...) and considered it a highly influencing factor. They did not consider the chip hold down effect. Bilgesu et al. (1997) generated data from drilling simulator including WOB, N, pumping rate, formation hardness and bit type to eliminate any errors contribution. They used artificial neural networks(ANN...

... Elkatatny Artificial Intelligence concentration input parameter abdulraheem eccentricity Support Vector Machine artificial neural network Mahmoud neural network society of petroleum engineers Upstream Oil & Gas Efficiency viscosity prediction empirical correlation correlation coefficient...

... exponent and m is 1.6 for practical usage. Artificial Intelligent Techniques: Artificial Neural Network: Artificial Neural Network is the tool used to recognize and determine the classification of complex systems which can’t be achieved using human brain ( Huang et al. , 1996 ; Castillo...

... of the apparent viscosity from the Marsh funnel viscosity. However, these models have the deficiency that the prediction is with high errors. For the first time, the solid percent was used to predict the rheological properties of the oil-based drilling fluid based on the artificial neural network using actual...

..., dual porosity, compositional reservoir simulation model. An algorithm is developed to capture varying fluid compositions and relative permeability data for the data set used in this study. Data collected from reservoir simulation cases are used to construct two Artificial Neural Network (ANN) based...

... funnel viscosity (MFV) and solid percent). D, MFV, and SV are measured frequently every 15-20 minutes in the well site. Artificial neural network (ANN) was used to build different models for PV, YP, AV, and n based on 3000 field data measurements. ANN was able to predict the rheological properties...

... with high level of certainty. neural network transfer function Upstream Oil & Gas two-phase flow machine learning abdulraheem correlation Artificial Intelligence mechanistic model optimization artificial neural network society of petroleum engineers FBHP Tariq multiphase flow...

... published literature papers and conventional PVT reports. Statistical analysis was performed to see which of these methods are more reliable and accurate method for predicting the inflow performance relationship for the gas reservoir. The FL model outperformed the artificial neural network (ANN) model...

... Abstract Objective The objective of this paper is to introduce a new methodology to increase the accuracy of the Artificial Neural Network (ANN) by improving the selection criteria of training dataset. Such approach will result in faster and better prediction models. Methods and Procedures...

... of artificial intelligence (AI) in the drilling applications will be a game changer since most of the unknown parameters are accounted for during the modeling or training process. The objective of this paper is to develop a rate of penetration model using artificial neural network (ANN) with the least possible...

.... In this study, five different artificial intelligence techniques are implemented to predict Z-factor. These techniques are neural network, radial basis function network, fuzzy logic, functional network, and support vector machine. To build and test these techniques, Standing-Katz charts data was used in which...

... evaluations. Several types of AI method were implements such as artificial neural network (ANN), fuzzy logic and support vector machine (SVM) ( Sargolzaei et al., 2006 ). Elkatatny et al., (2016) applied ANN technique to estimate the rheological properties of drilling fluids while drilling operations...

... analyses were conducted including using artificial neural network (ANN) to achieve the best results and prediction accuracy. Well-A was used to train and test the data with 70/30 ratio, while well-B was totally unseen data. The results obtained showed that ANN can predict formation tops with great...

... fracturing strength machine learning neural network Elkatatny closure stress rock strength neuron artificial neural network grid correlation prediction acid experimental data Artificial Intelligence Upstream Oil & Gas society of petroleum engineers fuzzy inference system variance...

... , M.A. , Al-Marhoun , M.A. 2013 . Application of Neural Network for Two-Phase Flow through Chokes . SPE Saudi Arab. Sect. Tech. Symp. Exhib. doi: 10.2118/169597-MS AL-Qutami , T.A. , Ibrahim , R. , Ismail , I. , Ishak , M.A. 2017 . Development of Soft Sensor To Estimate...

... Wellbore Design Engineering neural network predict fracture pressure drilling parameter average absolute percentage error correlation coefficient functional network penetration drilling fluid formulation drilling fluids and materials mud weight prediction fracture pressure society...

... exists among log analysts about which model can be universally used. The use of computer generated algorithms, fuzzy logic and neural networking is picking up pace in the petroleum industry. Consequently, in this paper we show how Machine Learning can be used to generate a correlation, to determine...

... parameters on the variations of torque and rate of penetration. Drilling parameters such as weight on bit (WOB), revolution per minute (RPM), fluid circulation rate (Q), and bit hydraulic horse power (HPb) has been studied. Thereafter, artificial neural network (ANN) model was developed to predict the torque...

... Engineering drilling parameter pressure prediction pore pressure prediction Elkatatny society of petroleum engineers coefficient neural network correlation prediction formation pressure penetration artificial neural network predict formation pressure Mahmoud neural network Introduction...

... of artificial networks that combining fuzzy logic with the neural network. ANFIS has the capability to extract the benefits of AI techniques in a single or multi framework(s). The model parameters are optimized or tuned using hybrid algorithm and sugeno-fuzzy inference system (FIS), by applying back-propagation...