A prediction model of specific productivity index using least square support vector machine method

Chunxin Wu, Shaopeng Wang, Jianwei Yuan, Chao Li, Qi Zhang

Abstract view|222|times       PDF download|150|times


In the design of oilfield development plans, specific productivity index plays a vital role. Especially for offshore oilfields, affected by development costs and time limits, there are shortcomings of shorter test time and fewer test sampling points. Therefore, it is very necessary to predict specific productivity index. In this study, a prediction model of the specific productivity index is established by combining the principle of least squares support vector machine (LS-SVM) with the calculation method of the specific productivity index. The model uses logging parameters, crude oil experimental parameters and the specific productivity index of a large number of test well samples as input and output items respectively, and finally predicts the specific productivity index of non-test wells. It reduces the errors caused by short training time, randomness of training results and insufficient learning. A large number of sample data from the Huanghekou Sag in Bohai Oilfield were used to verify the prediction model. Comparing the specific productivity index prediction results of LS-SVM and artificial neural networks (ANNs) with actual well data respectively, the LS-SVM model has a better fitting effect, with an error of only 3.2%, which is 12.1% lower than ANNs. This study can better reflect the impact of different factors on specific productivity index, and it has important guiding significance for the evaluation of offshore oilfield productivity.

Cited as: Wu, C., Wang, S., Yuan, J., Li, C., Zhang, Q. A prediction model of specific productivity index using least square support vector machine method. Advances in Geo-Energy Research, 2020, 4(4): 460-467, doi: 10.46690/ager.2020.04.10


Least square support vector machines, productivity evaluation, specific productivity index, Huanghekou Sag, Bohai Oilfield

Full Text:



Al-Rbeawi, S. The optimal reservoir configuration for maximum productivity index of gas reservoirs depleted by horizontal wells under Darcy and non-Darcy flow conditions. J. Nat. Gas Sci. Eng. 2018, 49: 179-193.

Bai, X., Jiang, H., Wang, S., et al. Multi-step forecasting model of dynamic index for oilfield development. Fault Block Oil and Gas Field 2010, 17(3): 345-347. (in Chinese)

Boyrdet, D. Pressure behavior of layered reserviors with crossflow. Paper SPE 13628 Presented at the SPE California Regional Meeting, Bakersfield, California, 27-29 March, 1985.

Chen, C., Wang, Z., Niu, W., et al. Quantitative calculation method of thief zone based on least square support vector machine. Fault Block Oil and Gas Field 2015, 22(1): 74-77. (in Chinese)

Chen, Z., Zheng, Y. Productivity evaluation and optimization of exploitation methods of S reservoir. Journal of Southwest Petroleum University 2012, 34(2): 111-118. (in Chinese)

Cheng, M.L., Leal, M.A., Mcnaughton, D. Productivity prediction from well logs in variable grain size reservoirs cretaceous Qishn formation, republic of Yemen. Log Anal. 1999, 40(1): 34-32.

Dong, P., Liao, X., Chen, Z., et al. An improved method for predicting CO2 minimum miscibility pressure based on artificial neural network. Adv. Geo-Energy Res. 2019, 3(4): 355-364.

Li, P., Hao, M., Hu, J., et al. A new production decline model for horizontal wells in low-permeability reservoirs. J. Pet. Sci. Eng. 2018, 171: 340-352.

Liu, S., Zhang, L., Zhang, K., et al. A simplified and efficient method for water flooding production index calculations in low permeable fractured reservoir. J. Energy Resour. Technol. 2019, 141(11): 112905.

Luo, X., Zhao, C., Liu, Y. Study on deliverability evaluation of offshore heavy oil field at initial stage of production. Fault Block Oil and Gas Field 2011, 18(5): 630-633. (in Chinese)

Qian, W., Yin, T., Hou, G. A new method for clastic reservoir prediction based on numerical simulation of diagenesis: A case study of the Ed1 clastic sandstones in the Bozhong depression, Bohai Bay Basin, China. Adv. Geo-Energy Res. 2019, 3(1): 82-93.

Rinaldi, Harris, H.D. Prediction of specific productivity index for sihapas formation in uncored wells of minas field using limited available core and log data. Paper SPE 38037 Presented at the SPE Asia Pacific Oil and Gas Conference, Kuala Lumpur, Malaysia, 14-16 April, 1997.

