Geological storage of CO2: Application, feasibility and efficiency of global sensitivity analysis and risk assessment using the arbitrary polynomial chaos

Ashraf, Muhammad; Oladyshkin, Sergey; Nowak, Wolfgang

doi:10.1016/j.ijggc.2013.03.023

Cited by 37 publications

(26 citation statements)

References 52 publications

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“…In general, CO 2 -EOR uncertainty sources may be classified as geological, physical and operational (Ashraf et al, 2013;Yang et al, 2014). Geological uncertainties associated with heterogeneity have attracted significant attention in recent years, with many studies suggesting heterogeneity as the primary source of uncertainty in model forecasts of CO 2 storage capacity, CO 2 plume extent, CO 2 migration and storage performance, and CO 2 leakage (Deng et al, 2012;Li and Zhang, 2014;Tian et al, 2014;Dai et al, 2014a;Liu and Zhang, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…However, typical Monte Carlo methods are computationally expensive as they require a large number of model simulations. Recently, several studies applied polynomial chaos expansion (PCE) to quantify risks of geological CO 2 storage (GCS) (Zhang and Sahinidis, 2013;Oladyshkin et al, 2010Oladyshkin et al, , 2011Ashraf et al, 2013). The PCE approach was first introduced by Wiener (1938) to determine the evolution of uncertainty in dynamic systems based on homogenous chaos theory.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty analysis of carbon sequestration in an active CO2-EOR field

Pan

McPherson

Dai

et al. 2016

International Journal of Greenhouse Gas Control

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a b s t r a c tEnhanced Oil Recovery (EOR) is perhaps the most feasible option for geologic CO 2 sequestration (GCS). However, the typical large extent of uncertainty in reservoir properties is a major obstacle to effective risk assessment of GCS. The primary objective of this study was to quantify uncertainties in key reservoir parameters for an active, commercial-scale CO 2 -EOR field. We selected the Morrow formation within the active Farnsworth Unit (FWU) EOR field in Texas for this case study. Critical for this study are historical and real-time CO 2 injection/production data as well as fundamental hydrologic and geologic characterization data from injection wells and three dedicated characterization/observation wells. We designed and applied a response surface methodology (RSM) integrated with Monte Carlo simulations to evaluate and quantify uncertainty. Previous sensitivity studies identified critical uncertain parameters including reservoir permeability, anisotropy ratio of permeability (k v /k h ), water-alternating-gas (WAG) time ratio, and initial oil saturation. Cumulative oil production, net CO 2 storage, net water stored (difference between the injection water and produced water), and reservoir pressure at the injection well were the primary dependent variables used to evaluate uncertainties of CO 2 storage associated with oil production and potential risk of reservoir pressure build-up. A 3-D static reservoir model was constructed based on the geology of the Farnsworth EOR site, serving as the basis for all multiple-realization reservoir simulations. After performing stepwise regression analyses, a series of response surface models of the dependent variables at each time step were constructed and validated using appropriate goodness-of-fit measures. Given the range of uncertainties in the independent variables, cumulative distribution functions (CDFs) and uncertainty bounds (5th and 95th percentiles) of output responses were estimated based on regression equations and Monte Carlo sampling. Forecasted cumulative oil production and net CO 2 storage varied from 54,696 bbl, and 22,784 t, respectively, at the 5th percentile to 203,989 bbl, and 39,525 t at the 95th percentile after 5 years. These results suggest that a significant proportion of forecasted output response uncertainty, including forecasted storage capacity, is propagated from parameter uncertainties. For this case study, response surface results suggest that maximum cumulative oil production could be achieved with permeability in a specific range (10.0-31.6 mD, which is close to the mean value of the actual strata). The pressure near the injection well exceeded 40 MPa, and 54 MPa at the 95th percentile after 1 and 5 years, respectively. The reservoir pressure fracturing threshold is just under 37 MPa in the FWU, indicating a significant risk of caprock fracturing in the low permeability zones due to pressure build-up.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Uncertainty analysis of carbon sequestration in an active CO2-EOR field

