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EM & Potential Methods

1 Schlumberger, Gatwick, UK.2 Schlumberger, Milan, Italy.* Corresponding Author, E-mail: [email protected]

Multi-disciplinary integration of seismic interpretation, AVO inversion and CSEM in the West Loppa exploration

Mehdi Paydayesh1*, Margaret Leathard1, Federico Ceci2 and Ajai Kumar Sharma1 describe the integration of multiple geophysical methods for reducing uncertainty in identifying hydrocar-bon potential during the predrill appraisal of a prospect in the West Loppa area of the Barents Sea.

T he key goals of most geophysical surveys per-formed for exploration and field development pur-poses include accurate reservoir characterization and estimation of parameters such as hydrocarbon

saturation. In principle, information about pore fluids can be extracted through amplitude-variation-with-offset (AVO) seismic inversion procedures, making this the primary tool for estimating saturation. However, there are often uncer-tainties in interpretation that cannot be addressed using this single method. In many situations, AVO and amplitude anomalies may be caused by lithology boundaries or thin-bed effects rather than changes in fluid content. Therefore, discrimination of hydrocarbons is often difficult on the basis of seismic data alone.

Over the past decade, the marine controlled-source electromagnetic (CSEM) method has proven to be a valuable tool for offshore exploration (e.g., Constable, 2010). CSEM methods use a high-powered source towed above the sea floor to transmit low-frequency signals through the earth that are subsequently recorded by an array of receivers (e.g., Young & Cox, 1981). Indications of the resistivity features of the subsurface can be determined by interpreting the received signals using forward modelling and inversion approaches. The strength of the method for exploration purposes lies in its potential to detect resistive layers associated with hydrocarbon reservoirs. In most hydrocarbon-bearing lay-ers, resistivity is between one and two orders of magnitude higher than in the surrounding water-saturated sediments, and theoretically, such resistivity variations should be readily detected using CSEM tools. However, similar to many other geophysical tools, CSEM inversion is often non-unique and subject to uncertainty. For example, whereas a zone of high resistivity can be caused by the presence of hydrocarbon fluids, other competing formation types, such as cemented sandstone, carbonates, volcanics, gas hydrates, salt, or coals, could lead to the measurement of high-resistivity anomalies.

Also, due to the relatively poor depth of the resolution of CSEM data and the constraints imposed by its inversion methods, the resulting resistivity models are often highly smoothed, typically underestimating the reservoir’s resistivity and overestimating its thickness.

The most reliable answer to hydrocarbon exploration is obtained by the combination of tools within an integrated framework. By integrating seismic and CSEM data in a staged workflow, limitations of each method can be over-come and the strengths of each exploited (e.g., Lovatini et al., 2010). Combining the two types of data should improve fluid detection and provide different and complementary images of the geology. The higher resolution of seismic imag-ing makes it possible to accurately determine the location of potential resistivity contrasts and the CSEM provides extra information about electrical conductivity.

This article describes the integration of multiple geo-physical methods for reducing uncertainty in identifying hydrocarbon potential during the predrill appraisal of a prospect in the West Loppa area of the Barents Sea. This study focused on Block 7220/2a, which is covered by 3D data acquired by Schlumberger and also a 3D CSEM survey conducted by EMGS. The work described in this article was made possible through a collaborative joint agree-ment between the two companies for an integrated study performed in Schlumberger.

