Accurate prediction of net hydraulic fracture propagation pressure is often impossible. Under some conditions the pressure drop along the fracture dominates while in other cases the pressure at the tip determines the net pressure. Properly scaled lab tests and modeling indicate that effective stress determines the tip propagation pressure, but this is hard to confirm with field data in specific cases.

We therefore gathered a large data base of fracture treatments from many areas to investigate the correlation between net pressure and effective stress. In order to avoid any spurious effect from fluid friction, tortuosity and height containment we limited the data to relatively small injections with water or linear gel in vertical wells. All treatments were in conventional clastic reservoirs, but over a large range of permeability, rock stiffness and geological age.

The data show a remarkably good correlation between net pressure and effective reservoir stress, with a slope of 0.46. Lower net pressure of 200-300 psi was found in over pressured reservoirs and higher net pressure of about 1500 psi was seen in depleted reservoirs. We checked that this is not due to another underlying parameter, such as modulus or depth, which could explain the correlation. It is concluded that the correlation is due to a true relation between net pressure (controlled by fracture propagation) and effective stress. Simulation of representative treatments with a new model that includes a cohesive zone at the fracture tip shows excellent agreement with the observed correlation, supporting a physical relation.

The relation between net pressure and effective stress in the reservoir can contribute to improved treatment design in green fields and also will aid in understanding fracture height growth, since effective stress will differ between formation layers. Calibrated models will still be important in view of lack of detailed formation knowledge, but a correct description of the physics of fracture propagation, based on effective stress at the tip, will facilitate more accurate model predictions.

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