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Abstract Compaction effects can obscure the impedance separation between hydrocarbon-bearing and fully brine-saturated sandstones. We have improved their discrimination by removing depth-related trends from inverted seismic impedance. Although the ratio of compressional- to shear-wave velocity versus seismic compressional-wave impedance crossplots shows differences among pay, brine sand, and shale trends, using absolute inverted impedances only imperfectly distinguishes hydrocarbon sands from brine sands due to outliers. In a given locality, statistical comparison of well log and seismic-derived impedances enables us to obtain a shale impedance model for a lithology baseline to detrend the impedance from the effects of burial and overburden. This has the effect of unmasking anomalies associated with hydrocarbon-bearing sands and serves as a reliable fluid discriminator. For an offshore Gulf of Mexico data set on the flank of a salt dome, with pay occurring over a wide range of depths, we identify hydrocarbon-bearing sands with a greater success rate after detrending the absolute seismic impedance.
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.87)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.71)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (0.70)
Porosity prediction using machine learning
Jiang, Lian (University of Houston) | Castagna, John P. (University of Houston) | Russell, Brian (CGG)
Abstract The mismatched scales of seismic attributes and geological layering limit porosity estimation directly from seismic data. This can be addressed by utilizing spectral decomposition attributes and machine learning techniques. First, we decompose the seismic data into different frequency components. A variety of seismic attributes then are extracted. We simultaneously predict porosity logs, filtered to different resolutions, using conventional and deep machine learning algorithms. Methods used include support vector regression (SVR), random forest (RF), and the multilayer perceptron (MLP). We then sum the results to create broadband porosity log predictions. We first use synthetic seismic data created from rock physics modeling to test the efficacy of the proposed method, followed by the testing of field seismic data from the North Sea. We compare our method with several different conventional methods and workflows commonly used in industry. The porosity prediction results indicate that the proposed method performs better than other conventional methods and workflows, with a highest correlation coefficient of 0.94 on synthetic seismic data and 0.81 on the field seismic data example.
- North America > United States (0.95)
- Europe > Norway > North Sea (0.35)
- Europe > United Kingdom > North Sea (0.25)
- (2 more...)
- Geology > Rock Type (0.70)
- Geology > Geological Subdiscipline > Geomechanics (0.50)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Åsgard Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Svarte Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Sleipner Formation (0.99)
- (17 more...)
The assumptions made in rock physics theories introduce many uncertainties into conventional rock physics modeling (RPM), which makes it difficult to implement accurate quantitative seismic interpretation workflows. We propose using machine learning algorithms to address this issue. First, we build a theoretical rock physics model using a conventional RPM workflow. We use Hertz-Mindlin grain contact theory to estimate the moduli of rock frame with optimized model parameters. We then use this rock physics model to create synthetic well logs by perturbing the rock properties, such as lithology and porosity. Next, we use the synthetic wells to test the efficacy of three common machine learning algorithms: support vector regression (SVR), random forest (RF), and the multi-layer perceptron (MLP). Finally, we predict P- and S-wave velocity by utilizing both the machine learning algorithms and the rock physics model on measured data from wells. Our proposed method achieves a better result than conventional rock physics modeling, with the average R2 score (coefficient of determination) increasing by 25.8% on P-wave prediction and 64.0% on Swave prediction. Presentation Date: Tuesday, October 13, 2020 Session Start Time: 9:20 AM Presentation Time: 11:25 AM Location: Poster Station 7 Presentation Type: Poster
- Geophysics > Seismic Surveying > Seismic Interpretation (0.55)
- Geophysics > Seismic Surveying > Seismic Processing (0.35)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Åsgard Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Svarte Formation (0.99)
- Europe > Norway > North Sea > Central North Sea > South Viking Graben > PL 046 > Block 15/9 > Volve Field > Shetland Group > Sleipner Formation (0.99)
- (17 more...)
ABSTRACT One of the primary fluid indicators for direct hydrocarbon detection in sandstones using seismic reflectivity is the difference between the saturated-rock P-wave impedance and the rock-frame impedance. This can be expressed in terms of the difference between the observed P-wave impedance squared and a multiplier times the square of the observed S-wave impedance. This multiplier is a fluid discrimination parameter that laboratory and log measurements suggest varies over a wide range. Theoretically, this parameter is related to the ratio of the frame bulk and shear moduli and the ratio of the frame and fluid-saturated rock densities. In practice, empirical determination of the fluid discrimination parameter may be required for a given locality. Given sufficient data for calibration, the parameter can be adjusted so as to best distinguish hydrocarbon-saturated targets from brine-saturated rocks. Using an empirically optimized fluid discrimination parameter has a greater impact on hydrocarbon detection success rate in the oil cases studied than for gas reservoirs, for which there is more latitude. Application to a wide variety of well-log and laboratory measurements suggests that the empirically optimized parameter may differ from direct theoretical calculations made using Gassmann’s equations. Combining laboratory and log measurements for sandstones having a broad range of frame moduli, varying from poorly consolidated to highly lithified, reveals a simple linear empirical relationship between the optimized fluid discrimination parameter and the squared velocity ratio of brine-saturated sandstones.
