Skip to main content

Advertisement

SpringerLink
Log in

Sembar Formation as an Unconventional Prospect: New Insights in Evaluating Shale Gas Potential Combined with Deep Learning

  • Original Paper
  • Published:
Natural Resources Research Aims and scope Submit manuscript

Abstract

Since most of the petroliferous basins of Pakistan have not been fully explored, the country's energy output falls short of its energy demands. Studies conducted both internationally and domestically show that it has far higher potential for hydrocarbons than its current proven reserves. It has been estimated that there are around 105 trillion cubic feet of natural gas resources that are potentially recoverable from the shale formations of Indus Basin. The Cretaceous shales of the Sembar Formation are one of the major prospects for shale gas exploitation in Central and Southern Indus Basins. An integrated study was conducted to evaluate its conventional and unconventional resource potential in terms of reservoir characteristics, organic richness and clay type. Conventional reservoir potential within the top sand facies of the Sembar Formation exhibited consistent behavior with regards to its porosity and fluid saturation. The lower part of this formation is characterized primarily by the prevalence of shale facies. Empirical log-based techniques were utilized to assess the organic richness of these facies, which showed total organic carbon (TOC) content ranging 1–1.4%. Along with its organic richness, the category of clay minerals is a very important factor in estimating unconventional resource potential. The log-based mineralogical assessment revealed the presence of brittle clay minerals (illite), which are suitable for fracking. The diagenetic changes of clay minerals may increase brittle components, which could lead to an increase in the integral rigidity of these rocks. Moreover, deep feedforward neural network (DFNN) analysis, which is a non-linear neural network regression technique, was used to delineate the spatial and vertical variations of TOC. The impedance sections, combined with the original seismic trace and TOC curve from the well, were utilized as input for training the seismic attributes for predicting seismic-based TOC. The findings of the DFNN analysis on the Duljan Re-Entry-01 well indicate a strong correlation coefficient of 93% between Passey TOC and the predicted TOC curve. Additionally, the root mean square error was found to be a mere 0.09. The TOC prediction based on seismic-based DFNN analysis yielded an average value of approximately 1.1%, with a maximum value of 2.3%. These two-dimensional TOC sections for the Sembar shales provide an extremely positive impact in evaluating its shale gas potential.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17

Similar content being viewed by others

Notes

  1. 1 trillion cubic feet = 0.0283168 trillion cubic meters

  2. * 1 µsec/ft = 3.28084 µsec/m

References

  • Abbasi, A.H., Mehmood, F., and Kamal, M. (2014). Shale oil and gas: lifeline for Pakistan. Sustainable Development Policy Institute Islamabad.

  • Abbasi, S. A., Asim, S., Solangi, S. H., & Khan, F. (2016). Study of fault configuration related mysteries through multi seismic attribute analysis technique in Zamzama gas field area, southern Indus Basin, Pakistan. Geodesy and Geodynamics, 7(2), 132–142.

    Google Scholar 

  • Abid, M., Niu, L., Ma, J., & Geng, J. (2021). Unconventional reservoir characterization and sensitive attributes determination: A case study of the Eastern Sembar Formation, Lower Indus Basin, Pakistan. Geophysics, 86(1), B1–B14.

    Google Scholar 

  • Aftab, F., Zafar, M., Hajana, M. I., & Ahmad, W. (2022). A novel gas sands characterization and improved depositional modeling of the Cretaceous Sembar Formation, Lower Indus Basin, Pakistan. Frontiers in Earth Science, 10, 1039605.

    Google Scholar 

  • Ahmad, N., Mateen, J., Chaudry, K. S., Mehmood, N., & Arif, F. (2013). Shale gas potential of lower cretaceous Sembar Formation in middle and lower Indus basin, Pakistan. Pakistan Journal of Hydrocarbon Research, 23, 51–62.

    Google Scholar 

  • Ali, A., Alves, T. M., & Amin, Y. (2022a). Integrated geophysical analysis of the Sembar Formation, Central Indus Basin, as an unconventional resource. Journal of Natural Gas Science and Engineering, 101, 104507.

