Recognizing Sequences and Systems Tracts from Well Logs, Seismic Data, and BiostratigraphyExamples from the Late Cenozoic of the Gulf of Mexico

1993 ◽  
Author(s):  
Robert M. Mitchum ◽  
John B. Sangree ◽  
Peter R. Vail ◽  
Walter W. Wornardt
Keyword(s):  
Gulf Of Mexico ◽  
Seismic Data ◽  
Well Logs ◽  
Late Cenozoic

Acquisition of 3D, 4C seismic data for offshore Gulf of Mexico time lapse studies

1998 ◽  
Author(s):  
Dan Ebrom ◽  
Paul Krail ◽  
Larry Scott
Keyword(s):  
Gulf Of Mexico ◽  
Seismic Data ◽  
Time Lapse

Potentially Large Subsurface Gas Hydrate Bodies in the Mexican Ridges, Southwestern Gulf of Mexico

2021 ◽  
pp. 1-29
Author(s):  
Papia Nandi ◽  
Patrick Fulton ◽  
James Dale
Keyword(s):  
Gulf Of Mexico ◽  
Gas Hydrate ◽  
Seismic Data ◽  
Poor Quality ◽  
Reflection Point ◽  
Seismic Reflection Data ◽  
Reflection Data ◽  
High Impedance ◽  
Acoustic Velocities ◽  
Seismic Images

As rising ocean temperatures can destabilize gas hydrate, identifying and characterizing large shallow hydrate bodies is increasingly important in order to understand their hazard potential. In the southwestern Gulf of Mexico, reanalysis of 3D seismic reflection data reveals evidence for the presence of six potentially large gas hydrate bodies located at shallow depths below the seafloor. We originally interpreted these bodies as salt, as they share common visual characteristics on seismic data with shallow allochthonous salt bodies, including high-impedance boundaries and homogenous interiors with very little acoustic reflectivity. However, when seismic images are constructed using acoustic velocities associated with salt, the resulting images were of poor quality containing excessive moveout in common reflection point (CRP) offset image gathers. Further investigation reveals that using lower-valued acoustic velocities results in higher quality images with little or no moveout. We believe that these lower acoustic values are representative of gas hydrate and not of salt. Directly underneath these bodies lies a zone of poor reflectivity, which is both typical and expected under hydrate. Observations of gas in a nearby well, other indicators of hydrate in the vicinity, and regional geologic context, all support the interpretation that these large bodies are composed of hydrate. The total equivalent volume of gas within these bodies is estimated to potentially be as large as 1.5 gigatons or 10.5 TCF, considering uncertainty for estimates of porosity and saturation, comparable to the entire proven natural gas reserves of Trinidad and Tobago in 2019.

Stochastic discrete fracture network modeling in shale reservoirs via integration of seismic attributes and petrophysical data

2021 ◽  
pp. 1-50
Author(s):  
Yongchae Cho
Keyword(s):  
Seismic Data ◽  
Stochastic Approach ◽  
Fracture Network ◽  
Natural Fracture ◽  
Well Logs ◽  
Fracture Model ◽  
Well Log ◽  
Discrete Fracture ◽  
Discrete Fracture Network Modeling ◽  
Fracture Network Modeling

The prediction of natural fracture networks and their geomechanical properties remains a challenge for unconventional reservoir characterization. Since natural fractures are highly heterogeneous and sub-seismic scale, integrating petrophysical data (i.e., cores, well logs) with seismic data is important for building a reliable natural fracture model. Therefore, I introduce an integrated and stochastic approach for discrete fracture network modeling with field data demonstration. In the proposed method, I first perform a seismic attribute analysis to highlight the discontinuity in the seismic data. Then, I extrapolate the well log data which includes localized but high-confidence information. By using the fracture intensity model including both seismic and well logs, I build the final natural fracture model which can be used as a background model for the subsequent geomechanical analysis such as simulation of hydraulic fractures propagation. As a result, the proposed workflow combining multiscale data in a stochastic approach constructs a reliable natural fracture model. I validate the constructed fracture distribution by its good agreement with the well log data.

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