The workflow followed a three-step approach. First, prestack seismic data were inverted for elastic attributes (acoustic impedance, Vp/Vs, and density). Then, the 3D CSEM data were inverted to generate an electrical resistivity cube. Next, a joint interpretation workflow was utilized to evaluate prospects in the Jurassic to Cretaceous sedimentary sequence. The results of both seismic and CSEM inversions were subsequently used as the inputs to a petrophysical joint inversion to estimate saturation and porosity distribution (see the work by Miotti et al., 2013). This article focuses

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Since 2008 Schlumberger has acquired and processed high-resolution 3D seismic data in the Barents Sea on a multi-client basis. Overall, seismic data is of high quality, although some areas are degraded due to the presence of gas clouds and shallow gas anomalies. Interpretation studies have identified several active petroleum systems in the area, including:n Cretaceous stacked submarine fan play – likely to be gas,

possibly oiln Shallow Tertiary regressive sequence play – gasn Jurassic/Triassic fault block plays – oil and gas

Initial seismic interpretations included picking horizons and identifying flat spots, examples of which are shown in Figure 3.

Seismic AVO inversionAVO techniques exploit relative changes in seismic ampli-tudes at varying incident angles to quantify changes in elastic properties. Elastic parameters can be related to the lithology and fluid content of the reservoir formations. Therefore, AVO techniques can be applied effectively in illuminating hydrocarbon-filled reservoirs. To improve prospect evalua-tion in new areas such as deep offshore environments, AVO attributes are being used as an analysis tool for quantitative prospect ranking. For this study, prestack simultaneous AVO inversion was performed to obtain acoustic impedance, Vp/Vs ratio, Poisson’s ratio, and density. This was followed by an additional AVO calculation to obtain intercept and gradient attributes.

on the joint interpretation of seismic and CSEM inversion results, and demonstrates how a systematic integrated interpretation can effectively improve the identification of prospective areas in the region, and hence reduce explora-tion risk.

West Loppa geological setting and seismic interpretationThe Barents Sea is a large continental shelf sea with mean water depths of 300 m. It is bounded by the north Norwegian and Russian coasts and the eastern margins of the Atlantic Ocean. Geologically, the Barents Sea is a complex mosaic of basins and platforms. Figure 1 shows the location of the study area and the Schlumberger West Loppa and Ice Bear multi-client 3D seismic surveys. The West Loppa survey, indicated by the blue polygon, was acquired between 2008 and 2010 and covers an area of 6255 km2. A complex of terraces off the Loppa High area downthrows towards the basin centre, and deposition is controlled by two dominant fault systems that set-up regional highs. Easternmost, the terraces off the Loppa High are characterised by Triassic sediments that are progressively overlain to the west by the Jurassic section on downthrown terraces. The basin contains a thick Cretaceous section that shows a stacked submarine fan system within a thick marine shale section. This is overlain by a Tertiary deltaic sequence. Both the Cretaceous and Tertiary plays have giant potential along with the prospective Jurassic/Triassic tilted fault blocks. Figure 2 shows the geological setting of the study area in Block 7220/2.

Figure 1 The 2008-2010 West Loppa multi-client 3D seismic survey (indicated by the blue polygon) cov-ers a 6255 km2 area. The study area for this article, located in Block7220/2, is within the red polygon.

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The simultaneous AVO inversion was performed on four angle stacks created by stacking over the ranges of incidence angles 3-12 (near), 12-21 (mid), 21-32 (far) and 32-45 (ultra-far) degrees. The inversion process included well-log editing and preparation, sonic calibration, well tie and wavelet extraction for each angle stack at the location of the well. The extracted wavelets are shown in Figure 4. The consistency of wavelets from near to far offsets indicates the high-quality seismic, even on far offsets. In addition, the initial models for inversion were constructed by extrapolat-ing the acoustic impedance, Vp/Vs, and density logs guided by seismic horizon interpretations.

Since there was no well within the study area, data from well 7219/9-1, located around 4.2 km from the centre of study area, were used for the inversion (see Figures 1 and 2). This well was drilled in 1987 at the crest of a fault block. Its main target was to verify hydrocarbon potential of Early-Middle Jurassic sandstones. Late Triassic sandstone of the Snadd Formation was a secondary target. A total thickness of 222 m of net sand was encountered for the three forma-tions (Stø, Nordmela and Tubåen) with average porosities of 17.8%, 16.5%, and 17.3% respectively. Logs and repeat formation tester (RFT) data indicated the reservoir to be water-bearing with possible residual oil.

