Key Factors Affecting 3D Reservoir Interpretation and Modelling Outcomes: Industry Perspectives

Abstract

To properly characterizing and modelling a hydrocarbon bearing reservoir is not an easy task because the reservoir properties vary spatially due to reservoir heterogeneities which occur at all scales, from pore scale to major reservoir units. The level of reservoir complexities under study determines the quantity and quality of data requirements for 3D reservoir modelling activity. An adequate understanding of the limitations imposed by the data, associated uncertainty, or the underlying geostatistical algorithms or approaches and their input requirements for the 3D reservoir models are absolutely necessary to obtain reasonable production forecasts. Generally, industry look-backs continue to show the difficulty of achieving a production forecast within an uncertainty band (P90 and P10) for both “Greenfield” projects with limited data and “Brownfield” projects with abundant data. Some of the identified key factors affecting production forecasts are: sparse and non-representative data, biased estimates of Original Hydrocarbon In-Place, non-representative inputs distribution in the reservoir models, inadequate static and dynamic models, poor use of seismic data, use of improper analogs, non-unique history matching calibration processes for brownfields and inappropriate use of uncertainty workflows and tools. This paper briefly discusses some of these factors which affect 3D reservoir interpretation and modelling outcomes for the conventional reservoirs, to provide better understanding, propose effective and practical solutions to improve production forecasts based on lessons learned from 3D reservoir modelling studies, authors and industry experiences. In recent years, the industry has developed and used some high-level fit-for-purpose workflows with a closed loop between 3D static and dynamic reservoir modelling under uncertainty with use of appropriate geo-statistical techniques and history look-backs approach which assist capturing the uncertainties in production forecasts and improving the project risks assessment. The evolution of closed loop modelling process will continue as new techniques and technologies are developed and implemented, enhancing our ability to capture the physical realities of the real subsurface world, generate better production forecasts to reduce the risk associated with field developments.