scholarly journals Porosity Distribution and Differentiation of Different Types of Fluids in Reservoir of Sawan Gas Field, Lower Indus Basin, Pakistan

2019 ◽  
Vol 3 (1) ◽  
pp. 28-37
Author(s):  
M. Asad ◽  
H.U. Rahim
Keyword(s):  
Acoustic Impedance ◽  
Reservoir Characterization ◽  
Three Dimensional ◽  
Seismic Inversion ◽  
Indus Basin ◽  
Gas Field ◽  
Porosity Distribution ◽  
Lower Indus Basin ◽  
Seismic Reservoir ◽  
Lower Goru Formation

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.

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.

scholarly journals Seismic Reservoir Characterization Using Model Based Post-stack Seismic Inversion: In Case of Fenchuganj Gas Field, Bangladesh

2016 ◽  
Vol 59 (6) ◽  
pp. 283-292 ◽  
Author(s):  
Shefa Ul KARIM ◽  
Md Shofiqul ISLAM ◽  
Mohammad Moinul HOSSAIN ◽  
Md Aminul ISLAM
Keyword(s):  
Reservoir Characterization ◽  
Seismic Inversion ◽  
Gas Field ◽  
Model Based ◽  
Seismic Reservoir

Application of geostatistical seismic inversion in reservoir characterization of Sapphire gas field, offshore Nile Delta, Egypt

2019 ◽  
Vol 38 (6) ◽  
pp. 474-479
Author(s):  
Mohamed G. El-Behiry ◽  
Said M. Dahroug ◽  
Mohamed Elattar
Keyword(s):  
Reservoir Characterization ◽  
Nile Delta ◽  
Seismic Inversion ◽  
Reservoir Modeling ◽  
Connectivity Analysis ◽  
Gas Field ◽  
Geostatistical Inversion ◽  
Well Data ◽  
Reservoir Connectivity ◽  
Inversion Techniques

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.

scholarly journals Petrophysical seismic inversion based on lithofacies classification to enhance reservoir properties estimation: a machine learning approach

2020 ◽  
Author(s):  
Amir Abbas Babasafari ◽  
Shiba Rezaei ◽  
Ahmed Mohamed Ahmed Salim ◽  
Sayed Hesammoddin Kazemeini ◽  
Deva Prasad Ghosh
Keyword(s):  
Acoustic Impedance ◽  
Reservoir Characterization ◽  
Seismic Inversion ◽  
Reservoir Properties ◽  
Well Location ◽  
The Neural Network ◽  
Machine Learning Approach ◽  
Lithofacies Classification ◽  
Elastic Inversion ◽  
Significant Match

Abstract For estimation of petrophysical properties in industry, we are looking for a methodology which results in more accurate outcome and also can be validated by means of some quality control steps. To achieve that, an application of petrophysical seismic inversion for reservoir properties estimation is proposed. The main objective of this approach is to reduce uncertainty in reservoir characterization by incorporating well log and seismic data in an optimal manner. We use nonlinear optimization algorithms in the inversion workflow to estimate reservoir properties away from the wells. The method is applied at well location by fitting nonlinear experimental relations on the petroelastic cross-plot, e.g., porosity versus acoustic impedance for each lithofacies class separately. Once a significant match between the measured and the predicted reservoir property is attained in the inversion workflow, the petrophysical seismic inversion based on lithofacies classification is applied to the inverted elastic property, i.e., acoustic impedance or Vp/Vs ratio derived from seismic elastic inversion to predict the reservoir properties between the wells. Comparison with the neural network method demonstrated this application of petrophysical seismic inversion to be competitive and reliable.

scholarly journals Erratum to: Integrated Reservoir Characterization and Petrophysical Analysis of Cretaceous Sands in Lower Indus Basin, Pakistan

2019 ◽  
Vol 94 (2) ◽  
pp. 220-220
Author(s):  
Perveiz Khalid ◽  
Muhammad Irfan Ehsan ◽  
Sohail Akram ◽  
Zia Ud Din ◽  
Shahid Ghazi
Keyword(s):  
Reservoir Characterization ◽  
Indus Basin ◽  
Petrophysical Analysis ◽  
Lower Indus Basin

Rock Physics modeling; attribute application and seismic inversion study in Miano, Lower Indus Basin of Pakistan

2015 ◽  
Author(s):  
Shazia Asim* ◽  
Peimin Zhu ◽  
Tayyab Naseer ◽  
Shabeer Ahmed ◽  
Farrukh Hussain ◽  
...  
Keyword(s):  
Rock Physics ◽  
Seismic Inversion ◽  
Indus Basin ◽  
Lower Indus Basin ◽  
Rock Physics Modeling ◽  
Physics Modeling
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