Iraqi Journal of Oil and Gas Research

The problems that accompany the drilling process of oil wells are often due to the different balance of ground stresses around the borehole and resulting from drilling operations inside the well, as well as because of the interactions between the drilling fluid and the rock formation. Even in very carefully planned wells, problems will almost certainly arise during the drilling of the well. For example, in areas where similar drilling practices are used, bore problems may be reported because formations are heterogeneous even though such problems have not occurred before. In well planning, the key to successfully achieving the objectives is to design the drilling program on the basis of anticipating potential drilling problems rather than being cautious or contained. Drilling problems can be very costly, so the design of drilling program must be carefully studied. The most common drilling problems included: Pipe sticking, drilling mud loss (Loss of circulation), Hole deviation, Drill pipe failures, Borehole instability, Formation damage, Hole cleaning, H2S fulcrum areas, Shallow gas zones, Equipment related drilling problems, and mud contamination. In this review, the most common well problems that are encountered during the drilling of oil and gas wells will be highlighted. The compilation of oil field data and previous studies related to the subject, as well as the field knowledge of the drilling supervisors, were all the information centers for this study. Especially the most important problems occurring in Iraqi fields and the solutions that these problems were dealt with were discussed in this review

Cost estimation is an essential part of every drilling project's well planning, and it involves predicting the rate of penetration (ROP) accurately. The ROP represents the amount of time required to drill a given depth, and maximizing it helps minimize the costs associated with the drilling budget. However, predicting the ROP accurately is challenging because it depends on numerous variables, including drilling parameters, drilling fluid properties, and drilled formation characteristics.
One approach to improving the accuracy of ROP prediction is by using machine learning techniques, such as gradient boosting. In a recent study conducted in the Rumaila oilfield, the researchers used gradient boosting to predict the ROP based on drilling operation parameters and drilling fluid properties for two wells used for training and testing and one well used for implementation. The results of the study showed that gradient boosting was successful in predicting the ROP, with R2 training and testing values of 0.9947 and 0.8611, respectively. This means that the model was highly accurate and could be used to improve cost estimation in drilling projects.
Overall, the use of machine learning techniques such as gradient boosting can help enhance the accuracy of cost estimation in drilling projects by predicting the ROP more accurately, minimizing the costs associated with the drilling budget, and improving the overall efficiency of the drilling process.

Asphaltene precipitation and deposition have a detrimental influence on a variety of oil and gas activities, including oil recovery, oil transportation, and petroleum processing. Modeling and predicting phase behavior and asphaltene precipitation are crucial since the Halfaya oil field is still in the early stages of development. Because of the significant expense of remediating asphaltene deposition in crude oil production and processing, screening techniques for assessing the stability of asphaltenes in crude oil have been developed. In this work, the Asphaltene-Resin Ratio, the Colloidal Stability Index, and the Modified Colloidal instability Index are utilized to predict the stability of asphaltene in crude oil. These techniques, as well as the experimental data derived from them, are described in detail. The parameters that control asphaltene precipitation vary from well to well, from high-pressure, high-temperature reservoirs to surface conditions, and must be investigated on an individual basis. Using Multiflash software, this study created a thermodynamic model for bottom-hole crude oil samples from a well in the Halfaya oil field/Nahr-Umr formation. Compositional analysis, PVT data, and reservoir conditions were among the data. The Cubic-Plus Association equation of state was used to develop a thermodynamic model of asphaltene phase behavior. All results indicate the presence of asphaltene precipitation problems. The conclusions of all screening techniques indicate the presence of an asphaltene precipitation problem (asphaltene instability), which was validated by a thermodynamic fluid model.

