Impact of Frac Hits on Production Performance- A Case Study in Marcellus Shale
Bibliographic record
Abstract
Abstract A new approach is proposed to quantify the communication between wells in a tight multi-layered shale gas reservoir. In this approach, a wide range of data sets, from microseismic monitoring to the production data are analyzed to evaluate the communication between wells in every stage of well development. The results provide new insight into how the initial well communication during stimulation impacts the well interference during production. Microseismic data collected during hydraulic fracturing are analyzed for a three-well pad drilled in the Appalachian basin. The microseismic event locations, magnitudes, and fracture plane characteristics are used to construct a discrete fracture network (DFN). The permeability of a numerical reservoir model is calculated from the generated DFN, and the model is further integrated with available reservoir data. History matching is carried out using three years of production data to calibrate the reservoir model, which was also used to predict water and gas production for thirty years. Finally, decline curve analysis (DCA) is used to examine the production behavior of the reservoir. The microseismic data monitored during hydraulic fracturing show the presence of communication between wells in the pad. This communication, also known as frac hits, is established as more events are recorded in the offset wells, indicating the extent of hydraulic fractures. In addition, the pressure perturbation in the offset wells confirms the presence of a pathway connecting the wells during stimulation. Furthermore, the reservoir model built in the numerical reservoir simulator shows overlap of drainage volumes. In the reservoir model, the depleted region expands across multilayer formations in both lateral and vertical directions. An analysis of the production behavior of the wells using DCA suggests an almost logarithmic trend which is expected for these types of reservoirs. Surprisingly, detailed analysis of the results reveals there is no significant deviation in the overall performance of wells associated with well interference, especially in the early years of production. However, at greater than 10 years of production, the expansion of the depleted zone accelerates the well interference which leads to lower performance of the pad in the long term. The results demonstrate that the enhanced permeability zones created due to frac hits may not have an immediate impact on production performance. Traditionally, frac hits and well communication were considered damaging to the reservoir production. However, this new result shows that the well communication during fracturing may not impact short-term production. Additionally, it highlights the importance of a thorough examination of the fracture network along with the reservoir properties to evaluate the potential impact of frac hits on reservoir production.
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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".