Effect of production system uncertainties on production forecast, energy demand, and carbon emissions

This work evaluates the impact of production system uncertainties in integrated simulations (reservoir-production system) for production, energy demand, and carbon emission forecasts through the application of a methodology based on discretized Latin-hypercube method with geostatistical realizations (DLHG) and a proposed energy model for a surface facility. The net present value, cumulative production (oil and water) and injection (water), total energy demand, and carbon emissions were used as indicators to investigate the impact of uncertainties in a benchmark case. The application of the DLHG method was made by generating risk curves for the technical and economic indicators. Three sets of uncertainties were compared: (1) reservoir attributes (R set), (2) reservoir including technical and operational attributes (RO set), and (3) reservoir including production system, technical, and operational attributes (RPO set). The results showed greater variation of uncertainties from the RPO set in relation to the R set. From a risk analysis, the RO set indicated limited influence of technical and operational attributes on the indicators. The risk analysis showed that the insertion of uncertain attributes of the production system is significant in the net present value, water production and injection, and CO2 emissions results. The production system affected water production and injection, which directly affects the energy demand and carbon emissions, which was consistent with other work. In the case studied, each ton of CO2 emitted recovered 14 m3 of oil in the pessimistic scenario and 24 m3 of oil in the optimistic scenario. The insertion of uncertain attributes of the production system can affect field development and management decision-making under uncertainties. These evaluations are important for production forecast and decision support over energy demand and greenhouse gas emissions from oil and gas production and processing.