Understanding the Impact of Stimulation Treatment on Gas Production from a Horizontally Drilled Marcellus Shale Well
Bibliographic record
Abstract
The Marcellus shale is one of the largest unconventional gas shale plays in the United States. It underlies much of Pennsylvania, West Virginia, and New York and even extends under Lake Erie and into Canada. The most effective methods to produce from this play is to drill horizontally into the shale formation and use hydraulic fracturing. Hydraulic fracturing creates pathways for the hydrocarbon to flow from the shale into the wellbore. These lateral sections of the well are typically completed from toe to heel over a number of stages using a plug and perforate method.;This research focuses on a Marcellus shale well drilled in Morgantown, West Virginia. The well was completed using five different fracture designs over a total of 28 stages throughout the lateral. This well served as more of a learning experience than a typical horizontal well. A flow scanner was also run through the well after hydraulic fracturing to discover more information that is typically not acquired in most wells. All data for this well was provided by the Marcellus Shale Energy and Environment Laboratory (MSEEL) research group. The MSEEL participants were Northeastern Natural Energy, Department of Energy, West Virginia University, Ohio State University, and others who reviewed all the collected information. The goal of research group was to improve the understanding the shale characteristics in this region in order to be more efficient in the completion of other wells in this location.;A neural network model was used to examine the efficiency and performance of different completion methods and their impact on gas production. Several input parameters such as plug depth, total shots, natural fractures, measured slurry, measured clean fluid, measured proppant, pump time and stage length were used to predict gas flowrate. Several combinations of training, validation, and testing sets were employed with different number of hidden layer neurons and the best combination was determined.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".