1887
Volume 42, Issue 2
  • ISSN: 0263-5046
  • E-ISSN: 1365-2397

Abstract

Abstract

This study illustrates a field application of a robust and reliable approach for assessing geomechanical parameters during drilling operation or as a post-mortem analysis. The methodology leverages surface logging drilling data (such as Rate of Penetration, Rotation Per Minute, Weight on Bit, Torque, Standpipe Pressure, and Flow rates) along with well log data (including Sonic log, Bulk Density log, and Gamma Ray log) as input of the methodology. This model is grounded on the integration of various data processing techniques and machine learning algorithms (encompassing Multiple Linear Regression, Support Vector Regression, Random Forest, Artificial Neural Network, and XGBoost), ensuring a comprehensive and accurate evaluation of geomechanical parameters in the area under evaluation.

The methodology is applied to a dataset of six wells drilled in the same geological units of an area located towards the eastern limit of the Neuquén Basin, north of the ‘Dorsal de Huincul’ (Gonzalez et al. 2016). The results associated with Young’s Modulus, Density, and UCS, here presented, provide evidence of its successful use in a challenging geological context such as the unconventional plays in Argentina.

EAGE Publications BV
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2024-02-01
2026-02-14

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References

  1. Ameen, M.S., Smart, B.G., Somerville, J.M., Hammilton, S. and Naji, N.A. [2009]. Predicting rock mechanical properties of carbonates from wireline logs (A case study: Arab-d Reservoir, Ghawar Field, Saudi Arabia), Marine And Petroleum Geology, 26(04), 430–444. DOI:10.1016/j.marpetgeo.2009.01.017.
     https://doi.org/10.1016/j.marpetgeo.2009.01.017 [Google Scholar]
  2. Anemangely, M., Ramezanzadeh, A., Amiri, H. and Hoseinpour, S.A. [2019]. Machine learning technique for the prediction of shear wave velocity using petrophysical logs, Journal of Petroleum Science and Engineering, 174, 306–327. DOI: S0920410518310246.
     https://doi.org/S0920410518310246 [Google Scholar]
  3. Asfari, M., Amani, M., Razmgir, S.A.M., Karimi, H. and Yousefi, S. [2010]. Using drilling and logging data for developing 1D mechanical earth model for a mature oil field to predict and mitigate wellbore stability challenges, Paper SPE – 132187 presented at the CPS/SPE International Oil & Gas Conference and Exhibition, Beijing, China, 8–10 June. DOI: 10.2523/132187‑MS.
     https://doi.org/10.2523/132187-MS [Google Scholar]
  4. Bourgoyne, A.T. and Young, F.S. [1974]. A Multiple Regression Approach to Optimal Drilling and Abnormal Pressure Detection, SPE Journal, 14(4), 371–384. DOI: 10.2118/4238‑PA.
     https://doi.org/10.2118/4238-PA [Google Scholar]
  5. Castagna, J.P., Batzle, M.L. and Eastwood, R.L. [1985]. Relationships Between Compressional-Wave And Shear-Wave Velocities In Clastic Silicate Rocks, Geophysics, 50(4), 571–581.
     [Google Scholar]
  6. Elkatatny, S. [2018]. New Approach to Optimize the Rate of Penetration Using Artificial Neural Network, Arabian Journal for Science and Engineering, 43(11), 6297–6304.
     [Google Scholar]
  7. Galle, E.H. and Woods, H.S. [1960]. How To Calculate Bit Weight And Rotary Speed For Lowest Cost Drilling, Oil And Gas Journal.
     [Google Scholar]
  8. González, G., Vallejo, M.D., Kietzmann, D., Marchal, D., Desjardins, P., Tomassini, F.G., Rivarola, L.G. and Domínguez, R.F. [2016]. Transecta regional de la Formación Vaca Muerta, IAPG.
     [Google Scholar]
  9. Goodman, R.E. [1989]. Introduction to Rock Mechanics 2nd Edition, John Wiley & Sons Ltd., New York.
     [Google Scholar]
