Fluid and Lithology Prediction within a Coal Sequence using Seismic Attribute Modelling and Analysis (Gippsland Basin) Martin Kim*
Jarrod Dunne
Boris Gurevich
Woodside Australia
[email protected]
Woodside Australia
[email protected]
Curtin University Australia
[email protected]
SUMMARY In the Latrobe Group of the Gippsland Basin (Australia) the seismic response of the reservoir is masked by the presence of coal seams. These coal seams possess a large contrast in their acoustic properties (density and Pwave velocity) to those of the surrounding rocks (sands and shales). This causes strong sidelobe interference and coherent noise that overprints the more subtle sand/shale and porefill responses that we aim to detect. This has prevented reliable delineation of existing fields and the possible discovery of new fields in the area. To study the effect of coal seams on seismic attributes we modelled the seismic response of sand-shale-coal sequences. Synthetic seismograms of a ‘coaly sequence’ were built from simple models consisting of three to four layers and also from blocked well log data. The synthetic seismograms were computed for these models using convolutional modelling as well as the reflectivity method, which takes into account multiple reflections and mode conversions. Input models created by randomly shuffling the blocked logs were used to analyse the sensitivity and robustness of AVO attributes to variations in rock and fluid properties. Fluid effects were detectable on the far-offset amplitudes for coal sequences with no more than fifteen to twenty percent coal content. For larger amounts of coal, fluid detection becomes ambiguous regardless of the distribution of coal layers in the sequence. A useful attribute for predicting coal content is the near-offset average absolute amplitude. A better attribute for detecting fluid effects can be formed as a linear combination of the far-offset event amplitude and the near-offset average amplitude.
Carboniferous Southern North Sea area, the Ruhr area in Germany, the Cooper Basin (Australia), and the Gippsland Basin in Bass Strait. The Latrobe Group holds a significant amount of oil and gas within the Gippsland Basin. The seismic response of this oil and gas is commonly ‘masked' by the presence of coal seams. These seams exhibit a large contrast in acoustic properties (density and P-wave velocity) to those of the surrounding rock (sand or shale). This large contrast in acoustic properties gives rise to a large reflection response, which can produce interbed multiples and mode conversions. Thus the sands, shales and fluid fill in between these seams are not resolved and are therefore ‘masked’. It is suspected that the main concentrations of the oil and gas are in sand channels of unknown dimensions, orientations and thickness. Imaging of these sand channels could possibly save millions of dollars in exploration and development costs. This paper proposes a method for detecting lithology and porefill effects within a coal sequence. We achieved this by modelling seismic data (based on well log information) to find seismic amplitude attributes that can be used to detect sands and shales within a coal sequence. Initially we used simple convolutional modelling without noise before using the reflectivity modelling method to generate synthetic gathers with coherent noise to simulate processing and real data effects. Different reservoir characterisation techniques were applied on the modelling results and generated synthetic gathers. These techniques were then compared to find the attributes most sensitive to the presence of sands and fluid fill, and establish an optimal workflow. In order to test and refine the workflow, the attributes were measured in a 3D PreSDM dataset and checked against existing wells in the study area (Figure 1).
These attributes were applied to a 3D prestack depth migrated dataset to characterise the ‘coaly sequence’ and calibrate it to nearby wells. The combined fluid attribute predicted the porefill in all five wells and indicated possible hydrocarbon extents in the known fields. Key words: AVO, modelling, coal, Latrobe, Gippsland.
INTRODUCTION AVO analysis of reflection seismic data is widely used to detect hydrocarbon reservoirs. There are several areas in the world where the seismic response of the reservoir is masked by the presence of coal seams. Such areas include the
ASEG 16th Geophysical Conference and Exhibition, February 2003, Adelaide.
