A probabilistic comprehensive evaluation method for the fracability of unconventional hydrocarbons reservoir considering description uncertainty of target formation

Abstract

Fracability evaluation is a crucial basis for fracturing and production enhancement in tight reservoirs. Due to the complexity of the geological environment, factors influencing reservoir fracability exhibit significant uncertainty. Ignoring the uncertainty of relevant parameters and conducting fracability evaluation based on deterministic parameters may lead to deviations from actual fracability results. To address this issue, this paper proposes a comprehensive evaluation method for the fracability of unconventional oil and gas reservoirs, considering reservoir description uncertainty. Based on fundamental evaluation methods, a reservoir fracability evaluation model is constructed, incorporating the Monte Carlo stochastic simulation method to determine the comprehensive probability distribution of the reservoir fracability evaluation index. This approach enables a more scientific and reliable evaluation of reservoir fracability. The research results indicate that the assumed distribution of input parameters has a certain impact on fracability evaluation results, with normal distribution demonstrating significant disturbance resistance. Additionally, brittleness index is found to be the most sensitive factor affecting fracability evaluation. The proposed evaluation method and insights can provide theoretical references for the fracability assessment of highly heterogeneous tight reservoirs.

Keywords

Unconventional oil and gas / Uncertainty / Probability distribution / Fracability evaluation

CRediT authorship contribution statement

Hongzhi Xu: Methodology. Hao Zhang: Writing – original draft, Investigation. Zizhen Wang: Writing – review & editing, Writing – original draft, Funding acquisition. Weidong Zhou: Methodology. Jintang Wang: Methodology. Wang Zhou: Investigation. Chengwen Wang: Writing – review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment

This research is supported by the National Key R&D Program of China (No. 2023YFC2811005), the Fundamental Research Funds for the Central Universities (No. 24CX02011A), and the Fund of State Key Laboratory of Deep Oil and Gas, China University of Petroleum (East China).

References

[1] L. Gandossi, U.V. Estorff, An Overview of Hydraulic Fracturing and Other Formation Stimulation Technologies for Shale Gas Production, M. Luxembourg: Publications Office of the European Union, 2015.

[2] J. Zhao, L. Ren, C. Shen, et al., Latest research progresses in network fracturing theories and technologies for shale gas reservoirs, J. Nat. Gas Industr. B 5 (5) (2018) 533-546.

[3] E. Enderlin, H. Alsleben, J. Beyer, Predicting Fracability in Shale Reservoirs, C. Houston, Texas, USA: AAPG Annual Convention and Exhibition, 2011.

[4] K.K. Chong , W.V. Grieser , A. Passman , et al. , in: A Completions Guide Book to shale-play Development: a Review of Successful Approaches Towards shale-play Stimulation in the Last Two Decades, C. SPE Canada Unconventional Resources Conference, SPE, 2010. SPE-133874-MS.

[5] D.M. Jarvie , R.J. Hill , T.E. Ruble , et al. , Unconventional shale-gas systems: the Mississippian Barnett Shale of north-central Texas as one model for thermogenic shale-gas assessment, J. AAPG Bull. 91 (4) (2007) 475-499.

[6] Z.P. Zeng , Z. Liu , J. Ma , et al. , A new method for fracability evaluation in deep and tight sandstone reservoir, J, J. Geomechanics 25 (2) (2019) 223-232.

[7] J.L. Yuan , J.G. Deng , D.Y. Zhang , et al. , Fracability evaluation of shale-gas reservoir, J. Acta Petrolei Sinica 34 (3) (2013) 523-527.

[8] J.A. Breyer , H. Alsleben , M.B. Enderlin , Predicting fracability in shale reservoirs, in: AAPG Annual Conference and Exhibition. Making the next Giant Leap in Geosciences, 2010. Houston, Texas, USA.

[9] J.C. Guo , B. Luo , H.Y. Zhu , et al. , Evaluation of fracability and screening of perforation interval for tight sandstone gas reservoir in western Sichuan Basin, J, J. Nat. Gas Sci. Eng. 25 (2015) 77-87.

[10] L.L. Sui, Y. Ju, Y.M. Yan, et al., A quantification method for shale fracability based on analytic hierarchy process, J. Energy 115 (2016) 637-645.

[11] M. Mullen, M. Enderlin, Fracability index-more than just calculating rock properties, in: SPE Annual technical conference and exhibition, SPE, 2012. SPE-159755-MS.