    Google Scholar 

  • Ali, J., Ashraf, U., Anees, A., Peng, S., Umar, M. U., Vo Thanh, H., et al. (2022b). Hydrocarbon potential assessment of carbonate-bearing sediments in a meyal oil field, Pakistan: Insights from logging data using machine learning and quanti elan modeling. ACS Omega, 7(43), 39375–39395.

    Google Scholar 

  • Amjad, M. R., Ehsan, M., Hussain, M., Al-Ansari, N., Rehman, A., Naseer, Z., et al. (2023a). Carbonate reservoir quality variations in basins with a variable sediment influx: A case study from the balkassar oil field, Potwar Pakistan. ACS Omega, 8(4), 4127–4145.

    Google Scholar 

  • Amjad, M. R., Hameed, M. S., Mujtaba, M., & Munir, M. N. (2019). Petrophysical and geochemical analysis of chichali formation for the source rock evaluation: A case study of Chanda-01 Well, Upper Indus Basin, Pakistan. International Journal of Economic and Environmental Geology, 8(3), 32–39.

    Google Scholar 

  • Amjad, M. R., Khan, S., Nisar, U. B., Shakir, U., & Ahmed, K. A. (2022). Exploring the untapped gas potential of Ghazij shale in Pirkoh area, Pakistan: Integrated approach of attribute analysis and maturity modeling. Turkish Journal of Earth Sciences, 31(1), 1–19.

    Google Scholar 

  • Amjad, M. R., Zafar, M., Malik, M. B., & Naseer, Z. (2023b). Precise geopressure predictions in active foreland basins: An application of deep feedforward neural networks. Journal of Asian Earth Sciences, 245, 105560.

    Google Scholar 

  • Ashraf, U., Zhang, H., Anees, A., Mangi, H. N., Ali, M., Zhang, X., et al. (2021). A core logging, machine learning and geostatistical modeling interactive approach for subsurface imaging of lenticular geobodies in a clastic depositional system, SE Pakistan. Natural Resources Research, 30(3), 2807–2830.

    Google Scholar 

  • Ashraf, U., Zhu, P., Yasin, Q., Anees, A., Imraz, M., Mangi, H. N., et al. (2019). Classification of reservoir facies using well log and 3D seismic attributes for prospect evaluation and field development: A case study of Sawan gas field, Pakistan. Journal of Petroleum Science and Engineering, 175, 338–351.

    Google Scholar 

  • Askari, H., & Krichene, N. (2010). An oil demand and supply model incorporating monetary policy. Energy, 35(5), 2013–2021.

    Google Scholar 

  • Asquith, G.B., and Krygowski, D. (2004). Basic well log analysis. American Association of Petroleum Geologists, Tulsa.

  • Azeem, T., Chun, W. Y., Khalid, P., Qing, L. X., Ehsan, M. I., Munawar, M. J., et al. (2017). An integrated petrophysical and rock physics analysis to improve reservoir characterization of Cretaceous sand intervals in Middle Indus Basin, Pakistan. Journal of Geophysics and Engineering, 14(2), 212–225.

    Google Scholar 

  • Aziz, H., Ehsan, M., Ali, A., Khan, H. K., & Khan, A. (2020). Hydrocarbon source rock evaluation and quantification of organic richness from correlation of well logs and geochemical data: A case study from the sembar formation, Southern Indus Basin, Pakistan. Journal of Natural Gas Science and Engineering, 81, 103433.

    Google Scholar 

  • Aziz, O., Hussain, T., Ullah, M., Bhatti, A. S., & Ali, A. (2018). Seismic based characterization of total organic content from the marine Sembar shale, Lower Indus Basin, Pakistan. Marine Geophysical Research, 39(4), 491–508.

    Google Scholar 

  • Baban, D. H., Qadir, F. M., & Mohammed, A. R. (2020). Reservoir rock properties of the Upper Cretaceous Shiranish Formation in Taq Taq Oilfield, Iraqi Kurdistan Region. Journal of Zankoy Slemani, 22, 363–388.

