Field experience shows that the cutting transportation and Hole-Cleaning phenomena are essential during the drilling phase. Particularly in directional drilling, when the accumulation of cutting has caused some drilling problems such as drill string sticking, formation failure, slow rate of penetration, drill bit abrasion, and the like. Through the study, a novel method for efficient Hole Cleaning, considering different parameters such as flow rate, the drill bit nozzles’ flow area, the consistency and flow behavior indices in the same time using PSO and ACO algorithms were implemented. Moreover, Power Law has been considered for the fluid rheology model. Based on this, the research parameter shows that the PSO algorithm is much more accurate than the ACO algorithm, improving objective function by 50% and 4%, respectively. The performance of each algorithm was evaluated, and the results show that Hole Cleaning has been significantly improved. The flow rate and the bit nozzle size, which play key roles, were selected as optimization variables. Effective parameters on Hole Cleaning were evaluated, and the results before and after optimization showed a significant improvement in the model. The PSO and ACO algorithms have been coded in MATLAB software, and the results are compared to the results of the ant colony. The amount of PV and YP has an inverse effect on the increment of minimum velocity required for cutting transport. Various model analyses reveal that the PSO algorithm is more accurate and robust than the Ant colony algorithm.

Hole Cleaning is one of the most important aspects of extractive drilling operations. When drill cuttings combine with drilling fluids, cutting bed gel is created, which becomes extremely challenging to remove while drilling. One of the functions of drilling fluid when the flow is static is to maintain the drill bits' suspension. However, the settling of drill bits makes this nearly impossible. In this paper, the influence of fluid viscosity on Hole Cleaning performance was critically studied through a series of laboratory experiments. The effects of different viscosity levels on Hole Cleaning were investigated. This paper evaluated the use of lecithin and carboxymethyl hydroxyethyl cellulose (CMHEC) as a viscosifier and fluid loss agent in oil-based drilling fluid. The rheological properties of the formulated drilling fluids were determined at various temperatures according to the API 13B-2 specifications in order to identify the most suitable mud type for high-temperature drilling operations. From this study, it was discovered that Formulation B has the best rheology, with a plastic viscosity (PV) of 12, 7, and 7 cP at 30, 40, and 50 °C, respectively, while its yield point (YP) was found to be 30, 24, and 16, respectively. These values were compared with the generic oil-based formulation (A), which had a plastic viscosity (VP) of 43, 39, and 22 cP at 30, 40, and 50 °C, respectively, while its yield point (YP) was found to be 38, 20, and 26, respectively. The findings show that formulation B has a significant resistance to high temperatures, which means that it can be applied to drilling operations in reservoirs with high temperatures. It is more environmentally friendly. Furthermore, this paper explores the implications of the results in terms of the efficiency of Hole Cleaning and the drilling process relative to the viscosity of oil-based drilling fluid. This paper concludes by providing suggestions for improving drilling efficiency through the selection of an appropriate drilling fluid viscosity.

A difficult problem in drilling operation that concerns the very drilling parameters is the cutting transport process. Correct calculation of the cuttings concentration (Hole Cleaning efficiency) in the wellbore annulus using drilling variables such as the geometry of wellbore, rheology, and density of drilling fluid, and pump rate is very important for optimizing these variables. In this study, a hybrid evolutionary fuzzy system (EFS) using artificial intelligent (AI) techniques is presented for estimation of the cuttings concentration in oil drilling operation using operational drilling parameters. A well-organized genetic learning algorithm that computes fitness values by symbiotic evolution is used for extraction of the Takagi– Sugeno– Kang (TSK) type fuzzy rule-based system for the EFS. A determination coefficient (R2) of 0. 877 together with a root mean square error (RMSE) of 1. 4 between prediction and measured data for test data verified a very satisfactory model performance. Results confirmed that the estimation accuracy of the proposed EFS is better than other models such as Multiple Linear Regression (MLR), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) for Hole Cleaning modeling.

In this paper, results obtained from investigations on a typical reverse osmosis (RO) membrane fouled by precipitation of materials which exist in water are presented. In these studies, effect of different Cleaning agents on recovery of the fouled membrane has been shown. Results show that a combination of sodium dodecyl sulfate, Na-EDTA and sodium hydroxide must be used as a Cleaning material to reach the optimum recovery of the polyamide membrane employed in petrochemical industries. It was found that Cleaning efficiency depends on type of the Cleaning agent and its concentration. It has been shown that the efficiency increases with increasing the concentration of Cleaning agent and reaches to a maximum value. The concentration that provides the highest Cleaning efficiency can be considered as the optimum concentration. This depends on the type of Cleaning agent. Operating conditions such as cross flow velocity, turbulence in the vicinity of the membrane surface, temperature, pH and Cleaning time also play an important role in the Cleaning processes. membrane Cleaning requires deep understanding of the interactions between the foulants and the membrane as well as the effect of the Cleaning procedure on deposit removal. In this paper the mechanism of deposit removal is also investigated.