Comparison of Oil Well Productivity Evaluation Methods Based on Different Data

Qin ZHU, Dong LIU, Dayong CHENG, Xinran WANG, Xiaolin ZHU

Abstract


Accurate prediction of well productivity is important to take proper engineering measures, and it has an important value on the increased cost of oil and gas exploration and development. Variety of different reservoir evaluation methods for well productivity based on different data, such as seismic data, mud logging data, well logging data, well testing data, formation test data etc. was compared in this paper, and the scale, scope and implications of the methods evaluation were also described. This provides a theoretical basis for comprehensive reservoir productivity assessment research, and can be a guidance for comprehensive evaluation of reservoir productivity based on variety of test data.


Keywords


Productivity assessment; Seismic productivity; Logging productivity; Well test productivity; Cable test productivity

Full Text:

PDF

References


‘Well Testing Manual’ writing group. (1992). Well testing manual (PP. 354-534). Beijing: Petroleum Industry Press.
Andre, C. A. D., Canas, J. A., & Low, S. (2005). Rigorous approach for viscous oil productivity forecast before well completion. (Report No.SPE 94837). Rio de Janeiro, Brazil : Society of Petroleum Engineers.
Chen, Q. D., & Guo, A. H. (1998). Discussion on seismic data prediction permeability problem. Oil Geophysical Prospecting, 33(s2), 86-90.
Cheng, M. L., & Yang, B. (1999). Productivity prediction from well logs in variable grain size reservoir cretaceous Qishn formation, republic of Yemen. Log Analyst, 40 (1), 24-32.
Cheng, S. Q., Li, X. F., An, X. P., & Liu, S. M. (2005). Research on reservoir flow potential by using high duty pump formation tester. Drilling & Production Technology, 28(5), 73-74.
Du, H. B., & Guo, Q. Z. (2005). Application of probabilistic neural network in reservoir productivity forecasting. Petroleum Instruments, 19(4), 54-56.
Gao, J. H. (2002). The method research of the reservoir productivity estimate based on logging data (Master’s thesis). Chengdu University of Technology, Sichuan.
Gao, Y., Tang, C. Y., Zhang, H. T., Liu, L., Li, J., & Yang, Z. Z. (2016). Well test analysis on abnormal high-pressure and low-permeability reservoirs and its application. Journal of Yangtze University (Natural Science ed.), 13(35), 47-52.
Gu, G. X., & Ding, J. (1993). A method for forecasting the productivity of an oil-producing pay by using logging interpretation data. Journal of Jianghan Petroleum Institute, (15), 43-49.
Guan, F. J., Li, X. F., Xu, H. B., Tang, E. G., & Yan, M. (2008). New method of deliverability forecasting by wireline formation testing with multi-well parameters. Journal of China University of Petroleum (Natural Science ed.), 32(2), 73-75.
He, X. Q. (2002). Application of new logging technology in productivity prediction of Oolitic Beach gas reservoir of Feixianguan formation in northeastern Sichuan. Natural Gas Exploration & Development, 25(4), 43-47.
Hu, Y. J. (2001). The study on the quantitative evaluation and prediction of the specific productivity index in clastic reservoirs by well logs (Doctoral dissertation). Research Institute of Petroleum Exploration and Development, Beijing.
Huang, X. P., Chen, Z. B., Chen, H. B., Zhang, Y. H., Li, M., & Xu, S. N. (2006). Application of compensatory fuzzy neural network model in reservoir productivity prediction. Inner Mongolia Petrochemical Industry. (10), 109 - 111.
Jean-François, M., Pablo, S., Pedro, A., & Riano, J. M. (2002). Reducing completion costs and enhancing productivity using nuclear magnetic resonance logs and formation tester data. (Report No.SPE 74362). doi: 10.2118/74362-MS
Lang, D. S., & Guo, S. S. (1995). Research on predicting reservoir productivity using pyrolysis parameters. Mud Logging Engineering, 6(4), 6-8.
Li, C. B., Song, J. P., & Xia, K. W. (2006). Using RBF neural networks adaptive adjustment algorithm to predict production capacity of reservoir. Oil Geophysical Prospecting, 41(1), 53-57.
Li, Q. D., Zhang, Y. X., Zhou, X. B., & Zhong, Z. L. (2001). Productivity prediction technology of heterogeneous reservoir in high water cut stage. Foreign Well Logging Technology, 16(4), 13-19.
Lin, L. (1994). Data interpretation theory and geological application of cable formation tester (pp. 25-60). Beijing: Petroleum Industry Press.
Lin, Z. X., Feng, C. Z., Li, H. L., Yan, H. P., Wang, H. B., & Hu, X. L. (2017). Application of envelope size method to productivity prediction. Well Logging Technology, 41(2), 226-230.
Liu, C. B., Schwab, K., Lin, X. J., et al. (2000). Layer productivity prediction based on wireline logs and formation tester dat. (Report No. SPE 64655).
Liu, X. H., Liu, J., Li, Z. L., Gao, M., Li, S. J., Zhuge, Y. Y., & Liu, H. X. (2003). Study on productivity forecast of sand reservoir. Well Logging Technology, 27(4), 325- 329.