    Google Scholar 

  • Bashir, Y., Faisal, M.A., Biswas, A., Babasafari, A.a., Ali, S.H., Imran, Q.S., et al. (2021). Seismic expression of miocene carbonate platform and reservoir characterization through geophysical approach: application in central Luconia, offshore Malaysia. Journal of Petroleum Exploration and Production 11(4), 1533-1544

  • Burke, J.A., Campbell, R.L., Jr., and Schmidt, A.W. (1969). The litho-porosity cross plot a method of determining rock characteristics for computation of log data. SPE Illinois Basin Regional Meeting, Evansville, Indiana, (SPE-2771-MS). https://doi.org/10.2118/2771-MS.

  • Burke, J.A., Campbell, R.L., Jr., and Schmidt, A.W. (1969). The Litho Porosity Cross Plot: A New Concept For Determining Porosity And Lithology From Logging Methods. SPWLA 10th Annual Logging Symposium, Houston, Texas, (SPWLA-1969-Y).

  • Chen, S., Dong, S., Wang, X., Yang, Q., Dai, C., Yang, H., et al. (2023). Using machine learning technology based on fuzzy neural network and geophysical data to predict the shale gas sweet spots: A case study of lower silurian longmaxi formation in Wy Block. Sichuan Basin. Energy Exploration & Exploitation, 41(2), 421–436.

    Google Scholar 

  • Chu, H., Dong, P., & Lee, W. J. (2023). A deep-learning approach for reservoir evaluation for shale gas wells with complex fracture networks. Advances in Geo-Energy Research, 7(1), 49–65.

    Google Scholar 

  • Cluff, R., Cluff, S., Sharma, R., and Sutton, C. (2015). A deterministic lithology model for the green river-upper wasatch interval of the Uinta basin. American Association of Petroleum Geologists Annual Convention & Exhibition. Denver, Colorado. Search and Discovery Article #10745

  • Cooper, J., Stamford, L., & Azapagic, A. (2016). Shale gas: A review of the economic, environmental, and social sustainability. Energy Technology, 4(7), 772–792.

    Google Scholar 

  • Diab, A., Sanuade, O., & Radwan, A. E. (2022). An integrated source rock potential, sequence stratigraphy, and petroleum geology of (Agbada-Akata) sediment succession, Niger delta: application of well logs aided by 3D seismic and basin modeling. Journal of Petroleum Exploration and Production Technology, 13(1), 237–257.

    Google Scholar 

  • Doyen, P. M. (1988). Porosity from seismic data: A geostatistical approach. Geophysics, 53(10), 1263–1275.

    Google Scholar 

  • Du, J., Cai, J., Long, S., Gao, B., Feng, D., Peng, Z., et al. (2021). The control of diagenesis and mineral assemblages on brittleness of mudstones. Frontiers in Earth Science, 9, 758046.

    Google Scholar 

  • Eberl, D., & Hower, J. (1976). Kinetics of illite formation. Geological Society of America Bulletin, 87(9), 1326–1330.

    Google Scholar 

  • Ehsan, M., & Gu, H. (2020). An integrated approach for the identification of lithofacies and clay mineralogy through Neuro-Fuzzy, cross plot, and statistical analyses, from well log data. Journal of Earth System Science, 129(1), 101.

    Google Scholar 

  • Ehsan, M., Gu, H., Ahmad, Z., Akhtar, M. M., & Abbasi, S. S. (2019). A modified approach for volumetric evaluation of shaly sand formations from conventional well logs: A case study from the talhar shale, Pakistan. Arabian Journal for Science and Engineering, 44, 417–428.

    Google Scholar 

  • Ehsan, M., Gu, H., Akhtar, M. M., Abbasi, S. S., & Ullah, Z. (2018). Identification of hydrocarbon potential of Talhar shale: Member of lower Goru Formation using well logs derived parameters, southern lower Indus basin, Pakistan. Journal of Earth Science, 29, 587–593.

    Google Scholar 

  • EIA. (2013). Technically recoverable shale oil and shale gas resources: an assessment of 137 shale formations in 41 countries outside the United States. United States Environmental Impact Assessment.

    Google Scholar 

  • Eickhoff, G., and Alam, S. (1991). On the petroleum geology and prospectivity of Kirthar range, Kirthar depression and Sibi trough, southern Indus basin, Pakistan. Islamabad: Hydrocarbon Development Institute of Pakistan.

