Instrumental and Laboratory Techniques for Characterization of Reservoir Rock

2016 ◽  
pp. 593-611
Author(s):  
Faruk Civan
2016 ◽  
Vol 9 (1) ◽  
pp. e2017007 ◽  
Author(s):  
Umberto Basile

Cryoglobulins are immunoglobulins that precipitate in serum at temperatures below 37°C and resolubilize upon warming. The clinical syndrome of cryoglobulinemia usually includes purpura, weakness, and arthralgia, but the underlying disease may also contribute other symptoms. Blood samples for cryoglobulin are collected, transported, clotted and spun at 37°C, before the precipitate is allowed to form when serum is stored at 4°C in a Wintrobe tube for at least seven days. The most critical and confounding factor affecting the cryoglobulin test is when the preanalytical phase is not fully completed at 37°C. The easiest way to quantify cryoglobulins is the cryocrit estimate. However, this approach has low accuracy and sensitivity. Furthermore, the precipitate should be resolubilized by warming to confirm that it is truly formed of cryoglobulins. The characterization of cryoglobulins requires the precipitate is several times washed, before performing immunofixation, a technique by which cryoglobulins can be classified depending on the characteristics of the detected immunoglobulins. These features imply a pathogenic role of these molecules which are consequently associated with a wide range of symptoms and manifestations. According to the Brouet classification, Cryoglobulins are grouped into three types by the immunochemical properties of immunoglobulins in the cryoprecipitate. The aim of this paper is to review the major aspects of cryoglobulinemia and the laboratory techniques used to detect and characterize cryoglobulins, taking into consideration the presence and consequences of cryoglobulinemia in Hepatitis C Virus (HCV) infection.


Fuel ◽  
2020 ◽  
Vol 276 ◽  
pp. 118062 ◽  
Author(s):  
Chrissie Wicking ◽  
Nathalia Tessarolo ◽  
Magdalena Savvoulidi ◽  
Jonathan Crouch ◽  
Ian Collins ◽  
...  

2021 ◽  
Author(s):  
Shadi Salahshoor

Abstract Leveraging publicly available data is a crucial stepfor decision making around investing in the development of any new unconventional asset.Published reports of production performance along with accurate petrophysical and geological characterization of the areashelp operators to evaluate the economics and risk profiles of the new opportunities. A data-driven workflow can facilitate this process and make it less biased by enabling the agnostic analysis of the data as the first step. In this work, several machine learning algorithms are briefly explained and compared in terms of their application in the development of a production evaluation tool for a targetreservoir. Random forest, selected after evaluating several models, is deployed as a predictive model thatincorporates geological characterization and petrophysical data along with production metricsinto the production performance assessment workflow. Considering the influence of the completion design parameters on the well production performance, this workflow also facilitates evaluation of several completion strategies toimprove decision making around the best-performing completion size. Data used in this study include petrophysical parameters collected from publicly available core data, completion and production metrics, and the geological characteristics of theNiobrara formation in the Powder River Basin. Historical periodic production data are used as indicators of the productivity in a certain area in the data-driven model. This model, after training and evaluation, is deployed to predict the productivity of non-producing regions within the area of interest to help with selecting the most prolific sections for drilling the future wells. Tornado plots are provided to demonstrate the key performance driversin each focused area. A supervised fuzzy clustering model is also utilized to automate the rock quality analyses for identifying the "sweet spots" in a reservoir. The output of this model is a sweet-spot map that is generated through evaluating multiple reservoir rock properties spatially. This map assists with combining all different reservoir rock properties into a single exhibition that indicates the average "reservoir quality"of the formation in different areas. Niobrara shale is used as a case study in this work to demonstrate how the proposed workflow is applied on a selected reservoir formation whit enough historical production data available.


2015 ◽  
Author(s):  
Leng Zhenpeng ◽  
Lv Weifeng ◽  
Ma Desheng ◽  
Liu Qingjie ◽  
Jia Ninghong ◽  
...  

Author(s):  
Joyshree Barman ◽  
Subrata Borgohain Gogoi ◽  
Jayakumar Viswanathan ◽  
Debasish Konwar ◽  
Kumaresan Jagatheesan

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