Automated well log interpretation for seismic Inversion ...

Automated well log interpretation for seismic Inversion

Internal Report

Christopher Dyt, Irina Emelyanova, Michael Clennell

EP177125

August 2017

EXPLORATION GEOSCIENCE, DATA ANALYTICS

CSIRO Energy

Citation

Dyt C, Emelyanova I and Clennell MB (2017) Automated well log interpretation for seismic inversion. The CORE Data Analytics Project: Milestone report. CSIRO Energy, Australia Citation

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Internal Report | i

ii | Internal Report

Contents

Executive summary .................................................................................................................. v

Part I Classifying Rock Types 1

1 Introduction ................................................................................................................. 2

2 Classifying rock types .................................................................................................... 3

2.1 Methodology ................................................................................................... 3

2.2 Study area location........................................................................................... 4

2.3 Expert classification rules ................................................................................. 4

2.4 Well data ......................................................................................................... 5

2.5 Rules for log classification................................................................................. 5

2.6 Velocity and impedance profiles ....................................................................... 6

3 Results 7

3.1 Lauda 1 ............................................................................................................ 7

3.2 Martell 1 ........................................................................................................ 12

3.3 Iago 1............................................................................................................. 14

3.4 Achilles 1 ....................................................................................................... 16

4 Discussion .................................................................................................................. 18

5 Conclusion.................................................................................................................. 19

6 References ................................................................................................................. 20

Internal Report | iii

Figures

Figure 1: Location of the 4 exploration wells found used in the survey; Iago 1, Martell 1, Lauda 1

and Achilles 1 ........................................................................................................................... 4

Figure 2. The impedance versus depth for rocks classified as shale in Lauda 1 ............................ 7

Figure 3. The impedance versus depth for rocks classified as marls in Lauda 1 ........................... 8

Figure 4. The impedance versus depth for rocks classified as medium to fine grain siliciclastic in

Lauda 1 .................................................................................................................................... 8

Figure 5. The impedance versus depth for rocks classified as medium to coarse grain siliciclastic

in Lauda 1 ................................................................................................................................ 9

Figure 6. The impedance versus depth for rocks classified as sands in Lauda 1 ........................... 9

Figure 7.The impedance versus depth for rocks classified as gas filled sands in Lauda 1 ............ 10

Figure 8. The impedance versus depth for rocks classified as radiolarite in Lauda 1 .................. 10

Figure 9. The impedance versus depth for rocks classified as pyritic material in Lauda 1 ........... 11

Figure 10. The impedance versus depth for rocks classified as limestone in Lauda 1................. 11

Figure 11. The velocity profile for all material from Lauda 1. the grey data points are the actual

velocities from the well log. The orange data points are the values from the various calculated

velocity curves for the rock type at that depth. ....................................................................... 12

Figure 12: The velocity profile for all material from Lauda 1. the grey data points are the actual



Figure 13. The impedance versus depth for rocks classified as shale in Martell 1 ...................... 13

Figure 14. The impedance versus depth for rocks classified as sand in Martell 1....................... 13

Figure 15. The impedance versus depth for rocks classified as coal in Martell 1........................ 14

Figure 16. The velocity profile for all material from Martell 1. The grey data points are the

actual velocities from the well log. The orange data points are the values from the various

calculated velocity curves for the rock type at that depth. ....................................................... 14

Figure 17. The impedance versus depth for rocks classified as shale in Iago 1 .......................... 15

Figure 18. The impedance versus depth for rocks classified as sand in Iago 1 ........................... 15

Figure 19. The impedance versus depth for rocks classified as marl in Iago 1 ........................... 16

Figure 20. The velocity profile for all material from Iago 1. the grey data points are the actual



Figure 21. The velocity profile for all material from Achilles 1. the grey data points are the actual



iv | Internal Report

Internal Report | v

Executive summary

To accurately perform seismic inversions, accurate velocity and impedance profiles need to be defined for the various rock types present in the subsurface, both reservoir lithology (e.g. clean sandstone, porous limestone) and the overburden rocks (shales, marls). In order to generate these

profiles rapidly and in an objective and repeatable manner we attempt to automatically determine lithology from well logs before analysing the velocity and density trends for each of the identified categories, so that they are “inversion ready” facies descriptions, for a number of commonly occurring rock types.