Lu, Y., Sundararajan, N., & Saratchandran, P. (1998). Performance evaluation of sequential minimal radial basis function (RBF) neural network learning algorithm. IEEE Transactions on Neural Networks, 9(2), 308-318.
Luo, L., Yao, S. X., Ren, X. G., & Yang, J. (2002). Applications of numerical calculation and model identification technology based on neural network in log interpretation. Well Logging Technology, 26 (5), 364 -368.
Mao, Z. Q., & Li, J. F. (2000). Method and models for productivity prediction of hydrocarbon reservoirs. ACTA Petrolei Sinica, 21(5), 58-61.
Meng, Y. X., Li, X. F., Du, H., & Sui, X. X. (2010). A New Accurate Calculation Method of Reservoir Production by Using Wireline for Mation Test Data. Drilling & Production Technology, 33 (6), 64-67.
Ouyang, J. (1994). Well log interpretations and reservoir description (pp. 148-152). Peking: Petroleum Industry Publishing House.
Peng, D. L., Xu, S. J., & Wang, R. C. (1999). Study on the application of the neural network expert system to the prediction of daily output per well. Experimental Petroleum Geology, 21(2), 20-22.
Rinaldi, & Djauhari, H. H. (1997). Prediction of Specific Productivity Index for Sihapas Fozmation in Uncured Wells of Minas Field Using Limited Available Gore and Log Data. Society of Petroleum Engineers, 183-190. doi: https://doi.org/10.2118/38037-MS
Tan, C. Q., Song, Z. Q., & Wu, S. B. (2001). Comprehensive evaluation of reservoir production capacity through grey association analysis. Henan Petroleum, 15(2), 20-23.
Tan, C. Q., Song, Z. Q., & Wu, X. H. (2001). The predicting method of reservoir production capacity by grey system theory. Systems Engineering-theory & Practice, 10(10), 101-106.
Tong, K. J., Shan, Y. M., Liao, T. Q., & Wang, D. C. (2008). Statistics-based reservoir-productivity ranking prediction by well logging. Natural Gas Exploration and Development, 31(2), 40-43.
Wang, Y., & Zhang, J. L. (2007). Reservoir productivity prediction method based on seismic data. Journal of Oil and Gas Technology, 29(3), 87-89.
Wang, Z., Xu, J. W., & Gu, B. (2009). Low porosity and low permeability reservoir deliverability based on well logging data. Journal of Southwest Petroleum University (Science & Technology ed.), 31(6), 51-55.
Whitehead, B. A., & Choate, T. D. (1996). Cooperative-competitive genetic evolution of radial basis function centers and widths for time series prediction. IEEE Transactions on Neural Networks, 7(4), 869-880.
Xia, H. R. (2013). Application of pressure build up test interpretation in an oil well in the deep sea of West Africa. Petroleum Geology and Engineering, 27(1), 100-103.
Xiao, C. X., Xiao, C. L., Li, Y. Z., Hu, H. W., & Chen, B. X. (1999). Log evaluation and prediction of gas reservoir productivity. Oil & Gas Geology, 20(4), 279-282.
Xu, L. Q., & Li, X. S. (2005). Comparative analysis of different deliverability evaluation methods. Well Testing, 14(6), 15-17.
Xu, Y. Q., Li, Z. B., & Lu, J. A. (1999). Study on the prediction methods of oil-gas reservoir performance with well logging. Journal of Changchun University of Science and Technology, 29(2), 179-183.
Yang, C. B., Nie, L. S., & Sun, P. Y. (2003). A study of using improved fuzzy neural network to forecast reservoir productivity. Journal of Changchun University of Science and Technology, 33(1), 48-50.
Yang, X. W., Fan, H. H., Liao, X. W., & Zhao, L. X. (2008). Reservoir production prediction based on wireline formation test. Offshore Oil, 28(2), 105-107.
Yang, Y., Kang, Y. L., Guo, C. H., & Yang, Y. J. (2006). Application of GA-BP neural network model in production prediction of Luotou Gas Field. Petroleum Geology and Recovery Efficiency, 13(6), 84-86.
Zhang, C. (2007) Summary of a method for reservoir productive capacity prediction with well logging data. World Well Logging Technology, 22(2), 23-25
Zhang, F., Zhang, X., Zhang, Le., Hao, Y. M., & Shan, W. (2007). Prediction of reservoir productivity by support vector machine. Journal of Southwest Petroleum University, 29(3), 24-27.
Zhang, W. C., Zhu, X. M., & Wang, C. X. (2002). Comparison and Analysis of RFT and FMT Pressure Test. Well Testing, 11(5), 41-44.
Zhao, J., Hou, K. J., Jiang, Z. G., Wu, Y. D., & Yang, L. (2009). A new method for productivity prediction in low porosity and permeability reservoir. Petroleum Geology and Recovery Efficiency, 16 (1), 72-74.
Zhao, L. X., & Liao, X. W. (2009). An approach for reservoir yield prediction using WFT and its applications. Well Logging Technology, 33(9), 283-288.
Zhu, C. S., Cheng, L. S., Yang, Z. H., & Chen, P. (2008). Flowing characteristics in extra-low permeability sandstone reservoirs. Petroleum Geology and Recovery Efficiency, 15(2), 102-104.
Zhu, S. Z., Ren, J. H., & Wei, L. B. (1996). Quality evaluation and productivity prediction of single well reservoir. Jianghan Petroleum Science and Technology, 6(3), 36-40.