Stalgorova, E.L. Practical analytical model to simulate production of horizontal wells with branch fractures. Paper SPE 162515 Presented at the SPE Canadian Unconventional Resources Conference, Calgary, Canada, 30 October-1 November, 2012.

Stalgorova, K., Mattar, L. Analytical model for unconventional multifractured composite systems. SPE Reserv. Eval. Eng. 2013, 16(3): 246-256.

Suykens., J.A.K., Vandewalle, J. Least squares support vector machines. Int. J. Circuit Theory Appl. 2002, 27(6): 605-615.

Tang, E., Han, G., Zhang, X., et al. The impact of unbalanced reservoir pressure on lateral well deliverability. Special Oil and Gas Reservoirs 2009, 16(4): 60-62. (in Chinese)

Torcuk, M.A., Kurtoglu, B., Fakcharoenphol, P., et al. Theory and application of pressure and rate transient analysis in unconventional reservoirs. Paper SPE 166147 Presented at the SPE Annual Technical Conference and Exhibition, New Orleans, USA, 30 September-2 October, 2013.

Wang, J., Xu, J., Wang, Y., et al. Productivity of hydraulically-fractured horizontal wells in tight oil reservoirs using a linear composite method. J. Pet. Sci. Eng. 2018, 164: 450-458.

Wang, W., Shahvali, M., Su, Y. A semi-analytical model for production from tight oil reservoirs with hydraulically fractured horizontal wells. Fuel 2015, 158: 612-618.

Wang, W., Su, Y., Sheng, G., et al. A mathematical model considering complex fractures and fractal flow for pressure transient analysis of fractured horizontal wells in unconventional oil reservoirs. J. Nat. Gas Sci. Eng. 2015, 23: 139-147.

Wang, Z., Sun, B., Sun, X. Calculation of temperature in fracture for carbon dioxide fracturing. SPE J. 2016, 21(5): 1491-1500.

Wei, G., Liu, J., Sun, J., et al. Study on nonlinear multi-functional sensor signal reconstruction method based on LS-SVM. Acta Automatica Sinica 2008, 34(8): 869-875. (in Chinese)

Xie, X., Zhou, B., Wen, F. A research method of production capacity for new oil field. Petroleum Exploration and Development 1997, 24(4): 57-60. (in Chinese)

Xu, Y., Ren, Y., Ding, L., et al. Prediction of profile control result using support vector machihes. Fault Block Oil and Gas Field 2007, 14(2): 50-52. (in Chinese)

Yuan, B., Moghanloo, G.R. Analytical modeling improved well performance by nanofluid pre-flush. Fuel 2017, 202: 380-394.

Yuan, B., Moghanloo, G.R., Zheng, D. A novel integrated production analysis workflow for evaluation, optimization and predication in shale plays. Int. J. Coal Geol. 2017, 180: 18-28.

Zhang, F., Xu, N., Yu, T., et al. Impact of vertical heterogeneity on well productivity in multilayered low-permeable reservoir. Special Oil and Gas Reservoirs 2002, 9(4): 39-42. (in Chinese)

Zhang, L., Tian, J., Zhu, G. Evaluation methods for initial productivity of directional wells in offshore fault block oilfields. Petroleum Drilling Techniques 2015, 43(1): 111-116. (in Chinese)

Zhang, L., Zhang, J., Li, Y., et al. The research and application of new horizontal well productivity formula based on pseudo steady state time. Science Technology and Engineering 2014, 14(32): 38-42. (in Chinese)

Zhong, Y., Zhang, Z., Zhu, H. A new method to predict production of oilfields in ultrahigh water-cut stage. Fault Block Oil ang Gas Field 2011, 18(5): 641-644. (in Chinese)

Zhou, K., Yang, C., Mu, X., et al. Intelligent prediction algorithm for floatation key parameters based on image features extraction. Control and Decision 2009, 24(9): 1300-1305.

Zhu, S., Li, H., Sun, Z., et al. Unsteady productivity model of mult-stage fractured horizontal well in low permeability gas reservoir. Journal of Shenzhen University Science and Engineering 2014, 31(03): 266-272. (in Chinese)

DOI: https://doi.org/10.46690/ager.2020.04.10


  • There are currently no refbacks.

Copyright (c) 2020 The Author(s)

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Copyright ©2018. All Rights Reserved