Three-dimensional Seismic Inversion and Geostatistical Reservoir Modeling of the Rotliegend Gas Field

2006 ◽  
pp. 237-251
2019 ◽  
Vol 38 (6) ◽  
pp. 474-479
Author(s):  
Mohamed G. El-Behiry ◽  
Said M. Dahroug ◽  
Mohamed Elattar

Seismic reservoir characterization becomes challenging when reservoir thickness goes beyond the limits of seismic resolution. Geostatistical inversion techniques are being considered to overcome the resolution limitations of conventional inversion methods and to provide an intuitive understanding of subsurface uncertainty. Geostatistical inversion was applied on a highly compartmentalized area of Sapphire gas field, offshore Nile Delta, Egypt, with the aim of understanding the distribution of thin sands and their impact on reservoir connectivity. The integration of high-resolution well data with seismic partial-angle-stack volumes into geostatistical inversion has resulted in multiple elastic property realizations at the desired resolution. The multitude of inverted elastic properties are analyzed to improve reservoir characterization and reflect the inversion nonuniqueness. These property realizations are then classified into facies probability cubes and ranked based on pay sand volumes to quantify the volumetric uncertainty in static reservoir modeling. Stochastic connectivity analysis was also applied on facies models to assess the possible connected volumes. Sand connectivity analysis showed that the connected pay sand volume derived from the posterior mean of property realizations, which is analogous to deterministic inversion, is much smaller than the volumes generated by any high-frequency realization. This observation supports the role of thin interbed reservoirs in facilitating connectivity between the main sand units.


2019 ◽  
Vol 3 (1) ◽  
pp. 28-37
Author(s):  
M. Asad ◽  
H.U. Rahim

AbstractThe lower Indus basin is one of the prolific basins in Pakistan in which the C-interval of lower Goru formation act as a reservoir. With the help of petrophysical interpretation production zone is recognized and also porosity is calculated at the reservoir level. Through porosity we are able to calculate Ksat. A model based inversion of 2D seismic inversion was performed to ascertain three dimensional dispersion of acoustic impedance in the investigation zone and we have recognized new areas where porosity distribution is maximum and site which is suitable for new well. Porosity and Acoustic impedance are typically contrarily relative to each other. Presently porosity can be anticipated in seismic reservoir characterization by utilizing acoustic impedance from seismic inversion far from well position.


AAPG Bulletin ◽  
2018 ◽  
Vol 102 (11) ◽  
pp. 2333-2354 ◽  
Author(s):  
Guochang Wang ◽  
Shengxiang Long ◽  
Yiwen Ju ◽  
Cheng Huang ◽  
Yongmin Peng

2016 ◽  
Vol 90 (1) ◽  
pp. 209-221 ◽  
Author(s):  
OU Chenghua ◽  
WANG Xiaolu ◽  
LI Chaochun ◽  
HE Yan

2021 ◽  
Author(s):  
Zahid U. Khan ◽  
◽  
Mona Lisa ◽  
Muyyassar Hussain ◽  
Syed A. Ahmed ◽  
...  

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.


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