According to field data, there are several methods to reduce the drilling cost of new wells. One of these methods is the optimization of drilling parameters to obtain the maximum available rate of penetration (ROP). There are too many parameters affecting on ROP like hole cleaning (including drillstring rotation speed (N), mud rheology, weight on bit (WOB) and floundering phenomena), bit tooth wear, formation hardness (including depth and type of formation), differential pressure (including mud weight) and etc. Therefore, developing a logical relationship among them to assist in proper ROP selection is extremely necessary and complicated though. In such a case, Artificial Neural Networks (ANNs) is proven to be helpful in recognizing complex connections between these variables. In literature, there were various applicable models to predict ROP such as Bourgoyne and Young's model, Bingham model and the modified Warren model. It is desired to calculate and predict the proper model of ROP by using the above models and then verify the validity of each by comparing with the field data. To optimize the drilling parameters, it is required that an appropriate ROP model to be selected until the acceptable results are obtained. An optimization program will optimize the drilling parameters which can be used in future works and also leads us to more accurate time estimation. The present study is optimizing the drilling parameters, predicting the proper penetration rate, estimating the drilling time of the well and eventually reducing the drilling cost for future wells.

According to the field data, there are several methods to reduce the drilling cost of other wells. One of these methods is the optimization of drilling parameters to obtain the maximum available ROP. Considering the geology and rock mechanic parameters, each part of well has different recommended parameters. There are too many parameters affecting in rate of penetration like hole cleaning (including drillstring rotation speed, mud rheology, weight on bit and floundering phenomena), tooth wear, formation hardness (including depth and kind of formation), differential pressure (including mud weight) and etc. Therefore, developing a logical relationship among them to assist in proper ROP selection is extremely necessary and complicated though. In such a case, Artificial Neural Networks (ANNs) is proven to be helpful in recognizing complex connection between these variables. Genetic Algorithm (GA), as a class of optimizing methods for the complex functions, is applied to help ROP optimization and its related drilling parameters. Optimization program will optimize drilling parameters which will be used in future works and also leads us to proper time estimation. The present study is predicting the proper penetration rate, optimizing the drilling parameters, estimating the drilling time of well and eventually reducing the drilling cost for future wells.

There is no deep understanding of the application of nanoparticles in water-based muds (WBM). Therefore, such study that helps to enhance the knowledge in the field of well stability using modern methods in an unforgiving industry is very much needed. The nanoparticles accumulate on the wellbore wall and due to their very small sizes, they seal the pores in the mud cake, which plasters the wellbore. This paper focuses on empirical aspects of using nano-bentonite for filter loss control. The current work was applied on a nano-drilling fluid to improve filtration characteristics of drilling fluid in the wellbore. Therefore, nano-bentonite WBM (size between 90 and 100 nm) was introduced as the smart drilling fluid with abilities to overcome the tight spot problem in wellbores, which has been investigated in the paper. Three different drilling fluids were prepared using nano-bentonite clay with the main focus of enhancing those rheological features of the fluid expected to improve the mud characteristics, especially the plastering properties. Low pressure low temperature (LPLT) filter press test has been utilized to calculate the filter loss volume and the viscosity, yield point and gel strength of the understudy samples and the results have been compared. It was found that the filtration loss during the LPLT test was reduced by an overall average of 34% for all of the three samples, resulting in better filtration characteristics.

Two phase flow applications in petroleum industry are so widespread. It is a fact that UBD precise bottomhole pressure maintenance ascertains UBD success. UBD hydraulics design, especially for inclined trajectories, is a real challenge. This is greatly dependent on the pressure drop in the annulus. Two phase flow through annulus is an ambiguous area of study to evaluate the bottomhole pressure. Two phase flow correlations on which most of UBD simulators based on over predict and also make extrapolation risky. Although Mechanistic approaches increase the frequency for designing two phase flow systems in pipes, modeling them through annulus by using the hydraulic diameter concept is not so successful. For this reason, their corresponding errors are not small. Therefore, in this paper, Artificial Neural Network is made use of to evaluate BHP in the inclined annulus using two major Iranian Oil Fields. To compare BHP found by neural network, Naseri et al mechanistic model which is a popular mechanistic model for these two fields is applied. ANN shows to perform much better than Naseri et al mechanistic model. The results show that neural network can estimate bottomhole pressure with an error of less than 20%. This proves that in case of existence of measured BHP while under balanced drilling, it is worth to use ANN to simulate BHP rather than mechanistic modeling or correlations. ANN is highly shown to be useful for solving the non-straightforward problem of two phase flow in annulus. Few jobs have been done to prove the superiority of ANN to mechanistic modeling and correlations in terms of pressure prediction especially in under balanced drilling.

Injection wells have long been an essential asset in enhanced oil recovery, wastewater disposal and carbon dioxide sequestration in petroleum industries. The temperature profile of fluid flow in the injection well is one of the main parameters of interest for petroleum engineers to determine optimum injection conditions and wellbore completion design especially in thermal injection projects and deep wells. In this study, the calculation involved in determining the temperature profile along the depth of wellbore has been revised to be newer and more robust via solving governing wellbore equations. The wellbore is segmented into discrete counterparts for it to be solved simultaneously in terms of mass, momentum and energy balance via wellbore governing equations. Five injection cases from the literatures, incompressible and compressible fluid flows, were used to confirm that the procedure is reproducible in terms of its behaviour, which is similar to field data. The new results acquired from the new procedure are in good agreement with field data collected with a maximum absolute error less than 3 °C.