First Break - Volume 42, Issue 6, 2024

Velocity model building is important in providing subsurface velocity models for workflows such as seismic imaging and interpretation. Velocity model building techniques, such as ray-based tomographic approaches are not very effective in complex geological settings. Full waveform inversion (FWI) approaches are computationally intensive and sensitive to an initial model. The physics-guided deep learning-based velocity model building, that involves deterministic, physics-based modelling and data-driven deep learning components, is designed to capture the subsurface salt body shapes and locations, with a small amount of training models. In this paper, we discuss the influence of dominant frequency and training models on the velocity prediction by using a hybrid physics-guided neural network method. Our results show that, the higher the dominant frequency, the more accurate the prediction accuracy of the salt body shapes and background information. For more complicated velocity models and real datasets, simple synthetic training models are not capable of capturing the salt body shapes, nor the background information. A more practical synthetic training set with much more smoothed background layered structures is more suitable for predicting complicated models.

The offshore basins of Uruguay and Southern Brazil have a limited oil and gas exploration history. Since the announcements of light oil discoveries on the conjugate margin of Namibia, this area has become an epicentre of interest for hydrocarbon explorers. The Punta del Este and Pelotas basins are considered underexplored and, as such, possess an elevated risk profile. Identifying analogous, conjugate petroleum system elements is a component in the framework to reduce that risk. Additionally, the calibration and integration of subsurface data in the search for the next hydrocarbon discovery is paramount to a successful wildcat. Here, we highlight the seamless merge and calibration of >25,000 km² from four separate, vintage 3D seismic volumes into a single volume interpretation tool. This allows interpreters to gain a contiguous and unobstructed view and, therefore, an understanding of the regional geologic framework. When integrated with existing 2D data, the merged volume has permitted an improved understanding of the basin’s evolution, the tectonostratigraphic history and elements of the prospectivity for the region. As oil companies continue to flock to the region looking for the next discovery, advancing tools in the explorer’s toolkit is the key to success.

We present a remote sensing analysis to identify potential concentrations of natural hydrogen by observing fairy circles. Utilising Principal Component Analysis (PCA), distinct features of these formations were delineated, indicating their differentiation from the surrounding environment. It was noted that such distinctiveness occasionally arose from the presence of water and similarities in topography, which also manifested in the PCA contours of the fairy circles. The role of water or humidity emerged as significant in the Thermal Infrared (TIR) response of fairy circles, typically displaying negative anomalies. However, this correlation appeared less straightforward in specific cases such as Brazil. Band ratio methods revealed a pronounced association with ferric iron (Fe⁺³) and a less conspicuous link with Alunite-Kaolinite.

Additionally, vegetation indices primarily correlated with Normalised Difference Vegetation Index (NDVI) and Moisture Stress Index 1 (MSI1) in agricultural areas, and MSI1 and Water Index 1 (WI1) in water body regions, with other indices (e.g., OCVI, NDWI, and CIG) proved beneficial. Radiometric analysis suggested that low K/Th values were associated with this anomaly in Western Australia, whereas other radiogenic elements did not exhibit clear patterns in the areas studied. Future research directions are proposed, advocating utilising high-resolution geophysical data to gain deeper insights into the associations linked to fairy circles. The implementation of unsupervised and supervised classification algorithms was deemed crucial for identifying new formations, while longitudinal analysis would aid in understanding the evolving nature of these phenomena over time.

Methane is the second most potent greenhouse gas in the atmosphere, and emission reduction plans are reflected in most countries’ legislation. Though official plans for reducing fossil fuel usage or solid waste management may sometimes sound over-optimistic, the general vector for better control of methane leaks is obvious. It requires relevant methodology and technology to be spread widely. This article analyses legislation and technology options and explains drone-based methane screening, which may significantly optimise current methane detection and quantification surveys.

In the effort to combat climate change and meet sustainable development objectives, the energy sector is facing a critical juncture. Over the past few decades, the oil and gas industry has played an indisputable role in driving economic growth and technological progress, significantly contributing to the development and modernisation of contemporary societies while ensuring energy accessibility for diverse populations. Amid the current challenges of energy transition, the oil and gas industry holds a central position. It now must shift towards sustainable practices and decarbonisation, all while maintaining energy security and affordability.

Moreover, it is also easy to recognise that historically the oil and gas industry has been heavily skewed towards male representation. Yet, amidst these challenges lies a unique opportunity for the subsurface industry to transition towards greater gender equality. Subsurface green business is a unique opportunity to actively promote diversity and inclusion initiatives, to effectively address the imperative for innovation in the energy transition. By breaking down barriers and creating equal opportunities for all genders, the sector can benefit from a broader talent pool and a range of perspectives, ultimately driving innovation and success in the transition towards sustainable energy solutions.

This article explores the interdependent relationship between technology and talent in advancing carbon capture and storage (CCS) solutions while leveraging skills developed in the oil and gas sector. By integrating insights from CCS requirements and constraints alongside the impact of diversity on innovation and performance, we aim to underscore the pivotal role of cultivating diverse talent pools in driving transformative change within the energy sector.