1,721 research outputs found
Structural dynamics branch research and accomplishments to FY 1992
This publication contains a collection of fiscal year 1992 research highlights from the Structural Dynamics Branch at NASA LeRC. Highlights from the branch's major work areas--Aeroelasticity, Vibration Control, Dynamic Systems, and Computational Structural Methods are included in the report as well as a listing of the fiscal year 1992 branch publications
Characterization and Simulation of Discrete Fracture Networks in Unconventional Shale Reservoirs
Fracture characterization and simulation of complex fracture networks are investigated with the emphasis on better and faster approaches to generate fractures by conforming to available data resources, and on accurate, robust, and efficient techniques to grid and discretize complex fracture networks.
Three fracture characterization techniques such as fractal-based, microseismic-constrained, and outcrop-based are presented. Natural fractures are generated either stochastically from fractal-based theory, or constrained by microseismic information, or from outcrop maps. Hydraulic fractures are computed from a fast proxy model for fracture propagation that incooperates material balance and lab-measured conductivity data. Then, optimization-based unstructured gridding and discretization technique is developed to handle complex fracture networks with extensively fracture clustering, nonorthogonal and low-angle fracture intersections, and nonuniform fracture aperture distributions. Moreover, through fracture simulation, sensitivity analysis of natural fracture related parameters, nonuniform fracture aperture, and unstructured gridding related parameters on well production performance are investigated, which are followed by well testing behaviors and CO2 EOR of complex fracture networks.
This work presents an integrated workflow to model discrete fractures in unconventional shale reservoirs, together with detailed illustrations of each critical component using both synthetic and field application examples
Stacking Factorizing Partitioned Expressions in Hybrid Bayesian Network Models
Hybrid Bayesian networks (HBN) contain complex conditional probabilistic
distributions (CPD) specified as partitioned expressions over discrete and
continuous variables. The size of these CPDs grows exponentially with the
number of parent nodes when using discrete inference, resulting in significant
inefficiency. Normally, an effective way to reduce the CPD size is to use a
binary factorization (BF) algorithm to decompose the statistical or arithmetic
functions in the CPD by factorizing the number of connected parent nodes to
sets of size two. However, the BF algorithm was not designed to handle
partitioned expressions. Hence, we propose a new algorithm called stacking
factorization (SF) to decompose the partitioned expressions. The SF algorithm
creates intermediate nodes to incrementally reconstruct the densities in the
original partitioned expression, allowing no more than two continuous parent
nodes to be connected to each child node in the resulting HBN. SF can be either
used independently or combined with the BF algorithm. We show that the SF+BF
algorithm significantly reduces the CPD size and contributes to lowering the
tree-width of a model, thus improving efficiency
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