An Analytical Approach to Fracture Gradient Prediction Utilizing Leak Off Test Calibration Data: Case Study Application of a Deep Play Niger Delta Well
Bibliographic record
Abstract
Abstract Accurate fracture gradient prediction is crucial for safe drilling operations in oil and gas exploration, particularly when encountering normal and over-pressured zones. This study presents a workflow for fracture gradient prediction calibrated with leak-off test (LOT) data from a wide variety of Niger Delta wells. The methodology improves upon existing prediction methods that were primarily calibrated for the Gulf of Mexico. Analysis of drilling data reveals that fracture gradient prediction is primarily influenced by overburden stress, pore pressure, and depth. A key finding shows that shale formations exhibit higher effective stress coefficients at shallow depths compared to deeper sections. Based on this observation, we propose a depth-dependent effective stress coefficient for enhanced fracture gradient prediction. The study validates the proposed workflow through multiple case applications and comparative analysis with existing methodologies. By integrating fracture gradient and pore pressure analyses, this workflow helps optimize casing setting depths while minimizing risks of borehole instability and lost circulation. Furthermore, accurate fracture pressure estimation prevents potential production losses, injectivity issues, and environmental risks associated with hydrocarbon migration through induced fractures. This work contributes to the understanding of fracture gradient prediction in onshore Niger Delta wells by establishing the effectiveness of combining Matthew and Kelly's correlations with locally available Leak off test data. The methodology provides a practical framework for post-drill fracture pressure analysis that can be used to optimize well and casing designs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".