Practical and Operational Use of Assisted History Matching and Model-Based Optimisation in the Salym Field

Joosten, G. J. P.; Altintas, A.; Sousa, Paulino De

doi:10.2118/146697-ms

Cited by 20 publications

(6 citation statements)

References 6 publications

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“…It is also well known that the estimated parameter values in such unregularized illposed problems are extremely sensitive to noise in the data. Joosten et al (2011) introduced the model maturation concept and showed that sometimes the application of unregularized reservoir parameter estimation still appears to have added value. They showed that localized unrealistic parameter values can be used as an indicator of model errors (or under-modeling) in the underlying reservoir model.…”

Section: Bayesian Parameter Estimationmentioning

confidence: 99%

Hidden Information in Ill-posed Inverse Problems

Kahrobaei¹,

Mansoori²,

Joosten³

et al. 2014

Proceedings

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SUMMARYIt is well known that parameter updating of large-scale numerical reservoir flow models (a.k.a. 'computer assisted history matching') is an ill-posed inverse problem. Typically the number of uncertain parameters in a reservoir flow model is very large whereas the available information for estimating these parameters is limited. The classic solution to this problem is to regularize the unknowns, e.g. by penalizing deviations from a prior model. Attempts to estimate all uncertain parameters from production data without regularization typically lead to unrealistically high parameter values and therefore to updated parameter fields that have little or no geological realism. However, it has been suggested that the application of unregularized reservoir parameter estimation may still add value, because it, sometimes, gives an indication of the location of significant missing features in the model. We investigated under which conditions this perceived added value might occur. We conducted several twin experiments and applied unregularized parameter estimation to update uncertain parameters in a simple two-dimensional reservoir model that contained a major deficiency in the form of a missing high or low permeability feature. We found that in case of low-permeability barriers or high-permeability streaks it is indeed sometimes possible to localize the position of the model deficiency. To further analyze this behavior we conducted onedimensional experiments using a transfer function formalism to characterize the identifiability of the location and magnitude of model deficiencies (flow barriers).

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Section: Bayesian Parameter Estimationmentioning

confidence: 99%

Hidden Information in Ill-posed Inverse Problems

Kahrobaei¹,

Mansoori²,

Joosten³

et al. 2014

Proceedings

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“…As part of our Best Practices we employ a workflow consisting of Experimental Design and the Adjoint Method with an emphasis on understanding rather than on automatically obtaining history matched models (Joosten & Altintas (2011), Van Doren et.al. (2012, van Essen et.al.…”

Section: Assisted History Matchingmentioning

confidence: 99%

“…This is illustrated in Figure 3b from Joosten & Altintas (2011). However, best practice has shown that the resulting model parameters should never be used blindly without relating the updates to physical explanations.…”

Section: Assisted History Matchingmentioning

confidence: 99%

“…The method is applied in a workflow coined model maturation (Joosten & Altintas (2011)), where the local permeability updates are used to highlight deficiencies in the geological model (e.g. A powerful feature of the Shell proprietary reservoir simulator is the availability of the so-called adjoint method which allows history matching of gridblock parameters such as permeability and porosity in an efficient and effective manner.…”

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confidence: 99%

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Full Field History Matching For Chemical Flooding With The Adjoint Method

Douma

Abu-Shiekah

Kindi³

2013

All Days

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Enhanced Oil Recovery is firmly established in the hydrocarbon maturation plans of PDO and Shell with successful ongoing thermal and polymer field developments and many novel methods being piloted. The current developments and trials undergo in-depth study to leverage the learnings for portfolio-wide application. In optimizing a current or derisking a future EOR development, understanding of the reservoir is key and more crucial than during primary and secondary development because of the complexity of the processes and higher costs involved.

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“…The motivation stems from a paper by Joosten et al (2011) who showed that sometimes the application of unregularized reservoir parameter estimation still appears to have added value. They argued, based on numerical examples, that localized unrealistic parameter values can be used as an indicator of model errors in the underlying reservoir model.…”

Section: Introductionmentioning

confidence: 99%

Identifiability of Location and Magnitude of Flow Barriers in Slightly Compressible Flow

Kahrobaei¹,

Mansoori²,

Joosten³

et al. 2015

SPE Reservoir Simulation Symposium

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Classic identifiability analysis of flow barriers in incompressible single-phase flow reveals that it is not possible to identify the location and permeability of low-permeable barriers from production data (well bore pressures and rates), and that only averaged reservoir properties in-between wells can be identified. We extend the classic analysis by including compressibility effects. We use two approaches: 1) a twin-experiment with synthetic production data for use with a time-domain parameter estimation technique, and 2) a transfer function formalism in the form of bilaterally coupled four-ports allowing for an analysis in the frequency domain. We investigate the identifiability, from noisy production data, of the location and the magnitude of a low-permeable barrier to slightly-compressible flow in a one-dimensional configuration. We use an unregularized adjoint-based optimization scheme for the numerical time-domain estimation, using various levels of sensor noise, and confirm the results using the semi-analytical transfer function approach. Both the numerical and semi-analytical results show that it is possible to identify the location and the magnitude of the permeability in the barrier from noise-free data. By introducing increasingly higher noise levels the identifiability gradually deteriorates, but the location of the barrier remains identifiable for much higher noise levels than the permeability. The shape of the objective function surface, in normalized variables, indeed indicates a much higher sensitivity of the well data to the location of the barrier than to its magnitude. These theoretical results appear to support the empirical finding that unregularized gradient-based history matching in large reservoir models, which is well known to be a severely ill-posed problem, occasionally leads to useful results in the form of model parameter updates having unrealistic magnitudes but indicating the correct location of model deficiencies.

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Practical and Operational Use of Assisted History Matching and Model-Based Optimisation in the Salym Field

Cited by 20 publications

References 6 publications

Hidden Information in Ill-posed Inverse Problems

Hidden Information in Ill-posed Inverse Problems

Full Field History Matching For Chemical Flooding With The Adjoint Method

Identifiability of Location and Magnitude of Flow Barriers in Slightly Compressible Flow

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