Drilling Data Mining System for Inefficiency Detection
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Solution Overview
Problem
Hydrocarbon extraction sites face inefficiencies and non-productive time due to obstacles that are difficult to quantify and address, primarily due to challenges in processing large data sets and integrating relevant data sources for optimizing resource allocation and drilling processes.
Innovation Solution
A computer system that accesses and formats data from hardware sensors and historical records to identify inefficiencies, using data mining, natural language processing, and machine learning to analyze drilling and completion reports, and generate optimized rig scheduling sequences to maximize productivity and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual methods are used to process drilling data, then data processing time is long and analysis is thorough, but productivity is low and non-productive time increases
Solution Approach 1:
The patent replaces manual mechanical data processing with automated computer systems that use machine learning algorithms, natural language processing, and data mining techniques to analyze drilling data, identify inefficiencies, and generate optimization recommendations without human intervention in the analysis process
Solution Approach 2:
The system enables self-service by automatically monitoring drilling operations, detecting inefficiencies, and providing remediation recommendations without requiring continuous human oversight. The machine learning models continuously learn from historical data to improve their analysis accuracy over time
2Measurement precision
If comprehensive data mining and analysis is performed, then identification of inefficiencies is accurate, but processing time and system complexity increase
Solution Approach 1:
The patent segments the data analysis process into distinct modules: data collection from multiple sources, data cleaning and formatting, machine learning-based pattern recognition, natural language processing for report analysis, and optimization recommendation generation. Each module handles specific tasks independently, making the complex system manageable and maintainable
Solution Approach 2:
The system introduces intermediary components including a data formatter that standardizes data from multiple sources, a machine learning engine that processes raw data into actionable insights, and a natural language processing unit that extracts information from text reports. These intermediaries simplify the overall system architecture by abstracting complexity into dedicated processing layers
3Loss of information
If historical data is integrated with sensor data, then analysis comprehensiveness is improved, but data processing complexity and time increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and storing historical drilling data in structured formats before actual analysis is needed. The system maintains ready-to-query databases of historical performance metrics, equipment parameters, and outcome data that can be quickly retrieved and integrated with current sensor data without time-consuming on-the-fly processing
Data Source
AI summary
Embodiments are directed to managing and improving a drilling and completions process at a hydrocarbon extraction site, and to optimizing resource allocation at a hydrocarbon extraction site/region. In one scenario, a computer system accesses data generated by hardware sensors implemented by drilling and completion equipment at a hydrocarbon extraction site. The computer system formats the sensor data into a form readable by a data mining algorithm, and mines the formatted sensor data to identify characteristics related to the drilling and completion process. The computer system also accesses and integrates historical data related to the drilling and completion equipment at the hydrocarbon extraction site. The computer system then computes drilling and completion performance indicators that identify inefficiencies based on the characteristics identified for the equipment and based on the accessed historical data. Then, a remediation step is performed to resolve the identified inefficiency.