Data Analysis Used in Multiple-Realization Workflows for History Matching - A North Sea Case Study

Schulze-Riegert, Ralf; Daniali, A..; Nwakile, Michael; Selberg, Stig; Skripkin, Sergey; Chugunov, Nikita; Carter, J..

doi:10.2118/185877-ms

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…MCMC sampling algorithms are used to explore the full posterior probability density function which puts a large weight on cases with a small mismatch and a small weight on cases with a large mismatch between simulated and historical measurment data (Slotte and Smorgrav 2008; Schulze-Riegert et al 2016, 2017a. This process involves a large number of function evaluations which would be unfeasible using full field simulations.…”

Section: History Matching Using Markov Chain Monte Carlo (Mcmc)mentioning

confidence: 99%

Integrated modeling of a complex oil rim development scenario under subsurface uncertainty

Elharith

Huey

Tewari

et al. 2019

J Petrol Explor Prod Technol

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Evaluation of the economic benefit of field development projects in the oil and gas industry is naturally subject to subsurface uncertainties and risks. While high economic returns from produced volumes compensated for the lack of robust uncertainty estimates in the past, lower margins in tight market conditions and more complex project designs require increased efforts in delivering reliable uncertainty estimates for improving reservoir management decision support in times to come. An increasing number of field development projects include rigorous uncertainty quantification workflows based on parameterized subsurface uncertainties. Estimation and reduction of prediction uncertainty has no value unless it can potentially influence a decision process. This requires an integrated workflow design across disciplines and a proper risk mitigation plan. This work presents the implementation of an uncertainty workflow in reservoir management with a focus on reservoir modeling and simulation. Practical workflow design steps and intermediate milestones are discussed for generating alternative reservoir model realizations including historical production data. Calibrated model realizations define the basis for prediction estimates of a predefined field development plan. Practical steps are discussed with application to a complex oil rim development for an existing reservoir with long production history. This study aimed at an uncertainty assessment of a field development plan and a proposal for optimized relocations of infill wells considering multiple geological realizations. In conclusion, this work describes examples, experiences, and recommendations for an industry implementation for a risk assessment of a field development plan under subsurface uncertainties.

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Section: History Matching Using Markov Chain Monte Carlo (Mcmc)mentioning

confidence: 99%

Integrated modeling of a complex oil rim development scenario under subsurface uncertainty

Elharith

Huey

Tewari

et al. 2019

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

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Deriving Alkali Polymer Parameter Distributions from Core Flooding by Applying Machine Learning in a Bayesian Framework to Simulate Incremental Oil Recovery

et al. 2020

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Various physico-chemical processes are affecting Alkali Polymer (AP) Flooding. Core floods can be performed to determine ranges for the parameters used in numerical models describing these processes. Because the parameters are uncertain, prior parameter ranges are introduced and the data is conditioned to observed data. It is challenging to determine posterior distributions of the various parameters as they need to be consistent with the different sets of data that are observed (e.g. pressures, oil and water production, chemical concentration at the outlet). Here, we are applying Machine Learning in a Bayesian Framework to condition parameter ranges to a multitude of observed data. To generate the response of the parameters, we used a numerical model and applied Latin Hypercube Sampling (2000 simulation runs) from the prior parameter ranges. To ensure that sufficient parameter combinations of the model comply with various observed data, Machine Learning can be applied. After defining multiple Objective Functions (OF) covering the different observed data (here six different Objective Functions), we used the Random Forest algorithm to generate statistical models for each of the Objective Functions. Next, parameter combinations which lead to results that are outside of the acceptance limit of the first Objective Function are rejected. Then, resampling is performed and the next Objective Function is applied until the last Objective Function is reached. To account for parameter interactions, the resulting parameter distributions are tested for the limits of all the Objective Functions. The results show that posterior parameter distributions can be efficiently conditioned to the various sets of observed data. Insensitive parameter ranges are not modified as they are not influenced by the information from the observed data. This is crucial as insensitive parameters in history could become sensitive in the forecast if the production mechanism is changed. The workflow introduced here can be applied for conditioning parameter ranges of field (re-)development projects to various observed data as well.

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Data Analysis Used in Multiple-Realization Workflows for History Matching - A North Sea Case Study

Cited by 2 publications

References 15 publications

Integrated modeling of a complex oil rim development scenario under subsurface uncertainty

Integrated modeling of a complex oil rim development scenario under subsurface uncertainty

Deriving Alkali Polymer Parameter Distributions from Core Flooding by Applying Machine Learning in a Bayesian Framework to Simulate Incremental Oil Recovery

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