Converted simulation model

Discovered in 1991, its production phase began in November 1997. Gas export began in 2001, and gas injection ceased in 2005 and re-started in 2006. Production and injection history data for this analysis, cover the period between 06/11/1997 and 01/12/2006 (3312 days) (Rwechungura et al., 2010). The historical records (Qo, Qw and Qg) are available for 22 producers and the historical injection rates (Qwi, Qgi) for 9 injector wells. The pressure records are available for most producer wells. However, most of these wells have only a few and erratic records available.

The simulation model available in the Norne field benchmark database is built in the Eclipse 100 simulation software. Muñoz Mazo (2014) converted the benchmark simulation model from Eclipse 100 to the CMG-IMEX simulation software, resulting in a few modifications to adequate the procedures to the tools developed by the Unisim research group, while ensuring the physical and numerical consistency of the reservoir models. The most relevant differences between ECLIPSE-100 and CMG-IMEX models are (Muñoz Mazo, 2014):

 ECLIPSE-100 model introduces several tracer definitions, all of them representing a specific injected water flow. Is not possible to incorporate this representation in the CMG-IMEX model since the only valid tracer model is related to salt-water and only one tracer can be defined;

 The fluid model in ECLIPSE-100 is Volatile-Oil. When implementing this fluid model in CMG-IMEX software, the simulation time triplicates when compared with Black-Oil fluid model. Since no other significant differences are observed between the Volatile-Oil and Black-Oil models, the fastest fluid model is chosen;

 In ECLIPSE-100 model, production history is reported for the three phases (oil, water and gas). With these data, a single value of produced fluid is calculated and set as operational condition for each well in every history date. In the CMG-IMEX model, it is only informed the oil production rate in order to set an observed-data criterion for calibration process prior history matching;

 In the CMG-IMEX model, injection history is introduced by the definition of the injector well operation constraints via OPERATE keywords, for two main reasons:

o Some of the injector wells (belonging to C Manifold) inject gas and water in an alternating form. Thus, every time the injected fluid is changed, it is necessary to change the injector well definitions (injected fluid and operation constraints);

o Maintain the uniformity in the injection history setup, since the functionality of the TARGET and OPERATE keywords is equivalent, and injection behavior is not a critical issue for the CMG-IMEX model.

By incorporating these modifications we adequate and prepare our simulation models to the history matching tools and methods that have been developed by the Unisim research group in the last years, that is, a probabilistic and multi-objective history matching using both production and 4D seismic data.

The uncertain attributes defined by Muñoz Mazo (2014) are used as a starting point for the geostatistical-based history matching process presented in this study.

(APPENDIX A):

 200 porosity, permeability and NtG images (IM);

 Rock compressibility (cp) with five levels of uncertainty;

 Oil/water (dw) and gas/oil (dg) contacts for different initialization segments and with five levels of uncertainty;

 Relative permeability curve (kr) and capillary pressure with 3 levels of uncertainty;

 Vertical permeability multipliers for the layers 1, 8, 11, 12,15, 18, 20 with three levels of uncertainty;

 Fault transmissibility multipliers with four levels of uncertainty.

The uncertainty levels with the highest probability of occurrence are based in the information available in the Norne database. The remaining levels are defined after the sensitivity analysis of the uncertainties mapped for the model of the Norne field (Muñoz Mazo, 2014).

The OF´s taken into account are: Q_o, Q_w and Q_g and BHP for all production wells.

The production tolerances for the objective functions are: 5% for Qo, 10% for Qw and Qg and 25% for BHP. The constant C it was not used during the entire process. Nevertheless and in order to show the impact in the deviations, a few tests were done with 25 m³/day (see results chapter, C-segment case study). The BHP tolerance is higher due to its lower quality and the oil production rate tolerance is more restricted since it is informed to the simulator as a boundary condition.

The quality of a reservoir model is function of the NQDS for each OF. For the specific case of the Norne field and due to the higher complexity of the history matching

process in real reservoirs, the models within the acceptance range [-10 10] have an acceptable quality.

The uncertainties are combined, using the DLHG method, to create multiple simulation models (in this study, 200 models). In order to make the process geologically more consistent, some simulation uncertainties are changed during the history matching process, namely during the global adjustments, including:

 3D vertical permeability models instead of permeability multipliers (APPENDIX B);

 Different relative permeability curves defined to different facies (G-segment – APPENDIX C);

 Vertical transmissibility models (Case study 2 – C-segment).

5. Results

This chapter presents and discusses the results that outcome from the methodological approach proposed in this work. Are described the main achievements during the geological modeling stages, namely the electrofacies database and the high resolution 3D facies and petrophysical, essential during the implementation of the pilot wells (Correia and Schiozer, 2016). Then are shown the results that outcome from the geostatistical-based history matching process guided by the pilot wells method, separated in two case studies.