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.

Deep learning Q inversion from reflection seismic data with strong attenuation using an encoder-decoder convolutional neural network: an example from South China Sea

2020 ◽  
Author(s):  
Hao Zhang ◽  
Jianguang Han ◽  
Heng Zhang ◽  
Yi Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
High Resolution ◽  
Seismic Data ◽  
Semantic Segmentation ◽  
Attenuation Effect ◽  
Attenuation Model ◽  
Reflection Seismic ◽  
Q Model ◽  
Segmentation Task

<p>The seismic waves exhibit various types of attenuation while propagating through the subsurface, which is strongly related to the complexity of the earth. Anelasticity of the subsurface medium, which is quantified by the quality factor Q, causes dissipation of seismic energy. Attenuation distorts the phase of the seismic data and decays the higher frequencies in the data more than lower frequencies. Strong attenuation effect resulting from geology such as gas pocket is a notoriously challenging problem for high resolution imaging because it strongly reduces the amplitude and downgrade the imaging quality of deeper events. To compensate this attenuation effect, first we need to accurately estimate the attenuation model (Q). However, it is challenging to directly derive a laterally and vertically varying attenuation model in depth domain from the surface reflection seismic data. This research paper proposes a method to derive the anomalous Q model corresponding to strong attenuative media from marine reflection seismic data using a deep-learning approach, the convolutional neural network (CNN). We treat Q anomaly detection problem as a semantic segmentation task and train an encoder-decoder CNN (U-Net) to perform a pixel-by-pixel prediction on the seismic section to invert a pixel group belongs to different level of attenuation probability which can help to build up the attenuation model. The proposed method in this paper uses a volume of marine 3D reflection seismic data for network training and validation, which needs only a very small amount of data as the training set due to the feature of U-Net, a specific encoder-decoder CNN architecture in semantic segmentation task. Finally, in order to evaluate the attenuation model result predicted by the proposed method, we validate the predicted heterogeneous Q model using de-absorption pre-stack depth migration (Q-PSDM), a high-resolution depth imaging result with reasonable compensation is obtained.</p>

Cannibalization and sealing of deepwater reservoirs by mass-transport complexes — The Jubilee field, Gulf of Mexico

2020 ◽  
Vol 8 (4) ◽  
pp. SV17-SV30
Author(s):  
Sebastian Cardona ◽  
Lesli Wood ◽  
Lorena Moscardelli ◽  
Dallas Dunlap
Keyword(s):  
Gulf Of Mexico ◽  
Mass Transport ◽  
Seismic Data ◽  
Hydrocarbon Exploration ◽  
Fluid Migration ◽  
Gas Field ◽  
Mass Flows ◽  
Gas Chimneys ◽  
Migration Pathways ◽  
Reservoir Deposits

Mass-transport complexes (MTCs) are important stratigraphic elements in many deepwater basins. In hydrocarbon exploration, MTCs have traditionally been identified as seals although they can also act as migration pathways or cannibalize and compartmentalize adjacent reservoirs. Although the ever-improving resolution of seismic data has enhanced the knowledge about these deposits (e.g., geometry, distribution), at present the potential of MTCs to act as top and/or lateral seals is difficult to predict predrilling and few case studies are publicly available. The key objective here is to present examples of seismically resolvable characteristics of two MTCs in the Jubilee gas field, offshore Gulf of Mexico: one of the MTCs cannibalized part of the reservoir, and the other acted as the top seal. The Jubilee field is an area where the ability of MTCs to act as a top seal has been proven — the field produced approximately 205 billion cubic feet of natural gas until abandonment in 2016. When evaluating the sealing potential of MTCs, seismic interpretation can offer a powerful technique to identify indicators of hydrocarbon leakage. Additionally, mass flows that form MTCs can be highly erosive and cannibalize underlying reservoir deposits, which increase reservoir heterogeneity that can lead to compartmentalization. Our results indicate that the seal MTC in the Jubilee field is a detached MTC and that the translational morphodomain overlies the gas accumulation. Consequently, when predicting the seal potential of MTCs from seismic data, it is important to determine (1) the type of MTC (i.e., attached versus detached), (2) the specific MTC morphodomain overlying the hydrocarbon accumulation/prospect (i.e., the headwall, translational, or toe morphodomains), and (3) the presence of seismic indicators of fluid migration pathways (e.g., gas chimneys, pockmarks, etc.). These results shed some light on the present challenges of predicting the seal potential of MTCs in frontier basins around the world.

PROSPECTIVITY OF THE OTWAY SUPERGROUP IN THE CENTRAL AND WESTERN OTWAY BASIN

1990 ◽  
Vol 30 (1) ◽  
pp. 263 ◽  
Author(s):  
E. Kopsen ◽  
T. Scholefield
Keyword(s):  
High Resolution ◽  
Success Rate ◽  
Seismic Data ◽  
Reservoir Quality ◽  
Critical Elements ◽  
The Future ◽  
Group A ◽  
Coal Measures ◽  
Migration Pathways ◽  
Depth Of Burial

Recent hydrocarbon discoveries in the non-marine rift fill sequence of the Otway Basin at Windermere, Katnook and Ladbroke Grove have upgraded the importance of this relatively poorly known interval of the sedimentary column and provide hydrocarbon trapping models for future exploration. Using a seismic stratigraphic approach based on high resolution seismic data and the geological re-evaluation of many key early wells, a clearer pattern has emerged for the distribution of major reservoir and seal units.The best reservoirs occur in the Crayfish Group 'A', 'B' and 'D' units and the Windermere Member of the Lower Eumeralla Formation. One of the most critical elements in controlling the more prospective areas is the diagenetic characteristics of the main hydrocarbon objective units. Reservoir quality is significantly affected by the abundance or absence of volcanic detritus and depth of burial, and as a result, the most attractive reservoir is the Crayfish 'A' lying at depths shallower than 3000 m. Lateral fault seals and good vertical seals are present at various stratigraphic levels through the sequence for the development of effective traps in fault blocks and anticlines.The Casterton Group and the basal coal measures zone of the Lower Eumeralla Formation overlying the Windermere Member are identified as the most prospective oil sourcing units in the sequence. Secondary oil sourcing intervals occur within the Crayfish 'C' unit and at the top of the Lower Eumeralla Formation. A higher drilling success rate is now expected in the future with hydrocarbon fairways in the supergroup expected to comprise:Fault blocks and anticlines in the more basinal areas, e.g. the Katnook and Ladbroke Grove gas fields.The 'shoulders' of the main rift depocentres where fault traps will be most prevalent, e.g. the Kalangadoo CO2 discovery.Portions of the northern platform lying on migration pathways extending from the main graben (hydrocarbon kitchen) areas.

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.

Sign in / Sign up

Export Citation Format

Share Document