A new hydrocarbon prospect determination using the seismic interpretation and petrophysical evaluation of Bahariya reservoir in Nader field, north Western Desert, Egypt

Basheer, Alhussein Adham; Metwalli, Farouk I.; Amin, Ahmed T.; El-Dabaa, Shaimaa A.

doi:10.1016/j.jafrearsci.2023.104891

Cited by 4 publications

(1 citation statement)

References 57 publications

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“…The Upper Cretaceous layers in the Abu Gharadig, Shushan, and Alamin basins of the Western Desert are known for their significant hydrocarbon exploration. Among these layers, the Upper Cretaceous "Abu Roash Formation" is particularly noteworthy as it signifies the onset of a substantial sea incursion in the study area, marked by the dominance of carbonate deposits [1,18,19]. Acting as a supportive layer for the Bahariya Formation, the Abu Roash Formation is divided into seven lithostratigraphic groups, labeled "G" to "A," representing the Upper Cenomanian to Santonian periods.…”

Section: Geologic Settingmentioning

confidence: 99%

Rock Typing and Characterization of the Late Cretaceous Abu Roash "G" Reservoirs at East Alam El-Shawish Field, Western Desert, Egypt

Sabry,

Abdel-Fattah,

El-Shafie

2023

Int. J. Petrol. Technol.

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Rock typing and petrophysical characterization play a vital role in constructing reservoir models for petroleum exploration and development. This study focuses on evaluating the petrophysical characteristics of the Late Cretaceous Abu Roash "G" Reservoirs at the East Alam El Shawish field in Egypt's Western Desert. The study involved five vertical wells and employed various techniques and analyses to investigate the reservoir. Lithology determination utilizing well logs and core analysis helps identify the lithology types and corresponding porosity of the Abu Roash "G" reservoirs. Sandstone and limestone lithologies with varying porosity ranges were identified, along with the influence of shale on neutron porosity values. Facies analysis of the Abu Roash "G" Member identified seven lithofacies types, categorized into shallow marine and deeper marine depositional environments. The petrophysical analysis involves evaluating gamma-ray logs, porosity, permeability, flow zone indicator (FZI), and reservoir quality index (RQI) values for each lithofacies type. This analysis classifies the core samples into seven reservoir rock types (RRT1 to RRT7) based on petrophysical attributes, providing a clear classification of the Abu Roash "G" reservoir interval. RRT1, RRT2, and RRT3 exhibit the highest reservoir quality, while RRT4 and RRT5 indicate moderate reservoir quality. RRT6 and RRT7 exhibit low reservoir quality due to unfavorable petrophysical behavior. The findings of this study provide valuable insights into the Abu Roash "G" reservoir, including its lithofacies, reservoir properties, and depositional environments. This knowledge is crucial for reservoir characterization and optimizing oil production strategies in the region.

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Section: Geologic Settingmentioning

confidence: 99%

Rock Typing and Characterization of the Late Cretaceous Abu Roash "G" Reservoirs at East Alam El-Shawish Field, Western Desert, Egypt

Sabry,

Abdel-Fattah,

El-Shafie

2023

Int. J. Petrol. Technol.

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Seismic interpretation and sequence stratigraphic analysis of the Bahariya Formation in the South Umbaraka oilfields (Western Desert, Egypt): insights into reservoir distribution, architecture, and evaluation

Shehata,

Abdel-Fattah,

Hamdan

et al. 2023

Geomech. Geophys. Geo-energ. Geo-resour.

