Meysam Naderi

TL;DR: In this paper, the simultaneous effect of six variables on penetration rate using real field drilling data has been investigated, and the bat algorithm was used to identify optimal range of factors in order to maximize drilling rate of penetration.

TL;DR: Three meta–heuristics algorithms of genetic algorithm, particle swarm optimization and bat inspired algorithm are used for optimal determination of six production well locations and the simulation results show that the BA is superior since it reduces the number of functional evaluations and thus improving the computational efficiency.

TL;DR: Error analysis proves that the BA optimized method is superior over all other forecasting methods in terms of highest R2 and lowest RMSE, and construction of LSSVM, ARIMA, ANN, and GP has better prediction ability, respectively.

TL;DR: In this article, computational fluid dynamic (CFD) and design of experiment (DOE) techniques were used to predict the cutting transport efficiency as a function mud velocity (Vm), drilling rate of penetration (ROP), mud weight (MW), cutting weight (CW), mud viscosity (μm), pipe rotational speed (N), and cutting size (CS).

TL;DR: In this paper, a metaheuristic bat inspired algorithm (BA) was applied in optimization process of objective function for the determination of optimal number of wells (N), individual well locations (I and J), production rate (Q g ), perforation thickness (H p ), and the limit of tubing head pressure (THP) for different average reservoir permeability (K G ) and permeability anisotropy (K v /K h ).