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The aim of this study is the integration of mud logging and wire-line logging data to detect overpressure zones in Kareem Formation (Middle Miocene), Ashrafi Field, Gulf of Suez, Egypt. The study is performed for the three wells Ash_H_1X_ST2, Ash_I_1X_ST, and Ash_K_1X. The prediction of the abnormal pressure is a quite important factor in the design of the well, where it contributes to avoid many problems during the drilling process and maintain the formation fluids. The abnormal pressure zones occur due to major changes in lithology, petrophysical properties, and fluid type, where these factors lead to differences in pore pressure from hydrostatic pressure, and their prediction is achieved by utilizing rock cuttings, D-exponent, and methane gas; in addition, the porosity and water saturation are estimated from mud logging as real-time data and compared to wire-line logging (resistivity, porosity, and permeability) to determine these zones. The concept of detecting abnormal pressure zones in this study is based on defining the marked changes in the D-exponent trends that arise from the variations of the fluids and the lithology of the Kareem Formation. Therefore, these trends are integrated with the petrophysical parameters such as resistivity, porosity, and permeability from wire-line logging to detect the overpressure zones. So, the overpressure zones are detected in the intercalated sand and shale intervals of the studied wells within the Kareem Formation and are mostly marked by a decrease in the reservoir quality such as permeability, as well as an increase in the resistivity and D-exponent. The thickness of the overpressure zone in Ash_H_1X_ST2 well is influenced by the marl content that reaches up to 80%. The integration results are summarized to determine the average depths of the overpressure zones for the Kareem Formation in the three studied wells. The zone average depths in the Ash_H_1X_ST2 well range from 6022.50 to 6093.30 ft, whereas the zone top is detected in the Ash_I_1X_ST well at the top of the Kareem Formation (6580.00 ft), and the zone bottom at average depth of approximately 6704.20 ft, in addition, the zone average depths in the Ash_K_1X well range from 7718.33 (top) to 7833.33 ft (bottom).

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In upstream oil and gas industry, infill drilling is a vital practice to increase the productivity as well as recovery factor of hydrocarbons. It concerns with the implementation of accurate and reliable reservoir models in order to evaluate the reservoir behaviour response effectively and be able to predict its future performance. Given the nature of reservoir response of naturally fractured reservoirs, optimal well locations are extremely important to ensure the economic viability of infill drilling programs. However, optimal placement of infill wells in fractured fields is challenging. In this study a reservoir modelling-optimization workflow for fractured reservoir is developed. Firstly, an integrated static and dynamic reservoir model has been developed and validated using history matching process. For the purpose of maximizing economic recovery, an infill well placement optimization project has been considered for this field to find the best possible locations of infill wells, two different optimization approaches were adopted and implemented on reservoir model. The first method is an exhaustive method which uses the concept of design of experiment to search all grids available in the model in order to locate the best possible well locations. The second method is automatic optimization using Genetic Algorithm. That depends on the principle of natural selection as proposed by Darwin The genetic program was coupled with the reservoir flow model to re-evaluate the chosen wells at each iteration until obtaining the optimal choice. The proposed location of wells has improved Net Present Value (NPV) by + 10% higher than the base case without infill wells. Examining two different optimization approaches used in this work, the genetic algorithm program gave results similar to the results that were obtained by an exhaustive method with much less computation time which is a great issue mainly for large size fields or fields which possess condensate gas and require the use of compositional simulators.