Thapliyal, Anil (Oil and Natural Gas Corporation Ltd.) | Kundu, Sudeb (Oil and Natural Gas Corporation Ltd.) | Dimri, Sunil Kumar (Oil and Natural Gas Corporation Ltd.) | Dutt, Ankit (Schlumberger) | Mishra, Shubham (Schlumberger) | Aggarwal, Akshay (Schlumberger) | Agarwal, Ankit (Schlumberger) | Ojha, Aditya (Schlumberger) | Bradley, David (Schlumberger) | Giddins, Marie Ann (Schlumberger)
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.