Data-driven methods serve as robust tools to turn data into knowledge. Historical data generally has not been used in an effective way in analyzing processes due to lack of a well-organized data, where there is a huge potential of turning terabytes of data into knowledge. With the advances and implementation of data-driven methods data-driven models have become more widely-used in analysis, predictive modeling, control and optimization of several processes. Yet, the industry overall is still skeptical on the use of data-driven methods, since they are data-based solutions rather than traditional physics-based approaches; even though physics and geology are often part of this methodology. This study comprehensively evaluates the status of data-driven methods in oil and gas industry along with the recent advances and applications. This study outlines the development of these methods from the fundamentals, theory and applications perspective along with their historical acceptance and use in the industry. Major challenges in the process of implementation of these methods are reviewed for different areas of applications. The major advantages and drawbacks of data-driven methods over the traditional methods are discussed. Limitations and areas of opportunities are outlined. Recent advancements along with the latest applications, the associated results and value of the methods are provided. It is observed that the successful use of data-driven methods requires strong understanding of petroleum engineering processes and the physics-based conventional methods together with a good grasp of traditional statistics, data mining, artificial intelligence and machine learning. Data-driven methods start with a data-based approach to identify the issues and their solutions. Even though data-driven methods provide great solutions on some challenging and complex processes, that are new and/or hard to define with existing conventional methods, there is still skepticism in the industry on the use of these methods. This is strongly tied to the delicacy and sensitive nature of the processes and on the usage of the data. Organization and refinement of the data turn out to be important components of an efficient data-driven process. Data-driven methods offer great advantages in the industry over that of conventional methods under certain conditions. However, the image of these methods for most of the industry professionals is still fuzzy. This study serves to bridge the gap between successful implementation and more widely use and acceptance of data-driven methods, and the fuzziness and reservations on the understanding of these methods in the industry. Significant components of these methods along with clarification of definitions, theory, application and concerns are also outlined in this study.
Background: Bangladesh hosts more than 800,000 Rohingya refugees from Myanmar. The low health immunity, lifestyle, access to good healthcare services, and social-security cause this population to be at risk of far more direct effects of COVID-19 than the host population. Therefore, evidence-based forecasting of the COVID-19 burden is vital in this regard. In this study, we aimed to forecast the COVID-19 obligation among the Rohingya refugees of Bangladesh to keep up with the disease outbreak’s pace, health needs, and disaster preparedness. Methodology and Findings: To estimate the possible consequences of COVID-19 in the Rohingya camps of Bangladesh, we used a modified Susceptible-Exposed-Infectious-Recovered (SEIR) transmission model. All of the values of different parameters used in this model were from the Bangladesh Government’s database and the relevant emerging literature. We addressed two different scenarios, i.e., the best-fitting model and the good-fitting model with unique consequences of COVID-19. Our best fitting model suggests that there will be reasonable control over the transmission of the COVID-19 disease. At the end of December 2020, there will be only 169 confirmed COVID-19 cases in the Rohingya refugee camps. The average basic reproduction number (R0) has been estimated to be 0.7563. Conclusions: Our analysis suggests that, due to the extensive precautions from the Bangladesh government and other humanitarian organizations, the coronavirus disease will be under control if the maintenance continues like this. However, detailed and pragmatic preparedness should be adopted for the worst scenario.
Chronic wasting disease (CWD) is a spongiform encephalopathy disease caused by the transmission of infectious prion agents. CWD is a fatal disease that affects wild and farmed cervids in North America with few cases reported overseas. Social interaction of cervids, feeding practices by wildlife keepers and climate effects on the environmental carrying capacity all can affect CWD transmission in deer. Wildlife deer game hunting is economically important to the semi-arid South Texas region and is affected by climate change. In this paper, we model and investigate the effect of climate change on the spread of CWD using typical climate scenarios. We use a system of impulsive differential equations to depict the transmission of CWD between different age groups and gender of cervids. The carrying capacity and contact rates are assumed to depend on climate. Due to the polygamy of bucks, we use mating rates that depend on the number of bucks and does. We analyze the stability of the model and use simulations to study the effect of harvesting (culling) on eradicating the disease, given the climate of South Texas. We use typical climate change scenarios based on published data and our assumptions. For the climate indicator, we calculated and utilized the Standard Precipitation Evapotranspiration Index (SPEI). We found that climate change might hinder the efforts to reduce and effectively manage CWD as it becomes endemic to South Texas. The model shows the extinction of the deer population from this region is a likely outcome.
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