4d pre-stack inversion workflow integrating reservoir model control and lithology supervised...

7/30/2019 4D Pre-Stack Inversion Workflow Integrating Reservoir Model Control and Lithology Supervised Classification

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72nd EAGE Conference & Exhibition incorporating SPE EUROPEC 2010

Barcelona, Spain, 14 - 17 June 2010

A027

4D Pre-stack Inversion Workflow IntegratingReservoir Model Control and LithologySupervised Classification

S. Toinet* (Total E&P Angola), S. Maultzsch (Total), V. Souvannavong(CGGVeritas) & O. Colnard (CGGVeritas)

SUMMARY

4D pre-stack inversion is used in the industry to image reservoir changes due to production and injection,and to make reservoir management decisions in order to optimize hydrocarbon recovery. We present aninnovative workflow to prepare, constrain and compute 4D pre-stack inversion attributes. Specific

properties of the studied field (huge time-shifts due to gas coming out of solution, various turbiditiccontexts) implied building a composite warping result, filtered using a 4D mask to build the initial monitor

model for 4D inversion. The pre-stack 4D inversion workflow not only integrates seismic information, butalso well information, used to discriminate sand from shale during the 4D mask building, and a 4D rock-

physics model. Applied to simulated reservoir properties, the rock-physics model defines a range ofrelative density and velocity variations in which the inversion results can vary. Moreover, because water-

bearing sands are hard to discriminate from shales in some of the field reservoirs using a cross-plot of Pand S impedances, information from the reservoir grid was also introduced to help locating water-bearingsands in the 4D mask. Preliminary analyses of 4D inversion attributes show an improved image comparedto previous 4D attributes.


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Introduction

4D pre-stack inversion is used in the oil and gas industry primarily to image and analyse reservoir

changes due to production and injection (McInally et al., 2001), and ultimately to make reservoir

management decisions in order to optimise hydrocarbon recovery (Rutledal et al., 2003). In some

cases, quantitative analysis based on 4D pre-stack inversion attributes is carried out to access fluid

saturation and pressure changes in the reservoir (Lumley et al., 2003).

A 4D pre-stack inversion has been run on a giant field located offshore Angola, in average water

depths of 1400 meters. Oil production started in December 2006. Reservoirs are located in

unconsolidated sandy turbiditic deposits, confined (thick channels) and unconfined (lobes).

We present an innovative workflow to prepare, constrain and compute 4D pre-stack inversion

attributes. This is followed by a preliminary analysis of 4D inversion results obtained.

Warping of monitor before 4D inversion

A 4D high-resolution seismic survey was acquired in the summer of 2008 on the field, with several

objectives: monitor the effects of one year and a half of production and injection, understand verticalcommunications and fault behaviour, update the reservoir model according to the extension of 4D

anomalies, help reservoir management and the location of future development and infill wells.

4D seismic data first went through a fast-track processing sequence. Analysis of 4D images from fast-

track processing has shown very large time-shifts (up to +18 ms) at the base of produced reservoirs,

and amplitude variations of more than 100% between base and monitor seismic data. Such large

variations are due to the fact that initial reservoir pressures are close to the bubble point, in

unconsolidated sands with a shallow burial: production-induced depletion rapidly liberates gas, giving

rise to a strong P-wave velocity decrease.

The two different types of reservoirs of the field (confined and unconfined turbidites) induced large

differences of time-shifts and amplitude variations: largest time-shifts were observed in confined

turbidites due to stronger depletion and significant vertical communication (Figure 1), whereas in

unconfined turbidites the time shift values were generally much smaller (around 5 ms). Thisvariability in magnitude of the 4D anomalies for the different reservoir complexes required the use of

different algorithms to warp the monitor data to the base data and generate a cube of relative P-wave

velocity change (dv/v) as a 4D attribute. The warping techniques consist of existing and newly

developed TOTAL proprietary algorithms (Williamson et al., 2007).

Figure 1 left: amplitudes from base 99 survey. Right: amplitudes monitor 2008. Orange line (left and

right images) represents the initial isochron of the reservoir base.

