Zobrazeno 1 - 10
of 133
pro vyhledávání: '"Pyrcz, Michael J."'
The characterization of subsurface models relies on the accuracy of subsurface models which request integrating a large number of information across different sources through model conditioning, such as data conditioning and geological concepts condi
Externí odkaz:
http://arxiv.org/abs/2404.05068
High-dimensional datasets present substantial challenges in statistical modeling across various disciplines, necessitating effective dimensionality reduction methods. Deep learning approaches, notable for their capacity to distill essential features
Externí odkaz:
http://arxiv.org/abs/2402.11404
Publikováno v:
Comput Geosci (2024)
Subsurface datasets inherently possess big data characteristics such as vast volume, diverse features, and high sampling speeds, further compounded by the curse of dimensionality from various physical, engineering, and geological inputs. Among the ex
Externí odkaz:
http://arxiv.org/abs/2308.08079
Spatial nonstationarity, the location variance of features' statistical distributions, is ubiquitous in many natural settings. For example, in geological reservoirs rock matrix porosity varies vertically due to geomechanical compaction trends, in min
Externí odkaz:
http://arxiv.org/abs/2212.04633
Autor:
Liu, Wendi, Pyrcz, Michael J.
Production forecast based on historical data provides essential value for developing hydrocarbon resources. Classic history matching workflow is often computationally intense and geometry-dependent. Analytical data-driven models like decline curve an
Externí odkaz:
http://arxiv.org/abs/2209.11885
Autor:
Pisel, Jesse R., Dierker, Joshua A., Srivastava, Sanya, Ravilisetty, Samira B., Pyrcz, Michael J.
Geoscience domain experts traditionally correlate formation tops in the subsurface using geophysical well logs (known as well-log correlation) by-hand. Based on individual well log interpretation and well-to-well comparisons, these correlations are d
Externí odkaz:
http://arxiv.org/abs/2202.08869
Publikováno v:
In Geoenergy Science and Engineering January 2025 244
Estimating porosity models via seismic data is challenging due to the signal noise and insufficient resolution of seismic data. Although impedance inversion is often used by combining with well logs, several hurdles remain to retrieve sub-seismic sca
Externí odkaz:
http://arxiv.org/abs/2111.13581
Publikováno v:
In Geoenergy Science and Engineering December 2024 243