Figure 2 A thick Cretaceous section shows a stacked submarine fan system within a thick marine shale section. This is overlain by a Tertiary deltaic sequence. Both the Cretaceous and Tertiary plays have a giant potential along with the pro-spective Jurassic/Triassic tilted fault blocks.

Figure 3 Interpretations show prospects in the study area at Base Tertiary and Base Cretaceous levels. Flat spots observed within Jurassic/Triassic fault block plays are indicated by red ellipses.

Figure 4 Wavelets extracted at well location for different angle stacks.

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and density (Figure 5). This provided confidence to progress with performing the inversion away from the well and into the study area of interest.

The result of the inversion performed is shown in Figure 6, which shows how low values of Poisson’s ratio can clearly identify certain anomalies. Interestingly, these anoma-lies are consistently located where interpreters have picked flat spots in Jurassic sediments and where bright amplitudes in the Base Tertiary were observed.

Poisson’s ratio and Vp/Vs are seismic elastic attributes that are sensitive to gas content, (e.g., Hilterman, 2001). The Poisson’s ratio anomalies support the presence of gas for the Base Tertiary prospect. The main reason for this is that S-waves are less sensitive to the presence of gas in a reservoir

The inversion algorithm itself was as described by Tonellot (2001) and based on Aki and Richards approxima-tions (1980) to the Zoeppritz equation. It allows constraints on lateral smoothness in the solution, and has explicit control over the relative influence of the data and the initial model in the results, which are determined by estimating the signal-to-noise ratio of the data and misfit values of the initial models at well locations. The optimisation algorithm used was simulated annealing as described by Ma (2002). The inversion parameters were tuned around the well loca-tion to provide the optimised inversion products in the study area, which is located away from the well. Quality control analysis of the inversion result with well logs indicated a high quality of inversion estimates of acoustic impedance, Vp/Vs

Figure 5 Quality control analysis of inversion at well location (7219/9-1): a) acoustic impedance (AI) comparison with AI log, b) Vp/Vs comparison between inversion result and well log, c) density comparison between inversion result and well log.

Figure 6 Poisson’s ratio overlaid on seismic sec-tion. Poisson’s ratio anomalies are consistently located where interpreters have picked flat spots in Jurassic sediments and where bright amplitudes in Base Tertiary were observed.

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cluster of data located to the left of the data points from the brine-filled sand (Simm, White and Uden, 2000). According to the classification scheme in Figure 7b, it was revealed that the main AVO classes dominant around the Base Tertiary and within Jurassic sediments are Class IV and Class II.

The Base Tertiary is mainly associated with the Class IV anomaly indicated in yellow. AVO Class IV anomalies are mainly recognised as sands with negative intercept but positive gradient. The reflection coefficient becomes less negative with increasing offset, and amplitude decreases versus offset, even though these sands may be bright spots (Castagna and Swan, 1997). Class IV occurs when soft sands (low impedance and low shear impedance) with gas are capped by relatively stiff shales characterized by Vp/Vs ratios slightly higher than in the sands; e.g., highly compacted or silty shales (Castagna and Swan, 1997).

Jurassic sediments, however, are more allied to a combina-tion of Class II (green geobody) and Class IV, where Class II is located on top of Class IV geobody. The class II AVO anomaly represents transparent sands with weak intercept but strong negative gradient that is often associated with hydrocarbons. This is due to relatively large gradients that show up as anomalies in an intercept-gradient plot and plot off the background trend. Interestingly, at the flat spots the two AVO classes seem to separate from each other suggesting the probability of fluid contacts. This effect is recognizable from the results of both AVO inversion and Intercept-Gradient attributes performed on this dataset.

than P-waves, as the high compressibility of gas has more of an effect on the P-wave velocity. Jurassic sediments also show Poisson’s ratio anomalies, which are in agreement with the response expected when hydrocarbons are present.

