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K-mean cluster analysis for better determining the sweet spot intervals of the unconventional organic-rich shale: a case study

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The petrophysical analysis is the crucial task for evaluating the quality of unconventional organic-rich shale and tight gas reservoirs. The presence of organic matter and the ultra-tight with over complex pore system have remained a lack of understanding of how to evaluate the extensive parameters of porosity considering organic content, gas saturation, organic richness, brittleness index, and sweet spot interval by only using conventional log. Therefore, this study offers effectively applied techniques and better analysis for interpreting these parameters by maximizing and integrating geological, geochemical, rock mechanical and engineering data. In general, the field data used in this study are from the first dedicated well for source rock exploration in the North Sumatra Basin, Indonesia. The developed method was derived by using conventional log. All interpretation results were validated by laboratory data measurements of routine and special core analysis, petrography, total organic carbon (TOC) and organic maturation, and brittleness index (BI) calculation. Moreover, the high quality of NMR log data was used as well to ensure our developed techniques present good estimations. Briefly about the methods, we started to determine the total and effective porosity based on the density log by including the presence of organic matter and multi-mineral analysis in these estimations. Then, we used the revised water saturation-TOC of water saturation while the TOC was predicted in advance by averaging three results from the correlation of TOC-Density, modified CARBOLOG and Passey’s ΔlogR methods. Equally important, in order to obtain the reliable gas saturation prediction, we used saturation exponent (n), cementation factor (m), and the tortuosity factor (a) parameters which obtained from laboratory measurement of formation resistivity factor and resistivity index (FFRI). In addition, the brittleness index was predicted based on sonic log data. Finally, all parameters needed for determining gas shale sweet spot have been made. Then, we developed a way to evaluate the sweet spot interval by using K-mean clustering. In conclusion, this clustering result properly follows the shale quality index parameters which consist of organic richness and maturation, brittleness index, the storage capacity of porosity and gas saturation. This study shows that these petrophysical applied techniques leads us to interpret the best position of shale interval to be developed with a simple, fast, and accurate prediction way. Furthermore, as a novelty, this method can be used as rock typing method and obviously can reduce uncertainty and risks in organic-rich shale exploration.
Wydawca
Rocznik
Strony
200--213
Opis fizyczny
Bibliogr. 20 poz., rys., tab.
Twórcy
  • University of Miskolc, Petroleum Geoengineering, Miskolc Egyetemvaros, 3515, Hangary
  • China University of Petroleum-Beijing, 18 Fuxue Road, Changping, Beijing China 102249
Bibliografia
  • Akbar A, Nur M., Musu JT. (2017) Rock typing and shale quality index methods based on conventional log a case study for organicrich shale in the North Sumatra Basin. In: 23rd Formation Evaluation Symposium of Japan, Society of Petrophysicists and WellLog Analysts, Japan, 11-12 October (JFES-2017-T).
  • Akbar A, Nur M., Musu JT., and Milad B. (2018) Water Saturation Interpretation Model for Organic-Rich Shale Reservoir: A Case Study of North Sumatra Basin. The Unconventional Resources Technology Conference held in Houston, Texas, USA, 23-25 July. URTeC: 2879229, DOI: 10.15530/urtec-201 8-2879229.
  • Aranibar A., Saneifar, M., and Heidari, Z. (2013) Petrophysical rock typing in organic-rich source rocks using well logs. the Unconventional Resources Technology Conference held in Denver, Colorado, USA, 12-14 August 2013. SPE 168913/URTeC 1619574.
  • Carpentier B, Huc A.Y, Bessereau G. (1991) Wireline logging and source rocks - Estimations of organic carbon content by CARBOLOG method. Log Analysts, 1991, 32(3):279-297.
  • Geological Agency (2011) Map of Distribution and Potential of Shale Oil and Gas Bearing Formation of Indonesia. Quaternary. Neogene. Paleogene.
  • Grieser WV, Bray JM. (2007) Identification of production potential in unconventional reservoirs. Production and Operations. SPE Symposium of Oklahoma City, OK, USA, March 31-April 3. SPE 106623.
  • Gupta, N., Rai, C. S., and Sondergeld, C. H. (2012) Integrated Petrophysical Characterization of the Woodford Shale in Oklahoma. The SPWLA 53rd Annual Logging Symposium, Cartagena, Colombia, 16-20 June.
  • Hammes, U., Eastwood, R., Rowe, H. D., and Reed, R. M. (2009) Addressing Conventional Parameters in Unconventional Shale-Gas Systems: Depositional Environment, Petrography, Geochemistry, and Petrophysics of the Haynesville Shale. The 29th Annual GCSSEPM Foundation Bob F. Perkins Research Conference, Houston, USA, 6-8 December.
  • Jarvie DM, Hill RJ, Ruble TE, Pollastro RM. (2007) Unconventional shale-gas systems: The Mississippian Barnett Shale of northcentral Texas as one model for thermogenic shale-gas assessment. AAPG Bull 2007; 91:475-499.
  • Kale, S., Rai, C. S., and Sondergeld, C. H. (2010) Rock Typing in Gas Shales. The SPE ATCE, Florence, Italy, 19-22 September. SPE 134539.
  • LEMIGAS (Research and Development Center for Oil and Gas Technology), Ministry of Energy and Mineral Resources (ESDM). (2013) Pilot Project - Search and Discovery of Shale Gas Reserves: unpublished report.
  • Passey QR, Creaney S, Kulla JB, Moretti FJ, Stroud JD. (1990) A practical model for organic richness from porosity and resistivity logs: AAPG Bull 74:1777-1794.
  • Popielski, A. C., Heidari, Z., and TorresVerdin, C. (2012) Rock Classification from Conventional Well Logs in HydrocarbonBearing Shale. The SPE ATCE, San Antonio, Texas, USA, 8-10 October. SPE 159255.
  • Rickman R, Mullen MJ, Petre JE, Grieser WV, Kundert D. A. (2008) Practical use of shale petrophysics for stimulation design optimization: All shale plays are not clones of the Barnett Shale. SPE Annual Technical Conference and Exhibition, Denver, CO, USA, September 21–24. SPE 115258.
  • Sondergeld CH., Newsham KE., Comisky JT., Rice MC., Rai CS. (2010) Petrophysical considerations in evaluating and producing gas shale resources. SPE Unconventional Gas Conference; Pittsburgh, PA, USA. February 23-25. SPE131768.
  • Tissot, B. P., and Welte, D. H. (1984) Petroleum Formation and Occurrence. SpringerVerlag; New York, 699 pp.
  • Wang, F.P. and Gale, J. (2009) Screening criteria for shale-gas systems. Gulf Coast Association of Geological Societies 59th Annual Convention, 27-29 September, Shreveport, LA.
  • Xu J., Lei X., and Yuxing Q. (2017) Two effective methods for calculating water saturations in shale-gas reservoirs. Geophysics, vol. 82, no. 3 (may-june 2017); p. D187-d197.
  • Zhang, Baoying, and Jingling Xu (2016) Methods for the evaluation of water saturation considering TOC in shale reservoirs. Journal of Natural Gas Science and Engineering pp. 800e810, Elsevier, http://dx.doi.org/10.1016/j.jngse.2016.11.0 23.
  • Zhang T, Ellis GS, Ruppel SC, Milliken K, Yang R. (2012) Effect of organic-matter type and thermal maturity on methane adsorption in shale-gas systems. Org Geochem 2012;47:120-131.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-cfe1438e-8ca9-4463-9db7-062823249671
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