Geoscience Reference

In-Depth Information

Model

Data

Truth

Model 2

Data

Reservoir

(ground truth)

Model

Model 3

Fig. 3.14
Illustration of the axiom: Data

6
¼ Model

6
¼ Truth (Redrawn from Corbett and Jensen
1992
,

EAGE

#

reproduced with kind permission of EAGE Publications B.V., The Netherlands)

Our overall aim in reservoir modelling is to

estimate and compare distributions for:

1. The well data (observations);

2. The reservoir model (a hypothesis or postulate);

3. The population (the unknown “true” reservoir

properties).

We must always remember not to confuse

observations (data) with the model (a hypothesis)

and both of these with the “ground truth” (an

unknown). This leads us to one of the most

important axioms of reservoir modelling:

'The previous models were all wrong, but this

one must be right because it matches all the

data we have.'

Now statement A sounds good but begs the

questions what cut-off was applied and is the

geometric average indeed the appropriate aver-

age to use? Statement B is clearly arrogant but in

fact captures the psychology of every reservoir

model builder - we try to do our best with the

available data but are reluctant to admit to the

errors that must be present. Versions of these

statements that would be more consistent with

the inequality above might be:

Data

6
¼

Model

6
¼

Truth

Of course, we want our models to be consistent

with the available data (from wells, seismic, and

dynamic data) and we hope they will give us a

good approximation of the truth, but too often the

reservoir design engineer tries to force an artifi-

cial match which leads inevitably to great disap-

pointment. A common mistake is to try to

manipulate the data statistics to obtain an appar-

ent match between the model and data. You may

have heard versions of the following statements:

'We were able to match the well test perme-

ability (k
h
) to within 10 % of the log-derived

permeability data by applying the agreed cut-

off and using a geometric average, and a

power average with p

0.3 gave us an even

better match to within 1 %.'

¼

'The previous models had several serious

errors and weaknesses, but this latest set of

three models incorporates the latest data and

captures

the likely range of

subsurface

'We matched the well test permeability (k
h
)to

the log-derived permeability data by applying

a cut-off and using a geometric average.'

behaviour.'

Figure
3.14
illustrates what the statistical

objective of modelling should be. The available