Publications

Matteo, Edward N.; Conley, Donald M.; Verzi, Stephen J.; Roberts, Barry L.; Doyle, Casey L.; Sobolik, Steven R.; Gilletly, Samuel G.; Bauer, Stephen J.; Pyrak-Nolte, L.P.; Reda Taha, M.M.; Stormont, J.C.; Crandall, D.C.; Moriarty, Dylan; John, Esther W.; Wilson, Jennifer E.; Bettin, Giorgia B.; Hogancamp, Joshua H.; Fernandez, S.G.; Anwar, I.A.; Abdellatef, M.A.; Murcia, D.H.; Bland, J.B.

The main goal of this project was to create a state-of-the-art predictive capability that screens and identifies wellbores that are at the highest risk of catastrophic failure. This capability is critical to a host of subsurface applications, including gas storage, hydrocarbon extraction and storage, geothermal energy development, and waste disposal, which depend on seal integrity to meet U.S. energy demands in a safe and secure manner. In addition to the screening tool, this project also developed several other supporting capabilities to help understand fundamental processes involved in wellbore failure. This included novel experimental methods to characterize permeability and porosity evolution during compressive failure of cement, as well as methods and capabilities for understanding two-phase flow in damaged wellbore systems, and novel fracture-resistant cements made from recycled fibers.

Matteo, Edward N.; Roberts, Barry L.; Sobolik, Steven R.; Gilletly, Samuel G.; Doyle, Casey L.; John, Esther W.; Verzi, Stephen J.

Approximately 93% of US total energy supply is dependent on wellbores in some form. The industry will drill more wells in next ten years than in the last 100 years (King, 2014). Global well population is around 1.8 million of which approximately 35% has some signs of leakage (i.e. sustained casing pressure). Around 5% of offshore oil and gas wells “fail” early, more with age and most with maturity. 8.9% of “shale gas” wells in the Marcellus play have experienced failure (120 out of 1,346 wells drilled in 2012) (Ingraffea et al., 2014). Current methods for identifying wells that are at highest priority for increased monitoring and/or at highest risk for failure consists of “hand” analysis of multi-arm caliper (MAC) well logging data and geomechanical models. Machine learning (ML) methods are of interest to explore feasibility for increasing analysis efficiency and/or enhanced detection of precursors to failure (e.g. deformations). MAC datasets used to train ML algorithms and preliminary tests were run for “predicting” casing collar locations and performed above 90% in classification and identifying of casing collar locations.

Matteo, Edward N.; Roberts, Barry L.; Sobolik, Steven R.; Gilletly, Samuel G.; Doyle, Casey L.; John, Esther W.; Verzi, Stephen J.

Approximately 93% of US total energy supply is dependent on wellbores in some form. The industry will drill more wells in next ten years than in the last 100 years (King, 2014). Global well population is around 1.8 million of which approximately 35% has some signs of leakage (i.e. sustained casing pressure). Around 5% of offshore oil and gas wells “fail” early, more with age and most with maturity. 8.9% of “shale gas” wells in the Marcellus play have experienced failure (120 out of 1,346 wells drilled in 2012) (Ingraffea et al., 2014). Current methods for identifying wells that are at highest priority for increased monitoring and/or at highest risk for failure consists of “hand” analysis of multi-arm caliper (MAC) well logging data and geomechanical models. Machine learning (ML) methods are of interest to explore feasibility for increasing analysis efficiency and/or enhanced detection of precursors to failure (e.g. deformations). MAC datasets used to train ML algorithms and preliminary tests were run for “predicting” casing collar locations and performed above 90% in classification and identifying of casing collar locations.

Brown, Nathanael J.; Hoffman, Matthew J.; Bussell, Sammy J.; Gilletly, Samuel G.; Cauthen, Katherine R.; John, Esther W.; Nozick, Linda K.

Abstract not provided.

Proceedings - Winter Simulation Conference

John, Esther W.; Cauthen, Katherine R.; Brown, Nathanael J.; Nozick, Linda K.

Many individuals' mobility can be characterized by strong patterns of regular movements and is influenced by social relationships. Social networks are also often organized into overlapping communities which are associated in time or space. We develop a model that can generate the structure of a social network and attribute purpose to individuals' movements, based solely on records of individuals' locations over time. This model distinguishes the attributed purpose of check-ins based on temporal and spatial patterns in check-in data. Because a location-based social network dataset with authoritative ground-truth to test our entire model does not exist, we generate large scale datasets containing social networks and individual check-in data to test our model. We find that our model reliably assigns community purpose to social check-in data, and is robust over a variety of different situations.