Developing Methodology for DFIT Design and Pressure Interpretation by Coupled Reservoir Geomechanics Flow Simulation
Bibliographic record
Abstract
Abstract A numerical, coupled reservoir flow and geomechanics model has been built using a black-oil simulator and evaluated for diagnostic fracture injection test (DFITs) in unconventional reservoirs. DFITs have evolved into a commonly used technique to generate direct estimates of some key unconventional reservoir characteristics. Challenges, however, arise due to the complexity of the unconventional reservoir characteristics, long shut-in times to reach the closure pressure, and the long test durations to obtain realistic reservoir parameters from pseudoradial flow. One way to improve the analysis of minifrac tests in unconventional reservoirs is to use a coupled geomechanics and flow simulator, which is capable of representing the interactions between the reservoir fractures and minifrac developed during minifrac tests. The numerical model used in this work is based on coupling the geomechanical rock properties with the reservoir flow model. The geomechanical model simulates the growth and subsequent closure of the hydraulic and secondary fractures by modeling the change in the reservoir stresses. Furthermore, the model simulates the minifrac pressure response before fracture-closure as well as during the after-closure falloff period. The pressure response during the falloff period is then analyzed to evaluate the reservoir properties using Nolte pre-closure and after-closure analysis techniques. The coupled geomechanics and flow simulation of minifrac provides the capability of modeling the fracture breakdown and fracture closure and estimating the fracture dimensions. The traditional fracture design tools provide similar information focusing only on the geomechanics of the rock and ignoring the effect of the reservoir flow. An additional advantage of the simulator used in this work is to provide estimates of fracture stiffness, fracture closure permeability, residual permeability, and permeability of the matrix. The simulator allows pressure matching and successfully simulates the reservoir flow for extended shut-in periods. The simulator allows designing DFITs with different injection volumes, rates, and shut-in periods and thus helps provide the optimum parameters for the test. The after-shut-in flow regimes generated by the simulation model agree with the DFIT theory.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".