Data-Driven Sidetrack Well Placement Optimization

Sidetracking technology has become a relatively mature approach for redeveloping mature fields and restoring the productivity of old wells. However, the design of conventional sidetracking projects has largely relied on expert experience or numerical simulation, methods that are often time-consuming, labor-intensive, and subjective. To overcome these limitations, this study proposes a data-driven optimization framework for sidetrack well placement. It utilizes machine learning techniques trained on a large-scale synthetic dataset generated from field-informed numerical simulations, to establish a robust machine-learning proxy model. Four predictive models—Linear Regression, Polynomial Regression, Random Forest, and a Backpropagation (BP) Neural Network—were systematically compared, among which the Random Forest model achieved the best predictive accuracy. After hyperparameter optimization, a robust prediction model for sidetracking performance was established, achieving a Mean Squared Error (MSE) of 0.0008 (Root Mean Squared Error, RMSE, of 0.0283) and an R² of 0.8059 on the test set. To further optimize well placement, a mathematical model was formulated with the objective of maximizing the production enhancement rate. Three optimization algorithms—the Multi-Level Coordinate Search (MCS), Differential Evolution (DE), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES)—were evaluated, with the DE algorithm demonstrating superior performance. By integrating the optimized Random Forest predictor with the DE optimizer, a systematic methodology for sidetrack well placement optimization was developed. A field case study validated the approach, showing significant improvements, including a reduced water cut and an incremental cumulative oil production of 82.7 tons. This research demonstrates the simulation-based feasibility of intelligent sidetrack well placement optimization and provides practical guidance for future sidetracking development strategies.