Modern demographers analyse regional and other infant mortality differentials as important factors behind the current life expectancy of Russian citizens (Kumo, 2017). Historically, however, the Russian Empire is simply displayed as one block with high infant mortality rates (Klüsener et al, 2014). Also with respect to cultural background factors, Russia is often perceived as religiously homogeneous with the Orthodox Church dominating the country. In reality, Russia has a long history of coexisting religious traditions. This includes both provinces with a majority of Catholics, Muslims, Buddhists or shamanistic populations as well as territories characterized by religious diversity and significant minority religions. Our project studies minority religious groups in the Urals, a province by the Ural Mountains stretching into Asia. While no territory can claim to be truly representative of this mega-country, we believe that this centrally located province is well suited to show some of the Russian variety, including differential infant mortality among the followers of minority religions, which is the topic of this article. We employ church record microdata to study Catholics, Jews and Old Believers in the main metal producing city of Ekaterinburg. 1

The Ural Population Project (URAPP) is built from individual level data transcriptions of 19th- to early 20th-century parish records and mid-19th-century census-like tax revisions manuscripts. This article discusses the source material, the contents, the history of creation and the strategy of the URAPP database and the outcome of the main research topics so far, including historical demography, Jewish studies, indigenous studies and studies of religious minorities in the Urals and Siberia. Our studies of the ethno-religious cultural landscape of the Urals and northwestern Siberia as well as participation in population history projects was more vital backgrounds than the traditional focus on aggregates. The over 65,000 vital events transcribed from parish records of Russian Orthodox Churches and minority religions in and around Ekaterinburg have been the basis for studies of mortality, nuptiality, religion and other characteristics. We found that the Jewish population kept their traditions and connections with relatives in the Pale of Settlement. Prisoners of WWI usually marrying within their own religious group. Infant mortality in Ekaterinburg was lower among Jews and the Catholics, minorities with higher education and western background, while the Orthodox majority exposed their newborn to extremely tough baptism. The burial records show cases of the Spanish flu in 1918–1919, but on a lower level than in the West, supporting recent theories that estimates of flu mortality may be too high. Based on the tax revisions, polygyny was officially recognized among the indigenous Siberian people. The strategy of the URAPP project has evolved from transcribing microdata about minorities towards covering the whole population.

This paper discusses a case study of a 42-year-old mature offshore oilfield. For this field, with declining production trend and timeworn equipment and technology, understanding and analyzing the transient flow is not a well-defined process. The Integrated Operations (IO) project was kicked-off in 2012 in order to deploy an Asset Management Decision Support tool. One of the focus areas was ‘Flow Assurance Management’ to overcome challenges of well slugging, liquid surge management and to establish guidelines for Start-Up and Ramp-Up processes. Traditionally in this field, most decisions were based on steady-state well and network modelling without much emphasis on transient behavior. Moreover, lack of instrumentation and manual data processing and model updates made it difficult to estimate current reservoir/operating conditions accurately to support real-time decision making. To overcome these issues, a Dynamic Production Management System (DPMS) was designed and implemented based on a dynamic flow model describing multi-phase flow in the gas lifted wells of the field. This paper describes the system and how it aids in better understanding of flow performanxce issues, collaborative decision making, and improved communication between various operational locations and disciplines. As part of the IO project, Real-Time field measurements (pressure, temperature, flow etc.) were captured at high frequency (seconds) & validated to ensure the desired data quality. These measurements were automatically used as boundary information by the model which calculates pressure, temperature, flows and volumes in real time throughout the field. The model is used in different modes: (1) For real-time surveillance, the online model provides a series of virtual instruments at locations without actual instrumentation in the field. (2) For advance warning, a separate transient model is executed faster than real-time to predict future events for Slug/Surge Management. (3) Finally the model is also used for planning activities, such as Start-up/Ramp-up or pigging and can predict any alarming issues during these operations. Thus DPMS assists production engineers and operators to make proactive decisions for effectively managing flow assurance challenges and adds value in various areas. Surveillance: Continuous, real-time monitoring of operating conditions within the network, along with prediction of future conditions within the inlet separators.Safety (HSE): Prevention and mitigation of facility trips/shut-down due to slug and surge issues during start-up/ramp-upEfficiency: Improved utilization of engineers' time and experience with increased focus on data analysis instead of data manipulation.Production Gain: Proactive field management for improved production, rather than reactive decisions that lead to deferred production.Dynamic PMS: The system will help to fully optimize wells, networks and facilities in order to produce and operate asset to its fullest potential by minimizing unexpected downtimes It's one of the first fields in Asia to implement an integrated DPMS using the online transient model concept as a basis for effective, real-time and proactive decision support.

* Работа выполнена частично при финансовой поддержке РФФИ, грант № 15-06-08541 «Религиозное разнообразие евразийского города: статистический и картографический анализ (на примере Екатеринбурга в конце XIX-начале XXI вв.)» (анализ материалов переписи 1897 г.) и за счет Российского научного фонда, проект 16-18-10105 «Этно-религиозная и демографическая динамика в горной Евразии в конце XIX-начале XX в. на примере Урала и Скандинавии» (анализ материалов метрических книг).