Pan

McPherson

Dai

et al. 2016

International Journal of Greenhouse Gas Control

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“…evolutionary algorithms, machine learning and particle swarms) are continuously adapted to reservoir simulation, and industry workflows for uncertainty quantification have been updated accordingly (e.g. Hajizadeh et al 2011;Abdollazadeh et al 2013;Arnold et al 2013;Ashraf et al 2013;Dehdari et al 2013;He & Durlofsky 2013;Park et al 2013;Peters et al 2013;El-Sheikh et al 2014). However, it is not clear how readily those new algorithms, which are commonly developed for well-known and sometimes slightly idealized benchmark problems comprising clastic reservoir models, can be applied to carbonate reservoirs.…”

Section: Background and Challengesmentioning

confidence: 99%

Fundamental controls on fluid flow in carbonates: current workflows to emerging technologies

Agar

Geiger

2014

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The introduction reviews topics relevant to the fundamental controls on fluid flow in carbonate reservoirs and to the prediction of reservoir performance. The review provides research and industry contexts for papers in this volume only. A discussion of global context and frameworks emphasizes the value yet to be captured from compare and contrast studies. Multidisciplinary efforts highlight the importance of greater integration of sedimentology, diagenesis and structural geology. Developments in analytical and experimental methods, stimulated by advances in the materials sciences, support new insights into fundamental (pore-scale) processes in carbonate rocks. Subsurface imaging methods relevant to the delineation of heterogeneities in carbonates highlight techniques that serve to decrease the gap between seismically resolvable features and well-scale measurements. Methods to fuse geological information across scales are advancing through multiscale integration and proxies. A surge in computational power over the last two decades has been accompanied by developments in computational methods and algorithms. Developments related to visualization and data interaction support stronger geoscience-engineering collaborations. High-resolution and real-time monitoring of the subsurface are driving novel sensing capabilities and growing interest in data mining and analytics. Together, these offer an exciting opportunity to learn more about the fundamental fluid-flow processes in carbonate reservoirs at the interwell scale.

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“…Oladyshkin and Nowak observed that as every set of random data, as well as a continuous or discrete PDF, can be described using the moments without making any assumptions about the shape or existence of a suitable probability distribution, the moments provided a very general approach to propagate data without requiring the determination of deterministic PDFs. Oladyshkin and Nowak [7] promoted this concept in the geo-sciences by successfully applying it to identify uncertainties in carbon dioxide storage in geological formations [15,16,17] and also for robust design [18]. Moreover, Oladyshkin and Nowak presented a derivation of the optimal orthogonal polynomials from the moments.…”

Section: Introductionmentioning

confidence: 99%

SAMBA: Sparse Approximation of Moment-Based Arbitrary Polynomial Chaos

Ahlfeld

Belkouchi

Montomoli

2016

Journal of Computational Physics

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Please cite this article in press as: R. Ahlfeld et al., SAMBA: Sparse approximation of moment-based arbitrary polynomial chaos, J. Comput. Phys. (2016), http://dx.doi.org/10.1016/j. jcp.2016.05.014 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. SAMBA AbstractA new arbitrary Polynomial Chaos (aPC) method is presented for moderately high-dimensional problems characterised by limited input data availability.The proposed methodology improves the algorithm of aPC and extends the method, that was previously only introduced as tensor product expansion, to moderately high-dimensional stochastic problems. The fundamental idea of aPC is to use the statistical moments of the input random variables to develop the polynomial chaos expansion. This approach provides the possibility to propagate continuous or discrete probability density functions and also histograms (data sets) as long as their moments exist, are finite and the determinant of the moment matrix is strictly positive. For cases with limited data availability, this approach avoids bias and fitting errors caused by wrong assumptions. In this work, an alternative way to calculate the aPC is suggested, which provides the optimal polynomials, Gaussian quadrature collocation points and weights from the moments using only a handful of

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Geological storage of CO2: Application, feasibility and efficiency of global sensitivity analysis and risk assessment using the arbitrary polynomial chaos

Abstract: With them the seed of Wisdom did I sow, And with mine own hand wrought to make it grow; And this was all the Harvest that I reap'd-"I came like Water, and like Wind I go."

Cited by 37 publications

References 52 publications

Uncertainty analysis of carbon sequestration in an active CO2-EOR field

Uncertainty analysis of carbon sequestration in an active CO2-EOR field

Fundamental controls on fluid flow in carbonates: current workflows to emerging technologies

SAMBA: Sparse Approximation of Moment-Based Arbitrary Polynomial Chaos

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