Constructing a geologically realistic reservoir model and accurately evaluating petrophysical properties are critical components for effective reservoir management. Such efforts enable reliable assessment of hydrocarbon potential and support predictive development scenarios through numerical simulations. This study aims to characterize the Yamama carbonate reservoir in the Ratawi Field, southern Iraq, and to build a static geological model as a foundation for future dynamic simulation. Comprehensive well data, including wireline logs, core analysis, and geological reports, were integrated to interpret petrophysical parameters such as shale volume, effective porosity, and water saturation. Additionally, facies classification was performed using cluster analysis to define lithological variability across the reservoir. A structural framework was established based on contour maps and well tops, followed by 3D property modeling using geostatistical techniques. Experimental variogram models were constructed for each facies and zone to enhance distribution accuracy. The Yamama Formation was confirmed -consistent with previous studies- to consist of five vertical units, three reservoir-bearing (YA, YB, YC) and two barriers (C1, C2). However, this study uniquely establishes an evaluation framework using data from eight wells penetrating the formation, unlike earlier studies constrained by limited wells. Among these units, YB demonstrated the most favorable properties, accounting for approximately 63% of the estimated 1800 MMSTB OOIP, consistent with operator reports. Overall, reservoir quality improves northwestward and declines eastward, with moderate characteristics in the southern sector.
Reliable estimation of critical parameters such as hydrocarbon pore volume, water saturation, and recovery factor are essential for accurate reserve assessment. The inherent uncertainties associated with these parameters encompass a reasonable range of estimated recoverable volumes for single accumulations or projects. Incorporating this uncertainty range allows for a comprehensive understanding of potential outcomes and associated risks. In this study, we focus on the oil field located in the northern part of Iraq and employ a Monte Carlo based petrophysical uncertainty modeling approach. This method systematically considers various sources of error and utilizes effective interpretation techniques. Leveraging the current state of a
... Show MoreReliable estimation of critical parameters such as hydrocarbon pore volume, water saturation, and recovery factor are essential for accurate reserve assessment. The inherent uncertainties associated with these parameters encompass a reasonable range of estimated recoverable volumes for single accumulations or projects. Incorporating this uncertainty range allows for a comprehensive understanding of potential outcomes and associated risks. In this study, we focus on the oil field located in the northern part of Iraq and employ a Monte Carlo based petrophysical uncertainty modeling approach. This method systematically considers various sources of error and utilizes effective interpretation techniques. Leveraging the current state of a
... Show MoreMandali Dam is one of the small dams in Iraq; it is located on Haran Wadi, Gangir, just 3km north-east Mandali City. Mandali dam consists of four main parts, the dam body, the intake structure, the spillway, and the bottom outlet. The dam body is zoned earth filled with a central core. The main purposes of the dam are to maintain flow of Wadi Haran, supplying irrigation and drinking water to Mandali City, and recharging the groundwater. Over a period of seven years of operation, the dam lost its ability to store water due to accumulated sediments within its reservoir. The accumulated sediment is about 2.25million m3. The average annual rate of reduction during this period is about 0.321
... Show MoreThe aim of this study is to investigate the feasibility of underground storage of gas in Um El-Radhuma formation /Ratawi field. This formation is an aquifer consisting of a high permeable dolomitebeds overlain by impermeable anhydrite bed of Rus formation. Interactive petrophysics (IP), Petrel REand Eclipse 100 softwares were used to conduct a well log interpretation, build a reservoir simulationmodel and predict the reservoir behavior during storage respectively. A black oil, three dimensionaland two phase fluid model has been used. The results showed that the upper part of Um El-Radhumaformation is suitable for underground gas storage, because of the seal of its cap rock and capability ofreserving gas in the reservoir. It was foun
... Show MoreThe estimation of the initial oil in place is a crucial topic in the period of exploration, appraisal, and development of the reservoir. In the current work, two conventional methods were used to determine the Initial Oil in Place. These two methods are a volumetric method and a reservoir simulation method. Moreover, each method requires a type of data whereet al the volumetric method depends on geological, core, well log and petrophysical properties data while the reservoir simulation method also needs capillary pressure versus water saturation, fluid production and static pressure data for all active wells at the Mishrif reservoir. The petrophysical properties for the studied reservoir is calculated using neural network technique
... Show MoreAchieving an accurate and optimal rate of penetration (ROP) is critical for a cost-effective and safe drilling operation. While different techniques have been used to achieve this goal, each approach has limitations, prompting researchers to seek solutions. This study’s objective is to conduct the strategy of combining the Bourgoyne and Young (BYM) ROP equations with Bagging Tree regression in a southern Iraqi field. Although BYM equations are commonly used and widespread to estimate drilling rates, they need more specific drilling parameters to capture different ROP complexities. The Bagging Tree algorithm, a random forest variant, addresses these limitations by blending domain kno
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
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