Integrated seismic texture segmentation and cluster analysis applied to channel delineation and chert reservoir characterization

Matos, Marcílio Castro de; Yenugu, Malleswar; Angelo, Sipuikinene Miguel; Marfurt, Kurt J.

doi:10.1190/geo2010-0150.1

Cited by 53 publications

(11 citation statements)

References 20 publications

(36 reference statements)

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“…Interpretation / August 2017 SK129 feed the SOM are texture (homogeneity and entropy), peak frequency, and peak magnitude attributes. These attributes are extracted from the raw amplitude data using software developed at the University of Oklahoma (e.g., Matos et al, 2011;Qi et al, 2016).…”

Section: Soms and Geomorphologymentioning

confidence: 99%

Seismic expression and geomorphology of igneous bodies: A Taranaki Basin, New Zealand, case study

Infante-Paez

Marfurt

2017

Interpretation

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Very little research has been done on volcanic rocks by the oil industry due to the misconception that these rocks cannot be "good reservoirs." However, in the past two decades, significant quantities of hydrocarbons have been produced from volcanic rocks in China, New Zealand, and Argentina. In frontier basins, volcanic piles are sometimes misinterpreted to be hydrocarbon anomalies and/or carbonate buildups. Unlike clastic and carbonate systems, the 3D seismic geomorphology of igneous systems is only partially documented. We have integrated 3D seismic data, well logs, well reports, core data, and clustering techniques such as self-organizing maps to map two distinct facies (pyroclastic and lava flows), within a Miocene submarine volcano in the Taranaki Basin, New Zealand. Three wells; Kora-1-3 drilled the pyroclastic facies within the volcano encountering evidence of a petroleum system, whereas the Kora-4 well drilled the lava-flow facies, which was barren of hydrocarbons. By integrating results from geochemistry and basin modeling reports prepared for Crown Mineral, New Zealand, we concluded that the reason that Kora-4 was dry was due to a lack of source chargenot to the absence of reservoir quality. Moreover, the Kora-1 well drilled a thick sequence (>1000 m) of pyroclastic flows in this submarine volcano by chance and found high peaks of gas in the mudlogs near the top 25 m of this sequence. A long-term test in this upper volcanic section resulted in 32 API oil flow of 668 barrels of oil per day for 254 ha result that challenges the misconception that volcanic rocks cannot be good reservoirs.

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Section: Soms and Geomorphologymentioning

confidence: 99%

Seismic expression and geomorphology of igneous bodies: A Taranaki Basin, New Zealand, case study

Infante-Paez

Marfurt

2017

Interpretation

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“…Kachine learning (ML) techniques have been reliably used in geoscience interpretation for almost two decades including seismic-facies classification (de Matos et al, 2011;Qi et al, 2016;Zhao et al, 2017), electrofacies classification (Allen and Pranter, 2016), and analysis of seismicity (Kortström et al, 2016;Sinha et al, 2018). Traditionally, geoscience ML applications rely on a carefully selected set of features or attributes.…”

Section: Introductionmentioning

confidence: 99%

Progress and Challenges in Deep Learning Analysis of Geoscience Images

Lima¹,

Marfurt²,

Duarte³

et al. 2019

81st EAGE Conference and Exhibition 2019

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Deep learning and deep convolutional neural network (CNN) models have shown promising results and are gaining popularity in the geoscientific community. In contrast to traditional machine learning methodologies based on a suite of carefully selected attributes, deep learning is based on the raw images themselves. Deep CNNs are currently the tools of choice for computer vision tasks such as self-driving cars. Unfortunately, deep learning is encumbered by jargon that is unfamiliar to most geoscientists, providing black box applications resulting in two common reactions: deep learning models are the solution for everything or deep learning models is a modern fad that discards the interpreter's physical insight or experience with a given problem. In this presentation, we show that CNN models are based on attributes similar to those we use in seismic interpretation and remote sensing. We also show that through a process called transfer learning based on the analysis of 2D color images, we can exploit much of the previous work developed for transportation and biological applications to rocks. We illustrate the successful use transfer learning to microfossil classification, core description, petrographic analysis, and hand specimen identification. We also discuss some of the challenges in CNN analysis of 3D seismic data volumes.

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“…Machine learning (ML) techniques have been successfully applied, with considerable success, in the geosciences for almost two decades. Applications of ML by the geoscientific community include many examples such as seismic-facies classification (Meldahl et al, 2001;West et al, 2002;de Matos et al, 2011;Roy et al, 2014;Qi et al, 2016;Hu et al, 2017;Zhao et al, 2017), electrofacies classification (Allen and Pranter, 2016), and analysis of seismicity (Kortström et al, 2016;DeVries et al, 2018;Perol et al, 2018;Sinha et al, 2018), and classification of volcanic ash (Shoji et al, 2018), among others. Conventionally, ML applications rely on a set of attributes (or features) selected or designed by an expert.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2019

TSR

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This publication is the result of more than 3 decades of sequence stratigraphy research and application. The objective is to emphasize the most important aspects of Sequence Stratigraphy-a method to guide geologic interpretation of stratigraphic data (seismic profiles, well-logs, cores and outcrops) across scales (from local to regional and global) and depositional environments (from continental to deep marine).The stratigraphic concept of a depositional sequence was introduced to the scientific literature by Peter Vail and his colleagues in the late 70s, building on the shoulders of giants like Chamberlain, Sloss and Wheeler. Since then, several papers compared and contrasted the original sequence-stratigraphic school published in the AAPG Memoir 26 in 1977 with other approaches to subdivide the geologic record, as well as, debating the model validity and impact on the community. At its core, the "model" is really a stratigraphic interpretation method, which was never explicitly documented in the literature. The objective of this book is to present the sequence stratigraphic method in its current form in an attempt to clarify its usage and application in diverse geologic data and depositional environments. This publication is the result of more than 3 decades of sequence stratigraphy research and application. The objective is to emphasize the most important aspects of Sequence Stratigraphy-a method to guide geologic interpretation of stratigraphic data (seismic profiles, well-logs, cores and outcrops) across scales (from local to regional and global) and depositional environments (from continental to deep marine). This book in an 11 x 17 format is designed to be easily used for teaching or self-learning experiences. In the second edition of the "Atlas", the book was divided in 2 separately bound volumes-Exercises and Solutions-to make it easier to use the publication as text book for sequence stratigraphy courses in universities. Also, a new exercise was added and several of the existing exercises went through major updating and editing. Catalog #55020 • Softcover

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Integrated seismic texture segmentation and cluster analysis applied to channel delineation and chert reservoir characterization

Cited by 53 publications

References 20 publications

Seismic expression and geomorphology of igneous bodies: A Taranaki Basin, New Zealand, case study

Seismic expression and geomorphology of igneous bodies: A Taranaki Basin, New Zealand, case study

Progress and Challenges in Deep Learning Analysis of Geoscience Images

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