history matching

The authors of this paper define a work flow that constrains solutions that match models and field observations and obtains a more-representative model for forecasting and optimizing fracture behavior.

This paper presents an automated calibration process, probabilistic infill well ranking, and location optimization for a major heavy oil field in Colombia with original oil in place of more than 5 million STB.

The authors of this paper present an application of a Bayesian framework for uncertainty assessment and efficient history matching of a Permian CO2 enhanced oil recovery field for reliable production forecast.

The paper describes an approach to history matching and forecasting that does not require a reservoir simulation model, is data driven, and includes a physics model based on material balance.

This study describes application of the iterative ensemble Kalman smoother application to a low-permeability coalbed methane field in Australia.

This work evaluates and compares the performance of rate normalization and pressure deconvolution for both synthetic and tight-oil examples.

The authors describe an approach to achieve reliable estimation of field gas initially in place.

This study explores pitfalls experienced when using capacitance/resistance modeling as a plug-and-play technique for waterflood optimization and discusses workarounds and mitigations to improve its reliability.

The dynamic nature of unconventional-reservoir developments calls for the availability of fast and reliable history-matching methods for simulation models. In this paper, the authors apply an assisted-history-matching approach to a pair of wells in the Wolfcamp formation.

Most history-matching studies have fixed resources—that is, the team of engineers and geoscientists is predetermined. Moreover, the deadlines are always very strict. This constrained scenario often leads to an unfortunate result: The quality of the study suffers.