Visualizing Hydrocarbon Migration Pathways Associated with the Ringhorne Oil Field, Norway: An Integrated Approach

2021 ◽  
pp. 1-57
Author(s):  
David Connolly ◽  
Kristoffer Rimaila ◽  
Assia Lakhlifi ◽  
Gabor Kocsis ◽  
Ingrid Fæstø ◽  
...  

Norway’s Ringhorne Field is a faulted anticline which produces oil from Triassic (Statfjord) and Paleocene (Hermod) sands. It is located on the Utsira High. Geochemical studies of the produced oil indicate the oil is generated from mature Upper Jurassic marine shales in the adjacent Viking Graben. However, it has not been clear how oil migrated into the Triassic reservoirs and charged the overlying Paleocene reservoirs. Gas chimney detection using a proven neural network technique was used to detect the vertical hydrocarbon migration pathways on normally processed seismic data. The processing results were then validated using a set of criteria to determine if they represented true hydrocarbon migration rather than seismic artifacts. The chimney processing results using this traditional (shallow) neural network was compared with convolutional neural network (deep learning) results and geo-mechanical modeling on key lines. Key reservoirs were delineated using a stochastic (elastic) inversion approach. Reliable chimneys were then visualized in the vicinity of the producing reservoirs. The results showed pathways by which the Triassic fluvial sands received charge, and how these reservoirs had flank leakage to provide charge to shallower Paleocene reservoirs. This approach has now been used over hundreds of fields and dry holes in the Norwegian North Sea and worldwide as analogs to assess hydrocarbon charge and top seal risk predrill.

2015 ◽  
Vol 3 (3) ◽  
pp. SX21-SX27 ◽  
Author(s):  
David L. Connolly

Previous 3D visualization studies in seismic data have largely been focused on visualizing reservoir geometry. However, there has been less effort to visualize the vertical hydrocarbon migration pathways, which may provide charge to these reservoirs. Vertical hydrocarbon migration was recognized in normally processed seismic data as vertically aligned zones of chaotic low-amplitude seismic response called gas chimneys, blowout pipes, gas clouds, mud volcanoes, or hydrocarbon-related diagenetic zones based on their morphology, rock properties, and flow mechanism. Because of their diffuse character, they were often difficult to visualize in three dimensions. Thus, a method has been developed to detect these features using a supervised neural network. The result is a “chimney” probability volume. However, not all chimneys detected by this method will represent true hydrocarbon migration. Therefore, the neural network results must be validated by a set of criteria that include (1) pockmarked morphology, (2) tie to shallow direct hydrocarbon indicators, (3) origination from known or suspected source rock interval, (4) correlation with surface geochemical data, and (5) support by basin modeling or well data. Based on these criteria, reliable chimneys can be extracted from the seismic data as 3D geobodies. These chimney geobodies, which represent vertical hydrocarbon migration pathways, can then be superimposed on detected reservoir geobodies, which indicate possible lateral migration pathways and traps. The results can be used to assess hydrocarbon charge efficiency or risk, and top seal risk for identified traps. We investigated a case study from the Dutch North Sea in which chimney processing results exhibited vertical hydrocarbon pathways, originating in the Carboniferous age, which provided the charge to shallow Miocene gas sands and deep Triassic prospects.


Author(s):  
Lars Stemmerik ◽  
Gregers Dam ◽  
Nanna Noe-Nygaard ◽  
Stefan Piasecki ◽  
Finn Surlyk

