Identify overlooked hydrocarbon zone and forecast oil gain potential from well-logs and production data - ON-1170

Genre de projet: Innovation
Discipline(s) souhaitée(s): Informatique, Sciences mathématiques, Géographie / géologie / sciences de la terre, Sciences naturelles
Entreprise: I2G SOLUTIONS INC.
Durée du projet: 4 à 6 mois
Date souhaitée de début: Dès que possible
Langue exigée: Anglais
Emplacement(s): ON, Canada
Nombre de postes: 2
Niveau de scolarité désiré: MaîtriseDoctoratRecherche postdoctorale
Ouvert aux candidatures de personnes inscrites à un établissement à l’extérieur du Canada: No

Au sujet de l’entreprise: 

i2G Solutions Inc. is a Canadian technology company delivering innovative AI-driven data solutions on Open Subsurface Data Universal (OSDU) DataPlatform for the oil and gas and broader energy sector through its flagship software, i2G SEDD (Subsurface Energy Data Discovery). I2G SEDD represents a next-generation data solutions to energy companies, is OSDU native, providing Machine Learning Toolkits and Agentic AI framework, to fundamentally modernize how subsurface and operational data are managed, discovered and utilized.
At its core, SEDD addresses long-standing industry challenges related to fragmented legacy systems, limited interoperability, and labor-intensive workflows, issues that disproportionately affect small and mid-sized energy operators. By leveraging the open, cloud-native OSDU standard developed under The Open Group, SEDD enables vendor-neutral data management, improved transparency, and a scalable digital foundation for responsible resource development.
Building on this standardized data layer, SEDD applies agentic AI capabilities to automate data quality control, to dialog with data, and support data driven decision though advanced machine learning workflows. These innovations enable data driven decision-making in Exploration and Production, and support more efficient and environmentally responsible resource extraction. 

Veuillez décrire le projet.: 

This research project investigates a machine learning approach to identify unperforated (overlooked-pay) reservoir intervals and forecasting their oil production potential using wel-logs and aggregate well-level production data. The objective is to develop an interpretable and generalizable modeling framework capable of inferring depth-resolved production contributions of reservoir zones, assessing water breakthrough risk, and supporting economically informed perforation decisions.

I2G’s current workflow employs symbolic regression to discover analytical expressions linking well-log measurements and liquid production rate. Interval-level contributions are inferred implicitly by aggregating symbolic predictions across perforated intervals and fitting observed well-level production. While GP provides interpretability and extrapolation capability, it remains limited under aggregate supervision and does not explicitly model inter-well dependencies or dynamic production events.

Research Directions

The proposed research extends beyond classical Symbolic Regression by investigating Symbolic Regression and Graph Neural Networks (GNNs) to incorporate information from neighboring and hydraulically connected wells. GNN-based representations will enable the model to learn dynamic production and water-related events (e.g., water breakthrough, interference effects) across wells. This allows evaluation of whether unperforated intervals, once perforated, are likely to experience early water invasion and whether post-perforation production behavior is expected to be economically viable.

The intern will work on developing Machine Learning workflows of above direction on Canadian, South East Asia and NorthSea dataset.

The outcome will be a functional prototype to leverage AI/ML power to enhance Exploration & Production efficiency for Canadian oil & gas SMEs.

Expertise ou compétences exigées: 

The ideal candidate will have a strong background in Artificial Intelligence, Machine Learning, Data Engineering with hands-on experience in Python, data pipelines, APIs, and cloud-native systems. Familiarity with agent-based systems, workflow orchestration, and human-in-the-loop AI is considered an asset.
Equally important is domain knowledge in petroleum geoscience, petrophysics. Candidates with experience working with machine learning for well, production data will be well suited to the project.
Experience or interest in OSDU data platform, data quality control, metadata management, and interoperability frameworks is highly desirable. Strong analytical skills, problem-solving ability, and the capacity to collaborate across multidisciplinary teams—combining AI, data engineering, and petroleum geosciences expertise—are essential. Prior exposure to regulatory reporting, or energy-sector applications will be considered an advantage.