6 Lithology Predition

7/29/2019 6 Lithology Predition

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The ability to predict lithology is one example of the potential of

multi-component technology to minimize reservoir lithology

uncertainty and to exploit and produce reserves faster and atlower cost.


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The North Sea field in this example is characterized by heterogeneous Paleocene

sandstone reservoirs. The shaling out of the sand makes exploitation and delineation

of the reservoirs very difficult. Analysis of log data from two wells, one with goodreservoir quality sand and a high net gross, and one with low net gross and only a

few sand lenses of reservoir quality, showed a strong correlation between good

reservoir and low Vp/Vs ratio. A seafloor multi-component survey was shot to better

define the shaling out of the sand.

Interpretation of instantaneous Vp/Vs sections can be used to predictchanges in reservoir quality to distinguish between clean and shaly sands.The figure above shows the instantaneous VP/Vs section for this example. The

reservoir interval is characterized by a low Vp/Vs value (denoted by orange) on

the left side. The Vp/Vs value increases (denoted by green) moving from left to

right. This interpretation demonstrates the degradation of the reservoir qualityseen in the well data.


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Seismic Attributes

We can use them to predict physical

properties away from well locations

But do we always know why they work?

If we dont, should we use them? How can we find out?


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Definitions

Seismic attribute: A derivative of a basic seismic measurement

(Brown, 1996)

A specific measurement of geometric,

kinematic, dynamic or statistical featuresderived from seismic data

(Chen and Sydney, 1997)


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Attribute study approach number 1: Horizon-based Extract physical property from wells (net sand, average porosity, etc.)

Extract attributes from seismic data at corresponding TWT (i.e., horizon slice) Look for correlations between seismic attributes and physical property at well

locations

Use correlations, if found, to produce a map of the physical property


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Attribute study approach number 2: Interval-based Extract physical property from wells (net sand, average porosity, etc.)

Extract attributes from seismic data in appropriate window Look for correlations between seismic attributes and physical property at well

locations

Use correlations, if found, to produce a map of the physical property


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Attribute study approach number 3: Volume-based Convert target log (porosity, Vsh, etc.) to time domain for all wells

Look for correlations between seismic attributes and physical property on asample-by-sample basis within a specific interval

Use correlations, if found, to produce a volume of the physical property (e.g.,

replacing the amplitude traces with predicted porosity logs)


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Conventional InterpretationConventional Interpretation

Pearson and Hart, 2004Pearson and Hart, 2004

Workflow for seismic attribute studies


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Attribute Studies Need careful traditional geoscience

interpretation Good well ties

Good seismic/log picks

Need good understanding of geology,geophysics, (geo)statistics

Ask the right questions Know when the answer is wrong

Vehicle for integration of geological, geophysical

and engineering data and concepts


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Attribute Studies Promoted/developed by software vendors

Few rigorous scientific papers Little documentation describing attributes

Methods (e.g., neural networks) that capturenon-linear relationships often give best results

Geologically, statistically

Caution: may find patterns where none exist!


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Attribute Studies Volume-based methods better than horizon- or

interval-based 3-D distribution of properties

Visualization provides insights

Need well data

Generally development settings

Use forward modeling to predict responses in

exploration/sparse well areas??


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Seismic data is converted to a layer-orientated model of the subsurface,

which is calibrated to the measured well log impedance. This clarifies

lithologic and stratigraphic identification


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Documents

6 Lithology Predition