Two Phase Flow in Nodular Systems: Laboratory Experiments Écoulements polyphasiques dans des systèmes nodulaires : expériences de laboratoire

Heterogeneity effects within the grid block of a reservoir numerical model are generally taken into account by using pseudo-relative permeability and capillary pressure curves. Some insight into the physics of these pseudo-functions can be obtained from multiple scale analysis such as largescale averaging. Previous theoretical results have shown specific behaviour associated with nodular systems. Laboratory scale experiments were performed that emphasize the difference in behaviour of nodular systems when the properties of the different regions are interchanged. Sandstone nodules embedded in a more permeable continuous region made of sand showed a quasi-static behaviour under experimental conditions while the reverse situation, i. e. sand nodules in a sandstone continuous region, exhibited a totally different behaviour in terms of final saturation and recovery curves. These results are discussed with respect to the large-scale averaging objective. Notre étude s'inscrit dans le cadre général de la prise en compte des hétérogénéités dans la modélisation des écoulements polyphasiques en milieu poreux. Les modèles de gisement utilisent le plus souvent les méthodes de pseudo-fonctions , faisant l'hypothèse que les équations décrivant les écoulements polyphasiques ne sont pas modifiées, dans leur forme, par le changement d'échelle. En fait, il est reconnu que le processus de changement d'échelle peut conduire à des propriétés transitoires, directionnelles et dynamiques. Il est donc nécessaire de bien connaître la physique des écoulements à l'échelle locale pour décrire l'écoulement à une échelle plus grande incluant les hétérogénéités. Le problème physique auquel nous nous intéressons est représenté figure 1. Lors de travaux précédents, Quintard et Whitaker (1988) ont obtenu les résultats suivants : - Dans le cas quasi-statique (écoulement contrôlé par les forces capillaires), les équations à grande échelle sont de même nature qu'à l'échelle locale. - Dans le cas d'un système nodulaire, la désaturation quasi-statique peut conduire à une inversion du rôle des régions (la région initialement la plus perméable peut devenir la région la moins perméable et inversement). - Il y a une grande différence de comportement entre les deux systèmes nodulaires suivants : système I où la région oméga constitue le nodule et la région êta la région continue, et système II où la région oméga est la région continue et la région êta constitue le nodule

Two Phase Flow in Nodular Systems: Laboratory Experiments

Heterogeneity effects within the grid block of a reservoir numerical model are generally taken into account by using pseudo-relative permeability and capillary pressure curves. Some insight into the physics of these pseudo-functions can be obtained from multiple scale analysis such as largescale averaging. Previous theoretical results have shown specific behaviour associated with nodular systems. Laboratory scale experiments were performed that emphasize the difference in behaviour of nodular systems when the properties of the different regions are interchanged. Sandstone nodules embedded in a more permeable continuous region made of sand showed a quasi-static behaviour under experimental conditions while the reverse situation, i. e. sand nodules in a sandstone continuous region, exhibited a totally different behaviour in terms of final saturation and recovery curves. These results are discussed with respect to the large-scale averaging objective

Symptomatology comparison between persons with post-COVID conditions and U09.9 codes.

Symptomatology comparison between persons with post-COVID conditions and U09.9 codes.</p

One hundred lowest incidence ratios, comparing persons with COVID to age, month and propensity-score matched persons.

One hundred lowest incidence ratios, comparing persons with COVID to age, month and propensity-score matched persons.</p

One hundred highest incidence ratios, comparing persons with COVID to age, month and propensity-score matched persons.

One hundred highest incidence ratios, comparing persons with COVID to age, month and propensity-score matched persons.</p

Cohort selection process.

