Gas well performance prediction using deep learning jointly driven by decline curve analysis model and production data

Abstract

The prediction of gas well performance is crucial for estimating the ultimate recovery rate of natural gas reservoirs. However, physics-based numerical simulation methods require a significant effort to build a robust model, while the decline curve analysis method used in this field is based on certain assumptions, hence its applications are limited due to the strict working conditions. In this work, a deep learning model driven jointly by the decline curve analysis model and production data is proposed for the production performance prediction of gas wells. Due to the time-series characteristics of gas well production data, the long short-term memory neural network is selected to establish the architecture of artificial intelligence. The existing decline curve analysis model is first implicitly incorporated into the training process of the neural network and then used to drive the neural network construction along with the actual gas well production historical data. By applying the proposed innovative model to analyze the conventional and tight gas well performance predictions based on field data, it is demonstrated that the proposed long short-term memory neural network deep learning model driven jointly by the decline curve analysis model and production data can effectively improve the interpretability and predictive ability of the traditional long short-term memory neural network model driven by production data alone. Compared with the data-driven model, the jointly driven model can reduce the mean absolute error by 42.90% and 13.65% for a tight gas well and a carbonate gas well, respectively.

Document Type: Original article

Cited as: Xue, L., Wang, J., Han, J., Yang, M., Mwasmwasa, M. S., Nanguka, F. Gas well performance prediction using deep learning jointly driven by decline curve analysis model and production data. Advances in Geo-Energy Research, 2023, 8(3): 159-169. https://doi.org/10.46690/ager.2023.06.03

References

Almajid, M. M., Abu-Al-Saud, M. O. Prediction of porous media fluid flow using physics informed neural networks. Journal of Petroleum Science and Engineering, 2022, 208: 109205.

Arps, J. J. Analysis of decline curves. Transactions of the AIME, 1945, 160(1): 228-247.

Baydin, A. G., Pearlmutter, B. A., Radul, A. A., et al. Automatic differentiation in machine learning: A survey. Journal of Marchine Learning Research, 2018, 18: 1-43.

Blasingame, T. A., Poe, B. D. Semianalytic solutions for a well with a single finite-conductivity vertical fracture. Paper SPE 26424 Presented at the SPE Annual Technical Conference and Exhibition, Houston, Texas, 3-6 October, 1993.

Chi, D. Research on electricity consumption forecasting model based on wavelet transform and multi-layer LSTM model. Energy Reports, 2022, 8: 220-228.

Chung, W. H., Gu, Y. H., Yoo, S. J. District heater load forecasting based on machine learning and parallel CNNLSTM attention. Energy, 2022, 246: 123350.

Cuomo, S., Di Cola, V. S., Giampaolo, F., et al. Scientific machine learning through physics-informed neural networks: Where we are and what’s next. Journal of Scientific Computing, 2022, 92(3): 88.

Duong, A. N. Rate-decline analysis for fracture-dominated shale reservoirs. SPE Reservoir Evaluation & Engineering, 2011, 14(3): 377-387.

Fan, D., Sun, H., Yao, J., et al. Well production forecasting based on ARIMA-LSTM model considering manual operations. Energy, 2021, 220: 119708.

Fetkovich, M. J. Decline curve analysis using type curves. Paper SPE 4629 Presented at the Fall Meeting of the Society of Petroleum Engineers of AIME, Las Vegas, Nevada, 30 September-3 October, 1973.

Fraces, C. G., Hamdi, T. Physics informed deep learning for flow and transport in porous media. Paper SPE 203934 Presented at the SPE Reservoir Simulation Conference, On-Demand, 4-6 October, 2021.

Frooqnia, A., Torres-Verdín, C., Sepehrnoori, K. Numerical simulation and interpretation of production logging measurements using a new coupled wellbore-reservoir model. Paper SPWLA 2011 presented at the SPWLA 52nd Annual Logging Symposium, Colorado Springs, Colorado, 14-18 May, 2011.

