Measurement, Modeling, and Diagnostics of Flowing Gas Composition Changes in Shale Gas Wells
Bibliographic record
Abstract
Abstract Few attempts have been made to model shale gas reservoirs on a compositional basis. Multiple distinct micro-scale physical phenomena influence the transport and storage of reservoir fluids in shale, including differential desorption, preferential Knudsen diffusion, and capillary critical effects. Concerted, these phenomena cause a measureable compositional change in the produced gas over time. We developed a compositional numerical model capable of describing the coupled processes of diffusion and desorption in ultra-tight rocks as a function of pore size. The model captures the various fracture configurations believed to be induced by shale gas fracture stimulations. By combining the macro-scale (reservoir-scale fractures) and micro-scale (diffusion through nanopores) physics, we show how gas composition changes spatially and temporally during production. We compare our numerical model against measured gas composition data obtained at regular intervals from shale gas wells. We utilize the characteristic behavior illustrated in the model results to identify and to define features in the measured data. We present a workflow for the integration of measured gas composition data into production data analysis tools in order to develop a more complete well performance diagnostic process. The onset of fracture interference in horizontal wells with multiple transverse hydraulic fractures is shown to be uniquely identified by distinct fluctuations in the flowing gas composition. Using these measured composition data, the timescale and durations of the transitional flow regimes in shales are quantified, even for high levels of noise in the rate and pressure data. Reservoir properties are inferred from the integration of the compositional shift analysis of this work with modern production analysis. This work expands the current understanding of well performance for shale gas to include physical phenomena that lead to compositional change. This may be used to optimize fracture and completion design, improve well performance analysis and provide more accurate reserves estimation. This work demonstrates a numerical model which captures multicomponent desorption, diffusion, and phase behavior in ultra-tight rocks. We identify and validate diagnostic trends via high-resolution composition, saturation and pressure maps. We provide a workflow for incorporating measured gas composition data into modern production analysis.
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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".