Model Complexity in Reservoir Simulation

Pickup, Gillian Elizabeth; Valjak, Monika; Christie, Mike

doi:10.3997/2214-4609.20146407

Cited by 5 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The simplification in parameterising the very heterogeneous reservoir is the key to generating multiple models in history-matching, because the amount of computation required is prohibitive. The issues of model selection and model averaging have been discussed in other literature (e.g., Pickup et al, 2008;Christie et al, 2011).…”

Section: Description Of the Methodsmentioning

confidence: 99%

Reservoir Model History-Matching and Uncertainty Quantification in Reservoir Performance Forecast Using Bayesian Framework

Okano

2013

All Days

View full text Add to dashboard Cite

Reservoir performance forecasts are essentially uncertain due to the lack of data. The unknown parameters are calibrated so that the simulated profile can match the observed data. However the history-matching is ill-posed and may have non-unique solutions. The aim of our study is to quantify uncertainty of reservoir connectivity in a Turbidite sandstone reservoir where the wells have been stimulated by hydraulic fracturing. The reservoir properties were calibrated using a stochastic sampling algorithm called Differential Evolution (DE). Then a Bayesian framework along with Markov Chain Monte Carlo and Neighbourhood Approximation in parameter space is used to calculate the posterior probability.A Bayesian framework and DE have been applied to the evaluation of CO 2 injection test in the tight oil reservoir. The in-place volumes and connectivity between the wells have been calibrated in a simple model. The calibrated parameters include the length of a hydraulic fracture around the injector well and net-to-gross ratios. The observed data used for history-matching include the bottom-hole flowing pressure at the injector well and the gas composition at the wellhead of the producer wells.We showed the best fit model for the gas breakthrough and the P10-90 envelopes in the reservoir performance forecast. Assuming an on-going injection after the actual pilot test, the uncertainty envelopes in the CO 2 mole fraction in the produced gas were estimated to see a gas breakthrough at each of the producer wells. Our results contribute to the evaluation of the pilot test for a continuous CO 2 injection in the tight oil reservoir.The key is starting with a simple model, because it is much quicker to adjust large-scale heterogeneity in a simple model than in a detailed model. The simple model calibration with DE and the forecast of Bayesian inference have been successfully applied to the field test evaluation.

show abstract

Section: Description Of the Methodsmentioning

confidence: 99%

Reservoir Model History-Matching and Uncertainty Quantification in Reservoir Performance Forecast Using Bayesian Framework

Okano

2013

All Days

View full text Add to dashboard Cite

show abstract

Uncertainty Quantification in Geostatistical Modelling of Saltwater Intrusion at a Coastal Aquifer System

Lino Pereira,

Varouchakis,

Karatzas

et al. 2024

Math Geosci

View full text Add to dashboard Cite

Groundwater resources in Mediterranean coastal aquifers are under several threats including saltwater intrusion. This situation is exacerbated by the absence of sustainable management plans for groundwater resources. Management and monitoring of groundwater systems require an integrated approach and the joint interpretation of any available information. This work investigates how uncertainty can be integrated within the geo-modelling workflow when creating numerical three-dimensional aquifer models with electrical resistivity borehole logs, geostatistical simulation and Bayesian model averaging. Multiple geological scenarios of electrical resistivity are created with geostatistical simulation by removing one borehole at a time from the set of available boreholes. To account for the spatial uncertainty simultaneously reflected by the multiple geostatistical scenarios, Bayesian model averaging is used to combine the probability distribution functions of each scenario into a global one, thus providing more credible uncertainty intervals. The proposed methodology is applied to a water-stressed groundwater system located in Crete that is threatened by saltwater intrusion. The results obtained agree with the general knowledge of this complex environment and enable sustainable groundwater management policies to be devised considering optimistic and pessimistic scenarios.

show abstract

Hierarchical benchmark case study for history matching, uncertainty quantification and reservoir characterisation

Arnold

Demyanov

Tatum

et al. 2013

Computers & Geosciences

View full text Add to dashboard Cite

Model Complexity in Reservoir Simulation

Cited by 5 publications

References 0 publications

Reservoir Model History-Matching and Uncertainty Quantification in Reservoir Performance Forecast Using Bayesian Framework

Reservoir Model History-Matching and Uncertainty Quantification in Reservoir Performance Forecast Using Bayesian Framework

Uncertainty Quantification in Geostatistical Modelling of Saltwater Intrusion at a Coastal Aquifer System

Hierarchical benchmark case study for history matching, uncertainty quantification and reservoir characterisation

Contact Info

Product

Resources

About