Probabilistic Assessment and Uncertainty Analysis of CO2 Storage Capacity of the Morrow B Sandstone—Farnsworth Field Unit

This paper presents probabilistic methods to estimate the quantity of carbon dioxide (CO2) that can be stored in a mature oil reservoir and analyzes the uncertainties associated with the estimation. This work uses data from the Farnsworth Field Unit (FWU), Ochiltree County, Texas, which is currently undergoing a tertiary recovery process. The input parameters are determined from seismic, core, and fluid analyses. The results of the estimation of the CO2 storage capacity of the reservoir are presented with both expectation curve and log probability plot. The expectation curve provides a range of possible outcomes such as the P90, P50, and P10. The deterministic value is calculated as the statistical mean of the storage capacity. The coefficient of variation and the uncertainty index, P10/P90, is used to analyze the overall uncertainty of the estimations. A relative impact plot is developed to analyze the sensitivity of the input parameters towards the total uncertainty and compared with Monte Carlo. In comparison to the Monte Carlo method, the results are practically the same. The probabilistic technique presented in this paper can be applied in different geological settings as well as other engineering applications.

Ant Colony Optimization with Warm-Up

The Ant Colony Optimization (ACO) is a probabilistic technique inspired by the behavior of ants for solving computational problems that may be reduced to finding the best path through a graph. Some species of ants deposit pheromone on the ground to mark some favorable paths that should be used by other members of the colony. Ant colony optimization implements a similar mechanism for solving optimization problems. In this paper a warm-up procedure for the ACO is proposed. During the warm-up, the pheromone matrix is initialized to provide an efficient new starting point for the algorithm, so that it can obtain the same (or better) results with fewer iterations. The warm-up is based exclusively on the graph, which, in most applications, is given and does not need to be recalculated every time before executing the algorithm. In this way, it can be made only once, and it speeds up the algorithm every time it is used from then on. The proposed solution is validated on a set of traveling salesman problem instances, and in the simulation of a real industrial application for the routing of pickers in a manual warehouse. During the validation, it is compared with other ACO adopting a pheromone initialization technique, and the results show that, in most cases, the adoption of the proposed warm-up allows the ACO to obtain the same or better results with fewer iterations.

Microstructural Size Dependency in Nonlinear Lateral Stability of Random Reinforced Microshells via Meshfree-Based Applied Mathematical Modeling

The prime objective of this research work is to develop an efficient small scale-dependent computational framework incorporating microstructural tensors of dilatation gradient, rotation gradient, and deviatoric stretch gradient to analyze nonlinear lateral stability of cylindrical microshells. The numerical strategy is established based upon a mixed formation of the third-order shear deformable shell model and modified strain gradient continuum mechanics. The graphene nanoplatelet reinforcements are assumed to be randomly dispersed in a checkerboard scheme within the resin matrix. Accordingly, to extract the effective material properties, the Monte Carlo simulation together with a probabilistic technique are employed. The numerical solution for the microstructural-dependent nonlinear problem is carried out via the moving Kriging meshfree method having the capability to accommodate accurately the essential boundary conditions using proper moving Kriging shape function. It is represented that the role of the stiffening characters related to the effect of microstructural dilatation gradient, rotation gradient, and deviatoric stretch reduces continuously by going to deeper territory of the load-deflection stability path. Moreover, it is indicated that among various microstructural gradient tensors, the stiffening character of the rotation gradient is higher than deviatoric stretch gradient, and the stiffening character of the latter is more considerable than the dilatation gradient tensor.

TOURISTS’ MOTIVATIONS FOR VISITING DARK TOURISM SITES. CASE OF ROMANIA

Nowadays, tourism plays an essential role in economic growth and development in many countries. Tourists have different travelling motivations and behavioural manifestations, helping tour operators decide suitable for them. Developing a dark tourism niche concept is a necessary step for correct use of it in theory and practice. Dark tourism is an important concept studied in the past twenty years in worldwide literature. This empirical paper aimed to determine the tourists’ motivations for travelling to Romania’s dark tourism destinations and establishing them. A quantitative research method was applied based on a survey using a non-probabilistic technique based on convenience sampling. Secondary, it was studied the relationship between motivational determinants and tourists’ attitude-behaviour in visiting dark tourism sites (Theory of Planned Behaviour, TPB). The survey conducted online was primarily made of 40 items evaluated on the Likert scale. It has been used a sample based on 475 Romanian respondents: 72.3% who had already visited a dark tourism destination in Romania, and 27.8% who stated they planned to visit one. The theoretical framework has analysed five constructs as motivational determinants in the relationships with behavioural construct attitude (TPB). The results show support for two of five research hypotheses (H1 and H4). Dark experience (H1) and Unique learning experience (H4) have a significant and positive effect on Attitude. We did not find any significant effect of Engaging entertainment, Casual interest, and Emotional recreation on Attitude. Hence, H2, H3 and H5 are not supported. Based on these results, the government can develop tourism economic and marketing policy to develop dark tourism as important post-communist niche tourism. The practitioners can develop and promote tourism packages and products by applying the correct use of dark tourism sense. The present scientific paper’s originality is given by the lack of literature review studies based on empirical research on this topic. Limits of the research and future research directions are also pointed out in the conclusion part.

