Practical Assisted History Matching and Probabilistic Forecasting Procedure: A West Africa Case Study

Dujardin, Bruno; Matringe, Sebastien; Collins, Jye Raymond

doi:10.2118/146292-ms

Cited by 7 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The parameter ranges may be based on some revised assessment of their impact. The sensitivity study may use an experimental design or some form of Monte Carlo (Dujardin, Matringe, & Collins, 2011) (Adepoju, Odusote, & Novakovic, 2009 (Billiter, Dagistanova, & King, 2008) (Baslaib, et al, 2014) (King, et al, 2005) (Landa & Guyaguler, 2003).…”

Section: Sensitivity Around Best History Matchmentioning

confidence: 99%

Bridging the Gap Between Deterministic and Probabilistic Uncertainty Quantification Using Advanced Proxy Based Methods

Goodwin

2015

SPE Reservoir Simulation Symposium

View full text Add to dashboard Cite

The use of proxy models for optimisation of expensive functions has demonstrated its value since the 1990's in many industries. Within reservoir engineering, similar techniques have been used for over a decade for history matching, both in commercial tools and in-house software.In addition to efficient history matching, proxy models have a distinct advantage when performing uncertainty quantification of probabilistic forecasts. Markov Chain Monte Carlo (MCMC) methods cannot realistically be applied directly with reservoir simulations, and even fast proxy models can fail dramatically to adequately represent the range of uncertainty if implemented without due care.A pitfall of the use of proxy models is that they are considered 'black box' and their quality is difficult to measure. Engineers prefer to deal with deterministic simulation models which they can evaluate and understand.The main pitfall of simple random walk MCMC techniques, which have begun to appear within reservoir engineering workflows, is a focus on theoretical properties which are not observed in practical implementations. This gives rise to potential gross errors, which are not generally appreciated by practitioners. Advances in recent years within the field of Bayesian statistics have significantly improved this situation, but have not yet been disseminated within the oil and gas industry. This paper describes the limitations of random walk MCMC techniques which are currently used for reservoir prediction studies, and shows how Hamiltonian MCMC techniques, together with an efficient implementation of proxy models, can lead to a more reliable and validated probabilistic uncertainty quantification, whilst also generating a suitable ensemble of deterministic reservoir models. Scientific comparison studies are performed for both an analytical case and a realistic reservoir simulation case to demonstrate the validity of the approach.The benefit of this methodology is to allow asset teams to effectively manage reservoir decisions using a robust and validated understanding of uncertainty. It lays the scientific foundations for the next generation of uncertainty tools and workflows.

show abstract

Section: Sensitivity Around Best History Matchmentioning

confidence: 99%

Bridging the Gap Between Deterministic and Probabilistic Uncertainty Quantification Using Advanced Proxy Based Methods

Goodwin

2015

SPE Reservoir Simulation Symposium

View full text Add to dashboard Cite

show abstract

References

2015

Experimental Design in Petroleum Reservoir Studies

View full text Add to dashboard Cite

Methodology to Systematically Reduce Uncertainty Assimilating Quantitatively 4D Seismic and Well Data in a Probabilistic and Multi-Objective History Matching

Almeida

Davólio

Schiozer

2017

SPE Annual Technical Conference and Exhibition

View full text Add to dashboard Cite

History matching (HM) is a complex process that aims to increase the reliability of reservoir simulation models. HM is an inverse problem with multiple solutions that calls for a probabilistic approach. When observed data are integrated with sampling methods, uncertainty can be reduced by updating the probability density function (pdf) of the attributes. This work presents a practical methodology to systematically reduce uncertainties in a multi-objective probabilistic assisted history matching, integrating well and 4D seismic data (4DS) quantitatively. The pdf update goes through an iterative process. The pdf of the current iteration is combined with the pdf generated using the best-matched models to generate the posterior pdf. The multiple local objective functions (OF) are evaluated independently, allowing the identification of OFs that indicate the requirement of re-parameterization. This quantitative process was applied in two phases: (I) using only well data to constrain the models; (II) adding 4DS data. The methodology was successfully validated against a benchmark case of medium complexity, with the history-production data generated at fine scale (reference model). The proposed methodology reduced uncertainties for the majority of attributes, achieving the highest probable levels similar to those of the reference values. The identification of objective functions that restricts the match allowed improving of the parameterization employed, by locally adding probabilistic perturbations to the petrophysical realizations. The method efficiently achieved multiple matched simulation models, with all (87) local objective functions within the defined tolerance range. Each iteration increased the number of matched models, demonstrating good convergence. The uncertainties were reduced gradually, avoiding premature level elimination (minimizing convergence at an incorrect solution). In both phases, the matched simulation models presented similar production forecasts to the reference model. The 4D seismic data were included regionally with an acceptable increase in computation time. The quantitative incorporation of 4D seismic data generated models that forecasted production with less variability than models generated without 4DS data. This was expected as for this study, 4D seismic data provided perfect information.

show abstract

Practical Assisted History Matching and Probabilistic Forecasting Procedure: A West Africa Case Study

Cited by 7 publications

References 8 publications

Bridging the Gap Between Deterministic and Probabilistic Uncertainty Quantification Using Advanced Proxy Based Methods

Bridging the Gap Between Deterministic and Probabilistic Uncertainty Quantification Using Advanced Proxy Based Methods

References

Methodology to Systematically Reduce Uncertainty Assimilating Quantitatively 4D Seismic and Well Data in a Probabilistic and Multi-Objective History Matching

Contact Info

Product

Resources

About