AI Camera System for Real-Time Load Concentration Estimation

Abstract

Abstract This paper presents an innovative study that optimizes drilling operations by integrating advanced camera systems and computer vision image processing techniques. The core objective of this research is to monitor and quantify the drilled solids transported to the shale shaker in real-time. By employing a sophisticated camera system equipped with AI-based image processing capabilities, this study introduces a groundbreaking approach to estimating the concentration of drill cuttings as a function of time directly at the drilling site. This methodology aims to accurately measure the shaker load, thereby facilitating estimating time-dependent cutting concentration during drilling operations. The research enhances the system's precision by comparing the actual cuttings concentration measured from the loop with the estimates derived from the processed camera images. This comparative analysis aims to validate the effectiveness of the camera-based system and its potential to revolutionize the accuracy and efficiency of drilling operations. The findings of this study are anticipated to contribute significantly to increased operational efficiency in the oil and gas industry, marking a substantial step forward in applying AI in field-based drilling analytics.