Constraining reservoir models to available field observation is an important step in reservoir simulation process especially when models aim at further development decision making. In the past two decades, several assisted history-matching methodologies have been proposed to help in the matter (gradient methods, streamlines, etc. Oliver and Chen, 2011). More recently, ensemble based methodologi...

The solution of sparse linear systems is the most time-consuming step in running reservoir simulations; over 70% of time is spent on the solution of linear systems derived from the Newton methods [1]. If large highly heterogeneous reservoir models are applied, their linear systems are even harder to solve and require much more simulation time. Hence fast solution techniques are fundamental to l...

in this work, studies of underground gas storage (ugs) were performed on a partially depleted, naturally fractured gas reservoir through compositional simulation. reservoir dynamic model was calibrated by history matching of about 20 years of researvoir production. effects of fracture parameters, i.e. fracture shape factor, fracture permeability and porosity were studied. results showed that di...

In this paper the operation of multiple reservoirs by the utilization of analysis method of the system dynamics is considering and this analysis method is based on the feedback and object oriented events. The goal of this research is presentation of a new method by utilization of system dynamicity technique for simulation of the reservoir. This software is a product of Ventana Company and the e...

Displacement of oil and water in porous media of reservoir rocks is described by relative                      permeability curves, which are important input data for reservoir performance simulation and drive mechanism studies. Many core studies, such as multiphase relative permeability, capillary pressure and saturation exponent determination, depend on the volume fractions of multiphase flui...

We propose photonic reservoir computing as a new approach to optical signal processing and it can be used to handle for example large scale pattern recognition. Reservoir computing is a new learning method from the field of machine learning. This has already led to impressive results in software but integrated photonics with its large bandwidth and fast nonlinear effects would be a high-perform...

Uncertainty in production prediction has been subject to numerous investigations. Geological and reservoir engineering data comprise a huge number of data entries to the simulation models. Thus, uncertainty of these data can largely affect the reliability of the simulation model. Due to these reasons, it is worthy to present the desired quantity with a probability distribution instead of a sing...

Reduced-order modeling represents an attractive framework for accelerating computationally expensive reservoir simulation applications. In this paper we introduce and apply a reduced-order modeling approach for history matching. The method considered, trajectory piecewise linearization (TPWL), has been used previously for production optimization problems, where it has provided large computation...

The objective of this study is to demonstrate the power of integrating rock physics theory, measurement and simulation to improve facies prediction in an unconventional limestone and shale reservoir. Reliable facies prediction is a challenge in unconventional reservoir characterization because of complex geological heterogeneities. Both deterministic and probabilistic approaches are commonly us...

Accurate prediction of petroleum reservoir performance requires reliable models of the often complex reservoir heterogeneity. Geostatistical simulation techniques generate multiple realizations of the reservoir model, all equally likely to be drawn. Traditional to geostatistics, geological continuity is represented through the variogram. The variogram is limited in describing complex geological...