AEGC 2019: From Data to Discovery – Perth, Australia 1

Lithology characterisation of the roof and floor of the Moranbah measures coal seam using post-stack and pre-stack seismic inversion Margarita Pavlova* BHP 480 Queen St, Brisbane, Australia margarita.pavlova@bhp.com

INTRODUCTION

The focus of this paper is on using 3D seismic inversion attributes to help identify two geotechnical risks pertinent to longwall mining: strong roof and weak floor. Strong roof refers to a condition when abnormally high strength and massive rock is located in the roof close to the working seam. This could affect caving process and result in periodic weighting and impact longwall production. Weak/soft floor lithologies usually consist of fine-grained materials such as siltstones and claystones. Underground coal mining on weak floor may often lead to geotechnical issues ranging from small scale issues like floor heave to large scale instability and long-term subsidence (Kasangula, 2011).

Post-stack seismic inversion was previously used by Hatherly et al. (2008) to predict Geophysical Strata Rating from compressional impedance features within the roof of the working seam. This Moranbah project was designed to investigate the potential advantages of using pre-stack data along with the post-stack.

DATASET The longwall mine is located in the Bowen Basin, Australia. The main mining target is the seam within the Permian Moranbah coal measures. Coal thickness varies between 5.5 and 7 m and the target seam is located between 70-300 m below ground level. The 36 km2 3D seismic data set, consisting of 9 individual surveys, was jointly reprocessed in 2017. The individual surveys were acquired using a mix of dynamite and Vibroseis sources and acquisition spanned 1998-2015 year range. The processing flow aimed to match amplitude, phase and frequency spectrum across the merged volume. The dominant frequency of the volume is 65 cycles/km. With the average velocity estimate of 2800 m/s the limit of separability is expected to be ~11 m based on the ¼ wavelength, suggesting that the coal is below tuning thickness. The dataset was migrated using PSDM and had very small (+/- 7 m) residual misties for the target coal seam. The quality of the final depth domain migrated full stack data is generally good. There is some noticeable amplitude variation of the target reflection that is attributed to the filtering effect of a discontinuous seam, located 100 m above the target seam. More energy is transmitted where the upper seam is absent and the amplitude of the target seam appears brighter. The pre-stack data quality is variable. One of the main reason for this is that near offsets are contaminated by strong noise energy (mainly ground roll). Noise attenuation was applied during processing and a relatively harsh inner mute was also required. This resulted in limited useable near offset traces remaining in the dataset. Broadening of the wavelet can be observed at far offsets, and is caused by absorption, cyclic layering and by very-short-delay multiples. There is also an imprint of mode converted energy and NMO stretch on the far offsets. Consequently, the usable angle range for inversion was chosen to be 10-32 degrees. Data conditioning techniques such as generating super gathers and applying trim statics were used to increase signal to noise ratio and flatten the gathers, respectively.

SUMMARY 3D seismic technology has become a popular exploration and development tool among coal mining companies in Australia in the last ten years. Traditionally the main use of these seismic data is to inform the mine of the structural features such as faults, rolls and dykes. There have been very few attempts to use quantitative seismic interpretation to help understand geological risks ahead of longwall mining. The purpose of this paper is to provide an example of roof and floor characterisation using post-stack and pre-stack seismic inversion. First step in the study was to understand the relationship between elastic and rock properties using log data. Thus, when looking at the relationship between lithology/gamma ray response and compressional impedance I observed that massive, blocky sandstones have high velocity and density and hence high compressional impedance, while siltstones, claystones and fine-grained sands have low density, velocity and low compressional impedance. Lithology discriminators lambda-rho and mu-rho crossplots also showed good separation of blocky clean, sands and soft fine-grained sands. As a second step I ran pre-stack and post-stack inversions using a small subset of the boreholes. Thirdly, upon reviewing products of the inversions against all log data, I found the pre-stack results to be more superior than the post-stack. The current work highlighted a few areas of blocky sandstone roof and weak floor. This information could be used to inform the future mine operations and design. Key words: coal, seismic inversion, QI, longwall mining.

