Auto-detection interpretation model for horizontal oil wells using pressure transient responses

Abstract

Directional drilling is an excellent option to extend the limited reservoir reach and contact offered by vertical wells. Pressure transient responses (PTR) of horizontal wells provide key information about the reservoirs drilled. In this study multilayer perceptron (MLP) neural networks are used to correctly identify reservoir models from pressure derivative curves derived from horizontal wells. To this end, 2560 pressure derivative curves for six distinct reservoir models are generated and used to design a machine-learning classifier. A single hidden layer MLP network with 5 neurons, trained with a scaled conjugate gradient algorithm, is selected as the best classifier. This smart classifier provides total classification accuracy of 98.3%, mean square error of 0.00725, and coefficient of determination of 0.97332 over the whole dataset. Performance accuracy of the proposed classifier is verified with real field data, synthetically generated noisy PTR, and some signals outside the range initially assessed by the training plus testing data subsets. The developed network can correctly identify the reservoir-flow model with a probability of close to 0.9. The novelty of this work is that it employs a large dataset of horizontal (not vertical) well tests applied to six reservoir-flow models and includes noisy data to train and verify a neural network model to reliably achieve a high-level of prediction accuracy.

Cited as: Moosavi, S.R., Vaferi, B., Wood, D.A. Auto-detection interpretation model for horizontal oil wells using pressure transient responses. Advances in Geo-Energy Research, 2020, 4(3): 305-316, doi: 10.46690/ager.2020.03.08

References

Al-Rbeawi, S. Restoring disrupted data by wellbore storage effect using analytical models and type-curve matching techniques. Int. J. Oil Gas Coal Technol. 2018, 19(2): 163-196.

Al-Rbeawi, S., Tiab, D. Pressure behaviours and flow regimes of a horizontal well with multiple inclined hydraulic fractures. Int. J. Oil Gas Coal Technol. 2013, 6(1-2): 207-241.

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

Athichanagorn, S., Horne, R.N. Automatic parameter estimation from well test data using artificial neural network. Paper SPE 30566 Presented at Annual Technical Conference and Exhibition, Dallas, Texas, USA, 22-25 October, 1995.

Biryukov, D., Kuchuk, F.J. Transient pressure behavior of reservoirs with discrete conductive faults and fractures. Transp. Porous Media 2012, 95(1): 239-268.

Biryukov, D., Kuchuk, F.J. Pressure transient behavior of horizontal wells intersecting multiple hydraulic fractures in naturally fractured reservoirs. Transp. Porous Media 2015, 110(3): 369-408.

Bourdet, D., Ayoub, J.A., Pirard, Y.M. Use of pressure derivative in well test interpretation. SPE Form. Eval. 1989, 4(2): 293-302.

Brown, M., Ozkan, E., Raghavan, R., et al. Practical solutions for pressure-transient responses of fractured horizontal wells in unconventional shale reservoirs. SPE Reserv. Eval. Eng. 2011, 14(6): 663-676.

Chu, H., Liao, X., Dong, P., et al. An automatic classification method of well testing plot based on convolutional neural network (CNN). Energies 2019, 12(15): 2846.

Cybenko, G.V. Approximation by superposition of a sigmoidal function. Math. Control Signal. Syst. 1989, 2(4): 303-314.

Daviau, F., Mouronval, G., Bourdarot, G., et al. Pressure analysis for horizontal wells. SPE Form. Eval. 1988, 3(4): 716-724.

Dikken, B.J. Pressure drop in horizontal wells and its effect on production performance. J. Pet. Technol. 1990, 42(11): 1426-1433.

Duan, Y., Ren, K., Fang, Q., et al. Pressure transient analysis for a horizontal well in heterogeneous carbonate reservoirs using a linear composite model. Math. Probl. Eng. 2020, 2020: 3267458.

Ershaghi, I., Li, X., Hassibi, M. A robust neural network model for pattern recognition of pressure transient test data. Paper SPE 26427 Presented at SPE Annual Technical Conference and Exhibition, Houston, Texas, USA, 3-6 October, 1993.

Feng, Q., Xia, T., Wang, S., et al. Pressure transient behavior of horizontal well with time-dependent fracture conductivity in tight oil reservoirs. Geofluids 2017, 2017: 5279792.

Geng, Z., Hu, X., Ding, N., et al. A pattern recognition modeling approach based on the intelligent ensemble classifier: Application to identification and appraisal of water-flooded layers. Proc. Inst. Mech. Eng. Part I-J Syst. Control Eng. 2019, 233(7): 737-750.

Ghaffarian, N., Eslamloueyan, R., Vaferi, B. Model identification for gas condensate reservoirs by using ANN method based on well test data. J. Pet. Sci. Eng. 2014, 123: 20-29.

Goode, P.A., Thambynayagam, R.K.M. Pressure drawdown and buildup analysis of horizontal wells in anisotropic media. SPE Form. Eval. 1987, 2(4): 683-697.

