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Investigation of hydraulic fracture propagation in conglomerate rock using discrete element method and explainable machine learning framework

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Abstract

Conglomerate reservoirs are important oil and gas resources that require hydraulic fracturing for stimulation. However, the heterogeneity of conglomerate rocks causes fractures to propagate irregularly, complicating the fracturing design. To provide insight into the complex fracture behavior in conglomerate rocks, a three-dimensional (3D) hydromechanical numerical model based on the discrete element method (DEM) was proposed in this study. Besides, a novel approach combining the grain-based DEM with Voronoi tessellation was adopted to depict the geometrical characteristics of conglomerate rocks. Considering the rock matrix-interface-gravel structure, the effects of various influencing factors including the strength and permeability, in situ stress difference, fluid properties and injection scheme on the fracture propagation and induced microseismic events were investigated. Two fracture behaviors, penetration and deflection, were summarized. Finally, the mechanisms of different fracture behaviors were discussed, revealing the joint effects of various factors on the fracture behavior. To predict the fracture behavior in conglomerate rocks, an explainable machine learning framework comprising the extreme gradient boosting and the shapley additive explanations was adopted, which attained high accuracy on the testing set. It can also provide comprehensive explanations for the predicted results, offering support for practical decisions.

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References

  1. Adachi J, Siebrits mE, Peirce A et al (2007) Computer simulation of hydraulic fractures. Int J Rock Mech Min Sci 44(5):739–757

    Article  Google Scholar 

  2. Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp 785–794

  3. Chengzao J, Caineng Z, Zhi Y et al (2018) Significant progress of continental petroleum geological theory in basins of central and western china. Petrol Explor Develop 45(4):573–588

    Article  Google Scholar 

  4. Cundall PA, Strack OD (1979) A discrete numerical model for granular assemblies. Geotechnique 29(1):47–65

    Article  Google Scholar 

  5. Duan K, Kwok CY, Wu W et al (2018) Dem modeling of hydraulic fracturing in permeable rock: influence of viscosity, injection rate and in situ states. Acta Geotechnica 13:1187–1202

    Article  Google Scholar 

  6. Espinosa HD, Zavattieri PD (2003) A grain level model for the study of failure initiation and evolution in polycrystalline brittle materials. part i: Theory and numerical implementation. Mech Mater 35(3–6):333–364

    Article  Google Scholar 

  7. Guoxin L, Jianhua Q, Chenggang X et al (2020) Theoretical understandings, key technologies and practices of tight conglomerate oilfield efficient development: A case study of the mahu oilfield, junggar basin, nw china. Petrol Explor Develop 47(6):1275–1290

    Article  Google Scholar 

  8. Hazzard JF, Young RP (2002) Moment tensors and micromechanical models. Tectonophysics 356(1–3):181–197

    Article  Google Scholar 

  9. Huang L, He R, Yang Z et al (2023) Exploring hydraulic fracture behavior in glutenite formation with strong heterogeneity and variable lithology based on dem simulation. Eng Fract Mech 278:109020

    Article  Google Scholar 

  10. Itasca C (2014) Pfc (particle flow code in 2 and 3 dimensions), version 5.0 [user’s manual]. Numer Anal Methods Geomech 32(6):189–213

  11. Jianmin L, Baocheng W, Haiyan Z et al (2019) Adaptability of horizontal well volume fracturing to tight conglomerate reservoirs in mahu oilfield. China Petrol Explor 24(2):250

    Google Scholar 

  12. Kang X, Hu W, Cao J et al (2019) Controls on reservoir quality in fan-deltaic conglomerates: insight from the lower triassic baikouquan formation, junggar basin, china. Mar Petrol Geol 103:55–75

    Article  Google Scholar 

  13. Li L, Meng Q, Wang S et al (2013) A numerical investigation of the hydraulic fracturing behaviour of conglomerate in glutenite formation. Acta Geotechnica 8:597–618

