Unsupervised Machine Learning Applications for Seismic Facies Classification

2019 ◽  
Author(s):  
Satinder Chopra ◽  
Kurt J. Marfurt
Keyword(s):  
Machine Learning ◽  
Seismic Facies ◽  
Unsupervised Machine Learning ◽  
Facies Classification ◽  
Machine Learning Applications

scholarly journals Seismic facies analysis using machine learning

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. O83-O95 ◽  
Author(s):  
Thilo Wrona ◽  
Indranil Pan ◽  
Robert L. Gawthorpe ◽  
Haakon Fossen
Keyword(s):  
Machine Learning ◽  
Seismic Facies ◽  
Machine Learning Techniques ◽  
Seismic Section ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
Facies Classification ◽  
Learning Techniques ◽  
Seismic Facies Analysis ◽  
Northern North Sea

Seismic interpretations are, by definition, subjective and often require significant time and expertise from the interpreter. We are convinced that machine-learning techniques can help address these problems by performing seismic facies analyses in a rigorous, repeatable way. For this purpose, we use state-of-the-art 3D broadband seismic reflection data of the northern North Sea. Our workflow includes five basic steps. First, we extract seismic attributes to highlight features in the data. Second, we perform a manual seismic facies classification on 10,000 examples. Third, we use some of these examples to train a range of models to predict seismic facies. Fourth, we analyze the performance of these models on the remaining examples. Fifth, we select the “best” model (i.e., highest accuracy) and apply it to a seismic section. As such, we highlight that machine-learning techniques can increase the efficiency of seismic facies analyses.

Exhaustive Probabilistic Neural Network for attribute selection and supervised seismic facies classification

2020 ◽  
pp. 1-67
Author(s):  
David Lubo-Robles ◽  
Thang Ha ◽  
Sivaramakrishnan Lakshmivarahan ◽  
Kurt J. Marfurt ◽  
Matthew J. Pranter
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Probabilistic Neural Network ◽  
Learning Algorithms ◽  
Computational Cost ◽  
Component Analysis ◽  
Machine Learning Algorithms ◽  
Seismic Facies ◽  
Seismic Survey ◽  
Facies Classification

Machine learning algorithms such as principal component analysis (PCA), independent component analysis (ICA), self-organizing maps (SOM), and artificial neural networks (ANN), have been used by geoscientists to not only accelerate the interpretation of their data, but also to provide a more quantitative estimate of the likelihood that any voxel belongs to a given facies. Identifying the best combination of attributes needed to perform either supervised or unsupervised machine learning tasks continues to be the most-asked question by interpreters. In the past decades, stepwise regression and genetic algorithms have been used together with supervised learning algorithms to select the best number and combination of attributes. For reasons of computational efficiency, these techniques do not test all the seismic attribute combinations, potentially leading to a suboptimal classification. In this study, we develop an exhaustive probabilistic neural network (PNN) algorithm which exploits the PNN’s capacity in exploring non-linear relationships to obtain the optimal attribute subset that best differentiates target seismic facies of interest. We show the efficacy of our proposed workflow in differentiating salt from non-salt seismic facies in a Eugene Island seismic survey, offshore Louisiana. We find that from seven input candidate attributes, the Exhaustive PNN is capable of removing irrelevant attributes by selecting a smaller subset of four seismic attributes. The enhanced classification using fewer attributes also reduces the computational cost. We then use the resulting facies probability volumes to construct the 3D distribution of the salt diapir geobodies embedded in a stratigraphic matrix.

scholarly journals The application of volume texture extraction to three-dimensional seismic data – lithofacies structures exploration within the Miocene deposits of the Carpathian Foredeep

2021 ◽  
Vol 46 (4) ◽  
pp. 301-313
Author(s):  
Mariusz Łukaszewski
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Seismic Analysis ◽  
Seismic Facies ◽  
Machine Learning Techniques ◽  
Small Scale ◽  
Facies Classification ◽  
Carpathian Foredeep ◽  
Texture Attributes ◽  
Self Learning

There are numerous conventional fields of natural gas in the Carpathian Foredeep, and there is also evidence to suggest that unconventional gas accumulations may occur in this region. The different seismic sig-natures of these geological forms, the small scale of amplitude variation, and the large amount of data make the process of geological interpretation extremely time-consuming. Moreover, the dispersed nature of information in a large block of seismic data increasingly requires automatic, self-learning cognitive processes. Recent developments with Machine Learning have added new capabilities to seismic interpretation, especially to multi-attribute seismic analysis. Each case requires a proper selection of attributes. In this paper, the Grey Level Co-occurrence Matrix method is presented and its two texture attributes Energy and Entropy. Haralick’s two texture parameters were applied to an advanced interpretation of the interval of Miocene deposits in order to discover the subtle geological features hidden between the seismic traces. As a result, a submarine-slope channel system was delineated leading to the discovery of unknown earlier relationships between gas boreholes and the geological environment. The Miocene deposits filling the Carpathian Foredeep, due to their lithological and facies diversity, provide excellent conditions for testing and implementing Machine Learning techniques. The presented texture attributes are the desired input components for self-learning systems for seismic facies classification.

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