... AAPE coefficient artificial neural network Mahmoud ann model water-based drilling fluid empirical correlation neuron denormalized value Introduction Tradition measurements of drilling fluid’s rheological properties are time-consuming and also cannot be aligned with the real time...

... study is conducted to identify the formation density from the drilling surface parameters. Several ANN models, including the proposed method, are constructed on the same dataset. Results show that the new methodology can be 85% faster and 18% more accurate than the traditional ANN on average...

.... Different model's parameters were used to optimize the intelligent networks, average absolute error and correlation coefficient were utilized to measure the model performance. ANN model showed the best prediction performance, an average absolute error of 6.2 % and a correlation coefficient of 0.98...

... importantly, we present a project screening protocol that couples the expert system and particle swarm optimization ( PSO ) methodology to maximize the net present value ( NPV ) of polymer injection projects. In this way, we take the advantages of the fast computational speed of the ANN model to evaluate...

... empirical correlation for bubble point pressure (BPP) prediction using artificial intelligent techniques (AI) such as; artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and supper vector machine (SVM). For the first time we changed the ANN model to a white box by extracting...

... ANN structure, it consists of one input layer, one or several hidden layers (one layer was used in this work) and one output layer. 442 data sets from Barnett formation were used to train the ANN model, the model was trained using as an input: (1) Resistivity log (R ILD ), (2) Sonic transit time...

... data and try to correlate the varying trends of input parameters to give the required output. The resulting physical form of ANN model will be the respective correlation between the inputs and output. For horizontal wells, ( Chaperon, 1986 ; Giger, 1989 ; Guo and Lee, 1992 ; Ozkan and Raghavan...

... neural networks (ANN) are used to predict Z-factor. Data used for constructing the Standing-Katz charts and experimental data from the literature were used to build the ANN model and evaluate the quality of the new model compared with the other methods. Data used for constructing the Standing-Katz...

... r ≤ 0.35 Low or Weak Empirical Study Experimental set-up Upstream Oil & Gas Artificial Intelligence machine learning neural network Reservoir Characterization ANN model reservoir society of petroleum engineers olatunji neuron correlation-based feature selection...

... Abstract In this study, field data from Saudi Arabian gas condensate reservoirs were used to develop Artificial Neural Network (ANN) model to estimate gas rate from wells having Condensate to Gas Ratio (CGR) ranging from 10 to 400 STB/MMSCF. This model can predict gas rates for both critical...

... network (ANN) model was developed to predict permeability from the MICP measurements. The neural network consists of two hidden layers with 15 neurons each and one output layer. A dataset of 206 core samples were used to train the ANN model. The dataset was divided into three sets: 70% for training, 15...

... application graph choke size prediction coefficient neuron machine learning Artificial Intelligence PVT measurement correlation ANN model flow rate estimation spe 169597 Introduction Accurate correlation for estimating multiphase flow rate is important for quick evaluation of well...