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Bridging The Gap Between Material Balance And Reservoir Simulation For History Matching And Probabilistic Forecasting Using Machine Learning

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Course Credit: 0.15 CEU, 1.5 PDH

In this talk we will start by discussing probabilistic forecasting methods using reservoir simulation for complex reservoir studies. We will emphasise the use of ensembles of simulation models, and discuss some of the difficulties in creating a valid probabilistic ensemble. We will describe Markov Chain Monte Carlo methods, and in particular random walk and Hamiltonian methods. We will show how these methods may be used with proxy models to generate probabilistic simulation ensembles.

In some cases, the bottleneck for rapid reservoir decision support is building and maintaining a reservoir simulation model. We will show how the reservoir simulation methods can be modified to support a data driven approach which includes known physics such as a material balance.

To generate a data driven model, we take historical measurements of rates and pressures at each well, and apply multi-variate time series analysis, with automatic feature selection, to generate a set of differential-algebraic equations (DAE) which can then be integrated over time using a fully implicit solver. We combine the time series models with material balance equations, including a simple PVT and Z factor model. The parameters are adjusted in a fully Bayesian manner to generate an ensemble of forecasts. The use of a DAE distinguishes the approach from normal statistical time-series analysis, where an ARIMA model or state space model is used, and is only suitable for short term forecasting.

We illustrate these approaches with examples of case studies of complex field history matching and probabilistic forecasting.

These approaches have many possible applications within the oil and gas industry, from subsurface to downstream.

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Course Chapters

  • 1Bridging The Gap Between Material Balance And Reservoir Simulation For History Matching And Probabilistic Forecasting Using Machine Learning - Chapter 1
    Media Type: Video

Credits

Earn credits by completing this course0.15 CEU credit1.5 PDH credits

Speakers

Nigel Goodwin
Yasin Hajizadeh