A total of 215 sheep and 87 goats were carefully searched for ixodid ticks from January to December 2015 at different regions of the middle and south of Iraq. The detached ticks count 1533 ticks from sheep with intensity of 8.4 and count 332 ticks from goats with intensity of 6.8. Tick species recovered from sheep and their incidence rates were: Rhipicephalus turanicus (39%), Hyalomma anatolicum (28%), R. (Boophilus) annulatus (11%), Hyalomma sp. (9%), H. turanicum (6%), H. excavatum (6%) and R. leporis (1%) while the tick species recovered from goats and their incidence rates were: R. turanicus (64%), H. anatolicum (24%)

Knowing the distribution of the mechanical rock properties and in-situ stresses for the field of interest is essential for many applications concerning reservoir geomechanics, including wellbore instability analysis, hydraulic fracturing, sand production, reservoir compaction, subsidence and water/gas injection throughout the filed life cycle. Determining the rock's mechanical properties is challenging because they cannot be directly measured at the borehole. The recovered carbonate core samples are limited and only provide discrete data for specific depths. This study focuses on creating a detailed 1D geomechanical model of the Mishrif reservoir in the Nasriyah oil field to identify the fault regime type for each unit in the format

Knowing the distribution of the mechanical rock properties and the far field stresses for the field of interest is an important task for many applications concerning reservoir geomechanics, including wellbore instability analysis, hydraulic fracturing, sand production, reservoir compaction, and subsidence. A major challenge with determining the rock's mechanical properties is that they cannot be directly measured at the borehole. Furthermore, the recovered carbonate core samples for performing measurements are limited and they provide discrete data for specific depths.

   The purpose of this study is to build 2D and 3D geomechanical models of the Khasib reservoir in the East Baghdad oil field/ Central area. TECHLOG.2015.3 softwa

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

The Paleocene benthic foraminiferal zonation of the Umm Er Rhadhuma Formation from the borehole (K.H 12/7), South Anah City (Western Iraq), has been re-studied and re-analyzed precisely based on the large benthic foraminifera (LBF). They are represented by two biozone Rotorbinella hensoni Partial Range Zone, recorded from the Lower and middle parts of the Umm Er Rhadhuma Formation and Lockhartia praehaimei Partial Range Zone determined Uppermost of this unit, and dated to be the Selandian – Thanetian stage. Almost all the biogenic (micro and macro) and non-biogenic constituents, including large benthic foraminifera, Algae, Echinoderm, Bryozoans, Oyster, Gastropod fragments, and peloids, in addition to lithofacies types, indicate t