Analysis of Production Data from Communicating Multifractured Horizontal Wells Using the Dynamic Drainage Area Concept
Bibliographic record
Abstract
Summary Reduction of fracture/well spacing and increases in hydraulic fracture stimulation treatment size are popular strategies for improving hydrocarbon recovery from multifractured horizontal wells (MFHWs). However, these strategies can also increase the chance of fracture interference, which can not only negatively impact the overall production but also introduce complexities for production data analysis. A semianalytical model is therefore developed to analyze production data from two communicating MFHWs and applied to a field case. The new semianalytical model uses the dynamic drainage area (DDA) concept and assumes three porosity regions. The three-region model is comprised of a primary hydraulic fracture (PHF), an enhanced fractured region (EFR) adjacent to the PHF, and a nonstimulated region (NSR). 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 communication between the wells by matching the production data. The semianalytical model is first verified against a more rigorous, fully numerical simulation model for a range of fracture/reservoir properties. These comparisons demonstrate that there is excellent agreement between the fully numerical simulation model results and the new semianalytical model. The semianalytical model is then employed to history-match production data from six MFHWs (drilled from two adjacent well pads) exhibiting different degrees of communication. For the purpose of history matching the data, only strong communication between pairs of wells (intrapair communication) is considered in the three-region model, and the results show good agreement with the field data. A flexible, yet simple, semianalytical 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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".