The oil industry is a high risk high reward venture. The capital and operating expenses runs into tens of millions of dollars with oil production being the primary source of revenue. During the initial development phase of the field, few wells are completed. Data collection during this period verifies assumptions made during the modelling phase and forms the basis for future development. History matching can play a significant role in these plans since it can determine uncertainties in future production.

History matching algorithms must be able to make an accurate estimates of uncertainty in future production while being computationally light. Towards this end, a number of history matching methods have been developed with emphasis being on the ensemble Kalman filter (EnKF) in recent times. While the variants of the EnKF seek to improve different aspects of the method, few have been successful in addressing all of these concerns.

The Distributed Gauss Newton (DGN) was developed with the same goal- accurate uncertainty prediction at low computational cost. It is not a variant of the EnKF but uses a sensitivity matrix determined through linear regression which decreases the computational load compared to existing gradient based techniques. In their tests, the authors report superior performance of the DGN compared to a Gauss-Newton scheme. This thesis aims to provide a detailed understanding of the method and its dependencies. This is followed up with a comparison of the DGN with an EnKF variant known as the ES-MDA.