well log analysis is used to determine the rock properties like porosity, water saturation, and shale volume. Archie parameters in Archie equation, which sometimes considered constants greatly affect the determination of water saturation, also these parameters may be used to indicate whether the rocks are fractured or not so they should be determined. This research involves well logging analysis for Zubair formation in Luhais field which involves the determination of Archie parameters instead of using them as constant.

The log interpretation proved that the formation is hydrocarbon reservoir, as it could be concluded from Rwa (high values) and water saturation values (low values), the lithology of Zubair from cro

The petrophysical analysis is very important to understand the factors controlling the reservoir quality and production wells. In the current study, the petrophysical evaluation was accomplished to hydrocarbon assessment based on well log data of four wells of Early Cretaceous carbonate reservoir Yamama Formation in Abu-Amood oil field in the southern part of Iraq. The available well logs such as sonic, density, neutron, gamma ray, SP, and resistivity logs for wells AAm-1, AAm-2, AAm-3, and AAm-5 were used to delineate the reservoir characteristics of the Yamama Formation. Lithologic and mineralogic studies were performed using porosity logs combination cross plots such as density vs. neutron cross plot and M-N mineralogy plot. Thes

Reservoir study has been developed in order to get a full interesting of the Nahr Umr formation in Ratawi oil field. Oil in place has been calculated for Nahr Umr which was 2981.37 MM BBL. Several runs have been performed to get matching between measured and calculated of oil production data and well test pressure. In order to get the optimum performance of Nahr Umr many strategies have been proposed in this study where vertical and horizontal wells were involved in addition to different production rates. The reservoir was first assumed to be developed with vertical wells only using production rate of (80000–125000) STB/day. The reservoir is also proposed to produce using horizontal wells besides vertical wells with pr

The selection of proper field survey parameters of electrical resistivity can significantly provide efficient results within a reasonable time and cost. Four electrode arrays of 2D Electric Resistivity Imaging (ERI) surveys were applied to characterize and detect subsurface archaeological bodies and to determine the appropriate array type that should be applied in the field survey. This research is to identify the subsurface features of the Borsippa archaeological site, Babylon Governorate, Middle Iraq. Synthetic modeling studies were conducted to determine the proper array and parameters for imaging the shallow subsurface features or targets. The efficiency of many array types has been tested for the detection the buried archaeolog

This research aims to predict the value of the maximum daily loss that the fixed-return securities portfolio may suffer in Qatar National Bank - Syria, and for this purpose data were collected for risk factors that affect the value of the portfolio represented by the time structure of interest rates in the United States of America over the extended period Between 2017 and 2018, in addition to data related to the composition of the bonds portfolio of Qatar National Bank of Syria in 2017, And then employing Monte Carlo simulation models to predict the maximum loss that may be exposed to this portfolio in the future. The results of the Monte Carlo simulation showed the possibility of decreasing the value at risk in the future due to the dec

Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables

Visualization of subsurface geology is mainly considered as the framework of the required structure to provide distribution of petrophysical properties. The geological model helps to understand the behavior of the fluid flow in the porous media that is affected by heterogeneity of the reservoir and helps in calculating the initial oil in place as well as selecting accurate new well location. In this study, a geological model is built for Qaiyarah field, tertiary reservoir, relying on well data from 48 wells, including the location of wells, formation tops and contour map. The structural model is constructed for the tertiary reservoir, which is an asymmetrical anticline consisting of two domes separated by a saddle. It is found that

Drilling deviated wells is a frequently used approach in the oil and gas industry to increase the productivity of wells in reservoirs with a small thickness. Drilling these wells has been a challenge due to the low rate of penetration (ROP) and severe wellbore instability issues. The objective of this research is to reach a better drilling performance by reducing drilling time and increasing wellbore stability.

In this work, the first step was to develop a model that predicts the ROP for deviated wells by applying Artificial Neural Networks (ANNs). In the modeling, azimuth (AZI) and inclination (INC) of the wellbore trajectory, controllable drilling parameters, unconfined compressive strength (UCS), formation

Drilling deviated wells is a frequently used approach in the oil and gas industry to increase the productivity of wells in reservoirs with a small thickness. Drilling these wells has been a challenge due to the low rate of penetration (ROP) and severe wellbore instability issues. The objective of this research is to reach a better drilling performance by reducing drilling time and increasing wellbore stability.

In this work, the first step was to develop a model that predicts the ROP for deviated wells by applying Artificial Neural Networks (ANNs). In the modeling, azimuth (AZI) and inclination (INC) of the wellbore trajectory, controllable drilling parameters, unconfined compressive strength (UCS), formation