Close Encounters in the 3rd Dimension: Using Diagnostic Fracture Injection Tests DFITs from the Alberta Duvernay Shale Formation to Quantify Simultaneous Horizontal- & Vertical-Plane Hydraulic Fracture Growth
Bibliographic record
Abstract
Abstract This paper will benefit engineers and geoscientists interested in creating representative hydraulic fracture simulation models and optimizing commercial-scale fracture treatments. The paper focuses on the emerging Duvernay shale formation in Alberta, Canada. Well fracturing pressures are often significantly higher than the Overburden (OB, lithostatic) pressure. Pressures above OB likely create horizontal (hz) bedding plane fracture components since sedimentary rocks are almost always weaker along bedding planes. Most fracture design simulators do not account for the simultaneous existence of multi-plane fractures (Figure 1). Therefore, scaled treatment designs for optimizing fluids, proppant schedules and production performance may be flawed. A key question is: What proportion of the overall fracture volume do horizontal-plane features take? The answer can be sought using the Pressure Transient Analysis (PTA) workflow for Diagnostic Fracture Injection Tests (DFITs) described by Bachman et al (2012, 2015) combined with simple PKN and GDK fracture models to represent the hz and vertical plane fracture components. DFIT analysis techniques and interpretation are hotly debated topics of late. The authors believe a portion of the gap in the understanding of how hydraulic fractures behave is a result of assuming fracture components are fully, or dominantly, vertical. Analysts often interpret high fracturing pressures as tortuosity or near-well friction. However, during the fall-off period after pumping a DFIT, pressures above OB can persist for up to 20 minutes after pump shut-down. Analysis of these tests often exhibit early-time radial flow signatures which are coincident with the OB gradient of ~22kPa/m (1psi/ft) also indicative of hz plane fractures. In Nicholson et al 2017 four field DFIT examples were presented showing strong evidence of hz plane fractures in various depths and formations found in the Western Canadian Sedimentary Basin. In the current paper DFIT PTA analysis is applied to two West Shale Basin Duvernay datasets. A physical model is presented (Figure 1) that incorporates the in-situ stress regime, rock fabric, and pore pressure and that allows history matching of DFIT leak-off and closure behavior for fractures above OB pressure. Simple calculations are provided to estimate the volume and dimensions of these same components for a small volume, single viscosity, no-proppant injection DFIT. This unique approach provides a valuable calibration point for building more advanced simulation models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".