Data analysis used in multiple realisation work flows for history matching - A North Sea case study

R. Schulze-Riegert, A. Daniali, M. Nwakile, S. Selberg, S. Skripkin, N. Chugunov, Jonathan Carter

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Abstract

An increasing number of field development projects include rigorous uncertainty quantification workflows based on parameterized subsurface uncertainties. Model calibration workflows for reservoir simulation models including historical production data, also called history matching, deliver non-unique solutions and remain technically challenging. The objective of this work is to present a manageable workflow design with well-defined project workflow tasks for reproducible result presentation. Data analysis techniques are applied to explore the information content of multiple-realization workflow designs for decision support.
Experimental design, sampling and Markov Chain Monte Carlo (MCMC) techniques are applied for case generation. Data analytics is applied to identify patterns in data sets supporting the evaluation of the history matching process. Visualization techniques are used to present dependencies between contributions to the history matching error metric. Conflicting history matching responses are identified and add value to the interpretation of history matching results. Probability maps are calculated on the basis of multiple-realizations sampled from a posterior distribution to investigate potentially under-developed reservoir regions.
Technologies are applied to a real gas field in the Southern North Sea. For the purpose of the benchmark, a structured workflow design to history matching and estimation of prediction uncertainty is presented. Sensitivity evaluations are used to identify key uncertain input parameters and perform parameter reduction. Markov Chain Monte Carlo (MCMC) is applied for optimization and uncertainty quantification. Statistical stability of key performance parameters is verified by repeating relevant phases of the workflow several times. In conclusion practical consequences and best practices as well as the use of data analytics in history matching workflows are discussed.
Original languageEnglish
Title of host publication79th EAGE Annual Conference & Exhibition
PublisherSociety of Petroleum Engineering
Number of pages22
ISBN (Print)978-1-61399-539-6
Publication statusPublished - 2017
Event 79th EAGE Annual Conference & Exhibition : Energy, Technology, Sustainability - Time to open a new Chapter - Paris, France
Duration: 12 Jun 201715 Jun 2017
Conference number: 79
https://events.eage.org/en/2017/79th-eage-conference-and-exhibition-2017
https://events.eage.org/en/2017/79th-eage-conference-and-exhibition-2017

Conference

Conference 79th EAGE Annual Conference & Exhibition
CountryFrance
CityParis
Period12/06/1715/06/17
Internet address

Fingerprint

Markov processes
Design of experiments
Visualization
Calibration
Sampling
Uncertainty
Gases

Cite this

Schulze-Riegert, R., Daniali, A., Nwakile, M., Selberg, S., Skripkin, S., Chugunov, N., & Carter, J. (2017). Data analysis used in multiple realisation work flows for history matching - A North Sea case study. In 79th EAGE Annual Conference & Exhibition [SPE-185877-MS] Society of Petroleum Engineering.

Data analysis used in multiple realisation work flows for history matching - A North Sea case study. / Schulze-Riegert, R.; Daniali, A.; Nwakile, M.; Selberg, S.; Skripkin, S.; Chugunov, N.; Carter, Jonathan.

79th EAGE Annual Conference & Exhibition. Society of Petroleum Engineering, 2017. SPE-185877-MS.

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Schulze-Riegert, R, Daniali, A, Nwakile, M, Selberg, S, Skripkin, S, Chugunov, N & Carter, J 2017, Data analysis used in multiple realisation work flows for history matching - A North Sea case study. in 79th EAGE Annual Conference & Exhibition., SPE-185877-MS, Society of Petroleum Engineering, 79th EAGE Annual Conference & Exhibition , Paris, France, 12/06/17.
Schulze-Riegert R, Daniali A, Nwakile M, Selberg S, Skripkin S, Chugunov N et al. Data analysis used in multiple realisation work flows for history matching - A North Sea case study. In 79th EAGE Annual Conference & Exhibition. Society of Petroleum Engineering. 2017. SPE-185877-MS
Schulze-Riegert, R. ; Daniali, A. ; Nwakile, M. ; Selberg, S. ; Skripkin, S. ; Chugunov, N. ; Carter, Jonathan. / Data analysis used in multiple realisation work flows for history matching - A North Sea case study. 79th EAGE Annual Conference & Exhibition. Society of Petroleum Engineering, 2017.
@inproceedings{eddda1bc27024b409594deecf49812cc,
title = "Data analysis used in multiple realisation work flows for history matching - A North Sea case study",
abstract = "An increasing number of field development projects include rigorous uncertainty quantification workflows based on parameterized subsurface uncertainties. Model calibration workflows for reservoir simulation models including historical production data, also called history matching, deliver non-unique solutions and remain technically challenging. The objective of this work is to present a manageable workflow design with well-defined project workflow tasks for reproducible result presentation. Data analysis techniques are applied to explore the information content of multiple-realization workflow designs for decision support.Experimental design, sampling and Markov Chain Monte Carlo (MCMC) techniques are applied for case generation. Data analytics is applied to identify patterns in data sets supporting the evaluation of the history matching process. Visualization techniques are used to present dependencies between contributions to the history matching error metric. Conflicting history matching responses are identified and add value to the interpretation of history matching results. Probability maps are calculated on the basis of multiple-realizations sampled from a posterior distribution to investigate potentially under-developed reservoir regions.Technologies are applied to a real gas field in the Southern North Sea. For the purpose of the benchmark, a structured workflow design to history matching and estimation of prediction uncertainty is presented. Sensitivity evaluations are used to identify key uncertain input parameters and perform parameter reduction. Markov Chain Monte Carlo (MCMC) is applied for optimization and uncertainty quantification. Statistical stability of key performance parameters is verified by repeating relevant phases of the workflow several times. In conclusion practical consequences and best practices as well as the use of data analytics in history matching workflows are discussed.",
author = "R. Schulze-Riegert and A. Daniali and M. Nwakile and S. Selberg and S. Skripkin and N. Chugunov and Jonathan Carter",
year = "2017",
language = "English",
isbn = "978-1-61399-539-6",
booktitle = "79th EAGE Annual Conference & Exhibition",
publisher = "Society of Petroleum Engineering",