NOTE: This article was published in a former series of GEUS Bulletin. Please use the original series name when citing this article, for example: Stemmerik, L., Dam, G., Noe-Nygaard, N., Piasecki, S., & Surlyk, F. (1998). Sequence stratigraphy of source and reservoir rocks in the Upper Permian and Jurassic of Jameson Land, East Greenland. Geology of Greenland Survey Bulletin, 180, 43-54. https://doi.org/10.34194/ggub.v180.5085 _______________ Approximately half of the hydrocarbons discovered in the North Atlantic petroleum provinces are found in sandstones of latest Triassic – Jurassic age with the Middle Jurassic Brent Group, and its correlatives, being the economically most important reservoir unit accounting for approximately 25% of the reserves. Hydrocarbons in these reservoirs are generated mainly from the Upper Jurassic Kimmeridge Clay and its correlatives with additional contributions from Middle Jurassic coal, Lower Jurassic marine shales and Devonian lacustrine shales. Equivalents to these deeply buried rocks crop out in the well-exposed sedimentary basins of East Greenland where more detailed studies are possible and these basins are frequently used for analogue studies (Fig. 1). Investigations in East Greenland have documented four major organic-rich shale units which are potential source rocks for hydrocarbons. They include marine shales of the Upper Permian Ravnefjeld Formation (Fig. 2), the Middle Jurassic Sortehat Formation and the Upper Jurassic Hareelv Formation (Fig. 4) and lacustrine shales of the uppermost Triassic – lowermost Jurassic Kap Stewart Group (Fig. 3; Surlyk et al. 1986b; Dam & Christiansen 1990; Christiansen et al. 1992, 1993; Dam et al. 1995; Krabbe 1996). Potential reservoir units include Upper Permian shallow marine platform and build-up carbonates of the Wegener Halvø Formation, lacustrine sandstones of the Rhaetian–Sinemurian Kap Stewart Group and marine sandstones of the Pliensbachian–Aalenian Neill Klinter Group, the Upper Bajocian – Callovian Pelion Formation and Upper Oxfordian – Kimmeridgian Hareelv Formation (Figs 2–4; Christiansen et al. 1992). The Jurassic sandstones of Jameson Land are well known as excellent analogues for hydrocarbon reservoirs in the northern North Sea and offshore mid-Norway. The best documented examples are the turbidite sands of the Hareelv Formation as an analogue for the Magnus oil field and the many Paleogene oil and gas fields, the shallow marine Pelion Formation as an analogue for the Brent Group in the Viking Graben and correlative Garn Group of the Norwegian Shelf, the Neill Klinter Group as an analogue for the Tilje, Ror, Ile and Not Formations and the Kap Stewart Group for the Åre Formation (Surlyk 1987, 1991; Dam & Surlyk 1995; Dam et al. 1995; Surlyk & Noe-Nygaard 1995; Engkilde & Surlyk in press). The presence of pre-Late Jurassic source rocks in Jameson Land suggests the presence of correlative source rocks offshore mid-Norway where the Upper Jurassic source rocks are not sufficiently deeply buried to generate hydrocarbons. The Upper Permian Ravnefjeld Formation in particular provides a useful source rock analogue both there and in more distant areas such as the Barents Sea. The present paper is a summary of a research project supported by the Danish Ministry of Environment and Energy (Piasecki et al. 1994). The aim of the project is to improve our understanding of the distribution of source and reservoir rocks by the application of sequence stratigraphy to the basin analysis. We have focused on the Upper Permian and uppermost Triassic– Jurassic successions where the presence of source and reservoir rocks are well documented from previous studies. Field work during the summer of 1993 included biostratigraphic, sedimentological and sequence stratigraphic studies of selected time slices and was supplemented by drilling of 11 shallow cores (Piasecki et al. 1994). The results so far arising from this work are collected in Piasecki et al. (1997), and the present summary highlights the petroleum-related implications.


2018 ◽  
Vol 28 (09) ◽  
pp. 1850007
Author(s):  
Francisco Zamora-Martinez ◽  
Maria Jose Castro-Bleda

Neural Network Language Models (NNLMs) are a successful approach to Natural Language Processing tasks, such as Machine Translation. We introduce in this work a Statistical Machine Translation (SMT) system which fully integrates NNLMs in the decoding stage, breaking the traditional approach based on [Formula: see text]-best list rescoring. The neural net models (both language models (LMs) and translation models) are fully coupled in the decoding stage, allowing to more strongly influence the translation quality. Computational issues were solved by using a novel idea based on memorization and smoothing of the softmax constants to avoid their computation, which introduces a trade-off between LM quality and computational cost. These ideas were studied in a machine translation task with different combinations of neural networks used both as translation models and as target LMs, comparing phrase-based and [Formula: see text]-gram-based systems, showing that the integrated approach seems more promising for [Formula: see text]-gram-based systems, even with nonfull-quality NNLMs.


