Visualization of vertical hydrocarbon migration in seismic data: Case studies from the Dutch North Sea

2015 ◽  
Vol 3 (3) ◽  
pp. SX21-SX27 ◽  
Author(s):  
David L. Connolly
Keyword(s):  
Neural Network ◽  
North Sea ◽  
Seismic Data ◽  
3D Visualization ◽  
Three Dimensions ◽  
Rock Properties ◽  
Geochemical Data ◽  
Lateral Migration ◽  
Hydrocarbon Migration ◽  
Migration Pathways

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.

scholarly journals Organic content and maturation effects on elastic properties of source rock shales in the Central North Sea

2019 ◽  
Vol 7 (2) ◽  
pp. T477-T497 ◽  
Author(s):  
Jørgen André Hansen ◽  
Nazmul Haque Mondol ◽  
Manzar Fawad
Keyword(s):  
Elastic Properties ◽  
Source Rock ◽  
North Sea ◽  
Empirical Relation ◽  
Organic Content ◽  
Rock Properties ◽  
Geochemical Data ◽  
Well Log ◽  
Seismic Properties ◽  
Central North Sea

We have investigated the effects of organic content and maturation on the elastic properties of source rock shales, mainly through integration of a well-log database from the Central North Sea and associated geochemical data. Our aim is to improve the understanding of how seismic properties change in source rock shales due to geologic variations and how these might manifest on seismic data in deeper, undrilled parts of basins in the area. The Tau and Draupne Formations (Kimmeridge shale equivalents) in immature to early mature stages exhibit variation mainly related to compaction and total organic carbon (TOC) content. We assess the link between depth, acoustic impedance (AI), and TOC in this setting, and we express it as an empirical relation for TOC prediction. In addition, where S-wave information is available, we combine two seismic properties and infer rock-physics trends for semiquantitative prediction of TOC from [Formula: see text] and AI. Furthermore, data from one reference well penetrating mature source rock in the southern Viking Graben indicate that a notable hydrocarbon effect can be observed as an addition to the inherently low kerogen-related velocity and density. Published Kimmeridge shale ultrasonic measurements from 3.85 to 4.02 km depth closely coincide with well-log measurements in the mature shale, indicating that upscaled log data are reasonably capturing variations in the actual rock properties. Amplitude variation with offset inversion attributes should in theory be interpreted successively in terms of compaction, TOC, and maturation with associated generation of hydrocarbons. Our compaction-consistent decomposition of these effects can be of aid in such interpretations.

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 ◽  
...  
Keyword(s):  
Neural Network ◽  
Integrated Approach ◽  
Upper Jurassic ◽  
Oil Field ◽  
Hydrocarbon Migration ◽  
Gas Chimney ◽  
Hydrocarbon Charge ◽  
Geochemical Studies ◽  
Elastic Inversion ◽  
Migration Pathways

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.

Improving porosity and gamma ray prediction for the Middle Jurassic Hugin sandstones in southern Norwegian North Sea with the application of deep neural networks

2021 ◽  
pp. 1-46
Author(s):  
Satinder Chopra ◽  
Ritesh Sharma ◽  
Kurt J. Marfurt ◽  
Rongfeng Zhang ◽  
Renjun Wen
Keyword(s):  
Neural Network ◽  
North Sea ◽  
Seismic Data ◽  
Deep Neural Network ◽  
Gamma Ray ◽  
Accurate Determination ◽  
Empirical Relation ◽  
Reservoir Property ◽  
Well Data

The complete characterization of a reservoir requires accurate determination of properties such as porosity, gamma ray and density, amongst others. A common workflow is to predict the spatial distribution of properties measured by well logs to those that can be computed from the seismic data. Generally, a high degree of scatter of data points is seen on crossplots between P-impedance and porosity, or P-impedance and gamma ray suggesting large uncertainty in the determined relationship. Although for many rocks there is a well established petrophysical model correlating P-impedance to porosity, there is not a comparable model correlating P-impedance to gamma ray. To address this issue, interpreters can use crossplots to graphically correlate two seismically derived variables to well measurements plotted in color. When there are more than two seismically derived variables, the interpreter can use multilinear regression or artificial neural network (ANN) analysis that uses a percentage of the upscaled well data for training to establish an empirical relation with the input seismic data and then uses the remaining well data to validate the relationship. Once validated at the wells, this relationship can then be used to predict the desired reservoir property volumetrically. We describe the application of deep neural network (DNN) analysis for the determination of porosity and gamma ray over the Volve Field in the southern Norwegian North Sea. After employing several quality-control steps in the deep neural network workflow and observing encouraging results, we validate the final prediction of both porosity and gamma ray properties using blind well correlation. The application of this workflow promises significant improvement to the reservoir property determination for fields that have good well control and exhibit lateral variations in the sought properties.

