Multi-objective Genetic Algorithm for Well Production Optimization
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Solution Overview
Problem
The oil and gas industry faces challenges in maximizing hydrocarbon production and recovery due to computationally expensive and labor-intensive manual processes for calculating well production rates, which are prone to errors and inefficiencies in implementing strategically designed field development plans and reservoir production strategies.
Innovation Solution
A multi-objective genetic algorithm is used to determine optimal well production rates by defining a fitness function that meets target oil rates, maximizes bottom-hole reservoir pressure, and minimizes water cut, automating the process and reducing manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calculation methods are used to determine well production rates, then calculation accuracy can be maintained through expert judgment, but computational time and labor intensity increase significantly
Solution Approach 1:
The patent replaces manual mechanical calculation methods with an automated computer-based system that uses genetic algorithms and machine learning models to determine well production rates, eliminating labor-intensive manual computations while maintaining or improving accuracy through iterative optimization
Solution Approach 2:
The system enables self-service automation where the computer-based platform automatically performs production parameter calculations without requiring manual expert intervention, using trained machine learning models and genetic algorithms to independently optimize well production rates based on reservoir data
2Productivity
If traditional field development planning methods are used, then implementation simplicity can be maintained, but strategic optimization of hydrocarbon recovery is limited
Solution Approach 1:
The patent transforms production strategy optimization by changing key parameters through genetic algorithms that iteratively adjust well production rates, reservoir pressure constraints, and water cut limitations to maximize hydrocarbon recovery while adapting to changing field conditions
Solution Approach 2:
The patent introduces a computer-based intermediary system that acts as a mediator between reservoir engineering principles and field implementation, using genetic algorithms and machine learning models to translate complex optimization objectives into actionable well production strategies
3Manufacturing precision
If individual well control is implemented to meet production strategy requirements, then production target accuracy is improved, but operational complexity and monitoring requirements increase
Solution Approach 1:
The patent implements a universal computer-based control system that simultaneously manages multiple wells and performs diverse functions including production rate optimization, pressure constraint enforcement, water cut monitoring, and automated recommendation generation, simplifying operational complexity while maintaining precise control
Data Source
AI summary
Systems and methods for operating wells of a field using a multi-objective genetic algorithm are disclosed. In one embodiment, a method of operating a plurality of wells within a field includes determining an oil rate for each well of the plurality of wells by a multi-objective genetic algorithm. The multi-objective genetic algorithm is defined by a multi-objective fitness function including a first objective function that meets a target oil rate for the field and a second objective function that maximizes bottom-hole reservoir pressure, maximizes a distance of the wells to a crest line of the field, and minimizes a water cut of the field. The multi-objective genetic algorithm outputs the oil rate for each well that satisfies the multi-objective fitness function. The method further includes operating the plurality of wells at the oil rate for each well.