[fogri] Re: artikel menarik dari geophysics
- From: Rovicky D P Hari <ROVICKY@xxxxxxxxxxxx>
- To: "'fogri@xxxxxxxxxxxxx'" <fogri@xxxxxxxxxxxxx>
- Date: Thu, 9 Aug 2001 16:05:19 +0700
> -----Original Message-----
> From: Paulus Allo [mailto:Paulus_Allo@xxxxxxxxxx]
> rekan-rekan,
> ada artikel menarik dari geophysics edisi januari-feb 2001
Dibawah ini Abstractnya sedangkan filenya ndak mungkin dibagi 16 MB ?? :
Silahkan DL sendiri di :
http://www.seg.org/publications/geoarchive/2001/jan/geo6601r02200236.html
Saya juga mengundang rekan-rekan untuk memberikan tulisannya utk dishare
dalam bentuk Webpage di www.fogri.f2s.com siapa takut ?? Kirim saja tulisan
anda, pakai bhs indonesia juga ndak apa-apa, yang penting dishare ilmunya.
Kalo papernya ndak diterima di mana-mana disini pasti diterima dan
ditampilkan, tanpa id sumur/lokasi juga ndak apa-apa. Pakai Bhs Indoz lebih
baik bhs Inggeris ya ndak apa-apa.
Trims
RDP
============
Use of multiattribute transforms to predict log properties from seismic data
Daniel P. Hampson, Hampson-Russell Software Services Ltd., 510 - 715 Fifth
Avenue SW, Calgary, Alberta T2P 2X6, Canada. E-mail: dan@xxxxxxxxxxxxxxxxxxx
James S. Schuelke, Formerly Mobil Technology Company, Dallas, Texas;
presently ExxonMobil Upstream Research Company, Houston, Texas. E-mail:
james_s_schuelke@xxxxxxxxxxxxxxx
John A. Quirein, Formerly Mobil Technology Company, Dallas, Texas; presently
Halliburton Energy Services, Houston, Texas. E-mail:
john.quirein@xxxxxxxxxxxxxxx
ABSTRACT
We describe a new method for predicting well-log properties from seismic
data. The analysis data consist of a series of target logs from wells which
tie a 3-D seismic volume The target logs theoretically may be of any type;
however,the greatest success to date has been in predicting porosity logs.
From the 3-D seismic volume a series of sample-based attributes is
calculated. The objective is to derive a multiattribute transform, which is
a linear or nonlinear transform between a subset of the attributes and the
target log values. The selected subset is determined by a process of forward
stepwise regression, which derives increasingly larger subsets of
attributes. An extension of conventional crossplotting involves the use of a
convolutional operator to resolve frequency differences between the target
logs and the seismic data.
In the linear mode,the transform consists of a series of weights derived by
least-squares minimization. In the nonlinear mode, a neural network is
trained, using the selected attributes as inputs. Two types of neural
networks have been evaluated: the multilayer feedforward network (MLFN) and
the probabilistic neural network (PNN). Because of its mathematical
simplicity, the PNN appears to be the network of choice.
To estimate the reliability of the derived multiattribute transform,
crossvalidation is used. In this process, each well is systematically
removed from the training set, and the transform is rederived from the
remaining wells. The prediction error for the hidden well is then
calculated. The validation error, which is the average error for all hidden
wells, is used as a measure of the likely prediction error when the
transform is applied to the seismic volume.
The method is applied to two real data sets. In each case, we see a
continuous improvement in predictive power as we progress from
single-attribute regression to linear multiattribute prediction to neural
network prediction. This improvement is evident not only on the training
data but, more importantly, on the validation data. In addition, the neural
network shows a significant improvement in resolution over that from linear
regression.
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