Nicholas A. Azzolina

University of North Dakota

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Charles D. Gorecki

University of North Dakota

James A. Sorensen

University of North Dakota

David V. Nakles

University of North Dakota

Matthew E. Burton‐Kelly

University of North Dakota

Wesley D. Peck

University of North Dakota

John Hamling

University of North Dakota

Nicholas W. Bosshart

University of North Dakota

Xue Yu

University of North Dakota

Steven B. Hawthorne

University of North Dakota

David J. Miller

Takeda (United Kingdom)

Cooperative Institutions

University of North Dakota

Grand Forks Human Nutrition Research Center

National Energy Technology Laboratory

Lawrence Livermore National Laboratory

United States Department of Energy

United States Department of Commerce

Stanford University

Schlumberger (British Virgin Islands)

Carnegie Mellon University

University of Pittsburgh

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Deep Learning-Accelerated 3D Carbon Storage Reservoir Pressure Forecasting Based on Data Assimilation Using Surface Displacement from InSAR

arXiv (Cornell University) (2022)

Hewei Tang Pengcheng Fu Honggeun Jo Su Jiang Christopher Sherman

Fast forecasting of reservoir pressure distribution in geologic carbon storage (GCS) by assimilating monitoring data is a challenging problem. Due to high drilling cost, GCS projects usually have spatially sparse measurements from wells, leading to high uncertainties in reservoir pressure prediction. To address this challenge, we propose to use low-cost Interferometric Synthetic-Aperture Radar (InSAR) data as monitoring data to infer reservoir pressure build up. We develop a deep learning-accelerated workflow to assimilate surface displacement maps interpreted from InSAR and to forecast dynamic reservoir pressure. Employing an Ensemble Smoother Multiple Data Assimilation (ES-MDA) framework, the workflow updates three-dimensional (3D) geologic properties and predicts reservoir pressure with quantified uncertainties. We use a synthetic commercial-scale GCS model with bimodally distributed permeability and porosity to demonstrate the efficacy of the workflow. A two-step CNN-PCA approach is employed to parameterize the bimodal fields. The computational efficiency of the workflow is boosted by two residual U-Net based surrogate models for surface displacement and reservoir pressure predictions, respectively. The workflow can complete data assimilation and reservoir pressure forecasting in half an hour on a personal computer.

Reservoir Simulation

10.48550/arxiv.2201.08543

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Citations (0)

Quantifying CO2 storage efficiency factors in hydrocarbon reservoirs: A detailed look at CO2 enhanced oil recovery

International journal of greenhouse gas control (2017)

Wesley D. Peck Nicholas A. Azzolina J. Ge Nicholas W. Bosshart Matthew E. Burton‐Kelly

Reservoir Simulation

Storage efficiency

10.1016/j.ijggc.2017.12.005

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Citations (30)

Statistical analysis of pulsed-neutron well logs in monitoring injected carbon dioxide

International journal of greenhouse gas control (2018)

Nicholas A. Azzolina Nicholas W. Bosshart Matthew E. Burton‐Kelly John Hamling Wesley D. Peck

Petrophysics

Saturation (graph theory)

Wellbore

10.1016/j.ijggc.2018.05.023

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Citations (4)

Statistical techniques for analyzing of soil vapor intrusion data: A case study of manufactured gas plant sites

Journal of the Air & Waste Management Association (2012)

Anshuman Singh Edward F. Neuhauser Nicholas A. Azzolina Mark Distler Krista M. Anders

As part of an ongoing study of soil vapor intrusion (SVI), concentration data for approximately 2000 air and vapor samples were assembled from remedial site investigations and stand-alone assessments conducted at New York State Manufactured Gas Plant (MGP) sites. Vapor samples were collected from ambient outdoor air, indoor air, beneath building slabs, and from outside of buildings. Despite the large sample size, the considerable variability in compound and sample-specific censoring limits inhibited the use of conventional tools for statistical interpretation. This paper describes the development and application of improved statistical tools to address an unusually high degree of data censoring and possible artifacts related to uneven distributions of samples across sites and buildings. In addition to methods for calculating population percentiles and associated confidence intervals, methods for comparing the population of MGP-SVI data with a reference population were also developed and evaluated via illustrative comparisons with the published 2001 EPA Building Assessment Survey and Evaluation (BASE) study of industrial buildings. The focus of this work is on the development and evaluation of new statistical methods; a more complete summary and evaluation of the full NYS MGP-SVI data set will be presented in a companion paper. Implications: Data from vapor intrusion and other environmental studies are often stratified and/or censored, which complicates comparisons with background data or reference populations. In some cases, statistical methods for censored data can be modified to support population-based inference and reduce biases associated with the presence of repeated measurements from multiple sources. Such modifications are particularly appropriate for retrospective data mining studies that are not guided by a formal experimental design. Supplemental Materials: Supplemental materials are available for this paper. Go to the publisher's online edition of the Journal of the Air & Waste Management Association.

