Deep Recurrent Neural Network DRNN Model for Real-Time Step-Down Analysis
- Srinath Madasu (Halliburton) | Keshava Prasad Rangarajan (Halliburton)
- Document ID
- Society of Petroleum Engineers
- SPE Reservoir Characterisation and Simulation Conference and Exhibition, 17-19 September, Abu Dhabi, UAE
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- machine learning, fracturing, Deep Recurrent Neural Network, Step down analysis, real time
- 34 in the last 30 days
- 34 since 2007
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A new real-time machine learning model has been developed based on the deep recurrent neural network (DRNN) model for performing step-down analysis during the hydraulic fracturing process. During a stage of the stimulation process, fluids are inserted at the top of the wellhead, while the flow is primarily driven by the difference between the bottomhole pressure (BHP) and reservoir pressure. The major physics and engineering aspects involved are complex and, quite often, there is a high level of uncertainty related to the accuracy of the measured data, as well as intrinsic noise. Consequently, using a machine learning-based method that can resolve both the temporal and spatial non-linear variations has advantages over a pure engineering model.
The approach followed provides a long short-term memory (LSTM) network-based methodology to predict BHP and temperature in a fracturing job, considering all commonly known surface variables. The surface pumping data consists of real-time data captured within each stage, such as surface treating pressure, fluid pumping rate, and proppant rate. The accurate prediction of a response variable, such as BHP, is important because it provides the basis for decisions made in several well treatment applications, such as hydraulic fracturing and matrix acidizing, to ensure success.
Limitations of the currently available modeling methods include low resolution BHP predictions and an inability to properly capture non-linear effects in the BHP/temperature time series relationship with other variables, including surface pressure, flow rate, and proppant rate. In addition, current methods are further limited by lack of accuracy in the models for fluid properties; the response of the important sub-surface variables strongly depends on the modeled fluid properties.
The novel model presented in this paper uses a deep learning neural network model to predict the BHP and temperature, based on surface pressure, flow rate, and proppant rate. This is the first attempt to predict response variables, such as BHP and temperature, in real time during a pumping stage, using a memory-preserving recurrent neural network (RNN) variant, such as LSTM. The results show that the LSTM can successfully model the BHP and temperature in a hydraulic fracturing process. The BHP and temperature predictions obtained were within 5% relative error. The current effort to model BHP can be used for step-down analysis in real time, thereby providing an accurate representation of the subsurface conditions in the wellbore and in the reservoir. The new method described in this paper avoids the need to manage the complex physics of the present methods; it provides a robust, stable, and accurate numerical solution throughout the pumping stages. The method described in this paper is extended to manage step-down analysis using surface-measured variables to predict perforation and tortuosity friction.
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Augustine, J.R. and Meijs, R. 2010. What Do the Surface Pressures Tell Us During Shale Fracturing Operations? Presented at the SPE International Symposium and Exhibition on Formation Damage Control, Lafayette, Louisiana, USA, 10-12 February. SPE-127821-MS. https://doi.org/10.2118/127821-MS.
Barree, R.D. 1983. A Practical Numerical Simulator for Three-Dimensional Fracture Propagation in Heterogeneous Media. Presented at the SPE Reservoir Simulation Symposium, San Francisco California, USA, 15-18 November. SPE-12273-MS. https://doi.org/10.2118/12273-MS.
Madasu, S. and Rangarajan, K.P. 2018. Deep Recurrent Neural Network DRNN Model for Real-Time Multistage Pumping Data. Presented at OTC Arctic Technology Conference, Houston, Texas, USA, 5-7 November. OTC-29145-MS. https://doi.org/10.4043/29145-MS.
Mehle, M.P. and Hlidek, B.T. 2016. Real Time Friction Simulation of Accurate Calculation of Bottomhole Pressure During Stimulation in Open-Hole Sliding Sleeve Ball Seat Completions. Presented at ABU Dhabi International Petroleum Exhibition & Conference, Abu Dhabi, UAE, 7-10 November. SPE_183168-MS. https://doi.org/10.2118/183168-MS.
Pandey, V.J. and Robert, J.A. 2002. New Correlation for Predicting Frictional Pressure Drop of Proppant-Laden Slurries Using Surface Pressure Data. Presented at the International Symposium and Exhibition on formation Damage Control, Lafayette, Louisiana, USA, 20-21 February. SPE-73753-MS. https://doi.org/10.2118/73753-MS.
Pandya, N. and Jaripatke, O. 2014. Rate Step-Down Analysis Improves placement Efficiency of Stimulation Treatments in Unconventional Resource Play. Presented at the SPE/AAPG/SEG Unconventional Resources Technology Conference, Denver Colorado, USA, 25-27 August. URTEC-194637-MS. https://doi.org/10.15530/URTEC-2014-1943637.
Pascanu, R., Mikolov, T., and Bengio, Y. 2013. On the Difficulty of Training Recurrent Neural Networks. ArXiv. https://arxiv.org/abs/1211.5063.
Tariq, Z. 2016. An Automated Flowing Bottom-Hole Pressure Prediction for a Vertical Well Having Multiphase Flow Using Computational Intelligence Techniques. Presented at the SPE Kingdom of Saudi Arabia Annual Technical symposium and Exhibition, Dammam, Saudi Arabia, 23-26 April. SPE-192184-MS. https://doi.org/10.2118/192184-MS.
Umnuayponwiwat, S., Ozkan, E., Pearson, C.M.. 2000. Effect of Non-Darcy Flow on the Interpretation of Transient Pressure Responses of Hydraulically Fractured Wells. Presented at the SPE Annual Technical Conference and Exhibition, Dallas, Texas, USA 1-4 October. SPE-63176-MS. https://doi.org/10.2118/63176-MS.