scholarly journals A Thermo-Compositional Model of the African Cratonic Lithosphere

2021 ◽  
Author(s):  
Nils-Peter Finger ◽  
Mikhail K Kaban ◽  
Magdala Tesauro ◽  
Walter D. Mooney ◽  
Maik Thomas
Keyword(s):  
2021 ◽  
Vol 11 (12) ◽  
pp. 5743
Author(s):  
Pablo Gamallo

This article describes a compositional model based on syntactic dependencies which has been designed to build contextualized word vectors, by following linguistic principles related to the concept of selectional preferences. The compositional strategy proposed in the current work has been evaluated on a syntactically controlled and multilingual dataset, and compared with Transformer BERT-like models, such as Sentence BERT, the state-of-the-art in sentence similarity. For this purpose, we created two new test datasets for Portuguese and Spanish on the basis of that defined for the English language, containing expressions with noun-verb-noun transitive constructions. The results we have obtained show that the linguistic-based compositional approach turns out to be competitive with Transformer models.


2014 ◽  
Author(s):  
A.A. Gimazov ◽  
E.I. Sergeev ◽  
V.A. Nikolaev ◽  
E.A. Sadreev
Keyword(s):  

2021 ◽  
Author(s):  
Jiamin Jiang

Abstract It is very challenging to simulate unconventional reservoirs efficiently and accurately. Transient flow can last for a long time and sharp solution (pressure, saturation, compositions) gradients are induced because of the severe permeability contrast between fracture and matrix. Although high-resolution models for well and fracture are required to achieve adequate resolution, they are computationally too demanding for practical field models with many stages of hydraulic fracture. The paper aims to innovate localization strategies that take advantage of locality on timestep and Newton iteration levels. The strategies readily accommodate to complicated flow mechanisms and multiscale fracture networks in unconventional reservoirs. Large simulation speed-up can be obtained if performing localized computations only for the solution regions that will change. We develop an a-priori method to exploit the locality, based on the diffusive character of the Newton updates of pressure. The method makes adequate estimate of the active computational gridblock for the next iterate. The active gridblock set marks the ones need to be solved, and then the solution to local linear system is accordingly computed. Fully Implicit Scheme is used for time discretization. We study several challenging multi-phase and compositional model cases with explicit fractures. The test results demonstrate that significant solution locality of variables exist on timestep and iteration levels. A nonlinear solution update usually has sparsity, and the nonlinear convergence is restricted by a limited fraction of the simulation model. Through aggressive localization, the proposed methods can prevent overly conservative estimate, and thus achieve significant computational speedup. In comparison to a standard Newton method, the novel solver techniques achieve greatly improved solving efficiency. Furthermore, the Newton convergence exhibits no degradation, and there is no impact on the solution accuracy. Previous works in the literature largely relate to the meshing aspect that accommodates to horizontal wells and hydraulic fractures. We instead develop new nonlinear strategies to perform localization. In particular, the adaptive DD method produces proper domain partitions according to the fluid flow and nonlinear updates. This results in an effective strategy that maintains solution accuracy and convergence behavior.


2013 ◽  
Vol 17 (2) ◽  
pp. 461-478 ◽  
Author(s):  
L. Loosvelt ◽  
H. Vernieuwe ◽  
V. R. N. Pauwels ◽  
B. De Baets ◽  
N. E. C. Verhoest

Abstract. Compositional data, such as soil texture, are hard to deal with in the geosciences as standard statistical methods are often inappropriate to analyse this type of data. Especially in sensitivity analysis, the closed character of the data is often ignored. To that end, we developed a method to assess the local sensitivity of a model output with resect to a compositional model input. We adapted the finite difference technique such that the different parts of the input are perturbed simultaneously while the closed character of the data is preserved. This method was applied to a hydrologic model and the sensitivity of the simulated soil moisture content to local changes in soil texture was assessed. Based on a high number of model runs, in which the soil texture was varied across the entire texture triangle, we identified zones of high sensitivity in the texture triangle. In such zones, the model output uncertainty induced by the discrepancy between the scale of measurement and the scale of model application, is advised to be reduced through additional data collection. Furthermore, the sensitivity analysis provided more insight into the hydrologic model behaviour as it revealed how the model sensitivity is related to the shape of the soil moistureretention curve.


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