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- Volume 40, Issue 2, 2022
First Break - Volume 40, Issue 2, 2022
Volume 40, Issue 2, 2022
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Monitoring Dynamic Changes in Reservoir Production Using Time-Lapse Walkaway Das-Vsp Data
Authors G. Yu, Z.H. Zhao, S.M. He, X.L. Zhang and Y.Z. ChenAbstractWith the rapid development of optical fibre sensing in recent years, Distributed Acoustic Sensing (DAS) is gradually considered as a viable alternative to borehole geophone arrays for the acquisition of borehole seismic or Vertical Seismic Profile (VSP) data. The data collected by an optical cable permanently deployed behind casing have a high degree of consistency and high SRN, which is less affected by receiving factors such as the cable coupling issues and the tube wave. This paper describes the time-lapse Walkaway DAS-VSP data acquisition in Dagang Oilfield of China and the multi-stage time-lapse Walkaway DAS-VSP data consistency processing procedures. Through the fine imaging processing of multi-stage Walkaway DAS-VSP data and detailed geological interpretation, it is possible to identify and map the range of fluid migration around the wellbore due to production, perform cross-validation during the monitoring period using the borehole oil production statistics information, and analyse the dynamic changes of reservoir fluids within the monitoring field.
The Walkaway DAS-VSP survey has become an important development of the borehole seismic technique due to its advantages of high density, high efficiency, low cost and good consistency, which can be carried out with repeated observations and also for permanent monitoring. Therefore, the use of time-lapse Walkaway DAS-VSP surveys is proposed for the monitoring of oil and gas production in mature fields. The repeated time-lapse Walkaway DAS-VSP surveys can provide high-quality borehole seismic data for the study of dynamic changes of the reservoir fluid around the wellbore to a certain range. The key points of this paper mainly include: (1) Time-lapse Walkaway DAS-VSP data acquisition; (2) Special time-lapse Walkaway DAS-VSP data processing procedures; (3) Time-lapse Walkaway DAS-VSP data inversion; (4) Map reservoir fluid dynamic changes during the oil and gas production. In the application process of the time-lapse Walkaway DAS-VSP, the high-accuracy velocity fields, high-resolution structure imaging and multiple attribute inversion of the reservoir formation around the wellbore have been obtained. In addition, it also provides reservoir fluid identification and mapping based on high-accuracy Walkaway DAS-VSP and surface seismic data, which provide a basis for the establishment of multi-domain and multi-dimensional geological and reservoir models.
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Nordkapp Topseis/Node Acquisition — Lessons from a Modelling Study
Authors Jan-Erik Lie, Vetle Vinje, Per Eivind Dhelie, Hao Jiang, Vidar Danielsen and Nicolas SalaunAbstractBased on an extensive 3D modelling study utilising full-wavefield Finite-Difference modelling and Full-Waveform Inversion (FWI) we demonstrate that the TopSeis/OBN hybrid acquisition acquired from May to August 2021 in the Nordkapp basin in the Barents Sea has the potential to image salt flanks and sedimentary details, given an accurate initial model in the shallow and a carefully designed deblending and FWI workflow. As a part of this we demonstrate that the large offsets and multi-azimuth recorded by the ocean bottom nodes are crucial to map the complex salt diapirism in the area including the steeply dipping flanks.
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Efficient Swell Noise Removal Using a Global Deep Neural Network Model
Authors Alejandro Valeciano, Olga Brusova and Cheng ChengSummaryThis paper describes implementing a global deep learning (DL) model for swell noise removal. It uses new ways to generate training data from a worldwide data library that combines noise-free processed field shot gathers with swell noise recorded during acquisition. Using examples from around the world, we demonstrate that the DL model generalizes well, providing good denoising results in data not previously seen during training. Creating a global model allows for the disruption of the traditional processing sequence and improves efficiency.
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Reconstructing Seismic Images and Creating Pseudo-3D Volumes: a Machine Learning Approach
Authors Paul de Groot, Arnaud Huck and Marieke van HoutAbstractWe describe how machine learning can be used to solve a range of interpolation problems regularly encountered in post-stack seismic data. In all experiments we train a U-net type of deep learning model on examples extracted in good-quality data areas. The 2D or 3D input images utilized for our training set are manipulated such that the inputs exhibit the same kind of problems as observed in the areas with poor, or missing data. We repair these poor data areas by applying the trained model. We show examples of interpolating missing traces, decreasing the bin-size (quadrupling the number of traces), and replacing a bad data patch in an undershoot area. We also demonstrate that these trained models are generic interpolators that can be reused without retraining to solve similar problems in other data sets. Finally, we use the same technology to create pseudo-3D volumes from 2D data. We present two workflows: a direct transformation approach and a transformation that takes place in the flattened domain. The latter approach is more demanding as it involves interpretation followed by flattening and unflattening the data.
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Volumes & issues
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Volume 42 (2024)
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Volume 41 (2023)
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Volume 40 (2022)
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Volume 39 (2021)
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Volume 38 (2020)
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Volume 37 (2019)
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Volume 36 (2018)
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Volume 35 (2017)
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Volume 34 (2016)
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Volume 33 (2015)
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Volume 32 (2014)
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Volume 31 (2013)
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Volume 30 (2012)
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Volume 29 (2011)
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Volume 28 (2010)
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Volume 27 (2009)
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Volume 26 (2008)
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Volume 25 (2007)
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Volume 24 (2006)
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Volume 23 (2005)
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Volume 22 (2004)
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Volume 21 (2003)
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Volume 20 (2002)
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Volume 19 (2001)
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Volume 18 (2000)
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Volume 17 (1999)
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Volume 16 (1998)
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Volume 15 (1997)
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Volume 14 (1996)
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Volume 13 (1995)
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Volume 12 (1994)
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Volume 11 (1993)
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Volume 10 (1992)
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Volume 9 (1991)
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Volume 8 (1990)
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Volume 7 (1989)
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Volume 6 (1988)
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Volume 5 (1987)
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Volume 4 (1986)
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Volume 3 (1985)
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Volume 2 (1984)
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Volume 1 (1983)