Adaptive algorithms for history matching and uncertainty quantification

Numerical reservoir simulation models are the basis for many decisions in regard to predicting, optimising, and improving production performance of oil and gas reservoirs. History matching is required to calibrate models to the dynamic behaviour of the reservoir, due to the existence of uncertainty in model parameters. Finally a set of history matched models are used for reservoir performance prediction and economic and risk assessment of different development scenarios. Various algorithms are employed to search and sample parameter space in history matching and uncertainty quantification problems. The algorithm choice and implementation, as done through a number of control parameters, have a significant impact on effectiveness and efficiency of the algorithm and thus, the quality of results and the speed of the process. This thesis is concerned with investigation, development, and implementation of improved and adaptive algorithms for reservoir history matching and uncertainty quantification problems. A set of evolutionary algorithms are considered and applied to history matching. The shared characteristic of applied algorithms is adaptation by balancing exploration and exploitation of the search space, which can lead to improved convergence and diversity. This includes the use of estimation of distribution algorithms, which implicitly adapt their search mechanism to the characteristics of the problem. Hybridising them with genetic algorithms, multiobjective sorting algorithms, and real-coded, multi-model and multivariate Gaussian-based models can help these algorithms to adapt even more and improve their performance. Finally diversity measures are used to develop an explicit, adaptive algorithm and control the algorithm’s performance, based on the structure of the problem. Uncertainty quantification in a Bayesian framework can be carried out by resampling of the search space using Markov chain Monte-Carlo sampling algorithms. Common critiques of these are low efficiency and their need for control parameter tuning. A Metropolis-Hastings sampling algorithm with an adaptive multivariate Gaussian proposal distribution and a K-nearest neighbour approximation has been developed and applied

An innovative metaheuristic strategy for solar energy management through a neural networks framework

Proper management of solar energy as an effective renewable source is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO), is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for nonlinearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development

Numerical Analysis of Two Phase Flow and Heat Transfer in Condensers

Steam surface condensers are commonly used in the power generation industry and their performance significantly affects the efficiency of the power plant. Therefore, it is vital to acquire a better understanding of the complex phenomena occurring inside condensers. A general three-dimensional numerical model is developed in this study to simulate the two-phase flow and heat transfer inside full-scale industrial condensers with irregular tube bundle shapes. The Eulerian-Eulerian two-phase model is selected to simulate gas and liquid flows and the interaction between them. A porous media approach is adopted to model the presence of large number of tubes in the condenser. The effect of the turbulence on the primary phase is accounted for by solving the transport equations for turbulent kinetic energy and dissipation rate. Various types of turbulence models are evaluated to select the best model for the condenser analysis. Also, the modified k-ε and RNG k-ε models are proposed to model the flow and heat transfer in condensers by adding the corresponding terms to the transport equations of the turbulence model to account for the effects of the tube bundle and condensate droplets on the primary phase turbulence, momentum and heat transfer. The proposed model provides excellent match with the experimental data and a significant improvement over the previous models. Furthermore, the proposed numerical model is coupled with a novel swarm intelligence multi-objective optimization algorithm to evaluate the performance of the new design candidates and introduce a set of condenser designs based on various input parameters and objective functions

Topological optimization for tailored designs of advection–diffusion-reaction porous reactors based on pore scale modeling and simulation: A PNM-NSGA framework

Alizadeh Mehrzad, Gostick Jeff, Suzuki Takahiro, et al. Topological optimization for tailored designs of advection–diffusion-reaction porous reactors based on pore scale modeling and simulation: A PNM-NSGA framework. Computers & Structures 301, 107452 (2024); https://doi.org/10.1016/j.compstruc.2024.107452

Optimizing CO2CO_{2} Capture in Pressure Swing Adsorption Units: A Deep Neural Network Approach with Optimality Evaluation and Operating Maps for Decision-Making

This study presents a methodology for surrogate optimization of cyclic adsorption processes, focusing on enhancing Pressure Swing Adsorption units for carbon dioxide ($CO_{2}$) capture. We developed and implemented a multiple-input, single-output (MISO) framework comprising two deep neural network (DNN) models, predicting key process performance indicators. These models were then integrated into an optimization framework, leveraging particle swarm optimization (PSO) and statistical analysis to generate a comprehensive Pareto front representation. This approach delineated feasible operational regions (FORs) and highlighted the spectrum of optimal decision-making scenarios. A key aspect of our methodology was the evaluation of optimization effectiveness. This was accomplished by testing decision variables derived from the Pareto front against a phenomenological model, affirming the surrogate models reliability. Subsequently, the study delved into analyzing the feasible operational domains of these decision variables. A detailed correlation map was constructed to elucidate the interplay between these variables, thereby uncovering the most impactful factors influencing process behavior. The study offers a practical, insightful operational map that aids operators in pinpointing the optimal process location and prioritizing specific operational goals