The problem of predicting the geometry of the multi-stage multi-fracture horizontal wells is an extremely complex task as shown in the images below:

  • One question that one needs to answer is: what is the contribution of the generated fractures in the ultimate production increase?
  • The developed model uses the hydraulic fracturing parameters as input and predict the dynamic stimulated reservoir volume (DSRV) as an effectiveness measure for the stimulation process.

Our workflow for model development is shown below:

  1. Collect field data for training (hydraulic fracturing parameters and microseismic recording).
  2. Find a relationship between the inputs HF parameters and output cloud of MSE. We used machine learning to find the relationship.
  3. Do a Monte Carlo simulation and use the Sobol Technique to represent the relationships function as summation of polynomials.

The advantage of our approach is a model that runs in a few seconds (compared to high fidelity models that have CPU run time up to days ), represents the SRV as a polynomial combination of input parameters in hydraulic fracturing process, and has a controlled error.

In the figures below, the overall workflow of the FACT’s team development is presented from left to right.

  1. Table containing the hydraulic fracturing parameters
  2. ANN model predicting the SRV as a result of each fracturing step.
  3. Sobol technique to represent the relationship with simple polynomials.