SEISMIC RESERVOIR CHARACTERIZATION WITH LIMITED WELL CONTROL

Tanja Oldenziel1, Fred Aminzadeh

2, Paul de Groot

1, and Sigfrido Nielsen

3

1 De Groot-Bril Earth Sciences BV Boulevard 1945 # 24, 7511 AE Enschede, The Neetherlands

2 De Groot-Bril Earth Sciences 2500 Tanglewilde, Suite 120, Houston Texas 77063, USA

3 GeoInfo SRL, 25 de Mayo 168 9º piso, C1002ABD Buenos Aires, Argentina

Keywords Seismic reservoir characterization with limited well control

Abstract

In this paper, we present a reservoir

characterization workflow for fields with limited

well control. An onshore German gasfield case

study is presented to discuss different techniques.

Central to all techniques is the use of a set of 300

simulated pseudo-wells that was created to extend

the well data base of six real wells. The pseudo-

wells are simulated using statistical input derived

from the real wells and geological knowledge

supplied in the form of rules and constraints. The

simulated set is representative of the expected

variations in geology, petrophysics and seismic

response in the study area.

In the first technique seismic data is analysed by

segmenting seismic waveforms around the

reservoir level using an unsupervised neural

network. Subsequently, the seismic character of

each segment is quantified in terms of the

reservoir properties porosity and N*Phi using the

pseudo-wells. In the second technique seismic and

impedance cubes are inverted to a porosity

volume using a supervised neural network. The

neural network is trained on synthetic traces of the

pseudo-wells. The real wells are used as blind test

wells and indicate the high quality of the porosity

inversion.

The pseudo-wells are essential to the success of

this study. Without these we do not have enough

statistics to analyse the waveform segmentation

maps. Neither would it be possible to produce a

realistic porosity volume. The real wells in the

area are all drilled on amplitude character and

recorded similar porosities. Low and high

porosities, which are known to exist in the

geologial setting are not represented in the real

well data base, but are represented in the

simulated set.

Introduction

The example is from northwest Germany

where gas is present in Rotliegend (Permian)

aeolian sandstones. Two 3D seismic volumes

were available: zero-phase reflectivity and

acoustic impedance. Six wells fall inside the study

area. These were used to derive the statistical

variations needed by the pseudo-well simulator

and served as blind test locations to validate the

predictions.

In the workflow the stratigraphy, logs, and

relevant well data are fully integrated according to

a user-defined integration framework. The

framework defines the hierarchy of the

stratigraphic units and also what information can

be stored at each individual unit. A well (real or

simulated) therefore consists of layers with

stratigraphic identification and attached

petrophysical data. The integrated well data is

linked to the seismic data, after which inter-

relationships between the various datatypes can be

studied at the hierarchical scale levels defined by

the integration framework. The inter-relationships

are then used to predict the same features from the

factual seismic data.

The aim of seismic reservoir characterization

is to relate seismic measurements to relevant

geological and petrophysical reservoir properties.

The process involves analyzing complex

relationships between huge amounts of data

originating from different sources, acquired at

different scale levels and accuracies. In the last

decade artificial neural networks have been used

successfully by many workers to aid in the

process of finding these complex relationships. In

this study two types of seismic pattern recognition

techniques have been used: unsupervised and

supervised. The main difference between

supervised and unsupervised approaches lies in

the amount of a-priori knowledge, which is

supplied. Below a more detailed discussion on

neural networks will follow.

Neural networks enable computer systems to

imitate some desirable brain properties. Various

types of networks have been applied successfully

in a variety of scientific and technological fields.

Examples are applications in industrial process

modeling and control, ecological and biological

modeling, sociological and economical sciences,

as well as medicine (Kavli, 1992). Within the

exploration and production world, neural network

technology is routinely applied to geologic log

analysis (Doveton, 1994, Nikravesh and

Aminzadeh, 2001) and seismic attribute analysis

(e.g. Schultz, 1994, de Groot, 1998).

Basically, two learning approaches can be

recognized in neural network modeling:

supervised and unsupervised. The supervised

approach requires the existence of a representative

dataset. The network learns by feeding it

examples from the representative dataset (the

training set). The neural network then learns how

the input data is related to the desired output. The

supervised approach is a form of non-linear,

multivariate regression that is used to quantify or

classify data. Examples of quantification are

networks that predict, from the seismic response,

properties such as porosity or porevolume.

