Factors Contributing to Uncertain Production Forecasts: 3D Reservoir Modelling Perspectives

Abstract

Finding and developing oil and gas assets has always been a risky business. The industry has a history of technological advances that have helped to reduce the risk. However, risk has not yet been fully reduced due to inherent uncertainties in the workflows used to generate production forecasts of the oil and gas fields using 3D reservoir models. Since, the reservoir properties vary spatially due to reservoir heterogeneities (occur at all scales, from pore scale to major reservoir units), to obtain reasonable production forecasts, 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. Based on the lessons learned from 3D reservoir modelling studies performed in-house in different projects, available public domain literature, authors' and industry experiences, some of the identified key factors affecting production forecasts are: sparse and nonrepresentative data, biased estimates of Original Hydrocarbon In-Place, non-representative inputs distribution in the reservoir models due to lack of conceptual geologic model, 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. To demonstrate and quantify the impact of different key factors under uncertainty which affect Hydrocarbon-In-Place, recoverable resources and production forecasts, using real field data for a clastic reservoir, a 3D static reservoir model was built using appropriate geo-statistical techniques and closed feedback loop between 3D static and dynamic models. Finally, the results are discussed which indicate that the evolution of modelling process will continue as new techniques/technologies are developed and implemented. This will enhance our ability to capture the physical realities of the real subsurface world, generate better production forecasts to reduce the risk associated with field developments.