Scientific Software Engineer

Location

Abingdon, Oxfordshire

FDPlan is SLB's cloud-native field development planning application, hosted on the industry-leading DELFI platform. It combines subsurface data, well planning, and economic models to accelerate decision-making for operators worldwide.

We are looking for an engineer who combines solid knowledge of well placement and field development planning with a passion for scientific computing and algorithm implementation. Your primary focus will be the Development Concept Generator (DCG) — the engine that automatically proposes optimal field development layouts from reservoir data and operational constraints.

You will also have the opportunity to contribute to and learn newer engines within the FDPlan ecosystem, such as the AI-based Scheduler (a PDDL‑driven planning and scheduling engine), the Scenario Generator (SGen), and integrated well performance workflows.

Domain fluency in well placement is important, together with your ability to understand what the engine needs, how to optimize it, maintain and evolve it.

DCG Engine Development & Maintenance

• Implement, maintain, and improve the DCG engine: multi‑well pad layout generation, well trajectory planning, anti‑collision analysis, manifold design, and drilling constraint handling.
• Collaborate with algorithm designers and petroleum engineers to translate field development constraints into optimization problem formulations (GA, PSO, Tabu search, gradient‑based methods, or hybrid approaches).
• Improve solution quality, convergence speed, and robustness of existing algorithms through research, experimentation, and benchmarking against real‑world well planning data.
• Extend DCG coverage for brownfield scenarios, onshore multi‑pad layouts, and advanced anti‑collision workflows.

Well Placement & Performance

• Design and implement features that optimize well placement decisions under reservoir, surface, and operational constraints.
• Work on well performance modelling integrations — connecting DCG outputs to simulators such as INTERSECT or PIPESIM for closed‑loop evaluation.
• Identify and resolve performance bottlenecks in engine execution: profiling, algorithmic improvements, parallelization, and scalability for large multi‑well problems.
• Learn and contribute to the AI Scheduler engine (PDDL‑based planning and scheduling), helping integrate it with DCG workflows for automated sequence and resource planning.
• Explore AI/ML and agentic approaches (reinforcement learning, surrogate models) to enhance or accelerate optimisation within FDPlan engines.
• Prototype and propose new algorithmic ideas; present findings to the team and contribute to the FDPlan technical roadmap.
• Participate in SLB hackathons and internal innovation initiatives.

Software Quality & Delivery

• Build automated tests — unit, integration, and performance regression tests — to validate numerical correctness and engine scalability.
• Design and develop microservices and cloud‑native backend components that expose engine capabilities via well‑defined APIs.
• Participate in Agile ceremonies: sprint planning, design reviews, code reviews, and retrospectives.

Domain expertise in well placement and field development planning is the primary selection criterion for this role:

• Degree (BSc, MSc, or PhD) in Petroleum Engineering, Drilling Engineering, Applied Mathematics, Physics, Computer Science, or a closely related discipline.
• Solid understanding of well placement concepts: trajectory design, anti‑collision analysis, multi‑well pad design, drilling constraints, and wellbore geometry.
• Familiarity with field development planning workflows and the factors that drive optimal development concept selection (reservoir, surface, and commercial constraints).
• Experience using or working alongside industry tools and simulators such as Petrel, INTERSECT, ECLIPSE, PIPESIM, or equivalent (academic or professional).
• 2-5 years of experience in well engineering, reservoir engineering, field development planning, or scientific software development in a related domain (industry or graduate research).
• Strong mathematical and analytical skills: ability to read,…