Petroleum geology: 3D reservoir modelling in the central Gulf of Suez, Egypt, to estimate the hydrocarbon possibility via petrophysical analysis and seismic data interpretation

2021 ◽  
Author(s):  
Esam A. Gawad ◽  
Mohamed Fathy ◽  
Mohamed Reda ◽  
Hatem Ewida
Keyword(s):  
Seismic Data ◽  
Data Interpretation ◽  
Petroleum Geology ◽  
Gulf Of Suez ◽  
Reservoir Modelling ◽  
Petrophysical Analysis

Integrated Analysis of Borehole and Surface Seismic Data to Resolve Seismic Uncertainties in the Gulf Of Suez

2014 ◽  
Author(s):  
Mohamed S El-Hateel ◽  
Parvez Ahmad ◽  
Ahmed Hesham A Ismail ◽  
Islam A M Henaish ◽  
Ahmed Ashraf
Keyword(s):  
Seismic Data ◽  
Integrated Analysis ◽  
Gulf Of Suez

Fault Detection by Using Instance Segmentation

2021 ◽  
Author(s):  
Donglin Zhu ◽  
Lei Li ◽  
Rui Guo ◽  
Shifan Zhan
Keyword(s):  
Fault Detection ◽  
Seismic Data ◽  
Semantic Segmentation ◽  
Data Interpretation ◽  
Machine Learning Methods ◽  
Segmentation Algorithms ◽  
Direct Recognition ◽  
First Time ◽  
Seismic Images ◽  
Instance Segmentation

Abstract Fault detection is an important, but time-consuming task in seismic data interpretation. Traditionally, seismic attributes, such as coherency (Marfurt et al., 1998) and curvature (Al-Dossary et al., 2006) are used to detect faults. Recently, machine learning methods, such as convolution neural networks (CNNs) are used to detect faults, by applying various semantic segmentation algorithms to the seismic data (Wu et al., 2019). The most used algorithm is U-Net (Ronneberger et al., 2015), which can accurately and efficiently provide probability maps of faults. However, probabilities of faults generated by semantic segmentation algorithms are not sufficient for direct recognition of fault types and reconstruction of fault surfaces. To address this problem, we propose, for the first time, a workflow to use instance segmentation algorithm to detect different fault lines. Specifically, a modified CNN (LaneNet; Neven et al., 2018) is trained using automatically generated synthetic seismic images and corresponding labels. We then test the trained CNN using both synthetic and field collected seismic data. Results indicate that the proposed workflow is accurate and effective at detecting faults.

scholarly journals Seismic Data Interpretation and Identification of Hydrocarbon-Bearing Zones of Rajian Area, Pakistan

Minerals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 891
Author(s):  
Naveed Ahmad ◽  
Sikandar Khan ◽  
Eisha Fatima Noor ◽  
Zhihui Zou ◽  
Abdullatif Al-Shuhail
Keyword(s):  
Seismic Data ◽  
Foreland Basin ◽  
Data Interpretation ◽  
Indus Basin ◽  
Subsurface Structure ◽  
Vertical Wells ◽  
Salt Range ◽  
Seismic Sections ◽  
The Given ◽  
Triangular Zone

The present study interprets the subsurface structure of the Rajian area using seismic sections and the identification of hydrocarbon-bearing zones using petrophysical analysis. The Rajian area lies within the Upper Indus Basin in the southeast (SE) of the Salt Range Potwar Foreland Basin. The marked horizons are identified using formation tops from two vertical wells. Seismic interpretation of the given 2D seismic data reveals that the study area has undergone severe distortion illustrated by thrusts and back thrusts, forming a triangular zone within the subsurface. The final trend of those structures is northwest–southeast (NW–SE), indicating that the area is part of the compressional regime. The zones interpreted by the study of hydrocarbon potential include Sakessar limestone and Khewra sandstone. Due to the unavailability of a petrophysics log within the desired investigation depths, lithology cross-plots were used for the identification of two potential hydrocarbon-bearing zones in one well at depths of 3740–3835 m (zone 1) and 4015–4100 m (zone 2). The results show that zone 2 is almost devoid of hydrocarbons, while zone 1 has an average hydrocarbon saturation of about 11%.

Microseepage through Evaporite Sequences – a Gulf of Suez Example

2021 ◽  
pp. 1-30
Author(s):  
Alan H. Silliman ◽  
Rick Schrynemeeckers
Keyword(s):  
Grain Boundaries ◽  
Seismic Data ◽  
Oil And Gas ◽  
Seismic Imaging ◽  
Passive Samplers ◽  
Ductile Material ◽  
Gulf Of Suez ◽  
Calibration Data ◽  
Geochemical Survey ◽  
Molecular Movement

Salt is one of the most effective agents for trapping oil and gas. As a ductile material it can move and deform surrounding sediments and create traps. However, effective sealing of reservoirs for movement of hydrocarbons along breaching faults or fracture swarms (i.e. macroseepage) is a completely different mechanism than the molecular movement of hydrocarbons through grain boundaries and microfractures as found in microseepage. Forum Exploration chose to evaluate the applicability of passive surface geochemistry for mapping hydrocarbons in their onshore West Gebel El Zeit lease due to difficulties in seismic imaging through salt and anhydrites sequences. Two economic producing wells had been drilled in the lease, but due to compartmentalization and complexity in the area, three dry wells had also been drilled. Target formations included the Kareem Formation at ∼2,700 m and the Rudeis Formation at ∼3,000 m.The geochemical survey encompassed 100 passive geochemical modules. Passive samplers were also deployed around two producing wells and one dry well. Calibration data generated positive thermogenic signatures around the two producing wells in contrast to the background or baseline signature developed around the dry well. The Rudeis Formation calibration signature ranged from ∼nC5 - ∼nC9 while the Kareem Formation calibration signature ranged from ∼nC6 – nC12. This suggested the Rudeis calibration signature was lighter than the Kareem. This correlated with independent API gravity testing on produced oil samples (41o API gravity oil for the Rudeis, 35o API gravity oil for the Kareem).A post-survey well, Fh85-8, was drilled based on combined geochemical and seismic data results. The well was an oil discovery, with initial production of 800 BOPD. The evidence presented in this Gulf of Suez example shows that microseepage can occur through salt sequences. As such, ultrasensitive passive surface geochemical surveys provide a powerful tool for derisking salt plays.

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