[12] Y. Tang, Y. Xing, L.Z. Li, et al., Influence factors and evaluation methods of the gas shale fracability, J. Earth Sci. Front. 19 (5) (2012) 356-363.

[13] W.J. Xu , J.Z. Zhao , J.G. Xu , Fracability evaluation method for tight sandstone oil reservoirs, J. Nat. Resourc. Res. 30 (6) (2021) 4277-4295.

[14] Y. Yang, X. Li, Y. Ju, et al., Multifactor coupled evaluation model of fracability based on a combinatorial weighting method: a case study of the low-permeability sandstone reservoirs of Yanchang formation, Southwestern Ordos Basin, China, J. Rock Mech. Rock Eng. 58 (1) (2025) 905-925.

[15] C.G. Jia , S.M. Li , H.T. Wang , et al. , Shale reservoir network fracturing technology research and experiment, J. Strat. Study CAE 14 (6) (2012) 106-112.

[16] Q.H. Li , M. C , Fred P⋅Wang, et al. , Influence of engineering factors on shale gas productivity: a case study from the Haynesville shale gas reservoir in North America, J. Nat. Gas Industr. 32 (4) (2012) 54-59+123.

[17] T.X. Jiang , X.B. Bian , Y. Su , et al. , A new method for evaluating shale fracability index and its application, J. Petrol. Drill. Techniq. 42 (5) (2014) 16-20.

[18] B. Zeng, N.X. Feng, Z.G. Yao, et al., Influence mechanism of hydraulic fracturing fracture propagation in deep shale gas reservoirs, Fault-Block Oil Gas Field 31 (2) (2024) 246-256.

[19] H. Su, R.X. Li, H.C. Deng, et al., Comprehensive evaluation of geological and engineering factors affecting fracturing effectiveness in tight sandstone reservoirs, Petrol. Geol. Exp. 46 (6) (2024) 1349-1361.

[20] B. Hou, M. Chen, K. Wang, et al., The key index system of fracability evaluation in gas shale reservoir, J, J. Petrochem. Univ. 27 (6) (2014) 42-49.

[21] J.Z. Zhao , W.J. Xu , Y.M. Li , et al. , A new method for fracability evaluation of shale-gas reservoirs, J. Nat. Gas Geosci. 26 (6) (2015) 1165-1172.

[22] H. Amiri, A. Ramezanzadeh, M. Chamanzad, et al., Recognizing the best intervals for hydraulic fracturing using a new fracability index, J, J. Pet. Explor. Prod. Technol. 11 (8) (2021) 3193-3201.

[23] H.J. Fu , X.Z. Wang , L.X. Zhang , et al. , Geological controls on artificial fracture networks in continental shale and its fracability evaluation: a case study in the Yanchang Formation, Ordos Basin, China, J. J. Nat. Gas Sci. Eng. 26 (2015) 1285-1293.

[24] J.G. Liu , L.S. Qu , Z.Y. Song , et al. , Fracability evaluation method and influencing factors of the tight sandstone reservoir, J. Geofluids 2021 (1) (2021) 7092143.

[25] C.Y. Peng , J. Zhou , J.S. Wu , et al. , Evaluation of tight sandstone mechanical properties and fracability: an experimental Study of Reservoir sandstones from Lufeng Sag, Pearl River mouth Basin, Northern South China Sea, J. Process. 11 (7) (2023) 2135.

[26] Y. Zhang, L.F. Zhang, J.Y. He, et al., Fracability evaluation method of a fractured-vuggy carbonate reservoir in the shunbei block, J. ACS omega 8 (17) (2023) 15810-15818.

[27] W.B. Zhai , J. Li , Y.C. Zhou , Application of catastrophe theory to fracability evaluation of deep shale reservoir, J. Arab. J. Geosci. 12 (2019) 1-7.

[28] M.S. Song , Z. Liu , X.C. Zhang , et al. , Fracability evaluation of tight sandstone reservoirs based on improved entropy analytic hierarchy process: taking the Jurassic reservoirs of well Z109 in the junggar basin as an example, J, J. Geomechanics 25 (4) (2019) 509-517.

[29] Y. Ju, G.J. Wu, Y.L. Wang, et al., 3D Numerical model for hydraulic fracture propagation in tight ductile reservoirs, considering multiple influencing factors via the entropy weight method, J. SPE 26 (5) (2021) 2685-2702.

