Application of Machine Learning in Well Performance Prediction, Design Optimization and History Matching

Finite difference based reservoir simulation is commonly used to predict well rates in these reservoirs. Such detailed simulation requires an accurate knowledge of reservoir geology. Also, these reservoir simulations may be very costly in terms of computational time. Recently, some studies have used the concept of machine learning to predict mean or maximum production rates for new wells by utilizing available well production and completion data in a given field. However, these studies cannot predict well rates as a function of time. This dissertation tries to fill this gap by successfully applying various machine learning algorithms to predict well decline rates as a function of time. This is achieved by utilizing available multiple well data (well production, completion and location data) to build machine learning models for making rate decline predictions for the new wells. It is concluded from this study that well completion and location variables can be successfully correlated to decline curve model parameters and Estimated Ultimate Recovery (EUR) with a reasonable accuracy. Among the various machine learning models studied, the Support Vector Machine (SVM) algorithm in conjunction with the Stretched Exponential Decline Model (SEDM) was concluded to be the best predictor for well rate decline. This machine learning method is very fast compared to reservoir simulation and does not require a detailed reservoir information. Also, this method can be used to fast predict rate declines for more than one well at the same time. This dissertation also investigates the problem of hydraulic fracture design optimization in unconventional reservoirs. Previous studies have concentrated mainly on optimizing hydraulic fractures in a given permeability field which may not be accurately known. Also, these studies do not take into account the trade-off between the revenue generated from a given fracture design and the cost involved in having that design. This dissertation study fills these gaps by utilizing a Genetic Algorithm (GA) based workflow which can find the most suitable fracturing design (fracture locations, half-lengths and widths) for a given unconventional reservoir by maximizing the Net Present Value (NPV). It is concluded that this method can optimize hydraulic fracture placement in the presence of natural fracture/permeability uncertainty. It is also concluded that this method results in a much higher NPV compared to an equally spaced hydraulic fractures with uniform fracture dimensions. Another problem under investigation in this dissertation is that of field scale history matching in unconventional shale oil reservoirs. Stochastic optimization methods are commonly used in history matching problems requiring a large number of forward simulations due to the presence of a number of uncertain variables with unrefined variable ranges. Previous studies commonly used a single stage history matching. This study presents a method utilizing multiple stages of GA. Most significant variables are separated out from the rest of the variables in the first GA stage. Next, best models with refined variable ranges are utilized with previously eliminated variables to conduct GA for next stage. This method results in faster convergence of the problem

Ontology-based Classification and Analysis of non- emergency Smart-city Events

Several challenges are faced by citizens of urban centers while dealing with day-to-day events, and the absence of a centralised reporting mechanism makes event-reporting and redressal a daunting task. With the push on information technology to adapt to the needs of smart-cities and integrate urban civic services, the use of Open311 architecture presents an interesting solution. In this paper, we present a novel approach that uses an existing Open311 ontology to classify and report non-emergency city-events, as well as to guide the citizen to the points of redressal. The use of linked open data and the semantic model serves to provide contextual meaning and make vast amounts of content hyper-connected and easily-searchable. Such a one-size-fits-all model also ensures reusability and effective visualisation and analysis of data across several cities. By integrating urban services across various civic bodies, the proposed approach provides a single endpoint to the citizen, which is imperative for smooth functioning of smart cities

Digital Hospital and Patient Monitoring System

Diagnosis, and monitoring of health is a very important task in healthcare industry. Due to time constraint, people are not visiting hospitals, which might and possibly lead to a lot of health issues in one instant of time. Predominantly most of the healthcare systems have been developed to predict and diagnose the health of the patients by which people who are busy in their schedule can also monitor their health at regular intervals. Many studies show that early prediction is the best way to cure health because early diagnosis will help and alert the patients to know the health status. Healthcare being a global issue more particularly India being a most populated nation where majority of which live in villages deprived of healthcare facilities on real time basis continuously and regularly. With the increasing use of technology, there is an urgent need to have such a smart health monitoring system that can communicate between network devices and application which will help the patients and doctors to monitor, track and record the patient�s sensitive data containing medical information. This paper depicts the idea of solving health issues using the latest technology, Internet of Things (IoT). It presents the architectural review of smart healthcare system using Internet of Things(IoT) which is aimed to provide a Better HealthCare to everyone. Using this system architecture, patient�s body parameters can be measured in real time

