Gas Saturation Monitoring In Heterogeneous Reservoir Using Tdt Modeling Technique

The Zeit Bay field reservoir units consist of sandstone and carbonates, partially overlaying a tilted block of fractured basement reservoir with a complex drive mechanism. A secondary recovery scheme of gas re-injection into the original gas cap was initiated to maintain reservoir energy and to overcome pressure decline. Hence accurate detection of gas movement is very critical. Several difficulties to monitor gas-oil contacts were encountered in a considerable number of wells. Some of these difficulties were, gas channelling behind the casing, gas coning, wellbore fluid changes, porosity and lithology changes, wellbore fluid invasion into the reservoir and the presence of formation stimulation fluid. The application of conventional methods using the response of gas indicator curves could result in a false indication of formation gas-oil contacts. This paper discusses the approach adopted in order to determine the gas-oil contact in wells where such problems occur. A database was established including more than 70 TDT runs, open hole log and pressure data of 12 infill wells, and production performance records of all Zeit Bay wells. The approach follows the Polyachenko model of functional relationship between count rates and gas saturation. Several crossplots for the same range of porosity and connate water saturation, e.g. formation capture cross section (SIGM), total selected near detector counts (TSCN), total selected far detector counts (TSCF), the capture cross section of the borehole (SIBH), and inelastic far detector counts (INFD). Each crossplot gives a definite diagnostic shape around the depth of the formation gas-oil contact By using these crossplots it will be possible to calculate gas saturation from a stand alone run. The model was validated by RFT and open hole log data from infill wells. Also it was successfully applied in wells which showed an ambiguity in the detected formation gas-oil contact. The field gas-oil contact in Zeit Bay was revised using the results of the model. This revision lead to an accurate definition of the oil column and to the drilling of three additional wells in the field. The open hole log results of these wells verified the gas-oil contact determined by the model

Association between loop diuretic dose changes and outcomes in chronic heart failure: observations from the ESC-EORP Heart Failure Long-Term Registry

Aims: Guidelines recommend down-titration of loop diuretics (LD) once euvolaemia is achieved. In outpatients with heart failure (HF), we investigated LD dose changes in daily cardiology practice, agreement with guideline recommendations, predictors of successful LD down-titration and association between dose changes and outcomes. Methods and results: We included 8130 HF patients from the ESC-EORP Heart Failure Long-Term Registry. Among patients who had dose decreased, successful decrease was defined as the decrease not followed by death, HF hospitalization, New York Heart Association class deterioration, or subsequent increase in LD dose. Mean age was 66 ± 13 years, 71% men, 62% HF with reduced ejection fraction, 19% HF with mid-range ejection fraction, 19% HF with preserved ejection fraction. Median [interquartile range (IQR)] LD dose was 40 (25–80) mg. LD dose was increased in 16%, decreased in 8.3% and unchanged in 76%. Median (IQR) follow-up was 372 (363–419) days. Diuretic dose increase (vs. no change) was associated with HF death [hazard ratio (HR) 1.53, 95% confidence interval (CI) 1.12–2.08; P = 0.008] and nominally with cardiovascular death (HR 1.25, 95% CI 0.96–1.63; P = 0.103). Decrease of diuretic dose (vs. no change) was associated with nominally lower HF (HR 0.59, 95% CI 0.33–1.07; P = 0.083) and cardiovascular mortality (HR 0.62,. 95% CI 0.38–1.00; P = 0.052). Among patients who had LD dose decreased, systolic blood pressure [odds ratio (OR) 1.11 per 10 mmHg increase, 95% CI 1.01–1.22; P = 0.032], and absence of (i) sleep apnoea (OR 0.24, 95% CI 0.09–0.69; P = 0.008), (ii) peripheral congestion (OR 0.48, 95% CI 0.29–0.80; P = 0.005), and (iii) moderate/severe mitral regurgitation (OR 0.57, 95% CI 0.37–0.87; P = 0.008) were independently associated with successful decrease. Conclusion: Diuretic dose was unchanged in 76% and decreased in 8.3% of outpatients with chronic HF. LD dose increase was associated with worse outcomes, while the LD dose decrease group showed a trend for better outcomes compared with the no-change group. Higher systolic blood pressure, and absence of (i) sleep apnoea, (ii) peripheral congestion, and (iii) moderate/severe mitral regurgitation were independently associated with successful dose decrease. © 2020 European Society of Cardiolog

