Lost Circulation Prediction Method Based on an Improved Fruit Fly Algorithm for Support Vector Machine Optimization.

Area of Science:

• Petroleum Engineering
• Drilling Operations
• Predictive Modeling

Background:

• Lost circulation events during drilling are difficult to control due to their abrupt nature.
• Traditional diagnostic methods are subjective, empirical, and lead to delayed or inaccurate predictions.
• There is a critical need for efficient, timely, and accurate lost circulation prediction methods.

Purpose of the Study:

• To develop a novel, efficient, and accurate method for predicting lost circulation events.
• To enhance the prediction accuracy and speed compared to existing techniques.
• To improve the reliability and performance of drilling operations.

Main Methods:

• Dimensionality reduction using Principal Component Analysis (PCA) and empirical analysis.
• Integration of an Improved Fruit Fly Optimization Algorithm (IFOA) with Support Vector Machine (SVM) techniques.
• Incorporation of tent map strategy for balanced population distribution and sine-cosine search for optimization.

Main Results:

• The proposed IFOA-SVM model achieved exceptional performance metrics: 96.8% accuracy, 97% precision, 96% recall, and 96% F1 score.
• The method demonstrated high accuracy with a reduced number of iterations, indicating efficiency and speed.
• The integrated approach effectively overcomes challenges like irregular population distribution and premature convergence.

Conclusions:

• The developed IFOA-SVM approach is a highly accurate and efficient method for lost circulation prediction.
• This novel methodology significantly improves upon traditional prediction techniques.
• Implementation in drilling operations can enhance efficiency, reliability, and overall performance.