Multivariate analysis of histopathological features as prognostic factors in fifty cases of thyroid neoplasm: a retrospective study done at tertiary care centre

Background: Number of prognostic factors for thyroid carcinoma have been identified including age, gender and tumor characteristics, such as histology and stage. The importance of these factors as independent predictors of survival for patients with differentiated thyroid carcinoma has been extensively studied but remains uncertain. Methods: A retrospective analysis of 50 thyroid carcinomas was made to assess prognostic factors including histological variants from September 2019 to February 2022 at our centre. The surgical and histopathological data were studied. Results: 72% patients had papillary thyroid cancer. Multivariate analysis was done and factors showing prognostic significance were tumour size, extrathyroid extension, extranodal extension, lymphovascular, perineural invasion, histological type, necrosis, focality, capsular invasion were found to have poor prognosis. Conclusions: There are histopathological factors which can modify the course and influence the line of treatment of thyroid neoplasms

Public health matters: Innovative approaches for engaging medical students

Background: Public health faces the paradox of being increasingly emphasized by the key health and social care regulators and stakeholders, while remaining a largely under-represented discipline in the context of medical curricula. Enhancing medical student engagement in public health teaching is one way to address this concern. Methods: We discuss four key solutions to the challenges faced by public health educators in medical schools, and present five case studies which demonstrate innovative approaches to engaging medical students in our discipline. Results: Four different approaches have been piloted by members of the Public Health Educators in Medical Schools (PHEMS) network: (i) ensuring social accountability, (ii) demonstrating clinical relevance, (iii) mapping the core curriculum, and (iv) using technology enhanced learning. Preliminary student feedback suggests that these approaches can be used to position public health as an enabler of modern medical practice, and promote a more holistic understanding of medicine by linking patient-centred care to the population level. Conclusions: The zeitgeist in both academia and the healthcare system supports the teaching of public health within the medical curriculum; there is also consensus at the political and pedagogical level. The challenge of ensuring engagement now needs to be met at the student–teacher interface

Analyzing the potential benefits of CDN augmentation strategies for internet video workloads

Video viewership over the Internet is rising rapidly, and market pre-dictions suggest that video will comprise over 90 % of Internet traf-fic in the next few years. At the same time, there have been signs that the Content Delivery Network (CDN) infrastructure is being stressed by ever-increasing amounts of video traffic. To meet these growing demands, the CDN infrastructure must be designed, pro-visioned and managed appropriately. Federated telco-CDNs and hybrid P2P-CDNs are two content delivery infrastructure designs that have gained significant industry attention recently. We ob-served several user access patterns that have important implica-tions to these two designs in our unique dataset consisting of 30 million video sessions spanning around two months of video view-ership from two large Internet video providers. These include par-tial interest in content, regional interests, temporal shift in peak load and patterns in evolution of interest. We analyze the impact of our findings on these two designs by performing a large scale measurement study. Surprisingly, we find significant amount of synchronous viewing behavior for Video On Demand (VOD) con-tent, which makes hybrid P2P-CDN approach feasible for VOD and suggest new strategies for CDNs to reduce their infrastructure costs. We also find that federation can significantly reduce telco-CDN provisioning costs by as much as 95%

Formulation and Evaluation of Floating Tablet of Tropisetron

he purpose of this research was to develop a novel gastroretentive drug delivery system based on controlled delivery of active agent. Tropisetron is an indole derivative having the antiemetic activity. It’s a selective serotonin receptor antagonist. Tropisetron blocks the action of serotonin at 5HT3 receptors. It also results in suppression of chemotherapy-and radiotherapy-induced nausea and vomiting. The incorporation of swellable and natural polymer for binding action and also good water solublility with high molecular  weight such as carbopol present it in the gastro retentive floating tablets, which are designed to provide the desired controlled and complete release of drug for prolonged period of time. Lactose was used as filler. Buoyancy was achieved by adding an effervescent mixture of sodium bicarbonate and anhydrous citric acid. Floating tablets were prepared by direct compression method. All the required evaluation parameters such as hardness, friability, drug content uniformity and swelling index were performed and found within the acceptance limit.  The optimized formulation (F7) exhibited 63.87% drug release in 12 hrs emerged as best formulation based on drug release characteristics. Keywords: Tropisetron, Gastroretentive Drug Delivery System, Floating tablet