ML Well Trajectory Planning for Hydrocarbon Facility Placement
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Solution Overview
Problem
Current automated planning systems for hydrocarbon facilities and pipelines are inefficient, requiring significant processing power and time to determine optimal locations, leading to delayed operations, higher costs, and reduced efficiency due to their inability to handle complex topological and geographical considerations.
Innovation Solution
A modular hydrocarbon facility placement planning system using machine learning algorithms, specifically Artificial Neural Networks (ANN) and particle swarm optimization (PSO), to optimize well trajectories and facility placements, reducing computational requirements and improving analysis efficiency by simultaneously solving for well, facility, and pipeline placements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional automated planning systems are used to determine optimal facility locations, then comprehensive geographical and topological considerations can be analyzed, but processing time and computational resources increase significantly
Solution Approach 1:
The planning system is divided into modular components: machine learning model for initial facility placement prediction, trajectory generation module for well paths, and pipeline routing module for connecting facilities. Each module processes specific aspects independently, reducing overall computational time while maintaining analysis accuracy.
Solution Approach 2:
The machine learning model performs preliminary facility placement predictions before detailed trajectory and pipeline design. This preliminary action provides initial optimal locations that guide subsequent detailed planning, significantly reducing the search space and computational requirements for comprehensive geographical and topological analysis.
2Adaptability or versatility
If traditional automated planning systems analyze multiple components, then comprehensive solutions can be identified, but the number of components analyzed is limited to 10-20 components
Solution Approach 1:
The system processes well trajectories, facility placements, and pipeline routings as separate modular modules that can independently handle large numbers of components. This segmentation allows each module to scale independently, enabling analysis of hundreds or thousands of components without proportionally increasing overall system complexity or reducing analysis efficiency.
Solution Approach 2:
The machine learning model serves multiple functions: predicting facility locations, optimizing well trajectories, and guiding pipeline routing. This multi-functionality allows the system to analyze comprehensive numbers of diverse components (wells, facilities, pipelines) simultaneously without requiring separate specialized systems for each component type.
3Manufacturing precision
If traditional planning systems determine suitable locations, then accurate placement can be achieved, but operational delays and costs increase
Solution Approach 1:
The machine learning model performs preliminary predictions of optimal facility placements and well trajectories before detailed design and construction planning. This preliminary action provides accurate placement recommendations that guide subsequent detailed engineering, reducing iterative revisions and accelerating overall project delivery while maintaining high placement accuracy.
Solution Approach 2:
The system incorporates feedback loops where machine learning predictions are validated against comprehensive geographical and topological constraints, and results are continuously refined. This feedback mechanism ensures accurate facility placement while reducing operational delays by identifying and resolving potential issues before construction begins.
Data Source
AI summary
A method may include receiving input data of one or more reservoir well section locations and a facility location and initializing the machine learning algorithm based on the input data. Moreover, the machine learning model may be trained to determine one or more well trajectories that adhere to a set of constraints based on a training dataset of predefined well trajectory solutions. The method may also include determining, via the machine learning algorithm, a well trajectory design between the facility location and at least one of the reservoir well section locations based on the facility location and the reservoir well section location.