Multimineral Modeling and Estimation of Brittleness Index of Shaly Sandstone in Upper Assam and Mizoram Areas, India
- Triveni Gogoi (Indian Institute of Technology (Indian School of Mines), Dhanbad) | Rima Chatterjee (Indian Institute of Technology (Indian School of Mines), Dhanbad)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Evaluation & Engineering
- Publication Date
- May 2020
- Document Type
- Journal Paper
- 708 - 721
- 2020.Society of Petroleum Engineers
- shaly sandstone, multimineral model, Upper Assam, Mizoram, brittleness index
- 8 in the last 30 days
- 53 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 5.00|
|SPE Non-Member Price:||USD 35.00|
The brittleness index (BI) has major implications for hydraulic fracture studies and production toward optimized recovery in unconventional reservoirs. The paucity of brittleness studies in Mizoram and Upper Assam, located in northeastern India, motivates us to take up multimineral modeling and estimation of BI. Two commonly used BI estimation approaches, mineralogical and geomechanical, have been implemented to characterize the shaly sandstone in the study area. Laboratory analyses of the available drill-cutting samples and crossplots from well log data along with previous literature confirm the types of minerals present in the study area. With this mineralogical information, a new approach of BI log estimation from multimineral modeling is suggested here using conventional log data in the absence of core/drill cutting samples. A multimineral model for Mizoram and Upper Assam is developed by using bulk density (ρ), compressional sonic velocity (Vp), shear sonic velocity (Vs), lithodensity, and acoustic impedance (AI) logs to calculate volumetric percentage of minerals. Estimated mineralogical BI from well log data using four established models are compared and calibrated with X-ray diffraction (XRD)-derived BI to validate the proposed procedure. Most brittle zones having a BI ≥ 66% are demarcated for high Young’s modulus (Y ≥ 60 GPa) and low Poisson’s ratio (ν ≤ 0.25) values in the Y vs. ν crossplot for the study area. The presence of brittle minerals estimated from both XRD and the multimineral model suffices the reason for the high brittleness of shaly sandstone in Mizoram compared with Upper Assam.
|File Size||14 MB||Number of Pages||14|
Akhir, N. A. M., Gaafar, G. R., and Saaid, I. M. 2015. Quantification of Clay Mineral and Log Response Toward Reservoir Rock Properties. In ICIPEG 2014: Proc. of the International Conference on Integrated Petroleum Engineering and Geosciences, ed. M. Awang, B. Negash, Md N. Akhir, and L. Lubis, Part IV, 221–231. Singapore: Springer.
Altamar, R. P. and Marfurt, K. 2014. Mineralogy-Based Brittleness Prediction from Surface Seismic Data: Application to the Barnett Shale. Interpretation 2 (4): T1–T17. https://doi.org/10.1190/INT-2013-0161.1.
Amosu, A. and Sun, Y. 2018. MinInversion: A Program for Petrophysical Composition Analysis of Geophysical Well Log Data. Geosciences 8 (2): 65. https://doi.org/10.3390/geosciences8020065.
Bhandari, L. L., Fuloria, R. C., and Sastri, V. V. 1973. Stratigraphy of Assam Valley, India. AAPG Bull 57 (4): 642–654. https://doi.org/10.1306/819a4310-16c5-11d7-8645000102c1865d.
Bharali, B., Borgohain, P., Bezbaruah, D. et al. 2017. A Geological Study on Upper Bhuban Formation in Parts of Surma Basin, Aizawl, Mizoram. Sci Vis 17 (3): 128–147. https://doi.org/10.33493/scivis.17.03.02.
Bhuyan, D. 2016. Tertiary Stratigraphy and Sedimentology of Parts of Upper Assam Basin, first edition. Saarbrücken, Germany: Lambert Academic Publishing.
Chen, J. and Xiao, X. 2013. Mineral Composition and Brittleness of Three Sets of Paleozoic Organic-Rich Shales in China South Area. J China Coal Soc 38 (5): 822–826.
Clavier, C. and Rust, D. H. 1976. MID-Plot: A New Lithology Technique. Log Anal 17 (6): 16–24. SPWLA-1976-vXVIIn6a2.
