Critical point calculations of multi-component reservoir fluids using nature-inspired metaheuristic algorithms

Abstract

This study introduces the application of nature-inspired metaheuristic algorithms for performing critical point calculations in multicomponent reservoir fluids. These algorithms are Monkey – Krill Herd Hybrid (MAKHA), Intelligent Firefly Algorithm (IFA), Covariance Matrix Adaptation Evolution Strategy (CMAES), Artificial Bee Colony (ABC), Cuckoo Search (CS), Bare Bones Particle Swarm Optimization (BBPSO) and Flower Pollination Algorithm (FPA). Capabilities and limitations of these optimizers have been analyzed using black oil, volatile oil, and condensate reservoir fluids with fifty components. Results showed that BBPSO, IFA and FPA outperformed other nature-inspired methods for critical point calculations in tested fluids. In particular, BBPSO offered the best efficiency-reliability tradeoff for the accurate prediction of critical points in multicomponent mixtures.