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The upstream E&P industry is one of the riskiest businesses in which to invest and is dominated by different types of uncertainties: political, economic, social, and technical. Many factors can lead to optimistic or pessimistic risk assessment. Overestimation, underestimation, misidentifying critical risks, overselling projects, and underselling projects are some of the problems. For consistent risk analysis of exploration projects, a systematic approach is used that includes geologic risk, minimum economic field size (MEFS), resource-size distribution, development cost, rate streams, commodity price, and discount rate. This approach requires highly skilled geoscientists and reservoir, facility, and drilling engineers to estimate field-development costs, generate the economic indicator, rank the exploratory prospects' potential success, and support the business decisions. The definition of “exploration success” contains two main variables: (1) probability of geologic success (Pg) and (2) probability of economic success (Pe). To remove the risk of subeconomic volumes from the volumetric distribution, the industry uses the estimation of minimum required resources for the full project life cycle, considering the most likely development scenario. An effort is made to describe the appropriate MEFS estimation methodology, which is an essential component for consistent exploratory-project risk analysis. The influence of key parameters on MEFS estimation, including some illustrative examples, also are analyzed to demonstrate the criticality of MEFS estimation and its impact on exploratory-prospect risk assessment and to achieve an overall economic success.

SUMMARYTo ensure the correct and optimized workflow for the generation of reservoir model, a high-level fit-forpurpose RIM process along with sub-workflows for each discipline have been developed that can be applied at different stages of field development depending upon the business need, resource limitation and input data availability. Application of these workflows has been demonstrated through an example to assess the project risk by capturing the uncertainties in production and economic forecasts. Finally, this study emphasizes that the integrated reservoir modelling process will continue to evolve as new techniques and technologies are developed and implemented. This in turn, will enhance our ability to capture the physical realities of the real world and reduce the risk associated with the field developments. Second EAGE Integrated Reservoir Modelling Conference16 -19 November 2014 Dubai, UAE AbstractFinding and developing oil and gas assets has always been a risky business. The industry has witnessed several technological advances that have helped to reduce the risk. However, risk has not 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. Reservoir Integrated Modelling (RIM) and flow simulation plays a key role in maximizing the benefits at different stages of the field development. The effective 3D interpretation and modelling process requires application and integration of various data, technologies, tools and skills from subsurface to surface engineering, production operations/ surveillance and economics disciplines. Cross-discipline collaboration and integration and a better shared use of all available subsurface and analogue data are considered crucial in developing reliable methods and workflows to help the industry make informed business decisions related to development options, ultimate recovery, and enhanced project economics.

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.