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The integration of multiscale datasets, including seismic, well-logs and stratigraphy, is providing a precise picture about reservoir evaluation and spatial distribution. This study focuses on the seismic interpretation and sequence stratigraphic analysis of the Bahariya Formation in the South Umbaraka oilfields (Selkit, Khepri, and Sethos), located in the Western Desert of Egypt. Seismic interpretations reveal various structural closures, including anticlinal domes and faulted anticlines in different fields. The study area is divided into northern and southern blocks separated by a major fault, and structural cross sections provide valuable information on reservoir zones regarding lateral extension, thickness variation, and fault distribution. The Bahariya Formation is subdivided into two depositional sequences: Lower Bahariya sequence (BAH.SQ-1) and Upper Bahariya sequence (BAH.SQ-2) with distinct wireline-log characteristics, seismic reflections, and lithofacies variations. BAH.SQ-1 comprises mixed siliciclastic-dominated units with some carbonate units, while BAH.SQ-2 consists of mixed siliciclastic and carbonate units. Multiple sequence boundaries, depositional surfaces, and systems tracts are identified within BAH.SQ-1 and BAH.SQ-2, indicating changes in sedimentary environments and sea-level fluctuations. The petrophysical evaluation reveals promising reservoir zones in both the Lower and Upper Bahariya sequences. However, the Lower Bahariya sequence exhibits superior reservoir characteristics in terms of net-pay thickness, shale volume, effective porosity, and water saturation. Particularly, the sandstone-dominated LST-1 within the Lower Bahariya Formation is identified as a favorable reservoir zone. Conversely, the Upper Bahariya Formation has limited reservoir potential. This study underscores the significance of sequence stratigraphy and reservoir architecture, emphasizing the role of faulting, folding, and depositional environments in the distribution and quality of reservoir zones. The findings contribute valuable insights into the structural and stratigraphic characteristics of the Bahariya Formation, enhancing understanding of hydrocarbon plays and reservoir distribution in the studied area.

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Unsupervised machine learning-based multi-attributes analysis for enhancing gas channel detection and facies classification in the serpent field, offshore Nile Delta, Egypt

El-Dabaa,

Metwalli,

Maher

et al. 2024

Geomech. Geophys. Geo-energ. Geo-resour.

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The prediction of highly heterogeneous reservoir parameters from seismic amplitude data is a major challenge. Seismic attribute analysis can enhance the tracking of subtle stratigraphic features. It is challenging to investigate these subtle features, including channel systems, with conventional-amplitude seismic data. Over the past few years, the use of machine learning (ML) to analyze multiple seismic attributes has enhanced the facies analysis by mapping patterns in seismic data. The purpose of this research was to assess the efficiency of an unsupervised self-organizing map (SOM) approach supported by multi-attribute analysis that could improve gas channel detection and seismic facies classification in Serpent Field, offshore Nile Delta, Egypt. As well as evaluates the importance of several available seismic attributes in reservoir characterization rather than analyzing individual attribute volumes. In this study, the single attribute (spectral decomposition attribute) highlighted the gas channel spatial distribution using three distinct frequency magnitude values. Subsequently, we employ principal component analysis (PCA) as an attribute selection method, discovering that combining seismic attributes such as sweetness, envelope, spectral magnitude, and spectral voice as input for SOM reflects an effective method to determine facies. The clustering results distinguish between shale, shaly sand, wet sand, and gas-saturated sand and identify gas–water contact on a 2D topological map (SOM), where each pattern indicates certain facies. This is achieved by associating the SOM outputs with lithofacies determined from petrophysical logs. Reducing exploration and development risk and empowering the geoscientist to generate a more precise interpretation are the ultimate objectives of this multi-attribute analysis.

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A new hydrocarbon prospect determination using the seismic interpretation and petrophysical evaluation of Bahariya reservoir in Nader field, north Western Desert, Egypt

Cited by 4 publications

References 57 publications

Rock Typing and Characterization of the Late Cretaceous Abu Roash "G" Reservoirs at East Alam El-Shawish Field, Western Desert, Egypt

Rock Typing and Characterization of the Late Cretaceous Abu Roash "G" Reservoirs at East Alam El-Shawish Field, Western Desert, Egypt

Seismic interpretation and sequence stratigraphic analysis of the Bahariya Formation in the South Umbaraka oilfields (Western Desert, Egypt): insights into reservoir distribution, architecture, and evaluation

Unsupervised machine learning-based multi-attributes analysis for enhancing gas channel detection and facies classification in the serpent field, offshore Nile Delta, Egypt

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