A Stochastic Method to Estimate the Anisotropic Stress-Dependent Coal Permeability by Pore-Volume Distribution and Stress-Strain Measurements

SPE Journal ◽  
2020 ◽  
Vol 25 (05) ◽  
pp. 2582-2600
Author(s):  
Syed Shabbar Raza ◽  
Victor Rudolph ◽  
Tom Rufford ◽  
Zhongwei Chen
Keyword(s):  
History Matching ◽  
Absolute Magnitude ◽  
Volume Distribution ◽  
Stress Strain ◽  
Anisotropic Permeability ◽  
Anisotropic Stress ◽  
Flow Channels ◽  
Measured Stress ◽  
Stress Dependent ◽  
Or History

Summary A novel, simple, economical, and time-effective method to estimate the anisotropic permeability of coal is presented in this paper. This method estimates the coal’s anisotropic permeability by avoiding the tedious experimentation using triaxial permeameter or history-matching exercises. This method calculates the absolute magnitude of the permeability of the sample. In this regard, it is unlike other analytical permeability models, such as given by Palmer and Mansoori (1998) and Shi and Durucan (2014), that only calculate the permeability ratio (k/k0). The motivation is to find a method by which the permeability of the coal may be determined with reasonable accuracy by using only two easy measurements: mercury intrusion porosimetry (MIP) and anisotropic stress-strain (σ-ɛ) measurement. The main blocks of the method are based on cleat size that is obtained from MIP and randomly allocated to form flow channels/cleats through the coal; these cleats form parallel paths in the orthogonal face and butt cleat directions that provide the permeability; and the cleat width (b) is stress dependent. This method is further validated by comparing with the experimentally measured stress-dependent permeability of Surat Basin (Australia) coal and German coal in face cleat and butt cleat directions.

Mimicking “J-Shaped” and Anisotropic Stress–Strain Behavior of Human and Porcine Aorta by Fabric-Reinforced Elastomer Composites

2019 ◽  
Vol 11 (36) ◽  
pp. 33323-33335 ◽  
Author(s):  
Dinara Zhalmuratova ◽  
Thanh-Giang La ◽  
Katherine Ting-Ting Yu ◽  
Alexander R. A. Szojka ◽  
Stephen H. J. Andrews ◽  
...  
Keyword(s):  
Stress Strain ◽  
Anisotropic Stress ◽  
Strain Behavior ◽  
Elastomer Composites

Evaluate the Importance of Shear Modulus in Dynamic FEM of Flexible Pavement Considering AC Cross-Anisotropy

2016 ◽  
Author(s):  
Mesbah U. Ahmed ◽  
Rafiqul A. Tarefder
Keyword(s):  
Shear Modulus ◽  
Resilient Modulus ◽  
Vertical Plane ◽  
Element Model ◽  
Tire Pressure ◽  
Stress Strain ◽  
Viscoelastic Parameters ◽  
Cross Anisotropy ◽  
As Stress ◽  
Stress Dependent

Goal of this study is to evaluate the effect of shear modulus variation on pavement responses, such as stress-strain, under dynamic load incorporating the AC cross-anisotropy. A dynamic Finite Element Model (FEM) of an instrumented asphalt pavement section on Interstate 40 (I-40) near Albuquerque, New Mexico, is developed in ABAQUS to determine stress-strain under truck tire pressure. Laboratory dynamic modulus tests were conducted on the AC cores to determine the temperature and frequency varying modulus values along both vertical and horizontal directions. The test outcomes are used to produce cross-anisotropic and viscoelastic parameters. Resilient modulus tests are conducted on granular aggregates from base and subbase layer to determine the nonlinear elastic and stress-dependent modulus values. These material parameters are integrated to the FEM through a FORTRAN subroutine via User Defined Material (UMAT) in the ABAQUS. The developed FEM is validated using the pavement deflections and stress-strain data under Falling Weight Deflectometer (FWD) test. The validated dynamic FEM is simulated under the non-uniform vertical tire contact stress. For the parametric study to investigate the effect of shear modulus variation on pavement responses, the validated FEM is simulated by varying the shear modulus in the AC layer. The results show that the variation in shear modulus along a vertical plane barely affects the tensile strain at the bottom of the AC layer and vertical compressive strains in both AC and unbound layers.

History matching and pressure analysis with stress-dependent permeability using the In Salah CO2 storage case study

2019 ◽  
Vol 91 ◽  
pp. 102844 ◽  
Author(s):  
Ji-Quan Shi ◽  
Sevket Durucan ◽  
Anna Korre ◽  
Philip Ringrose ◽  
Allan Mathieson
Keyword(s):  
History Matching ◽  
Co2 Storage ◽  
Stress Dependent ◽  
Pressure Analysis

Comparing Steam Flood Analytical and Simulation Models ~ Strengths and Limitations

2021 ◽  
Author(s):  
Thomas J. Hampton ◽  
Mohamed El-Mandouh ◽  
Stevan Weber ◽  
Tirth Thaker ◽  
K.. Patel ◽  
...  
Keyword(s):  
History Matching ◽  
Reservoir Characterization ◽  
Simulation Models ◽  
Analytical Models ◽  
Reservoir Properties ◽  
Advantages And Disadvantages ◽  
Single Pattern ◽  
Steam Flood ◽  
Methods Analytical ◽  
Or History

Abstract Mathematical Models are needed to aid in defining, analyzing, and quantifying solutions to design and manage steam floods. This paper discusses two main modeling methods – analytical and numerical simulation. Decisions as to which method to use and when to use them, requires an understanding of assumptions used, strengths, and limitations of each method. This paper presents advantages and disadvantages through comparison of analytical vs simulation when reservoir characterization becomes progressively more complex (dip, layering, heterogeneity between injector/producer, and reservoir thickness).While there are many analytical models, three analytical models are used for this paper:Marx & Langenheim, Modified Neuman, and Jeff Jones.The simulator used was CMG Stars on single pattern on both 5 Spot and 9 Spot patterns and Case 6 of 9 patterns, 5-Spot. Results were obtained using 6 different cases of varying reservoir properties based on Marx & Langenheim, Modified Neuman, and Jeff Jones models.Simulation was also done on each of the 6 cases, using Modified Neuman steam rates and then on Jeff Jones Steam rates using 9-Spot and 5-Spot patterns.This was done on predictive basis on inputs provided, without adjusting or history matching on analog or historical performance.Optimization runs using Particle Swarm Optimization was applied on one case in minimizing SOR and maximize NPV. Conclusion from comparing cases is that simulation is needed for complex geology, heterogeneity, and changes in layering. Also, simulation can be used for maximizing economics using AI based optimization tool. While understanding limitations, the analytical models are good for quick looks such as screening, scoping design, some surveillance, and for conceptual understanding of basic steam flood on uniform geologic properties. This paper is innovative in comparison of analytical models and simulation modeling.Results that quantify differences of oil rate, SOR, and injection rates (Neuman and Jeff Jones) impact on recovery factors is presented.

Sign in / Sign up

Export Citation Format

Share Document