Examples of classification are: classifying seismic

waveforms into classes representing a specific

fluid-fill, or a lithology. Popular supervised

learning networks are: Multi-Layer Perceptrons

and Radial Basis Functions networks (e.g. de

Groot, 1995) or Hybrid Neural Networks (e.g.

Aminzadeh, et al, 2000)

In the unsupervised approach the aim is to

find structure in the data themselves, without

imposing an a-priori conclusion. Unsupervised

learning is used for data segmentation, i.e. data

clustering. The resulting segments (e.g. clusters of

similar seismic waveforms at the reservoir level)

remain to be interpreted. Popular networks that

use unsupervised learning are the Unsupervised

Vector Quantiser (de Groot, 1995) and Kohonen

Feature Maps (e.g. Lippmann, 1989).

Neural networks are simply a way of

mapping a set of input variables to a set of output

variables. In seismic reservoir characterisation the

input obviously comes from seismic data. This

can be in the form of amplitudes, or single and/or

multi-trace attributes derived from one or more

seismic volumes (e.g. full stack, near stack, far

stack, intercept, gradient, inverted acoustic

impedance etc). Input may also come from other

sources (e.g. co-ordinates, two-way time,

geological features etc). Basically any variable

that is available at each prediction position and

which may be related to the desired output can be

used.

The output depends on the type and design of the

network and how the trained network is applied.

The results are two-dimensional (grids) if the

network is steered along an interpreted horizon.

Three-dimensional results (volumes) are obtained

if the network is applied on a trace-by-trace and

sample-by-sample basis.

Pseudo-well simulation

In many fields, there is only limited well

control and thus there may be a problem that data

is not truly representative of the variations in the

data. Hence, the inversion is ill-based. This

problem can be bypassed by simulating additional

pseudo-wells with associated synthetic

seismograms (de Groot, 1996). These are

stratigraphic columns with attached well logs but

without spatial locations. The method assumes

geologically and petrophysically correct

simulations and good synthetic-to-seismic

matches. These pseudo-wells are representative

for the area and can be seen as possible geologic

realizations, i.e. each can be the next newly drilled

well. For this study, three hundred pseudo-wells

with sonic, density (hence impedance) and

porosity logs were simulated. The variations in

stratigraphy and log response were derived from

real well data. The simulator is based on a

constrained Monte Carlo procedure which is

steered by geological knowledge (de Groot,

1995). Geologic knowledge was incorporated in

the simulation model to cover the ranges, which

are to be expected in the study area. Sonic and

density distributions are correlated with a –0.9

cross-correlation coefficient. Gas columns are not

simulated in this case, because the reservoirs

occur at a depth of approx. 4km where the effect

of gas is not detectable on seismic. For each

stratigraphic unit, rules and constraints were

implemented. For example, 40% Net-to-Gross in

the middle reservoir layer, always a shale to

overly the reservoir, and volcanic intrusions

occurring only in 50% of the wells. For each

pseudo well a synthetic trace is generated, using

the convolution model.

Segmentation of seismic character

In the unsupervised (or competitive learning)

approach the aim is to find structure in the data

themselves and thus to extract relevant properties

/ features. Seismic waveforms around an

interpreted horizon are segmented (clustered) into

a specified number of segments. Each segment is

characterized by its waveform shaped class center.

Mainly visual inspection of these class centers is

used to determine the optimal number of classes

for segmentation of the waveforms, for this study

8.

The Unsupervised Vector Quantiser (UVQ)

network first has to learn how to segment the

seismic waveforms. This training is done on a

representative selection of seismic waveforms,

e.g. every 10th Inline and Crossline a waveform is

extracted. The network learns to cluster the input

into a pre-defined number of segments. We can do

this kind of segmentation with any seismic

attribute. The advantage of doing it with the

seismic amplitudes within a certain time window

is that the center vectors resemble seismic

waveforms which facilitates the interpretation.

Moreover, the segmentation is based on the entire

seismic waveform rather than some derived

attributes.

Application of the network to the entire

volume(s) yields two outputs at every sample

position: the segmentation result i.e. the index of

the winning segment and the match i.e. a measure

of confidence in the segmentation. This is a non-

quantitative result showing only areas with similar

seismic characteristics. In the interpretation of

these patterns one must take into account that the

seismic response pertaining to a certain geological

sequence is smeared across overlying and

underlying sequences. Vice-versa, the response

from these units may pollute the level of interest.