- Geology > Geological Subdiscipline (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Sandstone (0.92)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.88)
- Geophysics > Seismic Surveying > Seismic Processing (0.68)
- Geophysics > Seismic Surveying > Seismic Interpretation > Seismic Reservoir Characterization > Amplitude vs Offset (AVO) (0.68)
- 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)
P-to-S-wave velocity ratio in organic shales
Omovie, Sheyore John (Oxy) | Castagna, John P. (University of Houston)
ABSTRACT In situ P- and S-wave velocity measurements in a variety of organic-rich shales exhibit P-to-S-wave velocity ratios that are significantly lower than lithologically similar fully brine-saturated shales having low organic content. It has been hypothesized that this drop could be explained by the direct influence of kerogen on the rock frame and/or by the presence of free hydrocarbons in the pore space. The correlation of hydrocarbon saturation with total organic content in situ makes it difficult to separate these possible mechanisms using log data alone. Theoretical bounding equations, using pure kerogen as an end-member component without associated gas, indicate that kerogen reduces the P- and S-wave velocities but does not in general reduce their ratio enough to explain the observed low velocity ratio. The theoretical modeling is consistent with ultrasonic measurements on organic shale core samples that indicate no dependence of velocity ratios on the kerogen volume alone. Sonic log measurements of P- and S-wave velocities in seven organic-rich shale formations deviate significantly (typically more than 5%) from the Greenberg-Castagna empirical brine-saturated shale trend toward lower velocity ratios. In these formations, and on core measurements, Gassmann fluid substitution to 100% brine saturation yields velocity ratios consistent with the Greenberg-Castagna velocity trend for fully brine-saturated shales, despite the high organic content. These sonic and ultrasonic measurements, as well as theoretical modeling, suggest that the velocity ratio reduction in organic shales is best explained by the presence of free hydrocarbons.
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (1.00)
- Geophysics > Borehole Geophysics (1.00)
- North America > United States > West Virginia > Appalachian Basin > Marcellus Shale Formation (0.99)
- North America > United States > Virginia > Appalachian Basin > Marcellus Shale Formation (0.99)
- North America > United States > Texas > West Gulf Coast Tertiary Basin > Eagle Ford Shale Formation (0.99)
- (48 more...)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale oil (1.00)
- Reservoir Description and Dynamics > Unconventional and Complex Reservoirs > Shale gas (1.00)
- Reservoir Description and Dynamics > Reservoir Characterization > Seismic processing and interpretation (1.00)
- (3 more...)
ABSTRACT One of the primary fluid indicators for direct hydrocarbon detection using seismic amplitude anomalies is the difference between saturated-rock P-wave impedance and the rock-frame impedance. This can be expressed in terms of the difference between the observed P-wave impedance squared and a multiplier times the square of the observed S-wave impedance. This multiplier is a fluid discrimination parameter that laboratory and log measurements suggest varies over a wide range. Theoretically, this parameter is simply related to the ratio of frame bulk and shear moduli and the ratio of densities between the frame and fluid-saturated rock. In practice, empirical determination of the fluid discrimination parameter may be required for a given locality. Given sufficient data for calibration, the parameter can be adjusted so as to best distinguish hydrocarbon-saturated targets from brine-saturated rocks. Using an empirically optimized fluid discrimination parameter has a greater impact on hydrocarbon detection success rate in the oil cases studied than for gas reservoirs, for which there is more latitude. Application to a wide variety of well log and laboratory measurements suggests that the empirically optimized parameter may differ from direct theoretical calculations made using Gassmann's equations. Combining laboratory and log measurements for sandstones having a broad range of frame moduli, varying from poorly consolidated to highly lithified, reveals a simple linear empirical relationship between the optimized fluid discrimination parameter and the squared velocity ratio of brine-saturated sandstones. Presentation Date: Tuesday, September 17, 2019 Session Start Time: 8:30 AM Presentation Start Time: 8:30 AM Location: 217A Presentation Type: Oral
- North America > United States (0.16)
- North America > Mexico (0.16)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Seismic Processing (0.69)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.49)
- Geophysics > Seismic Surveying > Seismic Interpretation (0.46)
- 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)
Abstract The joint time-frequency and time-phase analysis applied to a field seismic data highlights lateral changes on preferential frequency and phase illumination at the target across secondary faults. Mutual thin-bed interference modeling suited for the case study area was performed using a well-tying well-based extracted wavelet assumed to be representative of the wavelet embedded on the input seismic data. The long coda of this wavelet is also present on the corresponding thin-bed waveform, indicating the possibility of more complex mutual interference patterns between thin beds and mutual interference at farther vertical separations between thin beds compared with what would occur for an embedded wavelet with a shorter coda. The observed lateral changes on preferential frequency and phase illumination on the seismic data are attributable to collocated lateral changes in the stacking patterns and variable occurrence of vertically adjacent thin beds, which are interpreted as lateral sediment deposition changes induced by the syndepositional activity of the secondary faults. This is a geologic scenario that had not been previously considered on the area until the evidence of this case study provide indirect support for it.