To classify the rock sequence into type’s relevant to seismic inversion we first captured an expert’s methodology of interpreting facies from the well logs, and convert these into mathematical algorithms.

These algorithms were implemented in a single computer code, which was applied to four oil and gas exploration wells drilled in the Northern Carnarvon Basin offshore Western Australia during

the period 2000 to 2009. The classifications derived from these wells generally seem to match the geologist’s interpretation, and in each case, take less than a second to run, as opposed to many hours of interpretation when performing the task manually.

Importantly, velocity and impedance profiles for each rock type are produced that could potentially be fed into seismic inversion software.

There are however some situations, either due to sparse data for that rock type, or extremely variable/noisy data, the produced depth-impedance curves of poor quality. It may also suggest that

there may be strong variation within the individual rock type groupings produced (e.g. marls may vary widely in there rock properties and hence there log response) , suggesting that they may be further separated by method such as clustering techniques.

Internal Report | 1

Part I Classifying Rock Types

Using wireline logs to determine facies

2 | Internal Report

1 Introduction

Seismic inversion is the process of turning seismic reflections into quantitative rock properties,

and is a relatively standard technique in exploration and development of oil and gas fields.

To conduct a seismic inversion, you need both seismic data and a wavelet statistically calculated

from the data. Importantly, the quality of the wavelet estimation is vital to the outcome of the

seismic inversion. The wavelet estimation can be greatly enhanced by a good understanding of

sonic and density curves of the material in the well.

Different rock types will have different velocity and impedance (velocity * density) characteristics

due to their grain’s size, mineralogy, degree of cementation etc. Similarly, the impedance of a rock

type can vary due to its burial history and degree of compaction.

Wireline well logs are often used to attempt to determine the velocity/impedance profiles of the

rocks in a well, so they can then be applied to the local field being explored. This can be a labour-

intensive practice requiring expert knowledge of a geophysicist to firstly interpret the log data to

determine the rock types and then put together the velocity curve

This work is an attempt to automate this process.

Internal Report | 3

2 Classifying rock types

2.1 Methodology

The aim of this work is to create an automated system to determine the impedance vs depth

profile of the different rock types (lithology) in a well and then use multiple wells to determine

how the different impedance profiles vary regionally.

In order to do this, the lithologies must first be identified. The aim is to break the rocks into broad

general classifications, not to exactly describe the lithology at each depth. This will allow each

lithology to have enough data points to create a usable impedance curve. To this end a geologist

was asked to interpret the well log date for a test well, determine what well logs were most

important and derive a set of rules to determine the rock type. Because some rock properties vary

with depth due to compaction the rules were requested to be as broadly applicable throughout

the well as possible.

The rules derived from the first well were then developed into a computer program and applied to

the initial well to test accuracy and then to subsequent wells to explore robustness in the

technique.

4 | Internal Report

2.2 Study area location

Figure 1: Location of the 4 exploration wells found used in the survey; Iago 1, Martell 1, Lauda 1 and Achilles 1

Four wells were used in this study; Lauda 1, Iago 1, Achilles 1 and Martell 1. They were chosen as

they are relatively recent wells, with the oldest being Iago 1 drilled in December 2000, and thus

have well logs taken with quite modern tools. They also all have very comprehensive well logs that

are publically available. All wells reside within the Carnarvon Basin, North West Shelf, Western

Australia and are within 160 km of each other as shown in Figure 1. It was decided to use just one

basin at this initial stage as the wells are likely to have similar rock types and properties, whilst

showing subtle variations between them.

