How a combination of human and artificial intelligence is driving project planning in oil & gas
InEight chief design officer Daniel Patterson comments on the benefits of collaboration between AI and humans to facilitate project planning
The science of project planning has something of a tenuous reputation. How often do large oil and gas capital expenditure (CAPEX) projects really come in according to plan? Almost never. Even during this era of digital transformation, project schedule and cost overruns are still the normal course of business, not the exception.
Arguably, the reason for this is less about poor execution and more about how we still struggle to accurately forecast how long these complex CAPEX projects will actually take to complete.
According to a 2017 McKinsey report, the Middle East has one of the most significant project pipelines anywhere in the world, with a total of US$396B of future projects planned across the region — this means there is a lot of money to be made and opportunities to engage new methodologies and approaches.
New approaches using digital planning and risk assessment tools are poised to change oil and gas project economics forever, bringing with them the potential to deliver successful and on-time CAPEX projects while unlocking significant value.
For the first time in the industry, project planning can combine artificial intelligence (AI) and human intelligence to create true risk intelligence. In a nutshell, this is achieved by bringing together historical project data and human expertise. This way, planners and project teams can produce more accurate and fully risk-adjusted schedules for their projects.
If oil and gas companies in the Middle East can use these tools to adapt their scheduling practices to meet the needs of their unique regional environment, the productivity improvements could deliver up to 30% in cost savings – a whopping US$118B.
A wide variety of factors can contribute to project delays and CAPEX mismanagement, but the root cause has less to do with the likes of planning techniques not being fit for purpose and more to do with inaccurate data being fed into those plans.
The tide is finally turning toward more accurate project forecasting with the advent of AI and the simple realization that it takes the expertise of a specialized team to build a plan, rather than a single planner working in a silo.
Next-Generation Risk Analysis
To help address the challenge of developing a meaningful risk model, a more team-centric and collaborative means of capturing risk and uncertainty inputs has been developed, along with more easily consumable and actionable risk reports. Enter the human intelligence element, where a team’s collective expertise is pooled together on a single platform.
Let the Software Compile the Uncertainty Ranges for You
Rather than force team members down the “describe the range of outcomes as a distribution” approach, why not capture such expert opinion through a simple scorecard instead? Simply ask team members to either buy in or push back on the proposed durations.
This approach carries the massive benefit of making the expert opinion and knowledge capture process very fast and easy for contributors, while still retaining the underlying modelling methodology. This approach also ensures that the total consensus of the team is accounted for in the risk model rather than being “the voice of one.”
Relating back to the challenge of owner/EPC contractor alignment, this concept of consensus-based planning helps drive that necessary synergy tremendously, which in turn drives buy-in and, ultimately, the project’s chances of on-time completion.
Use AI to Help Establish Your Risk Register
In addition to more efficiently capturing duration ranges through the approach described above, the second step in the risk model building process is to capture and quantify risk events.
Traditionally, risk events have been tracked in what is known as a project risk register. The modelling challenge arises when linking those identified risks from the risk register into the schedule risk model. Without overstating, this process is treacherous at best, and one that causes huge challenges in project risk workshops.
So instead of identifying risks in isolation of the schedule and then trying to embed them back in, why not provide an environment where risks are identified and scored directly in context of the schedule itself?
Taking this a step further, by leveraging AI, team members can also take advantage of the computer making suggestions as to common risks and their historical impact on similar scopes of work.
AI-Driven Guidance on Risk Event Identification
Rather than team members having to brainstorm from a blank sheet of paper, they can take into account previously realized risks and opportunities from similar historical projects. As new risks are identified, they can be automatically added to the enterprise risk register, ready for subsequent consumption. This self-perpetuating risk management loop is an entirely new and more effective way for an oil and gas company to adopt a more mature outlook on risk.
Risk-Adjusted Forecasting Is Applicable to All Project Stakeholders
Historically, project risk analysis has been available to larger project organizations and, typically, embraced more by business owners than EPC contractors. The advent of next-generation, risk-adjusted forecasting software is opening up the benefits of risk insight to the broader market.
By combining the data mining power of AI and pooled human intelligence, risk modelling is making huge strides forward.
Contractor organizations can now benefit from determining applicable contingency, along with appropriate margins, when developing their commercial bids. In short, contractors can ensure they are more competitive by following this risk-adjusted forecasting approach. Likewise, owners now get more insight into the realism and achievability of contractor schedules and, thus, can react and remediate faster.
In all instances, the benefit of providing an easier means of capturing risk inputs, applying them to a proven approach, and then gaining deeper and more meaningful insight through next-generation risk reporting, is hard to argue against.
The long overdue collaboration between human and artificial intelligence is finally becoming a reality. By enabling on-time project completion, this culmination of proven practices becomes a perfect union, and has the potential to unlock value across a project’s life cycle. The end result is that more projects will see the light of day.