Statistical Inference

Soil gas

Censoring (clinical trials)

Data set

10.1080/10962247.2012.742473

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Citations (1)

An image-based equation for estimating the prospective CO2 storage resource of organic-rich shale formations

International journal of greenhouse gas control (2020)

Alexander Azenkeng Blaise A.F. Mibeck Bethany A. Kurz Charles D. Gorecki Evgeniy M. Myshakin

Matrix (chemical analysis)

10.1016/j.ijggc.2020.103038

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Citations (15)

Refracturing in the Bakken - An Analysis of Data from Across North Dakota

Proceedings of the 9th Unconventional Resources Technology Conference (2022)

Chantsalmaa Dalkhaa Nicholas A. Azzolina Alex Chakhmakhchev Bethany A. Kurz James A. Sorensen

10.15530/urtec-2022-3723732

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Citations (3)

CO2 storage associated with CO2 enhanced oil recovery: A statistical analysis of historical operations

International journal of greenhouse gas control (2015)

Nicholas A. Azzolina David V. Nakles Charles D. Gorecki Wesley D. Peck Scott C. Ayash

Dimensionless quantity

Oil Production

Oil in place

10.1016/j.ijggc.2015.03.037

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Citations (134)

Optimizing conformance control for gas injection EOR in unconventional reservoirs

Fuel (2022)

Lu Jin Xincheng Wan Nicholas A. Azzolina Nicholas W. Bosshart Jin Zhao

Well control

Injection well

10.1016/j.fuel.2022.124523

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Citations (11)

Monitored Natural Attenuation of Manufactured Gas Plant Tar Mono‐ and Polycyclic Aromatic Hydrocarbons in Ground Water: A 14‐Year Field Study

Groundwater Monitoring & Remediation (2009)

Edward F. Neuhauser John A. Ripp Nicholas A. Azzolina Eugene L. Madsen David Mauro

Abstract Site 24 was the subject of a 14‐year (5110‐day) study of a ground water plume created by the disposal of manufactured gas plant (MGP) tar into a shallow sandy aquifer approximately 25 years prior to the study. The ground water plume in 1988 extended from a well‐defined source area to a distance of approximately 400 m down gradient. A system of monitoring wells was installed along six transects that ran perpendicular to the longitudinal axis of the plume centerline. The MGP tar source was removed from the site in 1991 and a 14‐year ground water monitored natural attenuation (MNA) study commenced. The program measured the dissolved mono‐ and polycyclic aromatic hydrocarbons (MAHs and PAHs) periodically over time, which decreased significantly over the 14‐year period. Naphthalene decreased to less than 99% of the original dissolved mass, with mass degradation rates of 0.30 per year (half‐life 2.3 years). Bulk attenuation rate constants for plume centerline concentrations over time ranged from 0.33 ± 0.09 per year (half‐life 2.3 ± 0.8 years) for toluene and 0.45 ± 0.06 per year (half‐life 1.6 ± 0.2 years) for naphthalene. The hydrogeologic setting at Site 24, having a sandy aquifer, shallow water table, clay confining layer, and aerobic conditions, was ideal for demonstrating MNA. However, these results demonstrate that MNA is a viable remedial strategy for ground water at sites impacted by MAHs and PAHs after the original source is removed, stabilized, or contained.

tar (computing)

Panache

Polycyclic aromatic hydrocarbon

10.1111/j.1745-6592.2009.01244.x

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Citations (25)

A deep learning-accelerated data assimilation and forecasting workflow for commercial-scale geologic carbon storage

International journal of greenhouse gas control (2021)

Hewei Tang Pengcheng Fu Christopher Sherman Jize Zhang Xin Ju

Fast assimilation of monitoring data to update forecasts of pressure buildup and carbon dioxide (CO2) plume migration under geologic uncertainties is a challenging problem in geologic carbon storage. The high computational cost of data assimilation with a high-dimensional parameter space impedes fast decision-making for commercial-scale reservoir management. We propose to leverage physical understandings of porous medium flow behavior with deep learning techniques to develop a fast data assimilation-reservoir response forecasting workflow. Applying an Ensemble Smoother Multiple Data Assimilation (ES-MDA) framework, the workflow updates geologic properties and predicts reservoir performance with quantified uncertainty from pressure history and CO2 plumes interpreted through seismic inversion. As the most computationally expensive component in such a workflow is reservoir simulation, we developed surrogate models to predict dynamic pressure and CO2 plume extents under multi-well injection. The surrogate models employ deep convolutional neural networks, specifically, a wide residual network and a residual U-Net. The workflow is validated against a flat three-dimensional reservoir model representative of a clastic shelf depositional environment. Intelligent treatments are applied to bridge between quantities in a true-3D reservoir model and those in a single-layer reservoir model. The workflow can complete history matching and reservoir forecasting with uncertainty quantification in less than one hour on a mainstream personal workstation.

Reservoir Simulation

10.1016/j.ijggc.2021.103488

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Citations (49)