Reliable Reservoir Predictions

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      Ensemble History Matching
      History matching is ill-posed inverse problem, meaning that a lot of different parameter combinations can give a good data match.

      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.

      Well Location & Trajectory Optimisation
      Well parameter optimisation is an important yet challenging task, as it involves an evaluation of multiple scenarios.

      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. 

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      Multi-objective History Matching and Optimisation
      In many real life cases, project objectives can be conflicting when improving results, for one leads to degrading results of the other.

      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 Symposium

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      Forecast Optimisation Under Uncertainty
      Understanding model uncertainty is paramount to the success of our operations. Understanding model uncertainty is paramount to the success of our operations. This is why we spend a large amount of time performing extensive analysis, which in most cases leaves us with a number of potential cases of varying probability.

      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:

      • Forecasting functions allow you to easily and automatically generate forecasts from an ensemble of cases.
      • In-built optimization tools enable you to go beyond simple forecasts, applying an optimization algorithm over any number of potential cases and automatically generating a curve of best cases – known as a pareto front.
      • Forecast and optimization under uncertainty gives you confidence about uncertainties, to decide the best possible course of action.
      Integrated Uncertainty and History Matching Workflows
      Classic uncertainty and history matching practices have limitations as to what is modifiable, and fails to take into consideration uncertainty of the model building process itself.

      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.   

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      Economic Optimisation
      Production optimization is a great exercise to perform on any asset, however sometimes it is hard to see the true monetary value the optimization has yielded.

      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. 

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      Forecast Optimisation Under Uncertainty

      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

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      Well Location & Trajectory Optimisation

      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

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