.... The model was developed using two AI techniques; artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS). Using this technique will save cost and time by eliminating needs for using expensive, complicated downhole tools like measurement while drilling (MWDs) and pressure while...

... slurry and determine the casing setting depth. In addition, the drilling engineers will be able to eliminate the common drilling problems such as loss of circulation. neural network wellbore integrity Artificial Intelligence Upstream Oil & Gas predict fracture pressure abdulraheem...

... 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...

... of pore pressure without the need of different trends as compared to other models which require a two different trend (normal or abnormal pressure). machine learning Reservoir Characterization abdulraheem log data artificial neural network predict formation pressure fuzzy logic pore pressure...

... Water Saturation A major outcome of this work is the development of an empirical model using the trained neural network which is based on a set of weights and biases related between both the input-hidden layer and hidden-output layer. The weights and biases corresponding to their neurons are shown...

... reservoirs. The models were developed using 207 data points collected from unpublished sources. Statistical analysis was performed to define the more reliable and accurate techniques to predict the IPR. According to the results, the new fuzzy logic well IPR model outperformed the artificial neural networks...

... two Artificial Neural Network (ANN) based proxy models. Namely, a proxy for performance prediction that utilizes specific injection design parameters to predict reservoir performance and an inverse proxy for injection design that provides injection design scheme for a desired reservoir performance...

... with the artificial neural network is to optimize the variable parameters of the ANN model automatically. The optimization process will result in the best combination of the ANN parameters, which results in the highest correlation coefficient and lowest average absolute percentage error. The ANN parameters...

... and its success in prolific production increments. This work develops an artificial-neural- network based expert system by utilizing numerically generated training data using a high-fidelity numerical simulation model. Injection-pattern-based reservoir models are structured to establish the knowledge-base...

... . Producing Wells on Casing Flow-An Analysis of Flowing Pressure Gradients . Pet. Trans. AIME 213 , 202 – 206 . Al-Khalifa , 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...

... wellbore were considered to develop effective models. To ensure a high level of model reliability, more than 220 data set was used for training and testing the proposed models. Temperature distribution was determined using the neural network, fuzzy logic system, and generalized intelligent networks...

... dataset: this usually represent 30% of the dataset. It will be used to test the ANN after it complete developing ANN. The testing dataset, will be consider as new dataset for the ANN prediction model. knowledge management Upstream Oil & Gas neural network machine learning training dataset...

... 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 about 70% of the data was used...

... 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 number of inputs. Actual field data of more than 4,500 data points were used to build the model...

... precise prediction for acid-fracture conductivity. Artificial neural network (ANN) and adaptive network- based fuzzy inference system (ANFIS) are used to develop intelligent models capable of delivering an accurate design to acid-fracture conductivity. Published experimental data from the literature...

... 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...

... 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. Upstream Oil & Gas Artificial Intelligence two-phase flow Brill neural network transfer function machine learning abdulraheem input parameter Elkatatny empirical correlation neuron mechanistic model optimization artificial neural network society...

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

... 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...