The advanced technology has made directional drilling widely used to enhance the production of mature fields. The rate of penetration (ROP) contributes strongly towards the cost of drilling operations, where achieving higher ROP leads to substantial cost saving. The main objective of this study is to develop a model that predicts the ROP for deviated wells using artificial neural networks (ANNs). The model was developed based on the most critical variables affecting ROP using ANNs. In addition to the azimuth and inclination of the well trajectory, the controllable drilling parameters, unconfined compressive strength (UCS), pore pressure, and in-situ stresses of the studied area were included as inputs. 1D Mechanical earth modeling (1D-MEM) data, geophysical logs, daily drilling reports, and mud logs (master logs) of deviated wells drilled in Zubair field located in Southern Iraq were used to develop the ANN model. The results displayed that the ANN’s outputs are close to the measured field data. The correlation coefficient (R) and average absolute percentage error (AAPE) were over 0.91 and 8.3%, respectively, for the training dataset. For testing data, the developed model achieved a reasonable correlation coefficient (R) of 0.89 and average absolute percentage error (AAPE) of 9.6%. Unlike previous studies, this paper investigates the effect of well trajectory’s (azimuth and inclination) and their influence on the ROP for deviated wells. The major advantage of the present study is calculating approximately the drilling time of the deviated well and eventually reducing the drilling cost for future neighboring wells.

Drilling deviated wells is a frequently used approach in the oil and gas industry to increase the productivity of wells in reservoirs with a small thickness. Drilling these wells has been a challenge due to the low rate of penetration (ROP) and severe wellbore instability issues. The objective of this research is to reach a better drilling performance by reducing drilling time and increasing wellbore stability. In this work, the first step was to develop a model that predicts the ROP for deviated wells by applying Artificial Neural Networks (ANNs). In the modeling, azimuth (AZI) and inclination (INC) of the wellbore trajectory, controllable drilling parameters, unconfined compressive strength (UCS), formation pore pressure, and in-situ stresses of the studied area were included as inputs. The second step was by optimizing the process using a genetic algorithm (GA), as a class of optimizing methods for complex functions, to obtain the maximum ROP along with the related wellbore trajectory (AZI and INC). Finally, the suggested azimuth (AZI) and inclination (INC) are premeditated by considering the results of wellbore stability analysis using wireline logging measurements, core and drilling data from the offset wells. The results showed that the optimized wellbore trajectory based on wellbore stability analysis was compatible with the results of the genetic algorithm (GA) that used to reach higher ROP. The recommended orientation that leads to maximum ROP and maintains the stability of drilling deviated wells (i.e., inclination ranged between 40°—50°) is parallel to (140°—150°) direction. The present study emphasizes that the proposed methodology can be applied as a cost-effective tool to optimize the wellbore trajectory and to calculate approximately the drilling time for future highly deviated wells.

The paper focuses on studying activated carbon and its uses then producing activated carbon using lote wood that is available in Iraq through the process of carbonization. Also, the paper defines the best temperature to produce huge size of coal. Then use the process of carbon activation through Physical activation. The latter process can be done through steam heating or oxygen for 1000 C°. After that, several measurements have been done to measure the efficiency of activated carbon, and other physical checks related to carbon like humidity and percentage of ash.

Nanofluids are suspensions of nanoparticles with a minimum size of 100 nanometers or less. A decade of investigation into this composite material’s wet-spreadin