Petrophysical parameters estimation of a reservoir using integration of wells and seismic data: a sandstone case study

Estimation of reservoir parameters (reservoir characterization) including lithology, porosity and water saturation is crucial in the oil and gas industry. These parameters can be calculated in the wells and/or laboratories but these methods are time-consuming, costly, and can cover only a small part of the reservoir. Therefore, integration of seismic and well data for reservoir characterization has received special attention in the oil and gas industry since it provides information from the entire volume of the reservoir. In this study, to establish a relationship between the seismic attributes and the petrophysical parameters of a reservoir at the location of the wells, seismic inversion was performed using model-based, linear programming sparse spike, and maximum likelihood sparse spike algorithms, and the acoustic impedance were calculated accordingly. The model-based inversion method has provided a better answer than the other two methods by providing 99% correlation between the actual and estimated acoustic impedance. Therefore, this method was used to calculate the acoustic impedance in the space between the wells. In the next step, porosity, water saturation, and lithology (quartz and dolomite volume) were estimated from different seismic attributes. In this paper, the multi-attributes regression (MAR) method and artificial neural network (ANN) were used to estimate each of the petrophysical parameters of the reservoir, and we found out that the ANN provided a more accurate estimate than the MAR method for our given dataset. The correlation between the actual and estimated values using the ANN method for porosity, water saturation, quartz volume and dolomite volume is 86, 93, 93 and 95% respectively in the training data and 75, 78, 79 and 82% in the validation data.