scholarly journals Representation of material balance for fractional crystallization of reciprocal salt pair Systems: KNO3 production case study

2010 ◽  
Vol 14 (2) ◽  
Author(s):  
Sattar Ghader ◽  
Vahid Shadravan ◽  
Seyed Soheil Mansouri ◽  
Ali Farsi
2010 ◽  
Vol 10 (23) ◽  
pp. 2989-2997
Author(s):  
Sattar Ghader ◽  
Seyed Soheil Mansouri ◽  
Vahid Shadravan ◽  
Ali Farsi

Lithos ◽  
2020 ◽  
Vol 352-353 ◽  
pp. 105271 ◽  
Author(s):  
Chao Li ◽  
Jun Yan ◽  
Chao Yang ◽  
Chuan-Zhong Song ◽  
Ai-Guo Wang ◽  
...  

2017 ◽  
Vol 48 (2) ◽  
pp. 1024-1034 ◽  
Author(s):  
Jose Alberto Muñiz-Lerma ◽  
Manas Paliwal ◽  
In-Ho Jung ◽  
Mathieu Brochu

2014 ◽  
Vol 56 (7) ◽  
pp. 783-800 ◽  
Author(s):  
Yanru Song ◽  
Haijin Xu ◽  
Junfeng Zhang ◽  
Deyuan Wang ◽  
Endong Liu

2020 ◽  
Vol 29 (2) ◽  
pp. 289-303
Author(s):  
Nazim A. Imamverdiyev ◽  
Minakhanym Y. Gasanguliyeva ◽  
Vagif M. Kerimov ◽  
Ulker I. Kerimli

The article is devoted to the petrogeochemical features of Neogene collision volcanism in the central part of the Lesser Caucasus within Azerbaijan. The main goal of the study is to determine the thermodynamic conditions for the formation of Neogene volcanism in the central part of the Lesser Caucasus using the available petrogeochemical material. Using factor analysis, as well as the “IGPET”, “MINPET”, “Petrolog-3” programs, material balance calculations were performed that simulate the phenocryst fractionation process, the crystallization temperature, pressure, and figurative nature of the rock-forming minerals of the formation rocks were calculated. It was determined that at the early and middle stages of crystallization of the rocks of the andesite-dacite-rhyolite formation, the fractionation of amphibole played an important role in the formation of subsequent differentiates. Based on computer simulation, it was revealed that rocks of the andesite-dacite-rhyolite formation were formed by fractional crystallization of the initial high-alumina basaltic magma of high alkalinity in the intermediate magma foci. The calculations of the balance of the substance, simulating the process of fractionation of phenocrysts, as well as magnetite, confirmed the possibility of obtaining rock compositions from andesites to rhyolites as a result of this process. In this case, the process of crystallization differentiation was accompanied by processes of contamination, hybridism and mixing. Based on the geochemical features of rare and rare-earth elements, changes in their ratios, the nature of the mantle source and the type of fractionation process are determined. It was revealed that the enrichment of formation rocks by light rare earths, as well as by many incoherent elements, is associated with the evolution of enriched mantle material. Under high water pressure, as a result of the fractionation of olivine and pyroxene, high-alumina basalts are formed from primary high-magnesian magma, which can be considered parental magma. It was established that, in contrast to the elevated Transcaucasian zone in the more lowered East Caucasus, under conditions of increased fluid pressure and reduced temperature, the melt underwent fractional crystallization in the intermediate centers, being enriched with alkaline, large-ion lithophilic elements, light REEs, etc. This is evidenced by the presence of large crystals of feldspars, the contamination of these minerals by numerous crystals of biotite, magnetite, several generations of these minerals, zonality, as well as the presence of related “water” inclusions, such as hornblendites, hornblende gabbro, etc. The physicochemical conditions for the formation of Neogene volcanic rocks of the Lesser Caucasus are determined.


2021 ◽  
Author(s):  
Javad Rafiee ◽  
Carlos Mario Calad Serrano ◽  
Pallav Sarma ◽  
Sebatian Plotno ◽  
Fernando Gutierrez

Abstract Allocation of injection and production by layer is required for several production and reservoir engineering workflows including reserves estimation, water injection conformance, identification of workover and infill drilling candidates, etc. In cases of commingled production, allocation to layers is unknown; running production logging tools is expensive and not always possible. The current industry practice utilizes simplified approaches such as K*H based allocation which provides a static and inaccurate approximation of the allocation factors; this manual approach requires trial and error and can take several weeks in complex fields. This paper presents a novel technique to solve this problem using a combination of reservoir physics and machine learning. The methodology is made up of four stages: Data Entry: includes production at well level (commingled), injection at layer level and injection patterns or a connectivity map (optional) Gross Match: in order to match gross production for each well, the tool solves for time-varying layer-level injection allocation factors using a total material balance equation across all wells. Phase Match: having the allocation factors from the previous step, the tool automatically tunes various petrophysical parameters (i.e. porosity, relative permeability, etc.) in the physics model for each injector-producer pair across all the connected layers to match the oil and water production in each producer. An ensemble of several models can be run simultaneously to account for the probabilistic nature of the problem. Output: The steps 2 and 3 can be performed at pattern level for all connected patterns or for the whole field. The application of the technology in a complex field with 80+ layers in Southern Argentina is demonstrated as a case study of the benefits of the adoption of the technology.


Sign in / Sign up

Export Citation Format

Share Document