Modeling the Depositional Environment of the Sandstone Reservoir in the Middle Miocene Sidri Member, Badri Field, Gulf of Suez Basin, Egypt: Integration of Gamma-Ray Log Patterns and Petrographic Characteristics of Lithology

Radwan, Ahmed E.

doi:10.1007/s11053-020-09757-6

Cited by 90 publications

(29 citation statements)

References 50 publications

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“…The basin hosts a cumulative thickness up to 8000 m of sedimentary succession (EGPC 1996). Most of the oil and gas accumulations in the Gulf of Suez Basin are located in the central and southern parts of the basin (Alsharhan, 2003;Radwan et al 2020a, b, c;Radwan 2021aRadwan , b, c, 2022). A regional lithostratigraphic section is provided in Fig.…”

Section: Geological Settingmentioning

confidence: 99%

Implications of machine learning on geomechanical characterization and sand management: a case study from Hilal field, Gulf of Suez, Egypt

Abdelghany

Hammed

Radwan

et al. 2022

J Petrol Explor Prod Technol

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Sand production is one of the major challenges in the oil and gas industry, so a comprehensive geomechanical analysis is necessary to mitigate sand production in mature fields. As the pore pressure drastically decline in depleted reservoirs, the sand production risk becomes more critical and needs to be studied. However, the absence of key logs in many wells is a big challenge in the petroleum industry, and most geologists and engineers use empirical equations to predict missed log intervals. We conducted a comprehensive geomechanical modeling study on a full set of logs from two wells from the Hilal field, Gulf of Suez, Egypt, to infer the geomechanical elements and predict sand production. We have used the multi-arm calipers to calculate the actual depth of damage ratio to validate the geomechanical parameters in the prognosis model and confirm the stress orientations. We used machine learning approach to infer key sonic log in X-10 well to replace the empirical equations. The multi-arm calipers analysis showed an observed anisotropy in the hole diameter size with more enlargement in the ENE direction and fits with the minimum horizontal stress direction in the direction of N 60oE. The later also deduced the maximum horizontal stress direction in N150 ° based on the induced fractures from borehole image data in a nearby field. We developed and compared two sand management models: one using empirical equation and the other using machine learning. The model driven by the Gardner equation suggests sand production from day one, which is not matched with the production data, while the model driven by machine learning suggests no sand production risk, which is matched with the actual production data. Our results demonstrate the advantage of using machine learning technique in geomechanical studies on the classical empirical equations in the area of study that can be applied in other basins. The findings of this study can help with a better understanding of the implications of machine learning on geomechanical characterization and sand management.

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Section: Geological Settingmentioning

confidence: 99%

Implications of machine learning on geomechanical characterization and sand management: a case study from Hilal field, Gulf of Suez, Egypt

Abdelghany

Hammed

Radwan

et al. 2022

J Petrol Explor Prod Technol

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“…The Hammam Faraun member is a stacked alluvial fan delta system deposited in a shallow marine environment. It is composed of shale interbedded with sandstone beds and thin carbonate beds (Khalil and Meshrif 1988;Rashed 1990;EGPC 1996;Hughes et al 1997;Gawad et al 1986;Alsharhan 2003;Jackson et al 2006;Radwan 2014;Nabawy and El Sharawy 2015;Ali et al 2016;Nabawy and Barakat 2017;Abudeif et al 2018;Radwan et al 2019a;Radwan 2020d). The general Lithostratigraphic column of El Morgan oil field is shown in Figure 2.…”

Section: Lithostratigraphy and Geologic Settingmentioning

confidence: 99%

Implementation of Rock Typing on Waterflooding Process During Secondary Recovery in Oil Reservoirs: A Case Study, El Morgan Oil Field, Gulf of Suez, Egypt

