This article, written by Senior Technology Editor Dennis Denney, contains highlights of paper SPE 118727, "Where Is the Gap? Is It in More Reservoir Engineers or in Leveraging New Skills and Workflows That Enhance Individual Productivity?" by C. Amudo, SPE, Chevron Australia, and T. Graf, SPE, Schlumberger, prepared for the 2009 SPE Middle East Oil & Gas Show and Conference, Kingdom of Bahrain, 15–18 March. The paper has not been peer reviewed. Primary functions of reservoir engineers include estimating hydrocarbons in place, evaluating the recovery factor, and scheduling of the recovery. These roles are central in meeting the complex challenges of the development life cycle of hydrocarbon resources. These challenges have increased because mature assets require more attention to maximize recovery. The traditional deterministic approach of working is people-intensive and is no longer adequate. Statistical techniques can help in many core reservoir-engineering roles such as surveillance, history matching, and reservoir management. Introduction Our industry needs more people to meet the growing energy demands. The skills gap appears more pronounced in reservoir engineering because the complex issues may not migrate to automation with current traditional workflows. Reservoir-engineering practice relies, in large part, on limited, unstructured, and, often, uncertain data. With these limitations, statistical methods could be a vital part of a reservoir engineer's toolkit. Unfortunately, this has not been the case, and statistics has failed to make its way into mainstream reservoir-engineering practice. Statistics offers a disciplined approach to collecting, organizing, analyzing, and interpreting data. Statistics also facilitates the making of inferences, predictions, and decisions about the characteristics of a data population on the basis of information obtained from a subset of the population. Reservoir engineers can improve productivity and bridge the skills gap in the industry by harnessing statistical concepts and stochastic workflows that depend on these concepts. Data Handling Reservoir engineering centers on pattern recognition. Pattern recognition underpins well-test analysis, estimates of ultimate recovery for different development decisions, and diagnosis of reservoir and drainage-point performance. It also forms the basis for seeking analogs to verify or corroborate technical evaluations independently. To identify a pattern, one must overcome many data-related problems. These problems vary from scarcity of data in many exploration assignments to data overload in intelligent fields. This also includes how the adopted workflow handles uncertainties associated with the data.