Towards Bayesian Quantification of Permeability in Micro-scale Porous Structures – The Database of Micro Networks

This article develops a Bayesian framework to quantify the absolute permeability of water in a porous structure from the geometry and clustering parameters of its underlying pore-throat network. These parameters include the network`s diameter, transivity, degree, centrality, assortativity, edge density, K-core decomposition, Kleinberg’s hub centrality scores, Kleinberg's authority centrality scores, length, and porosity. In addition, the incorporated clustering aspects of the networks have been determined with respect to several clustering criteria – edge betweenness, greedy optimization of modularity, multi-level optimization of modularity, and short random walks. As such, the article takes the first footsteps of creating a Database of Micro Networks for micro-scale porous structures, to be used as main input stream for the proposed Bayesian scheme.publishedVersionThis is an open access article under the CC-BY license (https://creativecommons.org/licenses/by/4.0/)

The Global Equity Market Reactions of the Oil & Gas Midstream and Marine Shipping Industries to COVID-19: An Entropy Analysis

oai:ojs.pkp.sfu.ca:article/152This article quantifies the information flow between major equities in the Oil & Gas Midstream and Marine Shipping industries, on the basis of the effective transfer entropy methodology. In addition, the article provides the first analysis of investor fear and market expectations in these sectors, according to the Rényi entropy approach. The period of study was extended over five years to fully capture the pre/post-COVID situations. The entropy results reveal a major change in the underlying information flow pattern among equities in the Oil & Gas Midstream and Marine Shipping sectors in the aftermath of COVID-19. According to the new (post-COVID) paradigm, the stocks in the Oil & Gas Midstream and Integrated Freight & Logistics industries have gained momentum in occupying six of the ten positions within the list of the most influential equities in the market, in terms of information transmission. The disorder and randomness have decreased for over 89% of the studied equities, after virus outbreak. For the equities detected with high information-transmission standing, the Rényi entropy results indicate that investors more likely showed a higher level of future expectations and a lower level of fear regarding frequent market events within the post-COVID timeline. Doi: 10.28991/HIJ-2021-02-04-07 Full Text: PD

On the prediction of pseudo relative permeability curves: meta-heuristics versus Quasi-Monte Carlo

International audienceThis article reports the first application of the Quasi-Monte Carlo (QMC) method for estimation of the pseudo relative permeability curves. In this regards, the performance of several meta-heuristics algorithms have also been compared versus QMC, including the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the Artificial Bee Colony (ABC). The mechanism of minimizing the objective-function has been studied, for each method. The QMC has outperformed its counterparts in terms of accuracy and efficiently sweeping the entire search domain. Nevertheless, its computational time requirement is obtained in excess to the meta-heuristics algorithms

On the prediction of pseudo relative permeability curves: meta-heuristics versus Quasi-Monte Carlo

This article reports the first application of the Quasi-Monte Carlo (QMC) method for estimation of the pseudo relative permeability curves. In this regards, the performance of several meta-heuristics algorithms have also been compared versus QMC, including the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the Artificial Bee Colony (ABC). The mechanism of minimizing the objective-function has been studied, for each method. The QMC has outperformed its counterparts in terms of accuracy and efficiently sweeping the entire search domain. Nevertheless, its computational time requirement is obtained in excess to the meta-heuristics algorithms

Nanotechnology and global energy demand: challenges and prospects for a paradigm shift in the oil and gas industry.

The exploitation of new hydrocarbon discoveries in meeting the present global energy demand is a function of the availability and application of new technologies. The relevance of new technologies is borne out of the complex subsurface architecture and conditions of offshore petroleum plays. Conventional techniques, from drilling to production, for exploiting these discoveries may require adaption for such subsurface conditions as they fail under conditions of high pressure and high temperature. The oil and gas industry over the past decades has witnessed increased research into the use of nanotechnology with great promise for drilling operations, enhanced oil recovery, reservoir characterization, production, etc. The prospect for a paradigm shift towards the application of nanotechnology in the oil and gas industry is constrained by evolving challenges with its progression. This paper gave a review of developments from nano-research in the oil and gas industry, challenges and recommendations

Uncertainty and energy-sector equity returns in Iran: A Bayesian and quasi-monte Carlo time-varying analysis

This study investigates whether the implied crude oil volatility and the historical OPEC price volatility can impact the return to and volatility of the energy-sector equity indices in Iran. The analysis specifically considers the refining, drilling, and petrochemical equity sectors of the Tehran Stock Exchange. The parameter estimation uses the quasi-Monte Carlo and Bayesian optimization methods in the framework of a generalized autoregressive conditional heteroskedasticity model, and a complementary Bayesian network analysis is also conducted. The analysis takes into account geopolitical risk and economic policy uncertainty data as other proxies for uncertainty. This study also aims to detect different price regimes for each equity index in a novel way using homogeneous/non-homogeneous Markov switching autoregressive models. Although these methods provide improvements by restricting the analysis to a specific price-regime period, they produce conflicting results, rendering it impossible to draw general conclusions regarding the contagion effect on returns or the volatility transmission between markets. Nevertheless, the results indicate that the OPEC (historical) price volatility has a stronger effect on the energy sectors than the implied volatility has. These types of oil price shocks are found to have no effect on the drilling sector price pattern, whereas the refining and petrochemical equity sectors do seem to undergo changes in their price patterns nearly concurrently with future demand shocks and oil supply shocks, respectively, gaining dominance in the oil market

A hybrid Bayesian-network proposition for forecasting the crude oil price

This paper proposes a hybrid Bayesian Network (BN) method for short-term forecasting of crude oil prices. The method performed is a hybrid, based on both the aspects of classification of influencing factors as well as the regression of the out-of-sample values. For the sake of performance comparison, several other hybrid methods have also been devised using the methods of Markov Chain Monte Carlo (MCMC), Random Forest (RF), Support Vector Machine (SVM), neural networks (NNET) and generalized autoregressive conditional heteroskedasticity (GARCH). The hybrid methodology is primarily reliant upon constructing the crude oil price forecast from the summation of its Intrinsic Mode Functions (IMF) and its residue, extracted by an Empirical Mode Decomposition (EMD) of the original crude price signal. The Volatility Index (VIX) as well as the Implied Oil Volatility Index (OVX) has been considered among the influencing parameters of the crude price forecast. The final set of influencing parameters were selected as the whole set of significant contributors detected by the methods of Bayesian Network, Quantile Regression with Lasso penalty (QRL), Bayesian Lasso (BLasso) and the Bayesian Ridge Regression (BRR). The performance of the proposed hybrid-BN method is reported for the three crude price benchmarks: West Texas Intermediate, Brent Crude and the OPEC Reference Basket