Cook, H. E., Johnson, P. D., Matti, J. C. et al. 1975. Methods of Sample Preparation and X-Ray Diffraction Data Analysis, X-Ray Mineralogy Laboratory, Deep Sea Drilling Project, University of California, Riverside. Initial Reports of the Deep Sea Drilling Project 28, 999–1007. https://hdl.handle.net/10.2973/dsdp.proc.28.app4.1975.
Dasgupta, S. and Nandy, D. R. 1982. Seismicity and Tectonics of Meghalaya Plateau, Northeastern India. Paper presented at the 7th Symposium on Earthquake Engineering, Roorkee, India. 10–12 November.
Erickson, S. N. and Jarrard, R. D. 1998. Velocity–Porosity Relationships for Water-Saturated Siliciclastic Sediments. J Geophys Res 103 (B12): 385–406. https://doi.org/10.1029/98JB02128.
Fisher, A. T. and Underwood, M. B. 1995. Calibration of an X-Ray Diffraction Method to Determine Relative Mineral Abundances in Bulk Powders Using Matrix Singular Value Decomposition: A Test from the Barbados Accretionary Complex. In Proceedings of the Ocean Drilling Program, Initial Reports, ed. T. H., Shipley, Y., Ogawa, P., Blum, et al., Vol. 156, 29–37. College Station, Texas, USA: Texas A&M University.
Gandhi, M. S., Solai, A., and Chandrasekar, N. 2010. Light Minerals, XRD and SEM Studies in the Depositional Environments between Tuticorin and Thiruchendur, South East Coast of India, Tamil Nadu. Int J Geomat Geosci 1 (2): 233–251.
Gholami, R., Rasouli, V., Sarmadivaleh, M. et al. 2016. Brittleness of Gas Shale Reservoirs: A Case Study from the North Perth Basin, Australia. J Nat Gas Sci Eng 33: 1244–1259. https://doi.org/10.1016/j.jngse.2016.03.013.
Gogoi, T. and Chatterjee, R. 2019. Estimation of Petrophysical Parameters Using Seismic Inversion and Neural Network Modeling in Upper Assam Basin, India. Geosci Front 10 (3): 1113–1124. https://doi.org/10.1016/j.gsf.2018.07.002.
Goktan, R. M. and Yilmaz, N. G. 2005. A New Methodology for the Analysis of the Relationship Between Rock Brittleness Index and Drag Pick Cutting. J. South Afr Inst Min Metall 105 (10): 727–733. https://www.saimm.co.za/Journal/v105n10p727.pdf.
Gray, D. 2005. Estimating Compressibility from Seismic Data. Paper presented at the 67th EAGE Conference & Exhibition, Madrid, Spain, 13–16 June. Cp-1-00317. https://doi.org/10.3997/2214-4609-pdb.1.P025.
Grieser, B. and Bray, J. 2007. Identification of Production Potential in Unconventional Reservoirs. Paper presented at the Production and Operations Symposium, Oklahoma City, Oklahoma, USA, 31 March–3 April. SPE-106623-MS. https://doi.org/10.2118/106623-MS.
Guo, T., Zhang, S., Ge, H. et al. 2015. A New Method for Evaluation of Fracture Network Formation Capacity of Rock. Fuel 140: 778–787. https://doi.org/10.1016/j.fuel.2014.10.017.
Handique, G. K., Sethi, A. K., and Sarma, S. C. 1989. Review of Tertiary Stratigraphy of Parts of Upper Assam Valley, GSI Spec Publ 23: 23–36.
Hillier, S. 2003. Quantitative Analysis of Clay and Other Minerals in Sandstones by X-Ray Powder Diffraction (XRPD). In Clay Mineral Cements in Sandstones, ed. R. H. Worden and S. Morad, 213–251. Oxford, UK: International Assoc of Sedimentologists/Blackwell Publishing. https://doi.org//10.1002/9781444304336.ch11.
Hu, Y., Gonzalez Perdomo, M. E., Wu, K. et al. 2015. A Novel Model of Brittleness Index for Shale Gas Reservoirs: Confining Pressure Effect. Paper presented at the SPE Asia Pacific Unconventional Resources Conference and Exhibition, Brisbane, Australia, 9–11 November. SPE-176886-MS. https://doi.org/10.2118/176886-MS.
