Attribute selection in seismic facies classification: Application to a Gulf of Mexico 3D seismic survey and the Barnett Shale

2019 ◽  
Vol 7 (3) ◽  
pp. SE281-SE297 ◽  
Author(s):  
Yuji Kim ◽  
Robert Hardisty ◽  
Kurt J. Marfurt
Keyword(s):  
Gulf Of Mexico ◽  
Supervised Learning ◽  
Depositional Environments ◽  
Machine Learning Algorithms ◽  
Attribute Selection ◽  
Seismic Facies ◽  
Seismic Survey ◽  
Barnett Shale ◽  
3D Seismic ◽  
Facies Classification

Automated seismic facies classification using machine-learning algorithms is becoming more common in the geophysics industry. Seismic attributes are frequently used as input because they may express geologic patterns or depositional environments better than the original seismic amplitude. Selecting appropriate attributes becomes a crucial part of the seismic facies classification analysis. For unsupervised learning, principal component analysis can reduce the dimensions of the data while maintaining the highest variance possible. For supervised learning, the best attribute subset can be built by selecting input attributes that are relevant to the output class and avoiding using redundant attributes that are similar to each other. Multiple attributes are tested to classify salt diapirs, mass transport deposits (MTDs), and the conformal reflector “background” for a 3D seismic marine survey acquired on the northern Gulf of Mexico shelf. We have analyzed attribute-to-attribute correlation and the correlation between the input attributes to the output classes to understand which attributes are relevant and which attributes are redundant. We found that amplitude and texture attribute families are able to differentiate salt, MTDs, and conformal reflectors. Our attribute selection workflow is also applied to the Barnett Shale play to differentiate limestone and shale facies. Multivariate analysis using filter, wrapper, and embedded algorithms was used to rank attributes by importance, so then the best attribute subset for classification is chosen. We find that attribute selection algorithms for supervised learning not only reduce computational cost but also enhance the performance of the classification.

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.

A comparison of classification techniques for seismic facies recognition

2015 ◽  
Vol 3 (4) ◽  
pp. SAE29-SAE58 ◽  
Author(s):  
Tao Zhao ◽  
Vikram Jayaram ◽  
Atish Roy ◽  
Kurt J. Marfurt
Keyword(s):  
Seismic Data ◽  
Gaussian Mixture ◽  
Seismic Facies ◽  
Support Vector ◽  
3D Seismic ◽  
Generative Topographic Mapping ◽  
Learning Methods ◽  
Facies Classification ◽  
Data Volume ◽  
3D Seismic Data

During the past decade, the size of 3D seismic data volumes and the number of seismic attributes have increased to the extent that it is difficult, if not impossible, for interpreters to examine every seismic line and time slice. To address this problem, several seismic facies classification algorithms including [Formula: see text]-means, self-organizing maps, generative topographic mapping, support vector machines, Gaussian mixture models, and artificial neural networks have been successfully used to extract features of geologic interest from multiple volumes. Although well documented in the literature, the terminology and complexity of these algorithms may bewilder the average seismic interpreter, and few papers have applied these competing methods to the same data volume. We have reviewed six commonly used algorithms and applied them to a single 3D seismic data volume acquired over the Canterbury Basin, offshore New Zealand, where one of the main objectives was to differentiate the architectural elements of a turbidite system. Not surprisingly, the most important parameter in this analysis was the choice of the correct input attributes, which in turn depended on careful pattern recognition by the interpreter. We found that supervised learning methods provided accurate estimates of the desired seismic facies, whereas unsupervised learning methods also highlighted features that might otherwise be overlooked.

scholarly journals ASSESSMENT OF THE OIL AND GAS POTENTIAL PROSPECTS OF THE NEOKOMIAN SEDIMENTS IN THE JOINT ZONE OF THE GYDAN, YAMAL AND NADYM-PURPEY OIL AND GAS BEARING REGIONS OF WESTERN SIBERIA ON THE BASIS OF 3D SEISMIC SURVEY

2021 ◽  
Vol 2 (1) ◽  
pp. 8-14
Author(s):  
Vladimir N. Borodkin ◽  
Oleg A. Smirnov
Keyword(s):  
Western Siberia ◽  
Oil And Gas ◽  
Seismic Facies ◽  
Seismic Survey ◽  
3D Seismic ◽  
Geological Modeling ◽  
Joint Zone ◽  
Water Zone ◽  
Gas Potential ◽  
Gas Bearing

The article presents a brief overview of the views on the stratification of the section of the neocomian deposits. As a basis for geological modeling, instead of formation units, seismic facies complexes were taken, including reservoirs in the coastal shallow-water zone, in a relatively deep-water zone - isochronous clinoform formations of the achimov strata. Within the researched territory, the characteristic of the established oil and gas potential of the complex is presented, on the basis of 3D seismic survey, perspective objects are identified, and their seismogeological characteristics are given.

Using Semi-Supervised Convolutional Neural Networks for Porosity Modeling Over a Fluvio-Deltaic Triassic Gas Field

2021 ◽  
Author(s):  
Haibin Di ◽  
Aria Abubakar
Keyword(s):  
Supervised Learning ◽  
Seismic Wave Propagation ◽  
Well Logs ◽  
Seismic Survey ◽  
Rock Properties ◽  
3D Seismic ◽  
Gas Field ◽  
Rock Property ◽  
Additional Information ◽  
Property Estimation

Abstract Robust estimation of rock properties, such as porosity and density, from geophysical data, i.e. seismic and well logs, is essential in the process of subsurface modeling and reservoir engineering workflows. Such properties are accurately measured in a well; however, due to high cost of drilling, such direct measurements are limited in amount and sparse in space within a study area. On the contrary, 3D seismic data illuminates the subsurface of the study area throughoutly by seismic wave propagation; however, the connection between seismic signals and rock properties is implicit and unknown, causing rock property estimation from seismic only to be a challenging task and of high uncertainty. An integration of 3D seismic with sparse wells is expected to eliminate such uncertainty and improve the accuracy of static reservoir property estimation. This paper investigates the application of a semi-supervised learning workflow to estimate porosity from a 3D seismic survey and 36 wells over a fluvio-deltaic Triasic gas field. The workflow is performed in various scenarios, including purely from seismic amplitude, incorporating a rough 6-layer deposition model as a constraint, and training with varying numbers of wells. Good match is observed between the machine prediction and the well logs, which verifies the capability of such semi-supervised learning in providing reliable seismic-well integration and delivering robust porosity modeling. It is concluded that the use of available additional information helps effectively constrain the learning process and thus leads to significantly improved lateral continuity and reduced artifacts in the machine learning prediction. The semi-supervised learning can be readily extended for estimating more properties and allows nearly one- click solution to obtain 3D rock property distribution in a study area where seismic and well data is available.

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