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The placement of infill producers and injectors is an important aspect in the overall development strategy of any field and is particularly challenging for mature fields with high levels of water-cut. Previous screening approaches based upon static reservoir quality maps have limited applicability as these do not account for the drainage and swept volumes from existing wells. In contrast, direct application of formal optimization methods such as evolutionary algorithms and adjoint-based methods to high resolution geologic models may better represent reservoir dynamics but can be complex to implement or computationally prohibitive. We propose a novel method for well placement optimization that relies on streamlines which represents the flow paths in the reservoir and the time of flight which represents the travel time of fluids along streamlines. Specifically, the streamline time of flight from the injectors provides swept volumes for injectors whereas streamline time of flight from producers gives drainage volumes for producers. These quantities can be effectively combined to a ‘total time of flight’ to locate the potential regions of unswept and undrained oil in the reservoir. Our approach utilizes a dynamic measure based on the total streamline time of flight combined with static parameters to identify potential locations for infill drilling. Areas having high value of the dynamic measure (sweet spots) are both poorly drained and poorly swept, making them attractive for drilling infill wells. We show the power and utility of our proposed method on a mature offshore carbonate field in western India. The simulation model was history matched using a hierarchical history matching approach that follows a sequence of calibrations from global to local parameters in coarsened and fine scales. Using our proposed method on the history matched model we obtained a dynamic measure map highlighting areas suitable for drilling infill wells. Finally, we compared the performance of infill wells located using the dynamic measure map with wells located using traditional well placement techniques, for example, oil saturation map from simulation. Our proposed method consistently outperforms the traditional approaches. Subsequent field infill drilling in the field has validated our approach.
The placement of infill producers and injectors is an important aspect in the overall development strategy of any field and is particularly challenging for mature fields with high levels of water-cut. Previous screening approaches based upon static reservoir quality maps have limited applicability as these do not account for the drainage and swept volumes from existing wells. In contrast, direct application of formal optimization methods such as evolutionary algorithms and adjoint-based methods to high resolution geologic models may better represent reservoir dynamics but can be complex to implement or computationally prohibitive. We propose a novel method for well placement optimization that relies on streamlines which represents the flow paths in the reservoir and the time of flight which represents the travel time of fluids along streamlines. Specifically, the streamline time of flight from the injectors provides swept volumes for injectors whereas streamline time of flight from producers gives drainage volumes for producers. These quantities can be effectively combined to a ‘total time of flight’ to locate the potential regions of unswept and undrained oil in the reservoir. Our approach utilizes a dynamic measure based on the total streamline time of flight combined with static parameters to identify potential locations for infill drilling. Areas having high value of the dynamic measure (sweet spots) are both poorly drained and poorly swept, making them attractive for drilling infill wells. We show the power and utility of our proposed method on a mature offshore carbonate field in western India. The simulation model was history matched using a hierarchical history matching approach that follows a sequence of calibrations from global to local parameters in coarsened and fine scales. Using our proposed method on the history matched model we obtained a dynamic measure map highlighting areas suitable for drilling infill wells. Finally, we compared the performance of infill wells located using the dynamic measure map with wells located using traditional well placement techniques, for example, oil saturation map from simulation. Our proposed method consistently outperforms the traditional approaches. Subsequent field infill drilling in the field has validated our approach.
For planning the operations of Oil and Natural Gas Corporation Limited (ONGC) in the complex Heera field, it was estimated that over one hundred simulation runs would be needed to complete the history match of the field and almost the same number of simulations would be needed for production forecasting. Heera is a large field, with multiple faults and seven stacked carbonate formations. There are significant variations in petrophysical properties, and variable degrees of communication between reservoir zones. The simulation models include 479 wells with commingled production or injection. Well trajectories are complex and include multilateral and horizontal configurations. Field development options include use of simultaneous water alternating gas (SWAG) for enhanced oil recovery. Combining all these features, it would be difficult to run all the necessary sensitivity cases within the required project timeline, using a conventional reservoir simulator. Therefore, it was decided to test the applicability of a new generation simulation tool to address the challenges of the study. To ensure that the change of simulator would not impact the integrity of the model, rigorous quality checks were performed on the input data. After successful evaluation, the new software was used for the reservoir engineering study. The decision to apply the new simulator significantly reduced the elapsed time, with some realizations over 20 times faster compared to the original base case. As a result of this speed-up, numerous runs could be carried out to refine the history match. Multiple sensitivities could be used to help understand and reduce the uncertainties in a more comprehensive manner. Moreover, the prediction cases could be optimized to identify the best recovery strategy. This study has demonstrated the value of reducing simulation run times, to complete the project with greater efficiency and more confidence in the results. In future studies, high performance software tools can also enable use of fine resolution models, to capture detailed heterogeneities and optimize areal and vertical sweep.
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