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

On the application of artificial bee colony (ABC) algorithm for optimization of well placements in fractured reservoirs; efficiency comparison with the particle swarm optimization (PSO) methodology

AbstractThe application of a recent optimization technique, the artificial bee colony (ABC), was investigated in the context of finding the optimal well locations. The ABC performance was compared with the corresponding results from the particle swarm optimization (PSO) algorithm, under essentially similar conditions. Treatment of out-of-boundary solution vectors was accomplished via the Periodic boundary condition (PBC), which presumably accelerates convergence towards the global optimum. Stochastic searches were initiated from several random staring points, to minimize starting-point dependency in the established results. The optimizations were aimed at maximizing the Net Present Value (NPV) objective function over the considered oilfield production durations. To deal with the issue of reservoir heterogeneity, random permeability was applied via normal/uniform distribution functions. In addition, the issue of increased number of optimization parameters was address, by considering scenarios with multiple injector and producer wells, and cases with deviated wells in a real reservoir model. The typical results prove ABC to excel PSO (in the cases studied) after relatively short optimization cycles, indicating the great premise of ABC methodology to be used for well-optimization purposes

A Data Mining Perspective on the Confluent Ions` Effect for Target Functionality

The production of hydrocarbon resources at an oil field is concomitant with challenges with respect to the formation of scale inside the reservoir rock, intricately impairing its permeability and hindering the flow. Historically, the effect of ions has been attributed to the undergone phenomenon; nevertheless, there exists a great deal of ambiguity about its relative significance compared to other factors, or the effectiveness as per the ion type. The present work applies a data mining strategy to uncover the influence hierarchy of the parameters involved in driving the process within major rock categories—sandstone and carbonate—to regulate a target functionality. The functionalities considered revolve around maximizing oil recovery and minimizing permeability impairment/scale damage. A pool of experimental as well as field data was used for this purpose, accumulating the bulk of the available literature data. The methods used for data analysis in the present work included the Bayesian Network, Random Forest, Deep Neural Network, as well as Recursive Partitioning. The results indicate a rolling importance for different ion species, altering under each functionality, which is not ranked as the most influential parameter in either case. For the oil recovery target, our results quantify a distinction between the source of an ion of a single type in terms of its influencing rank in the process. This latter deduction is the first proposal of its kind, suggesting a new perspective for research. Moreover, the machine learning methodology was found to be capable of reliably capturing the data, as evidenced by the minimal errors in the bootstrapped results. Doi: 10.28991/HIJ-2021-02-03-05 Full Text: PD

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

oai:ojs.pkp.sfu.ca:article/55This 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 steps towards creating a database of micro-networks for micro-scale porous structures, to be used as the main input stream for the proposed Bayesian scheme. Doi: 10.28991/HIJ-2020-01-04-02 Full Text: PD

QSPR study of viscoplastic properties of peptide-based hydrogels

In this study, the power of machine learning was harnessed to probe the link between molecular structures of peptide-based hydrogels and their viscoplastic properties. The selection of compounds was attempted in accordance with the prescribed full list of peptide-based materials exhibiting hydrogel functionality in the literature. In this pursuit, a complete set of molecular descriptors and fingerprints was considered – accounting for an entry of size 17,968 for each peptide-based structure analyzed. The elastic and viscous moduli response of materials were mapped over a wide frequency spectrum in the range [0.1–100] (rad/s). In general, the results indicate that the frequency-dependent mechanical response of peptide-based hydrogels is statistically correlated with its (inter)molecular attributes, such as charge, first ionization potential (or equivalently electronegativity), surface area, number of chemical substrates, bond type, and intermolecular interactions. The performance of several (supervised) soft computing techniques was measured, for our quantitative structure property relationships model. In addition, the hypothesis of mapping our databank to a new system of principal components was tested, by using an unsupervised methodology, which resulted in enhancement of the prediction accuracy. In terms of significance, the present article provides the first report of frequency-dependent elastic and viscous moduli, for a set of 70 peptide-based formulations with hydrogel functionality. Communicated by Ramaswamy H. Sarma</p