A New Simple Analytic Equation for Estimating Asymmetric Growth of Stimulated Reservoir Volume in Anisotropic Shales
Bibliographic record
Abstract
Summary The objective of this paper is to present the development and application of a simple equation for calculating the asymmetric growth of the stimulated reservoir volume (SRV) in an anisotropic shale–petroleum reservoir using microseismic data, and the hydraulic diffusivities of the anisotropic shale. Calculation of the SRV is a problem tackled with solutions that involve different degrees of complexity. Because shale reservoirs are anisotropic, microseismic events generally develop 3D nonuniform asymmetric patterns around the injection points. This paper presents a new method with an easy–to–use analytic equation that allows for reproducing the asymmetric growth of microseismic events as a function of time by considering reservoir anisotropy. Asymmetric growth refers to the fact that propagation of the microseismic cloud in a given direction can be larger, equal, or smaller compared with the propagation in other directions. Accurate determination of the SRV asymmetric pattern is critical for use in specialized material–balance and reservoir–simulation models of shale–petroleum reservoirs. This determination allows for more–realistic projections of reservoir performance. The novelty of the method is the development of an easy–to–use approach for estimating SRV in a spatially nonuniform asymmetric anisotropic reservoir using octants in a coordinate system. The SRV is calculated from the volume of a symmetric ellipsoid divided by a constant value Vc. This is despite the fact that the point of injection of the fracturing fluids in the asymmetric reservoir can be at, close to, or far from the center of the ellipsoid. The development of Vc is presented in this paper. Use of the SRV calculation model is illustrated with real microseismic data of the Horn River Shale in Canada for a case where Vc is equal to 1.3722. Also presented are calculations of hydraulic diffusivities in this anisotropic shale.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".