Adaptive algorithms for history matching and uncertainty quantification

Numerical reservoir simulation models are the basis for many decisions in regard to predicting, optimising, and improving production performance of oil and gas reservoirs. History matching is required to calibrate models to the dynamic behaviour of the reservoir, due to the existence of uncertainty in model parameters. Finally a set of history matched models are used for reservoir performance prediction and economic and risk assessment of different development scenarios. Various algorithms are employed to search and sample parameter space in history matching and uncertainty quantification problems. The algorithm choice and implementation, as done through a number of control parameters, have a significant impact on effectiveness and efficiency of the algorithm and thus, the quality of results and the speed of the process. This thesis is concerned with investigation, development, and implementation of improved and adaptive algorithms for reservoir history matching and uncertainty quantification problems. A set of evolutionary algorithms are considered and applied to history matching. The shared characteristic of applied algorithms is adaptation by balancing exploration and exploitation of the search space, which can lead to improved convergence and diversity. This includes the use of estimation of distribution algorithms, which implicitly adapt their search mechanism to the characteristics of the problem. Hybridising them with genetic algorithms, multiobjective sorting algorithms, and real-coded, multi-model and multivariate Gaussian-based models can help these algorithms to adapt even more and improve their performance. Finally diversity measures are used to develop an explicit, adaptive algorithm and control the algorithm’s performance, based on the structure of the problem. Uncertainty quantification in a Bayesian framework can be carried out by resampling of the search space using Markov chain Monte-Carlo sampling algorithms. Common critiques of these are low efficiency and their need for control parameter tuning. A Metropolis-Hastings sampling algorithm with an adaptive multivariate Gaussian proposal distribution and a K-nearest neighbour approximation has been developed and applied

Performance evaluation for tracker-level fusion in video tracking

PhDTracker-level fusion for video tracking combines outputs (state estimations) from multiple trackers, to address the shortcomings of individual trackers. Furthermore, performance evaluation of trackers at run time (online) can determine low performing trackers that can be removed from the fusion. This thesis presents a tracker-level fusion framework that performs online tracking performance evaluation for fusion. We first introduce a method to determine time instants of tracker failure that is divided into two steps. First, we evaluate tracking performance by comparing the distributions of the tracker state and a region around the state. We use Distribution Fields to generate the distributions of both regions and compute a tracking performance score by comparing the distributions using the L1 distance. Then, we model this score as a time series and employ the Auto Regressive Moving Average method to forecast future values of the performance score. A difference between the original and forecast returns the forecast error signal that we use to detect tracking failure. We test the method with different datasets and then demonstrate its flexibility using tracking results and sequences from the Visual Object Tracking (VOT) challenge. The second part presents a tracker-level fusion method that combines the outputs of multiple trackers. The method is divided into three steps. First, we group trackers into clusters based on the spatio-temporal pair-wise relationships of their outputs. Then, we evaluate tracking performance based on reverse-time analysis with an adaptive reference frame and define the cluster with trackers that appear to be successfully following the target as the on-target cluster. Finally, we fuse the outputs of the trackers in the on-target cluster to obtain the final target state. The fusion approach uses standard tracker outputs and can therefore combine various types of trackers. We test the method with several combinations of state-of-the-art trackers, and also compare it with individual trackers and other fusion approaches.EACEA, under the EMJD ICE Project