Jarvie, D. M., Hill, R. J., Ruble, T. E. et al. 2007. Unconventional Shale-Gas Systems: The Mississippian Barnett Shale of North-Central Texas as One Model for Thermogenic Shale-Gas Assessment. Am Assoc Pet Geol Bull 91 (4): 475–499. https://doi.org/10.1306/12190606068.
Javid, S. 2013. Petrography and Petrophysical Well Log Interpretation for Evaluation of Sandstone Reservoir Quality in the Skalle Well (Barents Sea). Master’s thesis, Norwegian University of Science and Technology, Trondheim, Norway (June 2013).
Jin, X., Shah, S. N., Roegiers, J. C. et al. 2014. Fracability Evaluation in Shale Reservoirs—An Integrated Petrophysics and Geomechanics Approach. Paper presented at the SPE Hydraulic Fracturing Technology Conference, The Woodlands, Texas, USA, 4–6 February. SPE-168589-MS. https://doi.org/10.2118/168589-MS.
Kias, E., Maharidge, R., and Hurt, R. 2015. Mechanical Versus Mineralogical Brittleness Indices across Various Shale Plays. Paper presented at the SPE Annual Technical Conference and Exhibition, Houston, Texas, USA, 28–30 September. SPE-174781-MS. https://doi.org/10.2118/174781-MS.
Kiwi, I. R., Ameri, M., and Molladavoodi. H. 2018. Shale Brittleness Evaluation Based on Energy Balance Analysis of Stress-Strain Curves. J Pet Sci Eng 167: 1–19. https://doi.org/10.1016/j.petrol.2018.03.061.
Kumar, K., Dasgupta, R., Singha, D. K. et al. 2017. Petrophysical Evaluation of Well Log Data and Rock Physics Modeling for Characterization of Eocene Reservoir in Chandmari Oil Field of Assam-Arakan Basin, India. J Pet Explor Prod Technol 8 (2): 323–340. https://doi.org/10.1007/s13202-017-0373-8.
Lai, J., Wang, G., Huang, L. et al. 2015. Brittleness Index Estimation in a Tight Shaly Sandstone Reservoir Using Well Logs. J Nat Gas Sci Eng 27: 1536–1545. https://doi.org/10.1016/j.jngse.2015.10.020.
Mallick, S. 1995. Model-Based Inversion of Amplitude-Variations-With Offset Data Using a Genetic Algorithm. Geophysics 60 (4): 939–954. https://doi.org/10.1190/1.1443860.
Mathia, E., Ratcliffe, K., and Wright, M. 2016. Brittleness Index—A Parameter to Embrace or Avoid? Paper presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, San Antonio, Texas, USA, 1–3 August. URTEC-2448745-MS. https://doi.org/10.15530/URTEC-2016-2448745.
Mews, K. S., Alhubail, M. M., and Barati R. G. 2019. A Review of Brittleness Index Correlations for Unconventional Tight and Ultra-Tight Reservoirs. Geoscience 9 (7): 1–21. https://doi.org/10.3390/geosciences9070319.
Miskimins, J. L. 2012. The Impact of Mechanical Stratigraphy on Hydraulic Fracture Growth and Design Consideration for Horizontal Wells. Search and Discovery Article #41102, 17 December 2012, http://www.searchanddiscovery.com/pdfz/documents/2012/41102miskimins/ndx_miskimins.pdf.html (accessed 20 October 2019).
Nandy, D. R., Dasgupta, S., Sarkar, K. et al. 1983. Tectonic Evolution of Tripura-Mizoram Fold Belt, Surma Basin, Northeast India. Quar J Geol, Mining, & Metall Soc India 55 (4): 186–194.
Pahari, S., Singh, H., Prasad, I. V. S. V. et al. 2008. Petroleum Systems of Upper Assam Shelf, India. Geohorizons December: 14–21.
Pye, K. and Krinsley, D. H. 1984. Petrographic Examination of Sedimentary Rocks in the SEM Using Backscattered Electron Detectors. J Sediment Petrol 54 (3): 877–888.
Raghukanth, S. T. G. and Dash, S. K. 2009. Deterministic Seismic Scenarios for North East India. J Seismol 14 (2): 143–167. https://doi.org/10.1007/s10950-009-9158-y.
