Increased Production Through Microseismic Monitoring of Hydraulic Fracturing Over a Multiwell Program
Bibliographic record
Abstract
Abstract Surface based microseismic monitoring enabled an E & P company to optimize their hydraulic fracture treatments. This case study covers a six well program, consisting of 25 stages with 84 hours of continuous microseismic monitoring. The first part of the program consisted of stimulations in three vertical wells. This part of the program provided information indicating fracture orientation and lateral extent of the asymmetrical hydraulic fractures. This was then used to design optimal spacing and fracture design of the stimulations in the remaining three horizontal wells. The common fracture azimuths are 320° +/− 10° and 40° +/− 8°. More complex multi-stage stimulations in the horizontal wells showed microseismic activity growing before the proppant injection with a significant fracture half length. However, microseismic activity occurred near the wellbore during the mid and late portion of the proppant injections indicating the bulk of the proppant load was delivered near the wellbore. High initial post-stimulation production with a rapid decline was observed, supporting this interpretation. This led to the redesign of the fracpack treatment using smaller proppant sizes resulting in larger fractures and more productive wells. We also estimated a stimulated reservoir volume for each stage. The data were then combined into a table for reference, along with aerial extents of activity for the six wells. The microseismic activity then increased with more optimized treatments. We show that generally the production of each well is proportional to the stimulated volume. These results have given the operator an insight to the fracture orientation, and fracture effectiveness of 25 individual hydraulic fracture treatments.
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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 | 0.000 |
| 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) | 0.001 | 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".