Socioscope: Human Relationship and Behavior Analysis in Mobile Social Networks

The widely used mobile phone, as well as its related technologies had opened opportunities for a complete change on how people interact and build relationship across geographic and time considerations. The convenience of instant communication by mobile phones that broke the barrier of space and time is evidently the key motivational point on why such technologies so important in people's life and daily activities. Mobile phones have become the most popular communication tools. Mobile phone technology is apparently changing our relationship to each other in our work and lives. The impact of new technologies on people's lives in social spaces gives us the chance to rethink the possibilities of technologies in social interaction. Accordingly, mobile phones are basically changing social relations in ways that are intricate to measure with any precision. In this dissertation I propose a socioscope model for social network, relationship and human behavior analysis based on mobile phone call detail records. Because of the diversities and complexities of human social behavior, one technique cannot detect different features of human social behaviors. Therefore I use multiple probability and statistical methods for quantifying social groups, relationships and communication patterns, for predicting social tie strengths and for detecting human behavior changes and unusual consumption events. I propose a new reciprocity index to measure the level of reciprocity between users and their communication partners. The experimental results show that this approach is effective. Among other applications, this work is useful for homeland security, detection of unwanted calls (e.g., spam), telecommunication presence, and marketing. In my future work I plan to analyze and study the social network dynamics and evolution

An overview of the main machine learning models - from theory to algorithms

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsIn the context of solving highly complex problems, Artificial Intelligence shows an exponential growth over the past years allowing the Machine Learning to augment and sometimes to outperform the human learning. From driverless cars to automatic recommendation on Netflix, we are surrounded by AI, even if we do not notice it. Furthermore, companies have recently adopted new frameworks in their routines which are mainly composed by algorithms able to solve complex problems in a short period of time. The growth of AI technologies has been absolutely stunning and yes, it is only possible because a sub-field of AI called Machine Learning is growing even faster. In a small scale, Machine Learning may be seen as a simple system able to find patterns on data and learn from it. However, it is precisely that learning process that in a large scale will allow machines to mimic the human behavior and perform tasks that would eventually require human intelligence. Just for us to have an idea, according to Forbes the global Machine Learning market was evaluated in $1.7B in 2017 and it is expected to reach almost$21B in 2024. Naturally, Machine Learning has become an attractive and profitable scientific area that demands continuous learning since there is always something new being discovered. During the last decades, a huge number of algorithms have been proposed by the research community, which sometimes may cause some confusion of how and when to use each one of them. That is exactly what is pretended in this thesis, over the next chapters we are going to review the main Machine Learning models and their respective advantages/disadvantages

Effectiveness of innovative interventions on curbing transmission of Mycobacterium leprae

