Modelling of Production and Fiber Optic Data for Analyzing Inter-Well Interactions in Fractured Shale Gas Reservoir
Bibliographic record
Abstract
Abstract Fiber optic techniques, including Distributed Temperature Sensing (DTS) and Distributed Acoustic Sensing (DAS), enable real-time monitoring and interpreting of fracture hits, stress shadowing, and production behavior. However, integrating field DTS/DAS data and production responses remains challenging. This work uses numerical simulation to model fracture propagation, stress evolution, and fluid production in a shale reservoir. The capability of the numerical model to address these coupled flow-geomechanical issues is systematically evaluated. The simulation responses are analyzed to understand various observations extracted from some field DTS/DAS data. While previous coupled flow-geomechanical simulation studies have compared their numerical results of fracture hits to DAS responses, few studies have examined how the observed fracture interference would affect the fracture development and production performance of other nearby well drilled subsequently (e.g., child well). There are even fewer attempts to incorporate DTS data when analyzing the production performance of these offset wells. Detailed mechanistic models are constructed to simulate various fracture hits and crossflow scenarios. 3D thermal flow models with wellbore modelling are coupled with geomechanical calculations. Multi-scale fracture responses are modelled, e.g., physical opening/closure of hydraulic fractures (HF), induced secondary fractures, and pre-existing natural fractures. A commercial simulator is used, a systematic examination of most available model setup options was performed to achieve the most accurate responses in the flow-geomechanical simulations. Two novel features are added: first, the apparent permeability of the matrix is updated based on pore pressure to capture the effects of nano-scale flow behaviors; next, natural fracture properties are updated based on the computed stress, capturing their closure/dilation. Several field cases based on the Montney Formation are replicated. Simulated strain rate and temperature responses are compared to field DAS/DTS and production data provided by an industrial partner. Simulation results reveal that while fracture hits and stress shadowing hinder the development of adjacent new fractures, they also boost the production of nearby stages, especially in the early phases. Frac hits lead to slower cooling during injection and faster warm-back during shut-in and flowback near the wellbore; they additionally induce unforeseen temperature reductions in areas devoid of any newly stimulated fractures, this demonstrates that DTS can detect the effects of fracture hits and crossflows in real time during treatment. These effects intensify with closer proximity but diminish with higher intensity of frac hits. For the first time, optimal model configurations have been introduced that are designed for deployment within the commercial software package to achieve precise simulations of the hydraulic fracturing process. A quantitative framework is presented for correlating simulation responses with DAS/DTS data. This type of analysis is useful for a variety of geological energy applications. The results highlight the sensitivity of downhole temperature, strain/stress and production responses to treatment-monitor well interactions. Different scenarios are simulated and compared with field data. The findings provide valuable insights for using real-time DTS/DAS data from the field in fracture hit and fracture diagnosis and production data analysis.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".