From 3D Seismic Inversion to Drilling Engineering: A Multidisciplinary Approach to Resolve Wellbore Stability Issues Offshore West Madura

Abstract

The drilling of wells offshore West Madura, East Java, can be challenging. The geological structure of the area often requires drilling at high deviations with large stepouts, through formations consisting of carbonates, shales and sands. As a result, wellbore stability issues are frequently encountered, such as total mud losses, stuck pipe, loss of bottom hole assemblies and associated sidetracking, leading to non-productive drilling time and unnecessary costs. In order to lower the associated risks the operator commissioned a geomechanics study, to identify the root causes of the wellbore stability issues, and provide recommendations for improved drilling of future development wells. Numerous wells had been drilled within the area of interest over more than three decades, resulting in a large variation in the availability and quality of data. Recently acquired 3D seismic data were also available. Therefore, a multidisciplinary approach was employed with geomechanics at its core, accompanied by well log conditioning, generation of synthesized shear sonic logs, simultaneous seismic inversion, and drilling engineering. The integration of the different disciplines ensured the development of robust 1D and 3D geomechanical models, which were applied to develop mud weight recommendations for the planned development wells. Firstly, a 1D geomechanical model was constructed. Two recently drilled wells had excellent data sets: extended leakoff and minifrac test results showed very consistent fracture closure pressures. This, combined with the presence of borehole breakouts and direct rock strength measurements on core, allowed the determination of the minimum and maximum horizontal stresses with only small ranges of uncertainty. The 1D geomechanical model was further calibrated by a detailed comparison with critically reviewed drilling incidents. Simultaneously, well logs were conditioned and pseudologs were created, which were used for 3D simultaneous seismic inversion, from which rock property volumes (P-impedance, S-impedance, and Vp/Vs) were derived in turn. Gardner’s relationship was used to transform the seismic velocity data to a density volume. The 1D geomechanical model was subsequently combined with the 3D seismic data via a structural model grid, resulting in a full 3D geomechanical model containing cubes of pore pressure, principal insitu stresses, elastic rock properties and rock strength. Finally, wellbore stability analyses were performed for the planned development wells, including a quantitative risk assessment to gauge the impact of uncertainties in various key variables on the overall potential drilling success. Well deviation and azimuth sometimes showed a counterintuitive effect on recommended mud weights, as illustrated by stereonet plots. A key factor in the execution of this project was the integration of data and expertise in petrophysics, seismic inversion, geomechanics and drilling engineering over a relatively short timeframe to deliver a technically robust set of mud weight windows, which, combined with recommendations based on a detailed review of passed drilling practices, should enable the successful drilling of the wells in this very challenging environment.

Harnessing Advanced Machine-Learning Algorithms for Optimized Data Conditioning and Petrophysical Analysis of Heterogeneous, Thin Reservoirs

“…Within the geoscience domain, the adoption of ML algorithms such as the tree-based approach dates to the early 1960s . Machine-learning applications within petrophysics include the prediction of reservoir properties, , outlier detection, prediction of missing acoustic slowness, and well log repair. ,, Within the petrophysical workflow, data quality assurance is an important step and can take up a significant amount of time within a project. Friedman and Smith identified that poor data quality leads up to a 20% reduction in labor productivity.…”