Modular Hydrocarbon Facility Layout for Complex Site Planning
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Solution Overview
Problem
Current automated planning systems for hydrocarbon production sites are inefficient due to high computational requirements and limited ability to process geographical complexities, leading to delayed operations and increased costs in determining optimal component placements for hydrocarbon extraction and processing.
Innovation Solution
A method utilizing a processor to determine optimal layouts for hydrocarbon production sites by integrating particle swarm optimization algorithms with A* algorithms, allowing simultaneous determination of well placements, facility placements, and pipeline placements based on geological and cost data, while considering topological complexities and prohibited areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional automated planning systems are used to determine component placements, then comprehensive analysis of geographical and cost considerations can be performed, but processing time increases to days and computational resources are significantly consumed
Solution Approach 1:
The planning problem is divided into separate modules: well placement module, facility placement module, and pipeline routing module. Each module is solved independently using specialized algorithms (particle swarm optimization for well/facility placement, A* algorithm for pipeline routing), avoiding the need to process all components simultaneously and reducing overall computational time while maintaining comprehensive analysis capability
Solution Approach 2:
The system implements multi-scenario planning where different levels of analysis comprehensiveness can be selected based on computational resource availability. Users can choose between rapid assessment scenarios (partial action) and comprehensive optimization scenarios (excessive action), allowing flexible trade-off between processing time and analysis depth
2Device complexity
If traditional automated planning systems analyze limited numbers of components (10-20 components), then computational requirements are manageable, but the ability to handle large-scale hydrocarbon operations with numerous wells and facilities is restricted
Solution Approach 1:
By segmenting the planning problem into independent modules (well placement, facility placement, pipeline routing), the system can scale to handle large numbers of components efficiently. Each module processes its specific component type using optimized algorithms, allowing the overall system to handle hundreds or thousands of components without exponential increase in computational complexity
Solution Approach 2:
The planning system is designed with universal algorithms that can handle any number of components. The particle swarm optimization algorithm works regardless of the number of wells or facilities, and the A* algorithm routes pipelines between any pair of facilities. This multi-functionality allows the system to scale from small to large-scale operations without requiring fundamentally different approaches
3Reliability
If comprehensive automated planning is performed for all components, then optimal placements can be identified, but computational power requirements and processing time increase significantly
Solution Approach 1:
The comprehensive planning problem is segmented into three independent optimization sub-problems, each solved with appropriate algorithms. This segmentation maintains optimization accuracy for each component type while avoiding the exponential computational power requirements of a monolithic optimization approach, as each sub-problem can be solved with moderate computational resources
Solution Approach 2:
The system uses intermediate results from each module as inputs to subsequent modules. Well placements from the first module inform facility placement options in the second module, which in turn inform pipeline routing in the third module. These intermediaries allow comprehensive optimization to be achieved through sequential processing rather than simultaneous optimization of all components, reducing peak computational power requirements
Data Source
AI summary
A method for identifying locations for components of a hydrocarbon production facility may involve receiving, via a processor, input data having one or more maps representative of an area, a plurality of sets of coordinates for a plurality of wells, and cost data associated with at least one of the plurality of components. The method may also involve determining a set of candidate components that corresponds to the plurality of locations based on the input data and an optimization algorithm and determining additional sets of candidate components that correspond to the plurality of locations based on the input data, the set of candidate locations, and the optimization algorithm. The method may then include generating one or more additional maps indicative of the plurality of locations for the plurality of components based on at least one of the one or more additional sets of candidate components.