Prediction of Completion Fluid Stability and Productivity Impact in Challenging Reservoir Environments Using Machine Learning Analysis

This study aims to develop and validate machine learning models to predict completion fluid stability and quantitatively assess its impact on well productivity in challenging reservoir environments. The study employed a quantitative, applied research design based on historical completion and production data from onshore oil and gas reservoirs in Iran. The dataset integrated reservoir properties, completion fluid physicochemical characteristics, operational parameters, and post-completion productivity indicators. After data preprocessing, feature engineering, and normalization, multiple supervised machine learning algorithms—including linear, kernel-based, and ensemble models—were trained and evaluated. Robust cross-validation and hyperparameter optimization strategies were applied to ensure model generalizability and prevent overfitting. Model interpretability was addressed through feature importance analysis and sensitivity evaluation. Inferential results indicated that nonlinear ensemble models significantly outperformed linear approaches in predicting completion fluid stability, achieving high explanatory power and low prediction error. Reservoir temperature and formation water salinity emerged as the most influential predictors, followed by fluid thermal stability limits and filtration loss characteristics. Predicted stability classes exhibited statistically meaningful differences in productivity outcomes, with high-stability completions associated with substantially higher normalized productivity indices and initial production rates. The relationship between predicted stability and productivity was nonlinear, revealing a threshold beyond which incremental stability improvements yielded diminishing productivity gains. The findings confirm that machine learning provides a robust and interpretable framework for predicting completion fluid stability and its productivity implications under complex reservoir conditions. By linking stability predictions to measurable production outcomes, the proposed approach offers a practical decision-support tool for optimizing completion fluid design, reducing formation damage risk, and enhancing economic performance in challenging reservoirs.This study aims to develop and validate machine learning models to predict completion fluid stability and quantitatively assess its impact on well productivity in challenging reservoir environments. The study employed a quantitative, applied research design based on historical completion and production data from onshore oil and gas reservoirs in Iran. The dataset integrated reservoir properties, completion fluid physicochemical characteristics, operational parameters, and post-completion productivity indicators. After data preprocessing, feature engineering, and normalization, multiple supervised machine learning algorithms—including linear, kernel-based, and ensemble models—were trained and evaluated. Robust cross-validation and hyperparameter optimization strategies were applied to ensure model generalizability and prevent overfitting. Model interpretability was addressed through feature importance analysis and sensitivity evaluation. Inferential results indicated that nonlinear ensemble models significantly outperformed linear approaches in predicting completion fluid stability, achieving high explanatory power and low prediction error. Reservoir temperature and formation water salinity emerged as the most influential predictors, followed by fluid thermal stability limits and filtration loss characteristics. Predicted stability classes exhibited statistically meaningful differences in productivity outcomes, with high-stability completions associated with substantially higher normalized productivity indices and initial production rates. The relationship between predicted stability and productivity was nonlinear, revealing a threshold beyond which incremental stability improvements yielded diminishing productivity gains. The findings confirm that machine learning provides a robust and interpretable framework for predicting completion fluid stability and its productivity implications under complex reservoir conditions. By linking stability predictions to measurable production outcomes, the proposed approach offers a practical decision-support tool for optimizing completion fluid design, reducing formation damage risk, and enhancing economic performance in challenging reservoirs.

کلیدواژه ها:

Completion fluid stability ، machine learning ، well productivity ، reservoir engineering ، formation damage ، data-driven modeling