Analysis of Communicating Multi-Fractured Horizontal Well Production Data Using the Dynamic Drainage Area Concept
Bibliographic record
Abstract
Abstract Reducing fracture/well spacing and increasing hydraulic fracture stimulation treatment size are popular strategies for increasing hydrocarbon recovery from multi-fractured horizontal wells (MFHWs). However, these strategies can also increase the chance of fracture interference, which not only can negatively impact the overall production, but also introduce complexities for production data analysis. To analyze the production data from two communicating wells, a semi-analytical model is developed and applied to a field case. The new semi-analytical model uses the dynamic drainage area (DDA) concept and assumes that the reservoir consists of two regions: a primary hydraulic fracture (PHF) and an adjacent enhanced fracture region (EFR) or non-stimulated region (NSR) in the reservoir. Assuming a well pair primarily communicates through PHFs, the equations for two communicating wells are coupled and solved simultaneously to model the fluid transfer between the wells. This method is used within a history matching framework to estimate the degree of communication between the wells by matching the production data. The model is first verified against more rigorous numerical simulation for a range of fracture/reservoir properties. These comparisons demonstrate that there is excellent agreement between the reservoir simulation results and the new semi-analytical model. The semi-analytical model is then employed to history match production data from six MFHWs (drilled from two adjacent well pads) exhibiting different degrees of communication. First, only strong communication between pairs of wells (intra-pair communication) is considered. Then sink/source terms are added to account for intermediate degrees of communication between well pairs (inter-pair communication). Addition of the source/sink terms improves the history-matching quality of the three well pairs, while total material balance of the entire section is honored. A flexible, yet simple, semi-analytical model is developed for the first time that can accurately model the communication between multiple well pairs. This approach can be used by reservoir engineers to analyze the production data from communicating MFHWs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".