An Intelligent Data-Driven Framework to Develop New Correlation to Predict Gas Deviation Factor for High-Temperature and High-Pressure Gas Reservoirs Using Artificial Neural Network

2019 ◽  
Author(s):  
Zeeshan Tariq ◽  
Mohamed Mahmoud ◽  
Abdul Asad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Pressure ◽  
High Temperature ◽  
Data Driven ◽  
Gas Reservoirs ◽  
New Correlation ◽  
Deviation Factor ◽  
Artificial Neural

scholarly journals Reservoir Characterisation of High-Pressure, High-Temperature Zone of Malay Basin Using Seismic Inversion and Artificial Neural Network Approach

2021 ◽  
Vol 11 (21) ◽  
pp. 10248
Author(s):  
Gulbahar Yazmyradova ◽  
Nik Nur Anis Amalina Nik Mohd Hassan ◽  
Nur Farhana Salleh ◽  
Maman Hermana ◽  
Hassan Soleimani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Pressure ◽  
High Temperature ◽  
Quality Factor ◽  
Rock Physics ◽  
Seismic Inversion ◽  
Reservoir Rock ◽  
High Pressure High Temperature ◽  
Artificial Neural

The growing demand for hydrocarbons has driven the exploration of riskier prospects in depths, pressures, and temperatures. Substantial volumes of hydrocarbons lie within deep formations, classified as high pressure, high temperature (HPHT) zone. This study aims to delineate hydrocarbon potential in the HPHT zone of the Malay Basin through the integrated application of rock physics analysis, pre-stack seismic inversion, and artificial neural network (ANN). The zones of interest lie within Sepat Field, located offshore Peninsular Malaysia, focusing on the HPHT area in Group H. The rock physics technique involves the cross-plotting of rock properties, which helps to differentiate the lithology of sand and shale and discriminates the fluid into water and hydrocarbon. The P-impedance, S-impedance, Vp/Vs ratio, density, scaled inverse quality factor of P (SQp), and scaled inverse quality factor of S (SQs) volumes are generated from pre-stack seismic inversion of 3D seismic data. The obtained volumes demonstrate spatial variations of values within the zone of interest, indicating hydrocarbon accumulation. Furthermore, the ANN model is successfully trained, tested, and validated using 3D elastic properties as input, to predict porosity volume. Finally, the trained neural network is applied to the entire reservoir volume to attain a 3D porosity model. The results reveal that rock physics study, pre-stack seismic inversion, and ANN approach helps to recognize reservoir rock and fluids in the HPHT zone.

Feed-forward artificial neural network provides data-driven inference of functional connectivity

2019 ◽  
Vol 29 (9) ◽  
pp. 091101 ◽  
Author(s):  
Nikita Frolov ◽  
Vladimir Maksimenko ◽  
Annika Lüttjohann ◽  
Alexey Koronovskii ◽  
Alexander Hramov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Functional Connectivity ◽  
Data Driven ◽  
Feed Forward ◽  
Artificial Neural

Application of Artificial Neural Network for the Heat Transfer Investigation Around a High-Pressure Gas Turbine Rotor Blade

2011 ◽  
Author(s):  
Ibrahim Eryilmaz ◽  
Sinan Inanli ◽  
Baris Gumusel ◽  
Suha Toprak ◽  
Cengiz Camci
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Artificial Neural Network ◽  
High Pressure ◽  
Rotor Blade ◽  
Incidence Angle ◽  
Transfer Coefficients ◽  
Experimental Conditions ◽  
Heat Transfer Coefficients ◽  
Artificial Neural

This paper presents the preliminary results of using artificial neural networks in the prediction of gas side convective heat transfer coefficients on a high pressure turbine blade. The artificial neural network approach which has three hidden layers was developed and trained by nine inputs and it generates one output. Input and output data were taken from an experimental research program performed at the von Karman Institute for Fluid Dynamics by Camci and Arts [5,6] and Camci [7]. Inlet total pressure, inlet total temperature, inlet turbulence intensity, inlet and exit Mach numbers, blade wall temperature, incidence angle, specific location of measurement and suction/pressure side specification of the blade were used as input parameters and calculated heat transfer coefficient around a rotor blade used as output. After the network is trained with experimental data, heat transfer coefficients are interpolated for similar experimental conditions and compared with both experimental measurements and CFD solutions. CFD analysis was carried out to validate the algorithm and to determine heat transfer coefficients for a closely related test case. Good agreement was obtained between CFD results and neural network predictions.

scholarly journals Data-driven modeling to optimize the injection well placement for waterflooding in heterogeneous reservoirs applying artificial neural networks and reducing observation cost

2020 ◽  
Vol 38 (6) ◽  
pp. 2413-2435 ◽  
Author(s):  
Xinwei Xiong ◽  
Kyung Jae Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Injection Well ◽  
Data Driven ◽  
Well Placement ◽  
Heterogeneous Reservoirs ◽  
Modeling Approach ◽  
Artificial Neural ◽  
Well Data ◽  
Data Driven Modeling

Secondary recovery methods such as waterflooding are often applied to depleted reservoirs for enhancing oil and gas production. Given that a large number of discretized elements are required in the numerical simulations of heterogeneous reservoirs, it is not feasible to run multiple full-physics simulations. In this regard, we propose a data-driven modeling approach to efficiently predict the hydrocarbon production and greatly reduce the computational and observation cost in such problems. We predict the fluid productions as a function of heterogeneity and injection well placement by applying artificial neural network with small number of training dataset, which are obtained with full-physics simulation models. To improve the accuracy of predictions, we utilize well data at producer and injector to achieve economic and efficient prediction without requiring any geological information on reservoir. The suggested artificial neural network modeling approach only utilizing well data enables the efficient decision making with reduced computational and observation cost.

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