Using in-situ strain measurements to evaluate the accuracy of stress estimation procedures from fracture injection/shut-in tests

Autor: Ajo-Franklin, J., Baumgartner, T., Beckers, K., Blankenship, D., Bonneville, A., Boyd, L., Brown, S., Burghardt, J.A., Chai, C., Chakravarty, A., Chen, T., Chen, Y., Chi, B., Condon, K., Cook, P.J., Crandall, D., Dobson, P.F., Doe, T., Doughty, C.A., Elsworth, D., Feldman, J., Feng, Z., Foris, A., Frash, L.P., Frone, Z., Fu, P., Gao, K., Ghassemi, A., Guglielmi, Y., Haimson, B., Hawkins, A., Heise, J., Hopp, C., Horn, M., Horne, R.N., Horner, J., Hu, M., Huang, H., Huang, L., Im, K.J., Ingraham, M., Jafarov, E., Jayne, R.S., Johnson, T.C., Johnson, S.E., Johnston, B., Karra, S., Kim, K., King, D.K., Kneafsey, T., Knox, H., Knox, J., Kumar, D., Kutun, K., Lee, M., Li, D., Li, J., Li, K., Li, Z., Maceira, M., Mackey, P., Makedonska, N., Marone, C.J., Mattson, E., McClure, M.W., McLennan, J., McLing, T., Medler, C., Mellors, R.J., Metcalfe, E., Miskimins, J., Moore, J., Morency, C.E., Morris, J.P., Myers, T., Nakagawa, S., Neupane, G., Newman, G., Nieto, A., Paronish, T., Pawar, R., Petrov, P., Pietzyk, B., Podgorney, R., Polsky, Y., Pope, J., Porse, S., Primo, J.C., Pyatina, T., Reimers, C., Roberts, B.Q., Robertson, M., Rodríguez-Tribaldos, V., Roggenthen, W., Rutqvist, J., Rynders, D., Schoenball, M., Schwering, P., Sesetty, V., Sherman, C.S., Singh, A., Smith, M.M., Sone, H., Sonnenthal, E.L., Soom, F.A., Sprinkle, D.P., Sprinkle, S., Strickland, C.E., Su, J., Templeton, D., Thomle, J.N., Ulrich, C., Uzunlar, N., Vachaparampil, A., Valladao, C.A., Vandermeer, W., Vandine, G., Vardiman, D., Vermeul, V.R., Wagoner, J.L., Wang, H.F., Weers, J., Welch, N., White, J., White, M.D., Winterfeld, P., Wood, T., Workman, S., Wu, H., Wu, Y.S., Yildirim, E.C., Zhang, Y., Zhang, Y.Q., Zhou, Q., Zoback, M.D., Guglielmi, Yves, McClure, Mark, Burghardt, Jeffrey, Morris, Joseph P., Doe, Thomas, Fu, Pengcheng, Knox, Hunter, Vermeul, Vince, Kneafsey, Tim

Publikováno v: In International Journal of Rock Mechanics and Mining Sciences October 2023 170

Physics-Informed Machine Learning Models for Predicting the Progress of Reactive-Mixing

Autor: Mudunuru, M. K., Karra, S.

This paper presents a physics-informed machine learning (ML) framework to construct reduced-order models (ROMs) for reactive-transport quantities of interest (QoIs) based on high-fidelity numerical simulations. QoIs include species decay, product yie

Externí odkaz: http://arxiv.org/abs/1908.10929

Using Machine Learning to Discern Eruption in Noisy Environments: A Case Study using CO2-driven Cold-Water Geyser in Chimayo, New Mexico

Autor: Yuan, B., Tan, Y. J., Mudunuru, M. K., Marcillo, O. E., Delorey, A. A., Roberts, P. M., Webster, J. D., Gammans, C. N. L., Karra, S., Guthrie, G. D., Johnson, P. A.

We present an approach based on machine learning (ML) to distinguish eruption and precursory signals of Chimay\'{o} geyser (New Mexico, USA) under noisy environments. This geyser can be considered as a natural analog of $\mathrm{CO}_2$ intrusion into

Externí odkaz: http://arxiv.org/abs/1810.01488

Reduced-Order Modeling through Machine Learning Approaches for Brittle Fracture Applications

Autor: Hunter, A., Moore, B. A., Mudunuru, M. K., Chau, V. T., Miller, R. L., Tchoua, R. B., Nyshadham, C., Karra, S., Malley, D. O., Rougier, E., Viswanathan, H. S., Srinivasan, G.

In this paper, five different approaches for reduced-order modeling of brittle fracture in geomaterials, specifically concrete, are presented and compared. Four of the five methods rely on machine learning (ML) algorithms to approximate important asp

Externí odkaz: http://arxiv.org/abs/1806.01949

Discovering hidden geothermal signatures using non-negative matrix factorization with customized k-means clustering

Autor: Vesselinov, V.V., Ahmmed, B., Mudunuru, M.K., Pepin, J.D., Burns, E.R., Siler, D.L., Karra, S., Middleton, R.S.

Publikováno v: In Geothermics December 2022 106