Modeling Complex Natural Fracture Network in Heterogeneous Tight Formations Using Semi-Analytical Strategy
Bibliographic record
Abstract
Abstract This paper presents a robust semi-analytical strategy to simulate natural fracture network system in heterogeneous tight formations. The natural fracture networking is modeled in a more realistically and physically sound manner that enables the capacity to treat actual fracture network data set likely to be acquired from field seismic survey and well logging/core interpretation. The source and sink function method was implemented extensively to study the natural setting of fracture systems. A pseudo- fracture body concept, which can be uniquely named as ghost fracture, has been proposed and implemented in the modeling strategy to achieve an effective handling and computing of random natural fracture and fracture network. This strategy greatly overcomes the modeling challenge in this technical domain and is very useful for future application. The details of fluid entering and leaving the fracture body are scrutinized to help build physically meaningful treatment of fluid flow process and ensure a reliable workflow in computing. This new modeling strategy is applied to simulate natural fracture networking systems with various complexities. Representation of the physics around fracture body with high accuracy greatly enhances our technical confidence to deal with more complex natural fracture system in field, where the involvement of complex fracture physics directly influences the flow regime around a well and its performance. Comparison study using other simulator had been performed to help verify the correctness of the proposed simulation scheme. The results from this new semi-analytical model are consistent with those computed from other commercial simulators under the condition of comparable and simplified fracture network, such as the orthogonal fracture system, which is the normal manner a fracture system constructed in commercial software. However, this new semi-analytical methodology creates results with accuracy near analytical solution and successfully consolidates the ability of rendering more complex and irregular fracture settings to satisfy the real physics in a highly effective computational fashion; thus, helps fulfill the objective of modeling natural fractures in actual reservoir comprehensively. Results for various synthetic cases under different conductivity conditions have been analyzed systematically. The effects of the fracture network pattern and orientation have also been studied. Under the current industry scenario of implementing massive multistage fracturing in horizontal wellbore for tight oil/gas reserve development, there exists a great need in understanding and analyzing the complex interference/communication among artificial and natural fracture systems. The modeling methodology presented here has built a powerful tool to help characterize and diagnose the fracture system and potentially assist in identifying the sweet fractured formation ranges, thus offer a more reliable way to map fracture network and optimize tight formation drilling and fracturing practice.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".