Hugh Daigle | Searcharxiv

arXiv2023

Ensuring reliability: what is the optimal time for power plant maintenance in Texas as the climate changes?

We analyzed data for the Electric Reliability Council of Texas (ERCOT) to assess shoulder seasons -- that is, the 45 days of lowest total energy use and peak demand in the spring and fall typically used for power plant maintenance -- and whether their occurrence has changed over time. Over the period 1996--2022, the shoulder seasons never started earlier than late March nor later than mid-October, corresponding well with the minimum of total degree days. In the temperature record 1959--2022, the minimum in degree days in the spring moved earlier, from early March to early February, and in the fall moved later, from early to mid-November. Warming temperatures might cause these minima in degree days to merge into a single annual minimum in December or January by the mid-2040s, a time when there is a non-trivial risk of 1-day record energy use and peak demand from winter storms.

eess.SY↗

arXiv2021

MudrockNet: Semantic Segmentation of Mudrock SEM Images through Deep Learning

Segmentation and analysis of individual pores and grains of mudrocks from scanning electron microscope images is non-trivial because of noise, imaging artifacts, variation in pixel grayscale values across images, and overlaps in grayscale values among different physical features such as silt grains, clay grains, and pores in an image, which make their identification difficult. Moreover, because grains and pores often have overlapping grayscale values, direct application of threshold-based segmentation techniques is not sufficient. Recent advances in the field of computer vision have made it easier and faster to segment images and identify multiple occurrences of such features in an image, provided that ground-truth data for training the algorithm is available. Here, we propose a deep learning SEM image segmentation model, MudrockNet based on Google's DeepLab-v3+ architecture implemented with the TensorFlow library. The ground-truth data was obtained from an image-processing workflow applied to scanning electron microscope images of uncemented muds from the Kumano Basin offshore Japan at depths < 1.1 km. The trained deep learning model obtained a pixel-accuracy about 90%, and predictions for the test data obtained a mean intersection over union (IoU) of 0.6591 for silt grains and 0.6642 for pores. We also compared our model with the random forest classifier using trainable Weka segmentation in ImageJ, and it was observed that MudrockNet gave better predictions for both silt grains and pores. The size, concentration, and spatial arrangement of the silt and clay grains can affect the petrophysical properties of a mudrock, and an automated method to accurately identify the different grains and pores in mudrocks can help improve reservoir and seal characterization for petroleum exploration and anthropogenic waste sequestration.

cs.CV↗