to 15 data points to compare it to perturbed-chain statistical associated fluid theory (PC SAFT) and the Peng-Robinson equation of state models and to the multivariate regression models of Fahim (2007) and Ameli et al. (2016). The results showed that the mean absolute error for predicting the asphaltene precipitation onset pressure was 2.82% using Peng-Robinson, 2.36% using the PC SAFT equation of state, 23.96% using the Fahim model, 24.80% using the model reported by Ameli et al., and 2.39% using the newly developed multivariate regression model. The developed multivariate model appears to be as accurate as the PC SAFT equation of state modeling with tuning parameters. The primary advantage of multivariate regression is that, unlike the PC SAFT equation of state model, it does not require saturates, aromatics, resins, and asphaltenes (SARA)-based characterization methodologies or rigorous parameter tuning. It is simple to use, quick, and it produces results in a short period of time.
Keywords
asphaltene precipitation; asphaltene prediction; asphaltene machine learning model
Subject
Engineering, Chemical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