- Geology > Geological Subdiscipline (1.00)
- Geology > Structural Geology > Fault (0.94)
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Interpretation (1.00)
Abstract Using time-frequency and time-phase analysis we found that for an isolated thin bed in a binary-impedance setting, there is no observable sensitivity in preferential illumination as layered net-to-gross (NTG) changes within the isolated thin bed, regardless of the way the internal layering is distributed — either uniformly or semirandomly. The NTG signature is observed on the amplitude (magnitude) responses, rather than any specific frequency or phase component. On the other hand, external mutual thin-bed interference can significantly change the preferred phase component for each participating target. This phenomenon is largely driven by the embedded seismic wavelet that determines the nominal seismic response of an isolated thin layer and what phase component would preferentially illuminate it. For vertical separations between mutually interfering and elastically comparable thin beds in which mutual constructive interference is achieved, the target bed will be preferentially illuminated at a phase component that is very close to that of a total seismic isolation, whereas the occurrence of mutual destructive interference will cause a significant departure on the phase preferential illumination from that of an isolated seismic thin bed. All these observations can provide an avenue to yield more robust stratigraphic interpretations of seismic data and enhance the confidence on subsurface description.
- South America > Venezuela (0.68)
- North America > United States > Texas (0.29)
- Geology > Geological Subdiscipline > Stratigraphy (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock (0.48)
Rose diagram analysis from 3D seismic-fault volume in the Bakken Formation, Williston Basin, North Dakota, USA
Jahan, Ismot (Department of Earth and Atmospheric Sciences, University of Houston, Houston, Tx, USA) | Castagna, John P. (Department of Earth and Atmospheric Sciences, University of Houston, Houston, Tx, USA) | Murphy, Michael A. (Department of Earth and Atmospheric Sciences, University of Houston, Houston, Tx, USA)
ABSTRACT Fault orientation rose diagrams from 3D seismic fault attributes help in establishing and understanding different fault patterns in the Bakken Formation. The rose diagrams from 54 sub-areas of the 3D survey are used to establish pre-existing fault patterns related to genetic origin. Sets dominant in the ~N45°E direction are primarily normal faults, while sets with dominantly ~N75°E and ~N15°E orientations are strike-slip. The seismic rose diagrams correlate well with borehole image log rose plots. Presentation Date: Tuesday, October 16, 2018 Start Time: 1:50:00 PM Location: Poster Station 6 Presentation Type: Poster
- North America > United States > North Dakota (1.00)
- North America > Canada > Saskatchewan (1.00)
- North America > Canada > Manitoba (1.00)
Seismic odd and even component amplitudes for estimating porosity in tight dolomite reservoirs
Zhou, Jian (Hohai University and University of Houston) | Ba, Jing (Hohai University) | Castagna, John P. (University of Houston) | Yu, Cun (Hohai University) | Jiang, Ren (RIPED-Langfang) | Ge, Qiang (RIPED-Langfang)
ABSTRACT Seismic amplitude analysis could provide valuable information regarding physical properties of reservoir rocks that is related to seismic reflection characteristics, e.g. porosity. However, for thin-layer reservoirs, limited seismic resolution and related wavelet tuning often hinders accurate interpretation of seismic amplitudes. We show that for generalized simple layer, i.e. without the same magnitudetop and base reflections, amplitude of composite top-base reflection signal is affected by both perturbations to thinlayer reservoir and overburden rocks. The complex tuning behavior can be simplified using the fact that any arbitrary seismic signal can be uniquely decomposed using the Fourier transform into odd and even components that have distinct sensitivities to variation in thin-layer and overburdenproperties. Numerical analysis based on true log parameters in a tight-dolomite reservoir in the Sichuan Basin, China show that amplitude at peak frequency of the seismic data odd part (OAPF) is more sensitive to thin-reservoir porosity change compared to that of original signal (total waveform, TAPF) and even part of the waveform (EAPF). When applied in analyzing real seismic data, conventional TAPF is not obviously correlated to porosity variations. However, the OAPF attribute responds well to the porosity measured in boreholes, and also relates to apparent seismic attenuation. These results suggest that, for thin layers, amplitude-based interpretation and inversion may benefit from isolation of even and odd amplitude attributes. Presentation Date: Monday, October 15, 2018 Start Time: 1:50:00 PM Location: 209A (Anaheim Convention Center) Presentation Type: Oral
- Asia > China > Sichuan > Sichuan Basin > Moxi Block > Longwangmiao Formation (0.99)
- North America > United States > Louisiana > China Field (0.97)