Five subsurface sections and a large number of thin sections of the Hartha Formation (age Late Campanian – Early Maastrichtian) were studied to unravel the depositional facies and environments. The Hartha Formation is important as an oil reservoir in Iraq.

Petrographic and microfacies analysis of selected wells from Balad and East Baghdad oil fields in Central Iraq, enable the recognition of three main Sedimentary paleoenvironments. These are restricted marine, the shallow open marine environment within the inner ramp, deep outer ramp.

The studied Formation represents by two asymmetrical cycles bounded below by sequence boundary (SB1) the contact between Hartha and Saadi Formations. The deep outer ramp facies of

The main objective of this study is to develop a rate of penetration (ROP) model for Khasib formation in Ahdab oil field and determine the drilling parameters controlling the prediction of ROP values by using artificial neural network (ANN).

     An Interactive Petrophysical software was used to convert the raw dataset of transit time (LAS Readings) from parts of meter-to-meter reading with depth. The IBM SPSS statistics software version 22 was used to create an interconnection between the drilling variables and the rate of penetration, detection of outliers of input parameters, and regression modeling. While a JMP Version 11 software from SAS Institute Inc. was used for artificial neural modeling.

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The downhole flow profiles of the wells with single production tubes and mixed flow from more than one layer can be complicated, making it challenging to obtain the average pressure of each layer independently.  Production log data can be used to monitor the impacts of pressure depletion over time and to determine average pressure with the use of Selective Inflow Performance (SIP). The SIP technique provides a method of determining the steady state of inflow relationship for each individual layer. The well flows at different stabilized surface rates, and for each rate, a production log is run throughout the producing interval to record both downhole flow rates and flowing pressure. PVT data can be used to convert measured in-situ r

The downhole flow profiles of the wells with single production tubes and mixed flow from more than one layer can be complicated, making it challenging to obtain the average pressure of each layer independently.  Production log data can be used to monitor the impacts of pressure depletion over time and to determine average pressure with the use of Selective Inflow Performance (SIP). The SIP technique provides a method of determining the steady state of inflow relationship for each individual layer. The well flows at different stabilized surface rates, and for each rate, a production log is run throughout the producing interval to record both downhole flow rates and flowing pressure. PVT data can be used to convert measured in-situ rates

     This study was the first of its kind on the Dejiala River, which is considered one of the main branches of the Tigris River in Wasit Province. Therefore, the study aimed to investigate of some physical and chemical properties of water in the Dejiala River. Monthly sampling stations were conducted for 12 months, which was starting from January to December 2016, during those five stations was chosen which divided along about 58 Km of river; each station was located at a distance of ±10 Km. The results of the study showed a clear correlation between air and water temperature in all stations. Turbidity was recorded a value ranging from 2.36-116 NTU. It was found that the water of the Dejiala was Oligohaline, weak al

A 3D Geological model was generated using an advanced geostatistical method for the Cretaceous reservoir in the Bai Hassan oil field. In this study, a 3D geological model was built based on data from four wells for the petrophysical property distribution of permeability, porosity, water saturation, and NTG by using Petrel 2021 software. The geological model was divided into a structural model and a property model. The geological structures of the cretaceous reservoir in the Bai Hassan oil field represent elongated anticline folds with two faults, which had been clarified in the 3D Structural model. Thirteen formations represent the Cretaceous reservoir which includes (Shiranish, Mashurah, U.kometan, Kometan Shale, L. Kometan, Gulnen

Net pay is one of the most important parameters used in determining initial oil in place of a reservoir. It can be delineated through the using of limiting values of the petrophysical properties of the reservoir. Those limiting values are named as the cutoff. This paper provides an insight into the application of regression line method in estimating porosity, clay volume and water saturation cutoff values in Mishrif reservoir/ Missan oil fields. The study included 29 wells distributed in seven oilfields of Halfaya, Buzurgan, Dujaila, Noor, Fauqi, Amara and Kumait.

This study is carried out by applying two types of linear regressions: Least square and Reduce Major Axis Regression.

The Mishrif formation was

     Akkas Field is a structural trap with a sandstone reservoir that contains proven gas condensate. The field is a faulted anticline that consists of the Ordovician Khabour Formation. The objective of this research is to use structural reservoir characterization for hydrocarbon recovery. The stratigraphic sequence of the Silurian and older strata was subjected to an uplift that developed a gentle NW-SE trending anticline. The uplifting and folding events developed micro-fractures represented by tension cracks.  These microfractures, whether they are outer arc or release fractures, are parallel to the hinge line of the anticline and perpendicular to the bedding planes. The brittle sandstone laye

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F