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Permeability prediction and distribution is very critical for reservoir modeling process. The conventional method for obtaining permeability data is from cores, which is a very costly method. Therefore, it is usual to pay attention to logs for calculating permeability where it has massive limitations regarding this step. The aim of this study is to use unique artificial intelligence (AI) algorithms to tackle this challenge and predict permeability in the studied wells using conventional logs and routine core analysis results of the core plugs as an input to predict the permeability in non-cored intervals using extreme gradient boosting algorithm (XGB). This led to promising results as per the R2 correlation coefficient. The R2 correlation coefficient between the predicted and actual permeability was 0.73 when using the porosity measured from core plugs and 0.51 when using the porosity calculated from logs. This study presents the use of machine-learning extreme gradient boosting algorithm in permeability prediction. To our knowledge, this algorithm has not been used in this formation and field before. In addition, the machine-learning model established is uniquely simple and convenient as only four commonly available logs are required as inputs, it even provides reliable results even if one of the required logs for input is synthesized due to its unavailability. [ABSTRACT FROM AUTHOR]

Abstract Permeability prediction and distribution is very critical for reservoir modeling process. The conventional method for obtaining permeability data is from cores, which is a very costly method. Therefore, it is usual to pay attention to logs for calculating permeability where it has massive limitations regarding this step. The aim of this study is to use unique artificial intelligence (AI) algorithms to tackle this challenge and predict permeability in the studied wells using conventional logs and routine core analysis results of the core plugs as an input to predict the permeability in non-cored intervals using extreme gradient boosting algorithm (XGB). This led to promising results as per the R 2 correlation coefficient. The R 2 correlation coefficient between the predicted and actual permeability was 0.73 when using the porosity measured from core plugs and 0.51 when using the porosity calculated from logs. This study presents the use of machine-learning extreme gradient boosting algorithm in permeability prediction. To our knowledge, this algorithm has not been used in this formation and field before. In addition, the machine-learning model established is uniquely simple and convenient as only four commonly available logs are required as inputs, it even provides reliable results even if one of the required logs for input is synthesized due to its unavailability.

Steeply dipping prograding fan deltas possess high reservoir quality facies that could be excellent targets while exploring for hydrocarbons. Due to their complex stacking nature, and limited examples, delineating their architectural elements is still challenging. In this paper we mainly performed sedimentary facies analysis; applying various disciplines e.g. sequence stratigraphy, seismic stratigraphy, GR-log motifs, and seismic waveform segmentation; so as to adequately depict the reservoir heterogeneity and quality of the Paleozoic Nubia clastics in West Esh El Mallaha Concession (southwest Gulf of Suez rift). Organic maturity prediction, to confirm the hydrocarbon charging from source units to reservoir intervals, was also of most importance in this study. Accordingly, 1D basin model was established to define the past geologic events; subsidence, and thermal maturity; and their controls on sedimentary basin evolution and associated petroleum potential. We utilized several key-information scales; e.g. wireline logs, and seismic profiles. Linking different disciplines applied in this study points to a successful integrated reservoir characterization workflow capable of unfolding ancient environments and the associated hydrocarbon potential. The results show that Nubia Formation was built during the lowstand−transgressive phase of a 3rd order depositional sequence. It encompasses fluvio-lacustrine system with eight sedimentary facies associations; form source to sink. Fluvial channels and mouth bars, settled in subaerial and subaqueous settings respectively, represent the most significant reservoir facies in the area. Given best hydrocarbon-reservoir quality, the deltaic mouth bars ought to attract attention of further oilfield development plans and be considered while investigating similar settings.

We present here a series of surficial geologic maps of 22,000 km 2 of the southeastern part of the Western Desert of Egypt showing 3084 bodies of wadi-floor sediment that have been inverted by erosion to form sinuous ridges capped by alluvial gravel, here described as “ inverted wadis” . These features represent fragments of one or more ancient drainage systems that developed at times when rainfall, and hence overland flow, was greater than it is today in this hyperarid region. While some of the inverted wadis were tributaries to a through-flowing river that followed the course of the modern Nile, others converge on what appear to have been internal closed basins west of the Nile. Several components of this ancient drainage system have yielded artifacts assigned to an Acheulian lithic tradition, but there is not yet enough stratigraphic information available to enable us to relate this ancient drainage system to any of the proposed scenarios of Nile evolution. [ABSTRACT FROM AUTHOR]

Gamma oil field is considered as oil producer from Nubia sandstone Formation. It lies in the southwestern part of the Gulf of Suez. The penetrated Nubia Formation in the available wells has good reservoir characteristics. It is mainly sand and shale (kaolinite) facies. Moreover, the porosity model reflects a good porosity in the area with values ranging from 11% to 22%. Conversely, the water saturation values show that all available wells are located under the oil-water contact and are highly saturated with water. Consequently, a 3D static model was carried out to distinguish the hydrocarbon distribution, mapping, and validate any Nubian sandstone reservoir upside exploration potential in the area. Accordingly, the property models and maps results show that the upside potential blocks at the southeastern direction reflected a highly promising for hydrocarbon prospecting where the water saturation values reflect an obvious decrease and the volumetric calculation of the OOIP (original oil in place) in the upside blocks is about 19.3 MMSTB, this reflects the economic importance of hydrocarbon accumulations in the Nubia sandstone Formation in the predicted area. • The Nubia formation composed of sandstone interbedded with thin streaks of kaolinite and has a good reservoir quality. • The Gamma oil field was affected by both the Clysmic fault trend and the Cross-faults trend. • The upside blocks at the southeastern part of the study area are very promising for hydrocarbon prospecting. [ABSTRACT FROM AUTHOR]