Direct Estimation of Porosity from Seismic Data using Rock and Wave Physics Informed Neural Networks (RW-PINN)

TL;DR

This paper introduces RW-PINN, a physics-informed neural network that estimates reservoir porosity directly from seismic data with limited well data, ensuring consistency with rock physics and geological knowledge.

Contribution

The study presents a novel weakly supervised neural network approach that integrates rock and wave physics to improve petrophysical inversion from seismic data.

Findings

RW-PINN accurately estimates porosity with limited well data.

It maintains consistency with rock physics and geological constraints.

Compared to fully supervised models, RW-PINN requires less labeled data and produces physically plausible results.

Abstract

Petrophysical inversion is an important aspect of reservoir modeling. However due to the lack of a unique and straightforward relationship between seismic traces and rock properties, predicting petrophysical properties directly from seismic data is a complex task. Many studies have attempted to identify the direct end-to-end link using supervised machine learning techniques, but face different challenges such as a lack of large petrophysical training dataset or estimates that may not conform with physics or depositional history of the rocks. We present a rock and wave physics informed neural network (RW-PINN) model that can estimate porosity directly from seismic image traces with no or limited number of wells, with predictions that are consistent with rock physics and geologic knowledge of deposition. As an example, we use the uncemented sand rock physics model and normal-incidence wave physics to guide the learning of RW-PINN to eventually get good estimates of porosities from normal-incidence seismic traces and limited well data. Training RW-PINN with few wells (weakly supervised) helps in tackling the problem of non-uniqueness as different porosity logs can give similar seismic traces. We use weighted normalized root mean square error loss function to train the weakly supervised network and demonstrate the impact of different weights on porosity predictions. The RW-PINN estimated porosities and seismic traces are compared to predictions from a completely supervised model, which gives slightly better porosity estimates but poorly matches the seismic traces, in addition to requiring a large amount of labeled training data. In this paper, we demonstrate the complete workflow for executing petrophysical inversion of seismic data using self-supervised or weakly supervised rock physics informed neural networks.