Data Physics, a quantitative optimization framework, has been validated with eight operating companies across 14 EOR projects using data from more than 10,000 wells to enable operators to increase production by greater than 20% and/or reduce their operating expenses by more than 40%. Decisions to optimize.
Models only accurately predicting already-measured responses in the reservoir;Susceptibility to significant prediction errors due to data quality issues;Inability to predict drilling responses given the absence of data at new locations; andPoor predictability over longer time horizons and changing reservoir conditions.
Data Physics merges modern data science and the physics of reservoir simulation.
Data Physics models integrate production data and well log data in a single assimilation step, unlike traditional sequential reservoir simulation workflows.
Data Physics models can therefore be rapidly built and continuously updated while assimilating various forms of data without inconsistencies.
After fitting historical data and validating predictive capacity, Data Physics models can be used to quantitatively optimize any future performance indicator such as short-term cash flow, net present value or ultimate recovery.
Closed-loop optimization becomes possible due to the speed of Data Physics models.