Rickman, R., Mullen, M., Petre, E. et al. 2008. A Practical Use of Shale Petrophysics for Stimulation Design Optimization: All Shale Plays Are Not Clones of the Barnett Shale. Paper presented at the SPE Annual Technical Conference and Exhibition, Denver, Colorado, USA, 21–24 September. SPE-115258-MS. https://doi.org/10.2118/115258-MS.
Schlumberger. 2009. Log Interpretation Charts, 2009 edition. Sugar Land, Texas, USA: Schlumberger.
Sharma, R. K. and Chopra, S. 2015. Determination of Lithology and Brittleness of Rocks with a New Attribute. Lead Edge 34 (5): 482–596. https://doi.org/10.1190/tle34050554.1.
Sharma, R. K. and Chopra, S. 2019. Replacing Conventional Brittleness Indices Determination with New Attributes Employing True Hydrofracturing Mechanism. Paper presented at the SEG International Exposition and 89th Annual Meeting, San Antonio, Texas, USA, 15–20 September. https://doi.org/10.1190/segam2019-3214095.1.
Singh, S., Qiu, F., Morgan, N. et al. 2013. Critical Comparative Assessment of a Novel Approach for Multi-Mineral Modeling in Shale Gas: Results from an Evaluation Study of Marcellus Shale. Paper presented at the SPE Unconventional Resources Conference and Exhibition-Asia Pacific, Brisbane, Australia, 11–13 November. SPE-167037-MS. https://doi.org/10.2118/167037-MS.
Sondergeld, C. H., Newsham, K. E., Comisky, J. T. et al. 2010. Petrophysical Considerations in Evaluating and Producing Shale Gas Resources. Paper presented at the SPE Unconventional Gas Conference, Pittsburgh, Pennsylvania, USA, 23–25 February. SPE-131768-MS. https://doi.org/10.2118/131768-MS.
Wandrey, C. J. 2004. Sylhet-Kopili/Barail-Tipam Composite Total Petroleum System, Assam Geologic Province, India. Reston, Virginia, USA: US Geological Survey Bulletin 2208-D, USGS.
Wang, D., Ge, H., Wang, X. et al. 2015. A Novel Experimental Approach for Fracability Evaluation in Tight-Gas Reservoirs. J Nat Gas Sci Eng 23: 239–249. https://doi.org/10.1016/j.jngse.2015.01.039.
Wang, F. P. and Gale, J. F. W. 2009. Screening Criteria for Shale-Gas Systems. Gulf Coast Assoc Geol Soc Trans 59: 779–794.
Yang, Y., Sone, H., Hows, A. et al. 2013. Comparison of Brittleness Indices in Organic-Rich Shale Formations. Paper presented at the 47th US Rock Mechanics/Geomechanics Symposium, San Francisco, California, USA, 23–26 June. ARMA-2013-403.
Yi-kai, G., Zhen-kui, J., Jian-hua, Z. et al. 2017. Clay Minerals in Shales of the Lower Silurian Longmaxi Formation in the Eastern Sichuan Basin, China. Clay Miner 52 (2): 217–233. https://doi.org/10.1180/claymin.2017.052.2.04.
Yuan, Y., Jiang, Z., Yu, C. et al. 2015. Mineral Compositions and Brittleness of the Middle Jurassic Lacustrine Shale Reservoir in Northern Qaidam Basin. Geol J China Univ 21 (1): 117–123.
Zaidi, S. and Chakrabarti, S. K. 2006. Sequence Stratigraphy and Depositional Environment of the Kopili Formation in the Area between Borholla and Khoraghat, Dhansiri Valley, South Assam Shelf. Paper presented at the 6th International Conference & Exposition on Petroleum Geophysics, Science City, Kolkata, 9–11 January.
Zhang, D., Ranjith, P. G., and Perera, M. S. 2016. The Brittleness Indices Used in Rock Mechanics and Their Application in Shale Hydraulic Fracturing: A Review. J Pet Sci Eng 143: 158–170. https://doi.org/10.1016/j.petrol.2016.02.011.
Zoback, M. D., Bartonb, C. A., Brudy, M. et al. 2003. Determination of Stress Orientation and Magnitude in Deep Wells. Int J Rock Mech Min Sci 40 (7–8): 1049–1076. https://doi.org/10.1016/j.ijrmms.2003.07.001.