The physical and elastic characteristics of rocks determine rock strengths in general. Rock strength is frequently assessed using porosity well logs such as neutron and sonic logs. The essential criteria for estimating rock mechanic parameters in petroleum engineering research are uniaxial compressive strength and elastic modulus. Indirect estimation using well-log data is necessary to measure these variables. This study attempts to create a single regression model that can accurately forecast rock mechanic characteristics for the Harth Carbonate Formation in the Fauqi oil field. According to the findings of this study, petrophysical parameters are reliable indexes for determining rock mechanical properties having good performance p

One of the most important problems in the oil production process and when its continuous flow, is emulsified oil (w/o emulsion), which in turn causes many problems, from the production line to the extended pipelines that are then transported to the oil refining process. It was observed that the nanomaterial (SiO₂) supported the separation process by adding it to the emulsion sample and showed a high separation rate with the demulsifiers (RB6000) and (sebamax) where the percentage of separation was greater than (90 and 80 )%  respectively, and less than that when dealing with (Sodium dodecyl sulfate and Diethylene glycol), the percentage of separation was (60% and 50%) respectively.

   The high proportion

    Building a geological model is an essential and primary step for studying the reservoir’s hydrocarbon content and future performance. A three-dimensional geological model of the Asmari reservoir in Abu- Ghirab oil field including structure, stratigraphy, and reservoir petrophysical properties, has been constructed in the present work. As to underlying Formations, striking slip faults developed at the flank and interlayer normal. Abu Ghirab oilfields are located on the eastern anticlinal band, which has steadily plunged southward. 3D seismic interpretation results are utilized to build the fault model for 43 faults of the Asmari Formation in Abu Ghirab Oilfield. A geographic facies model with six different rock facies types

The Zubair reservoir in the Abu-Amood field is considered a shaly sand reservoir in the south of Iraq. The geological model is created for identifying the facies, distributing the petrophysical properties and estimating the volume of hydrocarbon in place. When the data processing by Interactive Petrophysics (IP) software is completed and estimated the permeability reservoir by using the hydraulic unit method then, three main steps are applied to build the geological model, begins with creating a structural, facies and property models. five zones the reservoirs were divided (three reservoir units and two cap rocks) depending on the variation of petrophysical properties (porosity and permeability) that results from IP software interpr

Knowledge of permeability, which is the ability of rocks to transmit the fluid, is important for understanding the flow mechanisms in oil and gas reservoirs.
Permeability is best measured in the laboratory on cored rock taken from the reservoir. Coring is expensive and time-consuming in comparison to the electronic survey techniques most commonly used to gain information about permeability.
Yamama formation was chosen, to predict the permeability by using FZI method. Yamama Formation is the main lower cretaceous carbonate reservoir in southern of Iraq. This formation is made up mainly of limestone. Yamama formation was deposited on a gradually rising basin floor. The digenesis of Yamama sediments is very important due to its direct

Static reservoir modeling is the interacting and analysis of the geological data to visualize the reservoir framework by three-dimensional model and distribute the static reservoir properties. The Petrel E&P software used to incorporate the data. The interpreted log data and core report used in distribution of petrophysical properties of porosity, water saturation and permeability for Zubair reservoir in Luhais oil field.

   The reservoir discretized to 274968 cells in increments of 300, 200 and 1 meter in the direction of X, Y, and Z respectively. The geostatistical approach used in the distribution of the properties of porosity and water saturation overall the reservoir units. The permeability has been calculated

This research paper aimed to quantitively characterize the pore structure of shale reservoirs. Six samples of Silurian shale from the Ahnet basin were selected for nitrogen adsorption-desorption analysis. Experimental findings showed that all the samples are mainly composed of mesopores with slit-like shaped pores, as well as the Barrett-Joyner-Halenda pore volume ranging from 0.014 to 0.046 cm3/ 100 g, where the lowest value has recorded in the AHTT-1 sample, whereas the highest one in AHTT-6, while the rest samples (AHTT-2, AHTT-3, AHTT-4, AHTT-5) have a similar average value of 0.03 cm3/ 100 g. Meanwhile, the surface area and pore size distribution were in the range of 3.8 to 11.1 m2 / g and 1.7 to 40 nm, respectively.

Two oil wells were tested to find the abnormal pressure zones using sonic log technique. We found that well Abu-Jir-3 and Abu-Jir-5 had an abnormal pressure zones from depth 4340 to 4520 feet and 4200 to 4600 feet, respectively. The maximum difference between obtained results and the field measured results did not exceed 2.4%.
In this paper, the formation pressures were expressed in terms of pressure gradient which sometimes reached up to twice the normal pressure gradient.
Drilling and developing such formations were dangerous and expensive.
The plotted figures showed a clear derivation from the normal trend which confirmed the existence of abnormal pressure zones.

The investigation of machine learning techniques for addressing missing well-log data has garnered considerable interest recently, especially as the oil and gas sector pursues novel approaches to improve data interpretation and reservoir characterization. Conversely, for wells that have been in operation for several years, conventional measurement techniques frequently encounter challenges related to availability, including the lack of well-log data, cost considerations, and precision issues. This study's objective is to enhance reservoir characterization by automating well-log creation using machine-learning techniques. Among the methods are multi-resolution graph-based clustering and the similarity threshold method. By using cutti