Extended Abstracts
Fluid and Lithology Prediction (Gippsland Basin)
Kim, Dunne and Gurevich
METHOD AND RESULTS Modelling The initial modelling involved the use of a convolutionmodelling package where the reflection coefficient was calculated for each interface and sequence of these reflection coefficients was convolved with a source wavelet. This is a simple tool that provides a basic understanding of the seismic response without the influence of noise. A simple three-layer model was expanded to four to comprise the first and second phase of modelling the coal response. Each model consisted of a shale seal followed by a coal seam and then a laminated sand layer. The first model contained a relatively thick sand package, within the Top Coaly Sequence of the Latrobe Group. The second model involves a thinner sand sequence between two coal seams. In both cases the rock properties and reservoir parameters were varied to establish the most suitable attributes for fluid and lithology predictions. The effects of variations in compressional velocity VP, shear velocity VS, density and thickness in sand, shale and coal were tested to simulate natural variations of rock types. Net to gross refers to the ratio of sand to other non-pay sediments within the laminated sands, and variations in this ratio were also tested. Table 1 displays the parameter variability of the lithologies used in each model. S-wave logs were not acquired in any of the wells, and thus had to be estimated for this work from the P-wave sonic log and Poisson’s ratio. Within the Gippsland Basin shales are known to have Poisson’s ratio similar to sand so Poisson’s ratio can be assumed to decrease linearly with depth. Coal has a very different Poisson’s ratio, which was estimated from a regional database.
histogram analysis and trend regression of the sonic and density logs. Our modelling software generated seismograms at fixed angles that then can be used to calculate the intercept and gradient. These attributes can then be crossplotted to examine the AVO response in more detail. The majority of the data lies in the lower right quadrant of the AVO crossplot. According to Castagna and Swan (1997) this is known as a type I sand response (hard becoming softer). As the coal layers become thinner the sand response becomes a type III (soft becoming softer). Based on the AVO crossplot analysis of individual reflections, the intercept should be used for lithology detection, as this is the direction of change when decreasing or increasing the rock properties of the sand or the coal (Figure 2). The reflection gradient, or alternatively the faroffset amplitude, is of more use for fluid prediction (Figure 2).
Coal Vp=2700 m/s Vs=1400 m/s Density=2 g/cm3
Gradient
Figure 1. Map of well locations and study area. Well 1, 3 and 5 are dry while 2 and 4 contain oil.
Change in lithology, density and thickness of sands and coals
Coal Vp=2200 m/s Vs=1000 m/s Density=1.7 g/cm3
Shale
Parameters VP (m/s) VS (m/s) 3 Density (g/cm ) Thickness (m)
4 Layer Model Min Max 3900 4100 2080 2240 2.45 2.65 10 20
Coal
VP (m/s) VS (m/s) 3 Density (g/cm ) Thickness (m)
2200 1000 2.0 30
2700 1500 2.3 50
2200 1000 1.7 5
2700 1400 2 20
Sand
Table 1. Model parameters for crossplot modelling 3 Layer Model Min Max 3900 4100 2080 2240 2.45 2.65 10 20
VP (m/s) VS (m/s) 3 Density (g/cm ) Porosity (%/100) Thickness (m) Net to Gross
3700 2200 2.35 0.06 5 0.2
3900 2360 2.55 0.20 35 1
3700 2200 2.35 0.06 5 0.2
3900 2360 2.55 0.20 35 1
The important parameters to note are the coal P-wave velocity and density. On the well logs these values varied from 2200m/s to 2700m/s for the velocity and 1.7g/cc to 2.3g/cc for the density. These values were obtained through
Change in porefill
Intercept
Figure 2. A softer coal (right) shows more contrast between the brine and gas response and indicates the most useful attribute as the change in gradient while intercept is better for change in lithology (this is also the direction for decreasing coal and sand thickness). Brine is blue; Oil, red; Gas, green and residual gas is yellow. Convolution modelling allowed us to select the most informative AVO attributes for data dominated by P-wave primary reflections. Since coal has a large contrast in properties to sand and shale, it is expected to produce large multiple reflections and P to S conversions which may distort the AVO response. To test the sensitivity of these attributes to multiples and mode conversions, we performed fullwaveform modelling using the reflectivity method (Kennett 1980). We generated synthetic seismograms from well logs and processed them similarly to the field data in the area to partially remove the effect of multiples. The logs were manipulated to analyse the effect of shuffling the coals to
ASEG 16th Geophysical Conference and Exhibition, February 2003, Adelaide.
