Well Intervention Scheduling Using Machine Learning Feedback
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Solution Overview
Problem
Delays in well operations due to sub-task delays can lead to a domino effect, impacting hydrocarbon production targets, and existing human planning methods are prone to errors and inefficiencies.
Innovation Solution
A well intervention manager using machine learning algorithms automatically generates and adjusts well intervention plans based on real-time data inputs, including service providers, resources, and conditions, to optimize scheduling and minimize delays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human planning methods are used for well operations scheduling, then operational flexibility and adaptability are maintained, but errors and inefficiencies increase leading to delays
Solution Approach 1:
The patent replaces human mechanical planning processes with an automated machine learning-based system. The well intervention manager automatically generates and adjusts intervention plans by processing well data, provider availability, and scheduling criteria through algorithms, eliminating human error while maintaining operational flexibility through dynamic adaptability to changing conditions.
2Productivity
If automated machine learning-based planning is implemented, then operational efficiency and accuracy improve, but system complexity increases
Solution Approach 1:
The well intervention manager serves as an intermediary system between raw well data and intervention planning decisions. It processes multiple data inputs (well conditions, provider availability, scheduling criteria) through machine learning algorithms to generate optimized intervention plans, managing system complexity by centralizing the decision-making process in a dedicated computational component.
3Adaptability or versatility
If dynamic adjustment of scheduling criteria is performed based on real-time data, then adaptability to changing conditions improves, but computational requirements and processing time increase
Solution Approach 1:
The system implements continuous feedback loops where the well intervention manager monitors well data, execution progress, and changing conditions, then dynamically adjusts intervention plans in real-time. The machine learning model processes incoming data streams and modifies scheduling criteria automatically, enabling rapid adaptation without significant processing delays through optimized computational algorithms.
Data Source
AI summary
A method may include generating a first well intervention plan for a well site automatically based on a predetermined scheduling criterion. The first well intervention plan is generated using a first set of data inputs regarding one or more well intervention providers and one or more well conditions. The method may further include obtaining first well data regarding the well site. The method may further include adjusting the predetermined scheduling criterion to produce an adjusted scheduling criterion using the first well data. The adjusted scheduling criterion corresponds to a second set of data inputs that are different from the first set of data inputs. The method may further include generating a second well intervention plan for the well site based on the adjusted scheduling criterion. The method may further include transmitting a command to the well site that adjusts well operations based on the second well intervention plan.