Finally, three dv/v blocks were produced, using different algorithms and computation parameters.

Because the 4D inversion algorithm requires using a single dv/v cube to create the initial model for

the inversion of the monitor data, a composite dv/v cube was built by merging the different warping-

derived dv/v blocks, using seismic interpreted horizons. This composite dv/v volume is also used for

4D interpretation purposes, as it is valid in confined and unconfined systems.

72ndEAGE Conference & Exhibition incorporating SPE EUROPEC 2010



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4D inversion workflow

The 4D inversion workflow starts with a 3D simultaneous inversion of the base survey data after

additional specific pre-conditioning of the angle stacks. Then the 3D inversion result on base is

updated using the dv/v attribute from the warping process. Finally, a global pre-stack 4D inversion

scheme (Lafet et al., 2009) is applied, where all partial angle stacks from base and monitor are jointly

inverted.

During the update phase with the dv/v attribute, a 4D mask is used: it defines reservoir and non-

reservoir samples in the seismic volumes, and finally samples where 4D dv/v is applied or not to

create the initial model for the monitor. This masking process allows in some specific places to

remove unwanted noise in the dv/v attribute (Figure 2).

The 4D mask is a combination of several types of data: lithology classification, reservoir model

facies, and 4D seismic energy.

The lithology classification is carried out using a supervised Bayesian classification scheme. It is

based on sand/shale Probability Density Functions (PDFs) that are defined from a cross-plot of elastic

properties (Figure 3). Unfortunately in this field, PDFs overlap significantly for water-sands and

shales. Furthermore, the well training set for water-sands is poorly defined as the majority of original

log sample points correspond to oil-bearing sands. Discriminating water-bearing sands from shalesbecomes therefore very uncertain using this cross-plot-based approach only.

Figure 2 an example of unwanted noise in the

dv/v attribute. Below a strong anomaly due to

production, another anomaly is visible below a

reservoir without any production or injection.Figure 3 Cross plot of Vp/Vs versus P-wave

impedance showing PDFs and log data points

corresponding to water-sands, oil-sands, gas-sands and shales.

In order to reduce potentially large uncertainties in the cross-plot-based approach, the initial (before

production) reservoir model was used to constrain the lihology classification. Fluid contacts are

integrated in the reservoir model, based on well information or Direct Hydrocarbon Indicators. For a

given reservoir unit, all cells located below the oil-water contacts are flagged as water-bearing sands.

The reservoir model was converted from depth to time. After careful validation of the seismic-to-

reservoir grid tie in the time domain, the water-sand distribution from the reservoir model was

integrated in the sand/shale 4D mask (Figure 4).

Accounting for water-bearing sands is critical, in order to properly define the initial model for

inversion of the monitor data and especially to allow mapping of water injection in water. To finalise

the sand-shale classification, a lithofacies cube based on a Total proprietary classification algorithm




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was also integrated into the 4D mask. This lithofacies cube is used to optimize well locations and has

proven to be very predictive in oil-bearing reservoirs.

In addition to the lithology component of the 4D mask, 4D seismic information was introduced in

form of 4D energy. A threshold was applied to the cube of 4D seismic energy, computed from the

difference between the 1999 base and the 2008 warped seismic monitor. The dv/v is then only used in

areas where the 4D energy is greater than the threshold. As a result, isolated dv/v values, outside

sands and outside areas of significant 4D energy are not used to build the initial monitor model,

before inversion. An example of the final 4D mask is shown Figure 5.

Figure 4 Lithology discrimination integrating

information from reservoir model for water-

bearing sand (in blue).

Figure 5 example of the 4D mask. In red, areas

where 4D initial differences between base and

monitor models will be introduced through the

dv/v.

