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Results
Abstract Seismic reservoir characterization plays an important role in carbon capture and storage analysis. The Havnsø anticlinal structure in Denmark is a prospective CO2 storage site due to its proximity to two large emission sources—a coal-fired power station and a nearby refinery. Although legacy 2D seismic lines over the area outline the anticlinal structure, their quality is insufficient for quantitative interpretation. Earlier studies have shown that the natural gas stored in the Stenlille aquifer exhibits a seismic response similar to the modeled CO2 fluid in the Havnsø structure. Thus, seismic reservoir characterization carried out on the Stenlille aquifer gas storage in terms of identifying spatial distribution of gas and outlining faults would provide insight regarding value addition that seismic data can bring into the proposed CO2 storage at Havnsø. Using the available poststack seismic data, we apply an integrated reservoir characterization analysis. After performing the adequate data conditioning, the impedance of the target Stenlille Formation is estimated through generation of an accurate low-frequency model. Thereafter, multiattribute analysis was used to generate volumetric estimates of porosity, gamma ray, and water saturation within the target formation so that the spatial distribution of gas can be mapped. The resulting porosity and gamma-ray volumes indicate encouraging results and were used for Bayesian classification to predict the probability of the more important lithofacies, namely, sand, shale, moderate-porosity sand, and moderate-porosity shaly sand, which enabled the mapping of high-porosity/facies zones in the two aquifer storage levels. Independently, we make use of unsupervised machine learning applications for seismic facies prediction and compare them at the two storage levels, which will be presented in the part 2 of this paper.
- Geology > Geological Subdiscipline (1.00)
- Geology > Structural Geology > Tectonics > Compressional Tectonics > Fold and Thrust Belt (0.44)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.35)
- North America > United States > Texas > Permian Basin > Delaware Basin (0.99)
- North America > United States > New Mexico > Permian Basin > Delaware Basin (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Utsira Formation (0.99)
- (23 more...)
- Reservoir Description and Dynamics > Storage Reservoir Engineering > Natural gas storage (1.00)
- Reservoir Description and Dynamics > Storage Reservoir Engineering > CO2 capture and sequestration (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- (2 more...)
Improving porosity and gamma-ray prediction for the Middle Jurassic Hugin sandstones in the southern Norwegian North Sea with the application of deep neural networks
Chopra, Satinder (SamiGeo) | Sharma, Ritesh Kumar (SamiGeo) | Marfurt, Kurt J. (The University of Oklahoma) | Zhang, Rongfeng (Geomodeling Technology Corp.) | Wen, Renjun (Geomodeling Technology Corp.)
Abstract The complete characterization of a reservoir requires accurate determination of properties such as the porosity, gamma ray, and density, among 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. In general, a high degree of scatter of data points is seen on crossplots between P-impedance and porosity, or P-impedance and gamma ray, suggesting great uncertainty in the determined relationship. Although for many rocks there is a well-established petrophysical model correlating the P-impedance to porosity, there is not a comparable model correlating the 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 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 have described 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 using several quality-control steps in the DNN workflow and observing encouraging results, we validate the final prediction of the 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.
- Geology > Geological Subdiscipline (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.66)
- North America > United States > Texas > Permian Basin > Midland Basin > Pegasus Field > Pennsylvanian Formation (0.99)
- North America > United States > Texas > Permian Basin > Midland Basin > Pegasus Field > Ellenburger Formation (0.99)
- Europe > United Kingdom > North Sea > North Sea > Northern North Sea > Balder Formation (0.99)
- (36 more...)
Seismic characterization of a Triassic-Jurassic deep geothermal sandstone reservoir, onshore Denmark, using unsupervised machine learning techniques
Chopra, Satinder (SamiGeo) | Sharma, Ritesh Kumar (SamiGeo) | Bredesen, Kenneth (Geological Survey of Denmark and Greenland (GEUS)) | Ha, Thang (The University of Oklahoma) | Marfurt, Kurt J. (The University of Oklahoma)
Abstract The Triassic-Jurassic deep sandstone reservoirs in onshore Denmark are known geothermal targets that can be exploited for sustainable and green energy for the next several decades. The economic development of such resources requires accurate characterization of the sandstone reservoir properties, namely, volume of clay, porosity, and permeability. The classic approach to achieving such objectives has been to integrate well-log and prestack seismic data with geologic information to obtain facies and reservoir property predictions in a Bayesian framework. Using this prestack inversion approach, we can obtain superior spatial and temporal variations within the target formation. We then examined whether unsupervised facies classification in the target units can provide additional information. We evaluated several machine learning techniques and found that generative topographic mapping further subdivided intervals mapped by the Bayesian framework into additional subunits.
- Europe > Denmark (1.00)
- North America > United States > Oklahoma (0.28)
- North America > United States > Texas > Permian Basin > Delaware Basin (0.99)
- North America > United States > Texas > Fort Worth Basin > Barnett Shale Formation (0.99)
- North America > United States > New Mexico > Permian Basin > Delaware Basin (0.99)
- (5 more...)
