A comparison of porosity estimates obtained using post‐, partial‐, and prestack seismic inversion methods: Marco Polo Field, Gulf of Mexico

2007 ◽  
Author(s):  
G. Russell Young ◽  
Mrinal K. Sen
Keyword(s):  
Gulf Of Mexico ◽  
Seismic Inversion ◽  
Inversion Methods ◽  
Marco Polo ◽  
Prestack Seismic Inversion

Prestack Seismic Inversion Based on Anisotropic Markov Random Field

2018 ◽  
Vol 56 (2) ◽  
pp. 1069-1079 ◽  
Author(s):  
Qiang Guo ◽  
Hongbing Zhang ◽  
Feilong Han ◽  
Zuoping Shang
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Seismic Inversion ◽  
Markov Random ◽  
Prestack Seismic Inversion

scholarly journals Gas-bearing sands appraisal for Zamzama Gas field in Pakistan through inverted elastic attributes assisted with PNN approximation of petrophysical properties

2021 ◽  
Author(s):  
Zahid U. Khan ◽  
◽  
Mona Lisa ◽  
Muyyassar Hussain ◽  
Syed A. Ahmed ◽  
...  
Keyword(s):  
Elastic Properties ◽  
Clay Content ◽  
Seismic Inversion ◽  
Indus Basin ◽  
Minimal Risk ◽  
Gas Field ◽  
Lower Indus Basin ◽  
Heterogeneity Effects ◽  
Prestack Seismic Inversion ◽  
Gas Bearing

The Pab Formation of Zamzama block, lying in the Lower Indus Basin of Pakistan, is a prominent gas-producing sand reservoir. The optimized production is limited by water encroachment in producing wells, thus it is required to distinguish the gas-sand facies from the remainder of the wet sands and shales for additional drilling zones. An approach is adopted based on a relation between petrophysical and elastic properties to characterize the prospect locations. Petro-elastic models for the identified facies are generated to discriminate lithologies in their elastic ranges. Several elastic properties, including p-impedance (11,600-12,100 m/s*g/cc), s-impedance (7,000-7,330 m/s*g/cc), and Vp/Vs ratio (1.57-1.62), are calculated from the simultaneous prestack seismic inversion, allowing the identification of gas sands in the field. Furthermore, inverted elastic attributes and well-based lithologies are incorporated into the Bayesian framework to evaluate the probability of gas sands. To better determine reservoir quality, bulk volumes of PHIE and clay are estimated using elastic volumes trained on well logs employing Probabilistic Neural Networking (PNN), which effectively handles heterogeneity effects. The results showed that the channelized gas-sands passing through existing well locations exhibited reduced clay content and maximum effective porosities of 9%, confirming the reservoir's good quality. Such approaches can be widely implemented in producing fields to completely assess litho-facies and achieve maximum production with minimal risk.

Three term AVO approximation ofKf-fm-ρand prestack seismic inversion for deep reservoir

2016 ◽  
Author(s):  
Huixin Wang ◽  
Xingyao Yin ◽  
Cao Danping ◽  
Qijie Zhou ◽  
Wenguo Sun
Keyword(s):  
Seismic Inversion ◽  
Deep Reservoir ◽  
Prestack Seismic Inversion

Prestack Seismic Inversion for Sw Modeling

2005 ◽  
Author(s):  
J. Martin ◽  
B. Bankhead ◽  
A. Sena ◽  
R.S. Cardenas Hernandez ◽  
K.G. Rosas Lara ◽  
...  
Keyword(s):  
Seismic Inversion ◽  
Prestack Seismic Inversion

Mineralogy-based brittleness prediction from surface seismic data: Application to the Barnett Shale

2014 ◽  
Vol 2 (4) ◽  
pp. T255-T271 ◽  
Author(s):  
Roderick Perez Altamar ◽  
Kurt Marfurt
Keyword(s):  
Seismic Data ◽  
Seismic Inversion ◽  
Barnett Shale ◽  
Elastic Parameters ◽  
Well Location ◽  
Data Application ◽  
Empirical Calibration ◽  
Two Measures ◽  
Prestack Seismic Inversion ◽  
Microseismic Event

Differentiating brittle and ductile rocks from surface seismic data is the key to efficient well location and completion. Brittleness average estimates based only on elastic parameters are easy to use but require empirical calibration. In contrast, brittleness index (BI) estimates are based on mineralogy laboratory measurements and, indeed, cannot be directly measured from surface seismic data. These two measures correlate reasonably well in the quartz-rich Barnett Shale, but they provide conflicting estimates of brittleness in the calcite-rich Viola, Forestburg, Upper Barnett, and Marble Falls limestone formations. Specifically, the BI accurately predicts limestone formations that form fracture barriers to be ductile, whereas the brittleness average does not. We used elemental capture spectroscopy and elastic logs measured in the same cored well to design a 2D [Formula: see text] to brittleness template. We computed [Formula: see text] and [Formula: see text] volumes through prestack seismic inversion and calibrate the results with the [Formula: see text] template from well logs. We then used microseismic event locations from six wells to calibrate our prediction, showing that most of the microseismic events occur in the brittle regions of the shale, avoiding more ductile shale layers and the ductile limestone fracture barriers. Our [Formula: see text] to brittleness template is empirical and incorporates basin- and perhaps even survey-specific correlations of mineralogy and elastic parameters through sedimentation, oxygenation, and diagenesis. We do not expect this specific template to be universally applicable in other mudstone rock basins; rather, we recommend interpreters generate similar site-specific templates from logs representative of their area, following the proposed workflow.

Sign in / Sign up

Export Citation Format

Share Document