Drilling-Campaign Optimization Using Sequential Information and Policy Analytics

Summary Optimally designed drilling campaigns are essential for improving oil recovery and value creation. They are required at different stages of the hydrocarbon-field life cycle, including exploration, appraisal, development, and infill. A significant fraction of the revenue risk comes from geological uncertainty, and for this reason, subsurface teams are frequently responsible for optimizing campaign parameters such as the number of wells, the corresponding locations, and the drilling sequence. Companies use the information and learning from drilled wells to adapt the remainder of the campaign, but classical optimization methods do not account for such learning and flexibility over time. Accounting for sequential geological information acquisition and decision making in the optimization of drilling campaigns adds value to the project. We propose a method to optimize drilling campaigns under geological uncertainty by using a sequential-decision model to obtain the optimal drilling policy and applying analytics over the policy to obtain the optimal number of wells and corresponding locations. The novel contribution of policy analytics provides better access to information within the complex data structure of the optimal policy, providing decision support for different decision criteria. The method is demonstrated in two different cases. The first case considers a set of eight candidate wells on predefined locations, mimicking the situation where the method is used after a prior subsurface optimization. The second case considers a set of 12 candidate wells regularly scattered in the same area and uses the method as the first optimization approach to filter out less-attractive regions. Exploiting the geological information on a well-by-well basis improved the expected campaign value by 65% in the first case and by 183% in the second case. The value of spatial geological information and value of flexibility from having more drilling candidates are two byproducts of the method application.

The EU as multidisciplinary education podium: Fusing contextual and decisional policy analytics

The purpose of this article is to describe a multidisciplinary approach implemented in teaching public policy analysis at university level. The approach fuses (a) contextual policy analysis with (b) bivariate and multivariate analysis techniques and (c) data analytics skills to improve the learners’ competence to conduct “decisional” policy analysis. The teaching subject matter was based on European Union policies and Eurostat data and delivered over a period of 3 years in a semester course and summer school formats. The multidisciplinary fusion generated strong interest among learners and participants from various backgrounds and skill levels. At the end of the course, even those with little or no previous exposure in at least one of the teaching topics were able to quickly grasp the “big picture” and explore their newly acquired knowledge with their own analyses. As a whole, this is an educational design where the multidisciplinary fusion is instrumented in the scope of a single course, not as the combination of different courses in a curriculum. The outcome is a viable and contemporary way to sidestep the quantitative–qualitative analysis divide (where applicable) and help learners gain valuable insight into how seemingly different subjects eventually fuse into applicable knowledge. The multidisciplinary synergy that instigated the course design was mirrored in research effort by the authors leading to publishable work, suggesting that the approach can be adopted and adapted to foster or enhance multidisciplinary research efforts. Similar educational designs can be used in existing curricula to bridge seemingly “distant” subjects and improve the comprehension of how seemingly “distant” subjects relate to each other. As in the case described, in curricula where such subjects already exist, the “raw” expertise is already present and can be readily utilized.