    Article  Google Scholar 

  14. Li M, Wu J, Li J et al (2022) Modeling of hydraulic fracturing in polymineralic rock with a grain-based dem coupled with a pore network model. Eng Fract Mech 275:108801

    Article  Google Scholar 

  15. Li X, Li H, Zhao J (2017) 3d polycrystalline discrete element method (3pdem) for simulation of crack initiation and propagation in granular rock. Comput Geotech 90:96–112

    Article  Google Scholar 

  16. Li X, Zhang Q, Li H et al (2018) Grain-based discrete element method (gb-dem) modelling of multi-scale fracturing in rocks under dynamic loading. Rock Mech Rock Eng 51:3785–3817

    Article  Google Scholar 

  17. Liang H, Tang H, Qin J, et al (2022) Multi-scale investigations on the geometries of hydraulic fractures in conglomerate reservoirs. In: International petroleum technology conference, OnePetro

  18. Lisjak A, Grasselli G (2014) A review of discrete modeling techniques for fracturing processes in discontinuous rock masses. J Rock Mech Geotech Eng 6(4):301–314

    Article  Google Scholar 

  19. Liu NZ, Zou YS, Ma XF et al (2019) Study of hydraulic fracture growth behavior in heterogeneous tight sandstone formations using ct scanning and acoustic emission monitoring. Petrol Sci 16:396–408

    Article  Google Scholar 

  20. Liu P, Ju Y, Ranjith PG et al (2016) Experimental investigation of the effects of heterogeneity and geostress difference on the 3d growth and distribution of hydrofracturing cracks in unconventional reservoir rocks. J Nat Gas Sci Eng 35:541–554

    Article  Google Scholar 

  21. Liu X, Zhang A, Tang Y et al (2022) Investigation on the influences of gravel characteristics on the hydraulic fracture propagation in the conglomerate reservoirs. Nat Gas Ind B 9(3):232–239

    Article  Google Scholar 

  22. Lundberg SM, Lee SI (2017) A unified approach to interpreting model predictions. Ad Neural Inf Process Syst 30

  23. Lundberg SM, Nair B, Vavilala MS et al (2018) Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat Biomed Eng 2(10):749–760

    Article  Google Scholar 

  24. Luo S, Ge H, Wang J et al (2021) Numerical simulation study on the crack propagation of conglomerate. R Soc Open Sci 8(7):202178

    Article  Google Scholar 

  25. Lv Y, Li H, Zhu X et al (2017) Discrete element method simulation of random voronoi grain-based models. Cluster Comput 20:335–345

    Article  Google Scholar 

  26. Ma X, Zou Y, Li N et al (2017) Experimental study on the mechanism of hydraulic fracture growth in a glutenite reservoir. J Struct Geol 97:37–47

    Article  Google Scholar 

  27. Nie Y, Zhang G, Wen J et al (2021) Cyclic injection to reduce hydraulic fracture surface roughness in glutenite reservoirs. Int J Rock Mech Min Sci 142:104740

    Article  Google Scholar 

  28. Pierce M, Cundall P, Potyondy D, et al (2007) A synthetic rock mass model for jointed rock. In: 1st Canada-US rock mechanics symposium, OnePetro

  29. Potyondy DO, Cundall P (2004) A bonded-particle model for rock. Int J Rock Mech Min Sci 41(8):1329–1364

    Article  Google Scholar 

  30. Rogers JP (2007) New reservoir model from an old oil field: garfield conglomerate pool, pawnee county, kansas. AAPG Bull 91(10):1349–1365

    Article  Google Scholar 

  31. Rui Z, Guo T, Feng Q et al (2018) Influence of gravel on the propagation pattern of hydraulic fracture in the glutenite reservoir. J Petrol Sci Eng 165:627–639

    Article  Google Scholar 

  32. Savitski A, Detournay E (2002) Propagation of a penny-shaped fluid-driven fracture in an impermeable rock: asymptotic solutions. Int J Solids Struct 39(26):6311–6337

