Semi-Supervised Training for (Pre-Stack) Seismic Data Analysis

Abstract

A lack of labeled examples is a problem in different domains, such as text and image processing, medicine, and static reservoir characterization, because supervised learning relies on vast volumes of these data to perform successfully, but this is quite expensive. However, large amounts of unlabeled data exist in these domains. The deep semi-supervised learning (DSSL) approach leverages unlabeled data to improve supervised learning performance using deep neural networks. This approach has succeeded in image recognition, text classification, and speech recognition. Nevertheless, there have been few works on pre-stack seismic reservoir characterization, in which knowledge of rock and fluid properties is fundamental for oil exploration. This paper proposes a methodology to estimate acoustic impedance using pre-stack seismic data and DSSL with a recurrent neural network. The few labeled datasets for training were pre-processed from raw seismic and acoustic impedance data from five borehole logs. The results showed that the acoustic impedance estimation at the well location and outside it was better predicted by the DSSL compared to the supervised version of the same neural network. Therefore, employing a large amount of unlabeled data can be helpful in the development of seismic data interpretation systems.

Geological Insights from Porosity Analysis for Sustainable Development of Santos Basin’s Pre-Salt Carbonate Reservoir

“…However, inherent resolution limitations in seismic data ( [12]) underscore the challenges in achieving high-quality acquisition, given its associated costs. Addressing these resolution-related concerns, some works (e.g., [10,13]) have sought to improve seismic resolution data by developing methodologies such as spectral whitening, deconvolution, and inverse Q-filtering. By mitigating these resolution issues, the proposed methods enhance precision and confidence in seismic data interpretation, contributing to a more nuanced understanding of the complex geological features in carbonate reservoirs.…”