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Within the 3D survey area there are just over 1000 boreholes with logged density and GR, and half of these boreholes have compressional sonic data. Lithology log is composed of data logged by a geologist from core (35-40%) or from predicted lithology from other logs. One of the vital pieces of information required for pre-stack inversion is shear velocity log data. There are several ways to acquire this information: 1. Run dipole sonic tool, which is not routinely used by

mining companies. 2. Run monopole sonic tool with extended recording time

and perform Full Waveform Sonic interpretation. In the fast formations shear velocity could be inferred from refracted waves and in the slow/coal formations (where Vshear < Vmud) it can be estimated from Stoneley waves (Market, 2019).

3. Use previously derived transforms: Castagna mudrock line for non-coal and Morcote et al. (2010) for coal.

4. Use analogues: Bowen Basin Coal Seam Gas companies acquired some shear velocity data through Permian coal intervals and this data is publicly available.

In this project method 2 was utilized. Five boreholes at the mine site were logged with extended recording time using a monopole tool. The Vp-Vs relationship was derived from the interpretation of the refracted shear in fast formations and shear estimate from Stoneley in slow formations. This relationship was later used to create shear velocity logs for pre-stack simultaneous inversion.

METHOD To understand the impact of changing lithology on elastic properties of the roof and floor of the target seam a series of well log cross-plots were generated using a +20 to -10 m extraction window centred at the main target seam. It was observed that there is a relationship between compressional impedance (Zp) and the lithology/gamma ray (GR) response. The high Zp corresponded to coarse-grained sandstone with low GR, while low Zp was characteristic to fine grained sandstones, siltstones and claystones with high GR. Further, it appears that the coarse-grained sandstones generally are cemented and have higher compressional velocity (Vp), while fine-grained material is generally slower (velocity is a more discriminating factor than density in this case) (Figures 1a and 1b). When shear velocity information is available, it is possible to investigate lithology discrimination via a Zp versus compressional to shear velocity ratio (Vp/Vs) crossplot. The low Vp/Vs ratio is indicative of lower clay and higher cement content (Ødegaard and Avseth, 2003). Figure 1c shows that high impedance coarse sands have low Vp/Vs. It is also possible to distinguish lithological units based on Lamé parameters lambda and mu (shear modulus). Figures 1e and 1d show lambda-rho and mu-rho crossplot. High mu-rho values in particular should aid to highlight clean sand, because the main sand constituent quartz has higher rigidity than clay minerals. Seismic data conditioning and inversion work was done in Hampson and Russell software. Mistie corrections were run to tie seismic depth to borehole depth. The correlation window was chosen to be 100 m above to 20 m below the target seam. The well to seismic tie synthetics correlation factor varied from 0.4 to 0.9, and 19 boreholes with correlation >0.65 were used

in the inversion. The wavelet used was a constant phase wavelet extracted using borehole data, it was found to be optimal for both post-stack and pre-stack inversion. A low frequency model was created from smoothed log data and co-kriged with the migration seismic velocity. To further guide interpolation and extrapolation of the log data within the model the top coal horizon was also used.

RESULTS AND DISCUSSION The post-stack inversion products were Zp and pre-stack - Zp, shear impedance (Zs) and density. All four volumes were analysed in Petrel software. Figure 2 shows the cross-section, where high impedance areas are interpreted to correspond to massive blocky sands and low impedance areas correspond to siltstone, fine-grained sandstone (soft rock). The mean of the Zp attribute was extracted within a 14 m interval, 6 m offset above the target seam and compared to the mean values of GR within the corresponding interval. This interval represented the roof lithology and the offset was used to make sure that the residual depth mistie does not interfere with the results (Figure 3a and Figure 4a). In the similar way for the floor characterisation the mean of the Zp was extracted within 10 m, with 6 m offset below the top of the seam (the 6 m offset represented the average seam thickness). GR log mean was extracted within the same interval (Figure 3b and 4b). Correspondence between Zp and GR is good for the roof and moderate for the floor. The inferior floor lithology match could possibly be explained by attenuation of energy through the target seam. Results of the pre-stack inversion Zp had a better match with lithology than post-stack results, particularly for the floor. Pre-stack simultaneous inversion works at estimating Zs and density together with Zp. This requires background trends between Zp and Zs, and Zp and density. The number of iterations of the pre-stack vs post-stack inversion is considerably higher as it attempts to reach global minimum between the inverted result and the input gathers (Anderson, 2009). The fact that pre-stack Zp is more consistent with Zs should results in better separation of lithologies. Figure 5 shows lithology characterisation based on the lambda rho, mu rho crossplotting, which largely agree with the results from pre-stack Zp. A pre-stack inversion allows to estimate elastic properties, which could have several applications for mine geotechnical work: • Young’s Modulus and Vp seismic volumes together with

