Metrel Petrel Plug-in for · SCRF 2010 3 Predicting 10-year oil production… • From uncertain reservoir model given data – Uncertain structure – Uncertainty geological scenario

Stanford Center for Reservoir ForecastingStanford Center for Reservoir Forecasting

Annual Meeting 2010

Metrel: Petrel Plug-in for Modeling Uncertainty in Metric Space

Kwangwon Park, and Jef Caers

SCRF 2010 2

Ocean

• Application development framework– Tightly integrated with the Petrel Product Family– Visual C#– Creates new custom workflows in Petrel

SCRF 2010 3

Predicting 10-year oil production…

• From uncertain reservoir model given data– Uncertain structure– Uncertainty geological scenario– …

• Generate multiple model realizations• Calculate 10-year oil production curves• Estimate p10, p50, p90 of oil production

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Modeling Uncertainty in Metric Space

• Allows the efficient management of multiple models– Generation of multiple models – Selection of a few representative models for uncertainty– Sensitivity analysis with any type of parameters– Easy analysis of multiple models in 3D space

• Core technologies of metric space modeling implemented in this plug-in (Metrel)– Defining a distance between two models – Multi-dimensional scaling to represent the metric space– Kernel k-means clustering

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Structural Model 1

Property Model 1

Property Model 2

…

Each Model

Property 1

Property 2

…

In Petrel Database

Structural Model 2

Property Model 1

Property Model 2

…

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No Fault Model

Continuous 1

Continuous 2

Channel 1

…

One-Fault Model

Sparse Channel 1

Dense Channel 1

Dense Channel 2

…

Each Model

Permeability

Porosity

NTG

Gas Production

Fractional Flow

BHP

OOIP

….

OWC

For example,

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Various simulation cases

Case 1: No Fault, Permeability 1, Porosity 1, ……





Case 6: One Fault, Permeability 1, Porosity 1, ……




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Each of simulation cases has its own various properties










Properties, Simulation Results, ….









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Metric space and multi-dimensional scaling

Multi-dimensional Scaling Map

Case 1Case 2

Case 3

Case 4

Case 5

D(Prop of case 1, Prop of case 5)

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Kernel k-means clustering

Cluster 1Cluster 2

Cluster 3

Cluster 4

Cluster 5

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From the petrel project containing multiple models and simulation cases

104 models (Brugge)Input1 (Facies): Facies model (78) and no-facies model (26)Input2 (Fluvial): Porosity by multi-point geostat (39) and by sequential indicator simulation (65)Input3 (Permeability): Permeability by single poroperm regression (39), by poroperm regression per facies (26), and by coKriging on porosity (39)

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Run the plug-inDistance and

multi-dimensional scaling

• Choose multiple cases• Define a distance by

choosing a property or multiple properties or results (of Frontsim)

• Click the button for multi-dimensional scaling

• Result is stored in the input tab as a pointset3D

Kernel k-means clustering

• Choose which metric space to be used for clustering

• Dimension of the metric space and kernel bandwidth is determined automatically

• Choose the number of clusters

• Click the button for kernel k-means clustering

• Result is stored in the input tab as a pointset3D

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Result: Multi-dimensional scaling

Pointset3D is generated in the input tab

Each point represents each casel; the case name is displayed in the screen.

The cases are distributed based on their production response. The closer the cases are, the more similar the production responses are.

By visual inspection, 1. They are already clustered2. Some cases are far from the cloud

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Result: Kernel k-means clustering

Pointset3D is generated in the input tab

Cluster indices for cases are displayed. All the cases are divided into 6 groups.

6 representative models are BRUGGE_33, 48, 68, 78, 88, and 93 (also displayed).

Estimate p10, p50, p90 of production by using only the chosen 6 cases.

Production of all cases

Production of 6 cases

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Result: Sensitivity analysisDisplay the facies model (Yes: w/ facies; No: w/o facies)

Display the fluvial model (MPS: multiple point geostat; SIS: sequential indicator simulation)

Display the permeability model (KS: single regression; KM: regression per facies; KP: coKriging on porosity)

Clustering and the input parameters or the type of property generation are clearly related.Sensitivity analysis is possible for any type of variable: whether categorical or continuous.

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Conclusion• Metric Space Modeling Technologies

– Manage multiple models based on our interests– Generate multiple models constrained to all data: Geology, hard

and soft data, dynamic production data– Cluster multiple models and select a few representatives– Analysis and visualization of multiple models– coupled with many functions of Petrel

• Suggested applications– Determining P10, P50, P90 with the reduced number of

realizations– Sensitivity of the input parameters to the results

Documents

Metrel Petrel Plug-in for · SCRF 2010 3 Predicting 10-year oil production… • From uncertain reservoir model given data – Uncertain structure – Uncertainty geological scenario