2021 ◽  
Author(s):  
Malte Oeljeklaus

This thesis investigates methods for traffic scene perception with monocular cameras for a basic environment model in the context of automated vehicles. The developed approach is designed with special attention to the computational limitations present in practical systems. For this purpose, three different scene representations are investigated. These consist of the prevalent road topology as the global scene context, the drivable road area and the detection and spatial reconstruction of other road users. An approach is developed that allows for the simultaneous perception of all environment representations based on a multi-task convolutional neural network. The obtained results demonstrate the efficiency of the multi-task approach. In particular, the effects of shareable image features for the perception of the individual scene representations were found to improve the computational performance. Contents Nomenclature VII 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Outline and contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 Related Work and Fundamental Background 8 2.1 Advances in CNN...


2020 ◽  
pp. 4-15
Author(s):  
M.F. Tagiyev ◽  
◽  
I.N. Askerov ◽  
◽  
◽  
...  

Based on pyrolysis data an overview is given on the generative potential and maturity of individual stratigraphic units in the South Caspian sedimentary cover. Furthermore, the pyrolysis analyses indicate that the Lower Pliocene Productive Series being immature itself is likely to have received hydrocarbon charge from the underlying older strata. The present state of the art in studying hydrocarbon migration and the "source-accumulation" type relationship between source sediments and reservoired oils in the South Caspian basin are touched upon. The views of and geochemical arguments by different authors for charging the Lower Pliocene Productive Series reservoirs with hydrocarbons from the underlying Oligocene-Miocene source layers are presented. Quantitative aspects of hydrocarbon generation, fluid dynamics, and formation of anomalous temperature & pressure fields based on the results of basin modelling in Azerbaijan are considered. Based on geochemical data analysis and modelling studies, as well as honouring reports by other workers the importance and necessity of upward migration for hydrocarbon transfer from deep generation centers to reservoirs of the Productive Series are shown.


2021 ◽  
pp. 1-79
Author(s):  
Alin G. Chitu ◽  
Mart H. A. A. Zijp ◽  
Jonathan Zwaan

The fundamental assumption of many successful geochemical and geomicrobial technologies developed in the last 80 years is that hydrocarbons leak from subsurface accumulations vertically to the surface. Driven by buoyancy, the process involves sufficiently large volumes directly measurable or indirectly inferable from their surface expressions. Even when the additional hydrocarbons are not measurable, their presence slightly changes the environment, where complex microbial communities live, and acts as an evolutionary constraint on their development. Since the ecology of this ecosystem is very complicated, we propose to use the full-microbiome analysis of the shallow sediments samples instead of targeting a selected number of known species, and the use of machine learning for uncovering the meaningful correlations in these data. We achieve this by sequencing the microbial biomass and generating its “DNA fingerprint”, and by analyzing the abundance and distribution of the microbes over the dataset. The proposed technology uses machine learning as an accurate tool for determining the detailed interactions among the various microorganisms and their environment in the presence or absence of hydrocarbons, thus overcoming data complexity. In a proof-of-technology study, we have taken more than 1000 samples in the Neuqu謠Basin in Argentina over three distinct areas, namely, an oil field, a gas field, and a dry location outside the basin, and created several successful predictive models. A subset of randomly selected samples was kept outside of the training set and blinded by the client operator, providing the means for objectively validating the prediction performance of this methodology. Uncovering the blinded dataset after estimating the prospectivity revealed that most of these samples were correctly predicted. This very encouraging result shows that analyzing the microbial ecosystem in the shallow sediment can be an additional de-risking method for assessing hydrocarbon prospects and improving the Probability Of Success(POS) of a drilling campaign.


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