Prediction of plugging formulation based on PSO-BP optimization neural network

Abstract

In the context of drilling operations, the study investigated the ability of a combination of rigid mineral particles and composite plugging agents to seal simulated cracks effectively. The study used a neural network model to predict the outcomes of experiments using this combination, based on data collected during the research. Initially, a backpropagation (BP) neural network was used to establish the prediction model, which was later optimized using the particle swarm optimization (PSO) algorithm to improve its accuracy, stability, and learning abilities. As a result, the optimized prediction model was found to be capable of providing accurate and compliant drilling plugging formulas quickly. This feature helped guide targeted formula experiments and significantly reduced experimental time and costs. In five practices in a well area in the southern Sichuan region of China, the application success rate was as high as 60%, and the time spent on plugging was reduced by an average of 36%. Overall, this study contributes to the development of effective and efficient drilling techniques, which are essential in the exploration and production of hydrocarbon resources.