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Artificial Neural Network-based Evolution Prediction Model of Propped Fracture Conductivity

Bugao Chen, Shouxin Wang, Xiaolin Wen, Di Qi, Wenhuan Huang, Huixia Ding, Dingxiang Diao, Min Wang, Jiangbo Xu, Chi Chen, Cong Lu

Abstract


Propped fracture conductivity (PFC) is an important parameter required for the accu-rate prediction of hydraulic fracture production performance. In this study, a new model for the prediction of PFC was proposed based on a large volume of experimental data on PFC and back propagation (BP) and artificial neural network (ANN) tools. Our results show that the relative average error between the predicted and measured PFC is 14.31%, which indicates that PFC can be predicted. Our research provides new concepts and methods for the prediction of PFC and can serve as a reference for optimizing fracturing design in unconventional reservoirs and improv-ing the efficacy of fracturing.

Keywords


Hydraulic fracturing; Propped fracture; Conductivity; Artifi cial neural network

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References


[1] ZHANG JINCHUAN, TAO JIA, LI ZHEN, et al. Prospect of Deep Shale Gas Resources in China [J]. Natural Gas Industry, 2021,41(01): 15-28.

[2] ZOU CAINENG, DONG DAZHONG, WANG YUMAN, et al. Shale Gas in China: Characteristics, Challenges and Prospects(Ⅱ) [J]. Petroleum Exploration and Development, 2016,43(02): 166-178.

[3] HOU TENGFEI. Research on the Proppant Uneven Distribution and Conductivity of Complex Fracture Network in Shale Gas Reservoir [D]. China University of Petroleum (Beijing), 2018.

[4] ZHANG R H, ZHANG L H, TANG H Y, et al. A Simulator for Production Prediction of Multistage Fractured Horizontal Well in Shale Gas Reservoir Considering Complex Fracture Geometry [J]. Journal of Natural Gas Science and Engineering, 2019.

[5] YANG L, GE H, SHEN Y, et al. Imbibition Inducing Tensile Fractures and Its Influence on In-situ Stress Analyses: a Case Study of Shale Gas Drilling [J]. Journal of Natural Gas Science and Engineering, 2015,26(Complete): S876352398.

[6] FREDD C N, MCCONNELL S B, BONEY C L, et al. Experimental Study of Hydraulic Fracture Conductivity Demonstrates the Benefits of Using Proppants: SPE Rocky Mountain Regional/Low-Permeability Reservoirs Symposium and Exhibition [C], 2000.

[7] LU CONG, GUO JIANCHUN, WANG YAOWEN, et al. Experiments on Proppant Embedment and Damage to Fracture Conductivity [J]. Natural Gas Industry, 2008(02): 99-101.

[8] VAN D, HAAFKENS R, SCHIPPER B A, et al. Criteria For Proppant Placement and Fracture Conductivity.

[9] ZOU YUSHI, ZHAGN SHICHENG, MA XINFANG, et al. Evaluation of the Effectiveness of Propped Fractures in Shale Gas Reservoirs [J]. Natural Gas Industry, 2012,32(09): 52-55.

[10] QU ZHANQING, HUANG DESHENG, YANG YANG, et al. Experimental Research on Influence Factors of Fracture Conductivity in Gas Reservoir [J]. Fault-Block Oil & Gas Field, 2014,21(03): 390-393.

[11] RAHMAN R M, AWOLEKE O O, GODDARD S D. Meta-analysis of Propped Fracture Conductivity Data—Fixed Effects Regression Modeling [J]. Journal of Petroleum Science and Engineering, 2019,182(5): 106280.

[12] STUNDNER M, AL-THUWAINI J S. How Data-Driven Modeling Methods like Neural Networks can help to integrate different Types of Data into Reservoir Management: SPE Middle East Oil Show [C], 2001.

[13] AL-FATTAH S M, STARTZMAN R A. Neural Network Approach Predicts U.S. Natural Gas Production [J]. SPE Production & Facilities, 2003,18(02): 84-91.

[14] AYALA H. L F, ERTEKIN T. Neuro-simulation Analysis of Pressure Maintenance Operations in Gas Condensate Reservoirs [J]. Journal of Petroleum Science and Engineering, 2007,58(1): 207-226.

[15] WANG LEI, ZHANG SHICHENG, ZHANG WENZONG, et al. Experimental Research on Long-term Conductivity of the Proppant Combination with Different Grain Sizes in Complex Fracturing [J]. Natural Gas Industry, 2005(09): 64-66.

[16] WANG LEI, ZHANG SHICHENG, WEN QINGZHI, et al. Experimental Research on Conductivity of Different Types of Proppants [J]. Drilling & Production Technology, 2012,35(02): 81-83.

[17] JIN ZHIRONG, GUO JIANCHUN, ZHAO JINZHOU, et al. Experimental Study and Analyses on Flow Conductivity of Fracture Proppant under Extreme Conditions [J]. Journal of Oil and Gas Technology, 2007(03): 284-287.

[18] ZHU HUAJUN,ZHAO FENG. Effect of Combined Proppants with Different Particle Sizes on Supporting Fracture Conductivity of Tight Sandstone Reservoir [J]. Chemical Engineering Design Communications, 2022,48(02): 27-29.

[19] MILTON-TAYLER D. Realistic Fracture Conductivities of Propped Hydraulic Fractures: SPE Annual Technical Conference and Exhibition [C], 1993.

[20] JIANG JIANFANG, ZHANG ZHIYONG, XU YUN, et al. Laboratory Research on Propping Fracture Conductivity Based on Gas Logging and Liquid Logging [J]. Oil Drilling & Production Technology, 2008(01): 67-70.

