How can you identify which model realisation is the one to take forward for reservoir predictions?
Ensemble modelling helps to address this question by simultaneously matching an ensemble of model realisations to historical data. Predictions based on ensemble allows you to evaluate the range of different possible scenarios for more reliable decision making.
Automation coupled with cutting-edge optimisation algorithms dramatically increases the efficiency of such projects. Integration streamlines the optimisation process. Python based workflows help to introduce realistic constraints and ensure the most practical solution.Watch Video
A multi-objective approach aims to find a good trade-off for such objectives, resulting in not one, but a number of optimal solutions.
Christie, Eydinov, Demyanov, Talbot, Arnold, Shelkov, 2013. Use of Multi-Objective Algorithms in History Matching of a Real Field. Society of Petroleum Engineers – SPE Reservoir Simulation SymposiumWatch Video
It is then very difficult to assemble these results and generate forecasts from these potential cases. How can you tackle the challenges in hand without compromising, when bound by tight deadlines? tNavigator has many unique, advanced tools for handling uncertainty, meaning you do not have to settle on a single scenario, or reduce the number of parameters:
tNavigator’s integrated workflows address these issues by defining uncertainty and matching variables within the model building process. This allows you to take into consideration every possible source of uncertainty and generate models with different structural, property, fluid and dynamic properties.Watch Video
Very often there is a disconnect between the optimization and the econometric calculations. tNavigator allows you to use the optimization engine directly in the economic model by linking it to the reservoir model. Thus, you can see the true economic impact of uncertainty and optimize on the bottom line dollar value of your asset.Watch Video
SPE-205204 - Integrated Assisted History Matching and Forecast Optimisation Under Uncertainty for More Robust Mature Field Redevelopment Project. Simon Berry; Zahid Khan; Diego Corbo; Tom Marsh; Alexandra Kidd; Elliot Moore
SPE-205913 - Streamlining the Well Location Optimization Process – An Automated Approach Applied to a Large Onshore Carbonate Field. Bruno Roussennac; Gijs van Essen; Bert-Rik de Zwart; Claus von Winterfeld; Erika Hernandez; Robert Harris; Nuha Al Sultan; Basel Al Otaibi; Alexandra Kidd; Georgii Kostin
|This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics".
|The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".
|This cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".
|This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other.
|This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".