A simulator for production prediction of multistage fractured horizontal well in shale gas reservoir considering complex fracture geometry
Bibliographic record
Abstract
Shale gas is becoming an increasingly supplementary energy source because of its clean-burning and abundance. Economic gas production in shale requires the techniques of horizontal drilling and multistage hydraulic fracturing to create complex fracture network (CFN). How to accurately describe the characteristics of geometry and flow mechanisms of the CFN and select the most efficient approach for modeling are challenging. In this paper, a production forecasting model for multistage fractured horizontal well (MFHW) with CFN in shale is proposed based on the multiple interacting continua (MINC) theory (organic/inorganic matrix, natural fractures system) and lower-dimensional discrete fracture network (DFN) model (hydraulic fractures system). The model is designed to describe the unconventional flow mechanisms in shale system, such as fractal porous media and non-Darcy multiscale flow in ultra-tight matrix, ad-desorption on organic materials’ surface, rock un-consolidation within natural fractures , high-velocity turbulent flow near well range, and multiphase behaviors. We also propose a novel hybrid control volume finite element (CVFE) and finite-difference (FD) simulation method to obtain the numerical results of the model based on the unstructured 3D tri-prisms. The accuracy of the simulator is successfully validated and sensitivity analysis of some key factors (e.g.: fractal model permeability, Langmuir volume , heterogeneities of reservoir and fractures, well platform) are conducted to evaluate the impacts on production performance. Combing with the micro-seismic monitoring (MSM) data and engineering analyses, the DFN model is applied in Longmaxi shale formation to obtain the history matching with the field data and predict the production.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".