Extended Abstracts
Fluid and Lithology Prediction (Gippsland Basin)
Kim, Dunne and Gurevich
make sure that attribute selection is not biased by any specific distribution of coal layers in an individual well, given the high degree of lateral variation in the ‘coaly sequence’. This shuffling is achieved by first blocking the logs and determining which is a sand layer and which is coal. From this the individual layers can be moved and shuffled at random. The logs were also manipulated to analyse the effect of reducing the amount of coal in the sequence to determine how much coal was needed before the sand and fluid responses were masked. A total of sixty-three synthetic shot gathers were created for the brine, oil and gas case from the original log data and for 20 shuffles of the coal seams. Many attributes were analysed from the synthetic gathers including near- and far-offset amplitudes, background normal calculations, and windowed attributes to find the optimum fluid and lithology indicator. The best attribute that we found for enhancing lithology effects (mainly coal content) was an average absolute amplitude computed on the near offsets between the top and base of the ‘coaly sequence’. A horizon amplitude extracted on the far-offsets appeared to be the most suitable for determining porefill. The ‘coaly sequence’ has soft coals and harder sands and regardless of porefill the sands show higher impedance. Therefore in using the SEG negative polarity convention we picked horizons on troughs to map the coal/sand interfaces.
Figure 3. A reduction in coal content makes the fluid response more detectable in the far offsets. The reduction of coal can be seen most clearly on the near offsets where the greatest amplitude change occurs. Real Data Application The attributes identified from the modelling were measured on the processed gathers taken from a traverse between all the wells. The procedure was then applied to the pre-stack depth migrated data and existing workflow was refined. Well to seismic ties were not made as the synthetics were not created to match any single well but rather to be representative of the entire study area. We used a near-offset average absolute amplitude over a window covering the ‘coaly sequence’ to illustrate the lithology effects over the study area, remembering that the modelling suggests a higher coal content with increasing amplitude. Figure 4 shows this higher coal content as blue and purple while a decrease in coal is in yellow and red. Possible channel belts running in a north-south direction, including individual meander loops can be seen in this figure.
Figure 3 illustrates that fluid effects in reservoirs containing twenty-five percent coal can not be resolved (first upper 3 curves of green, red, and blue), using an average absolute amplitude window attribute of 100ms, below the Top Coaly horizon marker. The figure shows that the fluid response of brine, oil and gas are not separable at any offset. Furthermore this was observed in each of the shuffle experiments containing twenty-five percent coal. Decreasing the coal content by five to ten percent gave weak indications of fluid fill at the far-offsets. Further decreases in the coal content led to better detectability of porefill over a larger range of offsets. Figure 3 also illustrates this effect of decreasing the coal content. It shows a general trend across the offsets, where near-offset amplitudes are high and decrease towards the far offsets. If the coal content is decreased the near-offset amplitudes decrease while the fluid effects become more prominent on the far offsets. Therefore, once the lithology is estimated using a near average absolute amplitude window then fluid prediction can be done with greater certainty using the far amplitudes. Average Absolute Amplitude over 'Coaly Sequence' with Reduction in Coal Content 700
Amp (*10000)
600
25% Coal
500
15% Coal
400
B rine Oil
300
Gas
5% Coal
200 100 0 0
1000
2000 Off set (m)
3000
4000
Figure 4. Near stack average absolute amplitude on prestack depth migrated 3D volume. Figure 5 shows the map of far-offset amplitude that was measured on a trough fifteen milliseconds below the Top Coaly horizon marker. This map can be used in conjunction with the lithology map to predict the porefill. Well-2 and Well-4, which are known to have oil, show larger amplitudes (yellow to red) while smaller amplitudes (blue to purple) are at Well-1, Well-3, and Well-5, which are dry. The large red event in the East of the map is not due to fluids but to coals, illustrated by the dark blues in the lithology map (Figure 4). Figure 5. Far stack amplitude fifteen milliseconds below Top Coaly horizon using prestack depth migrated 3D volume. The near- and far-offset amplitude maps gave some encouraging lithology and fluid indicators but are not independent, perhaps they can be used together to achieve better predictions. The near average absolute amplitude and the far amplitude are plotted against each other in Figure 6(a). This ‘areal’ histogram crossplot illustrates where the greatest accumulation of far amplitude and near average
ASEG 16th Geophysical Conference and Exhibition, February 2003, Adelaide.
Extended Abstracts
Fluid and Lithology Prediction (Gippsland Basin)
Kim, Dunne and Gurevich
absolute amplitude lie. Each histogram bin has a specified size and is coloured according to the number of points falling in it.