Geobody analysis was performed based on the crossplot of acoustic impedance versus Vp/Vs. This was particularly useful as the density attribute (incorporated in acoustic impedance) is strongly affected by the presence of gas. The highlighted points in green and yellow polygon on the crossplot in Figure 7a indi-cate points that are anomalous due to fluid effects. Two main anomalies were detected and their corresponding geobodies were mapped (Figure 7c). These anomalies were deemed to be hydrocarbon reservoirs where Vp/Vs ratio shows low values and density is less due to fluid presence.

To further evaluate this interpretation and identify the type of AVO anomalies, AVO attributes of intercept and gradient were computed. The details of how to compute these attributes are described by Shuey (1985). Figures 7b and 7d show AVO class identification (Castagna and Swan, 1997) based on intercept and gradient crossplots and cor-responding geobodies. The dotted line through the centre of the crossplot shows the background trend, which is the theoretical average of the rock property for brine-filled rocks (Simm, White and Uden, 2000). The spread between the points for the hydrocarbon-filled data points and the brine-filled data points depends on the effect the hydrocarbon has on the Vp/Vs ratio of the sand. The effect of the hydrocarbon does not create a very clear trend, but it will create a larger

Figure 7 Crossplots of elastic attributes along with geobody identification by utilizing both simulta-neous inversion (a and c) and AVO 2 term inversion (b and d) indicated that the AVO anomalies are mainly Class II and Class IV.

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areas could indicate areas with porous sands of significant thickness. Some of the targets highlighted by the interpreter at Triassic-Jurassic level (flat spots) do not show a clear high resistivity anomaly. Their dimension (lateral extentand thick-ness) may be too small to be detected by the method; hence the uncertainty on this feature is higher than the base of the tertiary prospect. Calibration of the interpretation with well log data (e.g., resistivity, AI, Vp/Vs, and density logs) closely located to the study area is required to accurately inspect what should be expected in the presence of hydrocarbon, in this area and what are the possible uncertainties attached to its interpretation. The result of such investigation, along with an accurate 3D CSEM modelling, can be used to decisively address the estimation of the distribution of reservoir quality in the Jurassic sands.

ConclusionThis study showed how a detailed AVO inversion analysis can be complemented with CSEM technology in de-risking potential play prospects. Such an integrated approach can be a powerful tool in the early exploration stages of frontier areas.

In the West Loppa example, seismic interpretation sug-gested prospects of hydrocarbons in the study area in Block 7220/2. Subsequently, seismic inversion and CSEM inversion consistently highlighted similar features and anomalies confirming the likely presence of hydrocarbons when placed in a regional geological context.

Seismic and CSEM methods assessed the same strata using fundamentally different techniques. Seismic inter-pretation formed a good understanding of the geological structure of the strata. AVO inversion provided indications of potential hydrocarbon areas. In contrast, CSEM produced

Integrating seismic and CSEM inversion3D anisotropic modelling and inversion of the CSEM elec-tric fields was performed (for more details, see Guerra et al., 2013). The initial resistivity models incorporated the seismic horizons picked in the seismic interpretation stage; hence, they were converted to depth beforehand.

The result of CSEM inversion showed resistive anomalies concentrated in certain regions of the sedimentary sequences. Figure 8 shows the seismic inversion geobodies co-visualized with the vertical resistivity cube (Rz). Vertical resistivity was selected because the inverted horizontal resistivity model is mainly sensitive to the background resistivity and not to thin resistors; however, the vertical resistivity model is sensitive to both (Gabrielsen et al., 2013).

Co-visualization with seismic sections suggests the main regions of resistivity anomalies and their boundaries. The main anomalies are described as follows: Cretaceous (Base Tertiary) prospect: the resistivity cubes indicated the presence of strong resistive features of interest in the Cretaceous sedi-ments. This strongly supports the likelihood of the presence of hydrocarbons (likely gas), which consistently coincides with AVO class IV geobody and bright amplitudes in the seismic interpretation.

