Lubo D.,University of Oklahoma |
Lubo D.,Simon Bolivar University |
Jayaram V.,University of Oklahoma |
Marfurt K.J.,University of Oklahoma
Society of Petroleum Engineers - SPE/AAPG/SEG Unconventional Resources Technology Conference | Year: 2016
Tying detailed well log measurements to lower resolution but a really extensive 3D seismic data volumes is key to quantitative seismic interpretation. Ties using a poststack or prestack convolution model are routine, while supervised classification tying well data to seismic attributes using neural networks and geostatistics are also well established. However, unsupervised classification ties where the objective is to identify unknown patterns in the data is less well established. In this paper, we use an automatic learning Gaussian Mixture Model to statistically characterize the well logs, evaluate the probability distribution functions of different lithologies and then tie them to corresponding 3D seismic attribute volumes. We precondition our four-dimensional data by projecting onto two dimensions using Independent Component Analysis. We apply this workflow to Diamond M Field within the Horseshoe Atoll Reef Complex, Scurry County, TX, and find the Gaussian Mixture Model is able to statistically characterize and resolve lithological variations seen in the logs. In particular, we are able to clearly distinguish between lithologies from six different wells in the region of interest. The final result is a probabilistic map that statistically measures the variability of the seismic lithologies from the well logs. Copyright 2014, Unconventional Resources Technology Conference (URTeC).
Sanchez-Mendoza E.H.,University of Duisburg - Essen |
Carballo J.,University of Duisburg - Essen |
Carballo J.,Institute for Advanced Studies IDEA |
Carballo J.,Simon Bolivar University |
And 3 more authors.
Journal of Visualized Experiments | Year: 2016
Pharmacological treatment in animal models of cerebral disease imposes the problem of repeated injection protocols that may induce stress in animals and result in impermanent tissue levels of the drug. Additionally, drug delivery to the brain is delicate due to the blood brain barrier (BBB), thus significantly reducing intracerebral concentrations of selective drugs after systemic administration. Therefore, a system that allows both constant drug delivery without peak levels and circumvention of the BBB is in order to achieve sufficiently high intracerebral concentrations of drugs that are impermeable to the BBB. In this context, miniosmotic pumps represent an ideal system for constant drug delivery at a fixed known rate that eludes the problem of daily injection stress in animals and that may also be used for direct brain delivery of drugs. Here, we describe a method for miniosmotic pump implantation and post operatory care that should be given to animals in order to successfully apply this technique. We embed the aforementioned experimental paradigm in standard procedures that are used for studying neuroplasticity within the brain of C57BL6 mice. Thus, we exposed animals to 30 min brain infarct and implanted with miniosmotic pumps connected to the skull via a cannula in order to deliver a pro-plasticity drug. Behavioral testing was done during 30 days of treatment. After removal the animals received injections of anterograde tract tracers to analyze neuronal plasticity in the chronic phase of recovery. Results indicated that neuroprotection by the delivered drug was accompanied with increase in motor fibers crossing the midline of the brain at target structures. The results affirm the value of these techniques for drug administration and brain plasticity studies in modern neuroscience. © 2016 Journal of Visualized Experiments.
News Article | December 5, 2016
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