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Central North Sea, Upper Jurassic Fulmar Inversion Case Study. Utilising Blueback ODiSI (One Dimensional Stochastic Inversion). L. Casteleyn 1., P. Ashton 2., A. D’ Alessandria 2., P. Connolly 3. and J. Sayer. 1. The reservoir is an Upper Jurassic Fulmar interpod (depocentre) and is flanked by Triassic Smith

Bank shale pods. Underlain by Triassic shales and Zechstein Evaporites and overlain by

Cretaceous lithologies, the field provides a challenge for seismic analysis, particularly inversion

and quantitative interpretation.

1. Cegal Limited, Aberdeen UK.

2. Repsol-Sinopec Resources UK Limited, Aberdeen UK.

3. Cegal Limited, Aberdeen and Patrick Connolly Associates Limited, Berkshire UK.

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Acknowledgments

Cegal would like to thank Repsol Sinopec Resources UK Limited for their support and engagement to

undertake the ODiSI Inversion and their partners for their agreement and support to publish the case

study. Particularly the contributions of Peter Ashton and Alessandra D’Alessandria as well as the

support of Paul Conway and Jon Seedhouse in securing approval for use of this case study in the public

domain. Thanks, must also be given to Patrick Connolly for his guidance and support throughout the

project.

Finally, Cegal would also like to thank CGG, the seismic data owners, for their permission to use and

show the data.

Summary

An ODiSI inversion was performed on a central North Sea field in an attempt to quantify the extent

of high quality sand. The field presents an Upper Fulmar reservoir bounded by mixed lithology

Cretaceous, Triassic shales and Zechstein Anhydrites.

The presence of the relatively high impedance over- and under-burden, combined with a low

acoustic differentiation within the reservoir, are the main challenges to any seismic analysis.

Initially developed by BP, ODiSI is a One Dimensional Stochastic Inversion method that uses only the

most robust prior information. ODiSI works by creating a set of pseudo-wells (1000-20000) at each

seismic trace position, populated with samples drawn from prior information such as rock physics

models. Synthetic seismic traces created from every pseudo well are then statistically matched to

the seismic trace. Typically, the properties attached to the 30 best matching pseudo wells are

averaged in order to obtain different outputs such as the average net reservoir fraction, porosity,

hydrocarbon pore volume, all with their associated uncertainties (standard deviation), and

lithofacies probabilities at each trace location.

Results acquired on the field present a very good delineation of high quality sands, provide new

definition of zone bounding surfaces and alternative structural interpretations. Good lateral

continuity was obtained in the property and lithofacies outcomes, providing a strong validation of

the ODiSI method and process.

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The Target Reservoir

The reservoir is an Upper Jurassic Fulmar interpod (depocentre) and is flanked by Triassic Smith Bank

shale pods. Underlain by Triassic shales and Zechstein Evaporites and overlain by Cretaceous

lithologies, the field provides a challenge for seismic analysis, particularly inversion and quantitative

interpretation.

The ODiSI parameterisation was performed on four wells located within the field and crossing the

reservoir. All wells had the suite of well logs needed for an ODiSI parameterisation: Vp, Vs, , total

porosity, Vsh, water and HC saturation and a facies log.

The provided facies logs represented four lithologies within the reservoir: Clean sand, Shaley sand,

Cemented sand and Shale. Alternation of thin Clean sands and thicker Shaley Sands can be found in

the uppermost Fulmar. In order to facilitate the delineation of this higher net sand fraction, a new

lithofacies was created called ‘Clean + Shaley Sands’. This new lithofacies is only present within the

upper reservoir zone.

The seismic data used in this study derive from a PreSDM project carried out by the data owners,

CGG, on behalf of the field operator in 2010. The portion of the survey covers an area of 6.7 by 8.2

km with a total of 337,858 traces. Only the full-stack was used in this study as AVA provided no

additional ability to separate the lithofacies.

ODiSI Method

ODiSI is a new approach to seismic inversion, initially developed by BP (Connolly and Hughes, 2016)

and implemented within Petrel by Cegal. ODiSI jointly estimates facies, reservoir properties and

impedances, and their associated uncertainties, by matching large numbers of pseudo-wells

simultaneously to multiple angle-stacks.