ObjectiveTo create a data-driven definition of post-COVID conditions (PCC) by directly measure changes in symptomatology before and after a first COVID episode.Materials and methodsRetrospective cohort study using Optum® de-identified Electronic Health Record (EHR) dataset from the United States of persons of any age April 2020-September 2021. For each person with COVID (ICD-10-CM U07.1 “COVID-19” or positive test result), we selected up to 3 comparators. The final COVID symptom score was computed as the sum of new diagnoses weighted by each diagnosis’ ratio of incidence in COVID group relative to comparator group. For the subset of COVID cases diagnosed in September 2021, we compared the incidence of PCC using our data-driven definition with ICD-10-CM code U09.9 “Post-COVID Conditions”, first available in the US October 2021.ResultsThe final cohort contained 588,611 people with COVID, with mean age of 48 years and 38% male. Our definition identified 20% of persons developed PCC in follow-up. PCC incidence increased with age: (7.8% of persons aged 0–17, 17.3% aged 18–64, and 33.3% aged 65+) and did not change over time (20.0% among persons diagnosed with COVID in 2020 versus 20.3% in 2021). For cases diagnosed in September 2021, our definition identified 19.0% with PCC in follow-up as compared to 2.9% with U09.9 code in follow-up.ConclusionSymptom and U09.9 code-based definitions alone captured different populations. Maximal capture may consider a combined approach, particularly before the availability and routine utilization of specific ICD-10 codes and with the lack consensus-based definitions on the syndrome.</div

Propensity score description.

ObjectiveTo create a data-driven definition of post-COVID conditions (PCC) by directly measure changes in symptomatology before and after a first COVID episode.Materials and methodsRetrospective cohort study using Optum® de-identified Electronic Health Record (EHR) dataset from the United States of persons of any age April 2020-September 2021. For each person with COVID (ICD-10-CM U07.1 “COVID-19” or positive test result), we selected up to 3 comparators. The final COVID symptom score was computed as the sum of new diagnoses weighted by each diagnosis’ ratio of incidence in COVID group relative to comparator group. For the subset of COVID cases diagnosed in September 2021, we compared the incidence of PCC using our data-driven definition with ICD-10-CM code U09.9 “Post-COVID Conditions”, first available in the US October 2021.ResultsThe final cohort contained 588,611 people with COVID, with mean age of 48 years and 38% male. Our definition identified 20% of persons developed PCC in follow-up. PCC incidence increased with age: (7.8% of persons aged 0–17, 17.3% aged 18–64, and 33.3% aged 65+) and did not change over time (20.0% among persons diagnosed with COVID in 2020 versus 20.3% in 2021). For cases diagnosed in September 2021, our definition identified 19.0% with PCC in follow-up as compared to 2.9% with U09.9 code in follow-up.ConclusionSymptom and U09.9 code-based definitions alone captured different populations. Maximal capture may consider a combined approach, particularly before the availability and routine utilization of specific ICD-10 codes and with the lack consensus-based definitions on the syndrome.</div

Comparison of data-driven definition to ICD-10-CM code U09.9.

Comparison of data-driven definition to ICD-10-CM code U09.9.</p

Cohort characteristics.

ObjectiveTo create a data-driven definition of post-COVID conditions (PCC) by directly measure changes in symptomatology before and after a first COVID episode.Materials and methodsRetrospective cohort study using Optum® de-identified Electronic Health Record (EHR) dataset from the United States of persons of any age April 2020-September 2021. For each person with COVID (ICD-10-CM U07.1 “COVID-19” or positive test result), we selected up to 3 comparators. The final COVID symptom score was computed as the sum of new diagnoses weighted by each diagnosis’ ratio of incidence in COVID group relative to comparator group. For the subset of COVID cases diagnosed in September 2021, we compared the incidence of PCC using our data-driven definition with ICD-10-CM code U09.9 “Post-COVID Conditions”, first available in the US October 2021.ResultsThe final cohort contained 588,611 people with COVID, with mean age of 48 years and 38% male. Our definition identified 20% of persons developed PCC in follow-up. PCC incidence increased with age: (7.8% of persons aged 0–17, 17.3% aged 18–64, and 33.3% aged 65+) and did not change over time (20.0% among persons diagnosed with COVID in 2020 versus 20.3% in 2021). For cases diagnosed in September 2021, our definition identified 19.0% with PCC in follow-up as compared to 2.9% with U09.9 code in follow-up.ConclusionSymptom and U09.9 code-based definitions alone captured different populations. Maximal capture may consider a combined approach, particularly before the availability and routine utilization of specific ICD-10 codes and with the lack consensus-based definitions on the syndrome.</div

Total variation distance measures of post-COVID conditions and U09.9 populations.

Total variation distance measures of post-COVID conditions and U09.9 populations.</p