scholarly journals Unsupervised Machine Learning Applied to Seismic Interpretation: Towards an Unsupervised Automated Interpretation Tool

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6347
Author(s):  
Alimed Celecia ◽  
Karla Figueiredo ◽  
Carlos Rodriguez ◽  
Marley Vellasco ◽  
Edwin Maldonado ◽  
...  
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Clustering Algorithms ◽  
Seismic Interpretation ◽  
Hydrocarbon Exploration ◽  
Seismic Facies ◽  
Well Logs ◽  
Unsupervised Machine Learning ◽  
High Data ◽  
Novel Approach

Seismic interpretation is a fundamental process for hydrocarbon exploration. This activity comprises identifying geological information through the processing and analysis of seismic data represented by different attributes. The interpretation process presents limitations related to its high data volume, own complexity, time consumption, and uncertainties incorporated by the experts’ work. Unsupervised machine learning models, by discovering underlying patterns in the data, can represent a novel approach to provide an accurate interpretation without any reference or label, eliminating the human bias. Therefore, in this work, we propose exploring multiple methodologies based on unsupervised learning algorithms to interpret seismic data. Specifically, two strategies considering classical clustering algorithms and image segmentation methods, combined with feature selection, were evaluated to select the best possible approach. Additionally, the resultant groups of the seismic data were associated with groups obtained from well logs of the same area, producing an interpretation with aggregated lithologic information. The resultant seismic groups correctly represented the main seismic facies and correlated adequately with the groups obtained from the well logs data.

Unsupervised machine learning using 3D seismic data applied to reservoir evaluation and rock type identification

2021 ◽  
pp. 1-69
Author(s):  
Marwa Hussein ◽  
Robert R. Stewart ◽  
Deborah Sacrey ◽  
Jonny Wu ◽  
Rajas Athale
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Rock Type ◽  
Seismic Attributes ◽  
Reservoir Quality ◽  
3D Seismic ◽  
Reservoir Properties ◽  
Unsupervised Machine Learning ◽  
Amplitude Data ◽  
3D Seismic Data

Net reservoir discrimination and rock type identification play vital roles in determining reservoir quality, distribution, and identification of stratigraphic baffles for optimizing drilling plans and economic petroleum recovery. Although it is challenging to discriminate small changes in reservoir properties or identify thin stratigraphic barriers below seismic resolution from conventional seismic amplitude data, we have found that seismic attributes aid in defining the reservoir architecture, properties, and stratigraphic baffles. However, analyzing numerous individual attributes is a time-consuming process and may have limitations for revealing small petrophysical changes within a reservoir. Using the Maui 3D seismic data acquired in offshore Taranaki Basin, New Zealand, we generate typical instantaneous and spectral decomposition seismic attributes that are sensitive to lithologic variations and changes in reservoir properties. Using the most common petrophysical and rock typing classification methods, the rock quality and heterogeneity of the C1 Sand reservoir are studied for four wells located within the 3D seismic volume. We find that integrating the geologic content of a combination of eight spectral instantaneous attribute volumes using an unsupervised machine-learning algorithm (self-organizing maps [SOMs]) results in a classification volume that can highlight reservoir distribution and identify stratigraphic baffles by correlating the SOM clusters with discrete net reservoir and flow-unit logs. We find that SOM classification of natural clusters of multiattribute samples in the attribute space is sensitive to subtle changes within the reservoir’s petrophysical properties. We find that SOM clusters appear to be more sensitive to porosity variations compared with lithologic changes within the reservoir. Thus, this method helps us to understand reservoir quality and heterogeneity in addition to illuminating thin reservoirs and stratigraphic baffles.

scholarly journals Ammonoid Taxonomy with Supervised and Unsupervised Machine Learning Algorithms

2021 ◽  
Author(s):  
Floe Foxon
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Learning Algorithms ◽  
Clustering Algorithms ◽  
Measurement Data ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Unsupervised Machine Learning

Ammonoid identification is crucial to biostratigraphy, systematic palaeontology, and evolutionary biology, but may prove difficult when shell features and sutures are poorly preserved. This necessitates novel approaches to ammonoid taxonomy. This study aimed to taxonomize ammonoids by their conch geometry using supervised and unsupervised machine learning algorithms. Ammonoid measurement data (conch diameter, whorl height, whorl width, and umbilical width) were taken from the Paleobiology Database (PBDB). 11 species with ≥50 specimens each were identified providing N=781 total unique specimens. Naive Bayes, Decision Tree, Random Forest, Gradient Boosting, K-Nearest Neighbours, and Support Vector Machine classifiers were applied to the PBDB data with a 5x5 nested cross-validation approach to obtain unbiased generalization performance estimates across a grid search of algorithm parameters. All supervised classifiers achieved ≥70% accuracy in identifying ammonoid species, with Naive Bayes demonstrating the least over-fitting. The unsupervised clustering algorithms K-Means, DBSCAN, OPTICS, Mean Shift, and Affinity Propagation achieved Normalized Mutual Information scores of ≥0.6, with the centroid-based methods having most success. This presents a reasonably-accurate proof-of-concept approach to ammonoid classification which may assist identification in cases where more traditional methods are not feasible.

