Optimize stimulated reservoir volume in brittle shale
The NETWORX predictive fracture network model helps operators calibrate fracture designs and potentially increase asset production.
The NETWORX data-driven predictive model is developed to predict fracture geometry using available fracture design parameters. This unique model is trained from more than a thousand fracture data stages obtained from various operators in brittle shale reservoirs.
The key benefits:
The NETWORX model provides a fast and simple way of predicting SRV in brittle shale formations by looking at only six parameters. After calibration by microseismic data, NETWORX models can act as a substitute for microseismic services in field development.
Neural network modeling techniques are used to find hidden patterns within all fracture datasets. The NETWORX model uses these relationships to make predictions for future fracture treatments, and allows identification of key stimulation factors to help model optimized treatment designs.
The NETWORX model aids understanding of fracture network development in shale reservoirs and provides recommendations on improving fracture designs. Analysts can study the impact of fracture design parameters on width, height and length separately, helping operators improve stimulation effectiveness and stage design for horizontal wellbores.
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