Facies models for meandering rivers

1981 ◽  
Keyword(s):  
Meandering Rivers ◽  
Facies Models

scholarly journals An Improved Method of Reservoir Facies Modeling Based on Generative Adversarial Networks

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3873
Author(s):  
Qingbin Liu ◽  
Wenling Liu ◽  
Jianpeng Yao ◽  
Yuyang Liu ◽  
Mao Pan
Keyword(s):  
Synthetic Data ◽  
Oil Field ◽  
Generative Adversarial Networks ◽  
Parameter Distribution ◽  
Good Prospect ◽  
Facies Modeling ◽  
Generative Adversarial Network ◽  
Hard Data ◽  
Reservoir Facies ◽  
Facies Models

As the reservoir and its attribute distribution are obviously controlled by sedimentary facies, the facies modeling is one of the important bases for delineating the area of high-quality reservoir and characterizing the attribute parameter distribution. There are a large number of continental sedimentary reservoirs with strong heterogeneity in China, the geometry and distribution of various sedimentary microfacies are relatively complex. The traditional geostatistics methods which have shortage in characterization of the complex and non-stationary geological patterns, have limitation in facies modeling of continental sedimentary reservoirs. The generative adversarial network (GANs) is a recent state-of-the-art deep learning method, which has capabilities of pattern learning and generation, and is widely used in the domain of image generation. Because of the similarity in content and structure between facies models and specific images (such as fluvial facies and the images of modern rivers), and the various images generated by GANs are often more complex than reservoir facies models, GANs has potential to be used in reservoir facies modeling. Therefore, this paper proposes a reservoir facies modeling method based on GANs: (1) for unconditional modeling, select training images (TIs) based on priori geological knowledge, and use GANs to learn priori geological patterns in TIs, then generate the reservoir facies model by GANs; (2) for conditional modeling, a training method of “unconditional-conditional simulation cooperation” (UCSC) is used to realize the constraint of hard data while learning the priori geological patterns. Testing the method using both synthetic data and actual data from oil field, the results meet perfectly the priori geological patterns and honor the well point hard data, and show that this method can overcome the limitation that traditional geostatistics are difficult to deal with the complex non-stationary patterns and improve the conditional constraint effect of GANs based methods. Given its good performance in facies modeling, the method has a good prospect in practical application.

Design of bridge crossings on meandering rivers

1990 ◽  
Vol 24 (11) ◽  
pp. 681-685
Author(s):  
V. G. Salikov
Keyword(s):  
Meandering Rivers

Upstream and downstream morphodynamic influence in simulated and real meandering rivers

2021 ◽  
Author(s):  
Hossein amini ◽  
Guido Zolezzi ◽  
Federico Monegaglia ◽  
Emanuele Olivetti ◽  
Marco Tubino
Keyword(s):  
Migration Rate ◽  
Machine Learning Techniques ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Lateral Migration ◽  
Remotely Sensed Data ◽  
Meandering Rivers ◽  
Meandering River ◽  
Migration Rates ◽  
Meander Migration

<p>This study investigates the dependency of meander lateral migration rates on the spatial distribution of channel centerline curvature in both synthetic and real meandering rivers. It employs Machine Learning techniques (hereafter ML) to relate observed local lateral meander migration rates with the local and the upstream/downstream values of the centerline curvature. To achieve this goal, it was primarily essential to identify the feasibility of using ML in the meandering river's morphodynamics. We then determined the ability of ML to predict the excess near bank velocity based a set of input data using different regression techniques (linear and polynomial, Stochastic Gradient Descent, Multi-Layer Perceptron, and Support Vector Machine). We then moved forward to study the upstream-downstream influence on local migration rate. Synthetic meandering river planforms, as obtained through the planform evolution model of Bogoni et al. (2017), which is based on Zolezzi and Seminara (2001) meander flow model, were used as test cases for the calibration and check of the different adopted ML algorithms. The calibrated algorithms were then applied to multi-temporal information on meander planform dynamics obtained through the PyRiS software (Monegaglia et al., 2018), to quantify to which extent the upstream and downstream distribution of meander centerline curvature affects the local meander migration rate in real rivers.</p><p>References </p><p>1- Zolezzi, G., & Seminara, G. (2001b). Downstream and upstream influence in river meandering. Part 1. General theory and application overdeepening. Journal of Fluid Mechanics, 438(September 2015), 183–211. https://doi.org/10.1017/S002211200100427X</p><p>2- Monegaglia, F., Zolezzi, G., Güneralp, I., Henshaw, A. J., & Tubino, M. (2018). Automated extraction of meandering river morphodynamics from multitemporal remotely sensed data. In Environmental Modelling & Software (Vol. 105, pp. 171–186). https://doi.org/10.1016/j.envsoft.2018.03.028</p><p>3- Bogoni, M., Putti, M., & Lanzoni, S. (2017). Modeling meander morphodynamics over self-formed heterogeneous floodplains. In Water Resources Research (Vol. 53, Issue 6, pp. 5137–5157). https://doi.org/10.1002/2017wr020726</p><p>4- Benozzo, D.,  Olivetti, E., Avesani, P. (2017). Supervised Estimation of Granger-Based Causality between Time series. In Frontiers in Neuroinformatics. </p><p>https://doi.org/10.3389/fninf.2017.00068 </p><p>5- Sharma A., Kiciman, E. (2020). DoWhy: An End-to-End library for Causal Inference. arXiv preprint arXiv:2011.04216. </p><p>https://arxiv.org/abs/2011.04216</p>

scholarly journals Geometry of meandering and braided gravel-bed threads from the Bayanbulak Grassland, Tianshan, P. R. China

2016 ◽  
Vol 4 (1) ◽  
pp. 273-283 ◽  
Author(s):  
François Métivier ◽  
Olivier Devauchelle ◽  
Hugo Chauvet ◽  
Eric Lajeunesse ◽  
Patrick Meunier ◽  
...  
Keyword(s):  
Grain Size ◽  
Scaling Laws ◽  
Sedimentary Basin ◽  
Bedload Transport ◽  
Braided River ◽  
Statistical Distributions ◽  
Meandering Rivers ◽  
Gravel Bed ◽  
Threshold Theory ◽  
Gravel Bed Rivers

Abstract. The Bayanbulak Grassland, Tianshan, P. R. China, is located in an intramontane sedimentary basin where meandering and braided gravel-bed rivers coexist under the same climatic and geological settings. We report and compare measurements of the discharge, width, depth, slope and grain size of individual threads from these braided and meandering rivers. Both types of threads share statistically indistinguishable regime relations. Their depths and slopes compare well with the threshold theory, but they are wider than predicted by this theory. These findings are reminiscent of previous observations from similar gravel-bed rivers. Using the scaling laws of the threshold theory, we detrend our data with respect to discharge to produce a homogeneous statistical ensemble of width, depth and slope measurements. The statistical distributions of these dimensionless quantities are similar for braided and meandering threads. This suggests that a braided river is a collection of intertwined threads, which individually resemble those of meandering rivers. Given the environmental conditions in Bayanbulak, we furthermore hypothesize that bedload transport causes the threads to be wider than predicted by the threshold theory.

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