[21] WANG YUDI, REN YI, ZHEGN BIN, et al. Study on the Influencing Fractors of Proppant Embedment in Hydraulic Fracturing of Shale Reservoir [J]. Drilling & Production Technology, 2020,43(04): 129-132.

[22] SHAUGHNESSY C M, KUNZE K R. Understanding Sandstone Acidizing Leads to Improved Field Practices [J]. Journal of Petroleum Technology, 1981,33(07): 1196-1202.

[23] ZHANG YI, ZHOU ZHIQI. Experimental Study on the Short-term Conductivity of Ceramsite Proppant for Fracturing [J]. Journal of Xi’an Shiyou University(Natural Science Edition), 2000(05): 39-41.

[24] ZHAGN YI, MA XIGNQIN, JIN BAOJUN. Long Term Fracture Conductivity of Fracturing Proppant [J]. Oil Drilling & Production Technology, 2004(01): 59-61.

[25] WENQIGNZHI, ZHAGN SHICHEGN, LI LINDI. Experimental Research of Long-term Flow Capacity of Propping Fractures in The Low Permeability Oil Reservoir [J]. Petroleum Geology and Recovery Efficiency, 2006(02): 97-99.

[26] YANG ZHENZHOU,CHEN MIAN, XUYUN, et al. An Experimental Study of Long-term Flow Conductivity of Volcanic Rock

Core Plate [J]. Natural Gas Industry, 2010,30(10): 42-44.

[27] LIU XIANG, YI XIANGYI, WANG YANLONG, et al. Influence of Testing Time on Core Conductivity under Different Closing Pressures [J]. Petroleum Geology and Engineering, 2017,31(06): 119-120.

[28] YUAN XU, XU DONGJIN, CHEN SHIHAI, et al. The Damage of Fracturing Fluid Intrusion to the Conductivity of Shale

Reservoir [J]. Science Technology and Engineering, 2020,20(09): 3591-3597.

[29] BI WENTAO, SHEN YATING, LUYONGJUN, et al. Effect of Reservoir Stress Fluctuation on Conductivity of Propped Fractures [J]. Oil Drilling & Production Technology, 2015,37(06): 78-80.

[30] LIU TIANSHU. The Research and Application on BP Neural Network Improvement [D]. Northeast Agricultural

University, 2011.

[31] QI DEHU, KANG CHANGJI. On Design of the BP Neural Network[J]. Computer Engineering and Design, 1998(02): 47-49.

[32] LI XIAOFENG, LIU GUANGZHONG. The lmprovement of BP Algorithm and lts Application [J]. Advanced Engineering Sciences, 2000(02): 105-109.

[33] ZHOU ZHENG. Survey of Current Progress in BP Neural Network [J]. Shanxi Electronic Technology, 2008(02): 90-92.

[34] LI YOUKUN. Analysis and Improvement Applications of BP Neural Network [D]. Anhui University of Technology, 2012.

[35] MCDANIEL B W. Conductivity Testing of Proppants at High Temperature and Stress: SPE California Regional Meeting [C], 1986.

[36] FREDD C N, MCCONNELL S B, BONEY C L, et al. Experimental Study of Hydraulic Fracture Conductivity Demonstrates the Benefits of Using Proppants: SPE Rocky Mountain Regional/Low-Permeability Reservoirs Symposium and Exhibition [C], 2000.

[37] RIVERS M, ZHU D, HILL A D. Proppant Fracture Conductivity with High Proppant Loading and High Closure Stress [M]. Proppant Fracture Conductivity with High Proppant Loading and High Closure Stress, 2012.

[38] AWOLEKE O O, ROMERO J D, ZHU D, et al. Experimental Investigation of Propped Fracture Conductivity in Tight Gas Reservoirs Using Factorial Design [M]. Experimental Investigation of Propped Fracture Conductivity in Tight Gas Reservoirs Using Factorial Design, 2012.

[39] ZHANG J, KAMENOV A, ZHU D, et al. Laboratory Measurement of Hydraulic-Fracture Conductivities in the Barnett Shale [J]. SPE Production & Operations, 2014,29(03): 216-227.

[40] QU ZHANQING, HUANG DESHENG, YANG YANG, et al. Experimental Study on the Factors Influencing the Fracture Conductivity of Fractured Gas Reservoirs [J]. Fault-Block Oil & Gas Field, 2014,21(03): 390-393.

[41] BRIGGS K, HILL A D, ZHU D, et al. The Relationship Between Rock Properties and Fracture Conductivity in the Fayetteville Shale: SPE Annual Technical Conference and Exhibition [C], 2014.

[42] LI K, GAO Y, LYU Y, et al. New Mathematical Models for Calculating Proppant Embedment and Fracture Conductivity [J]. Spe Journal, 2015,20(03): 496-507.

[43] MCGINLEY M, ZHU D, HILL A D. The Effects of Fracture Orientation and Elastic Property Anisotropy on Hydraulic Fracture Conductivity in the Marcellus Shale: Spe Technical Conference & Exhibition [C].

[44] BI WENTAO, LU YONGJU, MENG CHUANYOU, et al. Experimental Study on the Influence Capacity of Propped Fractures in Shale Reservoirs [J]. Fault-Block Oil & Gas Field, 2016,23(01): 133-136.

[45] PEREZ PENA P, ZHU D, HILL A D. The Effect of Rock Properties on Fracture Conductivity in The Marcellus Shale: SPE Asia Pacific Hydraulic Fracturing Conference [C], 2016.

[46] CHEN C, WANG S, LU C, et al. Experimental Study on The Effectiveness of Using 3D Scanning and 3D Engraving Technology to Accurately Assess Shale Fracture Conductivity [J]. Journal of Petroleum Science and Engineering, 2021: 109493.




DOI: http://dx.doi.org/10.18686/ahe.v7i19.9386

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