A Case Study for Evaluating Effective Geomechanical Parameters and the Influence of the Biot Coefficient on the Stability of a Wellbore Wall in the Asmari Formation using Machine Learning
DOI:
https://doi.org/10.48048/tis.2023.7036Keywords:
Oil well stability, Machine learning, XGBoost algorithm, Bayesian optimization, SHAP algorithm, Porous elasticity, Stress-pore pressure coupling, Mohr-Coulomb shear failure, Biot coefficientAbstract
In oil extraction projects, knowledge of reservoir geomechanics is essential for estimating the stability of wellbore walls drilled at great depths. In this regard, mechanics of porous media according to the definition of Biot’s coefficient instead of Terzaghi effective stress can provide a more accurate estimate compared to other analyses. Additionally, using artificial intelligence and machine learning algorithms such as XGBoost and optimizing it with algorithms like Bayesian, along with using SHAP algorithm as an interpretable AI model, can provide us with deeper insights into available data. In this research, 1st geomechanical data of a well in Asmari formation in southwest Iran was obtained through well logs and operational reports and then analyzed by machine learning. Also, a 1-way coupled reservoir rock-fluid model was built to investigate the volume of fractured rock around the well. The interpretation of machine learning results helps us better understand the parameters affecting instability in this well’s wall. Moreover, finite element model results indicate that assuming a value equal to 1 for Biot coefficient (or Terzaghi effective stress) leads to incorrect results and overestimates the volume of fractured rock around this well up to 13 times more than actual values. Therefore, any proper analysis regarding wellbore wall stability and evaluating effective stresses requires accurate knowledge about real and existing values of this coefficient due to simultaneous behavior of stress and pore pressure.
HIGHLIGHTS
- One of the major challenges in the oil industry is the factors affecting the stability of the walls of oil wells during the extraction process, which identifying and addressing these factors can greatly assist in reducing extraction costs
- Nowadays, the collection of precise and extensive data through well-logging is a reliable source for assessing reservoir geomechanics, which, alongside machine learning can help us better understand reservoir geomechanics
- Given the conditions of hydrocarbon reservoirs, the mechanics of porous media, along with the definition of the Biot coefficient, can provide us with a better understanding of how shear failure occurs in well walls
- The XGBoost algorithm is useful for data analysis through regression and classification, as it can easily be optimized using the Bayesian algorithm and interpreted using algorithms such as SHAP
- Assuming a value equal to 1 for the Biot coefficient, in comparison to its true and in-situ value can cause many errors in estimating the stress-pore pressure interaction and the sanding rate of the well due to shear failure in it
GRAPHICAL ABSTRACT
Downloads
Metrics
References
D Moos and M Zoback. Utilization of observations of well bore failure to constrain the orientation and magnitude of crustal stresses: Application to continental, Deep Sea Drilling Project, and Ocean Drilling Program boreholes. J. Geophys. Res. Solid Earth 1990; 95, 9305-25.
R Sanaee, SR Shadizadeh and MA Riahi. Determination of the stress profile in a deep borehole in a naturally fractured reservoir. Int. J. Rock Mech. Min. Sci. 2010; 47, 599-605.
BC Haimson and FH Cornet. ISRM suggested methods for rock stress estimation - part 3: Hydraulic fracturing (HF) and/or hydraulic testing of pre-existing fractures (HTPF). Int. J. Rock Mech. Min. Sci. 2003; 40, 1011-20.
E Fjaer, RM Holt, P Hordrud, AM Raaen and R Risnes. Developments in petroleum science. Elsevier Press, Hungary, 2008, p. 295-305.
M Zoback, CA Barton, M Brudy, DA Castillo, T Finkbeiner, BR Grollimund, DB Moos, P Peska, CD Ward and DJ Wiprut. Determination of stress orientation and magnitude in deep wells. Int. J. Rock Mech. Min. Sci. 2003; 40, 1049-76.
M Zoback. Reservoir geomechanics. Cambridge university press, Cambridge, 2010, p. 206-34.
M Ezati, M Azizzadeh, MA Riahi, V Fattahpour and J Honarmand. Wellbore stability analysis using integrated geomechanical modeling: A case study from the Sarvak reservoir in one of the SW Iranian oil fields. Arabian J. Geosci. 2020; 13, 149.
HS Almalikee and FM Al-Najm. Wellbore stability analysis and application to optimize high-angle wells design in Rumaila oil field, Iraq. Model. Earth Syst. Environ. 2019; 5, 1059-69.
D Raljević, JP Vuković, V Smrečki, LM Pajc, P Novak, T Hrenar, T Jednačak, L Konjević, B Pinević and T Gašparac. Machine learning approach for predicting crude oil stability based on NMR spectroscopy. Fuel 2021; 305, 121561.
L Qiang. Prediction of ultimate bearing capacity of oil and gas wellbore based on multi-modal data analysis in the context of machine learning. Int. J. Inform. Tech. Syst. Approach 2023; 16, 1-13.
CM Sayers, C Sayers, LD Boer, D Lee, P Hooyman and R Lawrence. Predicting reservoir compaction and casing deformation in deepwater turbidites using a 3D mechanical earth model. In: Proceedings of the International Oil Conference and Exhibition in Mexico, Cancun, Mexico. 2006.
