A Simplified Finite Element Algorithm and Coupled THM Problems in Stress-Sensitive Naturally Fractured Reservoirs During Injection/Production
Bibliographic record
Abstract
ABSTRACT: Stress concentration in vicinity of a wellbore may suppress fracture network and restrict the fluid flux in naturally fractured reservoirs (NFR). Primarily reducing the absolute permeability in the fracture network, such an impact may be reach a significant level when productivity may not increase after a drawdown increase. To cope with such a possible reversal incremental fluid flow, a critical drawdown is identified, for the case when the permeability reduction effect may surpass those inremental contributions of a drawdown increase to the flow flux. A different strategy by increasing the external fluid pressure and maintaining a constant bottom hole pressure (BHP) should be applied, so that the near-wellbore permeability may remain unchanged. Both simulation and field applications are performed and more than 30% production rate increase can be expected depending on the in-situ stresses level against permeability changes for each well. Finite element method is used for a coupled THM problem and simplified finite element algorithm is applied with a dual-porosity mode in NFR 1. INTRODUCTION Then the permeability perpendicular to a confining stress (Principal stress direction or fluid flow directions) is conventionally defined as a function of the effective stress. Practically speaking the principal directions of fluid flow and stresses seldom coincide and in what follows, we propose to define changes of the principal permeability as a function of the normal stress to the principal flow direction, the former may be calculated by the principal stress tensor and the latter is characterized of the natural fractures and the geological bedding. Furthermore, if plasticity, sliding along a fracture or joint, and dilatancy occur, the shear component of the stress tensor should be also used. Thirdly we can determine if a production decline can be reversed by controlling the wellbore drawdown, i.e. if the drawdown and stress concentrations fall into the aforementioned sensitive range. Fourthly we may diagnose if a production decline in a specific field is caused by these stress sensitive permeability change. Finally we may find alternative ways to increase drawdown without bringing down the permeability at the wellbore at the same time. In other words, once we can correlate producing well drawdown to the induced stress changes, which are in the stress-permeability sensitive range, then we can adjust the producing drawdown to serve the purpose for production enhancement. Once these factors are identified, a dual-porosity model is built and finite element method is utilized for production calculations. It should be realized that without considering these factors, any attempt to increase production by lifting drawdown will be neutralized by the consequence of permeability reduction. Production declines in a carbonated reservoir northwestern China are observed and enhancing strategies are proposed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".