lithological characterisation could help to understand rock strength variation within a mine.

• Bulk and Shear moduli, Young’s Modulus and Poisson ratio could be incorporated into the finite element models used to simulate the longwall extraction.

For use in geotechnical workflows, it is important to calibrate seismically derived elastic properties along with the borehole data which both are dynamic to the static lab measurements.

CONCLUSIONS

Despite minor variations between different inversion products, compressional impedances from post-stack and pre-stack

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inversion and lambda-rho mu-rho lithology indicators show similar distribution of blocky sandstones in the roof and soft lithology in the floor. Once calibrated by future drilling this information could improve lateral resolution of the 3D geological models and hence better inform the mine design and operations. The benefit of the pre-stack simultaneous inversion is that it allows to derive 3D elastic properties, which have a potential to improve geotechnical models.

ACKNOWLEDGEMENTS I would like to thank BHP Geophysics coal team and the leader Eric Battig for their support and to the mine site for allowing publishing the data. Also, special thanks to Balazs Nemeth, Heather Schinjs and Ben Forrest for their contributions in reviewing this abstract.

REFERENCES Andreson, P., 2009. Comparing post-stack AVO inversion to prestack inversion for estimating rock properties. CSPG CSEG CWLS Convention, 669-672 Hatherly, P., Zhou, B., Peters, and Urosevic, M., 2008. Acoustic impedance inversion for geotechnical evaluation in underground coal mining. SEG Las Vegas 2008 Annual Meeting, 3600-3603. Kasangula, M. 2011. BMA Guidelines to Geotechnical Constraints on Longwall Mining Projects. Morcote, A., Mavko, G., and Prasad, M., 2010. Dynamic elastic properties of coal. Geophysics, 75(6), E227-E234 Market, J., 2019. Internal BHP report. Ødegaard, E. and Avseth, P., 2003. Interpretation of Elastic Inversion Results Using Rock Physics Templates: EAGE, Expanded Abstracts, E1.

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Figure 1. Crossplots of compressional impedance with compressional velocity, coloured by gamma ray (GR) (a) and coloured by lithology (b); compressional impedance with compressional to shear velocity ratio, coloured by lithology (c); Lambda*Rho and Mu*Rho, coloured by gamma ray (GR) (d) and coloured by lithology (e).

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Figure 2. Section through pre-stack compressional impedance with logs of GR and lithology. The position of the section is marked on the Figure 3b (yellow line). Note, the inversion is run from 10 m above the upper seam to 30 m below the target seam and low frequency model values are displayed outside the inversion window.

Figure 3. Mean of roof (a) and floor (b) pre-stack compressional impedance. Point data show the average GR, calculated within the roof (a) and the floor (b) of the target seam.

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Figure 4. Mean of roof (a) and floor (b) post-stack compressional impedance. Point data show the average GR, calculated within the roof (a) and the floor (b) of the target seam.

Figure 5. Lithology identification based on lambda-rho, mu-rho crossplots for the roof (a) and for the floor (b). Red colour indicates siltstones and very fine-grained sandstone, while blue colour indicates coarse-grained sandstone. Point data show the average GR, calculated within the roof (a) and floor (b) of the target seam.