Skip to main content
SpringerLink
Log in

Fuzzy logic and grey clustering analysis hybrid intelligence model applied to candidate-well selection for hydraulic fracturing in hydrocarbon reservoir

  • Original Paper
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

Abstract

Candidate-well selection (CWS) aims to recognize wells that have potential for higher production after hydraulic fracturing stimulation in petroleum development process, which is natural nonlinear, strong-coupling, uncertain, multi-input, and single-output mathematical problem. CWS hybrid intelligence model is developed by integrating widely applied fuzzy logic systems (FLS), namely, type-2 Takagi-Sugeno-Kang (T2-TSK) FLS, with grey clustering analysis (GCA) for hydraulic fracturing in H gas field of Sichuan Basin, one of the large natural gas field in Southwest of China. The T2-TSK FLS is constructed based on field data involving 49 fractured wells, while the GCA is used to determine the dominant input variables data, and these dominant variables have great influence on post-fractured production. Then we use 39 fractured wells data to train the T1-TSK and T2-TSK FLS to predict post-fractured production. The accuracy of the trained models is validated by comparing predicted post-fractured production with real post-fractured production for the rest of the 10 fractured wells. The evaluation results for the gas field case demonstrate that the T2-TSK FLS is superior to the traditional T1-TSK FLS for CWS using the same input data. The T2-TSK FLS developed in this paper gives high accuracy predicted post-production in H gas field, which is very helpful in selecting the candidate well exactly for hydraulic fracturing.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Al-Mudhafer WJM, Alabbas M (2012) Application of a hybrid system of genetic algorithm & fuzzy logic as optimization techniques for improving oil recovery in a sandstone reservoir in Iraq. Soc Petrol Eng. https://doi.org/10.2118/149982-MS

  • Anifowose F, Abdulraheem A (2011) Fuzzy logic-driven and SVM-driven hybrid computational intelligence models applied to oil and gas reservoir characterization. J Nat Gas Sci Eng 3(3):505–517

    Article  Google Scholar 

  • Anifowose F, Labadin J, Abdulraheem A (2013) A least-square-driven functional networks type-2 fuzzy logic hybrid model for efficient petroleum reservoir properties prediction. Neural Comput & Applic 23:179–190. https://doi.org/10.1007/s00521-012-1298-2

    Article  Google Scholar 

  • Barree RD, Miskimins JL, Svatek KJ (2018) Reservoir and completion considerations for the refracturing of horizontal wells. SPE Prod Operations 33(01):1–11

    Article  Google Scholar 

  • Barrett JD (2007) Advanced fuzzy logic technologies in industrial applications. Springer London, 494-495

  • Castillo O and Melin P (2012) Type-2 fuzzy logic systems. Springer Berlin Heidelberg, 7-12

  • Cheng K, Wei Y, Wu W and Holditch SA (2010) A novel optimization model for analyzing production data. Society of Petroleum Engineers

  • Crowell RF and Jennings AR (1978) A diagnostic technique for restimulation candidate selection, SPE Annual Fall Technical Conference and Exhibition. Society of Petroleum Engineers

  • Deng JL (1989) Introduction to grey system theory. J Grey Syst 1(1):1–24

    Google Scholar 

  • East L, Craie D (2006) Refracs reverse production dips. Hart’s E & P 79(11):45–47

    Google Scholar 

  • Grieser WV, Shelley RF, Johnson BJ, Fielder EO, Heinze JR, Werline JR (2008) Data analysis of Barnett shale completions. SPE J 13(03):366–374

    Article  Google Scholar 

  • Guo J, Xiao Y, Zhu H (2014) A new method for fracturing wells reservoir evaluation in fractured gas reservoir. Math Probl Eng:2014

  • Khan WA, Sudirman SB, Qahtani GD (2017) Integrating field performance and history matching uncertainties using fuzzy logic to minimize well-placement risks. Soc Petrol Eng. https://doi.org/10.2118/187986-MS

