Abstract: A method and system for assessing and optimizing crude selection are provided. A predictive engine uses data from a database to execute at least one predictive performance model and/or at least one risk assessment model designed to optimize or improve refining operations during a refining process. The predictive engine takes as input key crude information corresponding to a particular crude or crude blend, e.g., at least one crude slate, and refinery operating parameters and/or conditions corresponding to a specific refinery and uses desirability metrics to assess the similarity to data in the database. Based on the resulting output, at least one predictive performance and/or at least one risk assessment model uses the output to predict performance or risk measures of refining the particular crude or crude blend using the specific refinery during the refining process, the probability of problems occurring during the refining process, the distribution of the problems throughout the refining process, etc.

Abstract: A technique is disclosed for evaluating and monitoring performance of a heat exchanger system. Operating parameters of the system are monitored and fouling factors for heat transfer surfaces of the exchanger are determined. Trending of fouling may be performed over time based upon the fouling factors, and a model of fouling may be selected from known sets of models, or a model may be developed or refined. Fluid treatment, such as water treatment regimes may be taken into account in evaluation of fouling. An automated knowledge based analysis algorithm may diagnose possible caused of fouling based upon sensed and observed parameters and conditions. Corrective actions may be suggested and the system controlled to reduce, avoid or correct for detected fouling.

Abstract: A method and system for assessing and optimizing crude selection are provided. A predictive engine uses data from a database to execute at least one predictive performance model and/or at least one risk assessment model designed to optimize or improve refining operations during a refining process. The predictive engine takes as input key crude information corresponding to a particular crude or crude blend, e.g., at least one crude slate, and refinery operating parameters and/or conditions corresponding to a specific refinery and uses desirability metrics to assess the similarity to data in the database. Based on the resulting output, at least one predictive performance and/or at least one risk assessment model uses the output to predict performance or risk measures of refining the particular crude or crude blend using the specific refinery during the refining process, the probability of problems occurring during the refining process, the distribution of the problems throughout the refining process, etc.

Abstract: A knowledge management system and methods for crude oil refining are provided. The knowledge management system and methods enable information, such as crude oil characterizations, the experiences of processing crude oils in the refinery, problems encountered during the processing of crude oil in the refinery, and the treatments applied by personnel of the refinery to counteract those problems, to be captured, organized, and disseminated. The knowledge management system and methods for crude oil refining further provide a basis for running analytical models and other decision support applications to assess risks and financial impacts; thus, allowing users to make informed decisions for crude selection and treatment planning.