Successful leveraging of image processing and machine learning in seismic structural interpretation: A review

2018 ◽  
Vol 37 (6) ◽  
pp. 451-461 ◽  
Author(s):  
Zhen Wang ◽  
Haibin Di ◽  
Muhammad Amir Shafiq ◽  
Yazeed Alaudah ◽  
Ghassan AlRegib
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
Seismic Data ◽  
Domain Knowledge ◽  
Machine Learning Algorithms ◽  
Hydrocarbon Exploration ◽  
Machine Learning Techniques ◽  
Structural Interpretation ◽  
Salt Domes

As a process that identifies geologic structures of interest such as faults, salt domes, or elements of petroleum systems in general, seismic structural interpretation depends heavily on the domain knowledge and experience of interpreters as well as visual cues of geologic structures, such as texture and geometry. With the dramatic increase in size of seismic data acquired for hydrocarbon exploration, structural interpretation has become more time consuming and labor intensive. By treating seismic data as images rather than signal traces, researchers have been able to utilize advanced image-processing and machine-learning techniques to assist interpretation directly. In this paper, we mainly focus on the interpretation of two important geologic structures, faults and salt domes, and summarize interpretation workflows based on typical or advanced image-processing and machine-learning algorithms. In recent years, increasing computational power and the massive amount of available data have led to the rise of deep learning. Deep-learning models that simulate the human brain's biological neural networks can achieve state-of-the-art accuracy and even exceed human-level performance on numerous applications. The convolutional neural network — a form of deep-learning model that is effective in analyzing visual imagery — has been applied in fault and salt dome interpretation. At the end of this review, we provide insight and discussion on the future of structural interpretation.

scholarly journals Using Real-Time Data and Unsupervised Machine Learning Techniques to Study Large-Scale Spatio–Temporal Characteristics of Wastewater Discharges and their Influence on Surface Water Quality in the Yangtze River Basin

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1268 ◽  
Author(s):  
Zhenzhen Di ◽  
Miao Chang ◽  
Peikun Guo ◽  
Yang Li ◽  
Yin Chang
Keyword(s):  
Machine Learning ◽  
Surface Water ◽  
Real Time ◽  
Yangtze River Basin ◽  
Clustering Algorithms ◽  
Machine Learning Techniques ◽  
Unsupervised Machine Learning ◽  
Learning Techniques ◽  
The Yangtze River Basin ◽  
Spatio Temporal

Most worldwide industrial wastewater, including in China, is still directly discharged to aquatic environments without adequate treatment. Because of a lack of data and few methods, the relationships between pollutants discharged in wastewater and those in surface water have not been fully revealed and unsupervised machine learning techniques, such as clustering algorithms, have been neglected in related research fields. In this study, real-time monitoring data for chemical oxygen demand (COD), ammonia nitrogen (NH3-N), pH, and dissolved oxygen in the wastewater discharged from 2213 factories and in the surface water at 18 monitoring sections (sites) in 7 administrative regions in the Yangtze River Basin from 2016 to 2017 were collected and analyzed by the partitioning around medoids (PAM) and expectation–maximization (EM) clustering algorithms, Welch t-test, Wilcoxon test, and Spearman correlation. The results showed that compared with the spatial cluster comprising unpolluted sites, the spatial cluster comprised heavily polluted sites where more wastewater was discharged had relatively high COD (>100 mg L−1) and NH3-N (>6 mg L−1) concentrations and relatively low pH (<6) from 15 industrial classes that respected the different discharge limits outlined in the pollutant discharge standards. The results also showed that the economic activities generating wastewater and the geographical distribution of the heavily polluted wastewater changed from 2016 to 2017, such that the concentration ranges of pollutants in discharges widened and the contributions from some emerging enterprises became more important. The correlations between the quality of the wastewater and the surface water strengthened as the whole-year data sets were reduced to the heavily polluted periods by the EM clustering and water quality evaluation. This study demonstrates how unsupervised machine learning algorithms play an objective and effective role in data mining real-time monitoring information and highlighting spatio–temporal relationships between pollutants in wastewater discharges and surface water to support scientific water resource management.

Towards reproducing seismic interpretation uncertainties using open-source stochastic geomodeling in Python

2020 ◽  
Author(s):  
Alexander Schaaf ◽  
Miguel de la Varga ◽  
Clare E. Bond ◽  
Florian Wellmann
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Seismic Data ◽  
Seismic Interpretation ◽  
Machine Learning Algorithms ◽  
Foreseeable Future ◽  
Manual Labour ◽  
Northern North Sea ◽  
Ambiguous Data

&lt;p&gt;Seismic data plays a key role in developing our understanding of the subsurface by providing 2-D and 3-D indirect imaging. But the resulting data needs to be interpreted by specialists using time-intensive, error-prone and subjective manual labour. While the automation of data classification using Machine Learning algorithms is starting to show promising results in areas of good data quality, the classification of noisy and ambiguous data will continue to require geological reasoning for the foreseeable future. In Schaaf &amp; Bond (2019) we provided a first quantification of the uncertainties involved in the structural interpretation of a 3-D seismic volume by analysing 78 student interpretations of the Gullfaks field in the northern North Sea. Our work also concretized the question of to which degree the seismic data itself could provide useful information towards a prediction of interpretation uncertainty.&lt;/p&gt;&lt;p&gt;We now look at the same dataset in an effort to answer the question if we can adequately reproduce the observed interpretation uncertainties by approximating them as aleatoric uncertainties in a stochastic geomodeling framework. For this we make use of the Python-based open-source 3-D implicit structural geomodeling software GemPy to leverage open-source probabilistic programming frameworks and to allow for scientific reproducibility of our results. We identify potential shortcomings of collapsing interpretation uncertainties into aleatoric uncertainties and present ideas on how to improve stochastic parametrization based on the seismic data at hand.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Schaaf, A., &amp; Bond, C. E. (2019). Quantification of uncertainty in 3-D seismic interpretation: Implications for deterministic and stochastic geomodeling and machine learning. Solid Earth, 10(4), 1049&amp;#8211;1061. https://doi.org/10.5194/se-10-1049-2019&lt;/p&gt;

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