Caveats and Pitfalls of Production Forecast Uncertainty Analysis Using Design of Experiments

Design of Experiments (DoE) is one of the most commonly employed techniques in the petroleum industry for Assisted History Matching (AHM) and uncertainty analysis of reservoir production forecasts. Although conceptually straightforward, DoE is often misused by practitioners because many of its statistical and modeling principles are not carefully followed. Our earlier paper (Li et al. 2019) detailed the best practices in DoE-based AHM for brownfields. However, to our best knowledge, there is a lack of studies that summarize the common caveats and pitfalls in DoE-based production forecast uncertainty analysis for greenfields and history-matched brownfields. Our objective here is to summarize these caveats and pitfalls to help practitioners apply the correct principles for DoE-based production forecast uncertainty analysis. Over 60 common pitfalls in all stages of a DoE workflow are summarized. Special attention is paid to the following critical project transitions: (1) the transition from static earth modeling to dynamic reservoir simulation; (2) from AHM to production forecast; and (3) from analyzing subsurface uncertainties to analyzing field-development alternatives. Most pitfalls can be avoided by consistently following the statistical and modeling principles. Some pitfalls, however, can trap experienced engineers. For example, mistakes made in handling the three abovementioned transitions can yield strongly unreliable proxy and sensitivity analysis. For the representative examples we study, they can lead to having a proxy R2 of less than 0.2 versus larger than 0.9 if done correctly. Two improved experimental designs are created to resolve this challenge. Besides the technical pitfalls that are avoidable via robust statistical workflows, we also highlight the often more severe non-technical pitfalls that cannot be evaluated by measures like R2. Thoughts are shared on how they can be avoided, especially during project framing and the three critical transition scenarios.

Buy Now and Pay (Dearly) Later: Unraveling Consumer Financial Spinning

In this challenging and innovative article, we propose a framework for the consumer behavior named “consumer financial spinning”. It occurs when borrowers-consumers of products with high financial stakes accumulate unsustainable debt and disconnect from their initial financial hierarchy of needs, wealth-related goals, and preferences over their household portfolio of assets. Three behaviors characterize daredevil consumers as they spin their wheel of misfortune, which together form a dark financial triangle: overconfidence, use of rationed rationality, and deceitfulness. We provokingly adapt some of the tenets of the Markowitz and Capital Asset Pricing models in the context of the predatory paradigm that consumer financial spinning entails and use modeling principles from the data percolation methodology. We partially test the proposed framework and show under what realistic conditions the relationship between expected returns and risk may depart from linearity. Our analysis and results appear timely and important because a better understanding of the psychological conditions that fuel intense speculation may restrain market frictions, which historically have kept reappearing and are likely to reoccur on a regular basis.

To the Issue of Mathematical Modeling of the Red Mud Thickening Process

The article focuses on the main mathematical modeling principles for engineering processes. The physical model of the red mud thickening process has been formed. The choice of mathematical model type has been described where the mathematical model represents the physicochemical character of the thickening process and allows estimating pulp water-yielding features at the stage of compression. Mathematical modeling of the engineering process, based on the studies of physicochemical patterns in its course and consideration of these patterns in the mathematical model, does not have certain disadvantages. Experimental data, used at the mathematical model formation where the mathematical model represents the physicochemical mechanism of the process, serve for their further analysis, physicochemical and mathematical interpretation. The mathematical model should be used as a method for detecting internal patterns in the process and for identification and quantitative assessment of its features.

Modeling the Management of International Reserves from the Perspective of Financial Stability

The paper explores the issues and international practices of the management of international reserves. The link is described between financial stability and international reserves. Emphasis is put on the specific significance of this subject for emerging economies. The main directions are charted for developing a systemic management approach in the domain and a case is made for applying modeling principles.