Practical Application of Data-Driven Modeling Approach during Waterflooding Operations in Heterogeneous Reservoirs
Bibliographic record
Abstract
Abstract Quantitative assessment of different operating areas and uncertainty appraisal due to reservoir heterogeneities are crucial elements in optimization of production and development strategies in oil sands operations. Evaluation of waterflooding performance that involves detailed simulations is usually deterministic, cumbersome, expensive (manpower and time consuming), and not quite suitable for practical decision making and forecasting, particularly when dealing with a high-dimensional data space consisting of a large number of operational and geological parameters. Data-driven modeling techniques, which integrate a comprehensive data analysis and machine learning methods, provide an attractive alternative especially for extremely nonlinear system forecast. In this paper, an exploratory data analysis is applied to construct a comprehensive training data set from waterflooding field data, which entails various attributes describing characteristics associated with reservoir heterogeneities and other relevant operating parameters. Artificial neural network (ANN) is employed as a data-driven modeling alternative to predict waterflooding production in heterogeneous reservoirs, an important application that is lacking in the existing literature. ANN utilizes a training data set to identify all significant patterns and relationships that exist between the input and output attributes. The data-driven model is subsequently tested using a verification data set involving cases that have not been used at the training stage. This paper highlights the great potential of data-driven modeling techniques for practical applications and to be used as a viable tool for analyzing a large amount of competitor data efficiently. In addition, the proposed approach can be integrated directly into most existing reservoir management routines. Given that robust forecasting and optimization of heavy oil recovery processes is a major challenge faced by the industry, the proposed research also has great potential to be applied in other recovery projects such as steam assisted gravity drainage (SAGD) and solvent-additive steam injection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".