Drilling Activity Sequencing Using Conditional Probability Paths
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Solution Overview
Problem
Drill plans in the oil and gas field are often incomplete, leading to subjectivity and human error in drilling operations, as they rely on experience-based knowledge and lack a systematic approach to sequence activities efficiently.
Innovation Solution
A method and system that analyze an initial drill plan and current recommendations to determine a sequence of activities based on conditional probabilities, augmenting the initial plan with a modified drill plan that includes a path of drilling activities to enhance efficiency and reduce human error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If drill plans rely on experience-based knowledge from offset wells, then drilling operations can be guided by historical data, but the plans become incomplete and subject to human error and subjectivity
Solution Approach 1:
The patent replaces the manual, experience-based drill plan creation process with an automated machine learning system. The system uses trained models to automatically generate complete drill plans by analyzing offset well data and determining optimal activity sequences, eliminating human subjectivity and error while providing systematic, reproducible results
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between historical drilling data and drill plan generation. This intermediary processes offset well information and current recommendations to produce optimized activity sequences, bridging the gap between raw data and actionable drill plans while ensuring completeness and objectivity
2Productivity
If drill plans are generated using traditional methods, then the process is simple to implement, but the sequence of activities is not optimized and drilling efficiency is reduced
Solution Approach 1:
The patent replaces traditional manual drill plan generation with an automated machine learning system that optimizes activity sequences. The system analyzes multiple factors including offset well data and current recommendations to determine the most efficient drilling activity order, significantly improving drilling productivity while managing complexity through automation
Solution Approach 2:
The patent changes the parameters used in drill plan generation from simple experience-based heuristics to complex multi-factor analysis including conditional probabilities, activity sequences, and well-specific parameters. This enables optimized drilling efficiency by considering numerous variables simultaneously that traditional methods could not handle
3Ease of operation
If drill plans allow operators to improvise between successive activities, then flexibility is provided, but the process becomes open to subjectivity and human error
Solution Approach 1:
The patent performs preliminary action by generating complete, optimized drill plans before drilling operations begin. The machine learning system determines the full sequence of activities in advance based on comprehensive analysis, providing operators with a reliable roadmap that reduces the need for improvisation while maintaining operational flexibility through pre-calculated optimal paths
Solution Approach 2:
The patent incorporates feedback mechanisms by analyzing current recommendations and comparing them against historical data from offset wells. The system uses this feedback to refine and update drill plans, ensuring accuracy and reliability while adapting to specific well conditions and maintaining a balance between structured planning and operational flexibility
Data Source
AI summary
A method for planning a well that include receiving an initial drill plan and a current recommendation of a sequence of activities for drilling a target well. The method also include analyzing the initial drill plan and selecting a subject activity of the initial drill plan. The method additionally include analyzing the current recommendation and determining a sequence of activities including a prior activity that is prior to the subject activity from the current recommendation, wherein the sequence of activities include one or more drilling activities that occur between the prior activity and the subject activity that are determined based on a conditional probability of an occurrence. The method further include analyzing the sequence of the activities and determining a path that represents the one or more drilling activities, wherein the initial drill plan is augmented based on the path and generated into a modified drill plan for drilling the target well.