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

The Khabour reservoir, Ordovician, Lower Paleozoic, Akkas gas field which is considered one of the main sandstone reservoirs in the west of Iraq. Researchers face difficulties in recognizing sandstone reservoirs since they are virtually always tight and heterogeneous. This paper is associated with the geological modeling of a gas-bearing reservoir that containing condensate appears while production when bottom hole pressure declines below the dew point. By defining the lithology and evaluating the petrophysical parameters of this complicated reservoir, a geological model for the reservoir is being built by using CMG BUILDER software (GEM tool) to create a static model. The petrophysical properties of a reservoir were computed using

   Predicting permeability is a cornerstone of petroleum reservoir engineering, playing a vital role in optimizing hydrocarbon recovery strategies. This paper explores the application of neural networks to predict permeability in oil reservoirs, underscoring their growing importance in addressing traditional prediction challenges. Conventional techniques often struggle with the complexities of subsurface conditions, making innovative approaches essential. Neural networks, with their ability to uncover complicated patterns within large datasets, emerge as a powerful alternative. The Quanti-Elan model was used in this study to combine several well logs for mineral volumes, porosity and water saturation estimation. This model goes be

This study included the extraction properties of spatial and morphological basins studied using the Soil and Water Assessment Tool (SWAT) model linked to (GIS) to find the amount of sediment and rates of flow that flows into the Haditha reservoir . The aim of this study is determine the amount of sediment coming from the valleys and flowing into the Haditha Dam reservoir for 25 years ago for the period (1985-2010) and its impact on design lifetime of the Haditha Dam reservoir and to determine the best ways to reduce the sediment transport. The result indicated that total amount of sediment coming from all valleys about (2.56 * 10⁶ ton). The maximum annual total sediment load was about (488.22 * 10³ ton) in year 1988

Shehadt 683 fish of different fish present in the tank Haditha Dam during the period of February 1993 and the end Maes 1994 to identify the nutritional content of the digestive tract or the stomach for these fish and their impact on the environment of the tank and found that Fish fine linen and Alhalg animal feeding predators feed on small fish, mainly the fish brown plantnutrition

    Geodiversity is the variety within abiotic natural elements that include: rocks, minerals, landforms, soil types, and water resources. Recently ecologists and naturalists recognized that there is close relationship between geodiversity and ecosystems. Huwaiza marsh is located south eastern Iraq within Lower Mesopotamian plain. The main rock bed units which crop out north east of the studied area comprises many types of rocks: conglomerate, sandstone, mudstone, siltstone and claystone belong to Bai Hassan, Mukdadiya and Injana Formations. The general elevation of the area ranges around 5 meters (a. s. l.) near the marsh and increase northeast to more than 100 meters (a. s. l.) and the Land forms are: cuesta, oxbow lakes