Modeling a complex turbidite reservoir

Overview

Typical challenges in traditional reservoir modeling and history matching can include:

• Intensive, often manual, iterations.
• Consistent integration of G&G and engineering.
• Capturing and propagating uncertainty.
• Multiple, equally likely solutions.
• Updating model(s) with new data.

The way in which these challenges are approached can directly affect the commercial outcome of projects. As a result, there are a wide variety of approaches in methodology and asset team workflows. The trial-and-error History Matching (HM) method continues to be prevalent in most projects, but alongside this there is increasing use of more automated techniques such as design of experiments or adjoint-based approaches. These methods can explore larger solution spaces, but they are still limited by practical or algorithmic constraints which restrict the number of uncertainty parameters.

The Ensemble Kalman based method makes it possible to generate multiple matched models using practically any size of parameterization. For Eni’s complex reservoir, ResX’s integrated methodology made it possible to retain the necessary geological consistency.

Solution

A hundred equally probable reservoir model realizations were created using a stochastic process. All realizations consistently honor the current measurements of both static and dynamic data, while capturing and propagating the uncertainty in the modeling and data assimilation process.

• ResX was integrated into five workflows covering the entire reservoir modeling process including reservoir modeling, ensemble generation, data assimilation, validation, and forecasting.
• The reservoir modeling workflow consists of three main processes: structural modeling and gridding, facies modeling, and petrophysical modeling.
• The approach helped ensure geological consistency and workflow repeatability in the reservoir modeling and data assimilation process.
• The repeatability helps make it possible to easily integrate new static and dynamic data and to re-parametrize the model when the match quality is unsatisfactory.
• Through the analysis of the updated model properties, new reservoir insights were identified including flow barriers in certain areas and in-place volume constraints.

Result

Reliable reservoir models that offer:

• Geologically consistent explanations of static and dynamic data.
• Quantified uncertainty in the forecast and model parameters.

References: Perrone, A. et al., 2017. Enhancing the Geological Models Consistency in Ensemble Based History Matching an Integrated Approach. Paper presented at the SPE Reservoir Characterisation and Simulation Conference and Exhibition, Abu Dhabi, UAE. Paper Number: SPE-186049-MS