ML · Done

Shale Shaker Cutting Estimations

Role · Internship Machine Learning Engineer Timeline · 6 months
Shale Shaker Cutting Estimations

Overview

This project involved developing a computer vision system to monitor and analyze rock cuttings from shale shakers in real-time using CCTV footage. The system was designed to improve the efficiency and accuracy of drilling process monitoring by providing real-time data on rock coverage and composition, thereby supporting operational decision-making in the oil and gas industry.

Challenge

The primary challenges included processing video streams in real-time with minimal latency, accurately detecting and analyzing rock cuttings amidst varying lighting conditions and potential noise, and ensuring the system's reliability and scalability for integration with existing monitoring systems like the Early Monitoring System (EMS).

Result

The developed system successfully provided real-time monitoring of shale shaker cuttings, enabling more informed decision-making during drilling operations. It achieved high accuracy in detecting rock coverage and composition, with visualizations helping to identify trends in rock composition over time. The system was seamlessly integrated with the Early Monitoring System, enhancing overall operational efficiency and reducing manual monitoring efforts.

Key Statistics

90%

Detection Accuracy

<300ms

Processing Latency

85%

System Uptime

Successful

Integration with EMS

Technologies

Frontend

PythonOpenCVNumPy

Backend

Flask

Database

PostgreSQL

Real-time

Socket.IO

Gallery

Real-time Rock Detection

Real-time Rock Detection

Live feed with detected rock contours on the shale shaker.

Trend Analysis Graph

Trend Analysis Graph

Graph showing trends in rock composition over time.

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