Determining the closure pressure is crucial for optimal hydraulic fracturing design and successful execution of fracturing treatment. Historically, the use of diagnostic tests before the main fracturing treatment has significantly advanced to provide more information about the pattern of fracture propagation and fluid performance to optimize the designs.

Many analytical methods such as the G-function, square root of time, etc. have been developed to determine the fracture closure pressure. In some cases, the difficulty in determining the fracture closure pressure, as well as personal bias and field experiences, make it challenging to interpret the changes in the pressure derivative slope and identify fracture closure. These conditions include:

  • High permeability reservoirs where fracture closure occurs very fast due to the quick fluid leak-off.

  • Extremely low permeability reservoirs, which require a long shut-in time for the fluid to leak-off and determine the fracture closure pressure.

  • The non-ideal fluid leak-off behavior under complex conditions.

The objective of the paper is to apply machine learning methods to implement predesigned algorithms to predict the fracture closure pressure and other minifrac parameters while minimizing the shortcomings in determining the closure pressure for non-ideal or subjective conditions. This study demonstrates training different supervised machine learning algorithms to help predict fracture closure pressure, fracture closure time, permeability and the time to reach late flow regimes. The workflow involves using the datasets to train and optimize the models, which are subsequently used to predict the minifrac parameters of testing data. The output results are then compared with actual results from more than 120 Diagnostic Fracture Injection Test (DFIT) data points. It is further proposed that an integrated approach helps feature selection, dataset processing and studying the effects of data processing on the success of the model prediction. The results from this study reduce the subjectivity and need for experience in interpreting DFIT data. We speculate that a linear regression and multilayer perceptron (MLP) neural network algorithms can yield high scores in the prediction of the minifrac parameters.

Keywords:
pressure transient analysis, artificial intelligence, complex reservoir, drillstem/well testing, fracture closure pressure, closure pressure, machine learning, flow in porous media, reservoir characterization, reservoir geomechanics
Subjects:
Hydraulic Fracturing, Reservoir Characterization, Reservoir Fluid Dynamics, Formation Evaluation & Management, Unconventional and Complex Reservoirs, Information Management and Systems, Reservoir geomechanics, Flow in porous media, Pressure transient analysis, Drillstem/well testing
This content is only available via PDF.
2020. Unconventional Resources Technology Conference
You can access this article if you purchase or spend a download.