Facies Models Revisited: Clastic Shelves

2006 ◽  
pp. 339-397 ◽  
Author(s):  
JOHN R. SUTER
Keyword(s):  
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.

THE SIGNIFICANCE OF PEATLAND AGGRADATION IN MODERN AND ANCIENT ENVIRONMENTS

Palaios ◽  
2017 ◽  
Vol 32 (10) ◽  
pp. 658-671 ◽  
Author(s):  
VERA A. KORASIDIS ◽  
MALCOLM W. WALLACE ◽  
BEN JANSEN
Keyword(s):  
Brown Coal ◽  
Environmental Changes ◽  
Important Process ◽  
Black Coal ◽  
Single Cycle ◽  
High Rainfall ◽  
Repetitive Nature ◽  
Brown Coals ◽  
Facies Models

Abstract: Peats are commonly used in paleoenvironmental and paleoclimatic studies but detailed sedimentological and facies models for peatlands are poorly developed relative to other sedimentary settings. A comparison of the palynology and charcoal abundances in modern and ancient Cenozoic peats (i.e., brown coals) demonstrates that, in a single cycle, their respective flora commonly evolves from inundated wetland assemblages to more elevated and well-drained forest. The repetitive nature of this pattern suggests that the changing floral compositions result from changes in substrate wetness during peatland aggradation in high rainfall settings. In this scenario, floristic changes within the peat are suggested to represent peatland facies that were controlled by the local peat-forming environment. We suggest that peatland aggradation is an important process that may ubiquitously control the floral and environmental changes documented in modern and Holocene ombrogenous peats, brown coal lithotype cycles, and perhaps black coal dulling-upwards cycles.

Morphology and Facies Models of Channel Confluences

2009 ◽  
pp. 89-100 ◽  
Author(s):  
C. S. Bristow ◽  
J. L. Best ◽  
A. G. Roy
Keyword(s):  
Facies Models
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