Using Multiple Realizations from an Integrated Uncertainty Analysis to Make More Robust Decisions in Field Development

Abstract

This paper describes the workflow for an uncertainty study for a field development case offshore Norway. It is a workflow where uncertainties in seismic time interpretation, depth conversion, contacts, fault scenarios, alternative conceptual models, facies models, relative permeability, schedule etc. are included in an automated way to generate multiple realizations (hundreds) of the geomodel and reservoir model. Hence all uncertainties in the geomodel are also included in the reservoir model explicitly, and are captured by the ensemble of realizations. Since structure, contacts and facies distribution are different from realization to realization the workflow ensures that the planned well trajectories are automatically adjusted accordingly. Having such a workflow which can generate and manage multiple realizations makes it straight forward to get robust and realistic estimates of uncertainties in in-place volumes and produced volumes. It is also used for risk mitigation and decision support as e.g. to evaluated robustness of well placement, well number, top side capacities etc. This automatic workflow made it easy to rerun the uncertainty study when new well-data arrived. It made it also easy to run sensitivities on any part of the reservoir modelling workflow to gain valuable insight. Furthermore, having such a workflow made it possible to do quick and simple soft conditioning on dynamic data (such as drill stem test data) or alternatively use the dynamic data as direct input to the geomodel. Few real data from the field under study will be included. Those who are included are anonymized and scales on axes are masked.

Influence of well management in the development of multiple reservoir sharing production facilities

“…Some authors [4] proposed a workflow that can generate and manage multiple realizations, which in turn promotes robust and realistic estimates of uncertainties in in-situ and produced volumes. It is also used for risk mitigation and decision support, for example: to evaluate the robustness of well placement, well number, top side capacities, etc.…”

Ranking of geostatistical models and uncertainty quantification using Signal Detection Principle (SDP)

“…In recent years, the selection of a candidate model from a set of model realizations for the purpose of solving various reservoir modelling problems has attracted a lot of research interests (Shirangi and Durlofsky, 2016). The areas of its application in the oil and gas industry range from history matching (Mtchedlishvili, Voigt and Haefner, 2004;Sahni and Horne, 2004;Shirangi, 2014;Shirangi and Emerick, 2016), well performance (Ballin, Journel, and Aziz, 1992), production forecast (Pratt et al, 1986), reservoir property distribution (Fanchi, 2006), reservoir management (Deutsch and Srinivasan, 1996;Fei et al, 2016), uncertainty analysis (Yadav, Bryant and Srinivasan, 2006;Rahim, Li and Trivedi, 2015;Shirangi and Durlofsky, 2016), well placement optimization (Guerra and Narayanasamy, 2006), to field development planning and decisionmaking (Hegstad and Saetrom, 2014) amongst others.…”

Effect of reservoir and production system integration on field production strategy selection

“…It is therefore possible to check if a production scenario is compatible with geologic data, or to quantify the production uncertainties resulting from geologic uncertainties. Hegstad and Saetrom (2014) proposed a workflow which can generate and manage multiple realizations makes it straight forward to get robust and realistic estimates of uncertainties in in-place volumes and produced volumes. It is also used for risk mitigation and decision support as e.g.…”