ODiSI has been applied to a number of BP fields to predict net-to-gross for siliciclastic fields offshore

Angola, West of Shetlands (Walker et al., 2016) and the Nile Delta (Grant and Dutton, 2016).

A set of Pseudo-wells (stochastic simulations of 1D stratigraphic profiles), typically between 1,000 to

10,000, are generated for each seismic trace. Physical properties are generated for each pseudo-well

from robust prior information utilising Rock Physics Models (RPM). Synthetics derived from the

pseudo-wells using Extended Elastic Impedance (EEI) are then matched to the appropriate seismic

volume.

In order to optimise the calculation time, ODiSI works on a macro-layer basis. Macro-layers represent

a “zonation” of the area of interest, where each zone is characterised by different bed thicknesses

and lithofacies distributions; for example, mass flow turbiditic sand or channel sands would carry

different lithological and bed-thickness distributions.

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The statistically best matched pseudo wells are selected and their properties averaged to provide an

estimate at each sample of the mean reservoir properties and their associated uncertainties

(standard deviations), and the probability of the presence of each lithofacies at every trace location.

ODiSI presents a number of key advantages, with one of the principle benefits being that it is

relatively simple to employ and understand. The user has complete control and is able to QC each

step of the process. ODiSI inversions are founded on the most robust geological prior information

extracted from well data and/or field analogues.

The algorithm converges so that each trace may be inverted independently of its neighbour, thus

being a one-dimensional statistical inversion. Additionally, algorithm stability is demonstrated by

strong lateral continuity without the need to apply spatial constraints such as a variogram.

Furthermore, no low frequency model is required as ODiSI utilises only the input seismic as a guide;

Colour Inverted, band limited impedance volumes. The low frequency information is introduced

through the differences in the parameterisation of the macro-layers. Well data is used to derive the

prior geological distributions and RPMs but the inversion result is not constrained to reproduce the

well properties at the well location. ODiSI can invert multiple volumes simultaneously, allowing the

creation of high quality reservoir property estimates with associated uncertainties (e.g. net reservoir

fraction, porosity, lithofacies distribution).

Finally, ODiSI takes advantage of the calculation power available in modern workstations. The

inversion process, being trace-by-trace, is highly parallelisable (you can still use Petrel while the

inversion is running) and inversions are consequently quick to run. For example, the field has 337,858

traces and during the inversion, 5,000 pseudo-wells were created for each trace. Seventeen

petrophysical curves were generated for each pseudo-well which resulted in a total of more than 28

billion curves. The whole inversion took around 3 hours to run on a workstation laptop.

Application

ODiSI was applied in an attempt to delineate the presence and lateral extent of the a range of

lithology types within the field.

Delineation is generally complicated by the relatively high impedance contrasts at the top and base

of the reservoir and poor acoustic differentiation of the Fulmar facies themselves. Figure 1, a cross

plot of Vp/Vs versus Acoustic Impedance (AI), shows very limited Vp/Vs contrast between the facies.

This would suggest a small gradient signal and hence limited AVO information. On the other hand,

Acoustic Impedance shows differentiation of the cemented sands and highest quality reservoir sands

from the shaley facies and is therefore the preferred elastic property for inversion.

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Figure 1: Vp/Vs versus Acoustic Impedance cross plot, data coloured by facies. Data shows very limited Vp/Vs contrast

between the facies characteristics and limited AVO information. Acoustic Impedance shows more differentiation

especially for Cemented Sand (Blue) and highest quality reservoir sands (Yellow).

ODiSI makes use of the extended elastic impedance (EEI) concept. A Chi angle stack was created from

3 angle stacks (Near, Mid, Far) at a chi projection of 5 degrees (Figure 2) to mimic the central angle

of the full-stack (no notable AVO effects were observed). The EEI 5 Stack was conditioned for

utilisation within ODiSI through the application of coloured inversion (Figure 3).

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Figure 2: EEI Chi 5˚ projection, NW/SE transept.

Figure 3: EEI Chi 5˚ projection, Colored Inverted NW/SE transept.

