Distance-of-Investigation Could be Misused in Unconventional Heterogeneous Reservoirs with Non-Static Properties
Bibliographic record
Abstract
Abstract The concept of distance-of-investigation (DOI) has been widely applied in rate- and pressure-transient analysis for estimating reservoir properties and for hydraulic fracture optimization. Despite its successful application in conventional reservoirs, significant errors arise when extending the concept to unconventional reservoirs. This work aims to clearly demonstrate such errors in the use of the traditional square-root-of-time model for DOI calculations in unconventional reservoirs, and to develop new models to improve the DOI calculations. In this work, the following mechanisms in unconventional reservoirs are first incorporated into the calculation of DOI: 1) pressure-dependency of rock and fluid properties; 2) continuous/discontinuous spatial variation of reservoir properties. To achieve this, pseudo-pressure, pseudo-time and pseudo-distance are introduced to linearize the diffusivity equation. Two novel methods are developed for calculating DOI, one using the concept of continuous succession of steady-states, and the other using the concept of dynamic-drainage-area (DDA). Both models are verified using a series of fine-grid numerical simulations. A production data analysis workflow using the new DOI models is proposed to analytically characterize reservoir heterogeneity and fracture properties. The new DOI models compensate for the inability of the traditional square-root-of-time model to capture spatial and temporal variations of reservoir and fluid properties. The pressure-dependency of fluids and reservoir (i.e., fluid density, viscosity, rock permeability and porosity) and reservoir heterogeneities (i.e., deterioration of reservoir quality from the primary fracture to the reservoir), can significantly retard the propagation of the DOI. Another important outcome of this work is to provide a practical and analytical approach to estimate the spatial heterogeneity directly from the production history of field cases.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".