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Kerogen as one the main constituents of mud rocks is not thoroughly understood in terms of its mechanical characteristics. Kerogen is not as stiff as inorganic minerals within the rock matrix, but it can have a significant impact on the propagation of fractures. This becomes more important in organic rich shale reservoirs. In this study, first we proposed a fast method to predict mechanical properties of kerogen using Raman spectroscopy, which is a function of its molecular structure and chemical compounds, and then incorporated the results in hydraulic fracturing simulation. FLAC software was used to simulate opening of a fracture in the shaly and kerogen rich members of the Bakken formation. Then results were compared with a shale simple model missing organic matter properties. Results showed organic matter mechanical characteristics affects the hydraulic fracturing simulation by having faster fracture closure and requiring more fracing volume to achieve a specified fracture length compared to the simple model. The proposed method of using Raman spectroscopy can be used to make a profile of mechanical properties of organic matter and then be used in hydraulic fracturing simulation to optimize the operation.

Quantitative ranking of different operating areas and assessment of uncertainty due to reservoir heterogeneities are crucial elements in optimization of production and development strategies in oil sands operations. Although detailed compositional simulators are available for recovery performance evaluation for SAGD, the simulation process is usually deterministic, cumbersome, expensive (manpower and time consuming), and not quite suitable for real-time decision making and forecasting. In this paper, Artificial Neural Network (ANN) is employed as a data-driven modeling alternative to predict SAGD recovery performance in heterogeneous reservoirs, an important application that is lacking in existing literature. In this study, numerical flow simulations are performed to construct a training data set consists of various attributes describing characteristics associated with reservoir heterogeneities and relevant production/injection parameters with the corresponding recovery factor as output. The network is trained using the data set to identify all significant patterns and relationships that exist between these attributes and the output parameters. The model is then tested using a verification data set (cases that have not been used at the training stage). Sensitivity studies on network configurations are also investigated. In addition, new modifications are proposed to identify and reduce extrapolations in predictions, which are often considered as major drawbacks in most data-driven modeling approaches. The approach described in this paper can be integrated directly into most existing reservoir management routines. In addition, the technique can be used as a viable tool for analyzing large amount of competitor data efficiently. Given that robust forecasting and optimization of heavy oil recovery processes is a major challenge faced by the industry, the proposed research has great potential to be applied in other recovery projects such as solvent-additive steam injection.