Sand body Architecture Predictive Model using Sequence Stratigraphy for Proper Thermal Recovery of Unconventional Resources in Kuwait

2014 ◽  
Author(s):  
Javier Llerena ◽  
Hasan Ferdous ◽  
Pradeep Kumar Choudhary ◽  
Saikia Pabitra ◽  
Deepender Bora ◽  
...  
Keyword(s):  
Predictive Model ◽  
Sequence Stratigraphy ◽  
Thermal Recovery ◽  
Unconventional Resources ◽  
Sand Body

Data-Driven Models to Predict Hydrocarbon Production From Unconventional Reservoirs by Thermal Recovery

2020 ◽  
Vol 142 (12) ◽  
Author(s):  
Kyung Jae Lee
Keyword(s):  
Test Data ◽  
Oil Shale ◽  
Approximate Solutions ◽  
Learning Technologies ◽  
Thermal Recovery ◽  
Unconventional Reservoirs ◽  
Data Driven ◽  
Efficient Computation ◽  
Hydrocarbon Production ◽  
Unconventional Resources

Abstract In the numerical simulations of thermal recovery for unconventional resources, reservoir models involve complex multicomponent-multiphase flow in non-isothermal conditions, where spatial heterogeneity necessitates the huge number of discretized elements. Proxy modeling approaches have been applied to efficiently approximate solutions of reservoir simulations in such complex problems. In this study, we apply machine learning technologies to the thermal recovery of unconventional resources, for the efficient computation and prediction of hydrocarbon production. We develop data-driven models applying artificial neural network (ANN) to predict hydrocarbon productions under heterogeneous and unknown properties of unconventional reservoirs. We study two different thermal recovery methods—expanding solvent steam-assisted gravity drainage for bitumen and in-situ upgrading of oil shale. We obtain training datasets by running high-fidelity simulation models for these two problems. As training datasets of ANN models, diverse input and output data of phase and component productions are generated, by considering heterogeneity and uncertainty. In the bitumen reservoirs, diverse permeability anisotropies are considered as unknown properties. Similarly, in the oil shale reservoirs, diverse kerogen decomposition kinetics are considered. The performance of data-driven models is evaluated with respect to the position of the test dataset. When the test data is inside of the boundary of training datasets, the developed data-driven models based on ANN reliably predict the cumulative productions at the end of the recovery processes. However, when the test data is at the boundary of training datasets, physical insight plays a significant role to provide a reliable performance of data-driven models.

Depositional Patterns of Dominant Sedimentary Time Units’ Reservoir in Daqing Oilfield

2011 ◽  
Vol 135-136 ◽  
pp. 365-368
Author(s):  
Guang Juan Fan ◽  
Yue Jun Zhao
Keyword(s):  
High Resolution ◽  
Sequence Stratigraphy ◽  
Large Scale ◽  
Difficult Question ◽  
Correlation Methods ◽  
Sand Body ◽  
Compound Type ◽  
Depositional Patterns ◽  
High Resolution Sequence Stratigraphy ◽  
Distributary Channel

Take the example of the PⅠ3 sublayers of the Twelve-section in the Xing region of the Daqing oilfield, in this paper, to analysis depositional patterns of reservoirs. To make a effectively reasonable development adjustment, correlation methods were raised, which are well-well、plane well net and identical formation cause boundary remotion according to fluvial sedimentology and high resolution sequence stratigraphy new theory, in order to solve the difficult question of sublayers’ unification and correlation in complicated fluvial-delta strata. To distinguishe one subfacies and seven microfacies, and then to establish logging microfacies model. The single sand body of identical formation cause is delibrately depicted. Finally, to establish three reservoir depositional patterns, which are large-scale compound type channel sandbody, medium-sized distributary channel sandbody, small size distributary channel sandbody, respectively.

Persistent Photoconductivity And Thermal Recovery Kinetics Of Low Energy Ar + Bombarded GaAs

1991 ◽  
Vol 223 ◽  
Author(s):  
A. Vaseashta ◽  
L. C. Burton
Keyword(s):  
Deep Level Transient Spectroscopy ◽  
Deep Level ◽  
Thermal Recovery ◽  
Low Energy ◽  
Persistent Photoconductivity ◽  
Transport Parameters ◽  
Excellent Correlation ◽  
Proposed Model ◽  
Transient Spectroscopy ◽  
Kinetics Of

ABSTRACTKinetics of persistent photoconductivity, photoquenching, and thermal and optical recovery observed in low energy Ar+ bombarded on (100) GaAs surfaces have been investigated. Rate and transport equations for these processes were derived and simulated employing transport parameters, trap locations and densities determined by deep level transient spectroscopy. Excellent correlation was obtained between the results of preliminary simulation and the experimentally observed values. The exponential decay of persistent photoconductivity response curve was determined to be due to metastable electron traps with longer lifetime and is consistent with an earlier proposed model.

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