Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach

TL;DR

This paper presents a Neuro-Fuzzy approach to model sand fraction from seismic attributes, effectively handling uncertainties and aiding reservoir characterization in complex geological settings.

Contribution

It introduces a hybrid Neuro-Fuzzy method for nonlinear mapping of seismic data to reservoir properties, improving modeling accuracy in challenging thin sand and shale layers.

Findings

Acceptable match between predicted and actual sand fraction.

Neuro-Fuzzy approach effectively manages uncertainties in data.

Visualization aids in identifying potential drilling sites.

Abstract

In this paper, we illustrate the modeling of a reservoir property (sand fraction) from seismic attributes namely seismic impedance, seismic amplitude, and instantaneous frequency using Neuro-Fuzzy (NF) approach. Input dataset includes 3D post-stacked seismic attributes and six well logs acquired from a hydrocarbon field located in the western coast of India. Presence of thin sand and shale layers in the basin area makes the modeling of reservoir characteristic a challenging task. Though seismic data is helpful in extrapolation of reservoir properties away from boreholes; yet, it could be challenging to delineate thin sand and shale reservoirs using seismic data due to its limited resolvability. Therefore, it is important to develop state-of-art intelligent methods for calibrating a nonlinear mapping between seismic data and target reservoir variables. Neural networks have shown its potential to model such nonlinear mappings; however, uncertainties associated with the model and datasets are still a concern. Hence, introduction of Fuzzy Logic (FL) is beneficial for handling these uncertainties. More specifically, hybrid variants of Artificial Neural Network (ANN) and fuzzy logic, i.e., NF methods, are capable for the modeling reservoir characteristics by integrating the explicit knowledge representation power of FL with the learning ability of neural networks. The documented results in this study demonstrate acceptable resemblance between target and predicted variables, and hence, encourage the application of integrated machine learning approaches such as Neuro-Fuzzy in reservoir characterization domain. Furthermore, visualization of the variation of sand probability in the study area would assist in identifying placement of potential wells for future drilling operations.