Macro Insights from Interval Pressure Transient Tests: Deriving Key Near-Wellbore Fracture Parameters in a Light Oil Reservoir Offshore Norway
Bibliographic record
Abstract
Abstract Fractures can be first-order controls on fluid flow in hydrocarbon reservoirs. Understanding the characteristics of fractures such as their aperture, density, distribution, conductivity, connectivity, etc, is key for reservoir engineering and production analysis. Well testing plays a key role in the the characterisation of fractured reservoirs, especially. New advances in the Pressure Transient Analysis (PTA) have enabled the interpretation of production data in a way where the resulting geological scenarios are in better agreement with fracture patterns observed in outcrop analogues. Traditionally, Drill Stem Test (DST) data have been the primay source of information for well testing. However, we hypothesise that wireline conveyed tools designed for Interval Pressure Transient Testing (IPTT) could yield a more throrough description of the near-wellbore heterogeneities, including fractures. Hence, we investigate the applicability of IPTT for characterising fractured reservoirs using detailed numerical simulations models with accurate wellbore representation to generate synthetic IPTT responses that can obtained through a next-generation wireline testing tool called SATURN. We particularly focus on cases where fractures are present in the near-wellbore region but do not intersect the wellbore. The study included parameters such as fracture densities and conductivities, distance between fractures and wellbore and the vertical extension of the fractures across geological beds. The impact of the different fracture scenarios on the pressure transient tests was recorded as characteristic signatures on diagnostic plots (pressure derivative curves). We have called these curves "IPTT-Geotypes"; they can be used to assist the interpretation process of IPTT responses. To the best of our knowledge, this is the first time pressure derivative type curves for IPTT in fractured reservoirs are presented in the literature. A field example of an IPTT case was analysed using the concept of geological well testing. We integrated the information from petrophysical logs and the IPTT-Geotypes to assist the calibration of a reservoir model developed to represent the geological setting of the tested reservoir interval. The results provided a sound interpretation of the reservoir geology and quantitative estimation of the matrix and fracture parameters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".