Machine Learning Applied to SRV Modeling, Fracture Characterization, Well Interference and Production Forecasting in Low Permeability Reservoirs
- Edgar Urban-Rascon (Schulich School of Engineering, University of Calgary) | Roberto Aguilera (Schulich School of Engineering, University of Calgary)
- Document ID
- Society of Petroleum Engineers
- SPE Latin American and Caribbean Petroleum Engineering Conference, 27-31 July, Virtual
- Publication Date
- Document Type
- Conference Paper
- 2020. Society of Petroleum Engineers
- 5.1.5 Geologic Modeling, 7.6.7 Neural Networks, 7.6.6 Artificial Intelligence, 5 Reservoir Desciption & Dynamics, 5.5 Reservoir Simulation, 3 Production and Well Operations, 2 Well completion, 3 Production and Well Operations, 2.4 Hydraulic Fracturing, 7.6.4 Data Mining, 5.6 Formation Evaluation & Management, 7.6 Information Management and Systems, 7 Management and Information, 5.1 Reservoir Characterisation, 5.6.9 Production Forecasting
- machine learning, low permeability, well interference, SRV, fracture characterization
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- 81 since 2007
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The objective of this paper is to develop predictive models to optimize the (1) characterization of the stimulated reservoir volume (SRV), (2) discretization of the fracture network, and (3) hydraulic fracturing modeling, by combining machine learning (ML) algorithms and reservoir engineering in low permeability reservoirs.
An unsupervised learning algorithm is implemented to characterize the fracture network developed by micro-seismic observations during hydraulic fracturing. A Self Organizing Map (SOM) and Multi-Attribute Analysis are performed on the available seismic data to map the extension of the hydraulic fracturing stages and the fracture network complexity in a low permeability reservoir.
To correlate the mapped fracture network and discretized SRV, a 3D Finite Element Model (FEM) is developed to estimate fracture behavior, stress response, and hydraulic fracture propagation, on the predicted and forecasted multi-attribute map of the reservoir.
A 3D hydraulic fracture propagation model (HFPM) is introduced, to delimit the fracture geometry and remove data outliers in the SOM algorithm. Unsupervised algorithms rely on data quality. The efficiency of hydraulic fracturing modeling is improved with a machine learning approach by refining the certainty and quality of the data. An Artificial Neural Network (ANN) model helps to select the most significant parameters related to fracture modeling and simulation in the field. This approach allows us to recreate and forecast complex fracture networks in low permeability reservoirs, based on the learned geostatistical maps and hydraulic fracturing parameters, particularly where the microseismicity is limited or unavailable.
To validate the implementation of the 3D-HFPM in the field, an earthquake model is compared with statistically significant microseismic events obtained by the unsupervised iso-cluster algorithm. The relationship showed a good agreement, which suggests the HFPM agrees with seismic observations in the field.
The machine learning application to fracture network modeling provides the capability to identify susceptible areas to well interference and possible frac hits with higher certainty. This is so because the approach improves the selection of seismic data and hydraulic fracturing parameters, employed to develop the complex fracture network in numerical commercial reservoir simulators. This helps to determinate the reservoir interconnectivity and flow patterns in the fracture network.
This approach presents a robust manner for characterizing the SRV using a relative fast methodology, based on the combination of geostatistical and unsupervised learning modeling. The seismicity and hydraulic fracturing are connected using a multi-attribute and multi-disciplinary interpretation. It is a powerful tool for characterizing problematic fracture networks in unconventional reservoirs.
|File Size||1 MB||Number of Pages||24|
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