Acute heart failure congestion and perfusion status – impact of the clinical classification on in-hospital and long-term outcomes; insights from the ESC-EORP-HFA Heart Failure Long-Term Registry

Aims: Classification of acute heart failure (AHF) patients into four clinical profiles defined by evidence of congestion and perfusion is advocated by the 2016 European Society of Cardiology (ESC)guidelines. Based on the ESC-EORP-HFA Heart Failure Long-Term Registry, we compared differences in baseline characteristics, in-hospital management and outcomes among congestion/perfusion profiles using this classification. Methods and results: We included 7865 AHF patients classified at admission as: ‘dry-warm’ (9.9%), ‘wet-warm’ (69.9%), ‘wet-cold’ (19.8%) and ‘dry-cold’ (0.4%). These groups differed significantly in terms of baseline characteristics, in-hospital management and outcomes. In-hospital mortality was 2.0% in ‘dry-warm’, 3.8% in ‘wet-warm’, 9.1% in ‘dry-cold’ and 12.1% in ‘wet-cold’ patients. Based on clinical classification at admission, the adjusted hazard ratios (95% confidence interval) for 1-year mortality were: ‘wet-warm’ vs. ‘dry-warm’ 1.78 (1.43–2.21) and ‘wet-cold’ vs. ‘wet-warm’ 1.33 (1.19–1.48). For profiles resulting from discharge classification, the adjusted hazard ratios (95% confidence interval) for 1-year mortality were: ‘wet-warm’ vs. ‘dry-warm’ 1.46 (1.31–1.63) and ‘wet-cold’ vs. ‘wet-warm’ 2.20 (1.89–2.56). Among patients discharged alive, 30.9% had residual congestion, and these patients had higher 1-year mortality compared to patients discharged without congestion (28.0 vs. 18.5%). Tricuspid regurgitation, diabetes, anaemia and high New York Heart Association class were independently associated with higher risk of congestion at discharge, while beta-blockers at admission, de novo heart failure, or any cardiovascular procedure during hospitalization were associated with lower risk of residual congestion. Conclusion: Classification based on congestion/perfusion status provides clinically relevant information at hospital admission and discharge. A better understanding of the clinical course of the two entities could play an important role towards the implementation of targeted strategies that may improve outcomes. © 2019 The Authors. European Journal of Heart Failure © 2019 European Society of Cardiolog

Performance of Prognostic Risk Scores in Chronic Heart Failure Patients Enrolled in the European Society of Cardiology Heart Failure Long-Term Registry

Objectives: This study compared the performance of major heart failure (HF) risk models in predicting mortality and examined their utilization using data from a contemporary multinational registry. Background: Several prognostic risk scores have been developed for ambulatory HF patients, but their precision is still inadequate and their use limited. Methods: This registry enrolled patients with HF seen in participating European centers between May 2011 and April 2013. The following scores designed to estimate 1- to 2-year all-cause mortality were calculated in each participant: CHARM (Candesartan in Heart Failure-Assessment of Reduction in Mortality), GISSI-HF (Gruppo Italiano per lo Studio della Streptochinasi nell'Infarto Miocardico-Heart Failure), MAGGIC (Meta-analysis Global Group in Chronic Heart Failure), and SHFM (Seattle Heart Failure Model). Patients with hospitalized HF (n = 6,920) and ambulatory HF patients missing any variable needed to estimate each score (n = 3,267) were excluded, leaving a final sample of 6,161 patients. Results: At 1-year follow-up, 5,653 of 6,161 patients (91.8%) were alive. The observed-to-predicted survival ratios (CHARM: 1.10, GISSI-HF: 1.08, MAGGIC: 1.03, and SHFM: 0.98) suggested some overestimation of mortality by all scores except the SHFM. Overprediction occurred steadily across levels of risk using both the CHARM and the GISSI-HF, whereas the SHFM underpredicted mortality in all risk groups except the highest. The MAGGIC showed the best overall accuracy (area under the curve [AUC] = 0.743), similar to the GISSI-HF (AUC = 0.739; p = 0.419) but better than the CHARM (AUC = 0.729; p = 0.068) and particularly better than the SHFM (AUC = 0.714; p = 0.018). Less than 1% of patients received a prognostic estimate from their enrolling physician. Conclusions: Performance of prognostic risk scores is still limited and physicians are reluctant to use them in daily practice. The need for contemporary, more precise prognostic tools should be considered