  • GSP (2012). Geological Map of Sindh, Pakistan. Geological Survey of Pakistan, Quetta, Pakistan.

  • Haider, B.A., Aizad, T., Ayaz, S.A., and Shoukry, A. (2012). A comprehensive shale gas exploitation sequence for Pakistan and other emerging shale plays. SPE/PAPG Annual Technical Conference, Islamabad, Pakistan. (SPE-163123-MS). https://doi.org/10.2118/163123-MS.

  • Hampson, D. P., Schuelke, J. S., & Quirein, J. A. (2001). Use of multiattribute transforms to predict log properties from seismic data. Geophysics, 66(1), 220–236.

    Google Scholar 

  • Imtiaz, F., Fazeelat, T., Nazir, A., & Ihsan, S. (2017). Geochemical characterization of sediments samples of Sembar Formation from three different wells of Southern Indus Basin. Petroleum Science and Technology, 35(7), 633–640.

    Google Scholar 

  • Jia, J., Liu, Z., Meng, Q., Liu, R., Sun, P., and Chen, Y. (2012). Quantitative evaluation of oil shale based on well log and 3-D seismic technique in the Songliao Basin, northeast China. Oil Shale 29(2). https://doi.org/10.3176/oil.2012.2.04.

  • Kadri, I.B. (1995). Petroleum geology of Pakistan. Pakistan Petroleum Limited, Karachi, Pakistan.

  • Kazmi, A.H., and Abbasi, I.A. (2008). Stratigraphy & historical geology of Pakistan. Department & National Centre of Excellence in Geology, Peshawar, Pakistan.

  • Kemal, A. (1992). Geology and new trends for petroleum exploration in Pakistan. Pakistan Association of Petroleum Geoscientists Bulletin, Islamabad, Pskistan, 16-57.

  • Khalid, P., Qayyum, F., & Yasin, Q. (2014). Data-driven sequence stratigraphy of the cretaceous depositional system, Punjab platform. Pakistan. Surveys in Geophysics, 35(4), 1065–1088.

    Google Scholar 

  • Khan, H. K., Ehsan, M., Ali, A., Amer, M. A., Aziz, H., Khan, A., et al. (2022). Source rock geochemical assessment and estimation of TOC using well logs and geochemical data of Talhar Shale, Southern Indus Basin. Pakistan. Frontiers in Earth Science, 10, 969936.

    Google Scholar 

  • Khan, I., Ahmad, S., Arif, M., & Ali, N. (2016). Impact of diagenetic and depositional fabric on reservoir quality; a synthesis from Late Cretaceous and Early Paleocene sandstones in Eastern Sulaiman Range. Pakistan. Arabian Journal of Geosciences, 9(5), 388.

    Google Scholar 

  • Khan, M. S., & Bibi, Z. (2016). Shale Gas Characterization of Sembar Formation, Khipro Area, Pakistan. International Journal of Geosciences, 7(08), 1009.

    Google Scholar 

  • Khoshdel, H., & Riahi, M. A. (2011). Multi attribute transform and neural network in porosity estimation of an offshore oil field—a case study. Journal of Petroleum Science and Engineering, 78(3–4), 740–747.

    Google Scholar 

  • Klaja, J., & Dudek, L. (2016). Geological interpretation of spectral gamma ray (SGR) logging in selected boreholes. Oil-Gas, 72, 3–14.

    Google Scholar 

  • Law, C.A. (1999). Evaluating source rocks. In: Beaumont, A. and Foster, N.H. (Ed.) Treatise of petroleum geology / handbook of petroleum geology: exploring for oil and gas traps. American Association of Petroleum Geologists special volumes. Chapter 6, 1-41.

  • Li, Y., Song, L., Tang, Y., Zuo, J., & Xue, D. (2022). Evaluating the mechanical properties of anisotropic shale containing bedding and natural fractures with discrete element modeling. International Journal of Coal Science & Technology, 9(1), 18.

    Google Scholar 

  • Li, Y., Wang, B., Song, L., Liu, J., Zuo, J., & Liu, D. (2019). Ultrasonic wave propagation characteristics for typical anisotropic failure modes of shale under uniaxial compression and real-time ultrasonic experiments. Journal of Geophysics and Engineering, 17(2), 258–276.

