Abstract:
Reservoir characterization is the process of determining the petrophysical properties of the
subsurface, including porosity, permeability, relative permeability, water saturation, fluid
saturation, capillary pressure, shale volume and sand volume. These petrophysical properties of
the subsurface can be determined using experimental, simulation and machine learning method.
Reservoir characterization using experimental method provides accurate results but it is costly and
time consuming. The accuracy of reservoir characterization using simulation method depends on
the amount of data. Moreover, the use of initial conditions and assumptions before the simulation
might lead to uncertainty. The use of complex algorithms, mathematical models and modeling
techniques in the Petrel software makes simulations computationally intensive and time consuming. So, to make reservoir characterization accurate and cost-effective machine learning is
applied to save both cost and time. Three machine learning algorithms Linear Regression (LR),
Support Vector Regression (SVR) and Artificial Neural Network (ANN) have been used to predict
porosity and permeability for reservoir characterization. LR and ANN have performed better in
the prediction of porosity and SVR and ANN have performed better in the prediction of
permeability. In both the prediction of porosity and permeability, ANN has provided the most
accurate results.
