Reduced-Physics Modeling and Optimization of Mature Waterfloods
- Fayadhoi Ibrahima (Quantum Reservoir Impact) | Agustin Maqui (Quantum Reservoir Impact) | Ana Suarez Negreira (Quantum Reservoir Impact) | Chao Liang (Quantum Reservoir Impact) | Feyisayo Olalotiti (Quantum Reservoir Impact) | Ouassim Khebzegga (Quantum Reservoir Impact) | Sebastien Matringe (Quantum Reservoir Impact) | Xiang Zhai (Quantum Reservoir Impact)
- Document ID
- Society of Petroleum Engineers
- SPE Abu Dhabi International Petroleum Exhibition & Conference, 13-16 November , Abu Dhabi, UAE
- Publication Date
- Document Type
- Conference Paper
- 2017. Society of Petroleum Engineers
- 5.6 Formation Evaluation & Management, 5.4 Improved and Enhanced Recovery, 2.1.3 Completion Equipment, 7.2 Risk Management and Decision-Making, 5 Reservoir Desciption & Dynamics, 5.5 Reservoir Simulation, 5.4.1 Waterflooding, 7 Management and Information, 5.5.8 History Matching, 2 Well completion, 2.2 Installation and Completion Operations, 7.2.1 Risk, Uncertainty and Risk Assessment, 5.6.5 Tracers
- Waterflood Management, time-of-flight, Waterflood optimization, Reduced-physics model, tracer
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Mature waterfloods often present significant Reservoir Management challenges. After an initial boost in oil production, water cuts tend to increase and flood performance starts to decline. Complex reservoirs that have been producing for decades through hundreds or thousands of wells are notoriously challenging to model. Creating and history-matching a simulation model usually take several months for subsurface teams, and operational teams can rarely rely on these models to make reservoir management decisions. In this paper, a novel methodology is presented that is being used in practice on large waterfloods or strong aquifer-supported reservoirs, to support operational decisions in near real-time. The proposed technology relies on a reduced-physics, data-driven reservoir model to quickly build and match a reservoir model that can be used to optimize waterfloods. The first stage of the workflow involves collecting and validating the field data, including rock and fluid properties, production, injection and pressure data as well as well information, such as trajectories and historical perforations. The reservoir behavior is then modeled following an approach similar to that of Thiele and Batycky (2006) in the context of streamline simulation. The model represents the reservoir as a network of inter-well connections described by their strengths and efficiencies. Contrary to traditional streamline-based method, the strength of connection is rather determined through the solution of a numerical tracer test, which generalizes the method to unstructured or locally refined grids as well as dual permeability systems, and allows the method to account for mild compressibility effects. An empirical fractional flow model is then used to calculate the connection efficiencies. Once the model is complete and calibrated, a cutting-edge optimization algorithm is used to optimize the production-injection strategy based on this network of subsurface connections. Recommendations for adjustments in the production-injection strategies are proposed and model uncertainties are computed through a novel algorithm to compute the associated risks. A new finite-volume based time-of-flight computation algorithm is developed based on the numerical tracer solution, which, combined with the empirical fractional flow model, can give a data-driven production mapping algorithm.
The proposed methodology was successfully applied to many reservoirs across the world, including several giant middle-east carbonates with hundreds of wells and decades of history. The approach consistenly identified an optimized strategy that could deliver several percentage points of incremental oil along with a reduction in water production. The methodology proposed is fast enough to build and match a new model in a few days; and updating an existing model takes less than an hour as new data comes available, avoiding expensive numerical simulations and helping engineers optimize daily production-injection strategy of reservoirs.
|File Size||1 MB||Number of Pages||15|
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