et al. 2021

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Waterflooding is one of the most common secondary recovery methods in the oil and gas industry. Globally, this process sometimes suffers a technical failure and inefficiency. Therefore, a better understanding of geology, reservoir characteristics, rock typing and discrimination, hydraulic flow units, and production data is essential to analyze reasons and mechanisms of water injection failure in the injection wells. Water injection failure was reported in the Middle Miocene Hammam Faraun reservoir at El Morgan oil field in the Gulf of Suez, where two wells have been selected as injector’s wells. In the first well (A1), the efficiency of injection was not good, whereas in the other analog A2 well good efficiency was assigned. Therefore, it is required to assess the injection loss in the low efficiency well, where all aspects of the geological, reservoir and production data of the studied wells were integrated to get a complete vision for the reasons of injection failure. The available data include core analysis data (vertical and horizontal permeabilities, helium porosity, bulk density, and water and oil saturations), petrographical studies injection and reservoir water chemistry, reservoir geology, production, and injection history. The quality of the data was examined and a set of reliable X–Y plots between the available data were introduced and the reservoir quality in both wells was estimated using reservoir quality index, normalized porosity index, and flow zone indicator. Integration and processing of the core and reservoir engineering data indicate that heterogeneity of the studied sequence was the main reason for the waterflooding inefficiency at the El Morgan A1 well. The best reservoir quality was assigned to the topmost part of the reservoir, which caused disturbance of the flow regime of reservoir fluids. Therefore, it is clearly indicated that rock typing and inadequate injection perforation strategy that has not been aligned with accurate hydraulic flow units are the key control parameters in the waterflooding efficiency.

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“…The Gulf of Suez is rich in oil and gas fields [50][51][52][53][54][55][56][57][58][59][72][73][74][75], and the Sidki is located in the southern part of the basin, close to significant oil and gas fields. The geological description of the Sidki field presented in Sect.…”

Section: Geological Characterization Of the Nubian 'C' Reservoirmentioning

confidence: 99%

Reservoir Formation Damage; Reasons and Mitigation: A Case Study of the Cambrian–Ordovician Nubian ‘C’ Sandstone Gas and Oil Reservoir from the Gulf of Suez Rift Basin

et al. 2021

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Reservoir formation damage is a major problem that the oil and gas industry has to mitigate in order to maintain the oil and gas supply. A case study is presented that identifies the impacts of formation damage and their causes in the Nubian ‘C’ hydrocarbon reservoir within Sidki field located in the Southern Gulf of Suez, Egypt. In addition, a formation damage mitigation program was designed and implemented involving an effective stimulation treatment for each well experiencing reservoir damage. The data available for this study include core analysis to provide rock mineralogy and lithology; analysis of production fluid data; water chemistry; drilling fluid composition; perforations and well completion details; workover operations; and stimulation history. The diagnosis of formation damage based on the integrated assessment of the available data is associated with several benefits, (1) The integration of the data available helps provide a robust analysis of formation damage causes and in establishing suitable remediation actions, (2) Workover fluid is confirmed as the primary cause of reservoir damage in the studied well, (3) Several reservoir damage mechanisms were identified including water blockage, solids and filtrate invasion, fluid/rock interaction (deflocculation of kaolinite clay), salinity shock and/or high-sulfate content of the invaded fluid, (4) Irrespective of the potential causes of formation damage, the primary objective of a gas production company is to mitigate its effects and the integrated dataset helps to design appropriate and effective stimulation treatments to overcome formation damage, and (5) In gas reservoirs, especially low permeability ones, extra precautions are necessary to avoid potential reservoir damage due to workover fluid invasion.

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Modeling the Depositional Environment of the Sandstone Reservoir in the Middle Miocene Sidri Member, Badri Field, Gulf of Suez Basin, Egypt: Integration of Gamma-Ray Log Patterns and Petrographic Characteristics of Lithology

Cited by 90 publications

References 50 publications

Implications of machine learning on geomechanical characterization and sand management: a case study from Hilal field, Gulf of Suez, Egypt

Implications of machine learning on geomechanical characterization and sand management: a case study from Hilal field, Gulf of Suez, Egypt

Implementation of Rock Typing on Waterflooding Process During Secondary Recovery in Oil Reservoirs: A Case Study, El Morgan Oil Field, Gulf of Suez, Egypt

Reservoir Formation Damage; Reasons and Mitigation: A Case Study of the Cambrian–Ordovician Nubian ‘C’ Sandstone Gas and Oil Reservoir from the Gulf of Suez Rift Basin

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