Exploring the effects of data quality, data worth, and redundancy of CO2 gas pressure and saturation data on reservoir characterization through PEST inversion

Fang, Zhufeng; Hou, Zhangshuan; Lin, Guang; Engel, Dave; Fang, Yilin; Eslinger, Paul W.

doi:10.1007/s12665-013-2680-9

Cited by 1 publication

(1 citation statement)

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“…Fang et al . [] use data worth analysis to optimize a monitoring network and reduce data redundancy in a CO 2 sequestration scenario. A major limitation of many existing approaches is that they rely on a single conceptual‐mathematical model of geologic or watershed makeup and of hydrologic processes therein.…”

Section: Introductionmentioning

confidence: 99%

Multimodel Bayesian analysis of groundwater data worth

Xue

Zhang

Guadagnini

et al. 2014

Water Resources Research

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We explore the way in which uncertain descriptions of aquifer heterogeneity and groundwater flow impact one's ability to assess the worth of collecting additional data. We do so on the basis of Maximum Likelihood Bayesian Model Averaging (MLBMA) by accounting jointly for uncertainties in geostatistical and flow model structures and parameter (hydraulic conductivity) as well as system state (hydraulic head) estimates, given uncertain measurements of one or both variables. Previous description of our approach was limited to geostatistical models based solely on hydraulic conductivity data. Here we implement the approach on a synthetic example of steady state flow in a two-dimensional random log hydraulic conductivity field with and without recharge by embedding an inverse stochastic moment solution of groundwater flow in MLBMA. A moment-equations-based geostatistical inversion method is utilized to circumvent the need for computationally expensive numerical Monte Carlo simulations. The approach is compatible with either deterministic or stochastic flow models and consistent with modern statistical methods of parameter estimation, admitting but not requiring prior information about the parameters. It allows but does not require approximating lead predictive statistical moments of system states by linearization while updating model posterior probabilities and parameter estimates on the basis of potential new data both before and after such data are actually collected.

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Section: Introductionmentioning

confidence: 99%