Leprosy or Hansen’s disease is a complex ancient infectious disease, caused by M.leprae and M.lepromatosis. The most believed frequent mode of transmission is airborne and therefore those in close contact with a new leprosy case are at the most risk of developing the disease although this depends on immunity heterogeneity. Despite leprosy has been the first infectious disease where the pathogen agent was identified, research and development have failed in the creation of reliable diagnostic tests for infection and disease. Therefore, the World Health Organization (WHO) recommends clinical cardinal signs and the ancient slit skin smear (SSS) for the diagnosis of the disease, and no diagnostic test for diagnosis of infection is currently recommended. Both clinical and laboratory skills and expertise are key for ensuring the reliability of diagnosis, which is dwindling due to the sustained decrease of leprosy prevalence worldwide. Nevertheless, the incidence has plateaued in the last decade around 200,000 new cases at the global scale and the highly effective treatment with multidrug therapy (MDT) has been insufficient to stop transmission. In 2018, the WHO has recommend single-dose rifampicin (SDR) as post-exposure prophylaxis (PEP) for the contacts of new leprosy patients without signs of leprosy disease. The protection of PEP is around 60% and is based on the pivotal COLEP trial in Bangladesh. The Leprosy post-exposure prophylaxis with single-dose rifampicin (LPEP) study has documented the feasibility of PEP under programmatic conditions, and there is also evidence that PEP is cost-effective. Nevertheless, operational challenges for the most cost-effective approach to the provision of PEP for the high-risk population without causing harm to the persons eligible for SDR, and avoiding the increase of prevalence of rifampicin resistance, remain. In this Ph.D., we developed and estimated the effectiveness of innovative active case detection strategies based on Geographic Information Systems-based (GIS-based) technologies for stopping transmission of M. leprae in high-priority countries i.e. Comoros, India, and Madagascar. We discussed the latest evidence of the natural history of leprosy and the most recent control strategies in Chapter 1. In chapter 2, we analyzed door-to-door screening for leprosy in four endemic villages of Comoros that received SDR-PEP and we calculated the spatial risk of contracting leprosy for contacts including the protective effect of SDR-PEP for those who received it. We found 114 new cases among 5760 contacts screened (2.0% prevalence), in addition to the 39 cases detected in the two preceding years. There were statistically significant associations of incident leprosy with physical distance to index cases ranging from 2.4 (95% confidence interval (95% CI) 1.5–3.6) for household contacts to 1.8 (95% CI 1.3–2.5) for those living at 1–25 m, compared to individuals living at ≥75 m. The effect of SDR-PEP appeared protective but did not reach statistical significance due to the low numbers.Chapter 3, describes the protocol of Post ExpOsure Prophylaxis for Leprosy in the Comoros and Madagascar (PEOPLE), a cluster-randomized trial to assess the effectiveness of three modalities of implementing PEP. In the PEOPLE trial, four annual door-to-door surveys will be performed in four arms. All consenting permanent residents will be screened for leprosy. Leprosy patients will be treated according to international guidelines and eligible contacts will be provided with SDR-PEP. Arm-1 is the comparator where no PEP will be provided. In arms 2, 3, and 4, SDR-PEP will be administered at a double dose (20 mg/kg) to eligible contacts aged two years and above. In arm 2, all household members of incident leprosy patients are eligible. In arm 3, not only household members but also neighborhood contacts living within 100-m of an incident case are eligible. In arm 4, such neighborhood contacts are only eligible if they test positive for anti-PGL-I, a serological marker. Incidence rate ratios calculated between the comparator arm 1 and each of the intervention arms will constitute the primary outcome. In chapter 4, we describe the findings of the baseline survey of the first year of the PEOPLE trial in Comoros and Madagascar. We also assessed clustering at the village level fitting a purely spatial Poisson model by Kulldorff’s spatial statistic and measured the distance risk of contact to the nearest leprosy patient. There were 455 leprosy patients; 200 (44.0%) belonged to 2735 households included in a cluster. Thirty-eight percent of leprosy patients versus 10% of the total population live 25 m from another leprosy patient. Risk ratios for being diagnosed with leprosy were 7.3, 2.4, 1.8, 1.4, and 1.7, for those in the same household, at 1–&lt;25 m, 25–&lt;50 m, 50–&lt;75 m, and 75–&lt;100 m as/from a leprosy patient, respectively, compared to those living at ≥100 m. Chapter 5, describes active case finding of household members of new cases detected in the preceding four years (2017-2020) in 32 villages not included in the PEOPLE trial in Anjouan, Comoros. Some neighbors requested to be screened for leprosy. We screened 131 out of 226 index case households aimed (58.8%), and 32 other nearby households. There were 945 persons recorded, 671 household contacts, and 274 neighborhood contacts. We examined 896 persons detecting 48(5.4%) leprosy cases. Among cases detected, 13(27.1%) had multibacillary (MB) leprosy, the median age was 18 years (IQR 8-34), 43% were below 15 years and two (4.2%) had visible deformities. The risk of contacts of developing leprosy was higher among 11 households linked to MB compared to one linked to a paucibacillary (PB) index case (OR 12.6, 95% CI 1.6-99.6). There were 12 new cases among 668 household contacts with a leprosy prevalence of 18.0 per 1,000 (95% CI 9.3-31.1). We found 30 new cases in neighbors and six additional cases were diagnosed between their households with a residual prevalence of 26.3 per 1,000 (95% CI 9.7-56.4). We found a high prevalence above 26‰ among household contacts. In chapter 6, we document the mobility of new leprosy cases in two endemic blocks of the State of Bihar, India. We also screened household contacts for leprosy. Finally, we developed a GIS-based system to outline the lowest administrative level (hamlets known as Tola) including its population for assessing clustering. We visited 169 patients and screened 1,044 household contacts in Bisfi and Benipatti blocks in the state of Bihar. Median number of years of residing in the village was 17, interquartile range (IQR)12-30. We found 11 new leprosy cases among 658 household contacts examined (167 per 10,000), of which seven had paucibacillary leprosy, one was a child under 14 years, and none had visible disabilities. We identified 739 hamlets with a total population of 802,788(median 163, IQR 65–774). There were five high-incidence clusters at the hamlet level including 12% of the population and 46%(78/169) of the leprosy cases. One highly significant cluster with a relative risk (RR) of 4.7(p&lt;0.0001) included 32 hamlets and 27 cases in 33,609 population. A second highly significant cluster included 32 hamlets and 24 cases in 33,809 population with a RR of 4.1(p&lt;0.001). The third highly significant cluster included 16 hamlets and 17 cases in 19,659 population with a RR of 4.8(p&lt;0.001). There was a high yield of active household contact screening. Conclusion Our findings highlighted the crucial role of geographical information systems in the control of leprosy while ensuring rational and efficient use of resources. As clustering is beyond the household level, regardless of the provision of PEP, there is a need 1) to explore the efficacy of adapted active case detection and PEP, 2) to monitor the success of control activities, and 3) to ensure surveillance in a post-elimination phase. All the tools we used are open-source and user-friendly, and the expertise we developed includes multidisciplinary partners i.e. the national leprosy programs, non-governmental organizations, and research institutions making them ready for scaling up in different leprosy prevalence settings while maximizing their cost-effectiveness.<br/

