Full waveform acoustic data as an aid in reducing uncertainty of mud window design in the absence of leak-off test

Abstract

Creating a mechanical earth model (MEM) during planning the well and real-time revision has proven to be extremely valuable to reach the total depth of well safely with least instability problems. One of the major components of MEM is determining horizontal stresses with reasonable accuracy. Leak-off and minifrac tests are commonly used for calibrating horizontal stresses. However, these tests are not performed in many oil and gas wellbores since the execution of such tests is expensive, time-consuming and may adversely impact the integrity of the wellbore. In this study, we presented a methodology to accurately estimate the magnitudes and directions of horizontal stresses without using any leak-off test data. In this methodology, full waveform acoustic data is acquired after drilling and utilized in order to calibrate maximum horizontal stress. The presented methodology was applied to develop an MEM in a wellbore with no leakoff test data. Processing of full waveform acoustic data resulted in three far-field shear moduli. Then based on the acoustoelastic effect maximum horizontal stress was calibrated. Moreover, maximum horizontal stress direction was detected using this methodology through the whole wellbore path. The application of this methodology resulted in constraining the MEM and increasing the accuracy of the calculated horizontal stresses, accordingly a more reliable safe mud weight window was predicted. This demonstrates that the presented methodology is a reliable approach to analyze wellbore stability in the absence of leak-off test.

A Study on Natural Fracture Characterization for Well Trajectory Design and Production Improvement: A Case Study from a Tight Gas Reservoir, Australia

“…A geomechanical model is built by integration of wireline logs with drilling events, core data and in-situ measurements. The Integration of these data constrains the geomechanical model and improve the estimation of in situ stresses and mechanical properties [9], [12]- [14]. Knowledge of rock strength is also essential for an accurate estimation of in situ stress during geomechanical modelling.…”

“…where UCS is in psi, is sonic wave travel time in , and E is dynamic Young's modulus in psi, calculated from density, shear and compressional sonic logs. Pore pressure was another parameter required for estimation of in-situ stress which can be obtained using Eaton's method [14], [15] calibrated by MDT data. The results achieved from this calculation indicated a pore pressure gradient of 1.04 SG in the well.…”

“…Herein, it was explored whether underbalanced drilling (UBD) operations within the reservoir are suitable in the context of rock strength and stress conditions. One target of this analysis has been to provide new information specific to the studied field in order to investigate UBD options, or at least, explore well trajectories that could be drilled while maintaining wellbore stability and minimizing formation damage [14].…”

“…In some situations, limiting wellbore breakout development is operationally easier than designing a mud program that prevents breakouts from ever forming. This is because excessively high mud weights might not be permissible due to fluid losses through either natural fractures or hydraulic fracturing [10], [14]- [23].…”

Machine Learning: A Useful Tool in Geomechanical Studies, a Case Study from an Offshore Gas Field

“…where σ h , σ H , ν, E, σ v , and P p are minimum horizontal stress, maximum horizontal stress, Poisson's ratio, Young's modulus, vertical stress and pore pressure, respectively. α as Biot's coefficient was set as 1.0, and ε x and ε y are two horizontal tectonic strains [35,49]. Trial and error on a range of values of ε x and ε y should be performed in a way that makes a balance between the value of σ h from Leak off test/Extended Leak off test [50] and observed drilling events [51].…”