Seismic attributes and the road ahead

Marfurt, Kurt J.

doi:10.1190/segam2014-1644.1

Cited by 14 publications

(4 citation statements)

References 18 publications

(13 reference statements)

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“…Shale anisotropy is currently the focus of academic and industrial research (Chopra et al, 2012;Guo et al, 2013;Sone and Zoback, 2013b;Marfurt, 2014). The shale is usually characterized by transversely isotropic (TI) media.…”

Section: Theory and Methodsmentioning

confidence: 99%

Effect of anisotropy, angle, and critical tensile stress and confining pressures on evaluation of shale brittleness index — Part 1: Methodology and laboratory study

et al. 2019

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Brittleness is an important evaluation parameter in shale fracturing. Current methods of brittleness evaluation can be classified into two categories: elastic parameter-based and mineral content-based methods. However, both categories neglect the effect of anisotropy on the brittleness index (BI) computation of shale resources. We have redefined a new BI by integrating failure criteria stress and anisotropy parameters estimated (BIac) from seismic waves. According to the new definition, the BI at one analysis point varies with the incident angle of the seismic wave and confining pressures. We applied the BIac method to laboratory-measured shale samples acquired from the Monterey Formation, Santa Maria Basin. We found that the delta parameter [Formula: see text] is more responsive to the BIac than the gamma [Formula: see text] and epsilon [Formula: see text] anisotropic parameters, and it indicates a good linear fit relationship with the BIac at different angles. The slope of the linear is variable with the angles, thus delta can be used to predict the BIac in the Monterey Formation, Santa Maria Basin.

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Section: Theory and Methodsmentioning

confidence: 99%

Effect of anisotropy, angle, and critical tensile stress and confining pressures on evaluation of shale brittleness index — Part 1: Methodology and laboratory study

et al. 2019

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“…In contrast to the stacked traces in post-stack seismic data, these gathers encapsulate a richer seismic dataset. This kind of information allows us to significantly enhance the performance of neural networks in seismic data analysis [46,47]. Research on using pre-stack seismic data for reservoir characterization in DSSL is still ongoing, as well as semi-supervised learning employing shallower models [48][49][50][51][52][53][54].…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Training for (Pre-Stack) Seismic Data Analysis

Ek-Chacón,

Molino-Minero-Re,

Méndez-Monroy

et al. 2024

Applied Sciences

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A lack of labeled examples is a problem in different domains, such as text and image processing, medicine, and static reservoir characterization, because supervised learning relies on vast volumes of these data to perform successfully, but this is quite expensive. However, large amounts of unlabeled data exist in these domains. The deep semi-supervised learning (DSSL) approach leverages unlabeled data to improve supervised learning performance using deep neural networks. This approach has succeeded in image recognition, text classification, and speech recognition. Nevertheless, there have been few works on pre-stack seismic reservoir characterization, in which knowledge of rock and fluid properties is fundamental for oil exploration. This paper proposes a methodology to estimate acoustic impedance using pre-stack seismic data and DSSL with a recurrent neural network. The few labeled datasets for training were pre-processed from raw seismic and acoustic impedance data from five borehole logs. The results showed that the acoustic impedance estimation at the well location and outside it was better predicted by the DSSL compared to the supervised version of the same neural network. Therefore, employing a large amount of unlabeled data can be helpful in the development of seismic data interpretation systems.

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“…Recent advances in multi-modal input technology for predictions in the fields of computer vision, e-commerce, health informatics, and other advanced artificial intelligence applications may be beneficial in this regard [16][17][18][19][20][21] . The availability of large amounts of data and computing resources have resulted in the resurgence of data-driven machine learning techniques for the problems where conventional physics-based modeling is deficient 20,21 .…”

mentioning

confidence: 99%

Characterization of complex fluvio–deltaic deposits in Northeast China using multi-modal machine learning fusion

Boateng

Danuor

2020

Sci Rep

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Due to the lack of petroleum resources, stratigraphic reservoirs have become an important source of future discoveries. We describe a methodology for predicting reservoir sands from complex reservoir seismic data. Data analysis involves a bio-integrated framework called multi-modal machine learning fusion (MMMLF) based on neural networks. First, acoustic-related seismic attributes from poststack seismic data were used to characterize the reservoirs. They enhanced the understanding of the structure and spatial distribution of petrophysical properties of lithostratigraphic reservoirs. The attributes were then classified as varied modal inputs into a central fusion engine for prediction. We applied the method to a dataset from Northeast China. Using seismic attributes and rock physics relationships as input data, MMMLF was performed to predict the spatial distribution of lithology in the Upper Guantao substrata. Despite the large scattering in the acoustic-related data properties, the proposed MMMLF methodology predicted the distribution of lithological properties through the gamma ray logs. Moreover, complex stratigraphic traps such as braided fluvial sandstones in the fluvio-deltaic deposits were delineated. These findings can have significant implications for future exploration and production in Northeast China and similar petroleum provinces around the world.

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Seismic attributes and the road ahead

Cited by 14 publications

References 18 publications

Effect of anisotropy, angle, and critical tensile stress and confining pressures on evaluation of shale brittleness index — Part 1: Methodology and laboratory study

Effect of anisotropy, angle, and critical tensile stress and confining pressures on evaluation of shale brittleness index — Part 1: Methodology and laboratory study

Semi-Supervised Training for (Pre-Stack) Seismic Data Analysis

Characterization of complex fluvio–deltaic deposits in Northeast China using multi-modal machine learning fusion

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