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

Asad, Muhammad; Rahim, Hamad-ur

doi:10.2478/pjg-2019-0004

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Although complex and nonlinear relationship exists between the thinly layered media and seismic waveforms, SVM established nonlinear projection relationship to capture the thinly layered media from the seismic waveform. In contrast, the inverted AI from model-based or other traditional algorithms applied previously in the same dataset have not been good enough to capture the small lithological variations [18,19]. What is more, overall lateral and vertical variations in the resolution of AI estimated from traditional inversion algorithms was poor and resulted in discontinuity of seismic event.…”

Section: Reservoir Characterization From Seismic Datamentioning

confidence: 91%

“…In the past few years, multiple studies have been carried out to evaluate the remaining potential of the C-sand interval using modern techniques [15][16][17]. Recent studies performed by Ali et al [18] and Asad and Rahim [19] indicated that model-based post-stack inversion presented good results for the spatial distribution of reservoir properties and mapping potential gas-saturated zones in the C-sand interval. Even though the model-based inversion algorithm achieved good results, the quantification of the highly productive area was scaled relative to the whole body of the reservoir.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Reservoir Quality from Log-Core and Seismic Inversion Analysis with an Artificial Neural Network: A Case Study from the Sawan Gas Field, Pakistan

et al. 2020

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This paper presents a novel approach that aims to predict better reservoir quality regions from seismic inversion and spatial distribution of key reservoir properties from well logs. The reliable estimation of lithology and reservoir parameters at sparsely located wells in the Sawan gas field is still a considerable challenge. This is due to three main reasons: (a) the extreme heterogeneity in the depositional environments, (b) sand-shale intercalations, and (c) repetition of textural changes from fine to coarse sandstone and very coarse sandstone in the reservoir units. In this particular study, machine learning (ML) inversion algorithm was selected to predict the spatial variations of acoustic impedance (AI), porosity, and saturation. While trained in a supervised mode, the support vector machine (SVM) inversion algorithm performed effectively in identifying and mapping individual reservoir properties to delineate and quantify fluid-rich zones. Meanwhile, the Sequential Gaussian Simulation (SGS) and Gaussian Indicator Simulation (GIS) algorithms were employed to determine the spatial variability of lithofacies and porosity from well logs and core analyses data. The calibration of the detailed spatial variations from post-stack seismic inversion using SVM and wireline logs data indicated an appropriate agreement, i.e., variations in AI is related to the variations in reservoir facies and parameters. From the current study, it was concluded that in a highly heterogeneous reservoir, the integration of SVM and GIS algorithms is a reliable approach to achieve the best estimation of the spatial distribution of detailed reservoir characteristics. The results obtained in this study would also be helpful to minimize the uncertainty in drilling, production, and injection in the Sawan gas field of Pakistan as well as other reservoirs worldwide with similar geological settings.

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Section: Reservoir Characterization From Seismic Datamentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Prediction of Reservoir Quality from Log-Core and Seismic Inversion Analysis with an Artificial Neural Network: A Case Study from the Sawan Gas Field, Pakistan

et al. 2020

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Evaluating the paleo-depositional environment of productive reservoir sand of Lower Goru Formation: an integrated stratigraphic and diagenetic study

Nisar,

Mughal,

Shahzad

et al. 2023

Environ Earth Sci

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Depositional trends and reservoir geometries of the Early Cretaceous Lower Goru Formation in Lower Indus Basin, Pakistan: evidence from sequence stratigraphy

Ahmad

Ghazi

2022

J Petrol Explor Prod Technol

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Lower Goru sand intervals of Early Cretaceous age are proven reservoirs in the Lower Indus Platform area. The delineation of reservoir geometries is of utmost importance while searching for stratigraphic and combination traps in clastic reservoirs. The data of ten wells and two 2D seismic lines from the northern part of the Lower Indus Basin are incorporated in this study. These reservoir intervals of the Lower Goru Formation are correlated in a third-order sequence stratigraphic framework to understand the depositional architecture and reservoir geometries. The Lower Goru Formation was deposited in westerly prograding river-dominated deltas developed by river system drained through the Indian Shield from east and south-east during Cretaceous time. Due to auto-cyclic switching of river distributaries, different lobes might have formed which if identified can point out the possible locations and configurations of stratigraphic and combination traps in this highly prospective area of Lower Indus Basin. The Lower Goru Formation (Barremian to Cenomanian) consists of one full second-order sequence of 20 Ma duration, having all the three fundamental system tracts in which eleven sequences of the third order, i.e. LG-1 to LG-11, were interpreted. Proven reservoir intervals of the Lower Goru Formation are concentrated in various third-order lowstand system tracts. The net sand and porosity maps of lowstand system tracts, gross depositional map and depositional cross section of the Lower Goru Formation point out the potential sites of good sandstone reservoirs and hint at the possible sand-body geometries.

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

Cited by 4 publications

References 13 publications

Prediction of Reservoir Quality from Log-Core and Seismic Inversion Analysis with an Artificial Neural Network: A Case Study from the Sawan Gas Field, Pakistan

Prediction of Reservoir Quality from Log-Core and Seismic Inversion Analysis with an Artificial Neural Network: A Case Study from the Sawan Gas Field, Pakistan

Evaluating the paleo-depositional environment of productive reservoir sand of Lower Goru Formation: an integrated stratigraphic and diagenetic study

Depositional trends and reservoir geometries of the Early Cretaceous Lower Goru Formation in Lower Indus Basin, Pakistan: evidence from sequence stratigraphy

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