The method by which the space of uncertainty should be sampled in reservoir models is an essential point of discussion that can have a major impact on the appraisal, development and economics of hydrocarbon fields.
Usually, uncertainty is assessed by way of many realizations of a given stochastic model with fixed parameters but this does not adequately sample the complete geological uncertainty space and it underestimates the uncertainty.
To improve the uncertainty assessment, in this article we propose a classification and hierarchy of the sources of internal reservoir architecture uncertainty.
The impact of the different levels and sources of uncertainty on elements such as original hydrocarbons in place or final recovery is quantified.
In this article we demonstrate that decisions as significant as stationarity, organization of reservoir heterogeneity, choice of the global statistics and variogramme range are of prime importance in relation to the other parameters. Furthermore, we also prove that the relative importance of any parameter in assessing the uncertainty depends on the type of result—volumetric or dynamic—on which the uncertainty calculation is computed.
Uncertainty of the global mean provided by hard data is assessed using sampling techniques.
The coefficients of variation obtained from many field cases allow a comparison between the different distributions. It lends support to the hierarchy of the sources of geological uncertainty into six levels.
The evolution of this uncertainty with the quantity of hard data, wells, and the quality of knowledge, zoning, has been quantified using synthetic models and a field case.
Both a general function for the decrease of uncertainty with the quantity of the data, and a possible increase—for some parameters—of uncertainty estimates with knowledge development are demonstrated herein.
The assessment phase is a crucial step in the life of a field, since the oil industry is faced today with titanic challenges, deep offshore, increasingly complex and small-size reservoirs and fierce international competition—which require a threefold approach:
financial risk related to the uncertain aspect of the natural element exploited by the industry;
technological research to reduce such uncertainties and the induced financial risks;
qualification and quantification of these uncertainties so that the risk is known, selected and managed by decision makers.
During the development of a reservoir, a number of parameters remain uncertain: original hydrocarbons in place (OHIP), production profiles, the final recovery factor, field zoning and compartmentalization, well productivity indexes (PI) etc.
Some of these parameters (OHIP, recovery factor) have been subjected to more or less valid uncertainty calculations for a number of years since they are key parameters for the economics of a project and because they can be quantified.
Other elements are just as uncertain but not so easy to quantify—will the well being drilled encounter a good or a deteriorated reservoir? These are usually not included in conventional uncertainty calculations.
In order to quantify the true uncertainty on different parameters, it appears necessary to quantify the uncertainty on the results provided by various sources of information on the reservoir, i.e., the different techniques implemented during the appraisal phase.
Among these different sources of uncertainty, geology plays a major role because it drives understanding of the internal reservoir architecture and the spatial distribution of reservoir characteristics.
Usually, the impact of the geological uncertainty is studied by varying the petrophysical parameters (f,Sw,k, kr, etc.) which are determinant on the uncertain parameter under scrutiny (OHIP, production profile, etc.).
In doing so, a direct relation is used between the actual geological uncertainty—facies proportion, size, organization, evolution in space, palaeogeography—and the uncertainty on the petrophysical parameter. The relation between the geological uncertainty and the uncertainty on a petrophysical parameter is known; it places the sources of geological uncertainty on the same level.
This approach appears too primitive since it presumes that the relation between the geological uncertainty and the uncertainty on the petrophysical parameter is known and it places all the uncertainties on the same level. It therefore appears necessary to produce a classification and hierarchical organization of the sources of geological uncertainty prior to assessing the uncertainties on parameters used in economic calculations.
In the present article we discuss a proposal for a six-level classification of geological uncertainty depending on the degree of interpretation and utilization in stochastic reservoir models.
The impact of the different levels of geological uncertainty on parameters such as OHIP or the field recovery factors has been evaluated. These results allow one to determine the key sources of geological uncertainty.
As described by Journel,1 the assessment of uncertainty is closely linked to the prediction of the uncertainty from the results of a model.
Since the models were constrained by available data, the uncertainty estimates based on the application of a model are also influenced by the data used to calibrate the model.
Consequently, the type, quantity and quality of the data used to build the model from which the uncertainty is estimated must be therefore seriously discussed before the uncertainty calculation is validated.