Assessment of Global Uncertainty for Early Appraisal of Hydrocarbon Fields

Abstract

Abstract We propose a workflow to assess the uncertainty about a global reservoir parameter such as net-to-gross during early exploration. As opposed to traditional statistical approaches that assume data independence and cannot easily account for either seismic data or geological interpretation, this model, based on multiple point statistics, integrates the main components of uncertainty, namely:the choice of a geological scenario, probably the most important factor at the appraisal stage.The location of wells, which could have been drilled elsewhere, giving a different picture of the reservoir.The calibration of the seismic to the well data. This global uncertainty model is demonstrated on a large 3D fluvial reservoir. Introduction Advances in deep water drilling technologies have cleared the path for new domains of hydrocarbon exploration. The appraisal of such deep offshore reservoirs is a high risk exercise: in addition to political and economical unknows, the sparsity of early exploration data compounds with the geological complexity of turbiditic formations, making any global reserve estimate highly uncertain. However, early in the appraisal stage, corporate decisions must be made about developing the field by drilling one or several new wells, or just abandoning the field and moving on to a safer prospect. Decision science provides tools to address this type of issue1, but calls for a sound assessment of uncertainty about the reservoir potential. In deep offshore reservoirs, the uncertainty on the oil in place is controlled in great part by the reservoir geometry and the pore volume, the latter depending on the net-to-gross (NTG). At this stage, such global quantities can only be estimated from 3D seismic and a small number of exploratory wells. Dynamic information about the reservoir is not yet available: no production or well test data can be used to constrain the petrophysical model. Moreover, the wells are typically drilled in likely high-pay areas, which may introduce a bias in the global estimates. In this article, we propose a new framework to quantify the uncertainty of such a global reservoir parameter. We first state the difficulty of assessing the uncertainty of a global event that has, by definition, no replicates in space or time; we point out that statistical techniques commonly used for that purpose rely on unrealistic data independence hypothesis and cannot handle jointly the different types of early exploration information available (wells, seismic, and geological interpretation). Then, we present our uncertainty assessment framework, which accounts for all data available and for the geological scenario(s) inferred from these data, the latter being a significant component of uncertainty. This model is demonstrated on a synthetic, exhaustively known reservoir for assessing the uncertainty on its net-to-gross. Uncertainty assessment Uncertainty assessment is an uneasy endeavor2,3 because it forces us to face our ignorance and then to quantify this ignorance. Even when abundant and reliable information are available, e.g., for mature reservoirs, uncertainty should not be disregarded in decision making4. This remark is even more relevant during early exploration of petroleum prospects5,6. What is an appropriate statement of uncertainty? By definition, uncertainty cannot be objectively measured or even checked. It is simply not possible to verify objectively the truth of a statement like "given the available data, the probability for the NTG being lower than 0.15 is 10%". Such a statement necessarily results from retaining a particular distribution model and from the choice of its parameters. This model and its parameters completely determine the results obtained, and they cannot ever be fully objective. Being subjective, a statement of uncertainty calls for a clear and honest description of the model used and for a justification of the parameters input to that model. This is the only way to judge whether a statement of uncertainty is appropriate for the problem at hand. Subjectivity in itself is not "good" or "bad": it just needs to be supported by sound expertise.