Journal of Telecommunications and Information Technology, 2012, nr 2

kwartalni

Novel sampling techniques for reservoir history matching optimisation and uncertainty quantification in flow prediction

Modern reservoir management has an increasing focus on accurately predicting the likely range of field recoveries. A variety of assisted history matching techniques has been developed across the research community concerned with this topic. These techniques are based on obtaining multiple models that closely reproduce the historical flow behaviour of a reservoir. The set of resulted history matched models is then used to quantify uncertainty in predicting the future performance of the reservoir and providing economic evaluations for different field development strategies. The key step in this workflow is to employ algorithms that sample the parameter space in an efficient but appropriate manner. The algorithm choice has an impact on how fast a model is obtained and how well the model fits the production data. The sampling techniques that have been developed to date include, among others, gradient based methods, evolutionary algorithms, and ensemble Kalman filter (EnKF). This thesis has investigated and further developed the following sampling and inference techniques: Particle Swarm Optimisation (PSO), Hamiltonian Monte Carlo, and Population Markov Chain Monte Carlo. The inspected techniques have the capability of navigating the parameter space and producing history matched models that can be used to quantify the uncertainty in the forecasts in a faster and more reliable way. The analysis of these techniques, compared with Neighbourhood Algorithm (NA), has shown how the different techniques affect the predicted recovery from petroleum systems and the benefits of the developed methods over the NA. The history matching problem is multi-objective in nature, with the production data possibly consisting of multiple types, coming from different wells, and collected at different times. Multiple objectives can be constructed from these data and explicitly be optimised in the multi-objective scheme. The thesis has extended the PSO to handle multi-objective history matching problems in which a number of possible conflicting objectives must be satisfied simultaneously. The benefits and efficiency of innovative multi-objective particle swarm scheme (MOPSO) are demonstrated for synthetic reservoirs. It is demonstrated that the MOPSO procedure can provide a substantial improvement in finding a diverse set of good fitting models with a fewer number of very costly forward simulations runs than the standard single objective case, depending on how the objectives are constructed. The thesis has also shown how to tackle a large number of unknown parameters through the coupling of high performance global optimisation algorithms, such as PSO, with model reduction techniques such as kernel principal component analysis (PCA), for parameterising spatially correlated random fields. The results of the PSO-PCA coupling applied to a recent SPE benchmark history matching problem have demonstrated that the approach is indeed applicable for practical problems. A comparison of PSO with the EnKF data assimilation method has been carried out and has concluded that both methods have obtained comparable results on the example case. This point reinforces the need for using a range of assisted history matching algorithms for more confidence in predictions