Assessing the accuracy of fault interpretation using machine-learning techniques when risking faults for CO2 storage site assessment

Generating an accurate model of the subsurface for the purpose of assessing the feasibility of a CO2 storage site is crucial. In particular, how faults are interpreted is likely to influence the predicted capacity and integrity of the reservoir; whether this is through identifying high-risk areas along the fault, where fluid is likely to flow across the fault, or by assessing the reactivation potential of the fault with increased pressure, causing fluid to flow up the fault. New technologies allow users to interpret faults effortlessly, and in much quicker time, using methods such as deep learning (DL). These DL techniques use knowledge from neural networks to allow end users to compute areas where faults are likely to occur. Although these new technologies may be attractive due to reduced interpretation time, it is important to understand the inherent uncertainties in their ability to predict accurate fault geometries. Here, we compare DL fault interpretation versus manual fault interpretation, and we can see distinct differences to those faults where significant ambiguity exists due to poor seismic resolution at the fault; we observe an increased irregularity when DL methods are used over conventional manual interpretation. This can result in significant differences between the resulting analyses, such as fault reactivation potential. Conversely, we observe that well-imaged faults indicate a close similarity between the resulting fault surfaces when DL and manual fault interpretation methods are used; hence, we also observe a close similarity between any attributes and fault analyses made. </jats:p

A multi-country test of brief reappraisal interventions on emotions during the COVID-19 pandemic.

The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions, we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation. Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions (reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings demonstrate the viability of creating scalable, low-cost interventions for use around the world

How Fault Interpretation Method may Influence the Assessment of a Fault-Bound C02 Storage Site

Interpretation of faults in the subsurface hinges on utilizing an optimum picking strategy, i.e. the seismic line spacing. Differences in line spacing lead to significant changes in subsequent fault analyses such as fault growth, fault seal and fault stability, all of which are crucial when analyzing a fault-bound CO2 storage site. With the ever-advancing technologies, machine learning techniques, such as Deep Neural Networks (DNN), used for fault extraction are becoming increasingly common, however their limitations and corresponding uncertainty is still largely unknown. Here, we show how fault extraction using DNN compares with faults that have been picked manually, and with using different line spacing. Uncertainty related to both manual and automated fault extraction methods are heavily reliant on seismic quality. As such, faults that are well-imaged show a closer similarity between those that have been manually picked and automatically extracted. In cases of poorly imaged faults, DNN picking on narrower line spacing creates a fault that is more irregular and with a lower predicted stability than the smoother and simpler fault model created by manual picking. Thereby, DNN creates fault surfaces that are less stable than those that have been picked manually, which is assumed to be associated with the increased irregularity of the fault segments. We conclude that fault picking by DNN without in-depth expertise works for well-imaged faults; poorly imaged faults require additional considerations and quality control for both manually and DNN picked faults

Statistical Analysis of Displacement and Length Relation for Normal Faults in the Barents Sea

This paper is devoted to the statistical analysis of dependence between fault length (L) and displacement (D). The main purpose of this work is to study the scaling relations between fault length and displacement using a database that includes datasets of 21 faults with geometric data extracted from 3D seismic coherence cubes of the Norwegian Barents Sea. Multiple linear regression and Bayesian and Akaike information criterions are applied to obtain optimal regression parameters. Our dataset is unique since it includes segment lengths of individual faults, unlike the previously published datasets. Hence, we studied both the dependence of fault segment length and accumulated fault length on displacement. The latter relation (accumulated fault length versus displacement) shows a general agreement (positive correlation and power-law relation) with the previously published results that are mainly obtained from outcrop studies, although the slopes vary for different lithologies. The differences could be attributed to the unique characteristics of our dataset that includes data of all segment lengths of individual faults