Solving iTOUGH2 simulation and optimization problems using the PEST protocol

Finsterle, Stefan; Zhang, Yingqi

doi:10.1016/j.envsoft.2011.02.008

Cited by 64 publications

(29 citation statements)

References 44 publications

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“…Rounding out the simulation components we used for estimating the oil and gas flow rate is the iTOUGH2 code with uncertainty and sensitivity analysis capabilities (11,12). Briefly, iTOUGH2 is a wrapper around the codes described above that calls the forward model repeatedly while varying key input parameters to produce multiple results as a function of input parameter variations.…”

Section: Methodsmentioning

confidence: 99%

Numerical simulations of the Macondo well blowout reveal strong control of oil flow by reservoir permeability and exsolution of gas

Oldenburg

Freifeld

Pruess

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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In response to the urgent need for estimates of the oil and gas flow rate from the Macondo well MC252-1 blowout, we assembled a small team and carried out oil and gas flow simulations using the TOUGH2 codes over two weeks in mid-2010. The conceptual model included the oil reservoir and the well with a top boundary condition located at the bottom of the blowout preventer. We developed a fluid properties module (Eoil) applicable to a simple twophase and two-component oil-gas system. The flow of oil and gas was simulated using T2Well, a coupled reservoir-wellbore flow model, along with iTOUGH2 for sensitivity analysis and uncertainty quantification. The most likely oil flow rate estimated from simulations based on the data available in early June 2010 was about 100,000 bbl∕d (barrels per day) with a corresponding gas flow rate of 300 MMscf∕d (million standard cubic feet per day) assuming the well was open to the reservoir over 30 m of thickness. A Monte Carlo analysis of reservoir and fluid properties provided an uncertainty distribution with a long tail extending down to 60,000 bbl∕d of oil (170 MMscf∕d of gas). The flow rate was most strongly sensitive to reservoir permeability. Conceptual model uncertainty was also significant, particularly with regard to the length of the well that was open to the reservoir. For fluid-entry interval length of 1.5 m, the oil flow rate was about 56,000 bbl∕d. Sensitivity analyses showed that flow rate was not very sensitive to pressuredrop across the blowout preventer due to the interplay between gas exsolution and oil flow rate.Gulf oil spill | wellbore-reservoir coupling | phase interference O n April 20, 2010, the Macondo well MC252-1 drilled from the Deepwater Horizon floating platform in the Gulf of Mexico suffered a blowout. Eleven people were killed by the explosion and fire on the platform shortly after the blowout, and the platform sank 2 d later. The failure of the blowout preventer (BOP) mounted on the wellhead at the seafloor allowed oil and gas to flow directly into the sea out of the mangled riser pipe, which would normally convey oil from the well to the platform. Later the riser pipe was cut off, and oil and gas flowed directly into the sea out the top of the BOP. These details were displayed to the public in unprecedented fashion in real time over the internet by live video feeds from several remotely operated vehicles.

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Section: Methodsmentioning

confidence: 99%

Numerical simulations of the Macondo well blowout reveal strong control of oil flow by reservoir permeability and exsolution of gas

Oldenburg

Freifeld

Pruess

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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“…If parameters of a pre-processor are varied as part of the analysis (as is done here for the parameters of the dual-permeability model, which enter the mesh generator), the PEST protocol is employed, as described in Finsterle and Zhang (2011). All simulations for the local and global sensitivity analysis are independent from each other and thus can be run in parallel, supported by iTOUGH2-PVM (Finsterle, 1998).…”

Section: Sensitivity Analysis Methodsmentioning

confidence: 99%

Microhole arrays for improved heat mining from enhanced geothermal systems

et al. 2013

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a b s t r a c t Keywords: Geothermal energy Heat extraction Microholes Numerical modeling EGS Numerical simulations are used to examine whether microhole arrays have the potential to increase the heat mining efficiency and sustainability of enhanced geothermal systems (EGS). Injecting the working fluid from a large number of spatially distributed microholes rather than a few conventionally drilled wells is likely to provide access to a larger reservoir volume with enhanced overall flow distances between the injection and production wells and increased contact area between permeable fractures and the hot rock matrix. More importantly, it reduces the risk of preferential flow and early thermal breakthrough, making microhole array-based EGS a more robust design. Heat recovery factors are calculated for EGS reservoirs with a conventional well configuration and with microhole arrays. The synthetic reservoir has properties similar to those of the EGS test site at Soultz-sous-Forêts. The wells and microholes are explicitly included in the numerical model. They intersect a stimulated reservoir region, which is modeled using a dual-permeability approach, as well as a wide-aperture zone, which is incorporated as a discrete feature. Local and global sensitivity analyses are used to examine the robustness of the design for a variety of reservoir and operating conditions. The simulations indicate that the flexibility offered by microhole drilling technology could provide an alternative EGS exploitation option with improved performance.

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“…Main methods for gradient-based optimization are the Levenberg-Marquardt formulation and the conjugate gradient or quasi-Newton approaches. For the Levenberg-Marquardt formulation, studies and algorithm development are included by Zhang et al [2003], Tonkin and Doherty [2005], Finsterle and Kowalsky [2011], and Finsterle and Zhang [2011]. For the conjugate gradient or quasi-Newton approach, Cheng et al [2003] and Oyerinde et al [2009] are examples.…”

Section: Literature Reviewmentioning

confidence: 99%

Estimation of plume distribution for carbon sequestration using parameter estimation with limited monitoring data

Espinet

Shoemaker

Doughty

2013

Water Resources Research

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[1] This study develops and evaluates an integrated methodology including parameter zonation, efficient global optimization, and multiple aquifer realizations that gives a useful forecast of the pressure distribution and the CO 2 plume migration through a heterogeneous storage formation, over time, using only a few monitoring locations in a feasibly short amount of computing time, while dealing with data error. Geological characteristics of the example application are similar to a CO 2 sequestration pilot test conducted in a fluvialdeltaic geological setting. In the example, the CO 2 injection problem is simulated with the TOUGH2 code, a numerical simulator for multiphase (gas and aqueous), multicomponent flow and transport. The inverse problem is difficult because the GCS optimization estimation problem has multiple local minima and the simulation is computationally expensive. The efficient surrogate response surface global optimization algorithm ''Stochastic RBF'' (stochastic radial basis function) is used to calibrate the model parameters. Results are averaged over three saline aquifer realizations. In the third numerical experiment using only pressure data, the CO 2 plume could be determined with an average correlation coefficient compared to the actual plume of up to R 2 ¼ 0.916 (for current plume at t ¼ 1.5 years) and average R 2 ¼ 0.80 (for forecasted plume estimated at t ¼ 7.5 years years, using only the first 1.5 years of monitoring data). Adding gas saturation data improved the 6 year forecast somewhat (increasing average R 2 ¼ 0.85) but it was not significantly helpful in estimating the current plume. Both our inverse methodology and findings can be broadly applicable to GCS in heterogeneous sedimentary formations.Citation: Espinet, A., C. Shoemaker, and C. Doughty (2013), Estimation of Plume distribution for carbon sequestration using parameter estimation with limited monitoring data, Water Resour. Res., 49,

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Solving iTOUGH2 simulation and optimization problems using the PEST protocol

Cited by 64 publications

References 44 publications

Numerical simulations of the Macondo well blowout reveal strong control of oil flow by reservoir permeability and exsolution of gas

Numerical simulations of the Macondo well blowout reveal strong control of oil flow by reservoir permeability and exsolution of gas

Microhole arrays for improved heat mining from enhanced geothermal systems

Estimation of plume distribution for carbon sequestration using parameter estimation with limited monitoring data

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