2.3 Expert classification rules

Lauda 1 was chosen as the test well to determine a set of rules. It was chosen due to its

comprehensive suite of 42 logs, long interval of logging, and decent quality readings. Initially a well

log expert analysed the logs and determined broad interpretation of the rocks, whilst capturing

why he chose those classifications. This then allowed him to derive a general set of initial rules for

determining five criteria for rock types; sand, shale, intermediate grain, radiolarite, and marl. Later

after reviewing the results against actual well logs, a further four lithologies were added to

include; limestone, coal, pyritic material, and sand containing gas (gas sand). Also, it was decided

Internal Report | 5

to break the intermediate grain sized material into coarse-medium and fine-medium as the

previous category was thought to be too broad.

Importantly it was possible to derive criteria for the different lithologies without needing to utilise

hard to define subjective terms such as “conforming” and “wavy” for example. Terms such as

these are often observable to a trained eye, but can be close to impossible to determine

mathematically from a noisy well log reading.

2.4 Well data

The amount of variables measured collected from a different wireline tool in a well can vary quite

significantly from more than fifty to just a few basic well logs. In designing our algorithms, we

chose to use those that are most commonly recorded across most wells, so as to give our solution

the most future flexibility. To this end we utilise, Photoe lectron factor (PEF), Gamma Ray(GR),

neutron porosity(NPhi), grain density (RhoB), slowness (DTCO) (from which you can calculate the

compression wave velocity (Vp)) and shear wave velocity(Vs) . The grain density and PEF are

utilised to calculate the density porosity (Dphi), which in turn is used to calculate the difference

between the two porosities (ND).

2.5 Rules for log classification

The classifications were determined as follows:

Limestone: PEF > 4.0 be, GR < 45 APS, DTCO < 80us/ft.

Coal: RhoB <1.7, DTCO >115 us/ft, NPhi > 0.6.

Pyritic Material: ND > 0.2, RhoB > 2.71, PEF > 3.2

Gas Sand: ND < -0.1

The remaining material after these distinctive lithologies were identified was broken up as:

Shale: ND > 0.15.

Radiolarite: 0.15 > ND > 0.05, RhoB < 2.4, NPhi > 0.25.

Fine medium: 0.15 > ND > 0.10.

Coarse medium: 0.10 > ND > 0.05.

Sand: ND < 0.05.

The classification of Marl was more problematic. Marl is a calcium carbonate or lime rich

mudstone, so it’s log characteristics are very similar to the medium and fine -grained material. The

addition of the calcite in Marls tends to make it harder and subsequently faster to transmit sound

than its comparable mudstone. However as the velocity in the rocks is also compaction and hence

depth related, separating the cemented Marl from its un-cemented Mudstone is difficult.

6 | Internal Report

Therefore, we divide the shales and medium grained material into a fast and slow grouping by

determining a depth related cut of velocity. Assigning the fast rocks to the category ‘Marl’.

To determine the cut off velocity, we calculate use an exponential fit of the compression wave

velocity (Vp) versus depth profile for all of the Marls, and another Vp versus depth profile for all

the sands. We then average these two profiles. This becomes the cut-off velocity between

cemented and un-cemented Marls. Sands typically have much high sound velocities than shales

under the same compression.

This is a somewhat crude cut off, as most siliciclastic sedimentary rocks have a degree of

cementation and therefore “marliness”, however a general classification was required to separate

the categories.

The above algorithm was coded as a Fortran90 program which runs on a basic laptop. The

software “ShaleType”, takes less than a second to classify a typical well log.

2.6 Velocity and impedance profiles

The shear wave velocity versus depth was calculated for each facies type using an exponential

least squares fit. http://mathworld.wolfram.com/LeastSquaresFittingExponential.html .

In general, this produced reasonable curves, however, when the rock type was poorly represented

or was clustered in just one part of the well, its ability to extrapolate was poor as will be seen in

the results section. This is also true of linear and quadratic extrapolations, meaning that an

extrapolation technique should be used cautiously outside of the depths where the rocks are

actually found.

Finally, an artificial velocity curve was created for the entire well segment analysed. This was done

by assigning the velocity profile for the interpreted rock type at that depth, reproducing quite well

the measured velocities of the well log.

http://mathworld.wolfram.com/LeastSquaresFittingExponential.html

Internal Report | 7

3 Results

Four wells were analysed with the software, to explore its robustness. All the wells are from the

Canarvon Basin, North West Shelf, Australia. The wells are Lauda-1, Archillies-1, Iago-1 and

Martell-1. All had reasonable log coverage which was freely available. For the sake of brevity, only

the complete results from Lauda 1 will be shown, with only interesting features of the other wells

being presented

3.1 Lauda 1

Location: Lat/Long: -20.818645/114.722635.

