Classification of reservoir facies using well log and 3D seismic attributes for prospect evaluation and field development: A case study of Sawan gas field, Pakistan

Ashraf, Umar; Zhu, Peimin; Yasin, Qamar; Anees, Aqsa; Imraz, Muhammad; Mangi, Hassan Nasir; Shakeel, Saiq

doi:10.1016/j.petrol.2018.12.060

Cited by 113 publications

(68 citation statements)

References 24 publications

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“…The wells of the SGF is situated in sand dunes topography, cut by different irrigation canals linked with the River Indus ( Figure 1b). The discovery of SGF took place in 1997 and started yielding the production in 2003, and as of today, 14 wells are giving production [31].…”

Section: Stratigraphic and Structural Characteristics Of The Study Areamentioning

confidence: 99%

“…Recent studies conducted in the LGF of the SGF were performed to highlight the possible gas-bearing sweet zones [31,32]. Various authors performed multiple post-stack inversion methods to calculate the reservoir parameters [33,34].…”

Section: Introductionmentioning

confidence: 99%

“…Various authors performed multiple post-stack inversion methods to calculate the reservoir parameters [33,34]. However, their studies were based only on attributes [27,28,31], inversion techniques [32][33][34], or well logging [29,35]. None of those studies have provided comprehensive research to incorporate all the necessary methods to present thorough research for classifying the reservoir thickness, quantitative and qualitative assessments of facies heterogeneity, and the factors controlling the reservoir heterogeneity of the Lower Goru sands (LGS).…”

Section: Introductionmentioning

confidence: 99%

“…Ref. [31] applied the seismic attributes to classify the sweet zones for future development and prospect evaluation of the SGF. Ref.…”

Section: Introductionmentioning

confidence: 99%

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Controls on Reservoir Heterogeneity of a Shallow-Marine Reservoir in Sawan Gas Field, SE Pakistan: Implications for Reservoir Quality Prediction Using Acoustic Impedance Inversion

Ashraf

Zhang

Anees

et al. 2020

Water

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The precise characterization of reservoir parameters is vital for future development and prospect evaluation of oil and gas fields. C-sand and B-sand intervals of the Lower Goru Formation (LGF) within the Lower Indus Basin (LIB) are proven reservoirs. Conventional seismic amplitude interpretation fails to delineate the heterogeneity of the sand-shale facies distribution due to limited seismic resolution in the Sawan gas field (SGF). The high heterogeneity and low resolution make it challenging to characterize the reservoir thickness, reservoir porosity, and the factors controlling the heterogeneity. Constrained sparse spike inversion (CSSI) is employed using 3D seismic and well log data to characterize and discriminate the lithofacies, impedance, porosity, and thickness (sand-ratio) of the C- and B-sand intervals of the LGF. The achieved results disclose that the CSSI delineated the extent of lithofacies, heterogeneity, and precise characterization of reservoir parameters within the zone of interest (ZOI). The sand facies of C- and B-sand intervals are characterized by low acoustic impedance (AI) values (8 × 106 kg/m2s to 1 × 107 kg/m2s), maximum sand-ratio (0.6 to 0.9), and maximum porosity (10% to 24%). The primary reservoir (C-sand) has an excellent ability to produce the maximum yield of gas due to low AI (8 × 106 kg/m2s), maximum reservoir thickness (0.9), and porosity (24%). However, the secondary reservoir (B-sand) also has a good capacity for gas production due to low AI (1 × 107 kg/m2s), decent sand-ratio (0.6), and average porosity (14%), if properly evaluated. The time-slices of porosity and sand-ratio maps have revealed the location of low-impedance, maximum porosity, and maximum sand-ratio that can be exploited for future drillings. Rock physics analysis using AI through inverse and direct relationships successfully discriminated against the heterogeneity between the sand facies and shale facies. In the corollary, we proposed that pre-conditioning through comprehensive petrophysical, inversion, and rock physics analysis are imperative tools to calibrate the factors controlling the reservoir heterogeneity and for better reservoir quality measurement in the fluvial shallow-marine deltaic basins.

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Section: Stratigraphic and Structural Characteristics Of The Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Ref. [31] applied the seismic attributes to classify the sweet zones for future development and prospect evaluation of the SGF. Ref.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Controls on Reservoir Heterogeneity of a Shallow-Marine Reservoir in Sawan Gas Field, SE Pakistan: Implications for Reservoir Quality Prediction Using Acoustic Impedance Inversion

Ashraf

Zhang

Anees

et al. 2020

Water

Self Cite

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“…This definition, itself, combines the disciplines of geophysics, geology, and petroleum engineering for the optimum production and operative description of the reservoirs [1]. Correct identification of high-quality regions requires a thorough understanding of porosity, permeability, fluid saturation, the volume of shale, areal extent, thickness, and mechanical properties (last but not least) through the integration of seismic data, wireline log, and core data analyses [2]. To quantify the high-quality regions, reservoir modeling, from drilling to reservoir management activities, plays a vital role in understanding the reservoir behavior in three-dimensional analysis.…”

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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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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Seismic Signal Interpretation for Reservoir Facies Classification

Saikia

Nankani

Baruah

2019

Lecture Notes in Computer Science

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Classification of reservoir facies using well log and 3D seismic attributes for prospect evaluation and field development: A case study of Sawan gas field, Pakistan

Cited by 113 publications

References 24 publications

Controls on Reservoir Heterogeneity of a Shallow-Marine Reservoir in Sawan Gas Field, SE Pakistan: Implications for Reservoir Quality Prediction Using Acoustic Impedance Inversion

Controls on Reservoir Heterogeneity of a Shallow-Marine Reservoir in Sawan Gas Field, SE Pakistan: Implications for Reservoir Quality Prediction Using Acoustic Impedance Inversion

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

Seismic Signal Interpretation for Reservoir Facies Classification

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