Rock Mechanical Properties of Shallow Unconsolidated Sandstone Formations

Abstract

Rock mechanical properties, such as the Poisson's ratio, Bulk, Shear and Young's moduli are required for various oilfield operations such as seismic processing, reservoir simulation studies, and hydraulic fracturing for sand control. These properties are usually determined from compressional (P) and shear (S) wave velocities which can be captured by the dipole sonic logging tool. For unconsolidated rocks (shallower than 6000 ft), the P and S-wave velocities are difficult to predict due to the dispersive nature of these rocks. Also, the available correlations in the open literature to determine these parameters should be used with caution since these were developed mainly for deeper formations.In this study, we compared the sonic wave velocities empirically predicted by Castagna et al., Eberhart-Phillips and Han with the logged sonic wave velocities across the Cruse Formation for eight onshore wells in South-Western Trinidad. We also investigated more suitable methods of matching the logged velocities across the Cruse, by developing new empirical models and an analytical method (Hertz-Mindlin contact model) to account for the unconsolidated rocks. The basic input data required for predicting the sonic velocities and dynamic rock mechanical properties were porosity, clay content and bulk density which were taken from the log data. The models for obtaining the static rock mechanical properties were tested against the results of Mini-Frac tests in two offset wells in the Cruse.The Artificial Neural Network (ANN) empirical method was shown to be best suited for predicting the dynamic rock mechanical properties for the Cruse Formation, since Average Absolute Errors (AAE) of 3 and 6 % were obtained in matching the P and S-wave velocities respectively. Similarly, the AAE obtained from applying the three empirical correlations were worse than 17 and 30 %. This error was generally observed to lessen for wells which encountered the Cruse at greater depths. While AAE of 6 and 12 % were obtained from applying the analytical method, reservoir depletion was found to significantly affect the results of the modeling due to the implied presence of hard-to-predict free gas saturations. Also, it was found that the analytical method is the only viable procedure to predict the static rock mechanical properties in lieu of laboratory core tests. The analytical method has been tuned in this study using True Vertical Depth (TVD) as a correlation parameter for the first time, and it can generally be used to estimate both the dynamic and static rock mechanical properties of the Cruse Formation for future oilfield operations.

Data-driven model for shear wave transit time prediction for formation evaluation

“…Rock elastic properties, particularly Young's modulus and Poisson's ratio, tell a lot about the formation because they are deformation properties ). Poisson's ratio is used as a calibration tool in the industry to determine the accuracy of well logs (Oloruntobi et al 2018;Onalo et al 2018aOnalo et al , 2019. In most cases, if a sonic log model is able to predict Poisson's ratio accurately, then, it can be said that the model is robust and reliable (Onalo et al 2018a).…”

“…Empirical relationships have been developed to estimate the shear wave velocity from compressional wave velocity in situations where the shear wave data were missing (Bailey 2012; Castagna et al 1985;Domenico 1984;Eberhart-Phillips et al 1989;Esene et al 2018;Gardner et al 1974;Greenberg and Castagna 1992b;Hamada 2004;Han et al 1986;Jorstad et al 1999;Krief et al 1990;Lee 2006;Miller andStewart 1974, 1990;Ramcharitar and Hosein 2016;Raymer et al 1980;Takahashi et al 2000;Vernik et al 2002). Though these estimations provide simple correlation for quick estimations, they are not as robust as modern-day machine learning techniques that have been applied in several engineering applications (Kumar et al 2014;Nourafkan and Kadkhodaie-Ilkhchi 2015;Onalo et al 2018aOnalo et al , 2019Ramcharitar and Hosein 2016;Reichel et al 2012).…”

Artificial Intelligence Method for Shear Wave Travel Time Prediction considering Reservoir Geological Continuity

“…In 2012, Quantico began to use artificial intelligence to generate acoustic and density logs from existing data flow [14]. Ramcharitar and Hosein established an artificial neural network with 10 hidden layers to estimate shear wave and P-wave by using depth, porosity, clay content, and bulk density [15]. Compared with the empirical model, the neural network model shows a lower absolute average error, so it is more suitable for the estimation of rock mechanical properties.…”