A new model selecting shale gas wells for foam deliquification based on geological and engineering conditions
Bibliographic record
Abstract
Abstract As many factors affect the selection of shale gas wells for foam deliquification treatment and these factors are in complex relationships, it is hard to pick out shale gas wells properly. In this work, to solve this issue, a new well selection model considering the influences of multiple geological and engineering factors was proposed. Firstly, the geological parameters of 3D sweet spots were obtained by establishing a geological model of the study area. Secondly, the reservoir numerical simulation was employed to quantitatively evaluate the distribution of remaining gas and formation energy of candidate wells, and the key geological and engineering parameters affecting productivity were identified by the grey correlation analysis method. Finally, according to the distribution ranges of key geological and engineering parameters of candidate wells, a new concept of optimal ideal well was proposed, and the Euclidean distances between candidate wells and the optimal ideal well were worked out to quantitatively evaluate and rank stimulation potentials of the candidate wells. The research results have been applied to the shale gas reservoir of Longmaxi Formation in Sichuan Basin. The results suggest that the main factors affecting the productivity include the length of horizontal section, porosity, permeability, gas saturation, swept volume of fracture network, recovery percent of reserves and formation energy retention degree. The field application has confirmed that the productivities of horizontal wells treated by foam deliquification process have a good correlation with their Euclidean distances, with a correlation coefficient of 100%. The wells had a daily gas production increment of 19.6% on average. The research results have an important guidance on well selection and popularization of foam deliquification.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".