}

TY - GEN

T1 - Data analysis used in multiple realisation work flows for history matching - A North Sea case study

AU - Schulze-Riegert, R.

AU - Daniali, A.

AU - Nwakile, M.

AU - Selberg, S.

AU - Skripkin, S.

AU - Chugunov, N.

AU - Carter, Jonathan

PY - 2017

Y1 - 2017

N2 - An increasing number of field development projects include rigorous uncertainty quantification workflows based on parameterized subsurface uncertainties. Model calibration workflows for reservoir simulation models including historical production data, also called history matching, deliver non-unique solutions and remain technically challenging. The objective of this work is to present a manageable workflow design with well-defined project workflow tasks for reproducible result presentation. Data analysis techniques are applied to explore the information content of multiple-realization workflow designs for decision support.Experimental design, sampling and Markov Chain Monte Carlo (MCMC) techniques are applied for case generation. Data analytics is applied to identify patterns in data sets supporting the evaluation of the history matching process. Visualization techniques are used to present dependencies between contributions to the history matching error metric. Conflicting history matching responses are identified and add value to the interpretation of history matching results. Probability maps are calculated on the basis of multiple-realizations sampled from a posterior distribution to investigate potentially under-developed reservoir regions.Technologies are applied to a real gas field in the Southern North Sea. For the purpose of the benchmark, a structured workflow design to history matching and estimation of prediction uncertainty is presented. Sensitivity evaluations are used to identify key uncertain input parameters and perform parameter reduction. Markov Chain Monte Carlo (MCMC) is applied for optimization and uncertainty quantification. Statistical stability of key performance parameters is verified by repeating relevant phases of the workflow several times. In conclusion practical consequences and best practices as well as the use of data analytics in history matching workflows are discussed.

AB - An increasing number of field development projects include rigorous uncertainty quantification workflows based on parameterized subsurface uncertainties. Model calibration workflows for reservoir simulation models including historical production data, also called history matching, deliver non-unique solutions and remain technically challenging. The objective of this work is to present a manageable workflow design with well-defined project workflow tasks for reproducible result presentation. Data analysis techniques are applied to explore the information content of multiple-realization workflow designs for decision support.Experimental design, sampling and Markov Chain Monte Carlo (MCMC) techniques are applied for case generation. Data analytics is applied to identify patterns in data sets supporting the evaluation of the history matching process. Visualization techniques are used to present dependencies between contributions to the history matching error metric. Conflicting history matching responses are identified and add value to the interpretation of history matching results. Probability maps are calculated on the basis of multiple-realizations sampled from a posterior distribution to investigate potentially under-developed reservoir regions.Technologies are applied to a real gas field in the Southern North Sea. For the purpose of the benchmark, a structured workflow design to history matching and estimation of prediction uncertainty is presented. Sensitivity evaluations are used to identify key uncertain input parameters and perform parameter reduction. Markov Chain Monte Carlo (MCMC) is applied for optimization and uncertainty quantification. Statistical stability of key performance parameters is verified by repeating relevant phases of the workflow several times. In conclusion practical consequences and best practices as well as the use of data analytics in history matching workflows are discussed.

M3 - Conference proceeding

SN - 978-1-61399-539-6

BT - 79th EAGE Annual Conference & Exhibition

PB - Society of Petroleum Engineering

ER -