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- Volume 39, Issue 11, 2021
First Break - Volume 39, Issue 11, 2021
Volume 39, Issue 11, 2021
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Lithology, porosity and saturation joint prediction using stochastic rock physics modelling and litho-petro-elastic inversion
Authors Khushboo Havelia, Surender Manral and Andrea MurinedduAbstractIn this paper we present the application of a single-loop litho-petro-elastic (LPE) inversion, which is a data assimilation algorithm that uses nonlinear Zoeppritz reflectivity operators with sequential filtering. It integrates rock physics models with seismic amplitude variation with offset (AVO) inversion and Bayesian inversion to define lithology, elastic, and petrophysical properties in a single loop, thus, combining several steps of the conventional reservoir characterization workflow. In the conventional multistep approach, the lithology and petrophysical properties are generated sequentially from elastic properties after AVO inversion is performed, which can add prediction uncertainty at each step and produce results that are not correlated with each other. The LPE inversion ensures that the predicted properties maintain the relationships defined by the rock physics model. The LPE inversion was tested on data from offshore Australia with three wells, for a faulted reservoir zone containing oil with a gas cap. It provided robust predictions for lithology, porosity, and water saturation, which matched acceptably at the three wells. The algorithm also accounted for subsurface uncertainties as it produced prediction probabilities of facies, porosity, and water saturation using multidimensional probability density functions. This approach can be effectively used to classify reservoir properties in a single-loop workflow.
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Variable separations for sources and streamers in marine seismic acquisition: a novel towing concept to improve survey efficiency
Authors Karim Souissi, Geir W. Simensen and Mark RhodesAbstractThis paper presents a novel acquisition methodology aimed at shallow targets and/or shallow water environments that require good near offset coverage and an improved crossline distance between sources and streamers. The proposed acquisition methodology features variable separations between adjacent sources and between adjacent streamers and builds on recent advances achieved by the seismic industry in towing the sources wide. This methodology results in better near offset coverage while improving survey efficiency, up to approximately 28% when compared to conventional configurations. A theoretical case study is presented to explain how to design this new towing concept and demonstrate the expected gains when compared to similar configurations.
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Thin sand identification in complex depositional environment by supervised artificial neural networks
Authors Si-Hai Zhang, Yin Xu and Mahdi AbuAliAbstractThe thin sands within the study interval developed in a complex fluvial-to-shallow marine environment. This variety results in extremely heterogeneously distributed thin sands. The objectives are to identify heterogeneous thin sands via machine learning and evaluate the impact of tuning thickness on the recognition. Multi-attribute classification using supervised ANNs is employed to predict the distribution of these thin sands within six subintervals. Before the prediction, the investigation of the six subinterval shows that the major part of each subinterval is greater than the tuning thickness and can be resolved by the available seismic data. An integrated workflow of multi-attribute classification based on supervised ANNs was established. The supervised classification cannot only add significant details and enhanced lateral resolution, but also allows the interpreters to avoid manual labelling. The thin sands of the six intervals predicted by the supervised ANNs are verified qualitatively and quantitatively. The predicted sand thickness is validated by a log-based thickness map and shows similar distribution of thin sands. The cross plots of the sand thickness between the seismic predictions and the log measurement at well locations quantify the seismic prediction. Therefore, both verifications show seismic prediction characterizes the thin sands in the study area and supervised ANNs have great potential in solving the challenge of thin bed identification. The positive correlation of the coefficients between the predicted and log-based thickness at the well locations and the average thickness of the subintervals suggest that seismic prediction still depends on the tuning thickness though multi-attribute classification based on the supervised ANNs used.
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A new wave in marine seismic source technology
Authors Nicolas Tellier, Julien Large, Shuki Ronen and Jeremy AznarSummaryWhile innovation in marine equipment has mainly concerned receiver technologies over the last few decades, a new focus within marine sources is drawing ever-increasing expectations in the industry to meet two key evolutions in offshore seismic acquisition. Reduced seismic signal energy in the audible bandwidth of marine mammals is becoming a must-have, either as a reflection of marine players’ environmental awareness or as a way to meet ever-demanding regulations. On the other hand, low frequencies have become paramount – if not a standard – to achieve superior seismic imaging and reservoir characterization, all the more when deep targets or complex geologies are at stake. After several years of development, optimization and field validation, two innovative marine sources intended to address these new requirements are herein introduced. The Bluepulse, available either as a complete source or as a straightforward upgrade of existing inventories, scales down the high-frequency output of conventional pneumatic sources. In a more disruptive approach, the Tuned Pulse Source (TPS) yields unprecedented performance in low-frequency signal generation.
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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)