An Analytical Approach to Fracture Gradient Prediction Utilizing Leak Off Test Calibration Data: Case Study Application of a Deep Play Niger Delta Well

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.