DOI: http://dx.doi.org/10.3968/10608

Refbacks

  • There are currently no refbacks.


Copyright (c) 2018 Qin Zhu, Liu Dong, Dayong Cheng, Xinran Wang, Xiaolin Zhu

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

Share us to:   

Reminder

  • How to do online submission to another Journal?
  • If you have already registered in Journal A, then how can you submit another article to Journal B? It takes two steps to make it happen:

1. Register yourself in Journal B as an Author

  • Find the journal you want to submit to in CATEGORIES, click on “VIEW JOURNAL”, “Online Submissions”, “GO TO LOGIN” and “Edit My Profile”. Check “Author” on the “Edit Profile” page, then “Save”.

2. Submission

  • Go to “User Home”, and click on “Author” under the name of Journal B. You may start a New Submission by clicking on “CLICK HERE”.

We only use three mailboxes as follows to deal with issues about paper acceptance, payment and submission of electronic versions of our journals to databases:
caooc@hotmail.com; aped@cscanada.net; aped@cscanada.org

 Articles published in Advances in Petroleum Exploration and Development are licensed under Creative Commons Attribution 4.0 (CC-BY). 

ADVANCES IN PETROLEUM EXPLORATION AND DEVELOPMENT Editorial Office

Address:1055 Rue Lucien-L'Allier, Unit #772, Montreal, QC H3G 3C4, Canada.

Telephone: 1-514-558 6138
Website: Http://www.cscanada.net
Http://www.cscanada.org
E-mail:office@cscanada.net;  office@cscanada.org

Copyright © 2010 Canadian Research & Development Centre of Sciences and Cultures