The October Oil Field is structurally complex due to its presence in the structurally complex system of the Gulf of Suez Rift Basin area, with the last updated structural model developed in 2012. Although the 2012 model defined the general structural framework and reservoir architecture, many challenges arose during the field development. The current study is focusing on the structural elements affecting this giant field to update the field structural model using the newly processed 3D seismic survey, the acquired data from newly drilled wells, and the associated different logging techniques. Several geological structure contour maps and cross‐sections were generated to help in delineating and understanding the reservoir's extension. Based on the detailed correlation study, we were able to detect the faults that affected the structure of the October Field in detail, define their throw amounts and directions, and identify the missed sections across the studied area. This study introduces a comparison between the old and updated model scenarios to show the differences and their effect on the field development plan and recommendations. The updated model shows differences between the 2012 model and the current study's modified model in the number, extension, and location of faults: the old model has 17 faults, while the modified model has 13 faults. The main clysmic fault “F1” has a significant impact on the entire field because it affects all studied wells. Furthermore, the F3 and F4 faults have a significant impact due to their ability to create and add compartmentalization within the area of study. This study revealed that the updated detailed 3D structural model can support the development plans for the Nubia reservoir and motivate drilling, workover, and dynamic operations to assign the development opportunities in the proper location. Based on the developed model, there are at least three opportunities in the attic areas of the study that could increase oil production and oil reserves for the field while avoiding any more failures.

Two sections of strata assigned to the Amran Group at Jabal Salab and Jabal Yam in the eastern Sana’a governorate were sampled and correlated. These sections are part of a carbonate platform that extends from the city of Marib in the east to Naqil Ibn Ghailan, 20 km east of the city of Sana’a to the west. Palaeontological analysis of samples recovered has resulted in identification of 123 foraminiferal species, which are used to subdivide the sequence of the Amran Group into five biostratigraphic zones, aged between Bathonian (Middle Jurassic) and Berriasian (Early Cretaceous). The proposed biozones are those of Riyadhella rotundata, Kurnubia jurassica, Ammomarginulina sinaica, Alveosepta jaccardi and Pseudocyclammina sulaiyana/Furitilla caspianseis. These biozones were constructed and correlated with the equivalent zones reported from several localities.

The area of study is a Pliocene gas field, located in the Eastern portion of the West Nile Delta Deep Marine Concession (WDDM) offshore Egypt. The primary aim of this study is to establish a methodology for direct porosity estimate from 3D post-stack inversion (Zp) and assess its reliability. Porosity estimation from seismic inversion is a commonly used technique in geophysics to predict subsurface porosity from seismic data. Seismic inversion is the process of converting seismic reflection data into a quantitative representation of subsurface properties. Seismic inversion methods aim to relate the seismic response (amplitude, phase, frequency content) to rock properties such as porosity. The inversion process typically involves the following steps: Acoustic impedance inversion from seismic data is a widely utilized technique in reservoir characterization. In cases where well penetrations are limited, the resulting impedance section can be employed to predict reservoir parameters, including porosity. However, the relationship between acoustic impedance (AI) and porosity is influenced by the lithofacies and requires geological interpretation. To construct a porosity map and porosity static model, a comprehensive methodology was developed, capitalizing on the expected porosity volumes. By applying cut-offs to shear and acoustic impedance logs, categorical facies or fluid classes were established. The mean porosity for each lithofacies category is determined from the porosity logs of the wells under study. The inverted porosity model is validated against well log data or other independent measurements like core porosity to assess its accuracy and reliability. If necessary, additional adjustments or calibration may be performed to improve the porosity estimation. Subsequently, a final trend porosity volume was generated to estimate the porosity in areas distant from the study wells by establishing a correlation between average porosity values and acoustic impedance. This process of creating a porosity map will significantly mitigate drilling uncertainties going forward.