Introduction and Validation of a Novel Acute Pancreatitis Digital Tool: Interrogating Large Pooled Data From 2 Prospectively Ascertained Cohorts

Objectives: Acute pancreatitis (AP) is a sudden onset, rapidly evolving inflammatory response with systemic inflammation and multiorgan failure (MOF) in a subset of patients. New highly accurate clinical decision support tools are needed to allow local doctors to provide expert care. Methods: Ariel Dynamic Acute Pancreatitis Tracker (ADAPT) is a digital tool to guide physicians in ordering standard tests, evaluate test results and model progression using available data, propose emergent therapies. The accuracy of the severity score calculators was tested using 2 prospectively ascertained Acute Pancreatitis Patient Registry to Examine Novel Therapies in Clinical Experience cohorts (pilot University of Pittsburgh Medical Center, n = 163; international, n = 1544). Results: The ADAPT and post hoc expert-calculated AP severity scores were 100% concordant in both pilot and international cohorts. High-risk criteria of all 4 severity scores at admission were associated with moderately-severe or severe AP and MOF (both P < 0.0001) and prediction of no MOF was 97.8% to 98.9%. The positive predictive value for MOF was 7.5% to 14.9%. Conclusions: The ADAPT tool showed 100% accuracy with AP predictive metrics. Prospective evaluation of ADAPT features is needed to determine if additional data can accurately predict and mitigate severe AP and MOF

Hydrocarbon Reservoir Parameter Estimation Using Production Data and Time-Lapse Seismic

The numerical simulation of hydrocarbon reservoir flow is necessarily an approximation of the flow in the real reservoir. The knowledge about the reservoir is limited and some of the processes occurring are either not taken into account or not described in an adequate way. The parameters influencing the flow are usually not known, except in a few well locations where they can be measured quite accurately. This knowledge, however, is too limited to describe the key reservoir properties in the domain of interest. Because of those imperfections the data gathered from a real field usually do not agree with the numerical simulation results. These data may therefore be used as input for an inversion process to update the most uncertain parameters of the numerical model, a process known as computer-assisted history matching. Typical uncertain parameters are (grid block) permeabilities and porosities, fault transmissbilities, aquifer strength or other reservoir or fluid properties. Different data sets can be used for the purpose of parameter estimation. Production data obtained from wells were shown to have a limited resolving power. They provide some information about parameters in the neighborhood of wells, but not further away from them. However, due to developments in geophysics, especially in the field of seismic, a new data set becomes available, namely time-lapse seismic, that can be used together with production data in the history matching process. This thesis focuses mainly on the incorporation of interpreted time-lapse seismic data in the form of time-lapse seismic density changes in computer-assisted history matching. For this purpose a particular variational data assimilation method was chosen, namely the representer method. Two-dimensional two-phase (oil-water) flow is considered in this thesis and the uncertain parameters are formed by the grid block permeabilities. The influence of seismic data on the final parameter estimate is analyzed for two synthetic examples. Although, seismic data give full field coverage, not all seismic measurements are used in the inversion process. An a-priori choice is made of the locations and number of seismic data used in the assimilation and only data at the saturation front moving over time are utilized, as they are considered to be the most informative ones. To investigate the influence of the prior knowledge on the estimation results, different correlation structures of the uncertain parameters are imposed and their impact on the final estimates is assessed. Additionally some attention is paid to the cases in which wrong prior information is utilized during assimilation. The estimation results are assessed in terms of the quality of the history match (mismatch between ‘true’ and simulated measurements), and in terms of predictions of water breakthrough time and water flow rates in producers after the history matching period.Department Imaging Science & TechnologyApplied Science