Automated real-time formation evaluation from cuttings and drilling data analysis: State of the art

Harpreet Singh, Chengxi Li, Peng Cheng, Xunjie Wang, Ge Hao, Qing Liu

Abstract view|4|times       PDF download|3|times Supplements download|0|times

Abstract


Traditional formation evaluation via laboratory testing and wireline logging of horizontal wells and deep formations face challenges due to several reasons and lead to uncertain results. Real-time cuttings and drilling data analysis of horizontal wells is an actively developing alternative approach to formation evaluation that can overcome several challenges faced by laboratory testing and wireline logging in providing improved estimates of formation parameters relevant to reservoir and completion quality. This study presents a state-of-the-art review of the latest methods and technologies in drill cuttings analysis to enable real-time characterization of the entire suite of formation properties, including chemical composition, densities and porosity, permeability, lithology, geomechanical properties, and characterization of fracture patterns. Specifically, the methods/techniques that enable characterizing drill cuttings in real-time and critically reviewed in this study include Raman spectroscopy for chemical composition, nuclear magnetic resonance for densities and porosity, liquid pressure pulse for permeability, deep learning for rock classification, 7 different methods for geomechanical properties, and mud loss signatures for characterization of fracture patterns. Benchmark comparison of drill cuttings analysis with the measurements from the core samples at similar depths is also reviewed. Key learnings are provided in 4 areas: to address the uncertainties in estimates of specific parameters affected by physical deformations due to drill bits, minimum cutting size for reliable nuclear magnetic resonance data, sweet spot identification, and power and network considerations for real-time analysis, respectively.

Document Type: Invited review

Cited as: Singh, H., Li, C., Cheng, P., Wang, X., Hao, G., Liu, Q. Automated real-time formation evaluation from cuttings and drilling data analysis: State of the art. Advances in Geo-Energy Research, 2023, 8(1): 19-36. https://doi.org/10.46690/ager.2023.04.03


Keywords


Drill cuttings, real-time, formation evaluation, industry 4.0, sustainability,

Full Text:

PDF Supplements

References


Abousleiman, Y. N., Hoang, S. K., Tran, M. H. Mechanical characterization of small shale samples subjected to fluid exposure using the inclined direct shear testing device. International Journal of Rock Mechanics and Mining Sciences, 2010, 47(3): 355-367.

Abousleiman, Y. N., Tran, M. H., Hoang, S. K., et al. Geomechanics field and laboratory characterization of the woodford shale: The next gas play. Paper SPE 110120 Presented at the SPE Annual Technical Conference and Exhibition, Anaheim, California, USA, 11-14 November, 2007.

Alipour, M., Esatyana, E., Sakhaee-Pour, A., et al. Characterizing fracture toughness using machine learning. Journal of Petroleum Science and Engineering, 2021, 200: 108202.

Althaus, S. M., Chen, J. H., Zhang, J. NMR measurement of porosity and density from drill cuttings of unconventional tight reservoirs. Paper SPWLA 2019-BBBBB Presented at the SPWLA 60th Annual Logging Symposium, The Woodlands, Texas, USA, 15-19 June, 2019.

Althaus, S. M., Chen, J. H., Zhang, J., et al. Low-field nuclear magnetic resonance methodology for analysis of drill cuttings from unconventional tight reservoirs. Energy & Fuels, 2020, 34(12): 15990-15994.

Canziani, A., Paszke, A., Culurciello, E. An analysis of deep neural network models for practical applications. ArXiv preprint, 2017: 1605.07678.

Chen, C., Ji, G., Wang, H., et al. Geology-engineering integration to improve drilling speed and safety in ultradeep clastic reservoirs of the Qiulitage structural belt. Advances in Geo-Energy Research, 2022, 6(4): 347-356.

Cheng, G., Guo, W. Rock images classification by using deep convolution neural network. Journal of Physics: Conference Series, 2017, 887(1): 012089.

Chiniwala, B., Palakurthi, A. K., Easow, I., et al. Measurement and analysis of wellbore micro losses and rock properties while drilling: A novel approach to identification of fractures in the osage and meramec formations of anadarko basin. Paper URTEC 2896976 Presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, Houston, Texas, USA, 23-25 July, 2018.

Dashti, J., Al-Ajmi, B., Farwan, H., et al. Identification of natural open fractures, induced fractures and matrix permeability in carbonates while drilling. Paper SPWLA 2021-0084 Presented at the SPWLA 62nd Annual Logging Symposium, Virtual Event, 15-19 May, 2021.

Di Santo, S., Yamada, T., Bondabou, K., et al. The digital revolution in mudlogging: An innovative workflow for advanced analysis and classification of drill cuttings using computer vision and machine-learning. Paper SEG 2022-3750340 Presented at the SEG/AAPG International Meeting for Applied Geoscience & Energy, Houston, Texas, USA, 28 August-1 September, 2022.

Dong, G., Chen, P. A comparative experimental study of shale indentation fragmentation mechanism at the macroscale and mesoscale. Advances in Mechanical Engineering, 2017, 9(8): 1-11.

Egermann, P., Lenormand, R., Longeron, D., et al. A fast and direct method of permeability measurements on drill cuttings. SPE Reservoir Evaluation & Engineering, 2005, 8(4): 269–275.

Equinor. Cuillin: Cuttings image lithology interpretation with Neural-Networks, 2019.

Esatyana, E., Alipour, M., Sakhaee-Pour, A. Characterizing anisotropic fracture toughness of shale using nanoindentation. SPE Reservoir Evaluation & Engineering, 2021, 24(3): 590-602.

Glover, K., Cui, A., Tucker, J., et al. The use of measurements made on drill cuttings to construct and apply geomechanical well profiles. Paper ARMA 2016-737 Presented at the 50th U.S. Rock Mechanics/Geomechanics Symposium, Houston, Texas, USA, 26-29 June, 2016.

