A Comprehensive Workflow for Near Real Time Waterflood Management and Production Optimization Using Reduced-Physics and Data-Driven Technologies
Bibliographic record
Abstract
Abstract Optimizing waterfloods in large fields with complex geology can be an extremely difficult engineering problem. Simulations become overwhelming and tremendously time consuming, while basic classical engineering will not capture enough physics to assess well by well decisions. In this paper we present a novel data-driven, reduced-physics workflow to manage and optimize an exceptionally complex reservoir in Latin America. 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 the one by 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 strength and efficiency. The strength of connection is determined through the solution of a numerical tracer test rather than through streamline-based method like in Thiele and Batycky (2006), which generalizes the method to unstructured or locally refined grids as well as dual permeability systems and allows the method to account for 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. The proposed methodology was successfully applied to a giant Latin American waterflooded reservoir with over 800 wells and nearly a hundred years of history. The approach identified an optimized strategy that could deliver a 5% increase in oil production with a 10% 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. The proposed method was designed to avoid expensive numerical simulation and to simplify the history-matching process. It can therefore be used daily to help engineers optimize the production-injection strategy of reservoirs. Furthermore, the model can be used for robust short-term forecasting as well as relatively elaborate production mapping.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".