The Gulf of Suez in Egypt contains more than 80 conventional oil fields with reservoirs from Precambrian up to Quaternary age. To date, these fields have all been conventional resources. This abstract will take part of the Gulf of Suez sequence within the fields of Petrogulf Misr Company and present a work process for unconventional resource assessment of the Brown Limestone formation within one of these areas. The Brown Limestone formation is a Late Cretaceous Pre-rift mega sequence succession and plays an important role in the conventional system of Gulf of Suez, Brown Limestone formation is not only as one of the important source rocks, but also a fractured carbonate reservoir in multiple fields especially is the southern Geisum oil field. However, this formation is characterized by uncertainty due to the complexity of reservoir architecture, various lithologies, lateral facies variations, and heterogeneous reservoir quality. These reservoir challenges, in turn, affect the effectiveness of further exploitation of this reservoir along the Gulf of Suez Basin. In this work, we conduct an integrated study using multidisciplinary datasets and techniques to determine the precise structural, petrophysical, and facies characteristics of the Brown Limestone Formation and predict their complex geometry in 3D space. The Brown Limestone formation is considered to be as a reservoir in the study area. The value of water saturation ranges from 15 to 45%, where the value of Effective Porosity ranges from 11 to 15% for the selected potential intervals in Brown Limestone due to the highly structural setting in the study area, so Reservoir thickness was used as the proxy for reservoir effectiveness where thicker reservoir had a higher chance of containing multiple intervals for good potential intervals.

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This article focuses on the efficient estimation problem of an arbitrary-order periodic integer-valued autoregressive (PINAR(p)) model. Both the local asymptotic normality (LAN) property and the local asymptotic linearity property satisfied by the central sequence of the underlying model are established. Using these results, we construct efficient estimators for the parameters in a parametric framework. The consistency property of these efficient estimations is evaluated via an intensive simulation study. Moreover, the performances of these efficient estimations, over the conditional maximum likelihood (CML) and the conditional least squares (CLS) estimations, are also illustrated via an intensive simulation study and an application on real data set.

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