    Google Scholar 

  • Liu, X., Shao, G., Liu, Y., Liu, X., Li, J., Chen, X., et al. (2022a). Deep classified autoencoder for lithofacies identification. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–14.

    Google Scholar 

  • Liu, X., Shao, G., Yuan, C., Chen, X., Li, J., & Chen, Y. (2022b). Mixture of relevance vector regression experts for reservoir properties prediction. Journal of Petroleum Science and Engineering, 214, 110498.

    Google Scholar 

  • Liu, X., Zhou, H., Guo, K., Li, C., Zu, S., & Wu, L. (2023). Quantitative characterization of shale gas reservoir properties based on BiLSTM with attention mechanism. Geoscience Frontiers, 14(4), 101567.

    Google Scholar 

  • Mahmood, M. F., Ahmad, Z., & Ehsan, M. (2018). Total organic carbon content and total porosity estimation in unconventional resource play using integrated approach through seismic inversion and well logs analysis within the Talhar Shale, Pakistan. Journal of Natural Gas Science and Engineering, 52, 13–24.

    Google Scholar 

  • Metwally, Y. M., & Chesnokov, E. M. (2012). Clay mineral transformation as a major source for authigenic quartz in thermo-mature gas shale. Applied Clay Science, 55, 138–150.

    Google Scholar 

  • MNPR (2013). Opportunities in Pakistan’s Upstream Oil & Gas Sector. Ministry of Petroleum and Natural Resources Government of Pakistan.

  • Myers, K., & Jenkyns, K. (1992). Determining total organic carbon contents from well logs: an intercomparison of GST data and a new density log method. Geological Society, London, Special Publications, 65(1), 369–376.

    Google Scholar 

  • Nazir, A., Fazeelat, T., & Asif, M. (2012). The geochemical characterization of sediments from early cretaceous sembar formation. Petroleum Science and Technology, 30(23), 2460–2470.

    Google Scholar 

  • Nazir, A., Fazeelat, T., & Asif, M. (2015). Petroleum Geochemistry of Lower Indus Basin, Pakistan: II. Oil-oil and Oil-source Rock Correlation. Petroleum Science and Technology, 33(12), 1295–1304.

    Google Scholar 

  • Passey, Q., Creaney, S., Kulla, J., Moretti, F., & Stroud, J. (1990). A practical model for organic richness from porosity and resistivity logs. American Association of Petroleum Geologists Bulletin, 74(12), 1777–1794. https://doi.org/10.1306/0C9B25C9-1710-11D7-8645000102C1865D

    Article  Google Scholar 

  • Pevear, D. R. (1999). Illite and hydrocarbon exploration. Proceedings of the National Academy of Sciences, 96(7), 3440–3446.

    Google Scholar 

  • Potter, D. K., Ali, A., & Abdalah, S. (2019). Templates for quantifying clay type and clay content from magnetic susceptibility and standard borehole geophysical measurements. Journal of Modern Physics, 10(7), 824–834.

    Google Scholar 

  • Qaiser, I. (2022). A comparison of renewable and sustainable energy sector of the South Asian countries: An application of SWOT methodology. Renewable Energy, 181, 417–425.

    Google Scholar 

  • Qayyum, F., Hanif, M., Mujtaba, M., Wahid, S., & Ali, F. (2016). Evaluation of source rocks using one dimensional maturity modeling in Lower Indus Basin, Pakistan. Arabian Journal of Geosciences, 9(4), 252.

    Google Scholar 

  • Radwan, A. E., Wood, D. A., & Radwan, A. A. (2022). Machine learning and data-driven prediction of pore pressure from geophysical logs: A case study for the Mangahewa gas field, New Zealand. Journal of Rock Mechanics and Geotechnical Engineering, 14(6), 1799–1809.

    Google Scholar 

  • Rao, X., Sha, J., Peng, B., Zhang, X., & Cai, H. (2019). Constraints of bipolar and tropical bivalves on the northward drifting of the Indian Plate. Journal of Asian Earth Sciences, 175, 68–73.

