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Quality assurance in forecast
Given the important role production profiles play in key decision making and the inherent uncertainty and bias existing in the process of building them, it is essential that the QA/QC process is sufficient to test the robustness of the forecast range and ensure there is a shared understanding and buy-in of the assumptions behind it across the stakeholders involved in an asset. Different ways to try and ensure the quality of the forecast are as follows:
Getting other qualified professionals not directly involved in the development of this particular forecast to review inputs, methodology and outputs of the forecast. This is a powerful tool, particularly in mitigating the effects of human bias. Each reservoir and development is unique so there are likely to be different approaches that should all be considered.
As has been mentioned throughout this document, it is essential to compare your forecast to the historical production of the field if it is already on production and appropriate analogues if it is not in production. This should include plateau length and production rates as well as individual well rates, decline, response to injection and recovery factors. If your field is already on production it is still useful to make a comparison of this production and the forecasts from developed models against more mature analogous fields. The use of analogues is discussed in more detail in production forecasting analog methods.
The purpose of an audit trail is to clearly state the assumptions and decisions underpinning the forecast so that it can be understood and replicated if necessary. Unfortunately, historically this has not always been done which prevents lessons being learned and improvements identified and implemented. Documentation is discussed in more detail in Documentation and reporting in production forecasting.
Look backs and forecast verification
Key to an effective improvement process is documentation of forecast cases to a level sufficient to understand the assumptions and replicate the results, regular comparison and reconciliation of actual results against predictions and a determination of the root causes of variances to determine process improvements. These improvements must be implemented so that subsequent forecasts improve. Look backs and forecast verification shares industry look back data, highlights some significant causes of poor forecasting and provides advice on implementing a successful look back process. Appendix C gives more detail on Forecasting Diagnostics workflows that can be applied as part of this process.