Flow Rate Profile Interpretation for a Two-Phase Flow in Multistage Fractured Horizontal Wells by Inversion of DTS Data
Bibliographic record
Abstract
The use of distributed temperature sensors (DTSs) has become a common practice in real-time downhole monitoring for horizontal wells in oil/gas reservoirs. However, great challenges still exist in translating measured DTS data to flow rate profiles due to lack of robust inversion approaches, especially for multistage fractured horizontal wells (MFHWs) with a gas–water two-phase flow. In this study, a comprehensive inversion system combined with a temperature prediction model and an inversion model has been developed to interpret flow rate profiles for MFHWs with a two-phase flow by inversing downhole DTS data. The temperature model serves as a forward model to predict temperature behaviors of MFHWs. The inversion model is derived from the Levenberg–Marquardt (L–M) algorithm to eliminate the errors between the measured DTS data and the simulated temperature profile. By simulating the temperature behaviors of two-phase flow MFHWs, it has been found that there exist abnormal decreases in the ΔT/xf ratio (temperature drop/fracture half-length) of the corresponding fractures with the production of water. According to this, two effective methods to diagnose water exit locations for an MFHW are introduced. Two synthetic cases are presented to illustrate the application of the inversion system in detail. Finally, a field application is analyzed and satisfactory inversion results are obtained. The maximum inversion temperature error is less than 0.03 K and the absolute error of the inversed gas production rate is less than 9 m3/day. The interpreted inflow rates of each stage are close to the measured data as well, which validates the reliability of the proposed inversion system. The findings of this study provide a promising tool to interpret flow rate profiles for an MFHW with a two-phase flow.
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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 | 0.000 | 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".