    Google Scholar 

  • Raza, A., Meiyu, G., Gholami, R., Rezaee, R., Rasouli, V., Sarmadivaleh, M., & Bhatti, A. A. (2018). Shale gas: A solution for energy crisis and lower CO2 emission in Pakistan. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 40(13), 1647–1656.

    Google Scholar 

  • Raza, H. A., Ali, S. M., & Ahmed, R. (1990). Petroleum geology of Kirthar Sub-basin and part of Kutch Basin. Pakistan Journal of Hydrocarbon Research, 2(1), 29–73.

    Google Scholar 

  • Rider, M. (1996). The geological interpretation of well logs. Scotland: Rider-French Consulting Ltd.

  • Schmoker, J. W. (1979). Determination of organic content of appalachian Devonian shales from formation-density logs: geologic notes. American Association of Petroleum Geologists Bulletin, 63(9), 1504–1509.

    Google Scholar 

  • Shah, S. (2009). Stratigraphy of Pakistan. Geological Survey of Pakistan. Quetta, Pakistan.

  • Shakir, U., Ali, A., Amjad, M. R., & Hussain, M. (2021). Improved gas sand facies classification and enhanced reservoir description based on calibrated rock physics modelling: A case study. Open Geosciences, 13(1), 1476–1493.

    Google Scholar 

  • Shakir, U., Hussain, M., Mahmood, M. F., Amjad, M. R., Zafar, W. A., Mehmood, S., et al. (2020). Prospect generation studies of cretaceous sands, Khipro Area by integrating seismic and well data. The Nucleus, 57(2), 67–75.

    Google Scholar 

  • Shalaby, M. R., Jumat, N., Lai, D., & Malik, O. (2019). Integrated TOC prediction and source rock characterization using machine learning, well logs and geochemical analysis: Case study from the Jurassic source rocks in Shams Field, NW Desert, Egypt. Journal of Petroleum Science and Engineering, 176, 369–380.

    Google Scholar 

  • Shar, A. M., Mahesar, A. A., & Memon, K. R. (2018). Could shale gas meet energy deficit: its current status and future prospects. Journal of petroleum exploration and production technology, 8, 957–967.

    Google Scholar 

  • Sheikh, N., & Giao, P. H. (2017). Evaluation of shale gas potential in the Lower Cretaceous Sembar Formation, the Southern Indus Basin, Pakistan. Journal of Natural Gas Science and Engineering, 44, 162–176.

    Google Scholar 

  • Sohail, G. M., Radwan, A. E., & Mahmoud, M. (2022). A review of Pakistani shales for shale gas exploration and comparison to North American shale plays. Energy Reports, 8, 6423–6442.

    Google Scholar 

  • Tian, Y., and Ayers, W.B. (2010). Barnett Shale (Mississippian), Fort Worth Basin, Texas: regional variations in gas and oil production and reservoir properties. Canadian Unconventional Resources and International Petroleum Conference, OnePetro. SPE-137766-MS. https://doi.org/10.2118/137766-MS.

  • Wang, X., Fan, A., van Loon, A.J., Yang, R., Han, Z., and Li, J. (2022). The influence of diagenesis on low-porosity, low-permeability gas reservoirs in the Sulige gas field (Ordos Basin, China). In: Yang, R. and Van Loon, A.J. (Eds.) The Ordos Basin. Sedimentological Research for Hydrocarbons Exploration, 191–215. https://doi.org/10.1016/B978-0-323-85264-7.00024-2.

  • Wang, H., Wu, W., Chen, T., Dong, X., & Wang, G. (2019). An improved neural network for TOC, S1 and S2 estimation based on conventional well logs. Journal of Petroleum Science and Engineering, 176, 664–678.

    Google Scholar 

  • Wang, L., Yao, B., Cha, M., Alqahtani, N. B., Patterson, T. W., Kneafsey, T. J., Miskimins, J. L., Yin, X., & Wu, Y. (2016). Waterless fracturing technologies for unconventional reservoirs-opportunities for liquid nitrogen. Journal of Natural Gas Science and Engineering, 35, 160–174.

