Application of Assisted History Matching to Unconventional Assets

Abstract

Abstract Characterization of key parameters in unconventional assets continues to be challenging due to the geologic heterogeneity of such resources and the uncertainty associated with fracture geometry in stimulated rock. Limited data and the accelerating pace of asset development in plays like the Permian present an increasing need for an efficient and robust assisted history matching methodology that produces better insights for asset development planning decisions, e.g. well spacing. A multi-scenario approach is presented to build an ensemble of history matched models that take into account existing uncertainty in reservoir description and well completions. We discuss parametrization of key uncertainties in the reservoir rock, fluid properties, fracture geometry and the effective permeability of stimulated rock. Ensemble-based assisted history matching algorithms are utilized to reduce and characterize the uncertainties in the model parameters by honoring various types of data including field dynamic data and measurements. We discuss the implementation of automated schemes for weighting of various types of data in the ensemble-based history matching algorithms. These schemes are introduced to define the history matching objective functions from various types of data including bottomhole pressure data, and the oil, water and gas productions rates. The computational results show that our adaptive scheme obtains better history match solutions. The presented multi-scenario approach, coupled with the ability to efficiently run a high number of scenarios, enables better understanding of reservoir and fracture properties and shortens the learning curve for new development in unconventional assets. The shown case study illustrates a comprehensive analysis, using thousands of simulation cases, to obtain multiple history match solutions. Given the non-uniqueness of reservoir history matched models presented in the scenarios, this workflow improves forecasting ability and enables robust business decision makings under uncertainty.