Project Title: Automated Drilling Time and Cost Data Extraction and Analysis System

Project Overview:
This project focused on designing and deploying an automated system to streamline the extraction and analysis of drilling time and cost data from over 50,000 daily drilling reports. Using Amazon Textract and custom Natural Language Processing (NLP) models, the solution significantly reduced manual data processing time by 85%, while identifying $2.3 million in potential cost savings.

Project Description:
In oil and gas operations, extracting actionable insights from unstructured drilling reports is critical for optimizing operational efficiency and reducing costs. This project involved the end-to-end development of an intelligent system to automate the processing of large volumes of daily drilling reports. By integrating advanced cloud-based machine learning services and custom NLP algorithms, the solution efficiently parsed and analyzed data, enabling faster decision-making and improved cost management.

Key Activities and Achievements:

Requirement Analysis and System Design:

Conducted a thorough analysis of existing manual processes to identify pain points and areas for automation.
Designed a scalable system architecture leveraging Amazon Web Services (AWS) for processing, analyzing, and storing extracted data.
Implementation of Amazon Textract for Data Extraction:

Utilized Amazon Textract to digitize and extract structured data, such as time, cost, and operational metrics, from unstructured drilling reports.
Configured Textract to handle diverse formats and layouts of the reports, ensuring high accuracy and reliability.
Development of Custom NLP Models:

Built and deployed custom NLP models to interpret complex language and contextual information in the drilling reports.
Fine-tuned models to classify and tag key data points, such as activity codes, downtime causes, and cost details, ensuring relevant insights.
Automated Workflow Deployment:

Integrated Textract and NLP pipelines into an automated workflow using AWS Lambda for real-time data processing.
Stored extracted and analyzed data in Amazon S3 and Amazon RDS for further analysis and reporting.
Analytics and Cost Optimization Insights:

Implemented dashboards using Amazon QuickSight to visualize trends in drilling time and costs.
Analyzed extracted data to identify inefficiencies, bottlenecks, and opportunities for cost reduction, resulting in $2.3 million in potential savings.
Results and Impact:

Efficiency Gains: Reduced manual data processing time by 85%, allowing teams to focus on high-value tasks.
Scalability: Architected a scalable system capable of processing thousands of reports daily, accommodating future growth.
Cost Savings: Identified $2.3 million in potential cost savings through actionable insights derived from the extracted data.
Enhanced Decision-Making: Provided real-time access to critical drilling metrics, enabling faster and more informed decision-making.
Technologies and Tools:

Cloud Services: Amazon Textract, AWS Lambda, Amazon S3, Amazon RDS, Amazon QuickSight.
Machine Learning: Custom NLP models developed using Python and AWS SageMaker.
Automation: Serverless architecture with event-driven processing pipelines for efficiency and scalability.