[30] Z.Y. Yu , Q. Ma , P.Y. Zheng , Research on the method of fracturing effect prediction based on improved support vector machine, J, J. Chongqing Univ. Sci. Technol. (Nat. Sci. Edit.) 18 (3) (2016) 116-118.

[31] S.Z. S , H.Y. Yu , Z.L. Sun , et al. , Forecast of fracturing effect based on gray correlation analysis and BP neural network, J. Yangtze Univ. (Nat. Sci. Edit.) 11 (31) (2014) 154-156+10.

[32] X.Z. Jiang , S.H. Xu , Oilfield measure effect prediction based on radial basis process neural network, J. Comp. Digit. Eng. 44 (8) (2016) 1443-1445+1492.

[33] Z.G. Wang , J.K. Liu , X.Y. Jiang , et al. , Evaluation method for well fracturing potential based on big-data analysis, J/OL, Pet. Geol. Oilfield Dev. Daqing (2025) 1-9, https://doi.org/10.19597/J.ISSN.1000-3754.202406010.

[34] J. Gai, Prediction method for hydraulic fracturing effect of oil production well based on automatic machine learning technology, J. Petrol. Geol. Recov. Eff. 30 (1) (2023) 161-170.

[35] M. Abdar, F. Pourpanah, S. Hussain, et al., A review of uncertainty quantification in deep learning: techniques, applications and challenges, Inf. Fusion 76 (2021) 243-297.

[36] M. Fathy, F.K. Haghighi , M. Ahmadi, Uncertainty quantification of reservoir performance using machine learning algorithms and structured expert judgment, Energy 288 (2024) 129906.

[37] D. Arnold, V. Demyanov, T. Rojas, et al., Uncertainty quantification in reservoir prediction: part 1-Model realism in history matching using geological prior definitions, Math. Geosci. 51 (2019) 209-240.

[38] J. Gao, F. Chen, Y. Zhao, et al., Quantitative risk analysis and parameter sensitivity evaluation of wellbore instability in poroelastic media considering uncertainty of geomechanical parameters, Comput. Geotech. 170 (2024) 106234.

[39] R. Rickman, M. Mullen, E. Petre, et al., in: A Practical Use of Shale Petrophysics for Stimulation Design Optimization: all Shale Plays are Not Clones of the Barnett Shale, C. SPE Annual Technical Conference and Exhibition, SPE, 2008. SPE-115258-MS.

[40] D.V. Deere , R.P. Miller , Engineering Classification and Index Properties for Intact Rock, J, 1966.

[41] J.C. Jaeger , N.G.W. Cook , R. Zimmerman , Fundamentals of rock mechanics, M. John Wiley & Sons, 2009.

[42] V. Hucka, B. Das, Brittleness determination of rocks by different methods, J. Int. Rock Mech. Min. Sci. 11 (10) (1974) 389-392.

[43] Y. Jin, M. Chen, X.D. Zhang, Determination of fracture toughness for deep well rock with geophysical logging data, J. Chin. Rock Mech. Eng. 20 (4) (2001) 454-456.

[44] B.R. Lawn, D.B. Marshall, Hardness, toughness and brittleness: an indentation analysis, J, J. Am. Ceram. Soc. 62 (7-8) (1976) 347-350.

[45] Y. Jin, M. Chen, H.Y. Wang, et al., Study on prediction method of fracture toughness of rock mode II by logging data, J. Chin. Rock Mech. Eng. 27 (S2) (2008) 3630-3635.

[46] H. Vahid, P. Kaiser, Brittleness of rock and stability assessment in hard rock tunneling, J. Tunnell. Undergr. Sp. Technol. 18 (1) (2003) 35-48.

[47] M.D. Zoback , Reservoir Geomechanics, M. Cambridge University Press, 2010.

[48] A. Chi, J. Zhang, Y.W. Li, et al., Estimation criteria for rock brittleness based on energy analysis during the rupturing process, J. Rock Mech. Rock Eng. 49 (12) (2016) 4681-4698.

[49] L.X. Zhao , S.D. Zhang , Interpretation and Applications of Imaging Logging, R. Sichuan:Sichuan Petroleum Administration, 1999, pp. 62-69.

[50] Q.R. Passey , S. Creaney , J.B. Kulla , et al. , A practical model for organic richness from porosity and resistivity logs, J. AAPG Bull. 74 (12) (1990) 1777-1794.

[51] Z.H. Chu , J. Gao , L.J. Huang , et al. , Principles and Methods of Geophysical Well Logging (Part 2), M. Beijing: Petroleum Industry Press, 2007, pp. 224-326.

