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Integration of data-driven modeling techniques for lean zone and shale barrier characterization in SAGD reservoirs
ادغام تکنیک های مدل سازی داده محور برای منطقه ناب و خصوصیات سد شیل در مخازن SAGD -2019 High water saturation zone, which is also known as lean zone, and shale barrier, are two common types of
heterogeneous features in steam-assisted gravity drainage (SAGD) reservoirs. Lean zone poses a detrimental
influence on conventional SAGD operations, as it causes steam utilization efficiency to decrease and increases
the steam-oil ratio. Shale barrier would also impede the vertical growth and lateral spread of a steam chamber
and potentially reduce oil production. An efficient characterization workflow is proposed to estimate the
quantity, location, and volume of these heterogeneous features by integration of data-driven modeling techniques.
Field data including geophysical, operational and production data corresponding to several existing SAGD
projects are extracted from the public domain and used to build a series synthetic homogeneous models. Lean
zones and shale barriers with varying distribution and volume are randomly added. From the corresponding
production time-series data, a set of input features are identified through a hybrid method comprised of discrete
wavelet transformation (DWT) and principal component analysis (PCA), while the output parameters are formulated
to describe the actual number and geological parameters of two types of reservoir heterogeneities. A
two-level data-driven model based on artificial neural networks (ANN) is employed to correlate the complex
relationship between input and output attributes. Finally, this calibrated model is integrated into a novel
characterization workflow to infer an ensemble of probable realizations of lean zone and shale barrier distribution
that are conditioned to a given production historical profile.
This work demonstrates the potential of practical application of data-driven models in correlating complex
reservoir heterogeneity properties and production time-series data. Results from the case study illustrate the
utility of the proposed workflow in facilitating the efficient identification of heterogeneous features from SAGD
profiles. The potential savings of computational efforts associated with the proposed methodology are also explained. Keywords: Artificial neural networks | Reservoir heterogeneities | Pattern recognition | Enhanced oil recovery | Time-series analysis |
مقاله انگلیسی |
2 |
Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes
مدل سازی داده محور و پیش بینی پویایی قند خون: کاربردهای یادگیری ماشین در دیابت نوع 1-2019 Background: Diabetes mellitus (DM) is a metabolic disorder that causes abnormal blood glucose (BG) regulation
that might result in short and long-term health complications and even death if not properly managed. Currently,
there is no cure for diabetes. However, self-management of the disease, especially keeping BG in the recommended
range, is central to the treatment. This includes actively tracking BG levels and managing physical
activity, diet, and insulin intake. The recent advancements in diabetes technologies and self-management applications
have made it easier for patients to have more access to relevant data. In this regard, the development
of an artificial pancreas (a closed-loop system), personalized decision systems, and BG event alarms are becoming
more apparent than ever. Techniques such as predicting BG (modeling of a personalized profile), and
modeling BG dynamics are central to the development of these diabetes management technologies. The increased
availability of sufficient patient historical data has paved the way for the introduction of machine
learning and its application for intelligent and improved systems for diabetes management. The capability of
machine learning to solve complex tasks with dynamic environment and knowledge has contributed to its
success in diabetes research.
Motivation: Recently, machine learning and data mining have become popular, with their expanding application
in diabetes research and within BG prediction services in particular. Despite the increasing and expanding popularity
of machine learning applications in BG prediction services, updated reviews that map and materialize
the current trends in modeling options and strategies are lacking within the context of BG prediction (modeling
of personalized profile) in type 1 diabetes.
Objective: The objective of this review is to develop a compact guide regarding modeling options and strategies
of machine learning and a hybrid system focusing on the prediction of BG dynamics in type 1 diabetes. The
review covers machine learning approaches pertinent to the controller of an artificial pancreas (closed-loop
systems), modeling of personalized profiles, personalized decision support systems, and BG alarm event applications.
Generally, the review will identify, assess, analyze, and discuss the current trends of machine learning
applications within these contexts.
Method: A rigorous literature review was conducted between August 2017 and February 2018 through various
online databases, including Google Scholar, PubMed, ScienceDirect, and others. Additionally, peer-reviewed
journals and articles were considered. Relevant studies were first identified by reviewing the title, keywords, and
abstracts as preliminary filters with our selection criteria, and then we reviewed the full texts of the articles that
were found relevant. Information from the selected literature was extracted based on predefined categories,
which were based on previous research and further elaborated through brainstorming among the authors.