Figure 2. The impedance versus depth for rocks classified as shale in Lauda 1

All the impedance curves for Lauda 1 show trend lines that would be ex pected for most wells, with

the impedance for each rock type increasing with depth in a reasonable curve (Figure 2 to Figure

9). The results for the sand profile shown in Figure 6 are somewhat fortunate, with only one small

packet of sand occurring high in the well providing enough influence to keep the curve reasonable.

It should be noted that even with the rocks grouped into their respective lithologies, there is still a

relatively large range of data values at each depth for each classification, and hence quite large R2

values. The large amount of noise is to be expected due to the natural variance in the rock as we

attempt to classify them into only a few categories.

y = 2.492E+03e3.641E-04x

R² = 9.396E-01

4000

5000

6000

7000

8000

9000

1800 2000 2200 2400 2600 2800 3000 3200

Imp

eda

nce

Depth

Lauda 1 Shale

8 | Internal Report

Figure 3. The impedance versus depth for rocks classified as marls in Lauda 1

Figure 4. The impedance versus depth for rocks classified as medium to fine grain siliciclastic in Lauda 1

y = 2.668E+03e3.816E-04x

R² = 7.212E-01

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5000

6000

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11000

12000

13000

1800 2000 2200 2400 2600 2800 3000 3200 3400

Imp

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Depth

Lauda 1 Marls

y = 2.313E+03e3.973E-04x

R² = 9.517E-01

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6500

7000

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1800 2000 2200 2400 2600 2800 3000

Imp

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Lauda 1 Medium - Fine Grain

Internal Report | 9

Figure 5. The impedance versus depth for rocks classified as medium to coarse grain siliciclastic in Lauda 1

Figure 6. The impedance versus depth for rocks classified as sands in Lauda 1

Figure 7 shows the only gas sands for the entire well log. In this case the resulting curve is

unusable as there is simply not enough spread of data to perform a meaningful extrapolation.

Likewise, for limestone shown in Figure 10, we see that that it only occurs low down in the well, so

extrapolating the date much beyond the depths covered could be misleading.

y = 2.445E+03e3.697E-04x

R² = 8.851E-01

4000

4500

5000

5500

6000

6500

1800 1900 2000 2100 2200 2300 2400 2500 2600 2700

Imp

eda

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Depth

Lauda 1 Medium-Coarse Grain

y = 4.283E+03e2.725E-04x

R² = 2.354E-01

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10000

11000

12000

1800 2000 2200 2400 2600 2800 3000 3200 3400

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Lauda 1 Sands

10 | Internal Report

Figure 7.The impedance versus depth for rocks classified as gas filled sands in Lauda 1

Figure 8. The impedance versus depth for rocks classified as radiolarite in Lauda 1

y = 4.256E+102e-6.982E-02x

R² = 8.332E-01

1800 2000 2200 2400 2600 2800 3000 3200 3400

Imp

eda

nce

Depth

Lauda 1 Gas Sands

y = 3.608E+03e2.909E-04x

R² = 6.372E-01

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7000

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9000

10000

1800 2000 2200 2400 2600 2800 3000 3200 3400

Imp

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Depth

Lauda 1 Radiolarite

Internal Report | 11

Figure 9. The impedance versus depth for rocks classified as pyritic material in Lauda 1

Figure 10. The impedance versus depth for rocks classified as limestone in Lauda 1

Figure 11 shows 3 different curves for the shear wave velocity. The grey curve is that measured by

the logging tool. The orange curve is created by using the calculated velocity profile for the rock

type identified at that depth. It does a reasonable effort at matching the actual curve given the

high range of variability in the data, indicating that our results are reasonable. Figure 12 is

identical to Figure 11, however it shows just the lower section of the well to highlight detail.

