In recent years, much effort has been spent in integration of the hydrocarbon E&P business processes. The new challenge lies in the use of the generated data for decision-making. In particular, in hydrocarbon assets, where large uncertainties occur, it is important to include the formal quantification of these uncertainties in the integrated workflow and allow for a decision framework based on a full characterization of these uncertainties. For quantification of uncertainties two classes of approach are currently in use –the probabilistic approach based on the description of a stochastic model for capturing all uncertainty and the scenario approach based on a definition of a number of conceptually different models expressing the uncertainty. Some hybrid versions of the approaches exist as well. The pros and cons of these approaches are discussed. Following this, an extended statistical framework is presented in which the previous approaches to subsurface uncertainty modelling have been combined. The framework is not limited to subsurface uncertainties, but can also be applied to the other uncertainties and decisions in the asset model. In the integrated framework several scenarios may be identified for the static earth model, the dynamic earth model, the drilling model, the surface facilities model and the economic model. A scenario tree can represent the combination of all these scenarios. Within each scenario a stochastic model can be applied to capture the within-scenario uncertainty. Calculation rules for the integration of the stochastic and scenario uncertainties are presented. Finally, decision rules are given that allow for decision-making based on the full uncertainty span in the integrated asset framework.
In recent years the emphasis in the development of new methodologies and tools has been on integration of data and applications. The new challenge lies in using the data generated by these packages to support the decision making process. In a joint industry project NITG-TNO have developed an E&P Decision Support System which helps the E&P asset manager to make more informed decisions regarding the exploration and development of hydrocarbon assets at any given moment in time. In the E&P DSS the data and information from earth modeling, surface engineering and economics are combined into an integrated asset model. It is then possible to place the technical decisions in an economic framework and come to a more transparent and auditable decision making process based on more diverse sources of information. The asset manager can define scenarios representing the key decision and uncertainties for the asset. From the defined scenarios a scenario tree is built automatically. Within each combination of scenarios, full probabilistic assessment of the decision indicators in the asset model is still possible. The assessment can either be done based on Monte Carlo analysis using fast evaluation models, or by loading data for multiple stochastic realizations generated by external software. The probability distributions for the scenarios are then merged to form the total uncertainty. The options for the key decisions are then evaluated using the full probabilistic assessment. Finally, the sensitivity of the decisions on the input parameters can be analyzed. The method is demonstrated for a typical development decision. In the case study, decisions must be made for the best type of surface facilities and wells, and whether water injection is preferred. Key uncertainties are the conductivity of a fault, the strength of the aquifer and the oil price. Application of the methodology allows the assessment. Introduction Several levels of decision making can be recognised in the E&P business (see Figure 1). The lowest level represents day-to-day decisions regarding activities within a single discipline, such as geological or seismic interpretation, reservoir simulation etc. The second level represents the workflow level. This level refers to decisions regarding the concatenation of several single activities into a workflow to achieve a larger technical task. Examples of such tasks are making a volumetric estimate, a production forecast or a surface facilities design. These are the two technical levels. The next level is the asset management level, where decisions are made for a complete asset. Here an asset is a hydrocarbon field or a set of hydrocarbon fields, which form a unit, for example because they are developed or produced through a common pipeline. Finally, the top level is the strategic level, where decisions are made at the portfolio or corporate level of an oil company. Decisions on this level deal with management of a portfolio of assets, or strategic decisions such as entering a new hydrocarbon province or not. In this paper we demonstrate a decision support system that supports decision making on the asset management level. For a particular asset, models have to be developed and evaluated for(Static and dynamic) earth modelSurface Facilities modelEconomic model Traditionally, such models were developed independently. Consequently, decisions based on the evaluations of the models were taken independently. With the change of focus from mono-disciplinary teams to multi-disciplinary asset teams in many oil companies, it has now become important to build integrated models where all three components are represented. With such an integrated model it is possible to quantify the influence of uncertainties and decisions in one component on the evaluation of a decision in another component.
A methodology is presented for uncertainty estimation in volumetrics. Firstly, we stress the need for an open hierarchical methodology. This allows for a flexible work process in estimating uncertainty throughout the asset life cycle, in which data of various scales and accuracy must be integrated. Secondly, a method is explained for the transfer of spatial uncertainty in structure and rock properties to uncertainty in hydrocarbon volume. Thirdly, a new technique is presented for calculating average water saturation. Application to a synthetic case study shows that scalar uncertainty calculation leads to an underestimation of the uncertainty compared to a spatial approach. Spatial mapping of standard deviation of net hydrocarbon column indicates extra potential in the field. Incorporation of correlations in the field between, for example, porosity, permeability and water saturation, increases the uncertainty range. Using extra wells in the uncertainty estimation reduces uncertainty. Now, the known volume lies within the estimated proven-to-probable range.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.