Hadi, F. A., Nygaard, R. Data driven in-situ sonic log synthesis in carbonate reservoirs. Paper ARMA 2021-1669 Presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, Virtual, 18-25 June, 2021.

Haftani, M., Bohloli, B., Moosavi, M., et al. A new method for correlating rock strength to indentation tests. Journal of Petroleum Science and Engineering, 2013, 112: 24-31.

Ismailova, L., Dochkina, V., Al Ibrahim M, et al. Automated drill cuttings size estimation. Journal of Petroleum Science and Engineering, 2022, 209: 109873.

Katende, A., Rutqvist, J., Benge, M., et al. Convergence of micro-geochemistry and micro-geomechanics towards understanding proppant shale rock interaction: A Caney shale case study in southern Oklahoma, USA. Journal of Natural Gas Science and Engineering, 2021, 96: 104296.

Kathrada, M., Adillah, B. J. Visual recognition of drill cuttings lithologies using convolutional neural networks to aid reservoir characterisation. Paper SPE 196675 Presented at the SPE Reservoir Characterisation and Simulation Conference and Exhibition, Abu Dhabi, UAE, 17-19 September, 2019.

Khoshouei, M., Bagherpour, R. Predicting the geomechanical properties of hard rocks using analysis of the acoustic and vibration signals during the drilling operation. Geotechnical and Geological Engineering, 2021, 39(3): 2087-2099.

Lenormand, R., Fonta, O. Advances in measuring porosity and permeability from drill cuttings. Paper SPE 111286 Presented at the SPE/EAGE Reservoir Characterization and Simulation Conference, Abu Dhabi, UAE, 19-21 October, 2007.

Mahmoud, A. A., Gamal, H., Mutrif, O., et al. Artificial neural networks-based equation for real-time estimation of the dynamic Young’s modulus. Paper ARMA 2021-1907 Presented at the 55th U.S. Rock Mechanics/Geomechanics Symposium, Virtual, 20-23 June, 2021.

Martogi, D., Abedi, S., Saadeh, C., et al. Mechanical properties of drill cuttings based on indentation testing and contact mechanics solutions. Paper SPE 196214 Presented at the SPE Annual Technical Conference and Exhibition, Calgary, Alberta, Canada, 30 September-2 October, 2019.

Mohnke, O., Bartetzko, A., Ritzmann, N., et al. Integration of advanced cuttings analysis and fluid typing with NMR and acoustic logs for petrophysical log interpretation without radioactive sources. Paper SPE 183845 Presented at the SPE Middle East Oil & Gas Show and Conference, Manama, Kingdom of Bahrain, 6-9 March, 2017.

Prioul, R., Nolen-Hoeksema, R., Loan, M., et al. Using cuttings to extract geomechanical properties along lateral wells in unconventional reservoirs. Geophysics, 2018, 83(3): MR167-MR185.

Sanei, H., Ardakani, O. H., Akai, T., et al. Core versus cuttings samples for geochemical and petrophysical analysis of unconventional reservoir rocks. Scientific Reports, 2020, 10(1): 7920.

Schlumberger. Fluid Inclusion Petrography and Microthermometry, 2020.

Shi, X., Jiang, S., Wang, Z., et al. The application of drill cuttings to evaluate the fracability in unconventional shale gas resources. Paper SPE 196529 Presented at the SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition, Bali, Indonesia, 29-31 October, 2019.

Singer, G., Flaum, M., Chen, S., et al. NMR drill cutting analysis: Methodology evaluation and operational best practices. Paper SPWLA 2021-0095 Presented at the SPWLA 62nd Annual Logging Symposium, Virtual Event, 17-20 May, 2021.

Solano, N. A., Clarkson, C. R., Krause, F. F., et al. Drill cuttings and characterization of tight gas reservoirs-an example from the nikanassin Fm. in the deep basin of Alberta. Paper SPE 162706 Presented at the SPE Canadian Unconventional Resources Conference, Calgary, Alberta, Canada, 30 October-1 November, 2012.

Tamaazousti, Y., François, M., Kherroubi, J. Automated identification and quantification of rock types from drill cuttings, in SEG Technical Program Expanded Abstracts, edited by Nedorub, O. and Swinford, B., Society of Exploration Geophysicists, Tulsa, pp. 1591-1595, 2020.

Truong-Lam, H. S., Cho, S. J., Lee, J. D. Simultaneous in-situ macro and microscopic observation of CH4 hydrate formation/decomposition and solubility behavior using Raman spectroscopy. Applied Energy, 2019, 255: 113834.

Welker, C., Feiner, S., Lishansky, R., et al. Trapped fluid analysis of 58 wells from the SCOOP and STACK plays, Oklahoma, in SEG Global Meeting Abstracts, edited by Blasingame T., Rhodes S. and Sparkman, G., Unconventional Resources Technology Conference, Texas, pp. 2922-2933, 2016.

Wittman, B., Hemenway, M., Dick, M., et al. Integration of geochemical and petrophysical measurements from drill cuttings for unconventional reservoir characterization, Converse County, Powder River Basin. Paper URTEC 2020-3290 Presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, Virtual, 20-22 July, 2020.

Yamada, T., Di Santo, S. Instance segmentation of piled rock particles based on mask R-CNN. Paper IGARSS 22092014 Presented at IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 17-22 July, 2022.

Yang, Z., Zou, C., Gu, Z., et al. Geological characteristics and main challenges of onshore deep oil and gas development in China. Advances in Geo-Energy Research, 2022, 6(3): 264-266.




DOI: https://doi.org/10.46690/ager.2023.04.03

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 The Author(s)

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright ©2018. All Rights Reserved