A Framework for Uncertainty Quantification and Technical-to-Business Integration for Improved Investment Decision-Making

Abstract

To support the E&P investment decision-making process we use computer models extensively. This paper discusses in a conceptual way the potential merits of moving much more than what is currently being practised into the direction of "fully probabilistic" and "fully holistic" modeling, initially at the cost of model precision. In our opinion, the currently prevailing paradigm of maximum model precision (i.e. more physics, more grid blocks, more detail) may severely limit the optimization of the E&P decision-making process. Evidence for this statement is obtained when analyzing why the average of production and cashflow forecasts generally fails to coincide with the truth, as revealed in time: high-precision models on average result in biased forecasts and, hence, often lead to sub-optimal decisions or missed opportunities. To explain our approach, we introduce two postulates, discuss three modeling dimensions and link this to the business process of decision-making. The first postulate, when modeling the E&P value chain for investment decision-making, is that all uncertainties having a potential material effect on the model outcomes must be quantified within a comprehensive, internally consistent framework, and be taken into account when making E&P investment decisions. The second postulate is that, when trading-off the degree of "model precision" vs. the degree of uncertainty modeling and/or the degree of holistic value chain modeling, the latter two are more important than the former, especially when uncertainties are large. Initially therefore, reduced-physics or "approximate" models may be used, enabling a more comprehensive decision analysis. After having thus optimized the decisions, the sensivity (or "robustness") of the optimal decision to model precision should be tested. Multi-tiered decision-making, real options and an extended definition of "value" are also discussed within the framework's context. Introduction The current debate on uncertainty modeling and decision-making, in our opinion, lacks a clear framework and, hence, lacks the conditions to make substantial progress in the area of improved forecasts and decision-making. A multitude of professional meetings have been held on this theme by a multitude of organizations. The meetings however generally result in limited guidance on how to proceed conceptually and often divert into rather detailed building blocks that fail to demonstrate their relevance within the overall framework of decision-making. While acknowledging the many excellent papers by a variety of authors, we would like to build on their work and present a framework aimed at putting the building blocks together. Since the final objective of our modeling activities is to support the decision-making process, the role of each detailed modeling activity should be understood within this context. The value loop The first step is to understand how "value" is generated. Value is not generated by an E&P company's physical assets only (i.e. hydrocarbon reservoirs). In order to capture the potential value of an asset, data on that asset must be acquired, the data must be processed and interpreted, mathematical models of the assets must be constructed in order to assess the benefits of certain actions (e.g. investments), decision options must be generated, decision criteria must be applied under various constraints, the optimal decision is then implemented, which again results in the intrinsic value being realized and new data being acquired. In this "value loop", the role of models is essential. Models allow decisions to be made and activities to be implemented, thereby generating new data and closing the loop. Let us therefore look more in detail into how we can conceptualise our modeling work.

Effective EOR Decision Strategies With Limited Data: Field Cases Demonstration

“…This is the case analytical-simulation and conceptual 5000 10000 5000 10000 sector models for IOR/EOR. Given limitations and uncertainties in data sources (models) and time constraints, simplified analyses are often a better solution to certain decision-making problems (Bos 2005). Embarking on IOR/EOR projects with a hierarchy of successive interdependent decisions can facilitate the task of promoting further development in a field.…”

“…Although this will not always be correct or true, what turns out to be true is that even if a complete and sophisticated model were available, the reality of production can be poorly predicted. Moreover, if time constraints are stringent, more-complex models tend to yield suboptimal decisions (Bos 2005). …”

“…Bos (2005) shows that lower precision and a higher level of modeling of uncertainty and integration might be necessary to optimize the exploration and production decision-making process. This may be attainable at the expense of trading off the degree of model precision vs. the degree of uncertainty modeling and integration in favor of the latter.…”

“…This paper briefly describes steps of our screening method, providing more details of various analytical-and numerical-simulation approaches used for different field studies as part of the continuous development of the proposed IOR/EOR screening method. Performance estimation by means of simplified models allows one to explore a wider range of decision opportunities, as highlighted by Bos (2005). Six case studies summarized in this paper present the levels of information available and the decisions made by the operators on the basis of the results of the screening.…”

Geological and Technological Parameters and Development of Deposits in Carbonate Reservoirs to Solve Exploration Tasks

“…It is important to know what effect various geological and technological parameters have on the depletion of deposits to analyze, manage and design oilfield development [1][2][3][4][5][6][7][8]. This is of particular importance to produce deposits in carbonate reservoirs of the Volga-Ural oil and gas province that contains significant residual oil reserves.…”