Integrating geological uncertainty and dynamic data into modelling procedures for fractured reservoirs

Modelling, simulating and characterising flow through naturally fractured reservoirs is a multi-disciplinary effort. The scarcity of data combined with the additional layer of complexity that fractures add to a reservoir makes an efficient integration of all available data fundamental. However, the vast range of data types to be considered and the multitude of disciplines giving their input often results in communication barriers and individuals working within their comfort area, creating further challenges for uncertainty propagation. It is however critical for decision-making to develop geologically consistent reservoir models that recognise the challenges of simulating flow through systems with high permeability and scale contrasts and address the need for an ensemble of reservoir models to sufficiently cover geological uncertainties and their impact on fluid flow. In this work I developed several workflows for naturally fractured reservoir modelling that invite cross-disciplinary thinking by integrating geological uncertainties and dynamic data into the modelling procedure and foster ensemble modelling from the start. The workflows are tested on a synthetic field that is based upon a conceptual model for fold-related fracture distributions. The first workflow involves the use of multiple-point statistics to efficiently model reservoir-scale fracture distribution by upscaling discrete fracture networks and converting them into training images. To cover the impact of fracture-related geological uncertainties on fluid flow efficiently, flow diagnostics were used to screen and afterwards cluster and select training images according to their flow response for further reservoir modelling. The second workflow proposes a novel reservoir modelling technique that considers both static and dynamic data and utilises entropy to generate a diverse ensemble of reservoir models that all match an outset objective. Finally, an agent-based reservoir modelling workflow is introduced, where within a reservoir model, independent but interacting agents follow a set of rules to generate reservoir models that take into account geological prior information and expected dynamic flow responses to drive modelling efforts. Overall, we demonstrated that combining approaches from various disciplines into cross-disciplinary workflows provides great potential for subsurface characterisation. What workflow to adopt within a project, depends on various boundary conditions. The availability of data and time, the confidence in the understanding of the reservoir and the ultimate goal behind the modelling exercise. These factors can impact whether moving along with the simpler, more parametric multiple-point statistics workflow, the entropy-driven workflow that utilises static and dynamic data or the more data-driven agent-based modelling workflow is the right choice.James Watt Scholarshi

To which world regions does the valence–dominance model of social perception apply?

Over the past 10 years, Oosterhof and Todorov’s valence–dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov’s methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov’s original analysis strategy, the valence–dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence–dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution

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

Shared Adversity Increases Team Creativity Through Fostering Supportive Interaction

In the current era, building more innovative teams is key to organizational success, yet there is little consensus on how best to achieve this. Common wisdom suggests that positive reinforcement through shared positive rewards builds social support within teams, and in turn facilitates innovation. Research on basic group processes, cultural rituals, and the evolution of pro-group behavior has, however, revealed that sharing adverse experiences is an alternative path to promoting group bonding. Here, we examined whether sharing an adverse experience not only builds social support within teams, but also in turn enhances creativity within novel teams. Drawing on behavioral observation of an experimental group interaction we find evidence that sharing an adverse (vs. non-adverse) experience leads to increased supportive interactions between team members and this in turn boosts creativity within a novel team. These effects were robust across different indicators of creativity: objective measures of creativity, third party ratings of the creativity of group products, and participants' own perceptions of group creativity. Our findings offer a new perspective from which to understand how best to boost innovation and creative output within teams

Can Agents Model Hydrocarbon Migration for Petroleum System Analysis? A Fast Screening Tool to De-Risk Hydrocarbon Prospects

Understanding subsurface hydrocarbon migration is a crucial task for petroleum geoscientists. Hydrocarbons are released from deeply buried and heated source rocks, such as shales with a high organic content. They then migrate upwards through the overlying lithologies. Some hydrocarbon becomes trapped in suitable geological structures that, over a geological timescale, produce viable hydrocarbon reservoirs. This work investigates how intelligent agent models can mimic these complex natural subsurface processes and account for geological uncertainty. Physics-based approaches are commonly used in petroleum system modelling and flow simulation software to identify migration pathways from source rocks to traps. However, the problem with these simulations is that they are computationally demanding, making them infeasible for extensive uncertainty quantification. In this work, we present a novel dynamic screening tool for secondary hydrocarbon migration that relies on agent-based modelling. It is fast and is therefore suitable for uncertainty quantification, before using petroleum system modelling software for a more accurate evaluation of migration scenarios. We first illustrate how interacting but independent agents can mimic the movement of hydrocarbon molecules using a few simple rules by focusing on the main drivers of migration: buoyancy and capillary forces. Then, using a synthetic case study, we validate the usefulness of the agent modelling approach to quantify the impact of geological parameter uncertainty (e.g., fault transmissibility, source rock location, expulsion rate) on potential hydrocarbon accumulations and migrations pathways, an essential task to enable quick de-risking of a likely prospect

Hormone Replacement Therapy and Reduced Cognitive Decline in Older Women: The Cache County Study

Objective: To examine the association between postmenopausal hormone replacement therapy (HRT) and the trajectory of global cognitive change with age. Methods: The Modified Mini-Mental State Examination (MMSE) was administered to a population sample of 2,073 nondemented, community-dwelling female residents of Cache County, UT, aged 65 and older. Current and past HRT and other medications at a baseline interview and at follow-up 3 years later were assessed. Between interviews, a telephone Women’s Health Questionnaire was administered to assess initial exposure, duration, and recency of HRT. Generalized estimating equation marginal models were used to evaluate the cross-sectional and longitudinal relations of HRT and modified MMSE score. Also assessed were effects with multivitamins and calcium supplements as exposures likely to reflect a healthy lifestyle among HRT users. Model covariates included the presence of APOE {epsilon}4 alleles, age, education, concurrent depression, several chronic diseases, and self-perceived general health. Results: Age, lower education, depression, and APOE {epsilon}4 were all associated with lower baseline modified MMSE scores. With these covariates in the model, lifetime HRT use was associated with better baseline modified MMSE scores and a slower rate of decline. Stratification by APOE genotype did not alter these effects. Apparent benefits with HRT were attenuated but remained significant after elimination of scores from participants with incident dementia. A significant interaction between age and HRT indicated the strongest effects in women aged 85 and older. Measures of age at initial use of HRT, duration, and recency of exposure did not improve the models. No effects were seen with the healthy lifestyle control exposures. Conclusions: In a population cohort of older women, lifetime HRT exposure was associated with improved global cognition and attenuated decline over a 3-year interval. Improvements were greatest in the oldest old