This paper presents the results of applying a novel nonlinear regression method, Variable Structure Regression (VSR), to forecasting well performance given the well completion and rock composition data. We compiled and analyzed data from 79 producing wells from the same unconventional reservoir. Calibration using the performance data of 60 wells was used to predict the behavior for the rest, and the predictions are quite successful. Input parameters for the regression model were the number of frac stages, the average length of each stage, isochore, total organic content, proppant-to-fluid ratio and a rock brittleness metric derived from illite content. The cumulative oil production after three months and after 18 months was considered as outputs of the prediction model. Scatterplot analysis did not indicate any obvious correlations between the individual input parameters and the output, thereby necessitating the use of a more complex multi-parameter model. Given the relatively small training data size and the complexity of the problem, the VSR method achieved satisfactory prediction accuracy. For predicting 3-month cum oil, model calibration using the performance data of 60 wells was used to predict the behavior for the remaining 19. About 70% of the predictions were within a 30% margin of error. For predicting 18-month cum oil, data from 33 wells was used to predict the production of 10 wells. About 80% of the predictions for the 18-month cum production were within the 30% error margin.

Real Time Production Optimization (RTPO) solutions have been deployed in Chevron assets starting in the 2008-2009 timeframe as part of the i-field® program. These solutions provide a common platform for accessing and integrating real-time field and well performance data with predictive modeling results. RTPO is a solution in the i-field® program that encompasses all aspects of people, process, and technology, with the objective of fundamentally transforming the way highly instrumented assets of the future are monitored using the right combination of physics-based modeling and high frequency real time field data.Chevron uses the Digital Oil Field tool suite from Petroleum Experts Ltd. (Petex) for RTPO deployments. The enterprise common solution includes model-based surveillance and analysis workflows, data integration with real-time field data and well test data, and visualization interfaces. Over the past few years, RTPO solutions have been deployed to a large number of Chevron oilfield assets worldwide, each presenting its own opportunities and challenges. In this paper, we will summarize key lessons learned from our experience so far, and our vision of the future of RTPO in Chevron.