    Article  Google Scholar 

  33. Schmidt GA, Pemberton SG (2004) Stratigraphy and paleogeography of a conglomeratic shoreline: the notikewin member of the spirit river formation in the wapiti area of west-central alberta. Bull Can Petrol Geol 52(1):57–76

    Article  Google Scholar 

  34. SHAPLEY L (1953) A value for n-person games. Ann Math Stud 28:307–318

    MathSciNet  Google Scholar 

  35. Sharafisafa M, Sato A, Aliabadian Z (2023) Hydraulic fracture development in conglomerate reservoirs simulated using combined finite-discrete element method. Eng Fract Mech, p 109063

  36. Shentu J, Lin B (2023) A novel machine learning framework for efficient calibration of complex dem model: a case study of a conglomerate sample. Eng Fract Mech, p 109044

  37. Shentu J, Lin B, Dong J, et al (2019) Investigation of hydraulic fracture propagation in conglomerate reservoirs using discrete element method. In: ARMA-CUPB geothermal international conference, OnePetro

  38. Shi X, Qin Y, Xu H et al (2021) Numerical simulation of hydraulic fracture propagation in conglomerate reservoirs. Eng Fract Mech 248:107738

    Article  Google Scholar 

  39. Shimizu H, Murata S, Ishida T (2011) The distinct element analysis for hydraulic fracturing in hard rock considering fluid viscosity and particle size distribution. Int J Rock Mech Min Sci 48(5):712–727

    Article  Google Scholar 

  40. Suckale J (2009) Induced seismicity in hydrocarbon fields. In: Ad Geophys, vol 51. Elsevier, pp 55–106

  41. Tan P, Jin Y, Han K et al (2017) Vertical propagation behavior of hydraulic fractures in coal measure strata based on true triaxial experiment. J Petrol Sci Eng 158:398–407

    Article  Google Scholar 

  42. Tang J, Fan B, Xiao L et al (2021) A new ensemble machine-learning framework for searching sweet spots in shale reservoirs. SPE J 26(01):482–497

    Article  Google Scholar 

  43. Tianxi Y, Feng Y, Peiyao Z et al (2021) Fracture propagating shapes in gravel-supported conglomerate reservoirs of upper wuerhe formation on manan slope, mahu sag. Xinjiang Petrol Geol 42(1):53

    Google Scholar 

  44. Tong Z, Haibo W, Fengxia L et al (2020) Numerical simulation of hydraulic fracture propagation in laminated shale reservoirs. Petrol Explor Develop 47(5):1117–1130

    Article  Google Scholar 

  45. Wang J, Ge H, Liu J et al (2022) Effects of gravel size and content on the mechanical properties of conglomerate. Rock Mech Rock Eng 55(4):2493–2502

    Article  Google Scholar 

  46. Xiangjun L, Jian X, Lixi L, et al (2018) Rock mechanical characteristics and fracture propagation mechanism of sandy conglomerate reservoirs in baikouquan formation of mahu sag. Xinjiang Petrol Geol 39(zk (English)):103

  47. Xiaodong H, Junxiu M, Gang L et al (2019) Analysis of rock mechanics and assessments of hydraulic fracture network in conglomerate reservoirs of mahu oilfield. Xinjiang Petrol Geol 40(6):1

    Google Scholar 

  48. Xu Z, Liu X, Liang L (2022) Numerical investigation of hydraulic fracture propagation morphology in the conglomerate reservoir. Geofluids 2022

  49. Xv C, Zhang G, Yanjun L, et al (2019) Experimental study on hydraulic fracture propagation in conglomerate reservoirs. In: 53rd US rock mechanics/geomechanics symposium, OnePetro

  50. Yan Y, Li S (2021) A study on the influence of the conglomerate mesostructure on fracture failure behavior based on discrete element method. Geofluids 2021:1–13