4D global inversion applied uses a CCGVeritas proprietary algorithm that optimizes a multi-vintage

cost function that combines several terms. Time-lapse coupling of the inversion scheme is achieved

by restricting the range of perturbations between successive surveys according to user-specified

constraints. Specifically, between each consecutive vintage, perturbations are restricted to specific

min-max intervals of expected variations of density and P- and S-wave velocity. These intervals were

directly derived from a 4D rock-physics model and reservoir simulations performed. Simulated

reservoir parameters (fluid saturations, pressure, ) at the time of the 4D and at initial reservoir state

are used as inputs of the rock-physics model which predicts the corresponding density, P-wave and S-

wave velocity ranges in the reservoirs. Final inverted impedance variations are limited by this a priori

range of property variations.

Figure 6Example of simulated relative P and S

wave impedance variations between initial state

of reservoir and time of 4D seismic survey. The

cross-plot is computed from simulated reservoir

properties and a 4D rock-physics model.

In the 4D inversion workflow the 4D mask is used to create the initial monitor model, and can be used

during the inversion process in order to impose areas where no impedance variations are allowed. The

dv/v cube showed several places with significant 4D anomalies that had not been classified as

reservoir in the lithology classification cubes. Therefore there was a danger of the mask being too

restrictive in the actual 4D inversion process. 4D inversion tests without the mask confirmed theanomalies observed in the dv/v cube and also led to a decrease of residuals in these places. Therefore




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it was decided to use the 4D mask only for the initial monitor model building, but to run the final 4D

inversion in a more data-driven way, without a deterministic mask..

Example of 4D inversion result

Preliminary analysis of the 4D inversion results provided new information, compared to previous

attributes. In general, 4D inversion allows a better fine tuning of 4D anomalies with less noise. In

particular the image from 4D inversion around water-injectors is generally of better quality than the

image obtained on previous attributes, like dv/v, as shown on Figure 6: the anomalies visible on the

4D inversion are better aligned with sands. Moreover, the positive anomaly associated with injected

water was extending towards the reservoir top on dv/v, which was not consistent with gravitational

segregation. Relative P-impedance from inversion brings a more relevant image.

Figure 7 example of 4D inversion result. a: bandpass P-impedance (oil sands in yellow, brown.

shales in green. Debris flows and basal lags in blue). b: dv/v from warping. c: relative P-impedance

variation from 4D inversion.

Conclusions

An innovative 4D pre-stack inversion workflow was built. Specific properties of the studied field(huge time-shifts due to gas coming out of solution, various turbiditic contexts) implied building a

composite warping result, a mandatory step to build the initial monitor model for 4D inversion. The

pre-stack 4D inversion workflow not only integrates seismic information, but also well information,

used to discriminate sand from shale during the 4D mask building, and a 4D rock-physics model.

Moreover, because water-bearing sands are hard to discriminate from shales in some of the field

reservoirs, information from the reservoir grid was also introduced in the process. All these different

steps were carefully validated. On top of the technical challenges, operational deadlines were met as a

result of close interaction between TOTAL E&P ANGOLA, CGGVeritas teams in Luanda and

TOTAL Headquarters. Preliminary analyses of 4D pre-stack inversion results already show

encouraging results.

Acknowledgements:Total thanks the block concessionaire, Sonangol and its partners Statoil, ExxonMobil and BP for their

authorization to publish this work.

ReferencesMcInally, A., Kunka, J., Garnham, J., Redondo-Lopez, T., and Stenstrup-Hansen, L., 2001. Tracking Production Changes in

a turbidite Reservoir Using 4D Elastic Inversion, 63rdEAGE Conference And ExhibitionRutledal, H., Helgesen, J., and Buran, H., 2003. 4D Elastic Inversion helps locate in-fill wells at Oseberg field, First Break,Vol. 21, N8, August 2003.

Lumley, D., Adams, D., Meadows, M., Cole, S. and Ergas, R., 4D Seismic Pressure-Saturation Inversion at Gullfaks field,Norway, First Break, Vol 21, N9, September 2003.

Williamson, P.R., Cherrett, A.J., Sexton, P.A., 2007, A New Approach to Warping for Quantitative TimeLapseCharacterisation, EAGE, Expanded Abstracts

Lafet, Y., Roure, B., Doyen, P.M., and Buran, H., 2009, Global 4-D seismic inversion and time-lapse fluid classification.SEG Expanded Abstracts.


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