Reservoir characterization over the Lille Prinsen and Ivar Aasen fields in the Norwegian North Sea using ocean-bottom-node seismic data — A case study
Chopra, Satinder (TGS, SamiGeo) | Sharma, Ritesh Kumar (TGS, SamiGeo) | Trulsvik, Mikal (TGS) | Ramirez, Adriana Citlali (TGS) | Went, David (TGS) | Kjølhamar, Bent Erlend (TGS)
Abstract We have developed an integrated workflow for estimating elastic parameters within the Late Triassic Skagerrak Formation, the Middle Jurassic Sleipner and Hugin Formations, the Paleocene Heimdal Formation, and the Eocene Grid Formation in the Utsira High area of the Norwegian North Sea. Our workflow begins with petrophysical analysis carried out at the available wells. Then, model-based prestack simultaneous impedance inversion outputs were derived, and attempts were made to estimate the petrophysical parameters (the volume of shale, porosity, and water saturation) from seismic data using extended elastic impedance. On not obtaining convincing results, we switched over to multiattribute regression analysis for estimating them, which yielded encouraging results. Finally, the Bayesian classification approach was used for defining different facies in the intervals of interest.
- Research Report > New Finding (0.34)
- Research Report > Experimental Study (0.34)
- Phanerozoic > Mesozoic > Jurassic > Middle Jurassic (0.34)
- Phanerozoic > Mesozoic > Triassic > Upper Triassic (0.34)
- Geology > Geological Subdiscipline (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.35)
- Geophysics > Seismic Surveying > Surface Seismic Acquisition > Marine Seismic Acquisition (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation > Seismic Reservoir Characterization > Amplitude vs Offset (AVO) (0.93)
- Geophysics > Seismic Surveying > Seismic Processing > Seismic Migration (0.68)
- Europe > United Kingdom > North Sea > Central North Sea > Egersund Basin > PL 038 > Sleipner Formation (0.99)
- Europe > Norway > North Sea > Hugin Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > Utsira High > PL 501 > Block 16/5 > Johan Sverdrup Field > Zechstein Formation (0.99)
- (63 more...)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Exploration, development, structural geology (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
Abstract Shale resource plays are associated with low permeability; hence, hydraulic fracturing is required for their stimulation and production. Even though considerable nonuniqueness exists in identifying favorable zones for hydraulic fracturing, geophysicists seem to be avid followers of low-Poisson’s ratio and high-Young’s modulus brittleness criteria, proposed a decade ago. We highlight the misinterpretation that one may run into in following such a criterion for any shale play and develop a new attribute that makes use of strain energy density and fracture toughness. Although the former controls fracture initiation, the propagation of fractures is governed by the latter. Because hydraulic fracturing comprises both these properties, it is firmly believed that the new proposed attribute could be used to highlight the favorable intervals for fracturing. Core data, well log curves, along with mud logs have been used to authenticate the proposed attribute. Finally, computation of the new attributes is implemented on the seismic data with encouraging results.
- North America > United States > Texas (1.00)
- North America > Canada (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Petroleum Play Type > Unconventional Play > Shale Play > Shale Gas Play (0.46)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.39)
- Geophysics > Seismic Surveying (1.00)
- Geophysics > Borehole Geophysics (1.00)
- North America > United States > Texas > West Gulf Coast Tertiary Basin > Eagle Ford Shale Formation (0.99)
- North America > United States > Texas > Sabinas - Rio Grande Basin > Eagle Ford Shale Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- (52 more...)
Abstract Multicomponent seismic data offer several advantages for characterizing reservoirs with the use of the vertical component (PP) and mode-converted (PS) data. Joint impedance inversion inverts both of these data sets simultaneously; hence, it is considered superior to simultaneous impedance inversion. However, the success of joint impedance inversion depends on how accurately the PS data are mapped on the PP time domain. Normally, this is attempted by performing well-to-seismic ties for PP and PS data sets and matching different horizons picked on PP and PS data. Although it seems to be a straightforward approach, there are a few issues associated with it. One of them is the lower resolution of the PS data compared with the PP data that presents difficulties in the correlation of the equivalent reflection events on both the data sets. Even after a few consistent horizons get tracked, the horizon matching process introduces some artifacts on the PS data when mapped into PP time. We have evaluated such challenges using a data set from the Western Canadian Sedimentary Basin and then develop a novel workflow for addressing them. The importance of our workflow was determined by comparing data examples generated with and without its adoption.
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation > Well Tie (0.89)
- North America > United States > Texas > East Texas Salt Basin > Alba Field (0.99)
- North America > United States > Kansas > Donald Field (0.99)
- North America > Canada > Saskatchewan > Western Canada Sedimentary Basin > Alberta Basin (0.99)
- (4 more...)
