Automatic Fracture Network Model Update Using Smart Well Data and Artificial Neural Networks
Bibliographic record
Abstract
Abstract This paper presents a new methodology to continuously update and improve fracture network models. We begin with a hypothetical model whose fracture network parameters and geological information are known. After generating the "exact" fracture network with known characteristics, the data were exported to a reservoir simulator and simulations were run over a period of time. Intelligent wells equipped with downhole multiple pressure and flow sensors were placed throughout the reservoir and put on production. These producers were completed in different fracture zones to create a representative pressure and production response. We then considered a number of wells of which static and dynamic data were used to model well fracture density. When new wells were drilled, historical and new data were used to update the fracture density using Artificial Neural Networks (ANN). More dynamic data will be provided as well as more static data when additional wells are drilled. The accuracy of the prediction model depends significantly on the representation of the available data of the existing fracture network. The importance of conventional data and smart data prediction capability was also investigated. A highly sensitive input data was selected through forward selection scheme to train the ANN. Well geometric locations were included as a new link in the ANN regression process. Once the relationship between fracture network parameters and well performance data was established, the ANN model was used to predict fracture density at newly drilled locations. Finally, an error analysis through correlation coefficient and percentage absolute relative error performance was performed to examine the accuracy of the proposed inverse modeling methodology. It was shown that fracture dominated production performance data collected from both conventional and smart wells allow automatically updating the fracture network model. The technique proposed helps in generating another -readily available at no cost- data source for fracture characterization to be used as supplementary to limited 1-D data obtained from well logs and cores.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".