The kick is a prevalent problem related to drilling operations. Therefore, to reduce the likelihood of a blowout, it is essential to identify the kick as quickly as possible. Using a kick detection technique that depends mainly on monitoring surface parameters, there is a high likelihood of kick detection delay in addition to other problems. Techniques for down-hole monitoring may be capable of recognizing a kick in its early stages. Based on relative variations in the fluid's down-hole conductivity and flow rate, this study evaluates the occurrence of a kick. Moreover, there is provided a method to identify a kick without raising false alarms. In addition to, the variables most sensitive to formation fluid. In order to monitor the down hole parameters, three different instruments are used: density meter, the conductivity sensor, the Coriolis flow and the pressure transmitter. These results are place to use to evaluate the likelihood of the kick and risk of an associated blowout. The accurate interpretation of kick occurrence may be improved by monitoring a variety of down-hole data, which also greatly reduces on false alarms and gives a comprehensive view of down-hole situations. The kick detection time may be minimized by using down-hole monitoring methods. Well NR-C was selected to study the problem of the kick in Nahr Umr field. The result shown that the electrical conductivity reduced to 40 µs/cm when kick occurs and represents the best method to early kick detection combined with mud density.

The initial oil in place (IOIP) is an important factor that controls economic planning for production and field life. Therefore, this parameter must be determined precisely and carefully. The present paper is dealt with study the impact of adopting two different strategies of property modeling on IOIP value. 3D geological model constructed by using information likewise: contour map, oil formation volume factor, control processing interpretation (CPI) of well logs, and well heads and tops of twelve wells for selected field. The chosen oil field is feeding from Mishrif reservoir and located in southern Iraq. Mishrif reservoir is classified to six layers: MA, MB11, MB12, MB21, MC1, and MC2. Two strategies for distributing petrophysical properties: porosity, water saturation, and net to gross thickness of Mishrif reservoir are utilized. The two strategies are sequential Gaussian simulation and moving average. The volumetric method is used for estimating IOIP values. The impact of using the two strategies of property modeling was very clear where the values of IOIP had a significant difference. The IOIP value of 3D model whose petrophysical properties are distributed using moving average strategy is 5.145 billion barrels, while the 3D model distributed by Gaussian strategy had IOIP equal to 4.195 billion barrels. According to obtained results, the choice of distribution method is very important in estimation of IOIP. Selection of optimal property modeling strategy need to statistical comparison with selected oil field reports and input data, so finally, the closest representation of a protective reservoir and accurate value of IOIP.

Hydrocarbon exploration and production heavily rely on understanding the petrophysical properties and spatial distribution of reservoir rocks. This study delves into an in-depth analysis of the AB unit's petrophysical attributes, with a specific focus on its spatial distribution and lithological identification. The research encompasses a comprehensive investigation of essential petrophysical properties, including porosity, permeability, and fluid saturation, shedding light on their significance in effective reservoir evaluation and optimized hydrocarbon extraction. By harnessing the power of well logs, encompassing gamma-ray, resistivity, density, and neutron logs, we unravel the intricate nature of the AB unit's petrophysical characteristics. This comprehensive characterization is further corroborated and validated through rigorous statistical analysis. Moreover, an exploration into the spatial distribution of these petrophysical attributes is undertaken, employing the sophisticated geostatistical technique known as Ordinary Kriging. The results of this analysis reveal distinct patterns of porosity, permeability, and lithology, providing valuable insights for reservoir management and decision-making. The findings of this study contribute significantly to a holistic understanding of the AB unit's reservoir potential, thereby guiding prudent exploration, production, and development strategies. Results showed that most reservoirs contain hydrocarbons. Porosity throughout the reservoirs ranged from 0.06 to 0.20; permeability ranged from 1.58 to 624.34md; and average hydrocarbon saturation was 68.14%. These findings point to a reservoir system with a significant hydrocarbon potential and acceptable performance for hydrocarbon production.

In this paper, the importance of using well logging data in well completion take into consideration, specifically for detection of well optimum perforation interval by using well logs data of (NS-1) well in Nasiriyah oil field in Yamama formation, which is detected process done by using the available well logging recorders such as (Gamma Ray log, SP log, RHOB log, NPHI log and Resistivity logs), the environmental correction has been done for available logs recording using Interactive Petrophysics software (IP) which used to determine Petrophysical Properties, further, construct a geological model by (petrel) software which used to detect the Water Oil Contact (WOC) in the geological structure to avoid to perforate nearby zone. The results show that the average Porosity is (11.2 %) and average Permeability is (12 md), whereas the minimum water saturation is (20.5%) at depth interval (3170-3230 m), while the WOC is located at depth (3378 m). Finally the results shows the optimum perforation zone is located at depth interval (3190-3210 m).