Page 282 - Intelligent Digital Oil And Gas Fields

230 Intelligent Digital Oil and Gas Fields

historical data, particularly at the beginning of the field exploitation. The
effect of surface constraints on the predicted production forecast is more
pronounced when:
• the field is producing at or near capacity,
• field operating conditions are changing, or
• the simulation is over an extended duration.
The step forward in development of modern IAM workflows is the intro-
duction of tight iterative coupling between the subsurface model and surface
network workflow components; Fig. 6.12 shows an example. One novel
aspect of this IAM workflow is the seamless integration of components,
in the green-shaded rectangle, which are:
• Data aggregation. This module provides services for gathering and sum-
marizing the data along with techniques for data quality assurance and
quality control (QA/QC), in preparation for (statistical) analysis. Such
techniques may include data cleaning and manipulation with removal
of statistical outliers, imputation, and interpolation of missing
data, etc.
• Visualization. This module provides data visualization in real time, which
may include forecast, profiles, trends, and vertical flow performance
(VFP) data. The visualization may have color maps of saturation, pres-
sure, and composition, contours, and/or streamlines or flow vectors.
• Workflow process controller. This module provides interface and control
platform for dynamic coupling between reservoir simulation models
and surface network models. This workflow is designed to control well
operations such as those using artificial lift pumps, automated surface
chokes, downhole ICVs, etc. It also includes recovery process manage-
ment such as water, gas, or chemical injection. Note that the production
predictions are ultimately used in surface processing and economic
models, which are not covered in detail here.
The value of deploying the IAM workflow with tight iterative coupling is
significant; the workflow:
• Improves the integration efficiency by streamlining processes across mul-
tiple disciplines (reservoir, production, facilities, planning).
• Creates a more realistic production forecast prediction for planning and
economics.
• Serves as an efficient, robust, flexible subsurface-to-surface integration
building block for subsequent production optimization workflows;
whatever event happens at surface affects the reservoir condition and
vice versa.