y = 2.780E+03e4.188E-04x

R² = 6.855E-01

4000

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7000

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10000

11000

12000

1800 2000 2200 2400 2600 2800 3000 3200

Imp

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Depth

Lauda 1 Pyritic Material

y = 4.765E+03e2.389E-04x

R² = 6.421E-01

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7000

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9000

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11000

1800 2000 2200 2400 2600 2800 3000 3200 3400

Imp

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Depth

Lauda 1 Limestone


Figure 11. The velocity profile for all material from Lauda 1. the grey data points are the actual velocities from the

well log. The orange data points are the values from the various calculated velocity curves for the rock type at that

depth.

Figure 12: The velocity profile for all material from Lauda 1. the grey data points are the actual velocities from the well log. The orange data points are the values from the various calculated velocity curves for the rock type at that

depth.

3.2 Martell 1

Location: Lat/Long: -19.395022/114.310692

2000

2500

3000

3500

4000

4500

5000

1800 2000 2200 2400 2600 2800 3000 3200 3400

Vel

oci

ty

Depth

Lauda 1 Velocity Measured and Calculated

2000

2500

3000

3500

4000

4500

5000

2800 2850 2900 2950 3000 3050 3100 3150 3200 3250

Vel

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Depth

Lauda 1 Velocity Measured and Calculated


Figure 13. The impedance versus depth for rocks classified as shale in Martell 1

Figure 14. The impedance versus depth for rocks classified as sand in Martell 1

The calculated output for Martell 1 appears to be quite successful, although caution should be

exercised in interpreting the marls, as the sand profile (Figure 14) is used to distinguish between

marls and shales. As can be seen, there are few sands and they only occur low down in the well .

Coal (Figure 15) was identified (having been absent from Lauda 1) near the base of the well,

although only a few layers were observed leading to a poor approximated curve.

The calculated velocity profile again does a reasonable job of tracking the measured data, and

picks out sharp changes in lithology (which would show up as seismic reflections).

700

1200

1700

2200

2700

7000 7500 8000 8500 9000 9500 10000 10500 11000

Imp

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Depth

Martell 1 Shales

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1200

1700

2200

2700

7000 7500 8000 8500 9000 9500 10000

Imp

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Depth

Martell 1 Sands


Figure 15. The impedance versus depth for rocks classified as coal in Martell 1

Figure 16. The velocity profile for all material from Martell 1. The grey data points are the actual velocities from the well log. The orange data points are the values from the various calculated velocity curves for the rock type at that

depth.

3.3 Iago 1


The results for Iago 1 were less successful when identifying shales and marls. This highlights the

risk in using the sand profile when splitting the two categories. In this case the impedance curve

for the sand profile (Figure 18) is actually decreasing with depth due to sparse data.This in turn

700

1200

1700

2200

2700

7000 7500 8000 8500 9000 9500 10000 10500 11000

Imp

eda

nce

Martell 1 Coals

550

750

950

1150

1350

1550

1750

1950

7500 8000 8500 9000 9500 10000 10500 11000

Vel

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Depth

Martell 1 velocity v depth. Measured and calculated


lead to a possible over classification of marls (Figure 19) in the top half of the well and an under

identification of shales (Figure 17). These in turn resulted in a poor fit of the shear wave velocities

in the top of the well

Figure 17. The impedance versus depth for rocks classified as shale in Iago 1

Figure 18. The impedance versus depth for rocks classified as sand in Iago 1

y = 2.437E+03e3.235E-04x

R² = 2.920E-01

4000

4500

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5500

6000

6500

7000

7500

8000

8500

2000 2200 2400 2600 2800 3000 3200 3400

Imp

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Depth

Iago 1 Shales

y = 1.311E+04e-1.196E-04x

R² = 4.255E-01

4000

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12000

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2000 2200 2400 2600 2800 3000 3200 3400 3600

Imp

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Iago 1 Sands


Figure 19. The impedance versus depth for rocks classified as marl in Iago 1

Figure 20. The velocity profile for all material from Iago 1. the grey data points are the actual velocities from the

well log. The orange data points are the values from the various calculated velocity curves for the rock type at that

depth.

