Developing a New Workflow to Study the Effect of Soaking Process on Shale Well
Bibliographic record
Abstract
Abstract The most common stimulation technique of shale gas production is multistage hydraulic fracturing. However, the implementation of the technique brings in new formation damage considerations. Large quantities of water-based fracture fluids, over 75% of the injected volume, usually left unrecovered at the start of production that leads to permeability reduction and low productivity. Accidently, some operators found an improvement in gas recovery after shut in the wells after flowback due to pipeline restriction. They called this behavior as the soaking effect. This study presents a workflow to evaluate the effect of the soaking process on the well performance after the hydraulic fracturing process in actual field cases. Waterflow back analysis was conducted for 21 well to estimate the effective fracture volume before and after the soaking process. Rate transient analysis (RTA) was conducted on the production data to estimate the stimulated reservoir volume (SRV) in each well. SRV and the enhanced recovery were correlated to the soaking time. Decline curve analysis for water and gas flow rates were conducted to estimate the estimated ultimate gas and water recovery (EURg, and EURw) before and after the soaking process. An increase in the gas flow rate was observed with soaking time with low water production. SRV increased with the soaking process up to 53% of its initial value with shut-in the well for 180 days. EURw decreased by 52 % of its value before the soaking process, while EURg increased by 48%. Shut-in the well before gas-kick off after hydraulic fracturing operations negatively impact the well performance and the gas production can decrease by 22% even after soaking process for 315 days. This study will present a methodology to evaluate the soaking process, and recommendations to improve the impact of the soaking process on well performance.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".