Types of Hydrocarbon Migration Pathways and Hydrocarbon Accumulation in the Yakela Fault-Convex, Tarim Basin

2013 ◽  
Vol 295-298 ◽  
pp. 2749-2752
Author(s):  
Xiao Long Luo ◽  
Liang Jie Tang
Keyword(s):  
Tarim Basin ◽  
Hydrocarbon Accumulation ◽  
Lateral Migration ◽  
Long Distance ◽  
Hydrocarbon Migration ◽  
Migration Pathway ◽  
Fault Type ◽  
Migration Pathways ◽  
3D Seismic Data ◽  
Field Geology

The existence of abundant hydrocarbon has been discovered in the Yakela Fault-convex, Tarim Basin, after reconstructing and superimposing for several periods. Through interpretation of 3D seismic data up to date, combined with the circumferential field geology, after comprehensively analyzing the characteristics of development on hydrocarbon migration passages and its relationship with hydrocarbon accumulation, this paper holds that it is divided into fault type, unconformity type and carrier bed type in the Yakela fault-convex. The unconformities and the carrier beds are the main lateral migration passage of gas and oil for long distance. The faults are the main vertical migration pathway of hydrocarbon, and the hydrocarbon can arrive at any strata with fault. It is significant to know the hydrocarbon migration pathways in the study area for exploration in future.

Seismic attribute detection of faults and fluid pathways within an active strike-slip shear zone: New insights from high-resolution 3D P-Cable™ seismic data along the Hosgri Fault, offshore California

2016 ◽  
Vol 4 (1) ◽  
pp. SB131-SB148 ◽  
Author(s):  
Jared W. Kluesner ◽  
Daniel S. Brothers
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Seismic Data ◽  
Strike Slip ◽  
Fluid Migration ◽  
Seismic Attribute ◽  
Central California ◽  
Data Conditioning ◽  
3D Volume ◽  
Migration Pathways

Poststack data conditioning and neural-network seismic attribute workflows are used to detect and visualize faulting and fluid migration pathways within a [Formula: see text] 3D P-Cable™ seismic volume located along the Hosgri Fault Zone offshore central California. The high-resolution 3D volume used in this study was collected in 2012 as part of Pacific Gas and Electric’s Central California Seismic Imaging Project. Three-dimensional seismic reflection data were acquired using a triple-plate boomer source (1.75 kJ) and a short-offset, 14-streamer, P-Cable system. The high-resolution seismic data were processed into a prestack time-migrated 3D volume and publically released in 2014. Postprocessing, we employed dip-steering (dip and azimuth) and structural filtering to enhance laterally continuous events and remove random noise and acquisition artifacts. In addition, the structural filtering was used to enhance laterally continuous edges, such as faults. Following data conditioning, neural-network based meta-attribute workflows were used to detect and visualize faults and probable fluid-migration pathways within the 3D seismic volume. The workflow used in this study clearly illustrates the utility of advanced attribute analysis applied to high-resolution 3D P-Cable data. For example, results from the fault attribute workflow reveal a network of splayed and convergent fault strands within an approximately 1.3 km wide shear zone that is characterized by distinctive sections of transpressional and transtensional dominance. Neural-network chimney attribute calculations indicate that fluids are concentrated along discrete faults in the transtensional zones, but appear to be more broadly distributed amongst fault bounded anticlines and structurally controlled traps in the transpressional zones. These results provide high-resolution, 3D constraints on the relationships between strike-slip fault mechanics, substrate deformation, and fluid migration along an active fault system offshore central California.

Theory-guided data science-based reservoir prediction of a North Sea oil field

2020 ◽  
Vol 39 (10) ◽  
pp. 742-750
Author(s):  
Jonathan E. Downton ◽  
Olivia Collet ◽  
Daniel P. Hampson ◽  
Tanya Colwell
Keyword(s):  
Neural Network ◽  
North Sea ◽  
Seismic Data ◽  
Data Science ◽  
Oil Field ◽  
Sampled Data ◽  
Reservoir Properties ◽  
Reservoir Prediction ◽  
Data Set ◽  
The Neural Network

Data science-based methods, such as supervised neural networks, provide powerful techniques to predict reservoir properties from seismic and well data without the aid of a theoretical model. In these supervised learning approaches, the seismic-to-rock property relationship is learned from the data. One of the major factors limiting the success of these methods is whether there exists enough labeled data, sampled over the expected geology, to train the neural network adequately. To overcome these issues, this paper explores hybrid theory-guided data science (TGDS)-based methods. In particular, we build a two-component model in which the outputs of the theory-based component are the inputs in the data science component. First, we simulate many pseudowells based on the well statistics in the project area. The reservoir properties, such as porosity, saturation, mineralogy, and thickness, are all varied to create a well-sampled data set. Elastic and synthetic seismic data are then generated using rock physics and seismic theory. The resulting collection of pseudowell logs and synthetic seismic data, called the synthetic catalog, is used to train the neural network. The derived operator is then applied to the real seismic data to predict reservoir properties throughout the seismic volume. This TGDS method is applied to a North Sea data set to characterize a Paleocene oil sand reservoir. The TGDS results better characterize the geology and well control, including a blind well, compared to a solely theory-based approach (deterministic inversion) and a data science-based approach (neural network using only the original wells). These results suggest that theory and data science can complement each other to improve reservoir characterization predictions.

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