Multiphase Oil and Gas Network Flow Modeling for Bottleneck Detection
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Solution Overview
Problem
Existing production systems for transporting oil and gas fluids face challenges in efficiently managing multiphase flows and optimizing infrastructure investments due to complex network configurations involving hundreds or thousands of interconnected flowlines and production equipment, requiring advanced thermodynamic and fluid dynamic understanding.
Innovation Solution
A computer-implemented method and system that utilizes a flow simulation model incorporating network models, equations for multiphase flow, and simulation tools to analyze and optimize production systems, including nodal analysis, pressure-volume-temperature analysis, and equipment modeling, enabling identification of bottlenecks and optimization of production operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a network model with hundreds or thousands of interconnected flowlines and production equipment is used to represent the production system, then the system can accurately represent complex production configurations, but the complexity of thermodynamic and fluid dynamic calculations increases substantially
Solution Approach 1:
The production system network is divided into multiple sub-networks, each representing a specific portion of the system (e.g., individual well groups, processing sections, or geographic regions). Each sub-network can be modeled and analyzed independently, then integrated into the overall system analysis. This segmentation reduces the computational complexity of thermodynamic and fluid dynamic calculations while maintaining accurate system representation.
2Productivity
If advanced thermodynamic and fluid dynamic analysis is performed on the production system, then better optimization of multiphase flows can be achieved, but the computational resources and time required increase
Solution Approach 1:
Standard thermodynamic and fluid dynamic analysis procedures are pre-configured and prepared for different types of production equipment and flow conditions. These preliminary analysis templates can be quickly applied to various sub-networks without requiring full detailed analysis each time, reducing computational time while maintaining optimization effectiveness.
3Measurement precision
If detailed equipment modeling and nodal analysis are performed throughout the network, then production bottlenecks can be accurately identified, but the computational burden increases significantly
Solution Approach 1:
Different levels of modeling detail are applied to different parts of the network based on their importance and characteristics. Critical sections where bottlenecks are likely to occur (such as choke points, processing facilities, or high-flow areas) receive detailed equipment modeling and nodal analysis, while less critical sections use simplified models. This local differentiation maintains bottleneck detection accuracy while reducing overall computational burden.
Data Source
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AI summary
A method can include receiving information that includes data that correspond to a range of a fluid production network variable; based at least in part on a portion of the data, determining optimal parameter values of a multi-parameter proxy model for at least a portion of the range of the fluid production network variable; based at least in part on a portion of the optimal parameter values, deriving functions for the parameters of the multi-parameter proxy model; and based at least in part on the functions, determining a value of the fluid production network variable for a fluid production network.