Reduced-Physics Modeling and Optimization of Mature Waterfloods
Bibliographic record
Abstract
Abstract 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".