  10. Hamada, Y., Kitamura, M., Yamada, Y., Sanada, Y., Sugihara, T., Saito, S., Moe, K. and Hirose, T. [2018]. Continuous Depth Profile Of The Rock Strength In The Nankai Accretionary Prism Based On Drilling Performance Parameters, Scientific Reports, 8(1), 1–9.
     [Google Scholar]
  11. Hareland, G. and Hoberock, L.L. [1993]. Use Of Drilling Parameters To Predict In-Situ Stress Bounds, Paper SPE-25727 presented at the SPE/IADC Drilling Conference, Amsterdam, Netherlands, 22–25 February. DOI: 10.2118/25727‑MS.
     https://doi.org/10.2118/25727-MS [Google Scholar]
  12. Hareland, G. and Nygård, R. [2007]. Calculating Unconfined Rock Strength From Drilling Data, Paper ARMA- 07-214 presented at the 1st Canada-Us Rock Mechanics Symposium, Vancouver, Canada, 27–31 May.
     [Google Scholar]
  13. Howell, J.A., Schwarz, E., Spalletti, L.A. and Veiga, G.D. [2005]. The Neuquén Basin: An Overview, Geological Society London Special Publications, 252 pp. 1–14. http://dx.doi.org/10.1144/GSL.SP.2005.252.01.01.
     [Google Scholar]
  14. Jamshidi, E., Arabjamaloei, R., Hashemi, A., Ekramzadeh, M.A. and Amani, M. [2013]. Real-Time Estimation Of Elastic Properties Of Formation Rocks Based On Drilling Data By Using An Artificial Neural Network, Energy Sources, Part A: Recovery, Utilization, And Environmental Effects, 35(4), 337–351. DOI:10.1080/15567036.2010.495971.
     https://doi.org/10.1080/15567036.2010.495971 [Google Scholar]
  15. Legarreta, L. and Uliana, M.A. [1991]. Jurassic-Cretaceous marine oscillations and geometry of back-arc basin fill, central Argentina Andes. Sedimentation, tectonics and eustasy. International Association of Sedimentologists, Special Publication, 12, 368 pp.
     [Google Scholar]
  16. Lashkaripour, G.R. [2002]. Predicting mechanical properties of mudrock from index parameters.Bulletin of Engineering Geology and the Environment, 61(1), 73–77. DOI: 10.1007/s100640100116.
     https://doi.org/10.1007/s100640100116 [Google Scholar]
  17. Martinelli, M., Colombo, I. and Russo, E.R. [2021]. Predict Geomechanical Parameters with Machine Learning Combining Drilling Data and Gamma Ray, SPE Middle East Oil & Gas Show and Conference, Bahrein (event cancelled). DOI: 10.2118/204688‑MS.
     https://doi.org/10.2118/204688-MS [Google Scholar]
  18. Mitchum, R.M. and Uliana, M. [1986]. Seismic stratigraphy of carbonate depositional sequences, Upper Jurassic-Lower Creataceous, Neuquén Basin, Argentina. Seismic Stratigraphy II. An integrated approach to hydrocarbon analysis.American Association of Petroleum Geologists, Memoir39: pp. 255–283.
     [Google Scholar]
  19. Ohen, H.A. [2003]. Calibrated Wireline Mechanical Rock Properties Model for Predicting and Preventing Wellbore Collapse and Sanding, Paper SPE-82236-MS presented at the SPE European Formation Damage Conference, The Hague, Netherlands, 13–14 May. DOI:10.2118/82236‑MS.
     https://doi.org/10.2118/82236-MS [Google Scholar]
  20. Rampersad, P.R., Hareland, G. and Boonyapaluk, P. [1994]. Drilling Optimization Using Drilling Data and Available Technology, Paper SPE-27034-MS presentend at the SPE Latin America/Caribbean Petroleum Engineering Conference, Buenos Aires, Argentina, 27–29 April. DOI: 10.2118/27034‑MS.
     https://doi.org/10.2118/27034-MS [Google Scholar]
  21. Warren, T.M. [1987]. Penetration Rate Performance Of Roller Cone Bits, Spe Drilling Engineering, 2(1), 9–18.
     [Google Scholar]
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Geomechanical Parameter Derivation while Drilling in Unconventional Plays: a Combination of Surface Drilling Data, Gamma Ray Data, and Machine Learning Techniques
First Break 42, 65 (2024); https://doi.org/10.3997/1365-2397.fb2024015
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