Scientific Software Engineer in Abingdon

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.

Typical Responsibilities and Duties

• 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.

Skills and Competencies

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, understand, and implement optimisation algorithms from technical literature.

Software & Technical Skills

We welcome engineers with strong domain backgrounds who are growing their software skills. The following is what we expect and what we will help you develop:

• Required: Programming experience in Python and/or C++ is sufficient to implement numerical algorithms. Comfort with scientific computing concepts: numerical methods, linear algebra, iterative solvers, data structures for spatial problems. Ability to read and implement solutions from technical papers and algorithm specifications.
• Desirable (we will invest in your growth): Experience with Go, Java, or C# for backend service development. Familiarity with Git, pull requests, and collaborative code review. Exposure to cloud platforms (GCP, Azure), containerisation (Docker), or CI/CD pipelines. Knowledge of PDDL or AI planning/scheduling formalisms. Awareness of Agile/SCRUM practices: TDD, BDD, Azure DevOps.

What Success Looks Like

• You bring credible well placement knowledge to design discussions and help the team build physically meaningful, robust optimisation engines.
• You contribute to DCG milestones on the 2026 roadmap: on‑prem Petrel release, brownfield extensions, and improved automated well layout generation.
• You learn the AI Scheduler architecture and make your first contributions to its integration with DCG within your first two seasons.
• You identify and close at least one significant performance or algorithmic gap in an FDPlan engine, with measurable impact on solution quality or compute time.
• You grow into owning features end‑to‑end: from requirements and algorithm design through implementation, testing, and production release.

What we offer

• A multi‑disciplinary team combining petroleum engineering science with modern cloud software, at the frontier of digital field development planning.
• Direct exposure to real‑world well planning challenges from global operators.
• A structured path to grow software engineering skills alongside your domain expertise, with mentoring from experienced software engineers.
• Hybrid working (BlueFlex): flexible combination of on‑site at Abingdon Technology Centre and home working.
• Access to SLB hackathons, internal tech conferences, and SPE/industry events.
• Competitive base salary with bonus, private healthcare for employee & family, subsidised dental care.
• Health & Wellbeing programmes such as the Employee Mental health support, health & wellness coaching.
• Other benefits are also available through the SLB flexible benefits programme.

SLB is an equal employment opportunity employer. Qualified applicants are considered without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, age, disability, or other characteristics protected by law. The recruiting process and the position can be adapted to fit most disabilities, please do not hesitate to mention this when applying.