Horne, R.N. Modern Well Test Analysis. Palo Alto, USA, Petroway Inc., 1995.

Kharrat, R., Razavi, S.M. Determination of reservoir model from well test data, using artificial neural network. Sci. Iran. 2008, 15(4): 487-493.

Kuchuk, F., Biryukov, D. Pressure-transient tests and flow regimes in fractured reservoirs. SPE Reserv. Eval. Eng. 2015, 18(2): 187-204.

Kuchuk, F.J. Well testing and interpretation for horizontal wells. J. Pet. Technol. 1995, 47(1): 36-41.

Lichtenberger, G.J. Data acquisition and interpretation of horizontal well pressure transient tests. J. Pet. Technol. 1994, 46(2): 157-162.

Luo, S., Neal, L., Arulampalam, P., et al. Flow regime analysis of multi-stage hydraulically-fractured horizontal wells with reciprocal rate derivative function: Bakken case study. Paper SPE 137514 Presented at Canadian Unconventional Resources and International Petroleum Conference, Calgary, Alberta, Canada, 19-21 October, 2010.

May, E.A., Dagli, C.H. A hybrid system for well test analysis. Paper Presented at 2nd IEEE World Congress on Computational Intelligence (WCCI 98), Anchorage, Alaska, USA, 4-9 May, 1988.

Meng, M., Chen, Z., Liao, X., et al. A well-testing method for parameter evaluation of multiple fractured horizontal wells with non-uniform fractures in shale oil reservoirs. Adv. Geo-Energy Res. 2020, 4(2): 187-198.

Moosavi, S.R., Qajar, J., Riazi, M. A comparison of methods for denoising of well test pressure data. J. Pet. Explor. Prod. Technol. 2018b, 8(4): 1519-1534.

Moosavi, S.R., Vaferi, B., Wood, D.A. Applying orthogonal collocation for rapid and reliable solutions of transient flow in naturally fractured reservoirs. J. Pet. Sci. Eng. 2018a, 162: 166-179.

Nategh, M., Vaferi, B., Riazi, M. Orthogonal collocation method for solving the diffusivity equation: application on dual porosity reservoirs with constant pressure outer boundary. J. Energy Resour. Technol. 2019, 141(4): 042001.

Odeh, A.S., Babu, D.K. Transient flow behavior of horizontal wells, pressure drawdown, and buildup analysis. Paper SPE 18802 Presented at SPE California Regional Meeting, Bakersfield, California, USA, 5-7 April, 1989.

Raghavan, R.S., Chen, C., Agarwal, B. An analysis of horizontal wells intercepted by multiple fractures. SPE J. 1997, 2(3): 235-245.

Sung, W., Yoo, I., Ra, S., et al. Development of HT-BP neural network system for the identification of well test Interpretation model. Paper SPE 30974 Presented at SPE Eastern Regional Meeting, Morgantown, West Virginia, USA, 18-20 September, 1995.

Suzuki, K., Nanba, T. Horizontal well test analysis system. Paper SPE 20613 Presented at SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, USA, 23-26 September, 1990.

Taibi, F., Akbarizadeh, G., Farshidi, E. Robust reservoir rock fracture recognition based on a new sparse feature learning and data training method. Multidimens. Syst. Signal Process. 2019, 30(4): 2113-2146.

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 SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, USA, 30 September-2 October, 2013.

Vaferi, B., Eslamloueyan, R. Characterisation of hydrocarbon reservoirs by analysing deconvolved impulse response. Int. J. Oil. Gas Coal Technol. 2018, 17(2): 129-142.

Vaferi, B., Eslamloueyan, R., Ayatollahi, S. Automatic recognition of oil reservoir models from well testing data by using multilayer perceptron networks. J. Pet. Sci. Eng. 2011, 77(3-4): 254-262.

Vaferi, B., Eslamloueyan, R., Ayatollahi, S. Application of recurrent networks to classification of oil reservoir models in well-testing analysis. Energy Sources Part A-Recovery Util. Environ. Eff. 2015, 37(2): 174-180.

Vaferi, B., Eslamloueyan, R., Ghaffarian, N. Hydrocarbon reservoir model detection from pressure transient data using coupled artificial neural network-Wavelet transform approach. Appl. Soft. Comput. 2016, 47: 63-75.

Vaferi, B., Salimi, V., Dehghan Baniani, D., et al. Prediction of transient pressure response in the petroleum reservoirs using orthogonal collocation. J. Pet. Sci. Eng. 2012, 98: 156-163.

Velez-Langs, O. Genetic algorithms in oil industry: An overview. J. Pet. Sci. Eng. 2005, 47(1-2): 15-22.

Zhang, J., Cheng, S., Zhu, C., et al. A numerical model to evaluate formation properties through pressure-transient analysis with alternate polymer flooding. Adv. Geo-Energy Res. 2019, 3(1): 94-103.

Zhao, K., Du, P. A new production prediction model for multistage fractured horizontal well in tight oil reservoirs. Adv. Geo-Energy Res. 2020, 4(2): 152-161.