Gers, F. A., Schmidhuber, J., Cummins, F. Learning to forget: Continual prediction with LSTM. Neural Computation, 2000, 12(10): 2451-2471.

Jia, X., Zhang, F. Applying data-driven method to production decline analysis and forecasting. Paper SPE 181616 presented at the SPE Annual Technical Conference and Exhibition, Dubai, UAE, 26-28 September, 2016.

Karasu, S., Altan, A. Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization. Energy, 2022, 242: 122964.

Karniadakis, G. E., Kevrekidis, I. G., Lu, L., et al. Physicsinformed machine learning. Nature Reviews Physics, 2021, 3(6): 422-440.

Kuzma, H. A., Arora, N. S., Farid, K. Generative models for production forecasting in unconventional oil and gas plays. Paper URTEC 1928595 Presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, Denver, Colorado, 25-27 August, 2014.

Li, W., Wang, L., Dong, Z., et al. Reservoir production prediction with optimized artificial neural network and time series approaches. Journal of Petroleum Science and Engineering, 2022a, 215: 110586.

Li, X., Xiao, K., Li, X., et al. A well rate prediction method based on LSTM algorithm considering manual operations. Journal of Petroleum Science and Engineering, 2022b, 210: 110047.

Mollaiy, B. S., Shahbazian, M. An imperialist competitive algorithm artificial neural network method to predict oil flow rate of the wells. International Journal of Computer Applications, 2011, 26(10): 47-50.

Muther, T., Dahaghi, A. K., Syed, F. I., et al. Physical laws meet machine intelligence: Current developments and future directions. Artificial Intelligence Review, 2023, 56(7): 6947-7013.

Ning, Y., Kazemi, H., Tahmasebi, P. A comparative machine learning study for time series oil production forecasting: ARIMA, LSTM, and Prophet. Computers & Geosciences, 2022, 164: 105126.

Press, W. H., Teukolsky, S. A. Savitzky-Golay smoothing filters. Computers in Physics, 1990, 4(6): 669-672.

Raissi, M., Perdikaris, P., Karniadakis, G. E. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 2019, 378: 686-707.

Rojc, M., Mlakar, I. An LSTM-based model for the compression of acoustic inventories for corpus-based textto-speech synthesis systems. Computers and Electrical Engineering, 2022, 100: 107942.

Sagheer, A., Kotb, M. Time series forecasting of petroleum production using deep LSTM recurrent networks. Neurocomputing, 2019, 323: 203-213.

Shabro, V., Torres-Verdin, C., Javadpour, F. Numerical simulation of shale-gas production: From pore-scale modeling of slip-flow, Knudsen diffusion, and Langmuir desorption to reservoir modeling of compressible fluid. Paper SPE 144355 Presented at the North American Unconventional Gas Conference and Exhibition, Woodlands, Texas, 14-16 June, 2011.

Song X., Liu, Y., Xue, L., et al. Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model. Journal of Petroleum Science and Engineering, 2020, 186: 106682.

Temizel, C., Canbaz, C. H., Saracoglu, O., et al. Production forecasting in shale reservoirs through conventional DCA and machine/deep learning methods. Paper URTEC 20202878 Presented at the Unconventional Resources Technology Conference held in Denver, Colorado, 20-22 July, 2020.

Valkó, P. P., Lee, W. J. A better way to forecast production from unconventional gas wells. Paper SPE 134231 Presented at the SPE Annual Technical Conference and Exhibition, Florence, Italy, 20-22 September, 2010.

Wang, N., Zhang, D., Chang, H., et al. Deep learning of subsurface flow via theory-guided neural network. Journal of Hydrology, 2020, 584: 124700.

Werneck, R. O., Prates, R., Moura, R., et al. Data-driven deep-learning forecasting for oil production and pressure. Journal of Petroleum Science and Engineering, 2022, 210: 109937.

Zha, W., Liu, Y., Wan, Y., et al. Forecasting monthly gas field production based on the CNN-LSTM model. Energy, 2022, 260: 124889.