[52] W.D. McCain Jr. , Properties of Petroleum Fluids, J, 1973.

[53] F. Yang, F. Lu, X.Q. Ke, et al., Application of resistivity invasion difference ratio method in fracture identification, J. Petrochem. Industr. Appl. 39 (8) (2020) 65-72.

[54] J. Lai, G.W. Wang, S.M. Sun, et al., Research advances in logging recognition and evaluation methods of fractures in tight sandstone reservoirs, J. Progr. Geophys. 30 (4) (2015) 1712-1724.

[55] J.H. Zhong , S.X. Liu , Y.S. Ma , et al. , Macro-fracture mode and micro-fracture mechanism of shale, J. Petrol. Explorat. Dev. 42 (2) (2015) 242-250.

[56] L.T. Shang , Y.M. Zhang , Y.H. Wang , et al. , Application of comprehensive fracturing interpretation method of tight oil and gas reservoir in Ordos basin, J. Petrol. Geol. Eng. 35 (4) (2021) 38-42.

[57] H.M. Liu , J.K. Zheng , W.S. Zhao , et al. , A new method for evaluating brittleness index of deep tight sandstone reservoir, J, J. Geomechanics 25 (4) (2019) 492-500.

[58] F.Q. Lai , H. Luo , D.Y. Qin , et al. , Crushability evaluation of shale gas reservoir based on analytic hierarchy process, J. Spec. Oil Gas Reserv. 25 (3) (2018) 154-159.

[59] W. Zhang, X.J. Liu, L.X. Liang, et al., Fracture characteristics of reservoirs with different lithologies in marine-continental transitional facies and its influence on fracturing, Petrol. Geol. Recov. Eff. 31 (6) (2024) 74-88.

[60] Y. Yang, X. Li, Y. Ju, Influence mechanisms of plasticity and horizontal stress difference on the fracture propagation in plastic reservoir rocks: a 3D XFEM-based plastic fracturing model, J. Geomech. Geophys. Geo-Energy Geo-Resourc. 8 (5) (2022) 145.

[61] D. Zhou, P. Zheng, P. He, et al., Hydraulic fracture propagation direction during volume fracturing in unconventional reservoirs, J, J. Petrol. Sci. Eng. 141 (2016) 82-89.

[62] S.C. Zhang , T.K. Guo , T. Zhou , et al. , Fracture propagation mechanism experiment of hydraulic in natural shale, J. Acta Petrolei Sinica 35 (3) (2014) 496-503+518.

[63] J.Z. Zhao , L. Ren , Y.Q. H , Controlling factors of hydraulic fractures extending into network in shale formations, J. Southwest Petrol. Univ. (Sci. Technol. Edit.) 35 (1) (2013) 1-9.

[64] X. Xiong, X.F. Ma, Influence of natural fractures on hydraulic fracture propagation behaviour, J. Eng. Fract. Mech. 276 (2022) 108932.

[65] Z.Y. Li , J.B. Chen , H.L. Zuo , et al. , Interaction laws of hydraulic fractures and natural fractures in shale reservoirs, Fault-Block Oil Gas Field 31 (2) (2024) 232-240.

[66] M.J. Fang , X.L. Du , Y.H. Bai , et al. , Three-dimensional physical simulation experiments on large-scale hydraulic fracturing in multi-thin interbedded tight sandstone reservoirs, Petrol. Geol. Exp. 46 (4) (2024) 786-798.

[67] B.L. Wu, C.Y. Peng, G.A. Wu, et al., Effect of fracability index on fracture propagation: a case study of LF Oilfield in South China Sea, Petrol. Drill. Techn. 51 (3) (2023) 105-112.

[68] Y.W. Liu , D.P. Gao , Q. Li , et al. , Mechanical frontiers in shale-gas development, J. Adv. Mech. 49 (00) (2019) 1-236.

[69] Z. Yang, L.H. Hou, S.Z. Tao, et al., Formation conditions and “sweet spot” evaluation of tight oil and shale oil, J. Petrol. Explor. Dev. 42 (5) (2015) 555-565.

[70] Y. Yang, X. Li, Y. Ju, et al., Multifactor coupled evaluation model of fracability based on a combinatorial weighting method: a case study of the low-permeability sandstone reservoirs of Yanchang Formation, Southwestern ordos Basin, China, Rock Mech. Rock Eng. 58 (1) (2025) 905-925.

