Simulated Annealing Algorithm-Based Inversion Model To Interpret Flow Rate Profiles and Fracture Parameters for Horizontal Wells in Unconventional Gas Reservoirs
Bibliographic record
Abstract
Summary With the increasing application of distributed temperature sensing (DTS) in downhole monitoring for multifractured horizontal wells (MFHWs), well performance interpretation by inversing DTS data has become a popular topic around the world. However, because of the lack of efficient inversion models, great challenges still exist in interpreting flow rate profiles and fracture parameters for MFHWs in unconventional gas reservoirs from DTS data. In this paper, a robust inversion system is developed to interpret flow rate profiles and fracture parameters for MFHWs in unconventional gas reservoirs by inversion of DTS data. A temperature prediction model serves as a forward model to simulate the temperature behaviors of MFHWs. A new inversion model based on a simulated annealing (SA) algorithm is proposed to find inversion solutions to flow rate profiles and fracture parameters. The simulated results of temperature behaviors indicate that the temperature profile of each MFHW is irregularly serrated, and the temperature drop in each serration is positively correlated with the inflow rate and fracture half-length. These results provide an excellent method to identify and locate effective hydraulic fractures for field MFHWs. Because of the far more significant influence of fracture half-length than conductivity on a temperature profile, fracture half-length was chosen as the inversion target parameter when performing the inversion of DTS data for MFHWs. Then a synthetic inversion task was accomplished using the SA algorithm-based inversion system, and it took only 110 iterations to reach the target inversion accuracy (10−6 level). Real-time inversion error distributions indicate that this novel inversion system shows great advantages in computational efficiency. Finally, a field application in a shale gas reservoir is presented to validate the reliability of the new inversion model. Based on accurate identification of effective fractures from DTS profiles, satisfactory inversion solutions (the maximum temperature deviation of less than 0.03 K) are obtained. The absolute error of the inversed gas production rate is less than 4 m3/d. The SA algorithm-based inversion system proves reliable to interpret flow rate profiles and fracture parameters, which is a great help to postfracturing evaluation and productivity improvement for MFHWs in unconventional gas reservoirs.
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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".