Figure 7 shows the combined fluid attribute map draped over the Top Coaly time structure map. The fluid attribute map illustrates the correct fluid response in the immediate vicinity of the well locations. Well-2 and Well-4 have high negative numbers (reds and yellows) around them indicating an oil sand, while Well-1, Well-3, and Well-5 have high positive Well 5 Well 4
Well 3
Increasing FAMP
IncreasingNAAA
Well 1
Hydrocarbons
Well 2
Increasing coal content Brine
Figure 6. Histogram crossplot with (a) Map display ‘areal’ histogram crossplot and (b) diagrammatic representation of how the brine and oil/gas are expected to plot against each other based on our modelling. FAMP is the far-offset amplitude, NAAA is the near-offset average absolute amplitude, m is the gradient of the brine cloud, and c is the intercept. Figure 6(b) is a cartoon representation of how the brine and oil/gas are expected to plot against each other based on the modelling. The higher the near average absolute amplitude the more likely we have greater coal content, while increasing far amplitude means a higher likelihood of oil or gas. With a lower near average absolute amplitude there is greater separation between the fluids, due to the decrease in coal content. Therefore, there is an ambiguity in the far-offset amplitude where the larger values may be indicative either of larger coal content or hydrocarbons. The cartoon is accentuated to show a distinct difference between the fluids. The histogram has more overlap between the oil and gas clouds, mainly due to the signal-to-noise ratio of the seismic data. However, a lobe is visible at the lower amplitude end of the histogram and this becomes thinner with increasing amplitudes, as predicted by the modelling. A simple formula was employed to exploit the difference between high far-offset amplitudes, low near-offset amplitudes and the rest of the data by plotting a map of the following fluid attribute F = FAMP – NAAA*m + c , (1) where FAMP is the far-offset amplitude, NAAA is the nearoffset average absolute amplitude, m is the gradient of the brine section, and c is the intercept. This attribute uses the brine response as a base value. Equation (1) leaves the majority of the coal lithology and brine porefill values positive, while the oil/gas sand values give negative F values.
numbers (dark blues and purples), indicating a higher coal content and no hydrocarbons. Figure 7. Combined Fluid Prediction Map. Polygons illustrate probable extents of the known fields. Looking back on the lithology map (Figure 4) a major feature appears on the middle terrace to the west of Well-3. On the near average absolute amplitude it appears as a large value (dark blue to purple and pink), which implies that it is caused by greater coal content. On the far amplitude map it is a high negative value (yellow to red), which could imply hydrocarbons. However, on the combined fluid map the hydrocarbon response is weakened and is a large positive value (blue and purple) which indicates a coal prone area. This example illustrates that the fluid attribute map given by equation (1) is more representative of the fluid than the faroffset amplitude.
CONCLUSIONS We established useful attributes for fluid and lithology prediction through detailed modelling and field data testing. A useful lithology attribute is the near average absolute amplitude response for the entire ‘coaly sequence’. A useful fluid attribute is the far amplitude extracted at the top of the laminated sand event. The two attributes together make a combined fluid prediction attribute that removes anomalous highs on the far amplitude map related to coals. These attributes appear to work well for the Latrobe group in the Gippsland Basin where they predicted the well results and revealed likely sand channels and field extents. The results led us to the conclusion that further improvements
ASEG 16th Geophysical Conference and Exhibition, February 2003, Adelaide.
Extended Abstracts
Fluid and Lithology Prediction (Gippsland Basin)
Kim, Dunne and Gurevich
might result from a more general multi-attribute approach. The additional attributes might include measurements of local frequency content (i.e. peak frequency and spectral width). The optimal attributes could vary for other coalprone basins, but the same methodology could be used to find them.
ACKNOWLEDGEMENTS Thanks to the Curtin Reservoir Geophysics Consortium (CRGC) and the Australian Petroleum Cooperative Research Centre (APCRC) for their support and interest in this project. We also wish to thank Klaas Koster, Jeromy DePledge, Paul Martin, Terrance Folkers, Colin Hawke and Bas Spaargaren from Woodside Energy Ltd. for their valued information and support. REFERENCES Castagna, J.P., and Swan, H.W., 1997, Principles of AVO Crossplotting: The Leading Edge, 16, No. 4, 337-342. Kennett, B. L. N., 1980, Seismic waves in a stratified half space - II. Theoretical seismograms: Geophys. J. Roy. Astr. Soc., 61, 1-10.
ASEG 16th Geophysical Conference and Exhibition, February 2003, Adelaide.
Extended Abstracts
