Seismic Data Interpretation using Digital Image Processing

Al-Shuhail, Abdullatif A.; Al-Dossary, Saleh; Mousa, Wail A.

doi:10.1002/9781119125594

Cited by 21 publications

(5 citation statements)

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“…3) Reveal major common geo-bodies from all inputs. In our experiment, the geo-bodies are faults [1].…”

Section: A Comparing Fusion Modelsmentioning

confidence: 99%

“…To gather seismic data, seismic waves are sent into the Earth's subsurface, and the resulting reflection is recorded. Using these reflections, the underlaying structural information is obtained and the earth subsurface can be modeled and visualized [1]. The data that are obtained from the seismic data, to supplement and enhance the geological/geophysical information, are referred to as seismic attributes [2].…”

Section: Introductionmentioning

confidence: 99%

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Combining Multiple Seismic Attributes using Convolutional Neural Networks

Alotaibi¹,

Fadel²,

Jamal³

et al. 2022

IJACSA

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Seismic exploration involves estimating the properties of the Earth's subsurface from reflected seismic waves then visualizing the resulting seismic data and its attributes. These data and derived seismic attributes provide complementary information and reduce the amount of time and effort for the geoscientist. Multiple conventional methods to combine various seismic attributes exist, but the number of attributes is always limited, and the quality of the resulting image varies. This paper proposes a method that can be used to overcome these limitations. In this paper, we propose using Deep Learning-based image fusion models to combine seismic attributes. By using convolutional neural network (CNN) capabilities in feature extraction, the resulting image quality is better than that obtained with conventional methods. This work implemented two models and conducted a number of experiments using them. Several techniques have been used to evaluate the results, such as visual inspection, and using image fusion metrics. The experiments show that the Image-fusion Framework, using the Image Fusion Framework Based on CNN (IFCNN) approach, outperformed all other models in both quantitative and visual analysis. Its QAB/F and MS-SSIM scores are 50% and 10%, respectively, higher than all other models. Also, IFCNN was evaluated against the current state-of-the-art solution, Octree, in a comparative study. IFCNN overcomes the limitation of the Octree method and succeeds in combining nine seismic attributes with a better-combining quality, with QAB/F and NAB/F scores being 40% higher.

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“…3) Reveal major common geo-bodies from all inputs. In our experiment, the geo-bodies are faults [1].…”

Section: A Comparing Fusion Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combining Multiple Seismic Attributes using Convolutional Neural Networks

Alotaibi¹,

Fadel²,

Jamal³

et al. 2022

IJACSA

View full text Add to dashboard Cite

show abstract

“…Initially, seismic interpretation involved manual picking using printed seismic sections (Sheriff, 1981), which has since developed to provide users with the ability to interpret using a suite of digital environments (e.g. Al-Shuhail et al, 2017). The ease and accuracy of seismic interpretation is continually increasing, associated with advancing geophysical and rock physics tools (Chopra and Marfurt, 2007;Avseth et al, 2010), as well as the increased use of automated and semiautomated technologies (e.g.…”

Section: Deep Learningmentioning

confidence: 99%

Assessing the accuracy of fault interpretation using machine-learning techniques when risking faults for CO₂ storage site assessment

2021

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Generating an accurate model of the subsurface for the purpose of assessing the feasibility of a CO2 storage site is crucial. In particular, how faults are interpreted is likely to influence the predicted capacity and integrity of the reservoir; whether this is through identifying high risk areas along the fault, where fluid is likely to flow across the fault, or by assessing the reactivation potential of the fault with increased pressure, causing fluid to flow up the fault. New technologies allow users to interpret faults effortlessly, and in much quicker time, utilizing methods such as Deep Learning. These Deep Learning techniques use knowledge from Neural Networks to allow end-users to compute areas where faults are likely to occur. Although these new technologies may be attractive due to reduced interpretation time, it is important to understand the inherent uncertainties in their ability to predict accurate fault geometries. Here, we compare Deep Learning fault interpretation versus manual fault interpretation, and can see distinct differences to those faults where significant ambiguity exists due to poor seismic resolution at the fault; we observe an increased irregularity when Deep Learning methods are used over conventional manual interpretation. This can result in significant differences between the resulting analyses, such as fault reactivation potential. Conversely, we observe that well-imaged faults show a close similarity between the resulting fault surfaces when both Deep Learning and manual fault interpretation methods are employed, and hence we also observe a close similarity between any attributes and fault analyses made.

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“…Seismic images are data gathered during the exploration of the earth subsurface by sending seismic waves to the earth subsurface and recording the reflection. They provide subsurface structural information and allow the modeling and visualization of the earth subsurface [1]. Seismic attributes are quantities derived from seismic data that supplement and emphasize certain information to make the visualization process more informative [2].…”

Section: Introductionmentioning

confidence: 99%

Enhancement of 3D Seismic Images using Image Fusion Techniques

Alotaibi¹,

Fadel²,

Jamal³

et al. 2021

IJACSA

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Seismic images are data collected by sending seismic waves to the earth subsurface, recording the reflection and providing subsurface structural information. Seismic attributes are quantities derived from seismic data and provide complementary information. Enhancing seismic images by fusing them with seismic attributes will improve the subsurface visualization and reduce the processing time. In seismic data interpretation, fusion techniques have been used to enhance the resolution and reduce the noise of a single seismic attribute. In this paper, we investigate the enhancement of 3D seismic images using image fusion techniques and neural networks to combine seismic attributes. The paper evaluates the feasibility of using image fusion models pretrained on specific image fusion tasks. These models achieved the best results on their respective tasks and are tested for seismic image fusion. The experiments showed that image fusion techniques are capable of combining up to three seismic attributes without distortion, future studies can increase the number. This is the first study conducted using pretrained models on other types of images for seismic image fusion and the results are promising.

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Seismic Data Interpretation using Digital Image Processing

Cited by 21 publications

References 0 publications

Combining Multiple Seismic Attributes using Convolutional Neural Networks

Combining Multiple Seismic Attributes using Convolutional Neural Networks

Assessing the accuracy of fault interpretation using machine-learning techniques when risking faults for CO₂ storage site assessment

Enhancement of 3D Seismic Images using Image Fusion Techniques

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Seismic Data Interpretation using Digital Image Processing

Cited by 21 publications

References 0 publications

Combining Multiple Seismic Attributes using Convolutional Neural Networks

Combining Multiple Seismic Attributes using Convolutional Neural Networks

Assessing the accuracy of fault interpretation using machine-learning techniques when risking faults for CO2 storage site assessment

Enhancement of 3D Seismic Images using Image Fusion Techniques

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Assessing the accuracy of fault interpretation using machine-learning techniques when risking faults for CO₂ storage site assessment