Session 7. Oral Presentation for: Improving operational confidence and decision-making in the absence of key reservoir data through utilisation of a machine learning-based coal identification model in coal seam gas wells
Gonzalo Vazquez A *A
Gonzalo Vazquez is a seasoned drilling engineer with 26 years of experience in the oil and gas industry. Born in Venezuela, he started his career in 1999 at Intevep, a leading research and development institute, specialising in drilling fluids and cement. Gonzalo gained extensive field experience in offshore and onshore operations before earning a Master’s degree in Drilling and Well Engineering from Robert Gordon University in Aberdeen, UK, in 2010. Relocating to Australia, he worked with Arrow Energy and Santos before joining Senex Energy, where he has served as a Senior Drilling and Completions Engineer for 8 years. Gonzalo integrates sustainability with innovative solutions, while also mentoring young engineers. A lifelong learner, he applies his passion for machine learning and artificial intelligence to optimise processes. Outside of work, Gonzalo enjoys tennis, soccer and family time. Known for his positivity, he thrives in dynamic teams and champions efficiency in all endeavours. |
Abstract
Presented on 27 May 2025: Session 7
This study investigates the feasibility of using machine learning algorithms to identify the presence of coal (at a scale comparable to wireline logging), utilising drilling parameters from coal seam gas (CSG) wells located in the Surat Basin, Queensland, Australia. Generally, during the drilling operation and before wireline logging, the presence of coal lithologies is inferred from elevated gas levels liberated into the drilling mud. However, the reliability of gas sensors can be compromised, necessitating operations geologists to rely on fluctuating drilling parameters to make decisions. To address this, a supervised classification model using the XGBoost algorithm has been developed to predict coal in the absence of reliable gas sensor data, improving data available to the operations geologists for decision-making purposes. Trained on data from over 150 wells, the classification model identifies coal lithologies by analysing typically available, high-resolution drilling parameters. While these parameters vary due to physical changes in the drilled lithology, they are also significantly overprinted by operational factors. Underpinned by iterative exploratory data analysis, the machine learning workflow involved processing a large amount of raw data, defining the predictive target, feature engineering and model development. Traditional machine learning performance metrics, such as F1 score, recall and precision, have been used in conjunction with business-based metrics to compare model iterations. Even though the model faces challenges related to class imbalances, overfitting and variable operational environments, results demonstrate the utility in predicting the location of coal and assisting operational geology workflows in wells where gas readings are unreliable or unavailable.
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Keywords: AI, artificial intelligence, coal prediction, coal seam gas, CSG, drilling technology, machine learning, Surat Basin, unconventional, XGBoost.
Gonzalo Vazquez is a seasoned drilling engineer with 26 years of experience in the oil and gas industry. Born in Venezuela, he started his career in 1999 at Intevep, a leading research and development institute, specialising in drilling fluids and cement. Gonzalo gained extensive field experience in offshore and onshore operations before earning a Master’s degree in Drilling and Well Engineering from Robert Gordon University in Aberdeen, UK, in 2010. Relocating to Australia, he worked with Arrow Energy and Santos before joining Senex Energy, where he has served as a Senior Drilling and Completions Engineer for 8 years. Gonzalo integrates sustainability with innovative solutions, while also mentoring young engineers. A lifelong learner, he applies his passion for machine learning and artificial intelligence to optimise processes. Outside of work, Gonzalo enjoys tennis, soccer and family time. Known for his positivity, he thrives in dynamic teams and champions efficiency in all endeavours. |