Triassic-Jurassic (Base Cretaceous) prospect: this is the zone where the AVO class II was observed along with Class IV (Class II in green geobody overlays the Class IV in yellow geobody) and flat spots are reported by interpreters. Within this interval of interest, the mapped resistivity appears to be lower compared to the Cretaceous target zone. In particular, there are areas with low resistivity (blue to pink) to the west and south, where the Triassic-Jurassic sequence is deeper (see the resistivity map in Figure 8). These low resistivity

Figure 8 Covisualization of seismic sections, seismic inversion geobodies, and the resistivity cube indi-cated that the Cretaceous propspect is a hydrocar-bon-bearing target. The resistivity map on the left side shows the distribution of anomalies based on RMS surface attribute extracted from the top to the base of the vertical resistivity cube.

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Guerra I., Ceci F., Miotti F., Lovatini A., Milne G., Paydayesh M.,

Leathard M. and Sharma A. [2013] Multi-measurement integra-

tion – a case study from the Barents Sea. First Break, 31 (4), 55-62.

Hilterman, F.J. [2001] Seismic Amplitude Interpretation. SEG

Distinguished Instructor Series, No. 4.

Lovatini, A., Medina, E., Campbell, T. and Myers, K. [2010] The

Use of CSEM within an Integrated Exploration Project ‘’Best of

EAGE’’. 72nd EAGE Conference & Exhibition, Barcelona, Extended

Abstracts.

Ma, X.Q. [2002] Simultaneous inversion of prestack seismic data for rock

properties using simulated annealing. Geophysics, 67 (6), 1877–1885.

Miotti F., Guerra I., Ceci F., Lovatini A., Paydayesh M., Leathard M.

and Sharma A. [2013] Petrophysical Joint Inversion of seismic and

EM attributes: a case study. SEG’s 83rd Annual Meeting, Houston

Texas, US.

Shuey, R.T. [1985] A simplification of the Zoeppritz equations.

Geophysics, 50, 609-614.

Simm, R., White, R. and Uden. R. [2000] The anatomy of AVO cross-

plots. The Leading Edge, 150-155.

Tonellot, T., Mace, D. and Richard, V. [2001] Joint Stratigraphic Inversion

of Angle-Limited Stacks. SEG Expanded Abstracts, 20, 227.

Young, P. and Cox, C. [1981] Electromagnetic active source sounding

near the East Pacific Rise. Geophysics Research Letters, 8, 1043-

1046.

information on the fluid content of the rock. Seismic and CSEM could be used in an exploration workflow, simulta-neously or iteratively, to better understand a sedimentary basin. By exploiting the strengths of each geophysical tool, decision-making confidence is increased in selecting areas of interest for further exploration, denser acquisition of seismic and CSEM data, and identifying optimal drilling locations.

AcknowledgmentsThe authors would like to thank the Schlumberger Multi-client team and EMGS for permission to present these data and allowing publication of this work.

ReferencesAki, K., and Richards, P. [1980] Quantitative seismology: Theory and

Methods. W.H. Freeman Co., NY.

Castagna, J. P. and Swan, H. W. [1997] Principles of AVO crossplotting.

The Leading Edge, 16 (4), 337-342.

Constable, S. [2010] Ten years of marine CSEM for hydrocarbon

exploration. Geophysics, 75 (5), 75A67–75A81.

Gabrielsen, P. T., Abrahamson, P., Panzner, M., Fanavoll, S. and Ellingsrud,

S. [2013] Exploring frontier areas using 2D seismic and 3D CSEM

data, as exemplified by multi-client data over the Skrugard and Havis

discoveries in the Barents Sea. First Break, 31 (1), 63-71.

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