Uncertainty evaluation of reservoir simulation models using particle swarms and hierarchical clustering

History matching production data in finite difference reservoir simulation models has been and always will be a challenge for the industry. The principal hurdles that need to be overcome are finding a match in the first place and more importantly a set of matches that can capture the uncertainty range of the simulation model and to do this in as short a time as possible since the bottleneck in this process is the length of time taken to run the model. This study looks at the implementation of Particle Swarm Optimisation (PSO) in history matching finite difference simulation models. Particle Swarms are a class of evolutionary algorithms that have shown much promise over the last decade. This method draws parallels from the social interaction of swarms of bees, flocks of birds and shoals of fish. Essentially a swarm of agents are allowed to search the solution hyperspace keeping in memory each individual’s historical best position and iteratively improving the optimisation by the emergent interaction of the swarm. An intrinsic feature of PSO is its local search capability. A sequential niching variation of the PSO has been developed viz. Flexi-PSO that enhances the exploration and exploitation of the hyperspace and is capable of finding multiple minima. This new variation has been applied to history matching synthetic reservoir simulation models to find multiple distinct history 3 matches to try to capture the uncertainty range. Hierarchical clustering is then used to post-process the history match runs to reduce the size of the ensemble carried forward for prediction. The success of the uncertainty modelling exercise is then assessed by checking whether the production profile forecasts generated by the ensemble covers the truth case

Ансамблевий класифікатор на основі бустінгу

Робота публікується згідно наказу Ректора НАУ від 27.05.2021 р. №311/од "Про розміщення кваліфікаційних робіт здобувачів вищої освіти в репозиторії університету". Керівник роботи: д.т.н., професор, зав. кафедри авіаційних комп’ютерно-інтегрованих комплексів, Синєглазов Віктор МихайловичThis paper considers the construction of a classifier based on neural networks, nowadays AI is a major global trend, as an element of AI, as a rule, an artificial neural network is used. One of the main tasks that solves the neural network is the problem of classification. For a neural network to become a tool, it must be trained. To train a neural network you must use a training sample. Since the marked training sample is expensive, the work uses semi-supervised learning, to solve the problem we use ensemble approach based on boosting. Speaking of unlabeled data, we can move on to the topic of semi-supervised learning. This is due to the need to process hard-to-access, limited data. Despite many problems, the first algorithms with similar structures have proven successful on a number of basic tasks in applications, conducting functional testing experiments in AI testing. There are enough variations to choose marking, where training takes place on a different set of information, the possible validation eliminates the need for robust method comparison. Typical areas where this occurs are speech processing (due to slow transcription), text categorization. Choosing labeled and unlabeled data to improve computational power leads to the conclusion that semi-supervised learning can be better than teacher-assisted learning. Also, it can be on an equal efficiency factor as supervised learning. Neural networks represent global trends in the fields of language search, machine vision with great cost and efficiency. The use of "Hyper automation" allows the necessary tasks to be processed to introduce speedy and simplified task execution. Big data involves the introduction of multi-threading, something that large companies in the artificial intelligence industry are doing.У даній роботі розглядається побудова класифікатора на основі нейронних мереж, на сьогоднішній день AI є основним світовим трендом, як елемент AI, як правило, використовується штучна нейронна мережа. Однією з основних задач, яку вирішує нейронна мережа, є проблема класифікації. Щоб нейронна мережа стала інструментом, її потрібно навчити. Для навчання нейронної мережі необхідно використовувати навчальну вибірку. Оскільки позначена навчальна вибірка є дорогою, у роботі використовується напівконтрольоване навчання, для вирішення проблеми ми використовуємо ансамблевий підхід на основі бустингу. Говорячи про немарковані дані, ми можемо перейти до теми напівконтрольованого навчання. Це пов’язано з необхідністю обробки важкодоступних обмежених даних. Незважаючи на багато проблем, перші алгоритми з подібними структурами виявилися успішними в ряді основних завдань у додатках, проводячи експерименти функціонального тестування в тестуванні ШІ. Є достатньо варіацій для вибору маркування, де навчання відбувається на іншому наборі інформації, можлива перевірка усуває потребу в надійному порівнянні методів. Типовими областями, де це відбувається, є обробка мовлення (через повільну транскрипцію), категоризація тексту. Вибір мічених і немічених даних для підвищення обчислювальної потужності призводить до висновку, що напівкероване навчання може бути кращим, ніж навчання за допомогою вчителя. Крім того, воно може мати такий же коефіцієнт ефективності, як навчання під наглядом. Нейронні мережі представляють глобальні тенденції в області мовного пошуку, машинного зору з великою вартістю та ефективністю. Використання «Гіперавтоматизації» дозволяє обробляти необхідні завдання для впровадження швидкого та спрощеного виконання завдань. Великі дані передбачають впровадження багатопоточності, чим займаються великі компанії в індустрії штучного інтелекту

Model Order Reduction

An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This three-volume handbook covers methods as well as applications. This third volume focuses on applications in engineering, biomedical engineering, computational physics and computer science

Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described