Salehi, Amir (Quantum Reservoir Impact International LLC) | Hetz, Gill (Quantum Reservoir Impact International LLC) | Olalotiti, Feyisayo (Quantum Reservoir Impact International LLC) | Sorek, Nadav (Quantum Reservoir Impact International LLC) | Darabi, Hamed (Quantum Reservoir Impact International LLC) | Castineira, David (Quantum Reservoir Impact International LLC)
An integral aspect of smart reservoir management of oil and gas fields is the process of identifying and performance forecasting of the remaining, feasible, and actionable field development opportunities (FDOs). In the present work, we introduce an adaptive full-physics simulation-based forecasting framework that applies a series of cutting-edge technologies to provide short- and long-term forecasts for both field- and well-level performance. Our workflow can be applied to a comprehensive opportunities inventory including behind-pipe recompletion, infill drilling, and sidetrack opportunities. In our approach, we begin with a model order reduction technique, which involves a parsimonious elimination of redundancies existing in a given geologic model. This involves an adaptive model upscaling strategy that retains fine details in the vicinity of critical geological features by locally varying the resulting model grid resolution. Reduced models, which are validated using streamline-based flow metrics, are passed into an automated sensitivity study and model calibration engine for efficient reconciliation of observed production trends in the field. Here, we apply a recently proposed Ensemble Smoother robust Levenberg- Marquardt (ES-rLM) method to generate plausible model realizations that replicate the reservoir energy. Representative models are further improved in a sensitivity-based local inversion step to match multiphase production data at the well level. An approach alternative to streamlines, which is compliant with a general unstructured grid format, is utilized to directly compute production data sensitivities on the underlying grid in the local inversion module. Finally, calibrated models are directly passed to the optimization and forecasting engine to assess and optimize field opportunities and development scenarios. This framework has been successfully applied to several giant mature assets in the Middle East, North America, and South America. A case study for one of the giant reservoirs in Latin America is presented where hundreds of field development opportunities are initially identified. We then apply our forecasting framework to the various scenarios including all opportunities to deliver the optimum field development plan. We propose a systematic workflow for field-scale modeling and optimization using an adaptive framework. Our approach facilitates a flexible framework to rapidly generate reliable forecasts and quantify associated uncertainties in a robust manner. This advantage in flexibility and robustness is tied to our fast and automated two-stage model calibration module that leads to substantial savings in computational time. This makes it an efficient method for quantifying the uncertainty as demonstrated through improved estimation of the faults’ connectivity, permeability distribution, fluid saturation evolution, and swept volume.