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Surveillance and optimization of waterfloods in low-permeability carbonate reservoirs pose many challenges. Updating reservoir models is tedious and time-consuming, involving multiple data sources, model updates, and simulations. Technological challenges include large simulation models, waterflood complexities, and limited real-time data. Human challenges must be addressed as well, since waterflooding decisions affect multiple disciplines: reservoir engineers, production engineers, facilities engineers, IT, Operations, and asset managers. The ‘languages’ and interests of these disciplines are quite different; necessitating a workflow that satisfies the needs of all disciplines and integrates people, processes, and technology. This paper presents an innovative automated workflow to enable monitoring, diagnostics, forecasting, and optimization of waterflooding processes in days instead of weeks. This workflow seamlessly captures historical and monthly real-time data, updates simulation history, creates simulation restart prediction points, runs numerical simulations with optimization scenarios, selects the global optimum solution by scenario, and compares results so that multi-disciplinary teams can make reactive or proactive decisions to maximize short-term oil rates and long-term oil recovery, while honoring constraints on voidage replacement ratios, reservoir pressure, sweep efficiencies, production, and injection. The workflow automatically updates real-time production data in the simulator each month. A base case is run to recalculate waterflooding indicators. The process then starts a 24-month production forecast, running hundreds of scenarios under constrained optimization to achieve global optimization points. The optimizer changes control variables such as injection volumes, tubing head pressure, bottomhole pressure, and production allowable. The workflow ranks potential well decisions with important impacts on oil rate and water cut. The workflow uses an intuitive user interface incorporating the needs of multiple engineering and operations disciplines, and facilitates one common language while evaluating and optimizing waterfloods. This workflow has been implemented for a waterflood in a Middle East carbonate reservoir to help engineers evaluate the waterflood and make better, faster decisions.

Production from low permeability carbonate reservoirs is commonly supported by the waterflood (WF) process. Despite decades of research and field experience, oil recovery expectations in carbonates typically remain low due to factors such as high permeability streaks, poor microscopic sweep efficiency, and low mobility ratios, all of which can dramatically impair oil recoveries. Vast amounts of remaining oil-in-place have led operators to analyze opportunities to improve WF management and production.This paper presents an approach to increase recovery and improve performance indicators in a low permeability carbonate reservoir. The main objective of this effort is to maximize short-term oil rates and long-term recovery while honoring target constraints on voidage-replacement ratio (VRR), reservoir pressure, sweep efficiencies, and production and injection rates. In essence, our approach seeks optimum WF solutions by coupling a full-field reservoir simulator with an adaptive, simulated annealing optimization engine.Predefined scenarios impose hard constraints on production and injection rates, field conditions, and well operating constraints. Voidage replacement ratios (VRR) and nominal pressure (Pn), volumetric sweep efficiency (Evol) and displacement efficiency (Ed) are soft constraints used to calculate design feasibility. Reservoir recovery and displacement efficiency performance indicators are pursued at different levels in the optimization loop. The outcome is that the following month's operational decisions are suggested by the optimizer. This paper describes how this optimization methodology provides improvements in short-term production rates of about 10-20% while enhancing oil recovery between 1-8%. This paper also discusses additional strategies to further improve oil recovery expectations using this automated workflow as the foundation.

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