A New Approach to Deformable Fractured Reservoir Characterization: Case Study of the Ekofisk Field
Bibliographic record
Abstract
Abstract Production from a fractured reservoir is generally governed by the fracture network conductivity, which may be altered by changes in reservoir pressure. A new approach to naturally fractured reservoir description accounts for the variation in the fracture properties caused by the effective stress change. The basis of the proposed approach is a compressible discrete fracture network (CDFN) model with stress dependent fracture aperture and hence porosity and permeability. The new approach allowed us to develop a workflow to evaluate the stress dependent fracture properties. This workflow includes inverse and direct problem solutions. The inverse problem consists of interpretation of pressure transient well tests and production history integrating geomechanical effects. The results of core analyses, production logging and wellbore imaging provide important information for the CDFN characterization. The direct problem consists of numerical flow simulation focused on calibration of the obtained fracture properties to match well test data. Using the workflow, one can determine stress or pressure dependent fracture parameters to be used in single and dual porosity reservoir simulations. Using available field data, the proposed approach allowed us to improve characterization of the Ekofisk fracture network. The seafloor subsidence and chalk compaction observed at Ekofisk have initiated extensive studies of the reservoir geomechanics. The geomechanical effects were integrated in the reservoir characterization using the developed workflow. A decrease in well productivity and fracture conductivity due to pressure decline was obtained from the pressure transient and production analyses. This allowed us to describe the dynamic behavior of the fracture permeability and porosity. These fracture properties were then used in reservoir simulation. The simulation results have given evidence that dynamic reservoir behavior has an effect on multiphase flow comparable to well placement and relative permeability effects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".