Kinetic simulation model for steam pyrolysis of shale oil feedstock

1990 ◽  
Vol 29 (4) ◽  
pp. 527-534 ◽  
Author(s):  
Hamid R. Kavianian ◽  
Victor F. Yesavage ◽  
Phillip F. Dickson ◽  
Robert W. Peters
Keyword(s):  
Simulation Model ◽  
Shale Oil ◽  
Kinetic Simulation

scholarly journals A Kinetic Simulation Model That Describes Catalysis and Regulation in Nitric-oxide Synthase

2000 ◽  
Vol 276 (2) ◽  
pp. 1233-1243 ◽  
Author(s):  
Jérôme Santolini ◽  
Subrata Adak ◽  
Christine M. L. Curran ◽  
Dennis J. Stuehr
Keyword(s):  
Nitric Oxide ◽  
Nitric Oxide Synthase ◽  
Simulation Model ◽  
Kinetic Simulation ◽  
Oxide Synthase

An In-Depth Review of the Recovery Mechanisms for the Cyclic Gas Injection Process in Shale Oil Reservoirs

2021 ◽  
Author(s):  
Hilario Martin Rodriguez ◽  
Yalda Barzin ◽  
Gregory James Walker ◽  
Markus Gruenwalder ◽  
Matias Fernandez-Badessich ◽  
...  
Keyword(s):  
Simulation Model ◽  
Oil Recovery ◽  
Hydraulic Fracture ◽  
Gas Injection ◽  
Pressure Profile ◽  
Shale Oil ◽  
Gas Composition ◽  
Pure Methane ◽  
Recovery Mechanisms ◽  
Incremental Oil Recovery

Abstract This study has double objectives: investigation of the main recovery mechanisms affecting the performance of the gas huff-n-puff (GHnP) process in a shale oil reservoir, and application of optimization techniques to modelling of the cyclic gas injection. A dual-permeability reservoir simulation model has been built to reproduce the performance of a single hydraulic fracture. The hydraulic fracture has the average geometry and properties of the well under analysis. A history match workflow has been run to obtain a simulation model fully representative of the studied well. An optimization workflow has been run to maximize the cumulative oil obtained during the GHnP process. The operational variables optimized are: duration of gas injection, soaking, and production, onset time of GHnP, injection gas flow rate, and number of cycles. This optimization workflow is launched twice using two different compositions for the injection gas: rich gas and pure methane. Additionally, the optimum case obtained previously with rich gas is simulated with a higher minimum bottom hole pressure (BHP) for both primary production and GHnP process. Moreover, some properties that could potentially explain the different recovery mechanisms were tracked and analyzed. Three different porosity systems have been considered in the model: fractures, matrix in the stimulated reservoir volume (SRV), and matrix in the non-SRV zone (virgin matrix). Each one with a different pressure profile, and thus with its corresponding recovery mechanisms, identified as below: Vaporization/Condensation (two-phase system) in the fractures.Miscibility (liquid single-phase) in the non-SRV matrix.Miscibility and/or Vaporization/Condensation in the SRV matrix: depending on the injection gas composition and the pressure profile along the SRV the mechanism may be clearly one of them or even both. Results of this simulation study suggest that for the optimized cases, incremental oil recovery is 24% when the gas injected is a rich gas, but it is only 2.4% when the gas injected is pure methane. A higher incremental oil recovery of 49% is obtained, when injecting rich gas and increasing the minimum BHP of the puff cycle above the saturation pressure. Injection of gas results in reduction of oil molecular weight, oil density and oil viscosity in the matrix, i.e., the oil gets lighter. This net decrease is more pronounced in the SRV than in the non-SRV region. The incremental oil recovery observed in the GHnP process is due to the mobilization of heavy components (not present in the injection gas composition) that otherwise would remain inside the reservoir. Due to the main characteristic of the shale reservoirs (nano-Darcy permeability), GHnP is not a displacement process. A key factor in success of the GHnP process is to improve the contact of the injected gas and the reservoir oil to increase the mixing and mass transfer. This study includes a review of different mechanisms, and specifically tracks the evolution of the properties that explain and justify the different identified mechanisms.

Catalyst Grading Optimization and Kinetic Simulation of the Shale Oil Hydrotreating Process

2017 ◽  
Vol 31 (4) ◽  
pp. 4353-4360 ◽  
Author(s):  
Hongyan Wang ◽  
Fei Dai ◽  
Yiqian Yang ◽  
Zengxi Li ◽  
Chunshan Li ◽  
...  
Keyword(s):  
Shale Oil ◽  
Kinetic Simulation

Kinetic simulation model of protein secretion and accumulation in the cell microcapsules

2008 ◽  
Vol 10 (5) ◽  
pp. 575-582 ◽  
Author(s):  
J. Wikström ◽  
H. Syväjärvi ◽  
A. Urtti ◽  
M. Yliperttula
Keyword(s):  
Simulation Model ◽  
Protein Secretion ◽  
Kinetic Simulation

Effect of cure kinetic simulation model on optimized thermal cure cycle for thin-sectioned composite parts

2013 ◽  
Vol 34 (7) ◽  
pp. 1172-1179 ◽  
Author(s):  
M. Vafayan ◽  
H. Abedini ◽  
M.H.R. Ghoreishy ◽  
M.H. Beheshty
Keyword(s):  
Simulation Model ◽  
Kinetic Simulation ◽  
Thermal Cure ◽  
Cure Cycle ◽  
Cure Kinetic

scholarly journals Kinetic simulation of hydrocarbon generation and its application to in-situ conversion of shale oil

2019 ◽  
Vol 46 (6) ◽  
pp. 1288-1296
Author(s):  
Bin ZHANG ◽  
Cong YU ◽  
Jingwei CUI ◽  
Jingkui MI ◽  
Huadong LI ◽  
...  
Keyword(s):  
Hydrocarbon Generation ◽  
Shale Oil ◽  
Kinetic Simulation
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