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.

scholarly journals Unsupervised seismic facies using Gaussian mixture models

2019 ◽  
Vol 7 (3) ◽  
pp. SE93-SE111 ◽  
Author(s):  
Bradley C. Wallet ◽  
Robert Hardisty
Keyword(s):  
Machine Learning ◽  
Facies Analysis ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Seismic Facies ◽  
Machine Learning Techniques ◽  
Spatial Correlations ◽  
Learning Techniques ◽  
Turbidite System ◽  
Seismic Facies Analysis

As the use of seismic attributes becomes more widespread, multivariate seismic analysis has become more commonplace for seismic facies analysis. Unsupervised machine-learning techniques provide methods of automatically finding patterns in data with minimal user interaction. When using unsupervised machine-learning techniques, such as [Formula: see text]-means or Kohonen self-organizing maps (SOMs), the number of clusters can often be ambiguously defined and there is no measure of how confident the algorithm is in the classification of data vectors. The model-based probabilistic formulation of Gaussian mixture models (GMMs) allows for the number and shape of clusters to be determined in a more objective manner using a Bayesian framework that considers a model’s likelihood and complexity. Furthermore, the development of alternative expectation-maximization (EM) algorithms has allowed GMMs to be more tailored to unsupervised seismic facies analysis. The classification EM algorithm classifies data vectors according to their posterior probabilities that provide a measurement of uncertainty and ambiguity (often called a soft classification). The neighborhood EM (NEM) algorithm allows for spatial correlations to be considered to make classification volumes more realistic by enforcing spatial continuity. Corendering the classification with the uncertainty and ambiguity measurements produces an intuitive map of unsupervised seismic facies. We apply a model-based classification approach using GMMs to a turbidite system in Canterbury Basin, New Zealand, to clarify results from an initial SOM and highlight areas of uncertainty and ambiguity. Special focus on a channel feature in the turbidite system using an NEM algorithm shows it to be more realistic by considering spatial correlations within the data.

STUDY ON THE PARAMETERIZATION RESPONSE OF PROBABILISTIC NEURAL NETWORKS FOR SEISMIC FACIES CLASSIFICATION IN THE GULF OF MEXICO

2021 ◽  
pp. 1-105
Author(s):  
Diana Salazar Florez ◽  
Heather Bedle
Keyword(s):  
Neural Networks ◽  
Seismic Facies ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Small Scale ◽  
Probabilistic Neural Networks ◽  
Computational Costs ◽  
Facies Classification ◽  
Learning Techniques ◽  
The Impact

Nowadays, there are many unsupervised and supervised machine learning techniques available for performing seismic facies classification. However, those classification methods either demand high computational costs or do not provide an accurate measure of confidence. Probabilistic neural networks (PNNs) overcome these limitations and have demonstrated their superiority among other algorithms. PNNs have been extensively applied for some prediction tasks, but not well studied regarding the prediction of seismic facies volumes using seismic attributes. We explore the capability of the PNN algorithm when classifying large- and small-scale seismic facies. Additionally, we evaluate the impact of user-chosen parameters on the final classification volumes. After performing seven tests, each with a parameter variation, we assess the impact of the parameter change on the resultant classification volumes. We show that the processing task can have a significant impact on the classification volumes, but also how the most geologically complex areas are the most challenging for the algorithm. Moreover, we demonstrate that even if the PNN technique is performing and producing considerably accurate results, it is possible to overcome those limitations and significantly improve the final classification volumes by including the geological insight provided by the geoscientist. We conclude by proposing a new workflow that can guide future geoscientists interested in applying PNNs, to obtain better seismic facies classification volumes by considering some initial steps and advice.

Seismic characterization of a Triassic-Jurassic deep geothermal sandstone reservoir, onshore Denmark, using unsupervised machine learning techniques

2021 ◽  
pp. 1-48
Author(s):  
Satinder Chopra ◽  
Ritesh Kumar Sharma ◽  
Kenneth Bredesen ◽  
Thang Ha ◽  
Kurt J. Marfurt
Keyword(s):  
Machine Learning ◽  
Bayesian Framework ◽  
Green Energy ◽  
Machine Learning Techniques ◽  
Generative Topographic Mapping ◽  
Reservoir Properties ◽  
Facies Classification ◽  
Learning Techniques ◽  
Sandstone Reservoir

The Triassic-Jurassic deep sandstone reservoirs in onshore Denmark are known geothermal targets that can be exploited for sustainable and green energy for the next several decades. The economic development of such resources requires accurate characterization of the sandstone reservoir properties, namely, volume of clay, porosity, and permeability. The classic approach to achieving such objectives has been to integrate prestack seismic data and well logs with geologic information to obtain facies and reservoir property predictions in a Bayesian framework. Using this prestack inversion approach, we can obtain superior spatial and temporal variations within the target formation. We then examine whether unsupervised facies classification in the target units can provide additional information. We evaluated several machine learning techniques and find that generative topographic mapping further subdivided intervals mapped by the Bayesian framework into additional subunits.

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.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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