Optimization Design of Drilling Fluid Chemical Formula Based on Artificial Intelligence

Area of Science:

• Petroleum Engineering
• Materials Science
• Computational Science

Background:

• Drilling fluid formulation is critical for efficient oil and gas exploration.
• Traditional methods for predicting drilling fluid performance are labor-intensive and time-consuming.
• Optimizing drilling fluid properties requires accurate predictive models.

Purpose of the Study:

• To develop a support vector machine (SVM) based regression model for predicting key drilling fluid performance parameters.
• To apply the predictive model to enhance the efficiency of drilling fluid formulation design.
• To reduce the experimental workload associated with drilling fluid development.

Main Methods:

• Support Vector Machine (SVM) regression was employed to build a predictive model.
• The model was trained to predict apparent viscosity (AV), plastic viscosity (PV), API filter loss (FL API), and roll recovery (R).
• The predictive model was integrated into an overall drilling fluid formulation optimization design framework.

Main Results:

• The SVM model demonstrated accuracy in predicting drilling fluid performance parameters.
• The model facilitated reverse engineering of treatment agent additions based on desired performance.
• Experimental validation confirmed the prediction accuracy of the developed model.

Conclusions:

• The developed SVM regression model effectively predicts drilling fluid performance parameters.
• This approach significantly improves the efficiency of drilling fluid formulation design.
• The research offers a valuable tool for reducing experimental efforts in drilling fluid optimization.