Results: The initial search was done by analyzing the title, abstract, and keywords. A total of 624 papers were
retrieved from DBLP Computer Science (25), Diabetes Technology and Therapeutics (31), Google Scholar (193), IEEE
(267), Journal of Diabetes Science and Technology (31), PubMed/Medline (27), and ScienceDirect (50). After
removing duplicates from the list, 417 records remained. Then, we independently assessed and screened the
articles based on the inclusion and exclusion criteria, which eliminated another 204 papers, leaving 213 relevant
papers. After a full-text assessment, 55 articles were left, which were critically analyzed. The inter-rater
agreement was measured using a Cohen Kappa test, and disagreements were resolved through discussion.
Conclusion: Due to the complexity of BG dynamics, it remains difficult to achieve a universal model that produces
an accurate prediction in every circumstance (i.e., hypo/eu/hyperglycemia e |
مقاله انگلیسی |
3 |
Predictive modeling and optimization of a multi-zone HVAC system with data mining and firefly algorithms
مدل سازی پیش بینی و بهینه سازی یک سیستم HVAC چند منطقه ای با اگوریتم های کرم ب تاب و داده کاوی-2015 This research applies a data-driven approach to investigate energy savings of a multi-zone HVAC (heating, ventilating, and air conditioning) system. The predictive models of the HVAC energy con- sumption and the environment conditions of multiple zones are constructed by data mining algorithms. Two major environment conditions, the room temperature and the relative room humidity, are considered. Two variables of operating the HVAC system, the supply air temperature set point and the supply air static pressure set point, in the predictive models are optimized with respect to minimizing the HVAC energy while maintaining the predefined environment conditions of each zone. A novel heuristic search algorithm, the firefly algorithm, is utilized to solve the data-driven predictive models and derive the optimal settings of two set points under required HVAC operational constraints. The firefly algorithm is compared with the particle swarm optimization and evolutionary strategy to demonstrate its advantages in solving the proposed optimization problem. HVAC energy saving with the proposed data-driven framework is examined in the computational studies. A sensitivity analysis of the potential of energy saving based on different types of environment condition constraints is conducted.© 2015 Elsevier Ltd. All rights reserved.
Keywords: Energy conservation | Data-driven modeling | Multi-zone HVAC | Firefly algorithm | Predictive operation |
مقاله انگلیسی |
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مدل سازی پیش بینی و بهینه سازی سیستم HVAC چند منطقه¬ای با داده کاوی و الگوریتم کرم شب تاب
سال انتشار: 2015 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 32 این تحقیق، رویکردی داده محور برای بررسی صرفه جویی انرژی در سیستم چند منطقه¬ای تهویه مطبوع (HVAC) (گرمایش، تهویه و تهویه مطبوع) است. مدل¬های پیش بینی مصرف انرژی HVAC و شرایط محیطی مناطق متعدد توسط الگوریتم های داده کاوی ایجاد شد. دمای و رطوبت نسبی اتاق، شرایط محیطی غالب محسوب می شوند. نقطه تنظیم منبع دمای هوا و منبع فشار استاتیک هوا، دو متغیر عملیاتی سیستم های HVAC هستند و در مدل های پیش بینی برای کاهش انرژی HVAC با حفظ شرایط محیطی از پیش تعریف شده در هر منطقه، بهینه شدند. الگوریتم کرم شب تاب، الگوریتم جستجوی اکتشافی جدیدی است که برای حل مدل های پیش بینی داده محور و دستیابی به تنظیمات بهینه در مجموعه¬ی دو نقطه در صورت اعمال محدودیت های عملیاتی HVAC، مورد نیاز است. در صورت مقایسه بین الگوریتم کرم شب تاب با بهینه سازی ازدحام ذرات و استراتژی تکاملی، مزایای الگوریتم کرم شب تاب در حل مشکل بهینه سازی پیشنهادی اثبات می¬شود. در مطالعات محاسباتی، صرفه جویی در انرژی HVAC در چارچوب مبتنی بر داده پیشنهادی مورد بررسی قرار گرفت. آنالیز حساسیت پتانسیل صرفه جویی انرژی بر مبنای انواع مختلف محدودیت شرایط محیطی انجام شد.
کلمات کلیدی: حفاظت از انرژی | مدل سازی داده محور | HVAC چند منطقه ای | الگوریتم کرم شب تاب | عملیات پیش بینی شده |
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