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The inversion employed six macro-layers, two for the over- and under-burden and four within the

reservoir itself (Figure 4), namely Uppermost Fulmar (dark orange on Figure 4), Upper Fulmar (orange

on Figure 4), Cemented (blue on Figure 4) and Transition (green on Figure 4).

Figure 4: Macro-layer definition within the reservoir, overlain the EEI5 volume. The ODiSI inversion employed six macro-

layers defined by different bed thickness and facies proportions statistics, two for the over- and under-burden (not

showed in this figure) and four within the reservoir: Uppermost Fulmar (dark orange), Upper Fulmar (orange), Cemented

(blue) and Transition (green).

Each macro-layer is characterised by different bed thickness and facies proportions statistics. igure

5 presents lithofacies in thirty pseudo-wells, randomly drawn from the user defined and zone specific

prior thickness proportions and transition probability distributions, showing the difference between

each macro-layer. For example, the first macro-layer within the reservoir contains thinner beds than

the second macro-layer.

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igure 5: 30 random pseudo-wells showing the different lithofacies, bed thicknesses and facies proportion. For example,

the first macro-layer is characterised by the presence of thinner beds than the second macro-layer. Macro-layer 3

contained a high proportion of Cemented sand. Nonetheless, Cemented sands can be founded is all macro-layers.

Each lithofacies was associated with a rock physics model calibrated to known well data. Figure 6

shows an example of the Rock Physics Model derived from measured well data for the ‘Clean Sand’

lithofacies. The method used in this particular lithofacies was to define the porosity trend versus

two-way time (plot 6a), then Shear Modulus versus porosity (plot 6b) and, finally, Dry Bulk Modulus

versus Shear Modulus (plot 6c). The ‘Shaley + clean sand’ lithofacies was defined in the same manner.

Shale lithology is defined using different trends: compressional, P-wave, velocity versus time, then

shear, S-wave velocity versus P-wave velocity and density versus P-wave velocity. Finally, ‘Shaley

sand’ was modelled as a laminated formation, an effective medium mix between ‘Clean sand’ and

‘Shale’ according to the shale fraction (Backus averaging method).

Due to the lack of and/or inconsistent well data, simple trends are used for the over- and under-

burden either derived from available well data or taken from a regional study. Theses RPMs are then

used to create the suite of elastic curves for all pseudo-wells (Figure 7).

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Figure 6: Cross-plots used for the definition of Clean Sand Rock Physics Model. a) porosity versus time trend, b) porosity

versus shear modulus trend, c) shear versus bulk modulus trend.

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Figure 7: Example well logs for one of the parameterisation wells. Blue curves represent the actual well logs and black

lines represent the pseudo-well logs created without auto-regression. A very good correlation was obtained between

the real and pseudo well data.

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Results

The inversion was performed on the colour inverted (CI) Chi angle stack at 5 degrees to obtain several

output volumes: Most Probable Lithofacies, Mean Net Reservoir Fraction, Standard deviation of the

Net Reservoir Fraction and the probability of each lithofacies. EEI Synthetics and residual traces

(synthetic minus real trace) are also created by ODiSI to validate the matching of pseudo wells. At

each seismic trace, 5000 pseudo-wells were created and compared to the EEI CI input. The results at

each trace are the average of the properties from the 50 best pseudo wells, based on the lowest

residual energy, RMSE (Root Mean Square Error), value.

ODiSI successfully delineated the regions of high net reservoir quality sand within the field and these

are consistent with blind wells. The results also show good lateral continuity and the differentiation

between the expected lithologies is consistent with the expectations of the overall geological model.

An analysis of the lithofacies and net volume outputs (Figure 8 and Figure 9) together highlights the

extent of a high fraction of clean sand within the field. Good reservoir can be found in the uppermost

Fulmar reservoir and is characterised by the alternation between thin clean sand beds and Shaley

sands. ODiSI also shows the presence of thicker, consistent clean sands at the base of the reservoir

within the Upper Fulmar and within the Transition zone. Correlation with the well data is strong and

supports the robustness of the results.