    Google Scholar 

  • Wandrey, C.J., Law, B., and Shah, H.A. (2004). Sembar Goru/Ghazij composite total petroleum system, Indus and Sulaiman-Kirthar geologic provinces, Pakistan and India. US Department of the Interior, US Geological Survey Reston, VA, USA.

  • Xingyuan, Z., & Dongbo, H. (2012). Clay minerals and shale gas. Xinjiang Petroleum Geology, 33(6), 1.

    Google Scholar 

  • Yadav, A., Yadav, K., & Sircar, A. (2021). Feedforward Neural Network for joint inversion of geophysical data to identify geothermal sweet spots in Gandhar, Gujarat. India. Energy Geoscience, 2(3), 189–200.

    Google Scholar 

  • Yang, L. (2015). A petrophysical evaluation of the Trenton-Black river formation of the Michigan basin. Michigan Technological University.

  • Yang, J., Lin, N., Zhang, K., Zhang, C., Fu, C., Tian, G., et al. (2021). Reservoir characterization using multi-component seismic data in a novel hybrid model based on clustering and deep neural network. Natural Resources Research, 30(5), 3429–3454. https://doi.org/10.1007/s11053-021-09863-z

    Article  Google Scholar 

  • Yar, M., Haider, S. W., Nabi, G., Tufail, M., & Rahman, S. (2019). Reservoir characterization of sand intervals of lower Goru formation using petrophysical studies; A case study of Zaur-03 well, Badin block, Pakistan. International Journal of Economic and Environmental Geology, 10(3), 118–124.

    Google Scholar 

  • Yener, D. (2016). Petrophysical and empirical and theoretical rock physics modeling for the Moki formation in two fields in the Taranaki Basin. Michigan Technological University.

    Google Scholar 

  • Zaigham, N.A., Ahmad, M., and Hisam, N. (2000). Thar rift and its significance for hydrocarbons. Pakistan Association of Petroleum Geoscientists (PAPG), Islamabad, Pakistan.

  • Zaigham, N. A., & Mallick, K. A. (2000). Prospect of hydrocarbon associated with fossil-rift structures of the southern Indus basin, Pakistan. American Association of Petroleum Geologists bulletin, 84(11), 1833–1848. https://doi.org/10.1306/8626C3A7-173B-11D7-8645000102C1865D

    Article  Google Scholar 

Download references

Acknowledgments

The authors express their gratitude to the Department of Earth and Environmental Sciences at Bahria University in Islamabad for their assistance in providing support and access to a geophysical software lab, which was instrumental in facilitating the execution of this study. The authors would also like to express their sincere thanks to GeoSoftware and LMK Resources for the provision of geoscience interpretation software, specifically Hampson Russell and GVERSE GeoGraphix, respectively. Furthermore, we would like to extend our genuine gratitude to the Directorate General of Petroleum Concession for granting authorization to employ publicly available data in the investigation. This study is funded by the Higher Education Commission, Pakistan under Grant No. 20-14925/NRPU/R&D/HEC/2021/2020.

Author information

Authors and Affiliations

  1. Department of Earth and Environmental Sciences, Bahria School of Engineering and Applied Sciences, Bahria University, Islamabad, 44000, Pakistan

    Muhammad Raiees Amjad, Urooj Shakir, Awais Rasul, Saqib Mehmood & Muhsan Ehsan

  2. Advance Reservoir Characterization, LMK Resources, Islamabad, 44000, Pakistan

    Muyyassar Hussain

Authors
  1. Muhammad Raiees Amjad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Urooj Shakir
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muyyassar Hussain
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Awais Rasul
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Saqib Mehmood
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Muhsan Ehsan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muhsan Ehsan.

Ethics declarations

Conflict of Interest

The authors assert that the study has been conducted without any commercial or financial affiliations that may be interpreted as a possible source of conflict of interest.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Amjad, M.R., Shakir, U., Hussain, M. et al. Sembar Formation as an Unconventional Prospect: New Insights in Evaluating Shale Gas Potential Combined with Deep Learning. Nat Resour Res 32, 2655–2683 (2023). https://doi.org/10.1007/s11053-023-10244-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11053-023-10244-x

Keywords

Search

Navigation