    Google Scholar 

  51. Yoon JS, Zang A, Stephansson O (2014) Numerical investigation on optimized stimulation of intact and naturally fractured deep geothermal reservoirs using hydro-mechanical coupled discrete particles joints model. Geothermics 52:165–184

    Article  Google Scholar 

  52. Yoon JS, Zimmermann G, Zang A (2015) Discrete element modeling of cyclic rate fluid injection at multiple locations in naturally fractured reservoirs. Int J Rock Mech Min Sci 74:15–23

    Article  Google Scholar 

  53. Yoon JS, Zimmermann G, Zang A et al (2015) Discrete element modeling of fluid injection-induced seismicity and activation of nearby fault. Can Geotech J 52(10):1457–1465

    Article  Google Scholar 

  54. Yushi Z, Shanzhi S, ZHANG S et al (2021) Experimental modeling of sanding fracturing and conductivity of propped fractures in conglomerate: A case study of tight conglomerate of mahu sag in junggar basin, nw china. Petrol Explor Develop 48(6):1383–1392

    Article  Google Scholar 

  55. Zang A, Yoon JS, Stephansson O et al (2013) Fatigue hydraulic fracturing by cyclic reservoir treatment enhances permeability and reduces induced seismicity. Geophys J Int 195(2):1282–1287

    Article  Google Scholar 

  56. Zhang C, Xinmin S, Xiaojun W et al (2020) Origin and depositional characteristics of supported conglomerates. Petrol Explor Develop 47(2):292–305

    Article  Google Scholar 

  57. Zhang F, Mack M (2017) Integrating fully coupled geomechanical modeling with microsesmicity for the analysis of refracturing treatment. J Nat Gas Sci Eng 46:16–25

    Article  Google Scholar 

  58. Zhang F, Damjanac B, Huang H (2013) Coupled discrete element modeling of fluid injection into dense granular media. J Geophys Res Solid Earth 118(6):2703–2722

    Article  Google Scholar 

  59. Zhang G, Sun S, Chao K et al (2019) Investigation of the nucleation, propagation and coalescence of hydraulic fractures in glutenite reservoirs using a coupled fluid flow-dem approach. Powder Technol 354:301–313

    Article  Google Scholar 

  60. Zhang H, Xu K, Liu L, et al (2017) Numerical simulation on hydraulic fracture propagation in glutenite under the effect of unsteady seepage. Oil Drill Prod Technol 39(1000-7393(2017)06-0751-05):751

  61. Zhou J, Zhang L, Pan Z et al (2016) Numerical investigation of fluid-driven near-borehole fracture propagation in laminated reservoir rock using pfc2d. J Nat Gas Sci Eng 36:719–733

    Article  Google Scholar 

  62. Zhuoxin D, Hui Z, Chi A et al (2021) Research on mechanism of influence of gravel on hydraulic crack growth in glutenite fracturing. Drill Prod Technol 44(4):57

    Google Scholar 

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (No. 42277122).

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Authors and Affiliations

  1. College of Information Science and Engineering / College of Artificial Intelligence, China University of Petroleum, Beijing, 102249, China

    Junjie Shentu & Botao Lin

  2. College of Petroleum Engineering, China University of Petroleum, Beijing, 102249, China

    Yan Jin

  3. DynaFrax UG, 14467, Potsdam, Germany

    Jeoung Seok Yoon

Authors
  1. Junjie Shentu
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  2. Botao Lin
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  3. Yan Jin
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  4. Jeoung Seok Yoon
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Contributions

JS contributed to Conceptualization, Methodology, Software, Investigation, and Writing— original draft. BL contributed to Methodology, Resources, and Writing— review and editing. YJ contributed to Project administration, Resources, and Funding acquisition. JSY contributed to Methodology and Software.

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Correspondence to Botao Lin.

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Shentu, J., Lin, B., Jin, Y. et al. Investigation of hydraulic fracture propagation in conglomerate rock using discrete element method and explainable machine learning framework. Acta Geotech. (2024). https://doi.org/10.1007/s11440-024-02317-9

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