[71] T. Wilson, P. Kavousi, T. Carr, et al., Relationships of Λρ, Мρ, Brittleness Index, Young’s Modulus, Poisson’s Ratio, and High Total Organic Carbon for the Marcellus Shale, West Virginia[C]// SEG International Exposition and Annual Meeting. SEG, Morgantown, 2017, pp. 2017-17631837.

[72] S.S. Ganguli , S. Sen , S. Verma , Characterization of organic content, brittleness index, and geomechanical properties of the Eocene Cambay Shales-Insights from the Ankleshwar oil field in western India, Interpretation 9 (1) (2021) T235-T252.

[73] J.L. Guo , C.Y. Jia , D.B. He , et al. , Classification and evaluation on shale gas reservoir for Wufeng-Longmaxi Formation in Chuannan Area, Sichuan Basin, J. Lithosphere 2021 (Special 1) (2021) 3364731.

[74] J.L. Yuan , J.L. Zhou J , J.L. Liu , et al. , An improved fracability-evaluation method for shale reservoirs based on new fracture toughness-prediction models, SPE J. 22 (5) (2017) 1704-1713.

[75] Z.H. Li , P. Deng , C.H. Yang , et al. , Experimental study on mechanical properties and fracability evaluation of carbonate reservoirs, J. Guangxi Univ. (Nat. Sci. Edit.) 44 (5) (2019) 1450-1460.

[76] J.F. Xiao , P.J. Hu , L.X. Han , et al. , Mechanical properties and compressibility evaluation of Qiongzhusi shale in Weiyuan Area in Southern Sichuan, J. Drill. Prod. Technol. 45 (2) (2022) 61-66.

[77] X. Jin, S.N. Shah, J.C. Roegiers, et al., in: Fracability Evaluation in Shale reservoirs-an Integrated Petrophysics and Geomechanics Approach, C. SPE Hydraulic Fracturing Technology Conference and Exhibition, SPE, 2014. SPE-168589-MS.

[78] B. Xu, Z.H. Wang, T. Song, et al., Logging evaluation method of fracturability of tight sandstone reservoirs, Petrol. Geol. Recov. Eff. 31 (6) (2024) 57-64.

[79] J.Y. Guo , Z.B. Zhang , Q.Y. Sun , Applications of AHP method in safety science, J, J. Safet. Sci. Technol. 4 (2) (2008) 69-73.

[80] J.E. Chang , T.L. Jiang , Research on the weight of coefficient through analytic hierarchy process, J, J. Wuhan Univ. Technol. (Inf. Manag. Eng.) 29 (1) (2007) 153-156.

[81] R.W. Saaty , The analytic hierarchy process-what it is and how it is used, J. Mathemat. Modell. 9 (3-5) (1987) 161-176.

[82] N. Dalkey, O. Helmer, An experimental application of the Delphi method to the use of experts, J. Manag. Sci. 9 (3) (1963) 458-467.

[83] C.E. Shannon , A mathematical theory of communication, J. Bell Syst. Techn. 27 (3) (1948) 379-423.

[84] R. Bro, A.K. Smilde , Principal component analysis, J. Analyt. Method. 6 (9) (2014) 2812-2831.

[85] D. Diakoulaki, G. Mavrotas, L. Papayannakis, Determining objective weights in multiple criteria problems: the critic method, J. Comp. Operat. Res. 22 (7) (1995) 763-770.

[86] Y.M. Wang , T.M.S. Elhag , An approach to avoiding rank reversal in AHP, J. Dec. Supp. Syst. 42 (3) (2006) 1474-1480.

[87] H. Wang , C. Xu , Z. Xu , An approach to evaluate the methods of determining experts’ objective weights based on evolutionary game theory, J. Knowledge-Based Syst. 182 (2019) 104862.

[88] J.L. Deng , Grey Prediction and Decision Making, M. Wuhan: Huazhong University of Science and Technology Press, 1986.

[89] H. Zhu, Y.B. Liu, Y. You, Monte Carlo method and quasi-Monte Carlo method, J. East China Univ. Technol. (Nat. Sci.) 33 (4) (2010) 357-362.

[90] Editorial Department of China Journal of Highway and Transport, Review on China’s automotive engineering research progress: 2023, J. China Highway Transp. 36 (11) (2023) 1-192.

[91] X.M. Song , J.Y. Zhang , C.S. Zhan , et al. , Review of methods of parameter sensitivity analysis in hydrologic modeling, J. Adv. Sci. Technol. Water Resourc. 35 (6) (2015) 105-112.