  • Khosravanian R, Sabah M, Wood DA, Shahryari A (2016) Weight on drill bit prediction models: Sugeno-type and Mamdani-type fuzzy inference systems compared. J Nat Gas Sci Eng 36:280–297

    Article  Google Scholar 

  • Klir GJ, Yuan B (1995) Fuzzy sets and fuzzy logic: theory and applications. Int Encycl Human Geogr:283–287

  • Kong L, Ostadhassan M, Tamimi N, Samani S, Li C (2019) Refracturing: well selection, treatment design, and lessons learned—a review. Arab J Geosci 12:117

    Article  Google Scholar 

  • Krasey R (1988) High grading fracture candidate selection. J Can Pet Technol 27(04):50–54

    Article  Google Scholar 

  • Lantz TG, Greene DT, Eberhard MJ, Norrid RS and Pershall RA (2007) Refracture treatments proving successful in horizontal Bakken wells; Richland Co, MT, Rocky Mountain Oil & Gas Technology Symposium. Society of Petroleum Engineers

  • Ma X, Sun Z, He Y (1998) Analysis and design of fuzzy controller and fuzzy observer. Fuzzy Syst IEEE Trans 6(1):41–51

    Article  Google Scholar 

  • McVey DS, Mohaghegh S (1996) Identification of parameters influencing the response of gas storage wells to hydraulic fracturing with the aid of a neural network. SPE Comput Appl 8(02):54–57

    Google Scholar 

  • Mendel JM (2007) Type-2 fuzzy sets and systems: an overview. IEEE Comput Intell Mag 2(1):20–29

    Article  Google Scholar 

  • Mohaghegh S, Reeves S and Hill D (2000) Development of an intelligent systems approach for restimulation candidate selection, SPE/CERI Gas Technology Symposium. Society of Petroleum Engineers

  • Mohaghegh SD, Gaskari R, Popa AS, Salehi IA, Ameri S (2005) Analysis of best hydraulic fracturing practices in the golden trend fields of Oklahoma. Soc Petrol Eng. https://doi.org/10.2118/95942-MS

  • Okwu MO, Nwachukwu AN (2019) A review of fuzzy logic applications in petroleum exploration, production and distribution operations. J Pet Explor Prod Technol 9:1555–1568. https://doi.org/10.1007/s13202-018-0560-2

    Article  Google Scholar 

  • Papadakis SE, Theocharis JB (2002) A GA-based fuzzy modeling approach for generating TSK models. Fuzzy Sets Syst 131(2):121–152

    Article  Google Scholar 

  • Reeves SR et al (1999) Restimulation of tight gas sand wells in the Rocky Mountain region, SPE Rocky Mountain regional meeting, 361-374

  • Reeves SR et al (2000) Benchmarking of restimulation candidate selection techniques in layered, tight gas sand formations using reservoir simulation, SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers

  • Ren Q, Balazinski M, Baron L, Jemielniak K, Botez R, Achiche S (2014) Type-2 fuzzy tool condition monitoring system based on acoustic emission in micromilling. Inf Sci 255(1):121–134

    Article  Google Scholar 

  • Rui Z, Guo T, Feng Q, Zhanqing Q, Qi N, Gong F (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 

  • Shen B and Zhang Y (2013) “Grey correlation analysis of tropical cyclone landing time,” 2013 Fourth International Conference on Digital Manufacturing & Automation, Qingdao, pp. 641-644, https://doi.org/10.1109/ICDMA.2013.152

  • Shi T, Jiang W, Luo P (2018) A method of clustering ensemble based on grey relation analysis. Wirel Pers Commun 103:871–885. https://doi.org/10.1007/s11277-018-5484-0

    Article  Google Scholar 

  • Smith MB (2006) Hydraulic fracturing: “THE” Multidisciplinary Technology

  • Sugeno M, Kang GT (1988) Structure identification of fuzzy model. Fuzzy Sets Syst 28(1):15–33

    Article  Google Scholar 

  • Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. Syst Man Cybernet IEEE Trans 1:116–132