Abstract Multicomponent seismic data analysis enhances confidence in interpretation by providing mode-converted PS data for imaging the subsurface. Integrated interpretation of PP and PS data begins with the identification of reflections corresponding to similar geologic events on both data sets. This identification is accomplished by carrying out well-log correlation through the generation of PP and PS synthetic seismograms. There are a few issues associated with the approach. One of the issues is that PS data have lower resolution than PP data. This presents difficulties in the correlation of equivalent reflection events on both data sets. Even if few consistent horizons are tracked, the horizon-matching process introduces artifacts on the PS data mapped in PP time. In this paper, we elaborate on such challenges with a data set from the Anadarko Basin in the United States. We then propose a novel workflow to address the challenges.
- North America > United States > Oklahoma (0.34)
- North America > United States > Texas (0.25)
- North America > United States > Kansas (0.25)
- North America > United States > Texas > Anadarko Basin (0.99)
- North America > United States > Oklahoma > Anadarko Basin (0.99)
- North America > United States > Kansas > Anadarko Basin (0.99)
Summary Multicomponent seismic data analysis enhances confidence in interpretation as it provides the mode-converted PS data for imaging of the subsurface. The integrated interpretation of PP and PS data begins with the identification of reflections corresponding to similar geologic events on both datasets. This identification is accomplished by carrying out well log correlation through the generation of PP and PS synthetic seismograms. Though it may seem to be a straightforward approach there are a few issues associated with it. One of them is the lower resolution of the PS data than the PP data which presents difficulties in the correlation of the equivalent reflection events on both the datasets. Even if few consistent horizons get tracked, the horizon matching process introduces some artifacts on the PS data mapped into PP time. In this exercise, we elaborate on such challenges with a dataset from the Anadarko Basin in the US, and then propose a novel workflow for addressing them.
- North America > United States > Oklahoma (0.35)
- North America > United States > Texas (0.25)
- North America > United States > Kansas (0.25)
- North America > United States > Texas > Anadarko Basin (0.99)
- North America > United States > Oklahoma > Anadarko Basin (0.99)
- North America > United States > Kansas > Anadarko Basin (0.99)
ABSTRACT One of the most important tools for carrying out seismic reservoir characterization is impedance inversion, which transforms seismic amplitudes representing subsurface rock interfaces into impedance attributes that represent interval properties. The key steps that lend confidence in impedance inversion and quantitative prediction made therefrom are, proper seismic data conditioning, robust initial models, and adequate parameterization in inversion analysis. In this study we elaborate on the data conditioning aspect. We begin by providing an appropriate workflow for seismic data conditioning in the offset-azimuth domain which enhances the quality of the far-offset stack and then highlight the impact of adequate velocity model used in offset-angle transformation. These key steps are often overlooked, and we demonstrate the added value that our proposed workflow brings about for effective seismic reservoir characterization by showing comparisons of data examples, with and without its application. Presentation Date: Tuesday, September 17, 2019 Session Start Time: 1:50 PM Presentation Time: 1:50 PM Location: Poster Station 7 Presentation Type: Poster
- Geology > Rock Type > Sedimentary Rock (0.48)
- Geology > Geological Subdiscipline (0.48)
- North America > Canada > British Columbia > Western Canada Sedimentary Basin > Alberta Basin > Montney Formation (0.99)
- North America > Canada > Alberta > Western Canada Sedimentary Basin > Alberta Basin > Montney Formation (0.99)
- North America > Canada > Alberta > Western Canada Sedimentary Basin > Alberta Basin > Duvernay Field > Duvernay Formation > Acl Duv 13-12-57-13 Well (0.89)
ABSTRACT Shale resource plays are associated with low permeability, and hence hydraulic fracturing is required for their stimulation and production. Even though considerable nonuniqueness exists in identifying favorable zones for hydraulic fracturing, geophysicists seem to be avid followers of Rickman et al.’s (2008), brittleness criteria of low Poisson’s ratio and high Young’s modulus, proposed a decade ago. In this study, we highlight the challenges in following such a criterion, and propose a new attribute that makes use of strain energy density and fracture toughness. While the former controls fracture initiation, the propagation of fractures is governed by the latter. As hydraulic fracturing comprises both these properties, we firmly believe that the new proposed attribute could be used to highlight the favorable intervals for fracturing. Core data, well-log curves along with mud-logs have been used to authenticate the proposed attribute. Finally, we implement it on the seismic data and observe encouraging results. Presentation Date: Tuesday, September 17, 2019 Session Start Time: 1:50 PM Presentation Start Time: 3:30 PM Location: 217A Presentation Type: Oral
- North America > United States > Texas (1.00)
- North America > Canada (0.70)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Petroleum Play Type > Unconventional Play > Shale Play (0.70)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.42)
- North America > United States > Texas > West Gulf Coast Tertiary Basin > Eagle Ford Shale Formation (0.99)
- North America > United States > Texas > Sabinas - Rio Grande Basin > Eagle Ford Shale Formation (0.99)
- North America > United States > Texas > Permian Basin > Delaware Basin > Wolfcamp Shale Formation (0.99)
- (35 more...)