The ‘Cemented sand’ lithofacies, coloured blue in Figure 8, shows a consistency and good lateral

continuity linked to its high reflectivity. One unexpected result for this facies was the high quality

definition of the top and base of the Cemented Zone produced by ODiSI. The boundaries of the macro

layering are not constrained to the controlling surfaces used as input to the inversion process (purple

and blue dotted surfaces); ODiSI does not force a layer boundary to tie the input but allows the

algorithm to resolve the optimal position during the matching process.

Interestingly, it was observed that ODiSI was able to discriminate and image the presence of faults

by remaining true to the seismic and avoiding the input of spatial controls. Notably, this does not

require the input of fault information as part of the parameterisation. For example, Figure 8 shows

the lack of cemented sand close to interpreted faults, applied retrospectively for the purpose of

imaging. In addition, ODiSI has provided an alternative interpretation of the fault as highlighted

within section in Figure 10.

Finally, the results in figures 8, 9 and 10 are consistent with the well data and demonstrate, at seismic

resolution, good lateral continuity. As the process is a one-dimensional trace-by-trace method with

no spatial smoothing the lateral continuity confirms the stability of the algorithm. High frequencies

are unconstrained by the seismic and hence will show some trace-to-trace discontinuity but this is a

useful visual cue indicating the level of uncertainty in the result. The quality inherent in the map

views in figures 11 and 12 show particularly clearly the benefit of retaining the lateral resolution of

the seismic data.

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Figure 8: Probable lithofacies output with faults. This volume shows a good delineation of high net reservoir quality

sands (Clean and Shaley + Clean). Good lateral continuity and strong well data correlation is observed showing the

stability of the algorithm.

Figure 9: Net mean. This volume highlights the delineation of high net sands. In the Uppermost Fulmar, the sands are

characterised by the alternation of Clean Sand and Shaley Sand (figure 8 and 10). High net zones are also found in the

Transition zone, consitent with the Probable lithofacies output and well log data.

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Figure 10: Probable Lithofacies NE / SW transect. ODiSI inversion was able to highlight the lack of Cemented Sand at

known fault contacts (NB: ODiSI doesn’t require faults information as input). The probable lithofacies also suggests that

alternative fault interpretations may be possible.

Figure 11: The Probable Lithofacies cube in 3D view. Dotted lines represent faults. Strong lateral continuity is observed

in the all 3D volumes. This figure shows the probable lithofacies and the strong link observed between facies deposition

and faults.

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Figure 12: Demonstrates chronostratigraphic slices for the cemented sand probability (left) and the probability of clean

sand within the transition zone (right). The output shows clear facies discrimination while retaining the full spatial

resolution of the seismic data.

Conclusion

An ODiSI inversion performed on the field was able to solve some known difficulties linked to this

Upper Fulmar reservoir: presence of relatively high impedance over- and under-burden combined

with a low acoustic differentiation within the reservoir.

ODiSI, which worked by creating a set of pseudo-wells at each seismic trace position and then

matched the best pseudo-wells to the trace, allowed the output of different 3D volumes such as

Probable Lithofacies and net to gross. The different volumes showed a good lateral continuity

providing a strong validation of the ODiSI method and process.

The ODiSI inversion results, particularly the Probable Lithology cube, showed a very good delineation

of high quality sands and were therefore incorporated in the screening of the next water injector

location to be drilled in 2017. The Probable Lithology cube was used as one of the seismic attributes

used to infer connectivity between producer and injector well locations in two separate reservoir

units and to increase confidence on the facies the well may encounter.

ODiSI Inversion –

Cemented Sand Probability

ODiSI Inversion –

Clean Sand Probability

1.0 0

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References Connolly, P.A. and M.J. Hughes, 2016. Stochastic inversion by matching to large numbers of pseudo-

wells, Geophysics, 81.

Grant, S.R. and B.J. Dutton, 2016. Stochastic Inversion by Trace Matching – Reservoir Property

Prediction Case Studies. 78th EAGE Conference, Vienna, Austria.

Walker, M., S. Grant, P. Connolly and L. Smith, 2016. Stochastic inversion for facies: A case study on

the Schiehallion field, Interpretation, Vol. 4, No. 3.

For more information regarding Blueback ODiSI and Cegal, please visit:

www.Cegal.com