Session 22. Oral Presentation for: Automatic rock strength prediction in data-limited wells
Max Millen A *A
Max Millen is an operations geologist, currently working with Origin Energy on APLNG’s coal seam gas operations. He holds a BSc (EarthSc) and a MPhil (Science) from Queensland University of Technology. Previous work includes a multidisciplinary seal capacity assessment for CO2 sequestration targets. Max’s recent work has focused on reducing the geological and geomechanical uncertainty in support of drilling, completions, workover and well abandonment operations. |
Abstract
Presented on 28 May 2025: Session 22
Understanding in situ mechanical rock properties is critical for wellbore stability, hydraulic fracturing, reservoir characterisation and remediation/abandonment operations. These properties are determined using specialised wireline log data and performing well and drill core testing to calibrate and constrain the log-derived models. However, a significant number of wells lack the required logs and tests, making accurate mechanical characterisation a significant challenge. This study presents a novel approach for the prediction of mechanical properties in these data-limited well scenarios. A specific application for this is identifying ‘weak’ points in a well to guide cement-plug pressure testing during well abandonment operations. In the area of interest for this work (Surat Basin), the most common dataset for development wells is a ‘triple-combo’ wireline logging suite. Curves acquired using this logging suite include gamma-ray, bulk density, neutron porosity and resistivity data. The proposed methodology leverages machine-learning and rock-physics relationships to predict mechanical properties in data-limited wells. Key wells were identified as having the required wireline log data, well test data and core testing to characterise and model in situ mechanical properties. Data from these wells were then used to train and test a machine-learning model and constrain rock-physics relationships allowing for characterisation of mechanical properties in data-limited wells. This work addresses a pressing industry need and is particularly relevant to operations with high well density, reducing costs and improving well integrity and regulatory compliance. Also, it highlights the potential of machine-learning and data integration in improving our understanding of subsurface rock properties in data-constrained settings.
To access the Oral Presentation click the link on the right. To read the full paper click here
Keywords: data-limited wells, elastic properties, geomechanics, horizontal stress, machine learning, petrophysics, supervised regression, well abandonment.
Max Millen is an operations geologist, currently working with Origin Energy on APLNG’s coal seam gas operations. He holds a BSc (EarthSc) and a MPhil (Science) from Queensland University of Technology. Previous work includes a multidisciplinary seal capacity assessment for CO2 sequestration targets. Max’s recent work has focused on reducing the geological and geomechanical uncertainty in support of drilling, completions, workover and well abandonment operations. |
