Spider Petroleum Services

Data management

Data management

  1. Our professionals in data management are fully customer oriented helping you and your company in:
  • Networks design including the different networks components (layer 2, 3 switches, routes, IPS, platforms)
    • Re-engineering of enterprise network designs to meet increasing demands
    • Preparation of new network technology strategies and implementation recommendations
    • Trouble-shooting and problem resolution of elusive network difficulties
    • Design and deploy secure, operable & scalable infrastructure that adheres to best practices & international compliance standards (ISO27001, PCI DSS).
  • Database: Design, Analyze and implement SQL server. Build capacity plans.
  • We are expert in programming languages such as: C/C++/Microsoft Visual C++, Microsoft Visual C#, ASP.NET, ASP Classic, PHP.

Data management: Responsabilities

  1. Some of the typical responsibilities are, but no limited to:
  • Create and maintain data management policies, procedures and standards for Well, Surface facilities and Production data.
  • Ensuring consistency of data between external and in-house software applications.
  • Data clean up, data consolidation, data loading for well & 2D/3D seismic data.
  • Extorting & compiling data for data packages to internal and external clients.
  • Subsurface mapping, database information management, gridding, geological reporting, data loading and Geo-science analysis.
  • Troubleshooting software / data problems.
  • Identify, analyze and architect solutions for well data and information environments.
  • Manage and ensure that data is available on-time and in the correct format for clients.
  • Define user data requirements through direct liaison with the user community and client representatives. Identify service improvement where possible.
  • Advise users on good data and database practices (e.g. version control, housekeeping, capturing interpretation data) and implementing any agreed actions on the users behalf.
  • Implement a QC checklist for each data type that can be used on each load in order to deliver a clean suite of data, which requires minimal QA from the user.