February 2020


Project Management: How human intelligence and AI are driving project planning in the oil and gas industry

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. Indeed, 30% of respondents to a 2018 PWC survey said they had experienced cost overruns of 10%–50% on their Middle East capital projects.

Patterson, D., InEight

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. Indeed, 30% of respondents to a 2018 PWC survey said they had experienced cost overruns of 10%–50% on their Middle East capital projects.1

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 the industry still struggles 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 $396 B of future projects planned across the region. In the U.S., the electricity and power sector accounted for $423 B in CAPEX projects—the largest of all sectors.2

New approaches using digital planning and risk assessment tools are poised to change oil and gas project economics, bringing with them the potential to deliver successful and on-time CAPEX projects, while unlocking significant value. Digital project planning solutions can combine artificial intelligence (AI) and human intelligence to create true risk intelligence. This is achieved by combining historical project data and human expertise. This path allows planners and project teams the ability to produce more accurate and fully risk-adjusted schedules for their projects (Fig. 1).

Fig. 1. Planners can now produce more accurate and fully-adjusted schedules.
Fig. 1. Planners can now produce more accurate and fully-adjusted schedules.

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—nearly $250 B.

Although a wide variety of factors can contribute to project delays and CAPEX mismanagement, 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.

To help address the challenge of developing a meaningful risk model, a more team-centric and collaborative means of capturing both risk and uncertainty, along with more easily consumable and actionable risk reports, has been developed.

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

Rather than force team members into the “describe the range of outcomes as a distribution” approach, why not capture such expert opinion through a simple scorecard? 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 fast and easy for contributors, while still retaining the underlying modeling methodology. This approach also ensures that the total consensus of the team is accounted for in the risk model rather than being a “the voice of one.”

Relating back to the challenge of owner/engineering, procurement and construction (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.

Applied in practice

Petra Nova, the world’s largest commercial-sized post-combustion carbon capture system, was one such infrastructure project that benefited from this change in approach. The facility’s project engineer’s use of an integrated platform prompted a total rethink on how to do daily work plans, quantity claiming and time collection. It also allowed users at every level of the job to access and maximize data from every aspect of the project.

The ability to clearly communicate work and project scope, provide daily work plans and ensure operational efficiency and compliance not only contributed to the project’s on-time and on-budget completion, but also increased productivity by 20%. The job required nearly 1.4 MM work-hours, but by pooling the team’s collective experience, it achieved a craft-to-staff ratio of 4.2/1—a 50% improvement on the average achieved by peer projects.

Use AI to help establish your risk register

In addition to more efficiently capturing duration ranges through this approach, the second step in the risk model building process is to capture and quantify risk events.

Traditionally, risk events have been tracked in a project risk register. The modeling challenge arises when linking those identified risks from the risk register into the schedule risk model. This process causes huge challenges in project risk workshops.

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?

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. 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 modeling is making huge strides forward.

Contractor organizations can benefit from determining applicable contingencies, 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 get more insight into the realism and achievability of contractor schedules and can react and remediate faster.

In all instances, the benefit of providing an easier means of capturing 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 intelligence and AI 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 lifecycle. The end result is that more projects will see the light of day. HP


  1. Wolfs, et. al, “An industry under pressure to reform: 2018 Middle East capital projects and infrastructure survey,” PwC, 2018.
  2. GlobalData, “Infrastructure insight: The U.S.,” August 2018, online: https://www.researchandmarkets.com/reports/4316734/infrastructure-insight-the-us

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