Production forecast plays a very important role in the global oil and gas industry to provide growth and ensure continuity in the exploration and production business. Investors usually expect a high rate of delivery so to improve this record, there is a need to provide the best possible range of production forecasts methodologies for better production rate.
During the forecasting process different groups of exploration, development and production use tools as well as dynamic reservoir simulation software to make realistic corrections during the process. It is also clear that a single model will not work properly and the experts in this industry rely on different methodologies for better outcomes.
Different long term Forecasting methodologies
The importance of providing realistic long-term forecasts cannot be underestimated because it effectively drives operational strategies to shareholders. There exists the possibility of combining different production forecasting methods to validate the solution to the problem statement.All methods inevitably have their advantages when used alone, but the limitations of individual methods can affect the validity of results. Various forecasting methodologies can be combined effectively by leveraging on the strengths of the individual methods to generate a more robust set of production forecasts.For example,
Numerical simulation
This method as reservoir simulation requires a history match to calibrate inputs.
The history-matching process
This methodology can be tedious and time-consuming, and it requires expertise and good judgment. Other limitations include applying proper mathematical theories and managing the non-uniqueness of solutions contributed by differing geological interpretations. The validity of reservoir simulation results can be questioned because of different issues.
Analogs
This methodology can be useful in predicting the future performance of new wells based on the performance of existing type wells. However, a big challenge to analog-based forecasting is the inability to focus on reservoir deliver ability and quantify the impact of variations in geology, semi-optimum well design, and unforeseen operational difficulties on production.
Analytical Models
This can be used based on the simplicity of the models. A big drawback is in incorporating heterogeneities into an analytical model.
Inflow performance relationship (IPR) curves
IPR curves represent the easiest method that requires a limited set of user input parameters, and they also produce single-point solutions. Material balance methods are easy and quick methods that honor material balance equations. Looking at other production technologies such as steam-assisted gravity drainage in thermal reservoirs, IPR methods may be used for pump optimization and design tasks but significantly misrepresent the production based on changing dynamic reservoir simulation.
Conclusion
Production forecasts can be improved through multiple methodologies that can be used to ultimately narrow down the variability and ultimately converge to some solution. There are various examples in which a combination of methods has been used to narrow down uncertainty in predictions and ultimately contribute to a more practical and perfect set of production forecasts.
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