Page 210 - Intelligent Digital Oil And Gas Fields

164 Intelligent Digital Oil and Gas Fields

Step 4: Reorganize the workflow. Reorganize the workflow focusing on
tasks that deliver business values in terms of engineering time and quality.
Avoid redundant activities and subtasks with many approval steps.
Step 5: Build a conceptual model. Prepare a preliminary design in a flow-
chart of the workflows, highlighting the main input, engineering pro-
cess, output, technical applications, and ownership. This is sometimes
referred to as functional design.
Step 6: Detail design for the UIs and tool selection. Workflows are made
accessible through high-end and visually appealing UIs and user naviga-
tion. Web-based workflow is the best technology to generate the visu-
alization for the workflows. To build Web pages, designers can build a
series of bluesheets showing the detailed process flow, results, perfor-
mance metrics, and statistical data enriched with graphical plots,
charts, etc.
Step 7: Scripting and programing. There are several technologies available
for programming the workflow automatically, sometimes referred to as a
“workflow engine.” For the authors, the most important criteria to
choose the right technology that allow easy, intuitive, and fast interface
with the user.
Step 8: Test and debug. This step is one of the most important phases dur-
ing workflow design and commonly is omitted. In those workflows
requesting multiple activities and coupling with several stand-alone
applications, comprehensive test protocols followed by debugging will
save considerable amount of time and wasted effort ultimately.
Step 9: Implement. The implementation process should assure that the UI
can be visualized from multiple devices including PC, mobile phones,
laptops, and tablets.
Step 10: Quantify and measure KPIs for the automated processes. Compare the
new KPI with step number 2 and show values of improvement.

5.2.7 Foundations of a Smart Workflow

The most effective DOF systems should be designed with advanced auto-
mated workflows. Such workflows must be capable of capturing and
retaining a company’s “knowledge capital” and applying this knowledge
to generate faster and more efficient operational solutions. These workflows
must also have access to all technical data and technical applications in a uni-
fied environment and accelerate repeatable activities based on consistent
rules. The difference between an advanced and a smart workflow is the degree