Qualitative and quantitative reservoir characterization using seismic inversion based on particle swarm optimization and genetic algorithm: A comparative case study

Abstract

Accurate reservoir characterization is necessary to effectively monitor, manage, and increase production. A seismic inversion methodology using a genetic algorithm (GA) and particle swarm optimization (PSO) technique has been devised in thisstudy to characterize the reservoir both qualitatively and quantitatively. It has always been difficult and expensive to disclose deeper reservoirs in exploratory operations when using traditional approaches for reservoir characterization hence inversion based on advanced technique (based on GA and PSO) is proposed in this study. The main goal is to use GA and PSO to significantly lower the fitness (error) function between real seismic data and modeled synthetic data, which will allow us to estimate subsurface properties and, ultimately, accurately characterize the reservoir. Using two synthetic data and one real data from the Blackfoot field in Canada, the study examined subsurface acoustic impedance and porosity in the inter-well zone. Porosity and acoustic impedance are layer features, but seismic data is an interface property, hence these characteristics provide more useful and applicable reservoir information. The inverted results aid in the understanding of seismic data by providing incredibly high-resolution images of the subsurface. Both the GA and the PSO algorithms deliver outstanding results for both simulated and real data. The inverted section accurately anticipated a high porosity zone (> 20%) that supported thehigh seismic amplitude anomaly by having a low acoustic impedance (6000-8500m/s∗ g/cc). This unusual zone is categorized as a reservoir (sand channel) and is located in the 1040–1065 ms time range. In this inversion process, after 400 iterations, thefitness error falls from 1 to 0.88 using GA optimization, compared to 1 to 0.25 using PSO. The convergence time for GA is 670680 seconds, but the convergence time for PSO optimization is 356400 seconds, showing that the former requires 88% more time than the latter. The study recommends PSO application over GA in any E & P project because PSO inversion delivers faster convergence times, excellent error minimization, and high-resolution subsurface data compared to inversion utilizing GA optimization.