Dynamic Integration of DTS Data for Hydraulically Fractured Reservoir Characterization with the Ensemble Kalman Filter
Bibliographic record
Abstract
Abstract The deployment of fiber-optic-based distributed temperature sensing (DTS) in hydraulically fractured wells has enabled us to observe the dynamic temperature profile along the wellbore during treatment, flow back and production not only as a postprocessing step but also in real-time monitoring applications of the hydraulic fracturing process. Fracture initiation points, vertical coverage and number of created fractures can be identified by DTS data. However, to evaluate the well performance, optimize future treatments and better understand fracture modeling, additional accurate quantitative information such as fracture conductivity and geometries need to be inferred from DTS data. In this study, we propose to set up a stochastic inverse problem to infer hydraulic fracture characteristics such as fracture conductivity and geometries by integrating real-time DTS monitoring data. We develop a synthetic non-isothermal simulation model containing a horizontal well with multi-stage transverse hydraulic fractures amenable for realist real-time DTS data. We also provide a comprehensive understanding of the effectiveness of different fracture and reservoir parameters in the monitored temperature data by means of sensitivity analysis. To estimate the hydraulic fracture characteristics, we employ the ensemble Kalman filter (EnKF), an ensemble based sequential model updating method, to assimilate DTS data. The EnKF enables us to perform quantitative fracture characterization and automatic history matching. The EnKF also offers several advantages for this application, including the ensemble formulation for uncertainty assessment, convenient gradient-free implementation, and the flexibility to incorporate additional monitoring data types. Examples are presented to illustrate the suitability of the EnKF-based fracture characterization for the inversion of DTS data to infer fracture geometries and conductivity. We demonstrate that by means of the EnKF we can identify accurately fracture halflength and fracture permeability from temperature inversion.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".