Machine Learning and Geo-Based Multi-Criteria Decision Support Systems in Analysis of Complex Problems

Many complex problems require a multi-criteria decision, such as the COVID-19 pandemic that affected nearly all activities in the world. In this regard, this study aims to develop a multi-criteria decision support system considering the sustainability, feasibility, and success rate of possible approaches. Therefore, two models have been developed: Geo-AHP (applying geo-based data) and BN-Geo-AHP using probabilistic techniques (Bayesian network). The ranking method of Geo-APH is generalized, and the equations are provided in a way that adding new elements and variables would be possible by experts. Then, to improve the ranking, the application of the probabilistic technique of a Bayesian network and the role of machine learning for database and weight of each parameter are explained, and the model of BN-Geo-APH has been developed. In the next step, to show the application of the developed Geo-AHP and BN-Geo-AHP models, we selected the new pandemic of COVID-19 that affected nearly all activities, and we used both models for analysis. For this purpose, we first analyzed the available data about COVID-19 and previous studies about similar virus infections, and then we ranked the main approaches and alternatives in confronting the pandemic of COVID-19. The analysis of approaches with the selected alternatives shows the first ranked approach is massive vaccination and the second ranked is massive swabs or other tests. The third is the use of medical masks and gloves, and the last ranked is the lockdown, mostly due to its major negative impact on the economy and individuals.

Investigation of probabilistic models for forecasting the efficiency of proppant hydraulic fracturing technology

To solve the problems accompanying the development of forecasting methods, a probabilistic method of data analysis is proposed. Using a carbonate object as an example, the application of a probabilistic technique for predicting the effectiveness of proppant hydraulic fracturing (HF) technology is considered. Forecast of the increase in the oil production of wells was made using probabilistic analysis of geological and technological data in different periods of HF implementation. With the help of this method, the dimensional indicators were transferred into a single probabilistic space, which allowed performing a comparison and construct individual probabilistic models. An assessment of the influence degree for each indicator on the HF efficiency was carried out. Probabilistic analysis of indicators in different periods of HF implementation allowed identifying universal statistically significant dependencies. These dependencies do not change their parameters and can be used for forecasting in different periods of time. Criteria for the application of HF technology on a carbonate object have been determined. Using individual probabilistic models, integrated indicators were calculated, on the basis of which regression equations were constructed. Equations were used to predict the HF efficiency on forecast samples of wells. For each of the samples, correlation coefficients were calculated. Forecast results correlate well with the actual increase (values ​​of the correlation coefficient r = 0.58-0.67 for the examined samples). Probabilistic method, unlike others, is simple and transparent. With its use and with careful selection of wells for the application of HF technology, the probability of obtaining high efficiency increases significantly.

ACO powered by Local Searches Algorithms for the Solution of TSP Problems

Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and other animals. Ants, in particular, have inspired a number of methods and techniques among which the most studied and successful is the general-purpose optimization technique, also known as ant colony optimization, In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Ant Colony Optimization (ACO) algorithm is used to arrive at the best solution for TSP. In this article, the researcher has introduced ways to use a great deluge algorithm with the ACO algorithm to increase the ability of the ACO in finding the best tour (optimal tour). Results are given for different TSP problems by using ACO with great deluge and other local search algorithms.

Probabilistic fatigue model for cast alloys of aero engine applications

PurposeThe purpose of this study is to address the complexity involved in computing the fatigue life of casted structure with porosity effects in aero engine applications. The uncertainty of porosity defects is addressed by introducing probabilistic models.Design/methodology/approachOne major issue of casted aluminium alloys in the application of aerospace industries is their internal defects such as porosities, which are directly affecting the fatigue life. Since there is huge cost and time effort involved in understanding the effect of fatigue life in terms of the presence of the internal defects, a probabilistic fatigue model approach is applied in order to define the realistic fatigue limit of the casted structures for the known porosity fractions. This paper describes the probabilistic technique to casted structures with measured porosity fractions and its relation to their fatigue life. The predicted fatigue life for various porosity fractions and dendrite arm spacing values is very well matching with the experimentally predicted fatigue data of the casted AS7G06 aluminium alloys with measured internal defects. The probabilistic analysis approach not only predicts the fatigue life limit of the structure but also provides the limit of fatigue life for the known porosity values of any casted aluminium bearing support structure used in aero engines.FindingsThe probabilistic fatigue model for addressing porosity in casting structure is verified with experimental results.Research limitations/implicationsThis is grey area in aerospace and automotive industry.Originality/valueThis work is original and not published anywhere else.

Computer Simulation of Stochastic Energy Fluctuations in Tensile Test of Elasto-Plastic Porous Metallic Material

The main aim of this work is the computational implementation and numerical simulation of a metal porous plasticity model with an uncertain initial microdefects’ volume fraction using the Stochastic Finite Element Method (SFEM) based on the semi-analytical probabilistic technique. The metal porous plasticity model applied here is based on Gurson–Tvergaard–Needleman theory and is included in the ABAQUS finite element system, while the external probabilistic procedures were programmed in the computer algebra system MAPLE 2017. Hybrid usage of these two computer systems enabled the determination of fluctuations in elastic and plastic energies due to initial variations in the ratio of the metal micro-voids, and the calculation of the first four probabilistic moments and coefficients of these energies due to Gaussian distribution of this ratio. A comparison with the Monte-Carlo simulation validated the numerical efficiency of the proposed approach for any level of input uncertainty and for the first four probabilistic characteristics traditionally seen in the experimental series.