Moreover, if the extraction window is not parallel

to the stratigraphy as in our case, we are cutting

through the geology and the results become

difficult to interpret. With these limitations in

mind we can still extract valuable geological and

petrophysical information from the observed

patterns. The interpretation can be based purely

on geological insight but a more quantitative

analysis can be done using the well data.

Simulated and / or real wells are segmented by the

trained UVQ network and the resulting well

groups are analyzed for geological and

petrophysical content.

Quantification of segments

To quantify the different seismic classes, the

300 pseudo-wells are segmented by the network

according to the corresponding synthetic seismic

response. In other words each synthetic seismic

response is compared to the UVQ class centers

and is assigned to the class it resembles most. The

segmentation result is used to analyze geological

and petrophysical variations per segment. In this

case, 300 simulated wells were segmented into the

8 segments. Subsequently, relevant well features

(e.g. porosity and N*Phi) are extracted from the

well group in each segment. Analyzing these

features reveals where the segments differ in

terms of geological and petrophysical content.

Table 1 shows the difference in porosity and

N*Phi for the 8 segments. Except for class 2, the

pseudo-wells are quite evenly distributed over all

segments indicating that the pseudo-wells cover

the seismic variety of our study area. No wells

were classified as class 2, which is therefore

missing from the table.

Usually one class acts as a ‘garbage bin’ to collect

all noise traces. None of the pseudo-wells has

similar low amplitude synthetics as in class 2,

which makes it most probably noise and not

related to a reservoir feature. Class 1 and 8 can be

quantified as good reservoir, i.e. high porosity and

NTG. On the other hand, class 3 and 6, are of

lower quality, i.e. low porosity and NTG.

Fig 1 Neural network topology for porosity prediction

1 3 4 5 6 7 8

Phi(%) 13 11 12 12 12 14 15

N*Phi 3.7 2.5 3.2 3.3 2.6 3.3 4.3

Table 1 Quantification of 8 UVQ

segments

Volume transformation to porosity

The supervised approach requires the

presence of a representative dataset comprising

seismic signals with corresponding geological /

petrophysical information. Neural networks

(MLP) are then trained to quantify the seismic

response into desired geological and/or

petrophysical target quantities.

The neural network input variables were

taken from the synthetics and the acoustic

impedance traces of the pseudo-wells. Seismic

waveforms of [-20,20] ms. length were extracted

relative to a reference time, sliding with 4 ms.

steps. Hence, seismic waveforms of 40 ms. length

were taken at -10, -6, -2 ms. etc. In the same way

the amplitude of the synthetic impedance trace

was extracted and given to the network. Also the

reference time itself served as an additional input

node to the neural network. Fig. 1 shows the

neural network topology. The porosity and

impedance logs for this purpose were converted to

time using the sonic log and resampled to 4 ms

using an anti-alias filter. To avoid overfitting the 6

real wells were used as test data during the

training of the network. Overfitting is a process,

which may occur with prolonged training when

the network starts to recognize individual

examples from the training set and deviates from

the general trend. Overfitting is especially a

problem when the training sets are small (few

wells) and the networks are large (many nodes in

the hidden layer means more degrees of freedom,

hence more complicated functions can be

modeled).

It is good practice to use a number of

examples as blind test locations. In this study the

6 real wells were used to validate the inversion

results. Fig. 2 shows the porosity predictions

versus the original porosity trace at one blind test

locations. All 6 blind test predictions are very

good, hence increasing our confidence in the

neural network performance and the

representativeness of the pseudo-wells. Fig. 3

shows the porosity prediction on one inline out of

the 3D porosity volume. The prediction agrees

well with the known stratigraphy of the

Rotliegend in the area.

Fig. 2 Porosity comparison

Conclusions

The following conclusions are drawn:

Quantification of the UVQ segments

indicates that segment 1 and 8 can be

characterized as good quality reservoir, 3 and 6 as

lower quality reservoir.

The most interesting result is obtained with

the volume-based neural network prediction

technique. The predicted porosity traces fit almost

perfect to the original porosity trace for the blind

test wells.

The pseudo-wells, generated within the GDI

software, have proven their value in the MLP

predictions and UVQ analysis quantification.

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Acknowledgments

The authors are grateful to Preussag Energie

GmbH for the permission to publish this paper.

Fig. 3 Inline through predicted porosity volume