X Chen, CP Tan and CM Haberfield. Guidelines for efficient wellbore stability analysis. Int. J. Rock Mech. Min. Sci. 1997; 34, 50.e1-50.e14.
L Qiuguo, X Zhang, K Al-Ghammari, L Mohsin, F Jiroudi and AA Rawahi. 3-D Geomechanical modeling and wellbore stability analysis in Abu Butabul Field. In: Proceedings of the Abu Dhabi International Petroleum Conference and Exhibition, Abu Dhabi, United Arab Emirates. 2012, p. 312-9.
JB Altmann, BIR Müller, TM Müller, O Heidbach, MRP Tingay and A Weißhardt. Pore pressure stress coupling in 3D and consequences for reservoir stress states and fault reactivation. Geothermics 2014; 52, 195-205.
K Lipnikov. 2002, Numerical methods for the Biot model in poroelasticity. Ph. D. Dissertation University of Houston, Texas.
G Gambolati and RA Freeze. Mathematical simulation of the subsidence of Venice: 1. Theory. Water Resour. Res. 1973; 9, 721-33.
RW Lewis and B Schrefler. A fully coupled consolidation model of the subsidence of Venice. Water Resour. Res. 1978; 14, 223-30.
A Lin, M Alali, S Almasmoom and R Samuel. Wellbore instability prediction using adaptive analytics and empirical mode decomposition. In: Proceedings of the IADC/SPE Drilling Conference and Exhibition, Fort Worth, Texas. 2018.
M Sabah, M Mehrad, SB Ashrafi, DA Wood and S Fathi. Hybrid machine learning algorithms to enhance lost-circulation prediction and management in the Marun oil field. J. Petrol. Sci. Eng. 2021; 198, 108125.
H AlBahrani and N Morita. Risk-controlled wellbore stability criterion based on a machine-learning-assisted finite-element model. SPE Drill. Completion 2022; 37, 38-66.
D Makowski, MS Ben-Shachar, I Patil and D Lüdecke. Methods and algorithms for correlation analysis in R. J. Open Source Software 2020; 5, 2306.
T Chen and C Guestrin. XGBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, California. 2016, p. 785-94.
R Dazeley, P Vamplew, C Foale, C Young, S Aryal and F Cruz. Levels of explainable artificial intelligence for human-aligned conversational explanations. Artif. Intell. 2021; 299, 103525.
[24] SC Chelgani. Estimation of gross calorific value based on coal analysis using an explainable artificial intelligence. Mach. Learn. Appl. 2021; 6, 100116.
S Mangalathu, SH Hwang and JS Jeon. Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach. Eng. Struct. 2020; 219, 110927.
[26] VH Truong, G Papazafeiropoulos, QV Vu, VT Pham and Z Kong. Predicting the patch load resistance of stiffened plate girders using machine learning algorithms. Ocean Eng. 2021; 240, 109886.
XGoost.readthedocs.io, Available at: https://xgboost.readthedocs.org/, accessed January 2023.
J Guo, L Yang, R Bie, J Yu, Y Gao, Y Shen and A Kos. An XGBoost-based physical fitness evaluation model using advanced feature selection and Bayesian hyper-parameter optimization for wearable running monitoring. Comput. Network. 2019; 151, 166-80.
W Zhang, C Wu, H Zhong, Y Li and L Wang. Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization. Geosci. Front. 2021; 12, 469-77.
Y Xia, C Liu, Y Li and N Liu. A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring. Expert Syst. Appl. 2017; 78, 225-41.
Bayesian optimization, Available at: https://github.com/fmfn/BayesianOptimization, accessed January 2023.
M Steurer, RJ Hill and N Pfeifer. Metrics for evaluating the performance of machine learning based automated valuation models. J. Property Res. 2021; 38, 99-129.
SC Chelgani, H Nasiri and M Alidokht. Interpretable modeling of metallurgical responses for an industrial coal column flotation circuit by XGBoost and SHAP-A “conscious-lab” development. Int. J. Min. Sci. Tech. 2021; 31, 1135-44.
SC Chelgani, H Nasiri and A Tohry. Modeling of particle sizes for industrial HPGR products by a unique explainable AI tool-A “Conscious Lab” development. Adv. Powder Tech. 2021; 32, 4141-8.
V Guerriero and S Mazzoli. Theory of effective stress in soil and rock and implications for fracturing processes: A review. Geosciences 2021; 11, 119.
A Merxhani. An introduction to linear poroelasticity. Geophysics 2016, https://doi.org/10.48550/arXiv.1607.04274
E Holzbecher. Poroelasticity benchmarking for FEM on analytical solutions. In: Proceedings of the COMSOL Conference, Rotterdam, Netherlands. 2013.
JF Labuz and A Zang. Mohr-Coulomb failure criterion. Rock Mech. Rock Eng. 2012; 45, 975-9.
JB Altmann. 2010, Poroelastic effects in reservoir modelling. Ph. D. Dissertation. Karlsruher Institut für Technologie, Karlsruhe, Germany.
R Suarez-Rivera, JW Martin and BJ Begnaud. Numerical analysis of open-hole multilateral completions minimizes the risk of costly junction failures. In: Proceedings of the Rio Oil and Gas Expo and Conference, Rio de Janeiro, Brazil. 2004.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Walailak University
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
