Main Duties and Responsibilities:
- Develop predictive models for reservoir performance forecasting, decline curve analysis, and production optimization.
- Apply machine learning algorithms (e.g., random forests, gradient boosting, time series models, deep learning) to identify patterns in production and well performance data.
- Integrate diverse datasets (PVT, SCADA, seismic, well logs, simulation outputs) to create actionable insights for reservoir management.
- Collaborate with reservoir engineers, geologists, and petroleum engineers to design data-enabled workflows.
- Support real-time data analytics and build dashboards for operational surveillance and anomaly detection.
- Participate in digital transformation initiatives and help implement AI/ML tools in the field and corporate settings.
- Ensure data quality, cleaning, normalization, and management of large datasets using appropriate data engineering tools.
- Provide statistical insights during field development planning, well intervention analysis, and history matching support.
- Stay attuned to industry trends and new technologies in petroleum data science and reservoir digitalization.
Requirements:
- Master’s or PhD in Data Science, Applied Mathematics, Compute Science, or related field.
- 3+ years of experience in data science, preferably within the oil and gas industry.
- Strong command of Python, SQL, and at least one ML framework (e.g., scikit-learn, TensorFlow, PyTorch).
- Experience with data visualization tools (e.g., Power BI, Spotfire, Dash, Tableau).
- Knowledge of statistics, time-series analysis, uncertainty quantification, and optimization techniques.
- Fluency in English.
- Experience with cloud platforms (AWS, Azure) and big data tools (e.g., Spark, Hadoop).
- Familiarity with reservoir simulation software (e.g., Eclipse, CMG, tNavigator).
Working Conditions:
Office based role - 8 hours a day - Sunday to Thursday.
Only qualified candidates will be contacted.