Empirical relationship between strength and geophysical properties for weakly cemented formations

• Published in: Journal of petroleum science & engineering Vol. 72; no. 1; pp. 134 - 142
• Main Authors: Sharma, M.S. Ravi, O'Regan, Matt, Baxter, C.D.P., Moran, Kate, Vaziri, Hans, Narayanasamy, Raja
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier B.V 01-05-2010; Elsevier
• Subjects: Applied sciences; Completion equipments and methods. Casing. Cementing. Tests and measurements in wells; Crude oil, natural gas and petroleum products; Drilling. Casing. Preparing wells for production; Energy; Exact sciences and technology; Fuels; multiple regression; neural network; porosity; Prospecting and production of crude oil, natural gas, oil shales and tar sands; sandstone; unconfined compressive strength; velocity; multiple regression; velocity; sandstone; neural network; porosity; unconfined compressive strength; Shale; Laboratory measurement; Compressive strength; Neural network; Density; Oil well; Gas well; Completion; Cement; Wireline logging; Porosity; Wave velocity; Sandstone; Strength
• ISSN: 0920-4105; 1873-4715
• DOI: 10.1016/j.petrol.2010.03.011

Estimation of strength profiles in a reservoir, particularly in weakly cemented formations, is important for completion design and predicting the onset of sanding in oil and gas wells. These strength profiles are often calculated using empirical relationships that relate measured log parameters (e.g. bulk density, compression wave velocity) to Unconfined Compressive Strength (UCS). A review of six published models developed specifically for weakly cemented formations showed that they significantly over-predicted measured strength values from four wells by up to 700%. Possible causes for this over-prediction include relating strength to only bulk density and compression wave velocity, using laboratory measurements on cores instead of log-derived values, and neglecting the significant effect that shale content has on measured velocities.
In this study, a multiple regression analysis was performed to develop a model that relates geophysical data to UCS for four oil wells where wireline logging data and measured strength profiles were available. The emphasis in this study was to evaluate the potential to develop a single regression model that could be applied to logging data collected from different geographic locations. To achieve this, the use of synthetic velocities calculated from velocity–porosity transform models was evaluated to remove the effects of varying pore fluids on the regression. Velocities calculated from the Xu and White (1995) model yielded better results when compared to regression analyses that incorporated measured velocities directly. A relationship was established between bulk density, synthetic velocities, volume of shale and UCS that predicted strength values within an error range of 100%. This is a significant improvement over published relationships and illustrates the benefits of using both synthetic velocities and multiple parameters in developing empirical strength predictions from logging data in weakly cemented formations.