3.4 Achilles 1


Achilles 1 like Lauda 1 produced excellent curves for nearly all lithologies due to a good spread of

data throughout the log. The only anomaly was limestone, with the resulting impedance curve

decreasing marginally with depth. The resulting artificial velocity profiles seem to do a good job of

representing the actual shear wave velocities.

y = 4.554E+03e1.983E-04x

R² = 1.864E-01

4000

6000

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10000

12000

14000

16000

2000 2200 2400 2600 2800 3000 3200 3400 3600

Imp

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Depth

Iago 1 Marls

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Vel

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Depth

Iago 1 velocity v depth. Measured and calculated


Figure 21. The velocity profile for all material from Achilles 1. the grey data points are the actual velocities from the well log. The orange data points are the values from the various calculated velocity curves for the rock type at that

depth.

2500

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4500

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10500 11000 11500 12000 12500 13000 13500 14000 14500 15000

Vel

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Achilles 1 velocity v depth. Measured and calculated


4 Discussion

Automated well log interpretation is by no means a new idea. Most commercial well log software

provides some form of automated log analysis from small start-up companies to massive service

companies such as Schlumburger. A list of the software will not be listed here, however a quick

search of “well log interpretation” will reveal many different products, all attempting to do the

same thing. Nearly all of them use much more advanced algorithms than that presented here, and

provide a much more refined interpretation.

We are deliberately attempting to keep our classifications extremely broad, and the number of

logs we utilise as small as possible. Our end target is to provide general impedance profiles for

different lithologies across multiple wells in a field extremely quickly. The end target is being able

to produce an accurate seismic inversion. Our current model shows promise, however the meth od

whilst working reasonably well for 3 wells, did not work for all 4. Sparseness of data for different

lithologies (sands in particular) causes a great deal of uncertainty. The accurate determination of

the degree of cementation in the siliciclastic rocks is the major problem. The use of clustering

techniques is being explored to break these rocks into meaningful groups, with very encouraging

initial work being realised when applied to the full range of lithologies (Emelyanova et al 2017).

Statistical techniques such as BFAST (Verbesselt et al 2010, 2010, 2013) were trialled. These

algorithms which have been designed to determine trends in data sets, however they could not

deal with the complexity and noise of well logs, nor the rapidly varying data. They also become

prohibitively time consuming when run at a resolution required to capture the detail required.


5 Conclusion

Whilst the software needs further improvement, the initial results are quite encouraging. The

largest problem is in interpreting the distinction between the marls and the shales/medium grain

sediments. Utilising the sand profile as a constraint only works when we have sufficient sand along

the length of the logged segment to construct a decent profile. Several other methods have been

attempted, (for example simply splitting the shales along their mean velocity depth curve), but all

have had their problems similar to the current approach.

The next stage will be to use more advanced methods such as ensemble clustering to separate

these sediments into groups as in reality the degree of calcification in a rock varies greatly and it

may be wrong to simply assign these elements just as shale or marl.


6 References

Emelyanova, Irina & Pervukhina, M & Clennell, Michael & Dyt, Chris. (2017). Unsupervised

identification of electrofacies employing machine learning. 79th EAGE Conference and Exhibition

2017 10.3997/2214-4609.201701655.

Verbesselt, J., Hyndman, R., Newnham, G., & Culvenor, D. (2010). Detecting trend and seasonal

changes in satellite image time series. Remote Sensing of Environment, 114, 106-115. DOI:

10.1016/j.rse.2009.08.014.

Verbesselt, J., Hyndman, R., Zeileis, A., & Culvenor, D. (2010). Phenological change detection while

accounting for abrupt and gradual trends in satellite image time series. Remote Sensing of

Environment, 114, 2970-2980. DOI: 10.1016/j.rse.2010.08.003.

Verbesselt, J., Zeileis, A., & Herold, M. (2013). Near real -time disturbance detection using satellite

image time series, Remote Sensing of Environment. DOI: 10.1016/j.rse .2012.02.022.


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