    Article  Google Scholar 

  • Tsai M, Hsu F (2010) Application of grey correlation analysis in evolutionary programming for distribution system feeder reconfiguration. IEEE Trans Power Syst Pwrs 25(2):1126–1133

    Article  Google Scholar 

  • Wang Y, Salehi S (2014) Refracture candidate selection using hybrid simulation with neural network and data analysis techniques. J Pet Sci Eng 123:138–146

    Article  Google Scholar 

  • Witten IH, Frank E (2005) Data mining: practical machine learning tools and techniques. Biomed Eng Online 5 51(1):95–97

    Google Scholar 

  • Xiang-jun X and Ting Y (2014) Application of T-S fuzzy model in candidate-well selection for hydraulic fracturing. Fuzzy Information & Engineering and Operations Research & Management. Springer, 563-570

  • Xiao Y, Guo J and Songgen S (2015) A comparison study of utilizing optimization algorithms and fuzzy logic for candidate-well selection, SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition. Society of Petroleum Engineers

  • Xiong H and Holditch SA (1995) Using a fuzzy expert system to choose target well and formations for stimulation. Artificial intelligence in the petroleum industry: symbolic and computational applications. Editions Technip, Paris: 361-379

  • Yang E (2009) Selection of target wells and layers for fracturing with fuzzy mathematics method, Fuzzy Systems and Knowledge Discovery, 2009. FSKD'09. Sixth International Conference on. IEEE, pp. 366-369

  • Zoveidavianpoor M and Gharibi A (2014) Applications of type-2 fuzzy logic system: handling the uncertainty associated with candidate-well selection for hydraulic fracturing. Neural Computing and Applications: 1-21

  • Zoveidavianpoor M, Samsuri A, Shadizadeh SR (2012) Fuzzy logic in candidate-well selection for hydraulic fracturing in oil and gas wells: a critical review. Int J Phys Sci 7(26):4049–4060

    Google Scholar 

  • Zoveidavianpoor M, Samsuri A, Shadizadeh SR (2012a) A review on conventional candidate-well selection for hydraulic fracturing in oil and gas wells. Int J Eng Technol 2(1):51–60

    Google Scholar 

  • Zoveidavianpoor M, Samsuri A and Shadizadeh SR (2012b) Development of a fuzzy system model for candidate-well selection for hydraulic fracturing in a carbonate reservoir, SPE Oil and Gas India Conference and Exhibition. Society of Petroleum Engineers

  • Zoveidavianpoor M, Samsuri A and Shadizadeh SR (2013) Hydraulic fracturing candidate-well selection by interval type-2 fuzzy set and system, IPTC 2013: International Petroleum Technology Conference

Download references

Funding

This study was supported by China Postdoctoral Science Foundation (No.2017 M623063), Open Fund of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Southwest Petroleum University) (No. PLN201608), Sichuan Postdoctoral Special Fund, Pre-teacher Postdoctoral Found of College of Petroleum Engineering at SWPU.

Author information

Authors and Affiliations

  1. State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu, 610500, Sichuan, China

    Bo Gou & Chuan Wang

  2. School of Science, Southwest Petroleum University, Chengdu, 610500, Sichuan, China

    Ting Yu

  3. Sinopec Southwest Petroleum Engineering co. LTD, Deyang, 618000, Sichuan, China

    Kunjie Wang

Authors
  1. Bo Gou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ting Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kunjie Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Bo Gou or Ting Yu.

Additional information

Responsible Editor: Santanu Banerjee

Appendix

Appendix

Table 8 Primary input and output original data for candidate-well selection
Full size table
Table 9 MFs initial values m1 for T2-TSK FLS
Full size table
Table 10 MFs initial values m2 for T2-TSK FLS
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gou, B., Wang, C., Yu, T. et al. Fuzzy logic and grey clustering analysis hybrid intelligence model applied to candidate-well selection for hydraulic fracturing in hydrocarbon reservoir. Arab J Geosci 13, 975 (2020). https://doi.org/10.1007/s12517-020-05970-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12517-020-05970-y

Keywords

Search

Navigation