Well identity is where integration fails. The same well appears in the drilling system, the production system, the reservoir model, the maintenance system and the regulatory return under different names, and joining data across them requires a mapping that someone maintains. Where that mapping does not exist or has drifted, every cross-system analysis silently loses or misattributes records. Establishing a unique well identifier with a maintained cross-reference is unglamorous work with a return that exceeds most analytics projects.

Production allocation is a calculation, not a measurement, and its basis is frequently undocumented. Individual well rates in a commingled system are derived from periodic well tests, extrapolated between tests and scaled to match a measured total. The frequency of testing, the validity criteria applied, the allocation method and the handling of wells that have not been tested recently all affect the numbers used for reserves, forecasting, reservoir management and partner accounting. Very few organisations can produce a clear description of their own allocation process.

Historians do not store measurements; they store compressed approximations. Deadband and swinging door compression algorithms discard points that the algorithm judges uninteresting, and the reconstruction of a trend from stored points is not the original signal. For trending and reporting this is adequate. For transient analysis, for event reconstruction, for any calculation involving rates of change, it is not, and analysts working from historian data without understanding its compression settings draw conclusions the data cannot support.

Finally, governance is what makes data quality persist. Cleaning a data set produces a clean data set once; establishing ownership, validation at entry, defined stewardship and a process for handling exceptions produces data that stays clean. Digital initiatives that fund analytics without funding governance produce a period of good results followed by gradual degradation as the underlying data drifts.

By the end of this training, participants will be able to:

• Map the reservoir and production data landscape and the systems holding each data type
• Establish master data management including unique well identity and cross-system reconciliation
• Assess data quality across completeness, accuracy, consistency, timeliness and validity
• Design and implement validation rules at entry and in downstream processing
• Describe and assess production allocation methodology and its integrity
• Handle historian and real time data with understanding of compression and its limits
• Design integration architecture connecting subsurface, production and operational data
• Establish data governance including ownership, stewardship and exception handling
• Specify data requirements for digital oilfield and analytics workflows
• Assess digital initiative proposals against their data foundation

Organisations sending participants to this training will:

• Reduce technical time spent finding, cleaning and reconciling data
• Improve the reliability of production allocation and everything built on it
• Enable cross-system analysis that currently fails on identity mismatch
• Improve the success rate of digital and analytics initiatives
• Establish governance so that data quality persists rather than degrading
• Improve regulatory and partner reporting accuracy

Participants will:

• Assess whether a data set can support an intended analysis
• Recognise allocation, identity and compression problems before they corrupt work
• Design validation and governance that prevents recurring problems
• Specify data requirements for analytics and digital work
• Evaluate digital proposals on their data foundation
• Build a capability that underpins every other technical activity

• Reservoir and production engineers working with field data
• Production accountants and allocation specialists
• Data management, data quality and information staff
• Digital, analytics and technology staff supporting subsurface and operations
• Surveillance and performance engineers
• Technical staff specifying or evaluating digital initiatives
• Managers accountable for data-dependent reporting

Upon successful completion of this training course, delegates will be awarded an official Certificate of Completion issued by the Petroleum Engineers Association (PEA), an ISO 9001:2015 certified training organization. The certificate carries 10 Credits and formally records the total learning hours completed.

Each certificate is signed by the Course Facilitator and the CEO of the Petroleum Engineers Association, and serves as verifiable proof of professional training that delegates can present to employers and professional bodies worldwide.